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Giuffrida, Erika, Chiara Bianca Maria Platania, Francesca Lazzara, et al. "The Identification of New Pharmacological Targets for the Treatment of Glaucoma: A Network Pharmacology Approach." Pharmaceuticals 17, no. 10 (2024): 1333. http://dx.doi.org/10.3390/ph17101333.
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Background: Glaucoma is a progressive optic neuropathy characterized by the neurodegeneration and death of retinal ganglion cells (RGCs), leading to blindness. Current glaucoma interventions reduce intraocular pressure but do not address retinal neurodegeneration. In this effort, to identify new pharmacological targets for glaucoma management, we employed a network pharmacology approach. Methods: We first retrieved transcriptomic data from GEO, an NCBI database, and carried out GEO2R (an interactive web tool aimed at comparing two or more groups of samples in a GEO dataset). The GEO2R statistical analysis aimed at identifying the top differentially expressed genes (DEGs) and used these as input of STRING (Search Tool for the Retrieval of Interacting Genes/Proteins) app within Cytoscape software, which builds networks of proteins starting from input DEGs. Analyses of centrality metrics using Cytoscape were carried out to identify nodes (genes or proteins) involved in network stability. We also employed the web-server software MIRNET 2.0 to build miRNA–target interaction networks for a re-analysis of the GSE105269 dataset, which reports analyses of microRNA expressions. Results: The pharmacological targets, identified in silico through analyses of the centrality metrics carried out with Cytoscape, were rescored based on correlations with entries in the PubMed and clinicaltrials.gov databases. When there was no match (82 out of 135 identified central nodes, in 8 analyzed networks), targets were considered “potential innovative” targets for the treatment of glaucoma, after further validation studies. Conclusions: Several druggable targets, such as GPCRs (e.g., 5-hydroxytryptamine 5A (5-HT5A) and adenosine A2B receptors) and enzymes (e.g., lactate dehydrogenase A or monoamine oxidase B), were found to be rescored as “potential innovative” pharmacological targets for glaucoma treatment.
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Korak, Tuğcan, Merve Gulsen Bal Albayrak, Gürler Akpınar, and Murat Kasap. "Identification of TIG1 Associated Molecular Targets for Breast Cancer Using Bioinformatic Approach." Gümüşhane Üniversitesi Sağlık Bilimleri Dergisi 13, no. 4 (2024): 1807–17. https://doi.org/10.37989/gumussagbil.1459020.
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e-posta/e-mail: tugcankorak@gmail.com/tugcan.korak@kocaeli.edu.tr Kabul Tarihi/Accepted: Tazarotene-induced gene 1 (TIG1) is involved in modulating the α-tubulin modification and effectively inhibiting tumor growth. In this bioinformatics study, we aim to propose novel therapeutic targets in breast cancer by utilizing differentially expressed genes (DEGs) of TIG1 in inflammatory breast cancer (IBC) and examining their correlation with the molecular and immune subtypes. Using the GEO2R tool, we analyzed DEGs in the GSE30543 dataset, specifically comparing suppressed TIG1 groups with control samples from SUM149 cells. Functional annotation analysis of DEGs were explored using SRplot with data from STRING (|log2(FC)| >2 and p
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Korbut, Edyta, Vincent T. Janmaat, Mateusz Wierdak, et al. "Molecular Profile of Barrett’s Esophagus and Gastroesophageal Reflux Disease in the Development of Translational Physiological and Pharmacological Studies." International Journal of Molecular Sciences 21, no. 17 (2020): 6436. http://dx.doi.org/10.3390/ijms21176436.
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Barrett’s esophagus (BE) is a premalignant condition caused by gastroesophageal reflux disease (GERD), where physiological squamous epithelium is replaced by columnar epithelium. Several in vivo and in vitro BE models were developed with questionable translational relevance when implemented separately. Therefore, we aimed to screen Gene Expression Omnibus 2R (GEO2R) databases to establish whether clinical BE molecular profile was comparable with animal and optimized human esophageal squamous cell lines-based in vitro models. The GEO2R tool and selected databases were used to establish human BE molecular profile. BE-specific mRNAs in human esophageal cell lines (Het-1A and EPC2) were determined after one, three and/or six-day treatment with acidified medium (pH 5.0) and/or 50 and 100 µM bile mixture (BM). Wistar rats underwent microsurgical procedures to generate esophagogastroduodenal anastomosis (EGDA) leading to BE. BE-specific genes (keratin (KRT)1, KRT4, KRT5, KRT6A, KRT13, KRT14, KRT15, KRT16, KRT23, KRT24, KRT7, KRT8, KRT18, KRT20, trefoil factor (TFF)1, TFF2, TFF3, villin (VIL)1, mucin (MUC)2, MUC3A/B, MUC5B, MUC6 and MUC13) mRNA expression was assessed by real-time PCR. Pro/anti-inflammatory factors (interleukin (IL)-1β, IL-2, IL-4, IL-5, IL-6, IL-10, IL-12, IL-13, tumor necrosis factor α, interferon γ, granulocyte-macrophage colony-stimulating factor) serum concentration was assessed by a Luminex assay. Expression profile in vivo reflected about 45% of clinical BE with accompanied inflammatory response. Six-day treatment with 100 µM BM (pH 5.0) altered gene expression in vitro reflecting in 73% human BE profile and making this the most reliable in vitro tool taking into account two tested cell lines. Our optimized and established combined in vitro and in vivo BE models can improve further physiological and pharmacological studies testing pathomechanisms and novel therapeutic targets of this disorder.
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Qian, Wang, Wang Xiaoyi, and Ye Zi. "Screening and Bioinformatics Analysis of IgA Nephropathy Gene Based on GEO Databases." BioMed Research International 2019 (July 16, 2019): 1–7. http://dx.doi.org/10.1155/2019/8794013.
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Purpose. To identify novel biomarkers of IgA nephropathy (IgAN) through bioinformatics analysis and elucidate the possible molecular mechanism. Methods. The GSE93798 and GSE73953 datasets containing microarray data from IgAN patients and healthy controls were downloaded from the GEO database and analyzed by the GEO2R web tool to obtain different expressed genes (DEGs). Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, protein-protein interaction (PPI), and Biological Networks Gene Oncology tool (BiNGO) were then performed to elucidate the molecular mechanism of IgAN. Results. A total of 223 DEGs were identified, of which 21 were hub genes, and involved in inflammatory response, cellular response to lipopolysaccharide, transcription factor activity, extracellular exosome, TNF signaling pathway, and MAPK signaling pathway. Conclusions. TNF and MAPK pathways likely form the basis of IgAN progression, and JUN/JUNB, FOS, NR4A1/2, EGR1, and FOSL1/2 are novel prognostic biomarkers of IgAN.
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Li, Jing, Ting Han, Zhenzhen Li, et al. "A Novel circRNA hsa_circRNA_002178 as a Diagnostic Marker in Hepatocellular Carcinoma Enhances Cell Proliferation, Invasion, and Tumor Growth by Stabilizing SRSF1 Expression." Journal of Oncology 2022 (August 27, 2022): 1–15. http://dx.doi.org/10.1155/2022/4184034.
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Background. Previous research studies have shown that the elevation of circular RNA (circRNA), hsa_circRNA_002178, was associated with the poor prognosis of breast cancer and colorectal cancer, while its molecular mechanisms underlying the effects on hepatocellular carcinoma (HCC) are still elusive. Methods. The microarray dataset GSE97332 was obtained from the Gene Expression Omnibus (GEO) database and calculated by using the GEO2R tool to identify differentially expressed circRNAs. Differentially expressed hsa_circRNA_002178, in 7 HCC tissue samples and paracancerous tissues, as well as in HCC cell lines and normal hepatocytes, was checked by RT-qPCR. Cell proliferation, invasion, migration, and epithelial-to-mesenchymal transition (EMT)-related proteins were tested in hsa_circRNA_002178-overexpressed or hsa_circRNA_002178-knocked down HCC cells. Subsequently, we identified whether hsa_circRNA_002178 binds to serine- and arginine-rich splicing factor 1 (SRSF1) and then analyzed their function in regulating HCC cell behavior. The effect on HCC cell xenograft tumor growth was observed by the knockdown of hsa_circRNA_002178 in vivo. Results. GEO2R-based analysis displayed that hsa_circRNA_002178 was upregulated in HCC tissues. Overexpression or knockdown of hsa_circRNA_002178 encouraged or impeded HCC cell proliferation, migration, invasion, and EMT program. Mechanically, hsa_circRNA_002178 bound to SRSF1 3′-untranslated region (UTR) and stabilized its expression. SRSF1 weakening eliminated the effects of pcDNA-hsa_circRNA_002178 on cell malignant behavior. Finally, the knockdown of hsa_circRNA_002178 was confirmed to prevent xenograft tumor growth. Conclusions. hsa_circRNA_002178 overexpression encouraged the stability of SRSF1 mRNA expression, and it may serve as an upstream factor of SRSF1 for the diagnosis of HCC.
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Khanam, Nargis, Mona Srivastava, Ankur Singh, et al. "Identifying Hub Genes in Autism Spectrum Disorder: A Bioinformatics Approach Using GEO Data Set and GEO2R Tool." International Journal of Science and Social Science Research 2, no. 4 (2025): 267–75. https://doi.org/10.5281/zenodo.15062493.
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Autism Spectrum Disorder (ASD) is a complex neurodevelopmental condition characterized by impairments in social interaction, communication, and behaviour. This study examined the genetic foundations of ASD through the analysis of RNA-sequencing data from two datasets (GSE107867 and GSE117776) obtained from the Gene Expression Omnibus (GEO). Using GEO2R, differentially expressed genes (DEGs) were identified, and a protein-protein interaction (PPI) network was constructed using STRING analysis. Among the upregulated genes, FCGR3A emerged as a central hub gene, indicating its potential involvement in the immune responses and neuroinflammation associated with ASD pathophysiology. Enrichment analysis revealed significant associations between immune system processes, molecular signaling, and neurodevelopmental pathways. This investigation underscores the complex molecular nature of ASD, with immune-related genes, particularly FCGR3A, playing a crucial role in the manifestation of the disorder. These findings provide insights into the genetic and immune pathways of ASD and suggest that FCGR3A is a potential therapeutic target. However, further experimental validation is required to confirm its functional relevance.
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Zhang, Si-ming, Cheng Shen, Jing Li, et al. "Identification of Hub Genes for Colorectal Cancer with Liver Metastasis Using miRNA-mRNA Network." Disease Markers 2023 (February 7, 2023): 1–14. http://dx.doi.org/10.1155/2023/2295788.
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Background. Liver metastasis is an important cause of death in patients with colorectal cancer (CRC). Increasing evidence indicates that microRNAs (miRNAs) are involved in the pathogenesis of colorectal cancer liver metastasis (CRLM). This study is aimed at exploring the potential miRNA-mRNA regulatory network. Methods. From the GEO database, we downloaded the microarray datasets GSE56350 and GSE73178. GEO2R was used to conduct differentially expressed miRNAs (DEMs) between CRC and CRLM using the GEO2R tool. Then, GO and KEGG pathway analysis for differentially expressed genes (DEGs) performed via DAVID. A protein-protein interaction (PPI) network was constructed by the STRING and identified by Cytoscape. Hub genes were identified by miRNA-mRNA network. Finally, the expression of the hub gene expression was assessed in the GSE81558. Results. The four DEMs (hsa-miR-204-5p, hsa-miR-122-5p, hsa-miR-95-3p, and hsa-miR-552-3p) were identified as common DEMs in GSE56350 and GSE73178 datasets. The SP1 was likely to adjust the upregulated DEMs; however, the YY1 could regulate both the upregulated and downregulated DEMs. A total of 3925 genes (3447 upregulated DEM genes and 478 downregulated DEM genes) were screened. These predicted genes were mainly linked to Platinum drug resistance, Cellular senescence, and ErbB signaling pathway. Through the gene network construction, most of the hub genes were found to be modulated by hsa-miR-204-5p, hsa-miR-122-5p, hsa-miR-95-3p, and hsa-miR-552-3p. Among the top 20 hub genes, the expression of CREB1, RHOA, and EGFR was significantly different in the GSE81558 dataset. Conclusion. In this study, miRNA-mRNA networks in CRLM were screened between CRC patients and CRLM patients to provide a new method to predict for the pathogenesis and development of CRC.
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Ponnusamy, Nirmaladevi, Keerthana Ganapathi, Rajkumar Sanjana Sri, Asma Ul Husna, and Mohanapriya Arumugam. "Identification of microRNA and protein interaction networks in human ovarian cancer." Research Journal of Biotechnology 18, no. 10 (2023): 148–53. http://dx.doi.org/10.25303/1810rjbt1480153.
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Ovarian cancer is one of the deadliest tumors in women, with a high mortality rate brought on by the lack of early detection. In this work, our main aim is to find promising biomarkers and pertinent mechanisms. GSE36668 was chosen from the Gene Expression Omnibus (GEO) to identify the differentially expressed genes (DEGs) using the GEO2R tool. To forecast gene ontology (GO) and pathway enrichment, online tools from ToppGene, FunRich and DAVID were employed. The protein-protein interaction (PPI) network is built via STRING v.11.5 and Cytoscape v.3.9.1. Following the detection of the hub genes, a Kaplan-Meire plotter was used to conduct additional validation survival analyses. A total of 1556 DEGs were identified using GEO2R, out of which 697 were upregulated and 859 were downregulated. According to GO analysis, DEGs were much more common in the online tools DAVID and ToppGene for cell adhesion, axoneme assembly and cilium assembly in the biological processs whereas cell surface is an essential component of the plasma membrane and extracellular matrix in the cellular component. In contrast, the plasma membranes are present in DAVID and FunRich. The DEGs are mostly linked to the MAPK, PI3K-Akt and RAP1 signaling pathways in KEGG and in the Reactome pathway, they are involved in cell-cell communication, cell and cell-cell junction organization The PPI network construct was used to find the gene clusters and to identify the hub genes MAPK1, CDH1, CBL and CCND1 by Cytoscape. The survival analysis of this hub gene CBL showed high expression in ovarian cancer which led to fewer survival chances. According to this study, ovarian cancer biomarkers are crucial to understand the molecular causes of the disease.
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Zamanian Azodi, Mona, Mostafa Rezaei-Tavirani, Mohammad Rostami-Nejad, and Majid Rezaei-Tavirani. "Comparative Bioinformatics Characteristic of Bladder Cancer Stage 2 from Stage 4 Expression Profile: A Network-Based Study." Galen Medical Journal 7 (December 17, 2018): e1279. http://dx.doi.org/10.31661/gmj.v7i0.1279.
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Background: Bladder cancer (BC) has remained as one of the most challenging issues in medicine. The aim of this study was to investigate the differential network analysis of stages 2 and 4 of BC to better understand the molecular pathology of these states. Materials and Methods: We chose gene expression data of GSE52519 from Gene Expression Omnibus (GEO) database analyzed by the GEO2R online tool. Cytoscape version 3.6.1 and its algorithms are the methods applied for the network construction and investigation of differentially expressed genes (DEG) in these states. Result: Our result revealed that the analysis DEGs provides useful information about a common molecular feature of stages 2 and 4 of BC. Conclusion: Consequently, the network finding revealed that more investigation about stage 2 is required to achieve an effective therapeutic protocol to block the transition from stage 2 to stage 4.[GMJ.2018;7:e1279]
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Tojo, Taiki, and Minako Yamaoka-Tojo. "Molecular Mechanisms Underlying the Progression of Aortic Valve Stenosis: Bioinformatic Analysis of Signal Pathways and Hub Genes." International Journal of Molecular Sciences 24, no. 9 (2023): 7964. http://dx.doi.org/10.3390/ijms24097964.
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The calcification of the aortic valve causes increased leaflet stiffness and leads to the development and progression of stenotic aortic valve disease. However, the molecular and cellular mechanisms underlying stenotic calcification remain poorly understood. Herein, we examined the gene expression associated with valve calcification and the progression of calcific aortic valve stenosis. We downloaded two publicly available gene expression profiles (GSE83453 and GSE51472) from NCBI-Gene Expression Omnibus database for the combined analysis of samples from human aortic stenosis and normal aortic valve tissue. After identifying the differentially expressed genes (DEGs) using the GEO2R online tool, we performed Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses. We also analyzed the protein–protein interactions (PPIs) of the DEGs using the NetworkAnalyst online tool. We identified 4603 upregulated and 6272 downregulated DEGs, which were enriched in the positive regulation of cell adhesion, leukocyte-mediated immunity, response to hormones, cytokine signaling in the immune system, lymphocyte activation, and growth hormone receptor signaling. PPI network analysis identified 10 hub genes: VCAM1, FHL2, RUNX1, TNFSF10, PLAU, SPOCK1, CD74, SIPA1L2, TRIB1, and CXCL12. Through bioinformatic analysis, we identified potential biomarkers and therapeutic targets for aortic stenosis, providing a theoretical basis for future studies.
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Dai, Peifang, Hang Wang, Xin Mu, Zhen Ren, Genli Liu, and Longying Gao. "Exploring Key Genes and Molecular Mechanisms Related to Myocardial Hypertrophy Based on Bioinformatics." Science of Advanced Materials 15, no. 6 (2023): 824–31. http://dx.doi.org/10.1166/sam.2023.4488.
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This study aimed to identify key genes and molecular mechanisms associated with cardiac hypertrophy using bioinformatics analysis. Datasets from the Gene Expression Omnibus (GEO) database were analyzed using the GEO2R tool to identify differentially expressed genes (DEGs) related to cardiac hypertrophy. The top 10 DEGs from two datasets (GSE18801 and GSE47420) were used to generate heatmaps and a volcano plot. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses were performed using the DAVID website. The protein interaction data for DEGs were visualized using Cytoscape software. A total of 767 DEGs were identified in GSE18801 and 447 DEGs in GSE47420, with 48 common differential genes named co-DEGs. GO enrichment analysis suggested these co-DEGs were mostly related to extracellular matrix organization, muscle system process, and tissue remodeling. KEGG pathway analysis demonstrated co-DEGs were related to malaria, estrogen signaling pathway, ECM-receptor interaction, and apelin signaling pathway. Eight hub genes were identified, including Fn1, Fbn1, Dcn, Ctgf, Timp1, Lox, Tlr4, and Lcn2. These hub genes might serve as therapeutic potential biomarkers of cardiac hypertrophy.
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Jiang, Xiao-wen, Hong-yuan Lu, Ziru Xu, et al. "In Silico Analyses for Key Genes and Molecular Genetic Mechanism in Epilepsy and Alzheimer’s Disease." CNS & Neurological Disorders - Drug Targets 17, no. 8 (2018): 608–17. http://dx.doi.org/10.2174/1871527317666180724150839.
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Background: Epilepsy and Alzheimer's disease are common neuropathies with a complex pathogenesis. Both of them have some correlations in etiology, pathogenesis, pathological changes, clinical manifestations and treatment. Objective: This study investigated the key genes and molecular genetic mechanism in epilepsy and Alzheimer’s disease by bioinformatics analysis. Method: Two gene expression profiles were used to screen differentially expressed genes by GEO2R tool. The Gene Ontology (GO) and Kyoto Encyclopedia of Gene and Genome (KEGG) pathway enrichment analysis was performed using the Database for Annotation, Visualization and Integrated Discovery (DAVID). Then the protein-protein interaction (PPI) network was constructed by Search Tool for the Retrieval of Interacting Genes (STRING) and Cytoscape software which can be used to analyze modules with MCODE. Results: A total of 199 differentially expressed genes (DEGs) in the two groups. According to GO_BP analysis and KEGG pathway enrichment by DAVID, we found DEGs referring to several pathways significantly down-regulated in endocytosis, such as endocytosis, synaptic vesicle cycle, lysosome, cAMP signaling pathway, circadian entrainment, LTP, glutamatergic synapse and GABAergic synapse pathway. The regulator genes of the upstream pathway of circadian rhythms were obviously downgraded. Conclusion: Our research demonstrated that the regulatory genes of the upstream pathway of circadian rhythms were obviously downgraded. These biological pathways and DEGs or hub genes may contribute to revealing the molecular relationship between Alzheimer's disease and epilepsy.
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Ghosal, Somnath, and Subrata Banerjee. "Identification of potential molecular players and therapeutic drug molecules in Melphalan resistant Multiple myeloma by integrated bioinformatics analysis." Research Journal of Biotechnology 17, no. 12 (2022): 6–15. http://dx.doi.org/10.25303/1712rjbt06015.
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Multiple myeloma (MM), second most common haematological malignancy, still remains incurable with the acquiring of drug resistance. Melphalan therapy which has been used as one of the key therapies against MM, is impeded by the occurrence of Melphalan resistance. The precise underlying mechanism of this acquired Melphalan resistance in MM is not yet well deciphered. Therefore, this present study was aimed to identify the differentially expressed genes (DEGs) associated micro RNAs (miRNAs) and transcription factors (TFs) from the microarray dataset of Melphalan resistant and Melphalan sensitive MM cell lines, obtained from Gene Expression Omnibus (GEO) database. DEGs were analysed using GEO2R tool from the dataset. Then the gene ontology (GO) and pathway enrichment analysis were executed by using DAVID database. Protein-protein interaction (PPI) network of DEGs was constructed and analysed by using STRING database and Cytoscape tool. Genetic alterations in DEGs were studied through COSMIC database. Network of interaction of DEGs and miRNAs as well as TFs was constructed and analysed by using mirDIP, TRRUST and miRNet tools. Drug gene interaction was examined to identify potential drug molecules by DGIdb tool. Various DEGs that might play pivotal role in Melphalan resistant MM, were detected and selected for further analysis. miRNA analysis detected hsa-mir- 21-3p, hsa-mir-27a-5p, hsa-mir-129-2-3p that could interact with maximum target DEGs. One TF, STAT1 was found to regulate the expression of selected DEGs. The whole study may give a better understanding about the Melphalan resistance in MM.
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Zhang, Xiaogen, Zhifa Wang, Li Hu, Xiaoqing Shen, and Chundong Liu. "Identification of Potential Genetic Biomarkers and Target Genes of Peri-Implantitis Using Bioinformatics Tools." BioMed Research International 2021 (December 11, 2021): 1–16. http://dx.doi.org/10.1155/2021/1759214.
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Objectives. To investigate potential genetic biomarkers of peri-implantitis and target genes for the therapy of peri-implantitis by bioinformatics analysis of publicly available data. Methods. The GSE33774 microarray dataset was downloaded from the Gene Expression Omnibus (GEO). The differentially expressed genes (DEGs) between peri-implantitis and healthy gingival tissues were identified using the GEO2R tool. GO enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed using the DAVID database and the Metascape tool, and the results were expressed as a bubble diagram. The protein-protein interaction network of DEGs was constructed using the Search Tool for the Retrieval of Interacting Genes (STRING) and visualized using Cytoscape. The hub genes were screened by the cytoHubba plugin of Cytoscape. The potential target genes associated with peri-implantitis were obtained from the DisGeNET database and the Open Targets Platform. The intersecting genes were identified using the Venn diagram web tool. Results. Between the peri-implantitis group and the healthy group, 205 DEGs were investigated including 140 upregulated genes and 65 downregulated genes. These DEGs were mainly enriched in functions such as the immune response, inflammatory response, cell adhesion, receptor activity, and protease binding. The results of KEGG pathway enrichment analysis revealed that DEGs were mainly involved in the cytokine-cytokine receptor interaction, pathways in cancer, and the PI3K-Akt signaling pathway. The intersecting genes, including IL6, TLR4, FN1, IL1β, CXCL8, MMP9, and SPP1, were revealed as potential genetic biomarkers and target genes of peri-implantitis. Conclusions. This study provides supportive evidence that IL6, TLR4, FN1, IL1β, CXCL8, MMP9, and SPP1 might be used as potential target biomarkers for peri-implantitis which may provide further therapeutic potentials for peri-implantitis.
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Liu, Hao, Yidan Qu, Hao Zhou, Ziwen Zheng, Junjiang Zhao, and Jian Zhang. "Bioinformatic analysis of potential hub genes in gastric adenocarcinoma." Science Progress 104, no. 1 (2021): 003685042110042. http://dx.doi.org/10.1177/00368504211004260.
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Gastric adenocarcinoma is the most common histologic type of gastric cancer; however, the pathogenic mechanisms remain unclear. To improve mechanistic understanding and identify new treatment targets or diagnostic biomarkers, we used bioinformatic tools to predict the hub genes related to the process of gastric adenocarcinoma development from public datasets, and explored their prognostic significance. We screened differentially expressed genes between gastric adenocarcinoma and normal gastric tissues in Gene Expression Omnibus datasets (GSE79973, GSE118916, and GSE29998) using the GEO2R tool, and their functions were annotated with Gene Ontology and Kyoto Encyclopedia of Genes and Genomes signaling pathway enrichment analyses in the DAVID database. Hub genes were identified based on the protein-protein network constructed in the STRING database with Cytoscape software. A total of 10 hub genes were selected for further analysis, and their expression patterns in gastric adenocarcinoma patients were investigated using the Oncomine GEPIA database. The expression levels of ATP4A, CA9, FGA, ALDH1A1, and GHRL were reduced, whereas those of TIMP1, SPP1, CXCL8, THY1, and COL1A1 were increased in gastric adenocarcinoma. The Kaplan–Meier online plotter tool showed associations of all hub genes except for CA9 with prognosis in gastric adenocarcinoma patients; CXCL8 and ALDH1A1 were positively correlated with survival, and the other genes were negatively correlated with survival. These 10 hub genes may be involved in important processes in gastric adenocarcinoma development, providing new directions for research to clarify the role of these genes and offer insight for improved treatment.
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Feng, Shunkang, Ping Sun, Chunhui Qu, et al. "Exploring the Core Genes of Schizophrenia Based on Bioinformatics Analysis." Genes 13, no. 6 (2022): 967. http://dx.doi.org/10.3390/genes13060967.
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Schizophrenia is a clinical syndrome composed of a group of symptoms involving many obstacles such as perception, thinking, emotion, behavior, and the disharmony of mental activities. Schizophrenia is one of the top ten causes of disability globally, accounting for about 1% of the global population. Previous studies have shown that schizophrenia has solid genetic characteristics. However, the diagnosis of schizophrenia mainly depends on symptomatic manifestations, and no gene can be used as a clear diagnostic marker at present. This study explored the hub genes of schizophrenia by bioinformatics analysis. Three datasets were selected and downloaded from the GEO database (GSE53987, GSE21138, and GSE27383). GEO2R, NCBI’s online analysis tool, is used to screen out significant gene expression differences. The genes were functionally enriched by GO and KEGG enrichment analysis. On this basis, the hub genes were explored through Cytoscape software, and the immune infiltration analysis and diagnostic value of the screened hub genes were judged. Finally, four hub genes (NFKBIA, CDKN1A, BTG2, GADD45B) were screened. There was a significant correlation between two hub genes (NFKBIA, BTG2) and resting memory CD4 T cells. The ROC curve results showed that all four hub genes had diagnostic value.
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Ramírez-Martínez, Carla Monserrat, Luis Fernando Jacinto-Alemán, Luis Pablo Cruz-Hervert, Javier Portilla-Robertson, and Elba Rosa Leyva-Huerta. "Bioinformatic Analysis for Mucoepidermoid and Adenoid Cystic Carcinoma of Therapeutic Targets." Vaccines 10, no. 9 (2022): 1557. http://dx.doi.org/10.3390/vaccines10091557.
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Salivary gland neoplasms are a heterogeneous neoplasm group, including mucoepidermoid carcinoma (MECa), adenoid cystic carcinoma (AdCC), and many others. Objective: We aimed to identify new critical genes of MECa and AdCC using bioinformatics analysis. Methods: Gene expression profile of GSE153283 was analyzed by the GEO2R online tool to use the DAVID software for their subsequent enrichment. Protein–protein interactions (PPI) were visualized using String. Cytoscape with MCODE plugin followed by Kaplan–Meier online for overall survival analysis were performed. Results: 97 upregulated genes were identified for MECa and 86 for AdCC. PPI analysis revealed 22 genes for MECa and 63 for AdCC that were validated by Kaplan–Meier that showed FN1 and SPP1 for MECa, and EGF and ERBB2 for AdCC as more significant candidate genes for each neoplasm. Conclusion: With bioinformatics methods, we identify upregulated genes in MECa and AdCC. The resulting candidate genes as possible therapeutic targets were FN1, SPP1, EGF, and ERBB2, and all those genes had been tested as a target in other neoplasm kinds but not salivary gland neoplasm. The bioinformatic evidence is a solid strategy to select them for more extensive research with clinical impact.
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Han, Chao, Lei Jin, Xuemei Ma, Qin Hao, Huajun Lin, and Zhongtao Zhang. "Identification of the hub genes RUNX2 and FN1 in gastric cancer." Open Medicine 15, no. 1 (2020): 403–12. http://dx.doi.org/10.1515/med-2020-0405.
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AbstractBackgroundThis study identified key genes in gastric cancer (GC) based on the mRNA microarray GSE19826 from the Gene Expression Omnibus (GEO) database and preliminarily explored the relationships among the key genes.MethodsDifferentially expressed genes (DEGs) were obtained using the GEO2R tool. The functions and pathway enrichment of the DEGs were analyzed using the Enrichr database. Protein–protein interactions (PPIs) were established by STRING. A lentiviral vector was constructed to silence RUNX2 expression in MGC-803 cells. The expression levels of RUNX2 and FN1 were measured. The influences of RUNX2 and FN1 on overall survival (OS) were determined using the Kaplan–Meier plotter online tool.ResultsIn total, 69 upregulated and 65 downregulated genes were identified. Based on the PPI network of the DEGs, 20 genes were considered hub genes. RUNX2 silencing significantly downregulated the FN1 expression in MGC-803 cells. High expression of RUNX2 and low expression of FN1 were associated with long survival time in diffuse, poorly differentiated, and lymph node-positive GC.ConclusionHigh RUNX2 and FN1 expression were associated with poor OS in patients with GC. RUNX2 can negatively regulate the secretion of FN1, and both genes may serve as promising targets for GC treatment.
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Hamdy, Hayam, Yi Yang, Cheng Cheng, and Qizhan Liu. "Identification of Potential Hub Genes Related to Aflatoxin B1, Liver Fibrosis and Hepatocellular Carcinoma via Integrated Bioinformatics Analysis." Biology 12, no. 2 (2023): 205. http://dx.doi.org/10.3390/biology12020205.
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The molecular mechanism of the hepatotoxicant aflatoxin B1 to induce liver fibrosis and hepatocellular carcinoma (HCC) remains unclear, to offer fresh perspectives on the molecular mechanisms underlying the onset and progression of AFB1-Fibrosis-HCC, which may offer novel targets for the detection and therapy of HCC caused by AFB1. In this study, expression profiles of AFB1, liver fibrosis and liver cancer-related datasets were downloaded from the Gene Expression Omnibus (GEO), and differentially expressed genes (DEGs) were identified by the GEO2R tool. The STRING database, CytoHubba, and Cytoscape software were used to create the protein-protein interaction and hub genes of the combined genes, and the ssGSEA score for inflammatory cells related gene sets, the signaling pathway, and immunotherapy were identified using R software and the GSEA database. The findings revealed that AFB1-associated liver fibrosis and HCC combined genes were linked to cell process disruptions, the BUB1B and RRM2 genes were identified as hub genes, and the BUB1B gene was significantly increased in JAK-STAT signaling gene sets pathways as well as having an immunotherapy-related impact. In conclusion, BUB1B and RRM2 were identified as potential biomarkers for AFB1-induced fibrosis and HCC progression.
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Zhou, Ting, Xianhong Meng, Daxiu Wang, Weiran Fu, and Xinrui Li. "Neutrophil Transcriptional Deregulation by the Periodontal Pathogen Fusobacterium nucleatum in Gastric Cancer: A Bioinformatic Study." Disease Markers 2022 (August 18, 2022): 1–10. http://dx.doi.org/10.1155/2022/9584507.
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Background. Infection with the periodontal pathogen Fusobacterium nucleatum (F. nucleatum) has been associated with gastric cancer. The present study is aimed at uncovering the putative biological mechanisms underlying effects of F. nucleatum–mediated neutrophil transcriptional deregulation in gastric cancer. Materials and Methods. A gene expression dataset pertaining to F. nucleatum-infected human neutrophils was utilized to identify differentially expressed genes (DEGs) using the GEO2R tool. Candidate genes associated with gastric cancer were sourced from the “Candidate Cancer Gene Database” (CCGD). Overlapping genes among these were identified as link genes. Functional profiling of the link genes was performed using “g:Profiler” tool to identify enriched Gene Ontology (GO) terms, pathways, miRNAs, transcription factors, and human phenotype ontology terms. Protein-protein interaction (PPI) network was constructed for the link genes using the “STRING” tool, hub nodes were identified as key candidate genes, and functionally enriched terms were determined. Results. The gene expression dataset GEO20151 was downloaded, and 589 DEGs were identified through differential analysis. 886 candidate gastric cancer genes were identified in the CGGD database. Among these, 36 overlapping genes were identified as the link genes. Enriched GO terms included molecular function “enzyme building,” biological process “protein folding,’” cellular components related to membrane-bound organelles, transcription factors ER71 and Sp1, miRNAs miR580 and miR155, and several human phenotype ontology terms including squamous epithelium of esophagus. The PPI network contained 36 nodes and 53 edges, where the top nodes included PH4 and CANX, and functional terms related to intracellular membrane trafficking were enriched. Conclusion. F nucleatum-induced neutrophil transcriptional activation may be implicated in gastric cancer via several candidate genes including DNAJB1, EHD1, IER2, CANX, and PH4B. Functional analysis revealed membrane-bound organelle dysfunction, intracellular trafficking, transcription factors ER71 and Sp1, and miRNAs miR580 and miR155 as other candidate mechanisms, which should be investigated in experimental studies.
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Yang, Qiannan, Bojun Yu, and Jing Sun. "TTK, CDC25A, and ESPL1 as Prognostic Biomarkers for Endometrial Cancer." BioMed Research International 2020 (November 17, 2020): 1–13. http://dx.doi.org/10.1155/2020/4625123.
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Objective. Endometrial cancer (EC) is one of the most common malignant gynaecological tumours worldwide. This study was aimed at identifying EC prognostic genes and investigating the molecular mechanisms of these genes in EC. Methods. Two mRNA datasets of EC were downloaded from the Gene Expression Omnibus (GEO). The GEO2R tool and Draw Venn Diagram were used to identify differentially expressed genes (DEGs) between normal endometrial tissues and EC tissues. Then, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed using the Database for Annotation, Visualization and Integrated Discovery (DAVID). Next, the protein-protein interactions (PPIs) of these DEGs were determined by the Search Tool for the Retrieval of Interacting Genes (STRING) tool and Cytoscape with Molecular Complex Detection (MCODE). Furthermore, Kaplan-Meier survival analysis was performed by UALCAN to verify genes associated with significantly poor prognosis. Next, Gene Expression Profiling Interactive Analysis (GEPIA) was used to verify the expression levels of these selected genes. Additionally, a reanalysis of the KEGG pathways was performed to understand the potential biological functions of selected genes. Finally, the associations between these genes and clinical features were analysed based on TCGA cancer genomic datasets for EC. Results. In EC tissues, compared with normal endometrial tissues, 147 of 249 DEGs were upregulated and 102 were downregulated. A total of 64 upregulated genes were assembled into a PPI network. Next, 14 genes were found to be both associated with significantly poor prognosis and highly expressed in EC tissues. Reanalysis of the KEGG pathways found that three of these genes were enriched in the cell cycle pathway. TTK, CDC25A, and ESPL1 showed higher expression in cancers with late stage and higher tumour grade. Conclusion. In summary, through integrated bioinformatics approaches, we found three significant prognostic genes of EC, which might be potential therapeutic targets for EC patients.
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Song, Yujie, Tao Feng, Wenping Cao, Haiyang Yu, and Zeng Zhang. "Identification of Key Genes in Nasopharyngeal Carcinoma Based on Bioinformatics Analysis." Computational Intelligence and Neuroscience 2022 (June 7, 2022): 1–7. http://dx.doi.org/10.1155/2022/9022700.
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Objective. This study aimed to identify key genes associated with the pathogenesis of nasopharyngeal carcinoma (NPC) by bioinformatics analysis. Methods. Datasets (GSE13597 and GSE34573) were screened and downloaded from the comprehensive gene expression database (GEO). GEO2R online tool was adopted to analyze microarray data GSE13597 and GSE34573 related to NPC. Volcano plot was generated using Bioconductor in R software. “Pheatmap” was used to draw heatmaps based on the top 10 regulated genes of GSE13597 and GSE34573. GO and KEGG analyses were conducted via online tool DAVID. We uploaded the DEGs of NPC to STRING software and then used Cytoscape software to draw PPI network of DEGs. Results. 216 DEGs were obtained in GSE13597 between patient and control group (111 up-regulated DEGs and 105 down-regulated DEGs). 1101 DEGs were obtained in GSE34573 (470 up-regulated DEGs and 641 down-regulated DEGs). 63 common differential genes were screened named co-DEGs in the two datasets. These DEGs were mainly associated with defense response to bacterium, cell-matrix adhesion, chemokine-mediated signaling pathway, tissue homeostasis, humoral immune response, cilium movement, cilium organization, cilium assembly, and epithelial cilium movement. KEGG pathway enrichment analysis showed that DEGs were mainly involved in viral protein interaction with cytokine and cytokine receptor, salivary secretion, p53 signaling pathway, IL-17 signaling pathway, cell cycle, PI3K-Akt signaling pathway, and ECM-receptor interaction. We identified seven hub genes, including FN1, MMP-10, MUC1, KIF23, CDK1, MUC5B, and MUC5AC. Conclusions. Seven hub genes, including FN1, MMP-10, MUC1, KIF23, CDK1, MUC5B, and MUC5AC, might be therapeutic potential biomarkers of NPC.
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Hao, Dexun, Yanshuang Li, Jiang Shi, and Junguang Jiang. "Bioinformatic Analysis Identifies of Potential miRNA-mRNA Regulatory Networks Involved in the Pathogenesis of Lung Cancer." Computational Intelligence and Neuroscience 2022 (September 30, 2022): 1–9. http://dx.doi.org/10.1155/2022/6295934.
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Objective. The purpose of the present study was to explore the biomarkers related to lung cancer based on the bioinformatics method, which might be new targets for lung cancer treatment. Methods. GSE17681 and GSE18842 were obtained from the Gene Expression Omnibus (GEO) database. The differentially expressed miRNAs (DEMs) and genes (DEGs) in lung cancer samples were screened via the GEO2R online tool. DEMs were submitted to the mirDIP website to predict target genes. Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were conducted via uploading DEGs to the DAVID database. The protein-protein interaction network (PPI) of the DEGs was analyzed by STRING’s online tool. Then, the PPI network was visualized using Cytoscape 3.8.0. Results. 46 DEMs were identified in GSE17681, and the website predicted that there were 873 target genes of these DEMs. 1029 DEGs were identified in the GSE18842 chip. GO analysis suggested that the co-DEGs participated in the canonical Wnt signaling pathway, regulation of the Wnt signaling pathway, a serine/threonine kinase signaling pathway, the Wnt signaling pathway, and cell-cell signaling by Wnt. KEGG analysis results showed the co-DEGs of GSE17681 and GSE18842 were related to the Hippo signaling pathway and adhesion molecules. In addition, six hub genes that were related to lung cancer were identified as hub genes, including mTOR, NF1, CHD7, ETS1, IL-6, and COL1A1. Conclusions. The present study identified six hub genes that were related to lung cancer, including mTOR, NF1, CHD7, ETS1, IL-6, and COL1A1, which might be a potential target for lung cancer.
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Yang, Guangda, Liumeng Jian, Xiangan Lin, Aiyu Zhu, and Guohua Wen. "Bioinformatics Analysis of Potential Key Genes in Trastuzumab-Resistant Gastric Cancer." Disease Markers 2019 (December 20, 2019): 1–13. http://dx.doi.org/10.1155/2019/1372571.
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Background. This study was performed to identify genes related to acquired trastuzumab resistance in gastric cancer (GC) and to analyze their prognostic value. Methods. The gene expression profile GSE77346 was downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were obtained by using GEO2R. Functional and pathway enrichment was analyzed by using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). Search Tool for the Retrieval of Interacting Genes (STRING), Cytoscape, and MCODE were then used to construct the protein-protein interaction (PPI) network and identify hub genes. Finally, the relationship between hub genes and overall survival (OS) was analyzed by using the online Kaplan-Meier plotter tool. Results. A total of 327 DEGs were screened and were mainly enriched in terms related to pathways in cancer, signaling pathways regulating stem cell pluripotency, HTLV-I infection, and ECM-receptor interactions. A PPI network was constructed, and 18 hub genes (including one upregulated gene and seventeen downregulated genes) were identified based on the degrees and MCODE scores of the PPI network. Finally, the expression of four hub genes (ERBB2, VIM, EGR1, and PSMB8) was found to be related to the prognosis of HER2-positive (HER2+) gastric cancer. However, the prognostic value of the other hub genes was controversial; interestingly, most of these genes were interferon- (IFN-) stimulated genes (ISGs). Conclusions. Overall, we propose that the four hub genes may be potential targets in trastuzumab-resistant gastric cancer and that ISGs may play a key role in promoting trastuzumab resistance in GC.
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Noor, Fatima, Muhammad Hamzah Saleem, Jen-Tsung Chen, Muhammad Rizwan Javed, Wafa Abdullah Al-Megrin, and Sidra Aslam. "Integrative bioinformatics approaches to map key biological markers and therapeutic drugs in Extramammary Paget’s disease of the scrotum." PLOS ONE 16, no. 7 (2021): e0254678. http://dx.doi.org/10.1371/journal.pone.0254678.
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Extramammary Paget’s disease (EMPD) is an intra-epidermal adenocarcinoma. Till now, the mechanisms underlying the pathogenesis of scrotal EMPD is poorly known. This present study aims to explore the knowledge of molecular mechanism of scrotal EMPD by identifying the hub genes and candidate drugs using integrated bioinformatics approaches. Firstly, the microarray datasets (GSE117285) were downloaded from the GEO database and then analyzed using GEO2R in order to obtain differentially expressed genes (DEGs). Moreover, hub genes were identified on the basis of their degree of connectivity using Cytohubba plugin of cytoscape tool. Finally, GEPIA and DGIdb were used for the survival analysis and selection of therapeutic candidates, respectively. A total of 786 DEGs were identified, of which 10 genes were considered as hub genes on the basis of the highest degree of connectivity. After the survival analysis of ten hub genes, a total of 5 genes were found to be altered in EMPD patients. Furthermore, 14 drugs of CHEK1, CCNA2, and CDK1 were found to have therapeutic potential against EMPD. This study updates the information and yields a new perspective in the context of understanding the pathogenesis of EMPD. In future, hub genes and candidate drugs might be capable of improving the personalized detection and therapies for EMPD.
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Xie, Binbin, Yiran Li, Rongjie Zhao, et al. "Identification of Key Genes and miRNAs in Osteosarcoma Patients with Chemoresistance by Bioinformatics Analysis." BioMed Research International 2018 (2018): 1–10. http://dx.doi.org/10.1155/2018/4761064.
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Chemoresistance is a significant factor associated with poor outcomes of osteosarcoma patients. The present study aims to identify Chemoresistance-regulated gene signatures and microRNAs (miRNAs) in Gene Expression Omnibus (GEO) database. The results of Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) included positive regulation of transcription, DNA-templated, tryptophan metabolism, and the like. Then differentially expressed genes (DEGs) were uploaded to Search Tool for the Retrieval of Interacting Genes (STRING) to construct protein-protein interaction (PPI) networks, and 9 hub genes were screened, such as fucosyltransferase 3 (Lewis blood group) (FUT3) whose expression in chemoresistant samples was high, but with a better prognosis in osteosarcoma patients. Furthermore, the connection between DEGs and differentially expressed miRNAs (DEMs) was explored. GEO2R was utilized to screen out DEGs and DEMs. A total of 668 DEGs and 5 DEMs were extracted from GSE7437 and GSE30934 differentiating samples of poor and good chemotherapy reaction patients. The Database for Annotation, Visualization, and Integrated Discovery (DAVID) was used to perform GO and KEGG pathway enrichment analysis to identify potential pathways and functional annotations linked with osteosarcoma chemoresistance. The present study may provide a deeper understanding about regulatory genes of osteosarcoma chemoresistance and identify potential therapeutic targets for osteosarcoma.
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Li, Yingyuan, Wulin Tan, Fang Ye, et al. "Inflammation as a risk factor for stroke in atrial fibrillation: data from a microarray data analysis." Journal of International Medical Research 48, no. 5 (2020): 030006052092167. http://dx.doi.org/10.1177/0300060520921671.
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Objective Stroke is a severe complication of atrial fibrillation (AF). We aimed to discover key genes and microRNAs related to stroke risk in patients with AF using bioinformatics analysis. Methods GSE66724 microarray data, including peripheral blood samples from eight patients with AF and stroke and eight patients with AF without stroke, were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) between AF patients with and without stroke were identified using the GEO2R online tool. Functional enrichment analysis was performed using the DAVID database. A protein–protein interaction (PPI) network was obtained using the STRING database. MicroRNAs (miRs) targeting these DEGs were obtained from the miRNet database. A miR–DEG network was constructed using Cytoscape software. Results We identified 165 DEGs (141 upregulated and 24 downregulated). Enrichment analysis showed enrichment of certain inflammatory processes. The miR–DEG network revealed key genes, including MEF2A, CAND1, PELI1, and PDCD4, and microRNAs, including miR-1, miR-1-3p, miR-21, miR-21-5p, miR-192, miR-192-5p, miR-155, and miR-155-5p. Conclusion Dysregulation of certain genes and microRNAs involved in inflammation may be associated with a higher risk of stroke in patients with AF. Evaluating these biomarkers could improve prediction, prevention, and treatment of stroke in patients with AF.
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Liu, Jun, Gui-Li Sun, Shang-Ling Pan, Meng-Bin Qin, Rong Ouyang, and Jie-An Huang. "Identification of hub genes in colon cancer via bioinformatics analysis." Journal of International Medical Research 48, no. 9 (2020): 030006052095323. http://dx.doi.org/10.1177/0300060520953234.
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Objectives This study aimed to investigate hub genes and their prognostic value in colon cancer via bioinformatics analysis. Methods Differentially expressed genes (DEGs) of expression profiles (GSE33113, GSE20916, and GSE37364) obtained from Gene Expression Omnibus (GEO) were identified using the GEO2R tool and Venn diagram software. Function and pathway enrichment analyses were performed, and a protein–protein interaction (PPI) network was constructed. Hub genes were verified based on The Cancer Genome Atlas (TCGA) and Human Protein Atlas (HPA) databases. Results We identified 207 DEGs, 62 upregulated and 145 downregulated genes, enriched in Gene Ontology terms “organic anion transport,” “extracellular matrix,” and “receptor ligand activity”, and in the Kyoto Encyclopedia of Genes and Genomes pathway “cytokine-cytokine receptor interaction.” The PPI network was constructed and nine hub genes were selected by survival analysis and expression validation. We verified these genes in the TCGA database and selected three potential predictors ( ZG16, TIMP1, and BGN) that met the independent predictive criteria. TIMP1 and BGN were upregulated in patients with a high cancer risk, whereas ZG16 was downregulated. The immunostaining results from HPA supported these findings. Conclusion This study indicates that these hub genes may be promising prognostic indicators or therapeutic targets for colon cancer.
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Li, Dongyang, Xuanyu Hao, and Yongsheng Song. "Identification of the Key MicroRNAs and the miRNA-mRNA Regulatory Pathways in Prostate Cancer by Bioinformatics Methods." BioMed Research International 2018 (June 20, 2018): 1–10. http://dx.doi.org/10.1155/2018/6204128.
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Objective. To identify key microRNAs (miRNAs) and their regulatory networks in prostate cancer.Methods. Four miRNA and three gene expression microarray datasets were downloaded for analysis from Gene Expression Omnibus database. The differentially expressed miRNA and genes were accessed by a GEO2R. Functional and pathway enrichment analyses were performed using the DAVID program. Protein-protein interaction (PPI) and miRNA-mRNA regulatory networks were constructed using the STRING and Cytoscape tool. Moreover, the results and clinical significance were validated in TCGA data.Results. We identified 26 significant DEMs, 633 upregulated DEGs, and 261 downregulated DEGs. Functional enrichment analysis indicated that significant DEGs were related to TGF-beta signaling pathway and TNF signaling pathway in PCa. Key DEGs such as HSPA8, PPP2R1A, CTNNB1, ADCY5, ANXA1, and COL9A2 were found as hub genes in PPI networks. TCGA data supported our results and the miRNAs were correlated with clinical stages and overall survival.Conclusions. We identified 26 miRNAs that may take part in key pathways like TGF-beta and TNF pathways in prostate cancer regulatory networks. MicroRNAs like miR-23b, miR-95, miR-143, and miR-183 can be utilized in assisting the diagnosis and prognosis of prostate cancer as biomarkers. Further experimental studies are required to validate our results.
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Zabihi, Mohammad Reza, and Mohammad Akhoondian. "The difference in gene expression network based on gender in the recovery of knee cruciate ligament injuries during exercise." Journal of Sports and Rehabilitation Sciences 1, no. 1 (2024): 4–9. http://dx.doi.org/10.32598/jsrs.2407.1002.
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Anterior cruciate ligament (ACL) injuries are common and often debilitating, requiring extensive rehabilitation. The recovery process involves complex physiological, biomechanical, and biochemical factors. Gender differences in the incidence and recovery from ACL injuries are well-documented, with recent advances in molecular biology suggesting that gene expression networks play a crucial role in tissue repair and regeneration. This study aims to compare gene expression networks between genders to provide strategies for better-managing sports injuries. Gene expression data were sourced from the Gene Expression Omnibus (GEO) database, with samples divided into four groups based on gender and age. Differentially expressed genes (DEGs) were identified using GEO2R, and biological interaction networks were constructed using the STRING (Search Tool for the Retrieval of Interacting Genes/Proteins). Network analysis was performed using Gephi, focusing on betweenness centrality and overall centrality. Two common genes, TRNA Aspartic Acid Methyltransferase 1 (TRDMT1) and NCF1, were identified in both sexes but exhibited different centrality measures. NCF1, associated with the production of superoxide anion and linked to chronic granulomatous disease, was expressed ten times higher in men than women. TRDMT1, responsible for RNA methylation, was twice as highly expressed in men. The study underscores the importance of gender-specific molecular mechanisms in ACL injury recovery and highlights the need for personalized treatment strategies.
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Tjipta, Arya, Dedy Hermansyah, Meiny Suzery, Bambang Cahyono, and Nur Dina Amalina. "Application of Bioinformatics Analysis to Identify Important Pathways and Hub Genes in Breast Cancer Affected by HER-2." International Journal of Cell and Biomedical Science 1, no. 1 (2022): 18–26. https://doi.org/10.59278/cbs.v1i1.11.
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Human epidermal growth factor receptor 2 (HER-2) is used as a marker for the diagnosis and prognosis of breast cancer. However, the molecular mechanisms involving HER2 in breast cancer require further study. Herein, we used the bioinformatics approaches to identify important pathways and hub genes in breast cancer affected by HER-2. The results showed that HER-2 is highly expressed in ovarian cancer and is closely related to the overall survival and progression-free survival of breast cancer. A total of 3014 downregulated genes and 4121 upregulated genes were identified under Gene Expression Omnibus (GEO) database with the GEO2R tool. Among them, the top 10 hub genes including CCNB1, KIF11, BUB1B, TOP2A, ASPM, MAD2L1, BUB1, RRM2, EGFR, and FN1 demonstrated by connectivity degree in the protein-protein interaction (PPI) network were screened out. In Kaplan–Meier plotter survival analysis, the overexpression of CCNB1, EGFR, MAD2L1, ASPM, and RRM2 were shown to be associated with an unfavourable prognosis in HER-2 positive breast cancer patients. In conclusion, we have identified important signalling pathways involving HER-2 that affect breast cancer. These findings could provide new insights outlining mechanisms involving HER-2 gene expression in breast cancer and provides a rationale for the novel treatment of breast cancer.
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Li, Huijuan, Shibin Liu, Xue Yang, et al. "Cellular Processes Involved in Jurkat Cells Exposed to Nanosecond Pulsed Electric Field." International Journal of Molecular Sciences 20, no. 23 (2019): 5847. http://dx.doi.org/10.3390/ijms20235847.
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Recently, nanosecond pulsed electric field (nsPEF) has been considered as a new tool for tumor therapy, but its molecular mechanism of function remains to be fully elucidated. Here, we explored the cellular processes of Jurkat cells exposed to nanosecond pulsed electric field. Differentially expressed genes (DEGs) were acquired from the GEO2R, followed by analysis with a series of bioinformatics tools. Subsequently, 3D protein models of hub genes were modeled by Modeller 9.21 and Rosetta 3.9. Then, a 100 ns molecular dynamics simulation for each hub protein was performed with GROMACS 2018.2. Finally, three kinds of nsPEF voltages (0.01, 0.05, and 0.5 mV/mm) were used to simulate the molecular dynamics of hub proteins for 100 ns. A total of 1769 DEGs and eight hub genes were obtained. Molecular dynamic analysis, including root mean square deviation (RMSD), root mean square fluctuation (RMSF), and the Rg, demonstrated that the 3D structure of hub proteins was built, and the structural characteristics of hub proteins under different nsPEFs were acquired. In conclusion, we explored the effect of nsPEF on Jurkat cell signaling pathway from the perspective of molecular informatics, which will be helpful in understanding the complex effects of nsPEF on acute T-cell leukemia Jurkat cells.
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Goldstein, Benjamin, Malav Trivedi, and Robert C. Speth. "Alterations in Gene Expression of Components of the Renin-Angiotensin System and Its Related Enzymes in Lung Cancer." Lung Cancer International 2017 (July 16, 2017): 1–8. http://dx.doi.org/10.1155/2017/6914976.
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Objectives. The study assessed the existence and significance of associations between the expression of fifteen renin-angiotensin system component genes and lung adenocarcinoma. Materials and Methods. NCBI’s built-in statistical tool, GEO2R, was used to calculate Student’s t-tests for the associations found in a DNA expression study of adenocarcinoma and matched healthy lung tissue samples. The raw data was processed with GeneSpring™ and then used to generate figures with and without Sidak’s multiple comparison correction. Results. Ten genes were found to be significantly associated with adenocarcinoma. Seven of these associations remained statistically significant after correction for multiple comparisons. Notably, AGTR2, which encodes the AT2 angiotensin II receptor subtype, was significantly underexpressed in adenocarcinoma tissue (p<0.01). AGTR1, ACE, ENPEP, MME, and PRCP, which encode the AT1 angiotensin II receptor, angiotensin-converting enzyme, aminopeptidase N, neprilysin, and prolylcarboxypeptidase, respectively, were also underexpressed. AGT, which encodes angiotensinogen, the angiotensin peptide precursor, was overexpressed in adenocarcinoma tissue. Conclusion. The results suggest an association between the expression of the genes for renin-angiotensin system-related proteins and adenocarcinoma. While further research is necessary to conclusively demonstrate a link between the renin-angiotensin system and lung cancers, the results suggest that the renin-angiotensin system plays a role in the pathology of adenocarcinoma.
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Ma, Xiaohan, Huijun Ren, Ruoyu Peng, Yi Li, and Liang Ming. "Identification of key genes associated with progression and prognosis for lung squamous cell carcinoma." PeerJ 8 (May 6, 2020): e9086. http://dx.doi.org/10.7717/peerj.9086.
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Background Lung squamous cell carcinoma (LUSC) is a major subtype of lung cancer with limited therapeutic options and poor clinical prognosis. Methods Three datasets (GSE19188, GSE33532 and GSE33479) were obtained from the gene expression omnibus (GEO) database. Differentially expressed genes (DEGs) between LUSC and normal tissues were identified by GEO2R, and functional analysis was employed using the Database for Annotation, Visualization and Integrated Discovery (DAVID) online tool. Protein–protein interaction (PPI) and hub genes were identified via the Search Tool for the Retrieval of Interacting Genes (STRING) and Cytoscape software. Hub genes were further validated in The Cancer Genome Atlas (TCGA) database. Subsequently, survival analysis was performed using the Kapla–Meier curve and Cox progression analysis. Based on univariate and multivariate Cox progression analysis, a gene signature was established to predict overall survival. Receiver operating characteristic curve was used to evaluate the prognostic value of the model. Results A total of 116 up-regulated genes and 84 down-regulated genes were identified. These DEGs were mainly enriched in the two pathways: cell cycle and p53 signaling way. According to the degree of protein nodes in the PPI network, 10 hub genes were identified. The mRNA expression levels of the 10 hub genes in LUSC were also significantly up-regulated in the TCGA database. Furthermore, a novel seven-gene signature (FLRT3, PPP2R2C, MMP3, MMP12, CAPN8, FILIP1 and SPP1) from the DEGs was constructed and acted as a significant and independent prognostic signature for LUSC. Conclusions The 10 hub genes might be tightly correlated with LUSC progression. The seven-gene signature might be an independent biomarker with a significant predictive value in LUSC overall survival.
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Cheng, Fang, Qiang Li, Jinglin Wang, Fang Zeng, Kaiping Wang, and Yu Zhang. "Identification of Differential Intestinal Mucosa Transcriptomic Biomarkers for Ulcerative Colitis by Bioinformatics Analysis." Disease Markers 2020 (October 21, 2020): 1–11. http://dx.doi.org/10.1155/2020/8876565.
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Background. Ulcerative colitis (UC) is a complicated disease caused by the interaction between genetic and environmental factors that affect mucosal homeostasis and triggers inappropriate immune response. The purpose of the study was to identify significant biomarkers with potential therapeutic targets and the underlying mechanisms. Methods. The gene expression profiles of GSE48958, GSE73661, and GSE59071 are from the GEO database. Differentially expressed genes (DEGs) were screened by the GEO2R tool. Next, the Database for Annotation, Visualization and Integrated Discovery (DAVID) was applied to analyze gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway. Then, protein-protein interaction (PPI) was visualized by Cytoscape with Search Tool for the Retrieval of Interacting Genes (STRING). Results. There were a total of 128 common DEGs genes, including 86 upregulated genes enriched in extracellular space, regulation of inflammatory response, chemokine-mediated signaling pathway, response to lipopolysaccharide, and cell proliferation, while 42 downregulated genes enriched in the integral component of the membrane, the integral component of the plasma membrane, apical plasma membrane, symporter activity, and chloride channel activity. The KEGG pathway analysis results demonstrated that DEGs were particularly enriched in cytokine-cytokine receptor interaction, TNF signaling pathway, chemokine signaling pathway, pertussis, and rheumatoid arthritis. 18 central modules of the PPI networks were selected with Cytotype MCODE. Furthermore, 18 genes were found to significantly enrich in the extracellular space, inflammatory response, chemokine-mediated signaling pathway, TNF signaling pathway, regulation of cell proliferation, and immune response via reanalysis of DAVID. Conclusion. The study identified DEGs, key target genes, functional pathways, and pathway analysis of UC, which may provide potential molecular targets and diagnostic biomarkers for UC.
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Geng, Rong-Xin, Ning Li, Yang Xu, et al. "Identification of Core Biomarkers Associated with Outcome in Glioma: Evidence from Bioinformatics Analysis." Disease Markers 2018 (October 10, 2018): 1–16. http://dx.doi.org/10.1155/2018/3215958.
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Glioma is the most common neoplasm of the central nervous system (CNS); the progression and outcomes of which are affected by a complicated network of genes and pathways. We chose a gene expression profile of GSE66354 from GEO database to search core biomarkers during the occurrence and development of glioma. A total of 149 samples, involving 136 glioma and 13 normal brain tissues, were enrolled in this article. 1980 differentially expressed genes (DEGs) including 697 upregulated genes and 1283 downregulated genes between glioma patients and healthy individuals were selected using GeoDiver and GEO2R tool. Then, gene ontology (GO) analysis as well as Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were carried out using the Database for Annotation, Visualization and Integrated Discovery (DAVID). Moreover, Cytoscape with Search Tool for the Retrieval of Interacting Genes (STRING) and Molecular Complex Detection (MCODE) plug-in was employed to imagine protein-protein interaction (PPI) of these DEGs. The upregulated genes were enriched in cell cycle, ECM-receptor interaction, and p53 signaling pathway, while the downregulated genes were enriched in retrograde endocannabinoid signaling, glutamatergic synapse, morphine addiction, GABAergic synapse, and calcium signaling pathway. Subsequently, 4 typical modules were discovered by the PPI network utilizing MCODE software. Besides, 15 hub genes were chosen according to the degree of connectivity, including TP53, CDK1, CCNB1, and CCNB2, the Kaplan-Meier analysis of which was further identified. In conclusion, this bioinformatics analysis indicated that DEGs and core genes, such as TP53, might influence the development of glioma, especially in tumor proliferation, which were expected to be promising biomarkers for diagnosis and treatment of glioma.
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Tian, Sha, Yinmei Guo, Jiajun Fu, Zijing Li, Jing Li, and Xuefei Tian. "Prognostic Value of Immunotyping Combined with Targeted Therapy in Patients with Non-Small-Cell Lung Cancer and Establishment of Nomogram Model." Computational and Mathematical Methods in Medicine 2022 (May 14, 2022): 1–9. http://dx.doi.org/10.1155/2022/3049619.
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Objective. Bioinformatics methods were used to analyze non-small-cell lung cancer gene chip data, screen differentially expressed genes (DEGs), explore biomarkers related to NSCLC prognosis, provide new targets for the treatment of NSCLC, and build immunotyping and line-map model. Methods. NSCLC-related gene chip data were downloaded from the GEO database, and the common DEGs of the two datasets were screened by using the GEO2R tool and FunRich 3.1.3 software. DAVID database was used for GO analysis and KEGG analysis of DEGs, and protein-protein interaction (PPI) network was constructed by STRING database and Cytoscape 3.8.0 software, and the top 20 hub genes were analyzed and screened out. The expression of pivot genes and their relationship with prognosis were verified by multiple external databases. Results. 159 common DEGs were screened from the two datasets. PPI network was constructed and analyzed, and the genes with the top 20 connectivity were selected as the pivotal genes of this study. The results of survival analysis and the patients’ survival curve was reflected in the line graph model of NSCLC. Conclusion. Through the screening and identification of the VIM-AS1 gene, as well as the analysis of immune infiltration and immune typing, the successful establishment of the rosette model has a certain guiding value for the molecular targeted therapy of patients with non-small-cell lung cancer.
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Fan, Rong, Lijin Dong, Ping Li, Xiaoming Wang, and Xuewei Chen. "Integrated bioinformatics analysis and screening of hub genes in papillary thyroid carcinoma." PLOS ONE 16, no. 6 (2021): e0251962. http://dx.doi.org/10.1371/journal.pone.0251962.
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Background With the increasing incidence of papillary thyroid carcinoma (PTC), PTC continues to garner attention worldwide; however its pathogenesis remains to be elucidated. The purpose of this study was to explore key biomarkers and potential new therapeutic targets for, PTC. Methods GEO2R and Venn online software were used for screening of differentially expressed genes. Hub genes were screened via STRING and Cytoscape, followed by Gene Ontology and KEGG enrichment analysis. Finally, survival analysis and expression validation were performed using the UALCAN online software and immunohistochemistry. Results We identified 334 consistently differentially expressed genes (DEGs) comprising 136 upregulated and 198 downregulated genes. Gene Ontology enrichment analysis results suggested that the DEGs were mainly enriched in cancer-related pathways and functions. PPI network visualization was performed and 17 upregulated and 13 downregulated DEGs were selected. Finally, the expression verification and overall survival analysis conducted using the Gene Expression Profiling Interactive Analysis Tool (GEPIA) and UALCAN showed that LPAR5, TFPI, and ENTPD1 were associated with the development of PTC and the prognosis of PTC patients, and the expression of LPAR5, TFPI and ENTPD1 was verified using a tissue chip. Conclusions In summary, the hub genes and pathways identified in the present study not only provide information for the development of new biomarkers for PTC but will also be useful for elucidation of the pathogenesis of PTC.
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Zhu, Huijing, Xin Zhu, Yuhong Liu, et al. "Gene Expression Profiling of Type 2 Diabetes Mellitus by Bioinformatics Analysis." Computational and Mathematical Methods in Medicine 2020 (October 21, 2020): 1–10. http://dx.doi.org/10.1155/2020/9602016.
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Objective. The aim of this study was to identify the candidate genes in type 2 diabetes mellitus (T2DM) and explore their potential mechanisms. Methods. The gene expression profile GSE26168 was downloaded from the Gene Expression Omnibus (GEO) database. The online tool GEO2R was used to obtain differentially expressed genes (DEGs). Gene Ontology (GO) term enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed by using Metascape for annotation, visualization, and comprehensive discovery. The protein-protein interaction (PPI) network of DEGs was constructed by using Cytoscape software to find the candidate genes and key pathways. Results. A total of 981 DEGs were found in T2DM, including 301 upregulated genes and 680 downregulated genes. GO analyses from Metascape revealed that DEGs were significantly enriched in cell differentiation, cell adhesion, intracellular signal transduction, and regulation of protein kinase activity. KEGG pathway analysis revealed that DEGs were mainly enriched in the cAMP signaling pathway, Rap1 signaling pathway, regulation of lipolysis in adipocytes, PI3K-Akt signaling pathway, MAPK signaling pathway, and so on. On the basis of the PPI network of the DEGs, the following 6 candidate genes were identified: PIK3R1, RAC1, GNG3, GNAI1, CDC42, and ITGB1. Conclusion. Our data provide a comprehensive bioinformatics analysis of genes, functions, and pathways, which may be related to the pathogenesis of T2DM.
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Xie, Rui-Ling, and Yu-Xin Xu. "Identification of hub genes for glaucoma: a study based on bioinformatics analysis and experimental verification." International Journal of Ophthalmology 16, no. 7 (2023): 1015–25. http://dx.doi.org/10.18240/ijo.2023.07.03.
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AIM: To explore hub genes for glaucoma based on bioinformatics analysis and an experimental model verification. METHODS: In the Gene Expression Omnibus (GEO) database, the GSE25812 and GSE26299 datasets were selected to analyze differentially expressed genes (DEGs) by the GEO2R tool. Through bioinformatics analysis, 9 hub genes were identified. Receiver operating characteristic (ROC) curves and principal component analysis (PCA) were performed to verify whether the hub gene can distinguish glaucoma from normal eyes. The mouse model of glaucoma was constructed, and the real-time reverse transcriptase-polymerase chain reaction (RT-qPCR) assay was performed to detect the expression levels of hub genes in glaucoma. RESULTS: There were 128 overlapping DEGs in the GSE25812 and GSE26299 datasets, mainly involved in intracellular signalling, cell adhesion molecules and the Ras signalling pathway. A total of 9 hub genes were screened out, including GNAL, BGN, ETS2, FCGP4, MAPK10, MMP15, STAT1, TSPAN8, and VCAM1. The area under the curve (AUC) values of 9 hub genes were greater than 0.8. The PC1 axle could provide a 70.5% interpretation rate to distinguish glaucoma from normal eyes. In the ocular tissues of glaucoma in the mice model, the expression of BGN, ETS2, FCGR4, STAT1, TSPAN8, and VCAM1 was increased, while the expression of GNAL, MAPK10, and MMP15 was decreased. CONCLUSION: Nine hub genes in glaucoma are identified, which may provide new biomarkers and therapeutic targets for glaucoma.
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Khan, Imteyaz Ahmad, and Srikant Sharma. "Bioinformatics Analysis of Key Genes and miRNAs Associated with Small-cell Lung Cancer." Asian Pacific Journal of Health Sciences 9, no. 4 (2022): 63–69. http://dx.doi.org/10.21276/apjhs.2022.9.4s.12.
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Small-cell lung cancer (SCLC) is a type of lung cancer, accounting for approximately 15% of lung cancers. SCLC is significantly associated with early recurrence, metastasis, and poor prognosis, with a 2-year survival rate after therapy, which is <5%. The molecular mechanisms underlying the development of SCLC are not clear. In this study, we identified SCLC-specific biomarkers by evaluating the differential expression of mRNA and microRNA (miRNA) profiles in SCLC tissue compared with normal lung tissue. A non-coding RNA sequence dataset (GSE19945) and transcriptome sequencing dataset (GSE6044) were downloaded from the GEO database. We identified 445 DEGs (differentially expressed genes) and 128 DE-miRNAs (differentially expressed miRNAs) using the GEO2R tool of the GEO and R limma software package. Furthermore, using the KEGG database, we identified 15 enrichment pathways, mostly associated with DNA replication, cell cycle, and oocyte meiosis mismatch repair, and the GO function was considerably enriched for 26 items. To investigate the molecular processes of key signaling pathways and cellular activity in SCLC, we used Cytoscape software to construct protein-protein interaction (PPI) networks. Using miRNAWalk, we identified 598 target genes of the 1380 miRNAs and constructed miRNA target networks. In addition, we identified eighteen overlapping genes that are regulated by 28 different miRNAs. The identified hub genes are important because they may be used as biomarkers for prognosis, diagnosis, and therapeutic target for SCLC.
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Huang, Hao, Wenhao Tang, and Chengliang Yang. "Bioinformatics analysis reveals the key factors affecting the progress of osteoporosis." International Journal of Public Health and Medical Research 1, no. 1 (2024): 102–16. http://dx.doi.org/10.62051/ijphmr.v1n1.12.
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The incidence rate of osteoporosis is high, and patients usually have decreased bone density and increased risk of fracture, which seriously affects their quality of life. This study aims to reveal key biomarkers that affect the progression of osteoporosis through bioinformatics methods. This study selected the GSE91033 and GSE93883 datasets for analysis, and obtained differentially expressed miRNAs using the GEO2R analysis tool; Predicting potential miRNA target factors through miRDIP and conducting pathway enrichment analysis of target factors through DAVID database; Analyze the interaction relationships between target factors through the STRING database, and construct a protein interaction network and a miRNA mRNA interaction network. The results showed that 73 and 79 differentially expressed genes were obtained in the GSE91033 and GSE93883 datasets, respectively. Common genes included hsa-miR-4508, hsa-miR-660-5p, and hsa-miR-424-5p. Pathway enrichment analysis showed that downstream target factors of differentially expressed miRNAs involved PIK/AKT, Wnt, Hippo, MAPK, and NF-κB pathway is closely related to cell proliferation and differentiation, and also involves intracellular phosphorylation activity. Protein interaction analysis revealed that CCND1, WEE1, MAP2K1, BTRC, FGF2, AXIN2, PTCH1, CCND2, KIF5C, DYNC1I1 and PAFAH1B1 are node genes involved in the progression of osteoporosis. The miRNA mRNA interaction network revealed that hsa-miR-424-5p is a key factor affecting the progression of osteoporosis.
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Martinez, Evelyn Calderon, Wern Lynn Ng, Maria Joseph та ін. "HUMAN β-DEFENSIN 2 ISOFORMS EXPRESSION ON PEDIATRIC IBD". Inflammatory Bowel Diseases 30, Supplement_1 (2024): S42—S43. http://dx.doi.org/10.1093/ibd/izae020.088.
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Abstract Inflammatory bowel disease (IBD), which includes Ulcerative Colitis (UC) and Chron’s disease, affects a million people around the world. IBD is developed during the different stages of human life and has mostly been studied in adults. However, this disease has been observed in childhood. In the case of pediatric IBD, the causes are mostly related to genetic disorders. Human β-defensin 2 isoforms (DEFB4A and DEFB4B) are one of the families strongly related to the development of multiple diseases such as asthma and colorectal cancer. The main purpose of this study was to determine the factors that can affect the progression of pediatric IBD. Using gene expression data from 190 pediatric patients (Control= 55 samples, CD = 92 samples, and UC= 43 samples) available at the Gene Expression Omnibus (Accession code = GSE117993), the differential gene expression was determined using GEO2R tool (significant level cut-ff = 0.05 and Log 2-fold threshold= 2). The results showed that DEFB4A/B were upregulated when comparing UC and CD with Control. On the other hand, DEFB4A/B was downregulated compared to CD with UC. Human β-defensin 2 isoforms have been strongly related to the development of multiple diseases, including IBD in adult patients. In the case of pediatric patients, these isoforms could represent a potential maker to assess the progression of IBD.
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Razzaghi, Zahra, Babak Arjmand, Maryam Hamzeloo-Moghadam, Mostafa Rezaei Tavirani, and Mona Zamanian Azodi. "Efficacy Evaluation of Human Skin Treatment with Photodynamic Therapy in Actinic Keratoses Patients." Journal of Lasers in Medical Sciences 14 (November 29, 2023): e60. http://dx.doi.org/10.34172/jlms.2023.60.
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Introduction: Photodynamic therapy (PDT) is a combined method of light and light-activated chemicals that are called photosensitizers (PSs). PDT is recommended as a high cure rate method with fewer side effects and a noninvasive tool to treat cancer. This study aimed to evaluate PDT efficacy as a therapeutic method against actinic keratoses in patients via protein-protein interaction (PPI) network analysis by using the gene expression profiles of Gene Expression Omnibus (GEO). Methods: Twenty-one gene expression profiles were extracted from GEO and analyzed by GEO2R to determine the significant differentially expressed genes (DEGs). The significant DEGs were included in PPI networks via Cytoscape software. The networks were analyzed by the "Network Analyzer", and the elements of the main connected components were assessed. Results: There were three main connected components for the compared sets of the gene expression profiles including the lesional region of skin before (Before set) and after (After set) PDT versus healthy (healthy set) skin and before versus after. The before-health comparison showed a partial similarity with the After-Healthy assessment. The before-after evaluation indicated that there were not considerable differences between the gene expression profile of the lesional region before and after PDT. Conclusion: In conclusion, PDT was unable to return the gene expression pattern of the actinic keratoses skin to a healthy condition completely.
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Yadalam, Pradeep Kumar, Raghavendra Vamsi Anegundi, and Ramya Ramadoss. "Abstract 91: Unsupervised Machine Learning Predicts Invasive and Undruggable Long Coding Rna Linc00662, Linc01234, and Spanxa1, Rabphilin 3a, Svil Antisense Rna 1 Like From Oral Cancer Transcriptomics." Cancer Epidemiology, Biomarkers & Prevention 32, no. 6_Supplement (2023): 91. http://dx.doi.org/10.1158/1538-7755.asgcr23-abstract-91.
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Abstract Purpose: Numerous cancer disease-modifying targets look “undruggable” or “difficult to target” because they lack accessible deep hydrophobic pockets where small molecules can bind, or they lack enzymatic activity (have no active site), RAS, and transcription factors (STAT3, TP53, MYC) are archetypal cancer undruggable. Designing a small molecule to bind to a PPI interface has proven difficult for several reasons. First, the unique interface structure is a challenge for drug design. Compared with the binding pockets of conventional protein targets, the interface of PPIs tends to be flat and contains few pockets, making it difficult for small-molecule compounds to bind. Designing a tiny chemical to bind to a PPI interface is tricky. First, the unusual interface structure presents a hurdle for drug design: the PPI interface is flat compared to traditional protein targets. It includes few pockets, making it challenging for small-molecule drugs to attach. Although initiatives to regulate RNA and protein expression with small molecules, biological compounds, such as anti-sense technology, have remained the most often deployed approach to target disease-associated RNAs. This study aims at predicting undruggable long coding RNA and transcription factors using unsupervised analysis from oral cancer transcriptomics. Methods: Using the NCBI geo database, GSE160395 data was retrieved for unsupervised analysis. PCA(Principal Component Analysis), T SNE (t-Distributed Stochastic Neighbor Embedding and MDS analysis of multi-dimensional data were analyzed for multi-dimensional data. Using the GEO2R online analysis tool (http://www.ncbi.nlm.nih.gov/geo/geo2r), the DEGs between HSC-3 and HSC-3-M3 cell lines samples were analyzed. DEGs with a threshold criterion of 1:0 log fold change and a P value of 0.05 were considered significantly differentially expressed. Results: Top differential genes include LINC00662, MYEOV, LGALS7, MAGED1, NT5M, AKR1C1, RPH3AL, LINC01234, KRT31, SPANXA1, SVIL ANTI-SENSE RNA 1.Clustering is an unsupervised machine learning technique that is effective at finding hidden groups in data. Targeting non-coding RNA and less hydrophobic proteins is crucial for cancer prevention and metastasis. By controlling the Wnt/-catenin pathway, LINC00662 and SVIL ANTISENSE RNA 1 can facilitate and hasten the formation of OSCC. Conclusion: Anti-sense technology and proteolysis-targeting chimeras will help solve complex biological undruggable proteins. Citation Format: Pradeep Kumar Yadalam, Raghavendra Vamsi Anegundi, Ramya Ramadoss. Unsupervised Machine Learning Predicts Invasive and Undruggable Long Coding Rna Linc00662, Linc01234, and Spanxa1, Rabphilin 3a, Svil Antisense Rna 1 Like From Oral Cancer Transcriptomics [abstract]. In: Proceedings of the 11th Annual Symposium on Global Cancer Research; Closing the Research-to-Implementation Gap; 2023 Apr 4-6. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2023;32(6_Suppl):Abstract nr 91.
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KOZALAK, Gül, Nazente ATCEKEN, and Rıza Köksal OZGUL. "IDENTIFICATION OF HUB GENES AND KEY PATHWAYS BETWEEN CELIAC AND CROHN'S DISEASES VIA BIOINFORMATICS TOOLS." Periódico Tchê Química 19, no. 41 (2022): 35–47. http://dx.doi.org/10.52571/ptq.v19.n41.2022.04_gul_pgs_35_47.pdf.
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Background: Chronic inflammatory diseases are the long-term response of the organism to any stimulus. Crohn's (CD) and Celiac (CeD) diseases are among chronic inflammatory diseases, and both cause chronic inflammation in the intestines. Both diseases are caused by polygenic, environmental, and lifestyle risk factors. Inflammation can perpetuate disease and cause it to become chronic. For this reason, CD and CeD that choose the intestine as the target organ may trigger each other. Although the relationship between these diseases is widely mentioned in the literature, scanty knowledge and research have been done on the immune mechanisms of these inflammatory diseases. Aim: This study aimed to determine hub genes, transcription factors-miRNAs, and protein-chemical interaction networks shared between CD and CeD. Methods: The NCBI-GEO datasets were downloaded and analyzed in GEO2R to identify differentially expressed genes (DEGs). STRING tool for Protein-Protein Interaction (PPI) and NetworkAnalyst tool were used for Gene Set Enrichment Analysis (GSEA), Transcription factor (TF) - miRNA Coregulatory Networks, and Protein-Chemical Interactions. Results and Discussion: GSE11501 and GSE3365 datasets were utilized to recognize 54 DEGs in CD, and CeD. 13 of these commonly expressed genes were defined as hub genes. GSEA has indicated that these genes are associated with immune system processes, cellular defense response, proteolysis, and apoptosis. KAT6A and SPI1 are transcription factors that direct the continuity of intestinal epithelial cells. Antirheumatic agents and Methotrexate are likely to be used to treat these diseases. Conclusions: In conclusion, we think that delayed-type hypersensitivity resulting from epitope propagation is a common immune mechanism of CD and CeD. Given the increasing prevalence of both CD and CeD in the population, it is clear that more studies are needed to understand the shared pathogenesis and overlapping immune mechanisms of these diseases.
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Marrugo-Padilla, Albeiro, Johana Márquez-Lázaro, and Antistio Álviz-Amador. "Identification of prognostic biomarkers of invasive ductal carcinoma by an integrated bioinformatics approach." F1000Research 11 (September 21, 2022): 1075. http://dx.doi.org/10.12688/f1000research.123714.1.
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Background: Invasive ductal carcinoma (IDC) is the most common breast cancer worldwide. Nowadays, due to IDC heterogeneity and its high capacity for metastasis, it is necessary to discover novel diagnostic and prognostic biomarkers. Thus, this study aimed to identify new prognostic genes of IDC using an integrated bioinformatics approach. Methods: Using the Gene Expression Omnibus (GEO) database, we downloaded publicly available data of the whole-genome mRNA expression profile from the first three stages of IDC in two expression profiling datasets, GSE29044 and GSE32291; intra-group data repeatability tests were conducted using Pearson’s correlation test, and the differentially expressed genes (DEGs) were identified using the online tool GEO2R, followed by the construction of a protein‑protein interaction network (PPI-net) with the common DEGs identified in the three analyzed stages using the Search Tool for the Retrieval of Interacting Genes (STRING) database and Cytoscape software, from these PPI-net we identify the hub genes (prognostic genes). Results: We found seven genes [WW domain-containing E3 ubiquitin-protein ligase 1 (WWP1), STIP1 homology and U-box containing protein 1 (STUB1), F-box and WD repeat domain containing 7 (FBXW7), kelch like family member 13 (KLHL13), ubiquitin-conjugating enzyme E2 Q1 (UBE2Q1), tripartite motif-containing 11 (TRIM11), and the beta-transducin repeat containing E3 ubiquitin-protein ligase (BTRC)] as potential candidates for IDC prognostic biomarkers, which were mainly enriched in the Ubiquitin-specific protease activity, cytoskeletal protein binding, and ligase activity. The role of these genes in the pathophysiology of IDC is not yet well characterized, representing a way to improve our understanding of the process of tumorigenesis and the underlying molecular events of IDC. Conclusions: Genes identified may lead to the discovery of new prognostic targets and precise therapeutics for IDC.
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Kaddoura, Rachid, Fatma Alqutami, Mohamed Asbaita, and Mahmood Hachim. "In Silico Analysis of Publicly Available Transcriptomic Data for the Identification of Triple-Negative Breast Cancer-Specific Biomarkers." Life 13, no. 2 (2023): 422. http://dx.doi.org/10.3390/life13020422.
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Background: Breast cancer is the most common type of cancer among women and is classified into multiple subtypes. Triple-negative breast cancer (TNBC) is the most aggressive subtype, with high mortality rates and limited treatment options such as chemotherapy and radiation. Due to the heterogeneity and complexity of TNBC, there is a lack of reliable biomarkers that can be used to aid in the early diagnosis and prognosis of TNBC in a non-invasive screening method. Aim: This study aims to use in silico methods to identify potential biomarkers for TNBC screening and diagnosis, as well as potential therapeutic markers. Methods: Publicly available transcriptomic data of breast cancer patients published in the NCBI’s GEO database were used in this analysis. Data were analyzed with the online tool GEO2R to identify differentially expressed genes (DEGs). Genes that were differentially expressed in more than 50% of the datasets were selected for further analysis. Metascape, Kaplan-Meier plotter, cBioPortal, and the online tool TIMER were used for functional pathway analysis to identify the biological role and functional pathways associated with these genes. Breast Cancer Gene-Expression Miner v4.7 was used to validify the obtained results in a larger cohort of datasets. Results: A total of 34 genes were identified as differentially expressed in more than half of the datasets. The DEG GATA3 had the highest degree of regulation, and it plays a role in regulating other genes. The estrogen-dependent pathway was the most enriched pathway, involving four crucial genes, including GATA3. The gene FOXA1 was consistently down-regulated in TNBC in all datasets. Conclusions: The shortlisted 34 DEGs will aid clinicians in diagnosing TNBC more accurately as well as developing targeted therapies to improve patient prognosis. In vitro and in vivo studies are further recommended to validate the results of the current study.
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Xiao, Lu, Zhou Yang, and Shudian Lin. "IRF9 and XAF1 as Diagnostic Markers of Primary Sjogren Syndrome." Computational and Mathematical Methods in Medicine 2022 (September 12, 2022): 1–12. http://dx.doi.org/10.1155/2022/1867321.
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Objective. Primary Sjogren syndrome (pSS) is characterized by lymphocytic infiltration of the salivary and lacrimal glands. It is a chronic systemic autoimmune disease. Genetic contributions and disturbed biological systems are the two major causes of pSS, but its etiology is unclear. This study is aimed at identifying potential pSS diagnostic markers and mechanisms at the transcriptome level. Methods. Whole blood datasets of patients with pSS were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified using the online tool, GEO2R. R software was used to perform enrichment analyses to understand the functions and enriched pathways of the DEGs. A protein–protein interaction network was constructed to identify hub genes and significant gene clusters. The least absolute shrinkage and selection operator logistic regression was used to screen pSS diagnostic markers. The expression level and diagnostic performance of the identified genes were tested using another GEO dataset. Results. A total of 221 DEGs were screened from the whole blood samples of 161 patients with pSS and 59 healthy controls. Functional enrichment analysis demonstrated that DEGs were mostly enriched in defense response to virus, response to virus, and type I interferon signaling pathway. Cytoscape identified 10 hub genes and two gene clusters. IRF9 ( AUC = 0.799 ) and XAF1 ( AUC = 0.792 ) were identified as pSS diagnostic markers. The expression levels of the two identified genes were validated by GSE51092. Conclusion. IRF9 and XAF1 were identified as diagnostic markers. The potential underlying molecular mechanism of pSS was explored.
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G., Divya. "Identification of Novel Cancer Stem Cell Markers in Glioblastoma by Comparing Tumor Cells with Stem-cell-like Cell Lines." Revista Gestão Inovação e Tecnologias 11, no. 2 (2021): 1567–83. http://dx.doi.org/10.47059/revistageintec.v11i2.1781.
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Aim. The aim of this study is to identify differential gene expression for glioblastoma tumor cells, normoxic and hypoxic glioblastoma stem-like cell lines. Finding the upregulated and downregulated gene and pathway interactions. Analysis to find the differential expression genes and pathway interactions. Materials and methods. The gene expression profiling data from the microarray dataset GSE45117 from the Gene Expression Omnibus (GEO) database, as well as differentially expressed genes (DEGs) between the 2 categories, are used in this analysis. 4 Samples of Glioblastoma tumors were considered as group 1 and 4 samples of normoxic and Hypoxic glioblastoma stem-like cell lines were considered as group 2 in the GEO2R web tool that has been used to screen them. Results. The gene-gene interactions among the DEGs and the GGI network with 37 nodes and 13 edges. The stem-cell-like cell lines showed lower expression of endothelin-related genes such as EDN3 and EDNRA along with dysregulation of enzymes such as PDK1, PGK1 which points to dysregulation of cellular respiratory pathways. This effect in consensus with under expression of cell attachment genes such as COL2A1, COL5A2, COL15A1 denotes a strong shift toward metastasis. Conclusion. Thus, a computational pipeline for identifying the significant genes and pathways involved in the glioblastoma tumors and glioblastoma stem-like cell lines. This study provides a path towards discovering potential leads for the treatment of glioblastoma and aids in comprehending the underlying novel molecular mechanisms.