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Academic literature on the topic 'GEO2R tool'
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Journal articles on the topic "GEO2R tool"
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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.
Conference papers on the topic "GEO2R tool"
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Kalyan, Gunturu Udith, Dodle Pranathi Reddy, Gummadivelly Chandrakanth, Boddu Pooja, V. Anitha, and D. Vivek. "Gene Association Disease Prediction by GEO2R Tool." In 2023 International Conference on Evolutionary Algorithms and Soft Computing Techniques (EASCT). IEEE, 2023. http://dx.doi.org/10.1109/easct59475.2023.10392802.
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