Статті в журналах: "Multiomics analysis"

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Lee, Jeongwoo, Do Young Hyeon, and Daehee Hwang. "Single-cell multiomics: technologies and data analysis methods." Experimental & Molecular Medicine 52, no. 9 (September 2020): 1428–42. http://dx.doi.org/10.1038/s12276-020-0420-2.

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Abstract Advances in single-cell isolation and barcoding technologies offer unprecedented opportunities to profile DNA, mRNA, and proteins at a single-cell resolution. Recently, bulk multiomics analyses, such as multidimensional genomic and proteogenomic analyses, have proven beneficial for obtaining a comprehensive understanding of cellular events. This benefit has facilitated the development of single-cell multiomics analysis, which enables cell type-specific gene regulation to be examined. The cardinal features of single-cell multiomics analysis include (1) technologies for single-cell isolation, barcoding, and sequencing to measure multiple types of molecules from individual cells and (2) the integrative analysis of molecules to characterize cell types and their functions regarding pathophysiological processes based on molecular signatures. Here, we summarize the technologies for single-cell multiomics analyses (mRNA-genome, mRNA-DNA methylation, mRNA-chromatin accessibility, and mRNA-protein) as well as the methods for the integrative analysis of single-cell multiomics data.

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Dai, Ling-Yun, Rong Zhu, and Juan Wang. "Joint Nonnegative Matrix Factorization Based on Sparse and Graph Laplacian Regularization for Clustering and Co-Differential Expression Genes Analysis." Complexity 2020 (November 16, 2020): 1–10. http://dx.doi.org/10.1155/2020/3917812.

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The explosion of multiomics data poses new challenges to existing data mining methods. Joint analysis of multiomics data can make the best of the complementary information that is provided by different types of data. Therefore, they can more accurately explore the biological mechanism of diseases. In this article, two forms of joint nonnegative matrix factorization based on the sparse and graph Laplacian regularization (SG-jNMF) method are proposed. In the method, the graph regularization constraint can preserve the local geometric structure of data. L 2,1 -norm regularization can enhance the sparsity among the rows and remove redundant features in the data. First, SG-jNMF1 projects multiomics data into a common subspace and applies the multiomics fusion characteristic matrix to mine the important information closely related to diseases. Second, multiomics data of the same disease are mapped into the common sample space by SG-jNMF2, and the cluster structures are detected clearly. Experimental results show that SG-jNMF can achieve significant improvement in sample clustering compared with existing joint analysis frameworks. SG-jNMF also effectively integrates multiomics data to identify co-differentially expressed genes (Co-DEGs). SG-jNMF provides an efficient integrative analysis method for mining the biological information hidden in heterogeneous multiomics data.

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Wang, Tzu-Hao, Cheng-Yang Lee, Tzong-Yi Lee, Hsien-Da Huang, Justin Bo-Kai Hsu, and Tzu-Hao Chang. "Biomarker Identification through Multiomics Data Analysis of Prostate Cancer Prognostication Using a Deep Learning Model and Similarity Network Fusion." Cancers 13, no. 11 (May 21, 2021): 2528. http://dx.doi.org/10.3390/cancers13112528.

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This study is to identify potential multiomics biomarkers for the early detection of the prognostic recurrence of PC patients. A total of 494 prostate adenocarcinoma (PRAD) patients (60-recurrent included) from the Cancer Genome Atlas (TCGA) portal were analyzed using the autoencoder model and similarity network fusion. Then, multiomics panels were constructed according to the intersected omics biomarkers identified from the two models. Six intersected omics biomarkers, TELO2, ZMYND19, miR-143, miR-378a, cg00687383 (MED4), and cg02318866 (JMJD6; METTL23), were collected for multiomics panel construction. The difference between the Kaplan–Meier curves of high and low recurrence-risk groups generated from the multiomics panel achieved p-value = 5.33 × 10−9, which is better than the former study (p-value = 5 × 10−7). Additionally, when evaluating the selected multiomics biomarkers with clinical information (Gleason score, age, and cancer stage), a high-performance prediction model was generated with C-index = 0.713, p-value = 2.97 × 10−15, and AUC = 0.789. The risk score generated from the selected multiomics biomarkers worked as an effective indicator for the prediction of PRAD recurrence. This study helps us to understand the etiology and pathways of PRAD and further benefits both patients and physicians with potential prognostic biomarkers when making clinical decisions after surgical treatment.

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Boroń, Dariusz, Nikola Zmarzły, Magdalena Wierzbik-Strońska, Joanna Rosińczuk, Paweł Mieszczański, and Beniamin Oskar Grabarek. "Recent Multiomics Approaches in Endometrial Cancer." International Journal of Molecular Sciences 23, no. 3 (January 22, 2022): 1237. http://dx.doi.org/10.3390/ijms23031237.

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Endometrial cancer is the most common gynecological cancers in developed countries. Many of the mechanisms involved in its initiation and progression remain unclear. Analysis providing comprehensive data on the genome, transcriptome, proteome, and epigenome could help in selecting molecular markers and targets in endometrial cancer. Multiomics approaches can reveal disturbances in multiple biological systems, giving a broader picture of the problem. However, they provide a large amount of data that require processing and further integration prior to analysis. There are several repositories of multiomics datasets, including endometrial cancer data, as well as portals allowing multiomics data analysis and visualization, including Oncomine, UALCAN, LinkedOmics, and miRDB. Multiomics approaches have also been applied in endometrial cancer research in order to identify novel molecular markers and therapeutic targets. This review describes in detail the latest findings on multiomics approaches in endometrial cancer.

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Rotroff, Daniel M., and Alison A. Motsinger-Reif. "Embracing Integrative Multiomics Approaches." International Journal of Genomics 2016 (2016): 1–5. http://dx.doi.org/10.1155/2016/1715985.

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As “-omics” data technology advances and becomes more readily accessible to address complex biological questions, increasing amount of cross “-omics” dataset is inspiring the use and development of integrative bioinformatics analysis. In the current review, we discuss multiple options for integrating data across “-omes” for a range of study designs. We discuss established methods for such analysis and point the reader to in-depth discussions for the various topics. Additionally, we discuss challenges and new directions in the area.

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Nassar, Sam F., Khadir Raddassi, and Terence Wu. "Single-Cell Multiomics Analysis for Drug Discovery." Metabolites 11, no. 11 (October 25, 2021): 729. http://dx.doi.org/10.3390/metabo11110729.

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Given the heterogeneity seen in cell populations within biological systems, analysis of single cells is necessary for studying mechanisms that cannot be identified on a bulk population level. There are significant variations in the biological and physiological function of cell populations due to the functional differences within, as well as between, single species as a result of the specific proteome, transcriptome, and metabolome that are unique to each individual cell. Single-cell analysis proves crucial in providing a comprehensive understanding of the biological and physiological properties underlying human health and disease. Omics technologies can help to examine proteins (proteomics), RNA molecules (transcriptomics), and the chemical processes involving metabolites (metabolomics) in cells, in addition to genomes. In this review, we discuss the value of multiomics in drug discovery and the importance of single-cell multiomics measurements. We will provide examples of the benefits of applying single-cell omics technologies in drug discovery and development. Moreover, we intend to show how multiomics offers the opportunity to understand the detailed events which produce or prevent disease, and ways in which the separate omics disciplines complement each other to build a broader, deeper knowledge base.

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Perkel, Jeffrey M. "Single-cell analysis enters the multiomics age." Nature 595, no. 7868 (July 19, 2021): 614–16. http://dx.doi.org/10.1038/d41586-021-01994-w.

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Marshall, John L., Beth N. Peshkin, Takayuki Yoshino, Jakob Vowinckel, Håvard E. Danielsen, Gerry Melino, Ioannis Tsamardinos, et al. "The Essentials of Multiomics." Oncologist 27, no. 4 (February 22, 2022): 272–84. http://dx.doi.org/10.1093/oncolo/oyab048.

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Abstract Within the last decade, the science of molecular testing has evolved from single gene and single protein analysis to broad molecular profiling as a standard of care, quickly transitioning from research to practice. Terms such as genomics, transcriptomics, proteomics, circulating omics, and artificial intelligence are now commonplace, and this rapid evolution has left us with a significant knowledge gap within the medical community. In this paper, we attempt to bridge that gap and prepare the physician in oncology for multiomics, a group of technologies that have gone from looming on the horizon to become a clinical reality. The era of multiomics is here, and we must prepare ourselves for this exciting new age of cancer medicine.

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Campuzano, Susana, Rodrigo Barderas, Paloma Yáñez-Sedeño, and José M. Pingarrón. "Electrochemical biosensing to assist multiomics analysis in precision medicine." Current Opinion in Electrochemistry 28 (August 2021): 100703. http://dx.doi.org/10.1016/j.coelec.2021.100703.

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Xing, Lu, Tao Wu, Li Yu, Nian Zhou, Zhao Zhang, Yunjing Pu, Jinnan Wu, and Hong Shu. "Exploration of Biomarkers of Psoriasis through Combined Multiomics Analysis." Mediators of Inflammation 2022 (September 23, 2022): 1–25. http://dx.doi.org/10.1155/2022/7731082.

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Background. Aberrant DNA methylation patterns are of increasing interest in the study of psoriasis mechanisms. This study aims to screen potential diagnostic indicators affected by DNA methylation for psoriasis based on bioinformatics using multiple machine learning algorithms and to preliminarily explore its molecular mechanisms. Methods. GSE13355, GSE14905, and GSE73894 were collected from the gene expression omnibus (GEO) database. Differentially expressed genes (DEGs) and differentially methylated region- (DMR-) genes between psoriasis and control samples were combined to obtain differentially expressed methylated genes. Subsequently, a protein-protein interaction (PPI) network was established to analyze the interaction between differentially expressed methylated genes. Moreover, the hub genes of psoriasis were screened by the least absolute shrinkage and selection operator (LASSO), Random Forest (RF), and Support Vector Machine (SVM), which were further performed single-gene gene set enrichment analysis (GSEA) to clarify the pathogenesis of psoriasis. The druggable genes were predicted using DGIdb. Finally, the expressions of hub genes in psoriasis lesions and healthy controls were detected by immunohistochemistry (IHC) and quantitative real-time PCR (RT-qPCR). Results. In this study, a total of 767 DEGs and 896 DMR-genes were obtained. Functional enrichment showed that they were significantly associated with skin development, skin barrier function, immune/inflammatory response, and cell cycle. The combined transcriptomic and DNA methylation data resulted in 33 differentially expressed methylated genes, of which GJB2 was the final identified hub gene for psoriasis, with robust diagnostic power. IHC and RT-qPCR showed that GJB2 was significantly higher in psoriasis samples than those in healthy controls. Additionally, GJB2 may be involved in the development and progression of psoriasis by disrupting the body’s immune system, mediating the cell cycle, and destroying the skin barrier, in addition to possibly inducing diseases related to the skeletal aspects of psoriasis. Moreover, OCTANOL and CARBENOXOLONE were identified as promising compounds through the DGIdb database. Conclusion. The abnormal expression of GJB2 might play a critical role in psoriasis development and progression. The genes identified in our study might serve as a diagnostic indicator and therapeutic target in psoriasis.

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Li, Lin, Long Bai, Huan Lin, Lin Dong, Rumeng Zhang, Xiao Cheng, Zexian Liu, Yi Ouyang, and Keshuo Ding. "Multiomics analysis of tumor mutational burden across cancer types." Computational and Structural Biotechnology Journal 19 (2021): 5637–46. http://dx.doi.org/10.1016/j.csbj.2021.10.013.

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He, Yong, Hao Chen, Hao Sun, Jiadong Ji, Yufeng Shi, Xinsheng Zhang, and Lei Liu. "High‐dimensional integrative copula discriminant analysis for multiomics data." Statistics in Medicine 39, no. 30 (October 15, 2020): 4869–84. http://dx.doi.org/10.1002/sim.8758.

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Nygren, Petra Johanna, Aino Häkkinen, Daehong Kim, Timo Jarvinen, Fumihiro Ishida, Stefania Bortoluzzi, Andrea Binatti, et al. "A Comprehensive, Multiomics Analysis of Natural Killer-Cell Malignancies." Blood 140, Supplement 1 (November 15, 2022): 6390–91. http://dx.doi.org/10.1182/blood-2022-162285.

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Sherrod, Stacy D., and John A. McLean. "Systems-Wide High-Dimensional Data Acquisition and Informatics Using Structural Mass Spectrometry Strategies." Clinical Chemistry 62, no. 1 (January 1, 2016): 77–83. http://dx.doi.org/10.1373/clinchem.2015.238261.

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Abstract BACKGROUND Untargeted multiomics data sets are obtained for samples in systems, synthetic, and chemical biology by integrating chromatographic separations with ion mobility–mass spectrometry (IM-MS) analysis. The data sets are interrogated using bioinformatics strategies to organize the data for identification prioritization. CONTENT The use of big data approaches for data mining of massive data sets in systems-wide analyses is presented. Untargeted biological data across multiomics dimensions are obtained using a variety of chromatography strategies with structural MS. Separation timescales for different techniques and the resulting data deluge when combined with IM-MS are presented. Data mining self-organizing map strategies are used to rapidly filter the data, highlighting those features describing uniqueness to the query. Examples are provided in longitudinal analyses in synthetic biology and human liver exposure to acetaminophen, and in chemical biology for natural product discovery from bacterial biomes. CONCLUSIONS Matching the separation timescales of different forms of chromatography with IM-MS provides sufficient multiomics selectivity to perform untargeted systems-wide analyses. New data mining strategies provide a means for rapidly interrogating these data sets for feature prioritization and discovery in a range of applications in systems, synthetic, and chemical biology.

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Taguchi, Y.-h., and Turki Turki. "Tensor-Decomposition-Based Unsupervised Feature Extraction in Single-Cell Multiomics Data Analysis." Genes 12, no. 9 (September 18, 2021): 1442. http://dx.doi.org/10.3390/genes12091442.

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Analysis of single-cell multiomics datasets is a novel topic and is considerably challenging because such datasets contain a large number of features with numerous missing values. In this study, we implemented a recently proposed tensor-decomposition (TD)-based unsupervised feature extraction (FE) technique to address this difficult problem. The technique can successfully integrate single-cell multiomics data composed of gene expression, DNA methylation, and accessibility. Although the last two have large dimensions, as many as ten million, containing only a few percentage of nonzero values, TD-based unsupervised FE can integrate three omics datasets without filling in missing values. Together with UMAP, which is used frequently when embedding single-cell measurements into two-dimensional space, TD-based unsupervised FE can produce two-dimensional embedding coincident with classification when integrating single-cell omics datasets. Genes selected based on TD-based unsupervised FE are also significantly related to reasonable biological roles.

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Kaur, Harpreet, Rajesh Kumar, Anjali Lathwal, and Gajendra P. S. Raghava. "Computational resources for identification of cancer biomarkers from omics data." Briefings in Functional Genomics 20, no. 4 (April 1, 2021): 213–22. http://dx.doi.org/10.1093/bfgp/elab021.

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Abstract Cancer is one of the most prevailing, deadly and challenging diseases worldwide. The advancement in technology led to the generation of different types of omics data at each genome level that may potentially improve the current status of cancer patients. These data have tremendous applications in managing cancer effectively with improved outcome in patients. This review summarizes the various computational resources and tools housing several types of omics data related to cancer. Major categorization of resources includes—cancer-associated multiomics data repositories, visualization/analysis tools for omics data, machine learning-based diagnostic, prognostic, and predictive biomarker tools, and data analysis algorithms employing the multiomics data. The review primarily focuses on providing comprehensive information on the open-source multiomics tools and data repositories, owing to their broader applicability, economic-benefit and usability. Sections including the comparative analysis, tools applicability and possible future directions have also been discussed in detail. We hope that this information will significantly benefit the researchers and clinicians, especially those with no sound background in bioinformatics and who lack sufficient data analysis skills to interpret something from the plethora of cancer-specific data generated nowadays.

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Pak, Kyoungjune, Sae-Ock Oh, Tae Sik Goh, Hye Jin Heo, Myoung-Eun Han, Dae Cheon Jeong, Chi-Seung Lee, et al. "A User-Friendly, Web-Based Integrative Tool (ESurv) for Survival Analysis: Development and Validation Study." Journal of Medical Internet Research 22, no. 5 (May 5, 2020): e16084. http://dx.doi.org/10.2196/16084.

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Background Prognostic genes or gene signatures have been widely used to predict patient survival and aid in making decisions pertaining to therapeutic actions. Although some web-based survival analysis tools have been developed, they have several limitations. Objective Taking these limitations into account, we developed ESurv (Easy, Effective, and Excellent Survival analysis tool), a web-based tool that can perform advanced survival analyses using user-derived data or data from The Cancer Genome Atlas (TCGA). Users can conduct univariate analyses and grouped variable selections using multiomics data from TCGA. Methods We used R to code survival analyses based on multiomics data from TCGA. To perform these analyses, we excluded patients and genes that had insufficient information. Clinical variables were classified as 0 and 1 when there were two categories (for example, chemotherapy: no or yes), and dummy variables were used where features had 3 or more outcomes (for example, with respect to laterality: right, left, or bilateral). Results Through univariate analyses, ESurv can identify the prognostic significance for single genes using the survival curve (median or optimal cutoff), area under the curve (AUC) with C statistics, and receiver operating characteristics (ROC). Users can obtain prognostic variable signatures based on multiomics data from clinical variables or grouped variable selections (lasso, elastic net regularization, and network-regularized high-dimensional Cox-regression) and select the same outputs as above. In addition, users can create custom gene signatures for specific cancers using various genes of interest. One of the most important functions of ESurv is that users can perform all survival analyses using their own data. Conclusions Using advanced statistical techniques suitable for high-dimensional data, including genetic data, and integrated survival analysis, ESurv overcomes the limitations of previous web-based tools and will help biomedical researchers easily perform complex survival analyses.

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Jiang, Aimin, Yewei Bao, Anbang Wang, Wenliang Gong, Xinxin Gan, Jie Wang, Yi Bao, et al. "Establishment of a Prognostic Prediction and Drug Selection Model for Patients with Clear Cell Renal Cell Carcinoma by Multiomics Data Analysis." Oxidative Medicine and Cellular Longevity 2022 (January 4, 2022): 1–30. http://dx.doi.org/10.1155/2022/3617775.

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Rationale. Patients with clear cell renal cell cancer (ccRCC) may have completely different treatment choices and prognoses due to the wide range of heterogeneity of the disease. However, there is a lack of effective models for risk stratification, treatment decision-making, and prognostic prediction of renal cancer patients. The aim of the present study was to establish a model to stratify ccRCC patients in terms of prognostic prediction and drug selection based on multiomics data analysis. Methods. This study was based on the multiomics data (including mRNA, lncRNA, miRNA, methylation, and WES) of 258 ccRCC patients from TCGA database. Firstly, we screened the feature values that had impact on the prognosis and obtained two subtypes. Then, we used 10 algorithms to achieve multiomics clustering and conducted pseudotiming analysis to further validate the robustness of our clustering method, based on which the two subtypes of ccRCC patients were further subtyped. Meanwhile, the immune infiltration was compared between the two subtypes, and drug sensitivity and potential drugs were analyzed. Furthermore, to analyze the heterogeneity of patients at the multiomics level, biological functions between two subtypes were compared. Finally, Boruta and PCA methods were used for dimensionality reduction and cluster analysis to construct a renal cancer risk model based on mRNA expression. Results. A prognosis predicting model of ccRCC was established by dividing patients into the high- and low-risk groups. It was found that overall survival (OS) and progression-free interval (PFI) were significantly different between the two groups ( p < 0.01 ). The area under the OS time-dependent ROC curve for 1, 3, 5, and 10 years in the training set was 0.75, 0.72, 0.71, and 0.68, respectively. Conclusion. The model could precisely predict the prognosis of ccRCC patients and may have implications for drug selection for ccRCC patients.

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Gao, Junpeng, Yuxuan Zheng, Lin Li, Minjie Lu, Xiangjian Chen, Yu Wang, Yanna Li, et al. "Integrated transcriptomics and epigenomics reveal chamber-specific and species-specific characteristics of human and mouse hearts." PLOS Biology 19, no. 5 (May 18, 2021): e3001229. http://dx.doi.org/10.1371/journal.pbio.3001229.

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DNA methylation, chromatin accessibility, and gene expression represent different levels information in biological process, but a comprehensive multiomics analysis of the mammalian heart is lacking. Here, we applied nucleosome occupancy and methylome sequencing, which detected DNA methylation and chromatin accessibility simultaneously, as well as RNA-seq, for multiomics analysis of the 4 chambers of adult and fetal human hearts, and adult mouse hearts. Our results showed conserved region-specific patterns in the mammalian heart at transcriptome and DNA methylation level. Adult and fetal human hearts showed distinct features in DNA methylome, chromatin accessibility, and transcriptome. Novel long noncoding RNAs were identified in the human heart, and the gene expression profiles of major cardiovascular diseases associated genes were displayed. Furthermore, cross-species comparisons revealed human-specific and mouse-specific differentially expressed genes between the atria and ventricles. We also reported the relationship among multiomics and found there was a bell-shaped relationship between gene-body methylation and expression in the human heart. In general, our study provided comprehensive spatiotemporal and evolutionary insights into the regulation of gene expression in the heart.

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Ugidos, Manuel, Sonia Tarazona, José M. Prats-Montalbán, Alberto Ferrer, and Ana Conesa. "MultiBaC: A strategy to remove batch effects between different omic data types." Statistical Methods in Medical Research 29, no. 10 (March 4, 2020): 2851–64. http://dx.doi.org/10.1177/0962280220907365.

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Diversity of omic technologies has expanded in the last years together with the number of omic data integration strategies. However, multiomic data generation is costly, and many research groups cannot afford research projects where many different omic techniques are generated, at least at the same time. As most researchers share their data in public repositories, different omic datasets of the same biological system obtained at different labs can be combined to construct a multiomic study. However, data obtained at different labs or moments in time are typically subjected to batch effects that need to be removed for successful data integration. While there are methods to correct batch effects on the same data types obtained in different studies, they cannot be applied to correct lab or batch effects across omics. This impairs multiomic meta-analysis. Fortunately, in many cases, at least one omics platform—i.e. gene expression— is repeatedly measured across labs, together with the additional omic modalities that are specific to each study. This creates an opportunity for batch analysis. We have developed MultiBaC (multiomic Multiomics Batch-effect Correction correction), a strategy to correct batch effects from multiomic datasets distributed across different labs or data acquisition events. Our strategy is based on the existence of at least one shared data type which allows data prediction across omics. We validate this approach both on simulated data and on a case where the multiomic design is fully shared by two labs, hence batch effect correction within the same omic modality using traditional methods can be compared with the MultiBaC correction across data types. Finally, we apply MultiBaC to a true multiomic data integration problem to show that we are able to improve the detection of meaningful biological effects.

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Lin, Jimmy, Eric Ariazi, Michael Dzamba, Teng-Kuei Hsu, Steven Kothen-Hill, Kang Li, Tzu-Yu Liu, et al. "Evaluation of a sensitive blood test for the detection of colorectal advanced adenomas in a prospective cohort using a multiomics approach." Journal of Clinical Oncology 39, no. 3_suppl (January 20, 2021): 43. http://dx.doi.org/10.1200/jco.2021.39.3_suppl.43.

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43 Background: Blood-based screening tests for colorectal cancer (CRC) with high sensitivity and specificity are needed to improve adherence, facilitate early detection, and ultimately reduce mortality from CRC. Current stool-based tests have a sensitivity of 24-42% for colorectal advanced adenomas (AAs), while blood tests that rely on tumor-derived cell-free DNA (cfDNA) methylation signatures have shown limited sensitivity for AAs. Here we demonstrate the ability to detect AAs from blood using a multiomics test that incorporates both tumor- and immune-derived signatures, and compare it to the performance of a cfDNA methylation-only test. Methods: Participants enrolled in a prospective study (NCT03688906) were included in this analysis. The multiomics test includes signatures for cell-free nucleic acids based on next-generation sequencing, and for plasma proteins based on high-throughput multiplexed assays. Signatures are integrated computationally with a combination of convolutional neural networks and regularized logistic regression. We compared the multiomics test with one based on cfDNA methylation only. Results: This sub-study included 542 participants (AA: n = 122; colonoscopy-confirmed negative controls: n = 420). Participants with AA were 56% male with a mean age of 63 years, and colonoscopy-confirmed negative controls were 54% male with a mean age of 61 years. The multiomics test achieved a sensitivity of 41% (n = 50/122, 95% CI 34-48%) at 90% specificity (377/420). By contrast, the cfDNA methylation-only test achieved a sensitivity of 20% (24/122, 95% CI 15-25%) at 91% specificity (383/420). Performance was also analyzed by histological subtype and location, and superiority of the multiomics test to the cfDNA-methylation-only test was consistently observed. Conclusions: A novel multiomics blood test can detect colorectal AAs at a sensitivity and specificity comparable to existing stool-based tests. Combining signatures from both tumor- and immune-derived sources resulted in AA sensitivity greater than that of cfDNA-methylation alone.

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Cancemi, Patrizia, Miriam Buttacavoli, Gianluca Di Cara, Nadia Ninfa Albanese, Serena Bivona, Ida Pucci-Minafra, and Salvatore Feo. "A multiomics analysis of S100 protein family in breast cancer." Oncotarget 9, no. 49 (June 26, 2018): 29064–81. http://dx.doi.org/10.18632/oncotarget.25561.

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Lin, Dan‐Yu, Donglin Zeng, and David Couper. "A general framework for integrative analysis of incomplete multiomics data." Genetic Epidemiology 44, no. 7 (July 21, 2020): 646–64. http://dx.doi.org/10.1002/gepi.22328.

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Taguchi, Y.-h., and Turki Turki. "Tensor-Decomposition-Based Unsupervised Feature Extraction Applied to Prostate Cancer Multiomics Data." Genes 11, no. 12 (December 11, 2020): 1493. http://dx.doi.org/10.3390/genes11121493.

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The large p small n problem is a challenge without a de facto standard method available to it. In this study, we propose a tensor-decomposition (TD)-based unsupervised feature extraction (FE) formalism applied to multiomics datasets, in which the number of features is more than 100,000 whereas the number of samples is as small as about 100, hence constituting a typical large p small n problem. The proposed TD-based unsupervised FE outperformed other conventional supervised feature selection methods, random forest, categorical regression (also known as analysis of variance, or ANOVA), penalized linear discriminant analysis, and two unsupervised methods, multiple non-negative matrix factorization and principal component analysis (PCA) based unsupervised FE when applied to synthetic datasets and four methods other than PCA based unsupervised FE when applied to multiomics datasets. The genes selected by TD-based unsupervised FE were enriched in genes known to be related to tissues and transcription factors measured. TD-based unsupervised FE was demonstrated to be not only the superior feature selection method but also the method that can select biologically reliable genes. To our knowledge, this is the first study in which TD-based unsupervised FE has been successfully applied to the integration of this variety of multiomics measurements.

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Carapito, Raphael, Christine Carapito, Aurore Morlon, Nicodème Paul, Alvaro Sebastian Vaca Jacome, Ghada Alsaleh, Véronique Rolli, et al. "Multi-OMICS analyses unveil STAT1 as a potential modifier gene in mevalonate kinase deficiency." Annals of the Rheumatic Diseases 77, no. 11 (July 20, 2018): 1675–87. http://dx.doi.org/10.1136/annrheumdis-2018-213524.

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ObjectivesThe objective of the present study was to explain why two siblings carrying both the same homozygous pathogenic mutation for the autoinflammatory disease hyper IgD syndrome, show opposite phenotypes, that is, the first being asymptomatic, the second presenting all classical characteristics of the disease.MethodsWhere single omics (mainly exome) analysis fails to identify culprit genes/mutations in human complex diseases, multiomics analyses may provide solutions, although this has been seldom used in a clinical setting. Here we combine exome, transcriptome and proteome analyses to decipher at a molecular level, the phenotypic differences between the two siblings.ResultsThis multiomics approach led to the identification of a single gene—STAT1—which harboured a rare missense variant and showed a significant overexpression of both mRNA and protein in the symptomatic versus the asymptomatic sister. This variant was shown to be of gain of function nature, involved in an increased activation of the Janus kinase/signal transducer and activator of transcription signalling (JAK/STAT) pathway, known to play a critical role in inflammatory diseases and for which specific biotherapies presently exist. Pathway analyses based on information from differentially expressed transcripts and proteins confirmed the central role of STAT1 in the proposed regulatory network leading to an increased inflammatory phenotype in the symptomatic sibling.ConclusionsThis study demonstrates the power of a multiomics approach to uncover potential clinically actionable targets for a personalised therapy. In more general terms, we provide a proteogenomics analysis pipeline that takes advantage of subject-specific genomic and transcriptomic information to improve protein identification and hence advance individualised medicine.

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Kalari, Krishna R., Jason P. Sinnwell, Kevin J. Thompson, Xiaojia Tang, Erin E. Carlson, Jia Yu, Peter T. Vedell, et al. "PANOPLY: Omics-Guided Drug Prioritization Method Tailored to an Individual Patient." JCO Clinical Cancer Informatics, no. 2 (December 2018): 1–11. http://dx.doi.org/10.1200/cci.18.00012.

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Purpose The majority of patients with cancer receive treatments that are minimally informed by omics data. We propose a precision medicine computational framework, PANOPLY (Precision Cancer Genomic Report: Single Sample Inventory), to identify and prioritize drug targets and cancer therapy regimens. Materials and Methods The PANOPLY approach integrates clinical data with germline and somatic features obtained from multiomics platforms and applies machine learning and network analysis approaches in the context of the individual patient and matched controls. The PANOPLY workflow uses the following four steps: selection of matched controls to the patient of interest; identification of patient-specific genomic events; identification of suitable drugs using the driver-gene network and random forest analyses; and provision of an integrated multiomics case report of the patient with prioritization of anticancer drugs. Results The PANOPLY workflow can be executed on a stand-alone virtual machine and is also available for download as an R package. We applied the method to an institutional breast cancer neoadjuvant chemotherapy study that collected clinical and genomic data as well as patient-derived xenografts to investigate the prioritization offered by PANOPLY. In a chemotherapy-resistant patient-derived xenograft model, we found that that the prioritized drug, olaparib, was more effective than placebo in treating the tumor ( P < .05). We also applied PANOPLY to in-house and publicly accessible multiomics tumor data sets with therapeutic response or survival data available. Conclusion PANOPLY shows promise as a means to prioritize drugs on the basis of clinical and multiomics data for an individual patient with cancer. Additional studies are needed to confirm this approach.

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Sugawara, Junichi, Daisuke Ochi, Riu Yamashita, Takafumi Yamauchi, Daisuke Saigusa, Maiko Wagata, Taku Obara, et al. "Maternity Log study: a longitudinal lifelog monitoring and multiomics analysis for the early prediction of complicated pregnancy." BMJ Open 9, no. 2 (February 2019): e025939. http://dx.doi.org/10.1136/bmjopen-2018-025939.

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PurposeA prospective cohort study for pregnant women, the Maternity Log study, was designed to construct a time-course high-resolution reference catalogue of bioinformatic data in pregnancy and explore the associations between genomic and environmental factors and the onset of pregnancy complications, such as hypertensive disorders of pregnancy, gestational diabetes mellitus and preterm labour, using continuous lifestyle monitoring combined with multiomics data on the genome, transcriptome, proteome, metabolome and microbiome.ParticipantsPregnant women were recruited at the timing of first routine antenatal visits at Tohoku University Hospital, Sendai, Japan, between September 2015 and November 2016. Of the eligible women who were invited, 65.4% agreed to participate, and a total of 302 women were enrolled. The inclusion criteria were age ≥20 years and the ability to access the internet using a smartphone in the Japanese language.Findings to dateStudy participants uploaded daily general health information including quality of sleep, condition of bowel movements and the presence of nausea, pain and uterine contractions. Participants also collected physiological data, such as body weight, blood pressure, heart rate and body temperature, using multiple home healthcare devices. The mean upload rate for each lifelog item was ranging from 67.4% (fetal movement) to 85.3% (physical activity), and the total number of data points was over 6 million. Biospecimens, including maternal plasma, serum, urine, saliva, dental plaque and cord blood, were collected for multiomics analysis.Future plansLifelog and multiomics data will be used to construct a time-course high-resolution reference catalogue of pregnancy. The reference catalogue will allow us to discover relationships among multidimensional phenotypes and novel risk markers in pregnancy for the future personalised early prediction of pregnancy complications.

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Lianqun, Jia, Ju Xing, Ma Yixin, Chen Si, Lv Xiaoming, Song Nan, Sui Guoyuan, et al. "Comprehensive multiomics analysis of the effect of ginsenoside Rb1 on hyperlipidemia." Aging 13, no. 7 (March 19, 2021): 9732–47. http://dx.doi.org/10.18632/aging.202728.

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Han, Leshan, Xiaomeng Liu, Chongchuan Wang, Jianhang Liu, Qinglong Wang, Shuo Peng, Xidong Ren, Deqiang Zhu, and Xinli Liu. "Breeding of a High-Nisin-Yielding Bacterial Strain and Multiomics Analysis." Fermentation 8, no. 6 (May 27, 2022): 255. http://dx.doi.org/10.3390/fermentation8060255.

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Nisin is a green, safe and natural food preservative. With the expansion of nisin application, the demand for nisin has gradually increased, which equates to increased requirements for nisin production. In this study, Lactococcus lactis subsp. lactis lxl was used as the original strain, and the compound mutation method was applied to induce mutations. A high-yielding and genetically stable strain (Lactobacillus lactis A32) was identified, with the nisin titre raised by 332.2% up to 5089.29 IU/mL. Genome and transcriptome sequencing was used to analyse A32 and compare it with the original lxl strain. The comparative genomics results show that 107 genes in the A32 genome had mutations and most base mutations were not located in the four well-researched nisin-related operons, nisABTCIPRK, nisI, nisRK and nisFEG: 39 single-nucleotide polymorphisms (SNPs), 34 insertion mutations and 34 deletion mutations. The transcription results show that the expression of 92 genes changed significantly, with 27 of these differentially expressed genes upregulated, while 65 were downregulated. Our findings suggest that the output of nisin increased in L. lactis strain A32, which was accompanied by changes in the DNA replication-related gene dnaG, the ABC-ATPase transport-related genes patM and tcyC, the cysteine thiometabolism-related gene cysS, and the purine metabolism-related gene purL. Our study provides new insights into the traditional genetic mechanisms involved nisin production in L. lactis, which could provide clues for a more efficient metabolic engineering process.

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Alam, Md Morshedul, Kanchan Chakma, Shahriar Mahmud, Mohammad Nazir Hossain, M. Rezaul Karim, and Md Ariful Amin. "Multiomics analysis of altered NRF3 expression reveals poor prognosis in cancer." Informatics in Medicine Unlocked 29 (2022): 100892. http://dx.doi.org/10.1016/j.imu.2022.100892.

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Surowiec, Izabella, Tomas Skotare, Rickard Sjögren, Sandra Gouveia-Figueira, Judy Orikiiriza, Sven Bergström, Johan Normark, and Johan Trygg. "Joint and unique multiblock analysis of biological data – multiomics malaria study." Faraday Discussions 218 (2019): 268–83. http://dx.doi.org/10.1039/c8fd00243f.

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In this work we used Joint and Unique MultiBlock Analysis (JUMBA) for the integrated analysis of lipidomic, metabolomic and oxylipins data sets obtained from profiling of plasma samples from children infected with P. falciparum malaria.

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Li, Xiunan, Jiayi Li, Leizuo Zhao, Zicheng Wang, Peizhi Zhang, Yingkun Xu, and Guangzhen Wu. "Comprehensive Multiomics Analysis Reveals Potential Diagnostic and Prognostic Biomarkers in Adrenal Cortical Carcinoma." Computational and Mathematical Methods in Medicine 2022 (August 9, 2022): 1–33. http://dx.doi.org/10.1155/2022/2465598.

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Adrenal cortical carcinoma (ACC) is a severe malignant tumor with low early diagnosis rates and high mortality. In this study, we used a variety of bioinformatic analyses to find potential prognostic markers and therapeutic targets for ACC. Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) data sets were used to perform differential expressed analysis. WebGestalt was used to perform enrichment analysis, while String was used for protein-protein analysis. Our study first detected 28 up-regulation and 462 down-regulation differential expressed genes through the GEO and TCGA databases. Then, GO functional analysis, four pathway analyses (KEGG, REACTOME, PANTHER, and BIOCYC), and protein-protein interaction network were performed to identify these genes by WebGestalt tool and KOBAS website, as well as String database, respectively, and finalize 17 hub genes. After a series of analyses from GEPIA, including gene mutations, differential expression, and prognosis, we excluded one candidate unrelated to the prognosis of ACC and put the remaining genes into pathway analysis again. We screened out CCNB1 and NDC80 genes by three algorithms of Degree, MCC, and MNC. We subsequently performed genomic analysis using the TCGA and cBioPortal databases to better understand these two hub genes. Our data also showed that the CCNB1 and NDC80 genes might become ACC biomarkers for future clinical use.

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Quirós, Pedro M., Miguel A. Prado, Nicola Zamboni, Davide D’Amico, Robert W. Williams, Daniel Finley, Steven P. Gygi, and Johan Auwerx. "Multi-omics analysis identifies ATF4 as a key regulator of the mitochondrial stress response in mammals." Journal of Cell Biology 216, no. 7 (May 31, 2017): 2027–45. http://dx.doi.org/10.1083/jcb.201702058.

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Mitochondrial stress activates a mitonuclear response to safeguard and repair mitochondrial function and to adapt cellular metabolism to stress. Using a multiomics approach in mammalian cells treated with four types of mitochondrial stressors, we identify activating transcription factor 4 (ATF4) as the main regulator of the stress response. Surprisingly, canonical mitochondrial unfolded protein response genes mediated by ATF5 are not activated. Instead, ATF4 activates the expression of cytoprotective genes, which reprogram cellular metabolism through activation of the integrated stress response (ISR). Mitochondrial stress promotes a local proteostatic response by reducing mitochondrial ribosomal proteins, inhibiting mitochondrial translation, and coupling the activation of the ISR with the attenuation of mitochondrial function. Through a trans–expression quantitative trait locus analysis, we provide genetic evidence supporting a role for Fh1 in the control of Atf4 expression in mammals. Using gene expression data from mice and humans with mitochondrial diseases, we show that the ATF4 pathway is activated in vivo upon mitochondrial stress. Our data illustrate the value of a multiomics approach to characterize complex cellular networks and provide a versatile resource to identify new regulators of mitochondrial-related diseases.

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Joseph, Serene, Jacquelyn M. Walejko, Sicong Zhang, Arthur S. Edison, and Maureen Keller-Wood. "Maternal hypercortisolemia alters placental metabolism: a multiomics view." American Journal of Physiology-Endocrinology and Metabolism 319, no. 5 (November 1, 2020): E950—E960. http://dx.doi.org/10.1152/ajpendo.00190.2020.

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Previous studies have suggested that increases in maternal cortisol or maternal stress in late pregnancy increase the risk of stillbirth at term. In an ovine model with increased maternal cortisol over the last 0.20 of gestation, we have previously found evidence of disruption of fetal serum and cardiac metabolomics and altered expression of genes related to mitochondrial function and metabolism in biceps femoris, diaphragm, and cardiac muscle. The present studies were designed to test for effects of chronically increased maternal cortisol on gene expression and metabolomics in placentomes near term. We hypothesized that changes in placenta might underlie or contribute to the alterations in fetal serum metabolomics and thereby contribute to changes in striated muscle metabolism. Placentomes were collected from pregnancies in early labor (143 ± 1 days gestation) of control ewes ( n = 7) or ewes treated with cortisol (1 mg·kg−1·day−1 iv; n = 5) starting at day 115 of gestation. Transcriptomics and metabolomics were performed using an ovine gene expression microarray (Agilent 019921) and HR-MAS NMR, respectively. Multiomic analysis indicates that amino acid metabolism, particularly of branched-chain amino acids and glutamate, occur in placenta; changes in amino acid metabolism, degradation, or biosynthesis in placenta were consistent with changes in valine, isoleucine, leucine, and glycine in fetal serum. The analysis also indicates changes in glycerophospholipid metabolism and suggests changes in endoplasmic reticulum stress and antioxidant status in the placenta. These findings suggest that changes in placental function occurring with excess maternal cortisol in late gestation may contribute to metabolic dysfunction at birth.

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Vasaikar, Suhas V., Abhijeet P. Deshmukh, Petra den Hollander, Sridevi Addanki, Nick Allen Kuburich, Sriya Kudaravalli, Robiya Joseph, Jeffrey T. Chang, Rama Soundararajan, and Sendurai A. Mani. "EMTome: a resource for pan-cancer analysis of epithelial-mesenchymal transition genes and signatures." British Journal of Cancer 124, no. 1 (December 10, 2020): 259–69. http://dx.doi.org/10.1038/s41416-020-01178-9.

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Abstract Background The epithelial-mesenchymal transition (EMT) enables dissociation of tumour cells from the primary tumour mass, invasion through the extracellular matrix, intravasation into blood vessels and colonisation of distant organs. Cells that revert to the epithelial state via the mesenchymal-epithelial transition cause metastases, the primary cause of death in cancer patients. EMT also empowers cancer cells with stem-cell properties and induces resistance to chemotherapeutic drugs. Understanding the driving factors of EMT is critical for the development of effective therapeutic interventions. Methods This manuscript describes the generation of a database containing EMT gene signatures derived from cell lines, patient-derived xenografts and patient studies across cancer types and multiomics data and the creation of a web-based portal to provide a comprehensive analysis resource. Results EMTome incorporates (i) EMT gene signatures; (ii) EMT-related genes with multiomics features across different cancer types; (iii) interactomes of EMT-related genes (miRNAs, transcription factors, and proteins); (iv) immune profiles identified from The Cancer Genome Atlas (TCGA) cohorts by exploring transcriptomics, epigenomics, and proteomics, and drug sensitivity and (iv) clinical outcomes of cancer cohorts linked to EMT gene signatures. Conclusion The web-based EMTome portal is a resource for primary and metastatic tumour research publicly available at www.emtome.org.

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Sakallioglu, Isin Tuna, Bridget Tripp, Jacy Kubik, Carol A. Casey, Paul Thomes, and Robert Powers. "Multiomics Approach Captures Hepatic Metabolic Network Altered by Chronic Ethanol Administration." Biology 12, no. 1 (December 23, 2022): 28. http://dx.doi.org/10.3390/biology12010028.

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Using a multiplatform and multiomics approach, we identified metabolites, lipids, proteins, and metabolic pathways that were altered in the liver after chronic ethanol administration. A functional enrichment analysis of the multiomics dataset revealed that rats treated with ethanol experienced an increase in hepatic fatty acyl content, which is consistent with an initial development of steatosis. The nuclear magnetic resonance spectroscopy (NMR) and liquid chromatography–mass spectrometry (LC-MS) metabolomics data revealed that the chronic ethanol exposure selectively modified toxic substances such as an increase in glucuronidation tyramine and benzoyl; and a depletion in cholesterol-conjugated glucuronides. Similarly, the lipidomics results revealed that ethanol decreased diacylglycerol, and increased triacylglycerol, sterol, and cholesterol biosynthesis. An integrated metabolomics and lipidomics pathway analysis showed that the accumulation of hepatic lipids occurred by ethanol modulation of the upstream lipid regulatory pathways, specifically glycolysis and glucuronides pathways. A proteomics analysis of lipid droplets isolated from control EtOH-fed rats and a subsequent functional enrichment analysis revealed that the proteomics data corroborated the metabolomic and lipidomic findings that chronic ethanol administration altered the glucuronidation pathway.

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Li, Yuanyuan, Hang Li, Yuping Xie, Shuo Chen, Ritian Qin, Hangyan Dong, Yongliang Yu, Jianhua Wang, Xiaohong Qian, and Weijie Qin. "An Integrated Strategy for Mass Spectrometry-Based Multiomics Analysis of Single Cells." Analytical Chemistry 93, no. 42 (October 13, 2021): 14059–67. http://dx.doi.org/10.1021/acs.analchem.0c05209.

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Li, Yuanyuan, Hang Li, Yuping Xie, Shuo Chen, Ritian Qin, Hangyan Dong, Yongliang Yu, Jianhua Wang, Xiaohong Qian, and Weijie Qin. "An Integrated Strategy for Mass Spectrometry-Based Multiomics Analysis of Single Cells." Analytical Chemistry 93, no. 42 (October 13, 2021): 14059–67. http://dx.doi.org/10.1021/acs.analchem.0c05209.

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Yang, Chengcong, Lijun You, Lai-Yu Kwok, Hao Jin, Jiangying Peng, Zhixin Zhao, and Zhihong Sun. "Strain-level multiomics analysis reveals significant variation in cheeses from different regions." LWT 151 (November 2021): 112043. http://dx.doi.org/10.1016/j.lwt.2021.112043.

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Van Pelt, Douglas W., Yalda A. Kharaz, Dylan C. Sarver, Logan R. Eckhardt, Justin T. Dzierzawski, Nathaniel P. Disser, Alex N. Piacentini, Eithne Comerford, Brian McDonagh, and Christopher L. Mendias. "Multiomics analysis of the mdx/mTR mouse model of Duchenne muscular dystrophy." Connective Tissue Research 62, no. 1 (July 15, 2020): 24–39. http://dx.doi.org/10.1080/03008207.2020.1791103.

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Ge, Siqi, Youxin Wang, Manshu Song, Xingang Li, Xinwei Yu, Hao Wang, Jing Wang, Qiang Zeng, and Wei Wang. "Type 2 Diabetes Mellitus: Integrative Analysis of Multiomics Data for Biomarker Discovery." OMICS: A Journal of Integrative Biology 22, no. 7 (July 2018): 514–23. http://dx.doi.org/10.1089/omi.2018.0053.

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Feng, Zheng, Danyi Wang, Francesco Vallania, Neil Smith, Anupriya Tripathi, and Juergen Scheuenpflug. "Abstract 5113: Liquid biopsy-based multiomics profiling using low-pass whole genome sequencing and proteomics with computational modeling reveals molecular correlates of disease severity in EGFR/ALK wild type NSCLC patients." Cancer Research 82, no. 12_Supplement (June 15, 2022): 5113. http://dx.doi.org/10.1158/1538-7445.am2022-5113.

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Abstract Background: Molecular features of disease severity provide a basis for patient stratification, and may hint at previously unrecognized drug targets. Liquid biopsy-based multiomics profiling of plasma coupled with computational modeling provides a promising approach to interrogate both tumor and non-tumor derived signals, to support patient-centric personalized precision oncology. Here, we utilized a blood-based multiomics platform to identify, refine, and combine signals to categorize late-stage, treatment-naive, EGFR/ALK wild type NSCLC. Methods: Cell-free DNA (cfDNA) was extracted from 34 late-stage, treatment-naive, EGFR/ALK wild type NSCLC patients and low pass whole-genome sequencing was performed to characterize cfDNA fragments, which reflect nucleosome protection and chromatin state. Gene activation for protein-coding genes (GAP scores) was modeled from fragment distribution around transcription start sites. The abundances of 644 plasma proteins including markers of the immune response, inflammation, cancer, and DNA repair were measured. Partial least-squares discriminant analysis (PLS-DA) was used for marker selection and sample separation by smoking status with either genomics or proteomics data. Results: By statistically modeling clinical data, we identified a significant association showing never-smokers being enriched in the highest N stage, (P = 7.95E-3). Using PLS-DA, we identified proteomic (top 3 proteins: ST1A1, AXIN1, STAMPB) and GAP score (top 3 genes: CHD3, FANCF, METTL3) markers that separated patients by smoking status, suggesting underlying molecular differences. By performing pathway analysis on GAP scores, we identified 108 significant pathways enriched in the never-smoker group (FDR < 5%), 18 of which were associated with immune cell signatures, specifically myeloid cells and macrophages. Based on our previous association of IL1RN GAP scores with disease progression in NSCLC, we examined whether these results are confirmed. In this cohort, patients with high IL1RN GAP scores were significantly associated with worse lymph-node staging (N) (P = 6E-3), which is indicative of worse prognosis. Pathway analysis comparing patients with high vs low IL1RN scores revealed 102 significant pathways (FDR < 5%), including leukocyte activity and regulation of inflammatory response. Conclusion: The liquid biopsy-based multiomics platform suggests an association between never-smoker status and pathways of inflammatory response, resulting in more severe lymph-node staging. Future applications of this multiomics platform provide promise for stratification of NSCLC patients for patient-centric precision oncology. Citation Format: Zheng Feng, Danyi Wang, Francesco Vallania, Neil Smith, Anupriya Tripathi, Juergen Scheuenpflug. Liquid biopsy-based multiomics profiling using low-pass whole genome sequencing and proteomics with computational modeling reveals molecular correlates of disease severity in EGFR/ALK wild type NSCLC patients [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5113.

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Bisht, Vartika, Katrina Nash, Yuanwei Xu, Prasoon Agarwal, Sofie Bosch, Georgios V. Gkoutos, and Animesh Acharjee. "Integration of the Microbiome, Metabolome and Transcriptomics Data Identified Novel Metabolic Pathway Regulation in Colorectal Cancer." International Journal of Molecular Sciences 22, no. 11 (May 28, 2021): 5763. http://dx.doi.org/10.3390/ijms22115763.

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Integrative multiomics data analysis provides a unique opportunity for the mechanistic understanding of colorectal cancer (CRC) in addition to the identification of potential novel therapeutic targets. In this study, we used public omics data sets to investigate potential associations between microbiome, metabolome, bulk transcriptomics and single cell RNA sequencing datasets. We identified multiple potential interactions, for example 5-aminovalerate interacting with Adlercreutzia; cholesteryl ester interacting with bacterial genera Staphylococcus, Blautia and Roseburia. Using public single cell and bulk RNA sequencing, we identified 17 overlapping genes involved in epithelial cell pathways, with particular significance of the oxidative phosphorylation pathway and the ACAT1 gene that indirectly regulates the esterification of cholesterol. These findings demonstrate that the integration of multiomics data sets from diverse populations can help us in untangling the colorectal cancer pathogenesis as well as postulate the disease pathology mechanisms and therapeutic targets.

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Ichihashi, Yasunori, Yasuhiro Date, Amiu Shino, Tomoko Shimizu, Arisa Shibata, Kie Kumaishi, Fumiaki Funahashi, et al. "Multi-omics analysis on an agroecosystem reveals the significant role of organic nitrogen to increase agricultural crop yield." Proceedings of the National Academy of Sciences 117, no. 25 (June 8, 2020): 14552–60. http://dx.doi.org/10.1073/pnas.1917259117.

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Both inorganic fertilizer inputs and crop yields have increased globally, with the concurrent increase in the pollution of water bodies due to nitrogen leaching from soils. Designing agroecosystems that are environmentally friendly is urgently required. Since agroecosystems are highly complex and consist of entangled webs of interactions between plants, microbes, and soils, identifying critical components in crop production remain elusive. To understand the network structure in agroecosystems engineered by several farming methods, including environmentally friendly soil solarization, we utilized a multiomics approach on a field planted withBrassica rapa. We found that the soil solarization increased plant shoot biomass irrespective of the type of fertilizer applied. Our multiomics and integrated informatics revealed complex interactions in the agroecosystem showing multiple network modules represented by plant traits heterogeneously associated with soil metabolites, minerals, and microbes. Unexpectedly, we identified soil organic nitrogen induced by soil solarization as one of the key components to increase crop yield. A germ-free plant in vitro assay and a pot experiment using arable soils confirmed that specific organic nitrogen, namely alanine and choline, directly increased plant biomass by acting as a nitrogen source and a biologically active compound. Thus, our study provides evidence at the agroecosystem level that organic nitrogen plays a key role in plant growth.

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Ohashi, Hiroyuki, Mai Hasegawa, Kentaro Wakimoto, and Etsuko Miyamoto-Sato. "Next-Generation Technologies for Multiomics Approaches Including Interactome Sequencing." BioMed Research International 2015 (2015): 1–9. http://dx.doi.org/10.1155/2015/104209.

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The development of high-speed analytical techniques such as next-generation sequencing and microarrays allows high-throughput analysis of biological information at a low cost. These techniques contribute to medical and bioscience advancements and provide new avenues for scientific research. Here, we outline a variety of new innovative techniques and discuss their use in omics research (e.g., genomics, transcriptomics, metabolomics, proteomics, and interactomics). We also discuss the possible applications of these methods, including an interactome sequencing technology that we developed, in future medical and life science research.

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Ma, Yawen, and Zhuo Xi. "Integrated Analysis of Multiomics Data Identified Molecular Subtypes and Oxidative Stress-Related Prognostic Biomarkers in Glioblastoma Multiforme." Oxidative Medicine and Cellular Longevity 2022 (September 22, 2022): 1–15. http://dx.doi.org/10.1155/2022/9993319.

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Glioblastoma multiforme (GBM) is a glioma in IV stage, which is one of the most common primary malignant brain tumors in adults. GBM has the characters of high invasiveness, high recurrence rate, and low survival rate and with a poor prognosis. GBM implicates various genetic changes and epigenetic and gene transcription disorders, which are crucial in developing GBM. With the progression and enhancement of high-throughput sequencing technologies, the acquirement and administering approaches of diverse biological omics data on distinctive levels are developing more advanced. However, the research of GBM with multiomics remains largely unknown. We identified GBM-related molecular subtypes by integrated multiomics data and exploring the connections of gene copy number variation (CNV) and methylation gene (MET) change data. The expression of CNV and MET genes was examined through cluster integration analysis. The present study confirmed three clusters (iC1, iC2, and iC3) with distinctive prognosis and molecule peculiarities. We also recognized three oxidative stress protecting molecules (OSMR, IGFBP6, and MYBPH) by contrasting gene expression, MET, and CNV in the three subtypes. OSMR, IGFBP6, and MYBPH were differentially expressed in the clusters, suggesting they might be recognized as characteristic markers for the three clusters in GBM. Through integrative investigation of genomics, epigenomics, and transcriptomics, we offer novel visions into the multilayered molecules of GBM and facilitate the accuracy remedy for GBM sufferers.

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Li, Hanwen, Shaohua Chen, and Hua Mi. "A Multiomics Profiling Based on Online Database Revealed Prognostic Biomarkers of BLCA." BioMed Research International 2022 (May 25, 2022): 1–19. http://dx.doi.org/10.1155/2022/2449449.

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Background. Bladder cancer (BLCA) is one of the most common urological malignancies globally, posing a severe threat to public health. In combination with protein-protein interaction (PPI) network analysis of proteomics, Gene Set Variation Analysis (GSVA) and “CancerSubtypes” package of R software for transcriptomics can help identify biomarkers related to BLCA prognosis. This will have significant implications for prevention and treatment. Method. BLCA data were downloaded from The Cancer Genome Atlas (TCGA) database and GEO database (GSE13507). GSVA analysis converted the gene expression matrix to the gene set expression matrix. “CancerSubtypes” classified patients into three subtypes and established a prognostic model based on differentially expressed gene sets (DEGSs) among the three subtypes. For genes from prognosis-related DEGSs, functional and pathway enrichment analyses and PPI network analysis were carried out. The Human Protein Atlas (HPA) database was used for validation. Finally, the proportion of tumor-infiltrating immune cells (TIICs) was determined using the CIBERSORT algorithm. Results. In total, 414 tumor samples and 19 adjacent-tumor samples were obtained from TCGA, with 145 samples belonging to subtype A, 126 samples belonging to subtype B, and 136 samples belonging to subtype C. Then, we identified 83 DEGSs and constituted a prognostic signature with two of them: “GSE1460_CD4_THYMOCYTE_VS_THYMIC_STROMAL_CELL_DN” and “MODULE_253.” Finally, five subnets of two PPI networks were established, and nine core proteins were obtained: CDH2, COL1A1, EIF2S2, PSMA3, NAA10, DNM1L, TUBA4A, KIF11, and KIF23. The HPA database confirmed the expression of the nine core proteins in BLCA tissues. Furthermore, EIF2S2, PSMA3, DNM1L, and TUBA4A could be novel BLCA prognostic biomarkers. Conclusions. In this study, we discovered two gene sets linked to BLCA prognosis. PPI analysis confirmed the network’s core proteins, and several newly discovered biomarkers of BLCA prognosis were identified.

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Liu, Dazhong, Pengfei Zhang, Jiaying Zhao, Lei Yang, and Wei Wang. "Identification of Molecular Characteristics and New Prognostic Targets for Thymoma by Multiomics Analysis." BioMed Research International 2021 (May 19, 2021): 1–15. http://dx.doi.org/10.1155/2021/5587441.

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Background. Thymoma is a heterogeneous tumor originated from thymic epithelial cells. The molecular mechanism of thymoma remains unclear. Methods. The expression profile, methylation, and mutation data of thymoma were obtained from TCGA database. The coexpression network was constructed using the variance of gene expression through WGCNA. Enrichment analysis using clusterProfiler R package and overall survival (OS) analysis by Kaplan-Meier method were carried out for the intersection of differential expression genes (DEGs) screened by limma R package and important module genes. PPI network was constructed based on STRING database for genes with significant impact on survival. The impact of key genes on the prognosis of thymoma was evaluated by ROC curve and Cox regression model. Finally, the immune cell infiltration, methylation modification, and gene mutation were calculated. Results. We obtained eleven coexpression modules, and three of them were higher positively correlated with thymoma. DEGs in these three modules mainly involved in MAPK cascade and PPAR pathway. LIPE, MYH6, ACTG2, KLF4, SULT4A1, and TF were identified as key genes through the PPI network. AUC values of LIPE were the highest. Cox regression analysis showed that low expression of LIPE was a prognostic risk factor for thymoma. In addition, there was a high correlation between LIPE and T cells. Importantly, the expression of LIPE was modified by methylation. Among all the mutated genes, GTF2I had the highest mutation frequency. Conclusion. These results suggested that the molecular mechanism of thymoma may be related to immune inflammation. LIPE may be the key genes affecting prognosis of thymoma. Our findings will help to elucidate the pathogenesis and therapeutic targets of thymoma.

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Liu, Li, Jianjun Huang, Yan Liu, Xingshou Pan, Zhile Li, Liufang Zhou, Tengfang Lai, et al. "Multiomics Analysis of Transcriptome, Epigenome, and Genome Uncovers Putative Mechanisms for Dilated Cardiomyopathy." BioMed Research International 2021 (March 29, 2021): 1–29. http://dx.doi.org/10.1155/2021/6653802.

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Objective. Multiple genes have been identified to cause dilated cardiomyopathy (DCM). Nevertheless, there is still a lack of comprehensive elucidation of the molecular characteristics for DCM. Herein, we aimed to uncover putative molecular features for DCM by multiomics analysis. Methods. Differentially expressed genes (DEGs) were obtained from different RNA sequencing (RNA-seq) datasets of left ventricle samples from healthy donors and DCM patients. Furthermore, protein-protein interaction (PPI) analysis was then presented. Differentially methylated genes (DMGs) were identified between DCM and control samples. Following integration of DEGs and DMGs, differentially expressed and methylated genes were acquired and their biological functions were analyzed by the clusterProfiler package. Whole exome sequencing of blood samples from 69 DCM patients was constructed in our cohort, which was analyzed the maftools package. The expression of key mutated genes was verified by three independent datasets. Results. 1407 common DEGs were identified for DCM after integration of the two RNA-seq datasets. A PPI network was constructed, composed of 171 up- and 136 downregulated genes. Four hub genes were identified for DCM, including C3 ( degree = 24 ), GNB3 ( degree = 23 ), QSOX1 ( degree = 21 ), and APOB ( degree = 17 ). Moreover, 285 hyper- and 321 hypomethylated genes were screened for DCM. After integration, 20 differentially expressed and methylated genes were identified, which were associated with cell differentiation and protein digestion and absorption. Among single-nucleotide variant (SNV), C>T was the most frequent mutation classification for DCM. MUC4 was the most frequent mutation gene which occupied 71% across 69 samples, followed by PHLDA1, AHNAK2, and MAML3. These mutated genes were confirmed to be differentially expressed between DCM and control samples. Conclusion. Our findings comprehensively analyzed molecular characteristics from the transcriptome, epigenome, and genome perspectives for DCM, which could provide practical implications for DCM.

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Shen, Yiqing, Wensong Yang, Xin Xiong, Xinhui Li, Zhongsong Xiao, Jialun Yu, Fangyu Liu, et al. "Integrated Multiomics Analysis Identifies a Novel Biomarker Associated with Prognosis in Intracerebral Hemorrhage." Oxidative Medicine and Cellular Longevity 2021 (December 14, 2021): 1–20. http://dx.doi.org/10.1155/2021/2510847.

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Existing treatments for intracerebral hemorrhage (ICH) are unable to satisfactorily prevent development of secondary brain injury after ICH and multiple pathological mechanisms are involved in the development of the injury. In this study, we aimed to identify novel genes and proteins and integrated their molecular alternations to reveal key network modules involved in ICH pathology. A total of 30 C57BL/6 male mice were used for this study. The collagenase model of ICH was employed, 3 days after ICH animals were tested neurological. After it, animals were euthanized and perihematomal brain tissues were collected for transcriptome and TMT labeling-based quantitative proteome analyses. Protein-protein interaction (PPI) network, Gene Set Enrichment Analysis (GSEA), and regularized Canonical Correlation Analysis (rCCA) were performed to integrated multiomics data. For validation of hub genes and proteins, qRT-PCR and Western blot were carried out. The candidate biomarkers were further measured by ELISA in the plasma of ICH patients and the controls. A total of 2218 differentially expressed genes (DEGs) and 353 differentially expressed proteins (DEPs) between the ICH model group and control group were identified. GSEA revealed that immune-related gene sets were prominently upregulated and significantly enriched in pathways of inflammasome complex, negative regulation of interleukin-12 production, and pyroptosis during the ICH process. The rCCA network presented two highly connective clusters which were involved in the sphingolipid catabolic process and inflammatory response. Among ten hub genes screened out by integrative analysis, significantly upregulated Itgb2, Serpina3n, and Ctss were validated in the ICH group by qRT-PCR and Western blot. Plasma levels of human SERPINA3 (homologue of murine Serpina3n) were elevated in ICH patients compared with the healthy controls (SERPINA3: 13.3 ng/mL vs. 11.2 ng/mL, p = 0.015 ). Within the ICH group, higher plasma SERPINA3 levels with a predictive threshold of 14.31 ng/mL ( sensitivity = 64.3 % ; specificity = 80.8 % ; AUC = 0.742 , 95% CI: 0.567-0.916) were highly associated with poor outcome (mRS scores 4-6). Taken together, the results of our study exhibited molecular changes related to ICH-induced brain injury by multidimensional analysis and effectively identified three biomarker candidates in a mouse ICH model, as well as pointed out that Serpina3n/SERPINA3 was a potential biomarker associated with poor functional outcome in ICH patients.

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