Toxicogenomics in Toxicologic Pathology
Pritam S. Sahota, James A. Popp, Jerry F. Hardisty, Chirukandath Gopinath, Page R. Bouchard in Toxicologic Pathology, 2018
Newer bioinformatics approaches are being developed to understand the toxicity profiles using unsupervised network-based approaches. Sutherland et al. (2017) have used a novel bio-informatics approach called the weighted gene co-expression network analysis (WGCNA) to organize the transcriptomic data in an unsupervised manner into well-defined gene networks or modules with unique biological functions. The assumption is that these gene networks or modules are conserved across species and hence any discovery in the rodents would have translational relevance. Using rat hepatic transcriptomic data from Drug Matrix and TG-GATEs, they have integrated the co-expressed modules with standard pathology endpoints to develop a framework called the ‘toxicogenomic module associations with pathogenesis’ (TXG-MAP) to characterize mechanisms of drug-induced liver injury that are translatable to human liver diseases (Sutherland et al. 2017). Such toxicogenomic approaches that integrate transcriptomics and pathology are likely to yield biologically meaningful conclusions.
Genetics and genomics of idiopathic pulmonary fibrosis
Muhunthan Thillai, David R Moller, Keith C Meyer in Clinical Handbook of Interstitial Lung Disease, 2017
A follow-up study performed by the same investigatory team utilized their previously described PBMC microarray dataset (56) to identify potential prognostic predictor genes and then utilized an unbiased analytic technique ‘Weighted Gene Co-expression Network Analysis’ (WGCNA) to find groups of genes whose expression patterns suggested co-regulation (57). These gene modules were then associated with pulmonary function and clinical outcome (57). The resulting functional genomic model illustrated diagnostic (IPF versus control) and prognostic utility. It also suggested down-regulation of immune system pathways were predictive of poor prognosis (57). This is consistent with the finding that immunosuppressive therapy (e.g. prednisone, azathioprine) can worsen the clinical course for IPF patients (5).
Identification of immune-associated prognostic biomarkers in lung adenocarcinoma on the basis of gene co-expression network
Published in Immunopharmacology and Immunotoxicology, 2023
Jianhai Zhang, Ange Chen, Zhang Xue, Chengzhi Liang
Weighted gene co-expression network analysis (WGCNA) is able to analyze expression patterns of genes in multiple samples. It mainly uses gene expression patterns to cluster similar genes into gene modules. By analyzing the correlation between specific traits and modules, candidate biomarkers or therapeutic targets can be identified [6]. In recent years, it has become increasingly common to use WGCNA to analyze public database data and mine cancer diagnostic and prognostic markers. By using WGCNA method, Yi Liao et al. [7] obtained green and blue modules of stem cell index based on mRNA expression, and finally constructed a 6-hub genes prognostic model for LUAD patients by protein-protein interaction network and other bioinformatics means. Tian et al. [8] mined the core modules and central genes related to breast cancer and found them being new sites for targeted therapy of breast cancer. Bao et al. [9] finally identified 16 potential targeted drugs for ovarian cancer treatment by drug-gene interaction analysis. These genes may be targets for these targeted drugs [9]. In a word, WGCNA method is suitable for screening genes with co-expression relationship.
Identification of a cancer-associated fibroblast signature for predicting prognosis and immunotherapeutic responses in bladder urothelial carcinoma
Published in The Aging Male, 2023
Weighted gene co-expression network analysis (WGCNA) is used to analyze gene expression patterns in multiple groups of samples. The method classifies genes with highly similar expression patterns into modules and further analyzes the internal relationship between the key genes in modules (or modules) and the clinical characteristics of patients [18]. It is most commonly applied to high-dimensional datasets in genomics. Further, WGCNA is widely used to screen candidate biomarkers or therapeutic targets. Zhen et al. reported CAF-associated biomarkers of gastric cancer using WGCNA and constructed gene signatures associated with prognosis and immunotherapy response [19]. However, few related studies have been conducted on BLCA. Here, we aimed to identify CAF-associated biomarkers and construct gene signatures for evaluating the prognosis of patients with BLCA and their response to immunotherapy.
Crucial genes of inflammatory bowel diseases explored by gene expression profiling analysis
Published in Scandinavian Journal of Gastroenterology, 2018
Dehong Xie, Yudong Zhang, Hao Qu
In 2008, Carey et al. have identified IL-6:STAT3-dependent biological networks upregulated in IBD patients using microarray analysis [4]. Furthermore, in 2013, Montero-Mele´ndez et al. have demonstrated a set of predictor classifiers correlated with immune responses to bacteria (OLFM4, PTN and LILRA2), autophagy and endocytocis processes (ATG16L1, VPS26B, DNAJC6, ITSN1, RABGEF1 and TMEM127) and glucocorticoid receptor degradation (MMD2 and STS) in IBD patients using microarray gene expression profiling [5]. Nevertheless, the interactions of differentially expressed genes (DEGs) in CD and UC were not explored. In this study, the microarray expression data deposited by Carey and Montero-Mele´ndez [4,5] were used to screen DEGs in CD and UC. Then, weighted gene co-expression network analysis (WGCNA) was performed and gene modules were identified. Moreover, pathway enrichment analysis for the genes in identified modules was conducted. The results were expected to provide theoretical basis for further experimental researches on IBD.
Related Knowledge Centers
- Gene
- Gene Expression
- Gene Regulatory Network
- Gene Expression Profiling
- Microarray
- Rna-Seq
- Pearson Correlation Coefficient
- Spearman'S Rank Correlation Coefficient
- Standard Score
- P-Value