Dataset Information

An epithelial-mesenchymal transition-related prognostic model for colorectal cancer based on weighted gene co-expression network analysis.

ABSTRACT:

Objective

To identify susceptibility modules and genes for colorectal cancer (CRC) using weighted gene co-expression network analysis (WGCNA).

Methods

Four microarray datasets were downloaded from the Gene Expression Omnibus database. We divided the tumor samples into three subgroups based on consensus clustering of gene expression, and analyzed the correlations between the subgroups and clinical features. The genetic features of the subgroups were investigated by gene set enrichment analysis (GSEA). A gene expression network was constructed using WGCNA, and a protein-protein interaction (PPI) network was used to identify the key genes. Gene modules were annotated by Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses.

Results

We divided the cancer cases into three subgroups based on consensus clustering (subgroups I, II, III). The green module identified by WGCNA was correlated with clinical characteristics. Ten key genes were identified according to their degree of connectivity in the protein-protein interaction network: FYN, SEMA3A, AP2M1, L1CAM, NRP1, TLN1, VWF, ITGB3, ILK, and ACTN1.

Conclusion

We identified 10 hub genes as candidate biomarkers for CRC. These key genes may provide a theoretical basis for targeted therapy against CRC.

SUBMITTER: Zhang L

PROVIDER: S-EPMC9751178 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature

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Publications

An epithelial-mesenchymal transition-related prognostic model for colorectal cancer based on weighted gene co-expression network analysis.

Zhang Lina L Qian Yucheng Y

The Journal of international medical research 20221201 12

<h4>Objective</h4>To identify susceptibility modules and genes for colorectal cancer (CRC) using weighted gene co-expression network analysis (WGCNA).<h4>Methods</h4>Four microarray datasets were downloaded from the Gene Expression Omnibus database. We divided the tumor samples into three subgroups based on consensus clustering of gene expression, and analyzed the correlations between the subgroups and clinical features. The genetic features of the subgroups were investigated by gene set enrichm ...[more]

PMID: 36510452

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Project description:BackgroundColorectal cancer (CRC) is one of the most prevalent malignancies and the third most lethal cancer globally. The most reported histological subtype of CRC is colon adenocarcinoma (COAD). The zinc transport pathway is critically involved in various tumors, and its anti-tumor effect may be through improving immune function. However, the Zn transport pathway in COAD has not been reported.MethodsThe determination of Zn transport-related genes in COAD was carried out through single-cell analysis of the GSE 161277 obtained from the GEO dataset. Subsequently, a weighted co-expression network analysis of the TCGA cohort was performed. Then, the prognostic model was conducted utilizing univariate Cox regression and least absolute shrinkage and selection operator (LASSO) Cox regression analysis. Functional enrichment, immune microenvironment, and survival analyses were also carried out. Consensus clustering analysis was utilized to verify the validity of the prognostic model and explore the immune microenvironment. Ultimately, cell experiments, including CCK-8,transwell and scratch assays, were performed to identify the function of LRRC59 in COAD.ResultsAccording to the Zn transport-related prognostic model, the individuals with COAD in TCGA and GEO databases were classified into high- and low-risk groups. The group with low risk had a comparatively more favorable prognosis. Two groups had significant variations in the immune infiltration, MHC, and the expression of genes related to the immune checkpoint. The cell experiments indicated that the proliferation, migration, and invasion of the HCT-116, DLD-1, and RKO cell lines were considerably increased after LRRC59 knockdown. It proved that LRRC59 was indeed a protective factor for COAD.ConclusionA prognostic model for COAD was developed using zinc transport-related genes. This model can efficiently assess the immune microenvironment and prognosis of individuals with COAD. Subsequently, the function of LRRC59 in COAD was validated via cell experiments, highlighting its potential as a biomarker.

Project description:Background: Most patients with idiopathic pulmonary fibrosis (IPF) have poor prognosis; Effective predictive models for these patients are currently lacking. Epithelial-mesenchymal transition (EMT) often occurs during idiopathic pulmonary fibrosis development, and is closely related to multiple pathways and biological processes. It is thus necessary for clinicians to find prognostic biomarkers with high accuracy and specificity from the perspective of Epithelial-mesenchymal transition. Methods: Data were obtained from the Gene Expression Omnibus database. Using consensus clustering, patients were grouped based on Epithelial-mesenchymal transition-related genes. Next, functional enrichment analysis was performed on the results of consensus clustering using gene set variation analysis. The gene modules associated with Epithelial-mesenchymal transition were obtained through weighted gene co-expression network analysis. Prognosis-related genes were screened via least absolute shrinkage and selection operator (LASSO) regression analysis. The model was then evaluated and validated using survival analysis and time-dependent receiver operating characteristic (ROC) analysis. Results: A total of 239 Epithelial-mesenchymal transition-related genes were obtained from patients with idiopathic pulmonary fibrosis. Six genes with strong prognostic associations (C-X-C chemokine receptor type 7 [CXCR7], heparan sulfate-glucosamine 3-sulfotransferase 1 [HS3ST1], matrix metallopeptidase 25 [MMP25], murine retrovirus integration site 1 [MRVI1], transmembrane four L6 family member 1 [TM4SF1], and tyrosylprotein sulfotransferase 1 [TPST1]) were identified via least absolute shrinkage and selection operator and Cox regression analyses. A prognostic model was then constructed based on the selected genes. Survival analysis showed that patients with high-risk scores had worse prognosis based on the training set [hazard ratio (HR) = 7.31, p < .001] and validation set (HR = 2.85, p = .017). The time-dependent receiver operating characteristic analysis showed that the area under the curve (AUC) values in the training set were .872, .905, and .868 for 1-, 2-, and 3-year overall survival rates, respectively. Moreover, the area under the curve values in the validation set were .814, .814, and .808 for 1-, 2-, and 3-year overall survival rates, respectively. Conclusion: The independent prognostic model constructed from six Epithelial-mesenchymal transition-related genes provides bioinformatics guidance to identify additional prognostic markers for idiopathic pulmonary fibrosis in the future.

Dataset Information

An epithelial-mesenchymal transition-related prognostic model for colorectal cancer based on weighted gene co-expression network analysis.

Objective

Methods

Results

Conclusion

Publications

An epithelial-mesenchymal transition-related prognostic model for colorectal cancer based on weighted gene co-expression network analysis.

Similar Datasets

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