An epithelial–mesenchymal transition-related prognostic model for colorectal cancer based on weighted gene co-expression network analysis
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 consen...
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| Format: | Article |
| Language: | English |
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SAGE Publishing
2022-12-01
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| Series: | Journal of International Medical Research |
| Online Access: | https://doi.org/10.1177/03000605221140683 |
| _version_ | 1811200773801902080 |
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| author | Lina Zhang Yucheng Qian |
| author_facet | Lina Zhang Yucheng Qian |
| author_sort | Lina Zhang |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-12T02:09:19Z |
| format | Article |
| id | doaj.art-9f0cd0f43c6848e6878ba9124fd3c503 |
| institution | Directory Open Access Journal |
| issn | 1473-2300 |
| language | English |
| last_indexed | 2024-04-12T02:09:19Z |
| publishDate | 2022-12-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | Journal of International Medical Research |
| spelling | doaj.art-9f0cd0f43c6848e6878ba9124fd3c5032022-12-22T03:52:27ZengSAGE PublishingJournal of International Medical Research1473-23002022-12-015010.1177/03000605221140683An epithelial–mesenchymal transition-related prognostic model for colorectal cancer based on weighted gene co-expression network analysisLina ZhangYucheng QianObjective 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.https://doi.org/10.1177/03000605221140683 |
| spellingShingle | Lina Zhang Yucheng Qian An epithelial–mesenchymal transition-related prognostic model for colorectal cancer based on weighted gene co-expression network analysis Journal of International Medical Research |
| title | An epithelial–mesenchymal transition-related prognostic model for colorectal cancer based on weighted gene co-expression network analysis |
| title_full | An epithelial–mesenchymal transition-related prognostic model for colorectal cancer based on weighted gene co-expression network analysis |
| title_fullStr | An epithelial–mesenchymal transition-related prognostic model for colorectal cancer based on weighted gene co-expression network analysis |
| title_full_unstemmed | An epithelial–mesenchymal transition-related prognostic model for colorectal cancer based on weighted gene co-expression network analysis |
| title_short | An epithelial–mesenchymal transition-related prognostic model for colorectal cancer based on weighted gene co-expression network analysis |
| title_sort | epithelial mesenchymal transition related prognostic model for colorectal cancer based on weighted gene co expression network analysis |
| url | https://doi.org/10.1177/03000605221140683 |
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