{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["16(1)"],"submitter":["Deng X"],"pubmed_abstract":["<h4>Background</h4>Esophageal cancer (EC) has a high incidence and is highly invasive. It is meaningful to employ invasion-related genes (IRGs) to predict patients' prognosis.<h4>Methods</h4>We launched a weighted correlation network analysis to screen for EC tumor differentially expressed IRGs (module genes) from the TCGA-ESCA dataset. By executing univariate-LASSO-multivariate Cox regression analyses, we stepwise selected module genes to obtain prognostic feature genes and create a model. We validated the model using the external GEO dataset GSE53624. We assessed the model's independent prognosis prediction ability. A nomogram was plotted and further validated later. GSEA was undertaken on high-risk groups (Group H). We compared immunity and tumor mutations in Group H and the low-risk gr"],"journal":["Discover oncology"],"pagination":["1695"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12460863"],"repository":["biostudies-literature"],"pubmed_title":["Selection of tumor invasion-related genes to build a prognostic model and predict immune response and potential drugs for esophageal cancer patients based on WGCNA."],"pmcid":["PMC12460863"],"pubmed_authors":["Liu Y","Deng X"],"additional_accession":[]},"is_claimable":false,"name":"Selection of tumor invasion-related genes to build a prognostic model and predict immune response and potential drugs for esophageal cancer patients based on WGCNA.","description":"<h4>Background</h4>Esophageal cancer (EC) has a high incidence and is highly invasive. It is meaningful to employ invasion-related genes (IRGs) to predict patients' prognosis.<h4>Methods</h4>We launched a weighted correlation network analysis to screen for EC tumor differentially expressed IRGs (module genes) from the TCGA-ESCA dataset. By executing univariate-LASSO-multivariate Cox regression analyses, we stepwise selected module genes to obtain prognostic feature genes and create a model. We validated the model using the external GEO dataset GSE53624. We assessed the model's independent prognosis prediction ability. A nomogram was plotted and further validated later. GSEA was undertaken on high-risk groups (Group H). We compared immunity and tumor mutations in Group H and the low-risk gr","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025 Sep","modification":"2026-05-31T03:10:37.86Z","creation":"2026-05-31T03:07:17.599Z"},"accession":"S-EPMC12460863","cross_references":{"pubmed":["40991071"],"doi":["10.1007/s12672-025-03095-w"]}}