{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Chen R"],"funding":["National Natural Science Foundation of China"],"pagination":["388"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12897147"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["18(3)"],"pubmed_abstract":["<h4>Purpose</h4>Despite existing signatures, there remains a lack of robust autophagy-based biomarkers validated across multi-omics datasets and independent clinical cohorts in ESCC. The aim of our study was to develop an autophagy-related prognostic model for ESCC.<h4>Methods</h4>Using transcriptomic data of ESCC from GEO/TCGA and autophagy-related genes (ARGs) from five autophagy-specific datasets, we identified the intersection between ARGs and tumor-normal differentially expressed genes (DEGs). We constructed a prognostic model using stepwise multivariate Cox regression based on these genes in GSE53625 (<i>n</i> = 179), validated in TCGA-ESCC (<i>n</i> = 94) through survival analysis and ROC curve, and analyzed the prognostic value of candidate genes in in-house ESCC samples.<h4>Result"],"journal":["Cancers"],"pubmed_title":["Identification and Validation of an Autophagy-Related Gene Signature for Prognostic Prediction and Immunotherapy Response in Esophageal Squamous Cell Carcinoma."],"pmcid":["PMC12897147"],"funding_grant_id":["Grant No. 62472415"],"pubmed_authors":["Li G","Fu F","Zhang H","Wang X","Chen R","Zhou H"],"additional_accession":[]},"is_claimable":false,"name":"Identification and Validation of an Autophagy-Related Gene Signature for Prognostic Prediction and Immunotherapy Response in Esophageal Squamous Cell Carcinoma.","description":"<h4>Purpose</h4>Despite existing signatures, there remains a lack of robust autophagy-based biomarkers validated across multi-omics datasets and independent clinical cohorts in ESCC. The aim of our study was to develop an autophagy-related prognostic model for ESCC.<h4>Methods</h4>Using transcriptomic data of ESCC from GEO/TCGA and autophagy-related genes (ARGs) from five autophagy-specific datasets, we identified the intersection between ARGs and tumor-normal differentially expressed genes (DEGs). We constructed a prognostic model using stepwise multivariate Cox regression based on these genes in GSE53625 (<i>n</i> = 179), validated in TCGA-ESCC (<i>n</i> = 94) through survival analysis and ROC curve, and analyzed the prognostic value of candidate genes in in-house ESCC samples.<h4>Result","dates":{"release":"2026-01-01T00:00:00Z","publication":"2026 Jan","modification":"2026-07-07T03:12:10.066Z","creation":"2026-07-07T03:08:39.122Z"},"accession":"S-EPMC12897147","cross_references":{"pubmed":["41681864"],"doi":["10.3390/cancers18030388"]}}