Development and Validation of Autophagy-Related Gene Signature and Nomogram for Predicting Survival in Oral Squamous Cell Carcinoma.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:33178587", "kind": "science-record", "revision": 6, "content_hash": "sha256:fb7b50adada9dca417f0c7de1f4ef994e31825779165cd4481ace6d25d3aa779", "created_at": "2026-11-01T15:41:15Z", "updated_at": "2026-11-01T21:24:04Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Development and Validation of Autophagy-Related Gene Signature and Nomogram for Predicting Survival in Oral Squamous Cell Carcinoma.", "doi": "10.3389/fonc.2020.558596", "pmid": "33178587", "authors": ["Chen Hou", "Hongshi Cai", "Yue Zhu", "Shuojin Huang", "Fan Song", "Jinsong Hou"], "journal": "Frontiers in oncology", "year": 2020, "abstract": "BACKGROUND: Autophagy, a highly conserved self-digesting process, has been deeply involved in the development and progression of oral squamous cell carcinoma (OSCC). However, the prognostic value of autophagy-related genes (ARGs) for OSCC still remains unclear. Our study set out to develop a multigene expression signature based on ARGs for individualized prognosis assessment in OSCC patients.\nMETHODS: Based on The Cancer Genome Atlas (TCGA) database, we identified prognosis-related ARGs through univariate COX regression analysis. Then we performed the least absolute shrinkage and selection operator (LASSO) regression analysis to identify an optimal autophagy-related multigene signature with the subsequent validation in testing set, GSE41613 and GSE42743 datasets.\nRESULTS: We identified 36 prognosis-related ARGs for OSCC. Subsequently, the multigene signature based on 13 prognostic ARGs was constructed and successfully divided OSCC patients into low and high-risk groups with significantly different overall survival in TCGA training set (p < 0.0001). The autophagy signature remained as an independent prognostic factor for OSCC in univariate and multivariate Cox regression analyses. The area under the curve (AUC) values of the receiver operating characteristic (ROC) curves for 1, 3, and 5-year survival were 0.758, 0.810, 0.798, respectively. Then the gene signature was validated in TCGA testing set, GSE41613 and GSE42743 datasets. Moreover, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, and single-sample gene set enrichment analysis (ssGSEA) revealed the underlying biological characteristics and signaling pathways associated with this signature in OSCC. Finally, we constructed a nomogram by combining the gene signature with multiple clinical parameters (age, gender, TNM-stage, tobacco, and alcohol history). The concordance index (C-index) and calibration plots demonstrated favorable predictive performance of our nomogram.\nCONCLUSION: In summary, we identified and verified a 13-ARGs prognostic signature and nomogram, which provide individualized prognosis evaluation and show insight for potential therapeutic targets for OSCC.", "claims": [{"id": "claim-method", "statement": "Univariate Cox proportional hazard regression analysis was performed to identify the ARGs significantly correlated with the overall survival (OS) in TCGA-OSCC cohort, using the survival package.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:33178587", "locator": "sections[5] \"Construction and Validation of the Autophagy-Related Genes-Based Prognostic Signature\" paragraph 2", "quote": "Univariate Cox proportional hazard regression analysis was performed to identify the ARGs significantly correlated with the overall survival (OS) in TCGA-OSCC cohort, using the survival package."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Publicly available datasets were analyzed in this study. This data can be found here: OSCC cohort from TCGA database, https://portal.gdc.cancer.gov/; GSE41613 and GSE42743 datasets from GEO database, https://www.ncbi.nlm.nih.gov/geo.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:33178587", "locator": "sections[15] \"Data Availability Statement\" paragraph 2", "quote": "Publicly available datasets were analyzed in this study. This data can be found here: OSCC cohort from TCGA database, https://portal.gdc.cancer.gov/; GSE41613 and GSE42743 datasets from GEO database, https://www.ncbi.nlm.nih.gov/geo."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": null, "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:33178587"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "complete", "blocked_on": null, "description": "The GEO series accession resolves and its sample accessions are listed. This lane did not download the dataset or read its licence.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE41613"], "outputs": ["the source data with its repository provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the published analysis", "kind": "analysis", "state": "complete", "blocked_on": null, "description": "Apply the paper's statistical method to the resolved series and compare the result with the published target.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE41613"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:33178587", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fonc.2020.558596", "sha256": "sha256:f9c812100427b190caf332c3c9861e34fa386a7abd62b40d7b99abfbb30586a9", "bytes": 42521, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3389/fonc.2020.558596", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC7596585", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE41613", "role": "raw", "format": "geo-series", "media_type": "application/vnd.ncbi.geo-series+json", "uri": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE41613", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE41613", "version": null, "note": "Publicly available datasets were analyzed in this study. 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