Identification and validation of an individualized autophagy-clinical prognostic index in gastric cancer patients.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:32477008", "kind": "science-record", "revision": 7, "content_hash": "sha256:4e5d48d438ad1455aebd24aed750a98985031d743f3f042ebd1889df8f3e4858", "created_at": "2026-11-01T15:41:02Z", "updated_at": "2026-11-01T22:01:42Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification and validation of an individualized autophagy-clinical prognostic index in gastric cancer patients.", "doi": "10.1186/s12935-020-01267-y", "pmid": "32477008", "authors": ["Jieping Qiu", "Mengyu Sun", "Yaoqun Wang", "Bo Chen"], "journal": "Cancer cell international", "year": 2020, "abstract": "BACKGROUND: The purpose of this study is to perform bioinformatics analysis of autophagy-related genes in gastric cancer, and to construct a multi-gene joint signature for predicting the prognosis of gastric cancer.\nMETHODS: GO and KEGG analysis were applied for differentially expressed autophagy-related genes in gastric cancer, and PPI network was constructed in Cytoscape software. In order to optimize the prognosis evaluation system of gastric cancer, we established a prognosis model integrating autophagy-related genes. We used single factor Cox proportional risk regression analysis to screen genes related to prognosis from 204 autophagy-related genes in The Atlas Cancer Genome (TCGA) gastric cancer cohort. Then, the generated genes were applied to the Least Absolute Shrinkage and Selection Operator (LASSO). Finally, the selected genes were further included in the multivariate Cox proportional hazard regression analysis to establish the prognosis model. According to the median risk score, patients were divided into high-risk group and low-risk group, and survival analysis was conducted to evaluate the prognostic value of risk score. Finally, by combining clinic-pathological features and prognostic gene signatures, a nomogram was established to predict individual survival probability.\nRESULTS: GO analysis showed that the 28 differently expressed autophagy-related genes was enriched in cell growth, neuron death, and regulation of cell growth. KEGG analysis showed that the 28 differently expressed autophagy-related genes were related to platinum drug resistance, apoptosis and p53 signaling pathway. The risk score was constructed based on 4 genes (GRID2, ATG4D,GABARAPL2, CXCR4), and gastric cancer patients were significantly divided into high-risk and low-risk groups according to overall survival. In multivariate Cox regression analysis, risk score was still an independent prognostic factor (HR = 1.922, 95% CI = 1.573-2.349, P < 0.001). Cumulative curve showed that the survival time of patients with low-risk score was significantly longer than that of patients with high-risk score (P < 0.001). The external data GSE62254 proved that nomograph had a great ability to evaluate the prognosis of individual gastric cancer patients.\nCONCLUSIONS: This study provides a potential prognostic marker for predicting the prognosis of GC patients and the molecular biology of GC autophagy.", "claims": [{"id": "claim-method", "statement": "Then, the multivariable Cox proportional risk regression analysis was carried out to establish the prognosis model of gastric cancer ATGs.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:32477008", "locator": "sections[5] \"Identification and verification of prognostic gene signatures\" paragraph 2", "quote": "Then, the multivariable Cox proportional risk regression analysis was carried out to establish the prognosis model of gastric cancer ATGs."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets used and/or analyzed in the present study are available from the corresponding author on reasonable request.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:32477008", "locator": "sections[22] \"notes\" paragraph 1", "quote": "The datasets used and/or analyzed in the present study are available from the corresponding author on reasonable request."}], "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:32477008"], "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:GSE62254"], "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": {"hazard_ratio": "dent prognostic factor (HR = 1"}, "inputs": ["data:geo:GSE62254"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:32477008", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1186/s12935-020-01267-y", 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