Development and Validation of an Autophagy Score Signature for the Prediction of Post-operative Survival in Colorectal Cancer.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:31552190", "kind": "science-record", "revision": 8, "content_hash": "sha256:588772be3b47c5e52cd52532e2caf3de6709877ea8cedb28d18f926c0908f90f", "created_at": "2026-11-01T15:40:50Z", "updated_at": "2026-11-01T22:44:13Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Development and Validation of an Autophagy Score Signature for the Prediction of Post-operative Survival in Colorectal Cancer.", "doi": "10.3389/fonc.2019.00878", "pmid": "31552190", "authors": ["Zheng Zhou", "Shaobo Mo", "Weixing Dai", "Zhen Ying", "Long Zhang", "Wenqiang Xiang", "Lingyu Han", "Zhimin Wang", "Qingguo Li", "Renjie Wang", "Guoxiang Cai"], "journal": "Frontiers in oncology", "year": 2019, "abstract": "Background: Survival rates for Colorectal cancer (CRC) patients who experienced early relapse have usually been relatively low. Our study aims at developing an autophagy signature that could help to detect early relapse cases in CRC. Methods: Propensity score matching analysis was carried out between patients from the early relapse group and the long-term survival group from GSE39582. For both groups, respectively, global autophagy expression changes were then analyzed to identify the differentially expressed prognostic autophagy related genes by conducting Linear Models for Microarray data method analysis. Then, the multi-gene signature was validated in TCGA and Fudan University Shanghai Cancer Center (FUSCC) cohorts. Time-dependent ROC were used to test the efficiency of this signature feature in predicting the prognosis of CRC patients. Results: 5 autophagy genes were finally identified to build an early relapse classifier. With specific risk score formula, patients were classified into low- or high-risk group. Time-dependent ROC analyses proved its prognostic accuracy, with AUC 0.841 and 0.803 at 1 and 3 years, respectively. Then, we validated its prognostic value in two external validation series (GSE17538 and GSE33113) and proved that the result is indeed significant irrespective of datasets in two external independent validation cohorts (TCGA and FUSCC cohorts). A nomogram was constructed to guide individualized treatment of patients with CRC. Conclusions: The identification of robust autophagy-related features can effectively classify CRC patients into groups with low and high risk of early relapse. This signature may be used to help select high-risk CRC patients who require more aggressive treatment interventions.", "claims": [{"id": "claim-data-access", "statement": "All data that support this manuscript are made available by the authors, without undue reservation, to any qualified researcher.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:31552190", "locator": "sections[20] \"Data Availability\" paragraph 2", "quote": "All data that support this manuscript are made available by the authors, without undue reservation, to any qualified researcher."}], "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:31552190"], "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:GSE17538"], "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:GSE17538"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:31552190", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fonc.2019.00878", "sha256": 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