A Prognostic Model for Colon Cancer Patients Based on Eight Signature Autophagy Genes.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:33324651", "kind": "science-record", "revision": 7, "content_hash": "sha256:7afe0a7e9459be2a342b098168f4bfbd4619dd01acbd0782011544fc264b0064", "created_at": "2026-11-01T15:41:19Z", "updated_at": "2026-11-01T22:02:44Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "A Prognostic Model for Colon Cancer Patients Based on Eight Signature Autophagy Genes.", "doi": "10.3389/fcell.2020.602174", "pmid": "33324651", "authors": ["Jiasheng Xu", "Siqi Dai", "Ying Yuan", "Qian Xiao", "Kefeng Ding"], "journal": "Frontiers in cell and developmental biology", "year": 2020, "abstract": "OBJECTIVE: To screen key autophagy genes in colon cancer and construct an autophagy gene model to predict the prognosis of patients with colon cancer.\nMETHODS: The colon cancer data from the TCGA were downloaded as the training set, data chip of GSE17536 as the validation set. The differential genes of the training set were obtained and were analyzed for enrichment and protein network. Acquire autophagy genes from Human Autophagy Database www.autophagy.lu/project.html. Autophagy genes in differentially expressed genes were extracted using R-packages limma. Using LASSO/Cox regression analysis combined with clinical information to construct the autophagy gene risk scoring model and divide the samples into high and low risk groups according to the risk value. The Nomogram assessment model was used to predict patient outcomes. CIBERSORT was used to calculate the infiltration of immune cells in the samples and study the relationship between high and low risk groups and immune checkpoints.\nRESULTS: Nine hundred seventy-six differentially expressed genes were screened from training set, including five hundred sixty-eight up-regulated genes and four hundred eight down regulated genes. These differentially expressed genes were mainly involved: the regulation of membrane potential, neuroactive ligand-receptor interaction. We identified eight autophagy genes CTSD, ULK3, CDKN2A, NRG1, ATG4B, ULK1, DAPK1, and SERPINA1 as key prognostic genes and constructed the model after extracting the differential autophagy genes in the training set. Survival analysis showed significant differences in sample survival time after grouping according to the model. Nomogram assessment showed that the model had high reliability for predicting the survival of patients with colon cancer in the 1, 3, 5 years. In the high-risk group, the infiltration degrees of nine types of immune cells are different and the samples can be well distinguished according to these nine types of immune cells. Immunological checkpoint correlation results showed that the expression levels of CTLA4, IDO1, LAG3, PDL1, and TIGIT increased in high-risk groups.\nCONCLUSION: The prognosis prediction model based on autophagy gene has a good evaluation effect on the prognosis of colon cancer patients. Eight key autophagy genes can be used as prognostic markers for colon cancer.", "claims": [{"id": "claim-method", "statement": "Kaplan-Meier method was used to estimate the overall survival rate of different groups, and log-rank was used to test the significance of the difference between different groups.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:33324651", "locator": "sections[13] \"Statistical Analysis\" paragraph 2", "quote": "Kaplan-Meier method was used to estimate the overall survival rate of different groups, and log-rank was used to test the significance of the difference between different groups."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets generated for this study can be found in the online repositories. 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This lane did not download the dataset or read its licence.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE17536"], "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:GSE17536"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:33324651", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fcell.2020.602174", "sha256": "sha256:ffbff556230466a884e156ef83cbb385f861f1e772817a4d4cb06721bb569faf", "bytes": 35014, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3389/fcell.2020.602174", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC7726244", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE17536", "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=GSE17536", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE17536", "version": null, "note": "The datasets generated for this study can be found in the online repositories. 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