Construction and Validation of an Immune-Related Prognostic Model Based on TP53 Status in Colorectal Cancer.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:31689990", "kind": "science-record", "revision": 8, "content_hash": "sha256:826e632b54e6df300e98da16d42e1e13b3af05e4c8bb83e632359ab03585c7b5", "created_at": "2026-11-01T15:40:51Z", "updated_at": "2026-11-01T22:00:58Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Construction and Validation of an Immune-Related Prognostic Model Based on TP53 Status in Colorectal Cancer.", "doi": "10.3390/cancers11111722", "pmid": "31689990", "authors": ["Xiaojuan Zhao", "Jianzhong Liu", "Shuzhen Liu", "Fangfang Yang", "Erfei Chen"], "journal": "Cancers", "year": 2019, "abstract": "Growing evidence has indicated that prognostic biomarkers have a pivotal role in tumor and immunity biological processes. TP53 mutation can cause a range of changes in immune response, progression, and prognosis of colorectal cancer (CRC). Thus, we aim to build an immunoscore prognostic model that may enhance the prognosis of CRC from an immunological perspective. We estimated the proportion of immune cells in the GSE39582 public dataset using the CIBERSORT (Cell type identification by estimating relative subset of known RNA transcripts) algorithm. Prognostic genes that were used to establish the immunoscore model were generated by the LASSO (Least absolute shrinkage and selection operator) Cox regression model. We established and validated the immunoscore model in GEO (Gene Expression Omnibus) and TCGA (The Cancer Genome Atlas) cohorts, respectively; significant differences of overall survival analysis were found between the low and high immunoscore groups or TP53 subgroups. In the multivariable Cox analysis, we observed that the immunoscore was an independent prognostic factor both in the GEO cohort (HR (Hazard ratio) 1.76, 95% CI (confidence intervals): 1.26-2.46) and the TCGA cohort (HR 1.95, 95% CI: 1.20-3.18). Furthermore, we established a nomogram for clinical application, and the results suggest that the nomogram is a better predictive model for prognosis than immunoscore or TNM staging.", "claims": [{"id": "claim-method", "statement": "In order to select the most useful prognostic genes, we applied the LASSO Cox regression algorithm to the prognosis-associated genes in the GEO cohort.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:31689990", "locator": "sections[19] \"4.8. Statistical Analysis\" paragraph 2", "quote": "In order to select the most useful prognostic genes, we applied the LASSO Cox regression algorithm to the prognosis-associated genes in the GEO cohort."}], "state": "complete"}, {"id": "claim-data-access", "statement": "In order to obtain a gene expression dataset of CRC, 566 CEL files of GSE39582 based on platform GPL570 were downloaded from the Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo/).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:31689990", "locator": "sections[12] \"4.1. Microarray Datasets\" paragraph 2", "quote": "In order to obtain a gene expression dataset of CRC, 566 CEL files of GSE39582 based on platform GPL570 were downloaded from the Gene Expression Omnibus (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:31689990"], "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. 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