Construction and Validation of Novel Ferroptosis-related Risk Score Signature and Prognostic Prediction Nomogram for Patients with Colorectal Cancer.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:38774759", "kind": "science-record", "revision": 3, "content_hash": "sha256:67f5356217775d71241ff9673f89474960c7523844ff589ec097285ac9e048c6", "created_at": "2026-11-01T19:20:44Z", "updated_at": "2026-11-01T22:11:54Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Construction and Validation of Novel Ferroptosis-related Risk Score Signature and Prognostic Prediction Nomogram for Patients with Colorectal Cancer.", "doi": "10.7150/ijms.91446", "pmid": "38774759", "authors": ["Ruibin Liu", "Yue Wang", "Jiawen Bu", "Qingqing Li", "Fang Chen", "Mengying Zhu", "Huanyu Chi", "Guilin Yu", "Tong Zhu", "Xudong Zhu", "Guohua Zhao"], "journal": "International journal of medical sciences", "year": 2024, "abstract": "Background: Colorectal cancer (CRC) has a high morbidity and mortality. Ferroptosis is a phenomenon in which metabolism and cell death are closely related. The role of ferroptosis-related genes in the progression of CRC is still not clear. Therefore, we screened and validated the ferroptosis-related genes which could determine the prevalence, risk and prognosis of patients with CRC. Methods: We firstly screened differentially expressed ferroptosis-related genes by The Cancer Genome Atlas (TCGA) database. Then, these genes were used to construct a risk-score model using the least absolute shrinkage and selection operator (LASSO) regression algorithm. The function and prognosis of the ferroptosis-related genes were confirmed using multi-omics analysis. The gene expression results were validated using publicly available databases and qPCR. We also used publicly available data and ferroptosis-related genes to construct a prognostic prediction nomogram. Results: A total of 24 differential expressed genes associated with ferroptosis were screened in this study. A three-gene risk score model was then established based on these 24 genes and GPX3, CDKN2A and SLC7A11 were selected. The significant prognostic value of this novel three-gene signature was also assessed. Furthermore, we conducted RT-qPCR analysis on cell lines and tissues, and validated the high expression of CDKN2A, GPX3 and low expression of SLC7A11 in CRC cells. The observed mRNA expression of GPX3, CDKN2A and SLC7A11 was consistent with the predicted outcomes. Besides, eight variables including selected ferroptosis related genes were included to establish the prognostic prediction nomogram for patients with CRC. The calibration plots showed favorable consistency between the prediction of the nomogram and actual observations. Also, the time-dependent AUC (>0.7) indicated satisfactory discriminative ability of the nomogram. Conclusions: The present study constructed and validated a novel ferroptosis-related three-gene risk score signature and a prognostic prediction nomogram for patients with CRC. Also, we screened and validated the ferroptosis-related genes GPX3, CDKN2A, and SLC7A11 which could serve as novel biomarkers for patients with CRC.", "claims": [{"id": "claim-method", "statement": "We used Cox proportional hazards regression and least absolute shrinkage and selection operator (LASSO) regression analysis to select 24 differentially expressed ferroptosis-related genes from the TCGA dataset.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38774759", "locator": "sections[6] \"Constructing and validating the risk-score signature\" paragraph 2", "quote": "We used Cox proportional hazards regression and least absolute shrinkage and selection operator (LASSO) regression analysis to select 24 differentially expressed ferroptosis-related genes from the TCGA dataset."}], "state": "complete"}, {"id": "claim-target-result", "statement": "A three-gene risk score model was then established based on these 24 genes and GPX3, CDKN2A and SLC7A11 were selected.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:38774759", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "A three-gene risk score model was then established based on these 24 genes and GPX3, CDKN2A and SLC7A11 were selected."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "A three-gene risk score model was then established based on these 24 genes and GPX3, CDKN2A and SLC7A11 were selected.", "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:38774759"], "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": "blocked", "blocked_on": "data:accession_absent", "description": "The GEO series accession resolves and its sample accessions are listed. 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