A novel ferroptosis-related 12-gene signature predicts clinical prognosis and reveals immune relevancy in clear cell renal cell carcinoma.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:34281531", "kind": "science-record", "revision": 6, "content_hash": "sha256:7d53ae598cb83f6e1f151b21b69a7be8c8eeb05b464405fc173bf95bee7f2294", "created_at": "2026-11-01T15:41:44Z", "updated_at": "2026-11-01T21:25:48Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "A novel ferroptosis-related 12-gene signature predicts clinical prognosis and reveals immune relevancy in clear cell renal cell carcinoma.", "doi": "10.1186/s12885-021-08559-0", "pmid": "34281531", "authors": ["Yingkai Hong", "Mingen Lin", "Dehua Ou", "Zhuangkai Huang", "Peilin Shen"], "journal": "BMC cancer", "year": 2021, "abstract": "BACKGROUND: Clear cell renal cell carcinoma (ccRCC) is still highly aggressive and lethal even with various therapeutic approaches. As the kidney is an iron metabolism-related organ, exploring and assessing the clinical value of ferroptosis, an iron-dependent regulated cell death, is practical and important.\nMETHODS: Prognostic ferroptosis-related differentially expressed genes (DEGs) were identified from the KIRC cohort in the cancer genome atlas (TCGA) database, from which a prognostic signature was established using Lasso-penalized Cox regression analysis. Each patient in the KIRC cohort and the E-MTAB-1980 cohort (from the ArrayExpress database) was assigned a calculated signature-correlated risk score and categorized to be either in the high- or low-risk group divided by the median risk score in the KIRC cohort. Then, the independent prognostic value of the signature was further assessed by Kaplan-Meier (K-M) survival, time-dependent receiver operating characteristic (ROC) and Cox regression analyses based on overall survival (OS) in both cohorts. Finally, risk-related DEGs were identified in both cohorts and subjected to enrichment analyses for Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) and immune infiltration.\nRESULTS: Among 60 ferroptosis-related genes, 32 prognostic DEGs were identified, from which we constructed a prognostic 12-gene signature with CARS1, HMGCR, CHAC1, GOT1, CD44, STEAP3, AKR1C1, CBS, DPP4, FANCD2, SLC1A5 and NCOA4. Patients in both cohorts were divided into high- and low-risk groups, which were visually distributed in two sets and had positive-risk-related mortality. The K-M survival and the ROC curves validated that the signature has prognostic value with P < 0.05 and area under the curve > 0.7 in both cohorts, respectively. Multivariate Cox regression further confirmed the risk score as an independent prognostic predictor for OS. Commonly enriched terms in GO and KEGG not only showed a high iron correlation but also, interestingly, immune relevance of 3 immune cells (macrophages, mast cells and regulatory T cells) and 1 immune-related function (antigen processing cell co-stimulation).\nCONCLUSION: We established a novel 12 ferroptosis-related-gene signature that was proven to be an independent prognostic predictor for OS and inferred to be related to tumour immunity in ccRCC; however, the underlying mechanism is still poorly characterized and needs further exploration.", "claims": [{"id": "claim-method", "statement": "To minimize the risk of overfitting, we used Lasso-penalized Cox regression analysis to rule out genes with an overfitting tendency and construct a prognostic signature with the “glmnet” R package [37–39].", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34281531", "locator": "sections[5] \"Construction and validation of a prognostic ferroptosis-related gene signature\" paragraph 2", "quote": "To minimize the risk of overfitting, we used Lasso-penalized Cox regression analysis to rule out genes with an overfitting tendency and construct a prognostic signature with the “glmnet” R package [37–39]."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets analyzed for this study can be found in the KIRC project in TCGA database (https://portal.gdc.cancer.gov/repository) and E-MTAB-1980 cohort in ArrayExpress database (https://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-1980/). The literature survey data from this study can be request from the corresponding author Peilin Shen.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34281531", "locator": "sections[22] \"Availability of data and materials\" paragraph 2", "quote": "The datasets analyzed for this study can be found in the KIRC project in TCGA database (https://portal.gdc.cancer.gov/repository) and E-MTAB-1980 cohort in ArrayExpress database (https://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-1980/). 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