Role of ferroptosis-related genes in prognostic prediction and tumor immune microenvironment in colorectal carcinoma.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:34316400", "kind": "science-record", "revision": 7, "content_hash": "sha256:fd041476e2e33a60885156b708febae015d0932bb19af4ec6061551ceffceda4", "created_at": "2026-11-01T15:41:45Z", "updated_at": "2026-11-01T22:04:19Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Role of ferroptosis-related genes in prognostic prediction and tumor immune microenvironment in colorectal carcinoma.", "doi": "10.7717/peerj.11745", "pmid": "34316400", "authors": ["Chao Yang", "Shuoyang Huang", "Fengyu Cao", "Yongbin Zheng"], "journal": "PeerJ", "year": 2021, "abstract": "BACKGROUND AND AIM: Colorectal cancer (CRC) ranks the second most common cause of cancer-related mortality worldwide. Ferroptosis, a recently discovered form of programmed cell death different from other, raises promising novel opportunities for therapeutic intervention of CRC. This study intended to systematically assess the prognosis value and multiple roles of the ferroptosis-related genes in the tumor immune microenvironment of CRC.\nMATERIALS AND METHODS: Of 1,192 CRC patients with complete information from the public datasets (TCGA CRC, GEO GSE39582 and GSE17538 cohorts) were selected for analysis. Firstly, K-means consensus clustering was performed to identify ferroptosis-associated subtypes in CRC patients. Subsequently, we constructed a risk signature based on ferroptosis-related genes in TCGA cohort and acquired its validation in two GEO cohorts. Additionally, we established a nomogram integrating the risk signature and clinical factors to improve risk assessment of CRC patients.\nRESULTS: Five molecular subtypes were identified by consensus clustering for ferroptosis-related genes. There were significant differences in the overall survival, immune cells infiltration status and PD1/PD-L1 mRNA among the five clusters. Then, a risk signature based on the ten-gene was constructed which could distinguish effectively high-risk group among CRC patients in both training and validation sets. The high-risk patients were more likely to have an inhibitory immune microenvironment and lower stemness features. A prognostic nomogram integrated risk signature and clinicopathological features could be used as a more accurate prognostic prediction visualization tool than TNM stage alone.\nCONCLUSION: This ferroptosis risk signature may accurately differentiate between different risk populations and predict the prognosis of CRC. Besides, this study elucidated the crucial role of ferroptosis in tumor immune microenvironment.", "claims": [{"id": "claim-data-access", "statement": "The following information was supplied regarding data availability:The raw transcriptome data of this study are available in the TCGA database (TCGA-COAD/READ cohorts)(https://portal.gdc.cancer.gov/) and GEO: GSE39582 and GSE17538.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34316400", "locator": "sections[22] \"Additional Information and Declarations\" paragraph 4", "quote": "The following information was supplied regarding data availability:The raw transcriptome data of this study are available in the TCGA database (TCGA-COAD/READ cohorts)(https://portal.gdc.cancer.gov/) and GEO: GSE39582 and GSE17538."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "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:34316400"], "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": "blocked", "blocked_on": "method:group_comparison_wilcoxon", "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": ["step:run_analysis blocked on method:group_comparison_wilcoxon"]}, "data": {"state": "partial", "assets": [{"id": "source:pmid:34316400", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": 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