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pmid:34252149

Identification and validation of a novel ferroptosis-related gene model for predicting the prognosis of gastric cancer patients.

Gang Liu · Jian-Ying Ma · Gang Hu · Huan Jin

PloS one · 2021 doi:10.1371/journal.pone.0254368

Paper

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pmid:34252149
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2026-11-01T15:41:43Z
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title
Identification and validation of a novel ferroptosis-related gene model for predicting the prognosis of gastric cancer patients.
doi
10.1371/journal.pone.0254368
pmid
34252149
authors array · 4 items
[0]
Gang Liu
[1]
Jian-Ying Ma
[2]
Gang Hu
[3]
Huan Jin
journal
PloS one
year
2021
abstract
BACKGROUND: Ferroptosis is a novel form of regulated cell death that plays a critical role in tumorigenesis. The purpose of this study was to establish a ferroptosis-associated gene (FRG) signature and assess its clinical outcome in gastric cancer (GC). METHODS: Differentially expressed FRGs were identified using gene expression profiles from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database. Univariate and least absolute shrinkage and selection operator (LASSO) Cox regression analyses were performed to construct a prognostic signature. The model was validated using an independent GEO dataset, and a genomic-clinicopathologic nomogram integrating risk scores and clinicopathological features was established. RESULTS: An 8-FRG signature was constructed to calculate the risk score and classify GC patients into two risk groups (high- and low-risk) according to the median value of the risk score. The signature showed a robust predictive capacity in the stratification analysis. A high-risk score was associated with advanced clinicopathological features and an unfavorable prognosis. The predictive accuracy of the signature was confirmed using an independent GSE84437 dataset. Patients in the two groups showed different enrichment of immune cells and immune-related pathways. Finally, we established a genomic-clinicopathologic nomogram (based on risk score, age, and tumor stage) to predict the overall survival (OS) of GC patients. CONCLUSIONS: The novel FRG signature may be a reliable tool for assisting clinicians in predicting the OS of GC patients and may facilitate personalized treatment.
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The performance of the FRG signature for predicting OS was validated using ROC curves and Kaplan-Meier analyses.
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sections[5] "External validation of prognostic FRG signature" paragraph 2
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The performance of the FRG signature for predicting OS was validated using ROC curves and Kaplan-Meier analyses.
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The datasets supporting the conclusions of this article are available in the public databases TCGA database (http://www.cancer.gov/tcga) project TCGA-STAD, and Gene Expression Omnibus (https://www.ncbi.nlm.nih.gov/gds/) with the accession numbers: GSE84437.
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sections[24] "Data Availability" paragraph 2
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The datasets supporting the conclusions of this article are available in the public databases TCGA database (http://www.cancer.gov/tcga) project TCGA-STAD, and Gene Expression Omnibus (https://www.ncbi.nlm.nih.gov/gds/) with the accession numbers: GSE84437.
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Molecular subtypes in gastric cancer.
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Clinicopathologic characteristics of gastric cancer patients in TCGA and GEO cohorts.
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Clinicopathologic features of patients
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Clinicopathologic characteristics of gastric cancer patients in TCGA and GEO cohorts.
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TCGA cohort
[2]
GEO cohort
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[0]
(n = 375)
[1]
(n = 431)
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N (%)
[1]
N (%)
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Age (M±SD, years)
[1]
65.22±10.51
[2]
60.02±11.58
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≤60
[1]
125 (33.3)
[2]
194 (28.3)
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> 60
[1]
250 (66.7)
[2]
237 (71.7)
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[1]
[2]
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[1]
135 (36.0)
[2]
137 (45.5)
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[1]
240 (64.0)
[2]
294 (54.5)
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Tumor location
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[2]
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Cardia
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96 (25.6)
[2]
/
[12] array · 3 items
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Non-cardia
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279 (74.4)
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Grade
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G1-2
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140 (37.3)
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G3
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235 (62.7)
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I
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47 (12.5)
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II
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122 (32.5)
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III
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172 (45.9)
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/
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IV
[1]
34 (9.1)
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/
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section[12]/table-wrap[1]#pone.0254368.t001
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The performance of the FRG signature for predicting OS was validated using ROC curves and Kaplan-Meier analyses.
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The datasets supporting the conclusions of this article are available in the public databases TCGA database (http://www.cancer.gov/tcga) project TCGA-STAD, and Gene Expression Omnibus (https://www.ncbi.nlm.nih.gov/gds/) with the accession numbers: GSE84437.
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The performance of the FRG signature for predicting OS was validated using ROC curves and Kaplan-Meier analyses.
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{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:34252149", "kind": "science-record", "revision": 6, "content_hash": "sha256:aa65960ac78eb0f0d067b87d7e113c37e372bba5d84149c62ee3f18f418f37bd", "created_at": "2026-11-01T15:41:43Z", "updated_at": "2026-11-01T21:25:44Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification and validation of a novel ferroptosis-related gene model for predicting the prognosis of gastric cancer patients.", "doi": "10.1371/journal.pone.0254368", "pmid": "34252149", "authors": ["Gang Liu", "Jian-Ying Ma", "Gang Hu", "Huan Jin"], "journal": "PloS one", "year": 2021, "abstract": "BACKGROUND: Ferroptosis is a novel form of regulated cell death that plays a critical role in tumorigenesis. The purpose of this study was to establish a ferroptosis-associated gene (FRG) signature and assess its clinical outcome in gastric cancer (GC).\nMETHODS: Differentially expressed FRGs were identified using gene expression profiles from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database. Univariate and least absolute shrinkage and selection operator (LASSO) Cox regression analyses were performed to construct a prognostic signature. The model was validated using an independent GEO dataset, and a genomic-clinicopathologic nomogram integrating risk scores and clinicopathological features was established.\nRESULTS: An 8-FRG signature was constructed to calculate the risk score and classify GC patients into two risk groups (high- and low-risk) according to the median value of the risk score. The signature showed a robust predictive capacity in the stratification analysis. A high-risk score was associated with advanced clinicopathological features and an unfavorable prognosis. The predictive accuracy of the signature was confirmed using an independent GSE84437 dataset. Patients in the two groups showed different enrichment of immune cells and immune-related pathways. Finally, we established a genomic-clinicopathologic nomogram (based on risk score, age, and tumor stage) to predict the overall survival (OS) of GC patients.\nCONCLUSIONS: The novel FRG signature may be a reliable tool for assisting clinicians in predicting the OS of GC patients and may facilitate personalized treatment.", "claims": [{"id": "claim-method", "statement": "The performance of the FRG signature for predicting OS was validated using ROC curves and Kaplan-Meier analyses.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34252149", "locator": "sections[5] \"External validation of prognostic FRG signature\" paragraph 2", "quote": "The performance of the FRG signature for predicting OS was validated using ROC curves and Kaplan-Meier analyses."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets supporting the conclusions of this article are available in the public databases TCGA database (http://www.cancer.gov/tcga) project TCGA-STAD, and Gene Expression Omnibus (https://www.ncbi.nlm.nih.gov/gds/) with the accession numbers: GSE84437.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34252149", "locator": "sections[24] \"Data Availability\" paragraph 2", "quote": "The datasets supporting the conclusions of this article are available in the public databases TCGA database (http://www.cancer.gov/tcga) project TCGA-STAD, and Gene Expression Omnibus (https://www.ncbi.nlm.nih.gov/gds/) with the accession numbers: GSE84437."}], "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:34252149"], "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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