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pmid:38743344

A ferroptosis-related signature predicts the clinical diagnosis and prognosis, and associates with the immune microenvironment of lung cancer.

Hua Zhou · Xiaoting Zhou · Runying Zhu · Zhongquan Zhao · Kang Yang · Zhenghai Shen · Hongwen Sun

Discover oncology · 2024 doi:10.1007/s12672-024-01032-x

Paper

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pmid:38743344
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2026-11-01T19:20:42Z
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title
A ferroptosis-related signature predicts the clinical diagnosis and prognosis, and associates with the immune microenvironment of lung cancer.
doi
10.1007/s12672-024-01032-x
pmid
38743344
authors array · 7 items
[0]
Hua Zhou
[1]
Xiaoting Zhou
[2]
Runying Zhu
[3]
Zhongquan Zhao
[4]
Kang Yang
[5]
Zhenghai Shen
[6]
Hongwen Sun
journal
Discover oncology
year
2024
abstract
Targeting ferroptosis-related pathway is a potential strategy for treatment of lung cancer (LC). Consequently, exploration of ferroptosis-related markers is important for treating LC. We collected LC clinical data and mRNA expression profiles from TCGA and GEO database. Ferroptosis-related genes (FRGs) were obtained through FerrDB database. Expression analysis was performed to obtain differentially expressed FRGs. Diagnostic and prognostic models were constructed based on FRGs by LASSO regression, univariate, and multivariate Cox regression analysis, respectively. External verification cohorts GSE72094 and GSE157011 were used for validation. The interrelationship between prognostic risk scores based on FRGs and the tumor immune microenvironment was analyzed. Immunocytochemistry, Western blotting, and RT-qPCR detected the FRGs level. Eighteen FRGs were used for diagnostic models, 8 FRGs were used for prognostic models. The diagnostic model distinguished well between LC and normal samples in training and validation cohorts of TCGA. The prognostic models for TCGA, GSE72094, and GSE157011 cohorts significantly confirmed lower overall survival (OS) in high-risk group, which demonstrated excellent predictive properties of the survival model. Multivariate Cox regression analysis further confirmed risk score was an independent risk factor related with OS. Immunoassays revealed that in high-risk group, a significantly higher proportion of Macrophages_M0, Neutrophils, resting Natural killer cells and activated Mast cells and the level of B7H3, CD112, CD155, B7H5, and ICOSL were increased. In conclusion, diagnostic and prognostic models provided superior diagnostic and predictive power for LC and revealed a potential link between ferroptosis and TIME.
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The genes were screened by the least absolute shrinkage and selection operator (LASSO) regression and intersected with univariate Cox regression models, and multivariate Cox regression analysis was carried on intersected genes to build prognostic risk scores of FRGs.
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sections[5] "Establishment and verification of FRGs prognostic model" paragraph 2
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The genes were screened by the least absolute shrinkage and selection operator (LASSO) regression and intersected with univariate Cox regression models, and multivariate Cox regression analysis was carried on intersected genes to build prognostic risk scores of FRGs.
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sections[29] "notes" paragraph 1
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The datasets supporting the conclusions of this article are included within the article.
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The ferroptosis-related prognostic risk score was associated with significantly shorter overall survival in the high-risk group (P = 2.3e-10, HR = 1.89, 95% CI 1.55–2.3).
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The results demonstrated that, compared with the low-risk group, OS was statistically dwindled in the high-risk group (Fig. 4A; P = 2.3e-10, HR = 1.89, 95% CI 1.55–2.3).
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sections[16] "Evaluating prognostic risk scores associated with ferroptosis" paragraph 2
quote
The results demonstrated that, compared with the low-risk group, OS was statistically dwindled in the high-risk group (Fig. 4A; P = 2.3e-10, HR = 1.89, 95% CI 1.55–2.3).
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The ferroptosis-related prognostic risk score was associated with significantly shorter overall survival in the high-risk group (P = 2.3e-10, HR = 1.89, 95% CI 1.55–2.3).
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570
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title
Multi-institutional prospective validation of prognostic mRNA signatures in early stage squamous lung cancer (Alliance)
resolution_checked_at
2026-11-01T20:16:08Z
resolution_check
identifier_resolution_only_no_download_no_licence_read
downloaded
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[2] object · 15 fields
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note
The datasets supporting the conclusions of this article are included within the article.
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442
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15048
taxon
Homo sapiens
title
KRAS mutation-associated gene expression, p53 and STK11 mutations, proliferation and immune surveillance in lung adenocarcinoma
resolution_checked_at
2026-11-01T15:39:55Z
resolution_check
identifier_resolution_only_no_download_no_licence_read
downloaded
false
licence_read
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gaps array · 4 items
[0]
asset:data:geo:GSE157011 not downloaded
[1]
asset:data:geo:GSE157011 licence not read
[2]
asset:data:geo:GSE72094 not downloaded
[3]
asset:data:geo:GSE72094 licence not read
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state
complete
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[0] object · 10 fields
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method-1
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[0]
cox_ph
missing_operations array · 0 items
parameters object · 0 fields
data_shapes array · 1 item
[0]
time_to_event_with_censoring
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The genes were screened by the least absolute shrinkage and selection operator (LASSO) regression and intersected with univariate Cox regression models, and multivariate Cox regression analysis was carried on intersected genes to build prognostic risk scores of FRGs.
[1] object · 10 fields
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method-2
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survival_analysis
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[0]
logrank_test
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parameters object · 0 fields
data_shapes array · 1 item
[0]
time_to_event_with_censoring
evidence object · 3 fields
score
0.8
band
high
quote
Log-rank
[2] object · 10 fields
id
method-3
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differential_expression
specific
deseq2
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Differential expression analysis
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[0]
normalise_counts
[1]
differential_expression_fit
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fdr_bh
missing_operations array · 0 items
parameters object · 0 fields
data_shapes array · 1 item
[0]
count_matrix_with_condition
evidence object · 3 fields
score
0.8
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high
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method-4
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[0]
normalise_counts
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differential_expression_fit
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fdr_bh
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parameters object · 0 fields
data_shapes array · 1 item
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count_matrix_with_condition
evidence object · 3 fields
score
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limma
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[0]
hierarchical_cluster
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parameters object · 0 fields
data_shapes array · 1 item
[0]
feature_matrix
evidence object · 3 fields
score
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hierarchical clustering
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[3]
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[4]
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[5]
published_precision_envelope
missing_operations array · 0 items
parameters object · 0 fields
data_shapes array · 1 item
[0]
reference_standard_and_test
evidence object · 3 fields
score
0.8
band
high
quote
ROC curve
targets array · 3 items
[0] object · 8 fields
id
target-published-estimate
claim_id
claim-target-result
operation
classification_metrics
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per cent
tolerance
null
state
complete
note
The published estimate as the reader quoted it from the paper. No tolerance is published because the paper states none.
[1] object · 8 fields
id
target-published-estimate-hazard-ratio
claim_id
claim-target-result
operation
classification_metrics
unit
hazard ratio
tolerance
null
state
complete
note
The published estimate as the reader quoted it from the paper. No tolerance is published because the paper states none.
[2] object · 8 fields
id
target-published-estimate-p-value
claim_id
claim-target-result
operation
classification_metrics
unit
p-value
tolerance
null
state
complete
note
The threshold the paper applied to its own result, as the paper states it. A run that reproduces the effect is judged against this threshold.
gaps array · 0 items
capability object · 3 fields
state
complete
requirements array · 1 item
[0] object · 6 fields
capability_id
capability:computational_analysis
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Computational analysis platform
assay_families array · 9 items
[0]
meta_analysis
[1]
machine_learning
[2]
survival_analysis
[3]
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[4]
pca
[5]
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[6]
network_analysis
[7]
differential_expression
[8]
gwas
candidate
Automated Assays Laravel application and simulated adapters
gaps array · 0 items
readiness object · 8 fields
state
complete
tier
R1
blocked_on
nothing:replicable_now
axes object · 3 fields
method object · 5 fields
tier
R1
expressible
true
required_operations array · 1 item
[0]
cox_ph
missing_operations array · 0 items
basis
The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: cox_proportional_hazards.
capability object · 4 fields
tier
R1
capability_id
capability:computational_analysis
availability_state
owned
basis
The computational analysis is owned. The physical bench the paper used is not, so a physical validation cannot be run here.
data object · 6 fields
tier
R1
reachable
true
accessions array · 2 items
[0]
GSE72094
[1]
GSE157011
resolved_accessions array · 2 items
[0]
GSE72094
[1]
GSE157011
statements array · 1 item
[0]
The datasets supporting the conclusions of this article are included within the article.
basis
The paper names GSE72094, GSE157011 in GEO and the accession resolves in the repository. No dataset was downloaded and no licence was read.
can_run object · 3 fields
dsl_operations array · 1 item
[0]
cox_ph
assets array · 2 items
[0]
source:pmid:38743344
[1]
data:geo:GSE157011
steps array · 1 item
[0]
run_analysis
must_build object · 5 fields
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readers array · 0 items
capabilities array · 0 items
data_gaps array · 1 item
[0]
data:geo:GSE157011
gaps array · 0 items
review object · 5 fields
state
partial
machine_critique object · 5 fields
kind
extraction_review
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MiniMax-M3.1-Flash
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minimax
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vironment was analyzed. Immunocytochemistry, Western blotting, and RT-qPCR detected the FRGs level. Eighte
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. Immunocytochemistry, Western blotting, and RT-qPCR detected the FRGs level. Eighteen FRGs were
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mmunocytochemistry, Western blotting, and RT-qPCR detected the FRGs level. Eighteen FRGs were
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The genes were screened by the least absolute shrinkage and selection operator (LASSO) regression and intersected with univariate Cox regression models, and multivariate Cox regression analysis was carried on intersected genes to build prognostic risk scores of FRGs.
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Complete file
{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:38743344", "kind": "science-record", "revision": 5, "content_hash": "sha256:2a42bc751f39a4ad6afb50844e4544a6f4de7f78e32ce81177dd1942cf99c8b9", "created_at": "2026-11-01T19:20:42Z", "updated_at": "2026-11-01T22:54:18Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "A ferroptosis-related signature predicts the clinical diagnosis and prognosis, and associates with the immune microenvironment of lung cancer.", "doi": "10.1007/s12672-024-01032-x", "pmid": "38743344", "authors": ["Hua Zhou", "Xiaoting Zhou", "Runying Zhu", "Zhongquan Zhao", "Kang Yang", "Zhenghai Shen", "Hongwen Sun"], "journal": "Discover oncology", "year": 2024, "abstract": "Targeting ferroptosis-related pathway is a potential strategy for treatment of lung cancer (LC). Consequently, exploration of ferroptosis-related markers is important for treating LC. We collected LC clinical data and mRNA expression profiles from TCGA and GEO database. Ferroptosis-related genes (FRGs) were obtained through FerrDB database. Expression analysis was performed to obtain differentially expressed FRGs. Diagnostic and prognostic models were constructed based on FRGs by LASSO regression, univariate, and multivariate Cox regression analysis, respectively. External verification cohorts GSE72094 and GSE157011 were used for validation. The interrelationship between prognostic risk scores based on FRGs and the tumor immune microenvironment was analyzed. Immunocytochemistry, Western blotting, and RT-qPCR detected the FRGs level. Eighteen FRGs were used for diagnostic models, 8 FRGs were used for prognostic models. The diagnostic model distinguished well between LC and normal samples in training and validation cohorts of TCGA. The prognostic models for TCGA, GSE72094, and GSE157011 cohorts significantly confirmed lower overall survival (OS) in high-risk group, which demonstrated excellent predictive properties of the survival model. Multivariate Cox regression analysis further confirmed risk score was an independent risk factor related with OS. Immunoassays revealed that in high-risk group, a significantly higher proportion of Macrophages_M0, Neutrophils, resting Natural killer cells and activated Mast cells and the level of B7H3, CD112, CD155, B7H5, and ICOSL were increased. In conclusion, diagnostic and prognostic models provided superior diagnostic and predictive power for LC and revealed a potential link between ferroptosis and TIME.", "claims": [{"id": "claim-method", "statement": "The genes were screened by the least absolute shrinkage and selection operator (LASSO) regression and intersected with univariate Cox regression models, and multivariate Cox regression analysis was carried on intersected genes to build prognostic risk scores of FRGs.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38743344", "locator": "sections[5] \"Establishment and verification of FRGs prognostic model\" paragraph 2", "quote": "The genes were screened by the least absolute shrinkage and selection operator (LASSO) regression and intersected with univariate Cox regression models, and multivariate Cox regression analysis was carried on intersected genes to build prognostic risk scores of FRGs."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets supporting the conclusions of this article are included within the article.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38743344", "locator": "sections[29] \"notes\" paragraph 1", "quote": "The datasets supporting the conclusions of this article are included within the article."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The ferroptosis-related prognostic risk score was associated with significantly shorter overall survival in the high-risk group (P = 2.3e-10, HR = 1.89, 95% CI 1.55–2.3).", "kind": "quantitative", "target": {"value": 95.0, "unit": "per cent", "tolerance": null, "source": "The results demonstrated that, compared with the low-risk group, OS was statistically dwindled in the high-risk group (Fig. 4A; P = 2.3e-10, HR = 1.89, 95% CI 1.55–2.3)."}, "evidence": [{"asset": "source:pmid:38743344", "locator": "sections[16] \"Evaluating prognostic risk scores associated with ferroptosis\" paragraph 2", "quote": "The results demonstrated that, compared with the low-risk group, OS was statistically dwindled in the high-risk group (Fig. 4A; P = 2.3e-10, HR = 1.89, 95% CI 1.55–2.3)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The ferroptosis-related prognostic risk score was associated with significantly shorter overall survival in the high-risk group (P = 2.3e-10, HR = 1.89, 95% CI 1.55–2.3).", "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:38743344"], "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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