Development of an Immune-Related Risk Signature for Predicting Prognosis in Lung Squamous Cell Carcinoma.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:33005178", "kind": "science-record", "revision": 9, "content_hash": "sha256:2da735c424894ab51a0632fc81e6feb09128a1c4643bd45ddb857d0aadd208cf", "created_at": "2026-11-01T15:41:12Z", "updated_at": "2026-11-01T22:45:25Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Development of an Immune-Related Risk Signature for Predicting Prognosis in Lung Squamous Cell Carcinoma.", "doi": "10.3389/fgene.2020.00978", "pmid": "33005178", "authors": ["Denggang Fu", "Biyu Zhang", "Lei Yang", "Shaoxin Huang", "Wang Xin"], "journal": "Frontiers in genetics", "year": 2020, "abstract": "Lung squamous cell carcinoma (LSCC) is the most common subtype of non-small cell lung cancer. Immunotherapy has become an effective treatment in recent years, while patients showed different responses to the current treatment. It is vital to identify the potential immunogenomic signatures to predict patient' prognosis. The expression profiles of LSCC patients with the clinical information were downloaded from TCGA database. Differentially expressed immune-related genes (IRGs) were extracted using edgeR algorithm, and functional enrichment analysis showed that these IRGs were primarily enriched in inflammatory- and immune-related processes. \"Cytokine-cytokine receptor interaction\" and \"PI3K-AKT signaling pathway\" were the most enriched KEGG pathways. 27 differentially expressed IRGs were significantly correlated with the overall survival (OS) of patients using univariate Cox regression analysis. A prognostic risk signature that comprises seven IRGs (GCCR, FGF8, CLEC4M, PTH, SLC10A2, NPPC, and FGF4) was developed with effective predictive performance by multivariable Cox stepwise regression analysis. Most importantly, the signature could be an independent prognostic predictor after adjusting for clinicopathological parameters, and also validated in two independent LSCC cohorts (GSE4573 and GSE17710). Potential molecular mechanisms and tumor immune landscape of these IRGs were investigated through computational biology. Analysis of tumor infiltrating lymphocytes and immune checkpoint molecules revealed distinct immune landscape in high- and low-risk group. The study was the first time to construct IRG-based immune signature in the recognition of disease progression and prognosis of LSCC patients.", "claims": [{"id": "claim-method", "statement": "The differentially expressed genes were identified between the LSCC and adjacent non-tumor samples by edgeR R package (Robinson et al., 2010).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:33005178", "locator": "sections[4] \"Identification of the Differentially Expressed Genes (DEGs) and IRGs\" paragraph 2", "quote": "The differentially expressed genes were identified between the LSCC and adjacent non-tumor samples by edgeR R package (Robinson et al., 2010)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Level 3 RNA-seq raw count data from 502 lung squamous cell carcinoma patients (LSCC) and 49 non-tumor samples were downloaded from the TCGA database. Clinical information of these patients was also downloaded and extracted.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:33005178", "locator": "sections[3] \"Gene Expression Datasets and IRGs\" paragraph 2", "quote": "Level 3 RNA-seq raw count data from 502 lung squamous cell carcinoma patients (LSCC) and 49 non-tumor samples were downloaded from the TCGA database. Clinical information of these patients was also downloaded and extracted."}], "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:33005178"], "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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multivariate Cox regression analysis of the risk score and clinical parameters in LSCC patients.", "rows": [["Variables", "Univariate analysis", "Multivariate analysis"], ["", "HR (95% CI)", "P-value", "HR (95% CI)", "P-value"], ["Age", "1.0189 (1.0042–1.0338)", "0.0116", "1.0158 (1.0007–1.0311)", "0.0405"], ["pM", "1.2810 (1.0533–1.5580)", "0.0131", "1.2609 (1.0328–1.5395)", "0.0228"], ["pN", "1.1680 (0.9839–1.3866)", "0.0761", "1.0929 (0.8826–1.3533)", "0.415"], ["pT", "1.3538 (1.1272–1.6259)", "0.0012", "1.3019 (1.0287–1.6476)", "0.0281"], ["Gender", "0.8031 (0.5752–1.1213)", "0.1978", "0.8395 (0.5981–1.1783)", "0.3118"], ["Tobacco_smoking_history", "0.8829 (0.7805–0.9987)", "0.0476", "0.8667 (0.7634–0.9841)", "0.0273"], ["Tumor_stage", "1.2859 (1.1008–1.5020)", "0.0015", "1.0861 (0.8593–1.3729)", "0.4895"], ["Tigarettes_exposures (per_day)", "1.0021 (0.9295–1.0803)", "0.9571", "1.0079 (0.9345–1.0870)", "0.8388"], ["Risk score", "1.5196 (1.3462–1.7155)", "1.32E–11", "1.5480 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The physical bench the paper used is not, so a physical validation cannot be run here."}, "data": {"tier": "R1", "reachable": true, "accessions": ["GSE4573", "GSE17710"], "resolved_accessions": ["GSE4573", "GSE17710"], "statements": ["Level 3 RNA-seq raw count data from 502 lung squamous cell carcinoma patients (LSCC) and 49 non-tumor samples were downloaded from the TCGA database. Clinical information of these patients was also downloaded and extracted."], "basis": "The paper names GSE4573, GSE17710 in GEO and the accession resolves in the repository. 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