A Novel Inflammatory-Related Gene Signature Based Model for Risk Stratification and Prognosis Prediction in Lung Adenocarcinoma.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:35069695", "kind": "science-record", "revision": 3, "content_hash": "sha256:5d46fd4290a27438585e7299724e83fd3b7a93334d80ca11c9d30cd12d769b95", "created_at": "2026-11-01T17:36:48Z", "updated_at": "2026-11-01T21:27:03Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "A Novel Inflammatory-Related Gene Signature Based Model for Risk Stratification and Prognosis Prediction in Lung Adenocarcinoma.", "doi": "10.3389/fgene.2021.798131", "pmid": "35069695", "authors": ["Wen-Yu Zhai", "Fang-Fang Duan", "Si Chen", "Jun-Ye Wang", "Yao-Bin Lin", "Yi-Zhi Wang", "Bing-Yu Rao", "Ze-Rui Zhao", "Hao Long"], "journal": "Frontiers in genetics", "year": 2021, "abstract": "Inflammation is an important hallmark of cancer and plays a role in both neogenesis and tumor development. Despite this, inflammatory-related genes (IRGs) remain to be poorly studied in lung adenocarcinoma (LUAD). We aim to explore the prognostic value of IRGs for LUAD and construct an IRG-based prognosis signature. The transcriptomic profiles and clinicopathological information of patients with LUAD were obtained from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO). Least absolute shrinkage and selection operator (LASSO) analysis and multivariate Cox regression were applied in the TCGA set to generate an IRG risk signature. LUAD cases with from the GSE31210 and GSE30219 datasets were used to validate the predictive ability of the signature. Analysis of the TCGA cohort revealed a five-IRG risk signature consisting of EREG, GPC3, IL7R, LAMP3, and NMUR1. This signature was used to divide patients into two risk groups with different survival rates. Multivariate Cox regression analysis verified that the risk score from the five-IRG signature negatively correlated with patient outcome. A nomogram was developed using the IRG risk signature and stage, with C-index values of 0.687 (95% CI: 0.644-0.730) in the TCGA training cohort, 0.678 (95% CI: 0.586-0.771) in GSE30219 cohort, and 0.656 (95% CI: 0.571-0.740) in GSE30219 cohort. Calibration curves were consistent between the actual and the predicted overall survival. The immune infiltration analysis in the TCGA training cohort and two GEO validation cohorts showed a distinctly differentiated immune cell infiltration landscape between the two risk groups. The IRG risk signature for LUAD can be used to predict patient prognosis and guide individual treatment. This risk signature is also a potential biomarker of immunotherapy.", "claims": [{"id": "claim-method", "statement": "Following this, the least absolute shrinkage and selection operator (LASSO) regression analysis was performed to narrow down the prognostically significant candidate IRGs. Then, multivariate Cox regression analysis was used to determine the best weighting coefficient of each prognostically significant candidate IRG.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35069695", "locator": "sections[4] \"Construction and Validation of the Prognostic IRG Signature\" paragraph 2", "quote": "Following this, the least absolute shrinkage and selection operator (LASSO) regression analysis was performed to narrow down the prognostically significant candidate IRGs. Then, multivariate Cox regression analysis was used to determine the best weighting coefficient of each prognostically significant candidate IRG."}], "state": "complete"}, {"id": "claim-data-access", "statement": "RNA-seq data and clinical information of patients with LUAD were downloaded from the TCGA (https://tcga-data.nci.nih.gov/tcga/) and GEO databases (https://www.ncbi.nlm.nih.gov/geo/).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35069695", "locator": "sections[3] \"Data Collection and Preparation\" paragraph 2", "quote": "RNA-seq data and clinical information of patients with LUAD were downloaded from the TCGA (https://tcga-data.nci.nih.gov/tcga/) and GEO databases (https://www.ncbi.nlm.nih.gov/geo/)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "A five-IRG risk signature was constructed to stratify LUAD patients by prognosis and was validated in two GEO cohorts.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:35069695", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "Analysis of the TCGA cohort revealed a five-IRG risk signature consisting of EREG, GPC3, IL7R, LAMP3, and NMUR1."}, {"asset": "source:pmid:35069695", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "This signature was used to divide patients into two risk groups with different survival rates."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "A five-IRG risk signature was constructed to stratify LUAD patients by prognosis and was validated in two GEO cohorts.", "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:35069695"], "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:GSE30219"], "outputs": ["the source data with its repository provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the published analysis", "kind": "analysis", "state": "complete", "blocked_on": null, "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:GSE30219"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:35069695", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fgene.2021.798131", "sha256": 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