A bioinformatics-based immune-related prognostic index for lung adenocarcinoma that predicts patient response to immunotherapy and common treatments.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:35813746", "kind": "science-record", "revision": 4, "content_hash": "sha256:9a493307a0499776a5d8c0281a118729dff4be6affe1c027514161bbc212c97e", "created_at": "2026-11-01T18:14:52Z", "updated_at": "2026-11-01T22:07:00Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "A bioinformatics-based immune-related prognostic index for lung adenocarcinoma that predicts patient response to immunotherapy and common treatments.", "doi": "10.21037/jtd-22-494", "pmid": "35813746", "authors": ["Chenghao Wang", "Tong Lu", "Ran Xu", "Xiaoyan Chang", "Shan Luo", "Bo Peng", "Jun Wang", "Lingqi Yao", "Kaiyu Wang", "Zhiping Shen", "Jiaying Zhao", "Linyou Zhang"], "journal": "Journal of thoracic disease", "year": 2022, "abstract": "Background: There is increasing evidence of the effectiveness of immune checkpoint blockade (ICB) therapy for the treatment of lung adenocarcinoma (LUAD). However, the benefits of ICB therapy vary among LUAD patients. Due to the research dimension, existing biomarkers, such as programmed death-ligand 1 (PD-L1) expression and tumor mutation burden (TMB), could not reflect the complex tumor environment, and had low prediction accuracy of ICB. Therefore, we aimed to uncover a prognostic biomarker that could also predict whether a patient would benefit from ICB therapy and other common treatments from multiple dimensions, so as to improve the prediction accuracy of pre-treatment patients.\nMethods: Based on the LUAD dataset retrieved from The Cancer Genome Atlas (TCGA) database, 50 immune-related hub genes were identified using weighted gene co-expression network analysis and univariate Cox regression analyses. An immune-related gene prognostic index (IRGPI) was constructed using a Cox proportional-hazards model based on 15 genes and validated using GSE72094 dataset. We tested its prognostic accuracy by Kaplan-Meier (K-M) survival curves of the two datasets and assessed its predictive power by comparing area under curve (AUC) of IRGPI with existing biomarkers. Subsequently, we analyzed the molecular and immune characteristics, and evaluated the benefits of ICB by PD-L1 expression and Tumor Immune Dysfunction and Exclusion (TIDE) analysis, predicted the inhibitory concentration 50 of common treatments drugs for two IRGPI score-related subgroups.\nResults: Patients in the IRGPI-high subgroup had lower overall survival (OS) than patients in the IRGPI-low subgroup in K-M survival curve in two cohorts. And IRGPI has AUC values of 0.715, 0.724, and 0.743 in 1, 2, and 3 years, respectively. A higher tumor mutation burden and PD-L1 expression and the tumor microenvironment (TME) landscape demonstrated that IRGPI-high subgroup patients may respond better to ICB therapy. Genomics of Drug Sensitivity in Cancer (GDSC) analysis indicated that the IRGPI-high subgroup showed greater sensitivity to chemotherapy.\nConclusions: IRGPI is a prospective biomarker for evaluating whether a patient will benefit from ICB therapy and other treatments, and distinguishing patients with different molecular and immune characteristics.", "claims": [{"id": "claim-method", "statement": "IRGPI, a Cox proportional-hazards model, was obtained by multiplying the expression values of specific genes by their coefficients.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35813746", "locator": "sections[5] \"Construction of IRGPI\" paragraph 2", "quote": "IRGPI, a Cox proportional-hazards model, was obtained by multiplying the expression values of specific genes by their coefficients."}], "state": "complete"}, {"id": "claim-data-access", "statement": "RNA-sequencing data for 594 LUAD cases (59 normal tissues and 535 LUAD samples) and clinical and simple nucleotide variation (varscan2) data for 486 samples were downloaded from The Cancer Genome Atlas (TCGA) database (https://portal.gdc.cancer.gov/).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35813746", "locator": "sections[3] \"Gene expression datasets and study design\" paragraph 2", "quote": "RNA-sequencing data for 594 LUAD cases (59 normal tissues and 535 LUAD samples) and clinical and simple nucleotide variation (varscan2) data for 486 samples were downloaded from The Cancer Genome Atlas (TCGA) database (https://portal.gdc.cancer.gov/)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "IRGPI had AUC values of 0.715, 0.724, and 0.743 at 1, 2, and 3 years, respectively.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:35813746", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "And IRGPI has AUC values of 0.715, 0.724, and 0.743 in 1, 2, and 3 years, respectively."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "IRGPI had AUC values of 0.715, 0.724, and 0.743 at 1, 2, and 3 years, respectively.", "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:35813746"], "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:GSE72094"], "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:GSE72094"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:35813746", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.21037/jtd-22-494", "sha256": 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