Lipid metabolism-related gene prognostic index (LMRGPI) reveals distinct prognosis and treatment patterns for patients with early-stage pulmonary adenocarcinoma.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:35582412", "kind": "science-record", "revision": 4, "content_hash": "sha256:db00eb96047b178ac9fcfe1369d0efc2834a0ffb31123553133036c5d075eb3d", "created_at": "2026-11-01T18:14:28Z", "updated_at": "2026-11-01T22:06:32Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Lipid metabolism-related gene prognostic index (LMRGPI) reveals distinct prognosis and treatment patterns for patients with early-stage pulmonary adenocarcinoma.", "doi": "10.7150/ijms.71267", "pmid": "35582412", "authors": ["Aimin Jiang", "Xue Chen", "Haoran Zheng", "Na Liu", "Qianqian Ding", "Yimeng Li", "Chaoxin Fan", "Xiao Fu", "Xuan Liang", "Tao Tian", "Zhiping Ruan", "Yu Yao"], "journal": "International journal of medical sciences", "year": 2022, "abstract": "Background: Lipid metabolism plays a pivotal role in cancer progression and metastasis. This study aimed to investigate the prognostic value of lipid metabolism-related genes (LMRGs) in early-stage lung adenocarcinoma (LUAD) and develop a lipid metabolism-related gene prognostic index (LMRGPI) to predict their overall survival (OS) and treatment response. Methods: A total of 774 early-stage LUAD patients were identified from The Cancer Genome Atlas (TCGA, 403 patients) database and Gene Expression Omnibus (GEO, 371 patients) database. The non-negative Matrix Factorization (NMF) algorithm was used to identify different population subtypes based on LMRGs. The Least Absolute Shrinkage and Selection Operator (LASSO) and multivariate Cox regression analyses were used to develop the LMRGPI, with receiver operating characteristic (ROC) curves and concordance index being used to evaluate its performance. The characteristics of mutation landscape, enriched pathways, tumor microenvironment (TME), and treatment response between different LMRGPI groups were also investigated. Results: We identified two population subtypes based on LMRGs in the TCGA-LUAD cohort, with distinct prognosis, TME, and immune status being observed. LMRGPI was developed based on the expression levels of six LMRGs, including ANGPTL4, NPAS2, SLCO1B3, ACOXL, ALOX15, and B3GALNT1. Higher LMRGPI was correlated with poor OS both in TCGA and GSE68465 cohorts. Two nomograms were established to predict the survival probability of early-stage LUAD, with higher consistencies being observed between the predicted and actual OS. Higher LMRGPI was significantly correlated with more frequent TP53 mutation, higher tumor mutation burden (TMB), and up-regulation of CD274. Besides, patients with higher LMRGPI presented unremarkable responses for gefitinib, erlotinib, cisplatin, and vinorelbine, while they tend to have a favorable response for immune checkpoint inhibitors (ICIs). The opposite results were observed in the low-LMRGPI group. Conclusions: We comprehensively investigated the prognostic value of LMRGs in early-stage LUAD. Given its good prognostic ability, LMRGPI could serve as a promising biomarker to predict the OS and treatment response of these patients.", "claims": [{"id": "claim-method", "statement": "The Least Absolute Shrinkage and Selection Operator (LASSO) and multivariate Cox regression analyses were used to develop the LMRGPI, with receiver operating characteristic (ROC) curves and concordance index being used to evaluate its performance.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35582412", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "The Least Absolute Shrinkage and Selection Operator (LASSO) and multivariate Cox regression analyses were used to develop the LMRGPI, with receiver operating characteristic (ROC) curves and concordance index being used to evaluate its performance."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The raw data were obtained from TCGA (https://portal.gdc.cancer.gov/) and GSE68465 series in GEO (https://www.ncbi.nlm.nih.gov/) databases.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35582412", "locator": "sections[26] \"Availability of data and material\" paragraph 2", "quote": "The raw data were obtained from TCGA (https://portal.gdc.cancer.gov/) and GSE68465 series in GEO (https://www.ncbi.nlm.nih.gov/) databases."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Higher LMRGPI was associated with poor overall survival in both the TCGA and GSE68465 cohorts.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:35582412", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "Higher LMRGPI was correlated with poor OS both in TCGA and GSE68465 cohorts."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Higher LMRGPI was associated with poor overall survival in both the TCGA and GSE68465 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:35582412"], "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:GSE68465"], "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:GSE68465"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:35582412", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.7150/ijms.71267", "sha256": 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