Constructing a sixteen lactate-related gene risk signature for LUAD to predict the prognosis and TME by machine learning.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:41266429", "kind": "science-record", "revision": 2, "content_hash": "sha256:0936da854143b91db8c65c24f5adf4886e12d72722f04f4017e64cc7089cf743", "created_at": "2026-11-01T20:31:17Z", "updated_at": "2026-11-01T21:36:04Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Constructing a sixteen lactate-related gene risk signature for LUAD to predict the prognosis and TME by machine learning.", "doi": "10.1038/s41598-025-24770-6", "pmid": "41266429", "authors": ["Jinjie Wang", "Deshen Shan", "Hailong Tang", "Ming Zhao", "Hao Wang"], "journal": "Scientific reports", "year": 2025, "abstract": "Although it is the most common subtype of lung cancer in clinical practice, lung adenocarcinoma (LUAD) was proven to be associated with a poor prognosis. In recent years, lactate metabolism has been considered an important biological mechanism in lung cancer. However, the mechanisms of lactate-related genes in tumour microenvironment (TME) of LUAD are unknown. Lactate-related genes modification patterns in 701 LUAD samples from TCGA and GEO database were systematically assessed on the basis of 16 lactate-related genes (LRGs).We also identified the correlation of these clusters to the TME and immune cell infiltration in the TME. After unsupervised clustering analysis was performed, the LUAD samples were divided into three lactate-related gene phenotypes on the basis of 36 prognostic lactate-related genes (PLRGs). These molecular subtypes have different immune cell infiltration characteristics and pathway enrichment. Using LASSO, a 16-LRG risk signature was constructed. The lactate-related signature demonstrated a stable and accurate ability to predict the prognosis of LUAD in patients. Furthermore, the lactate-related signature demonstrated good predictive ability for immune infiltrating cells, tumour mutation burden, and response to immunotherapy. The 16-LRG risk signature was subsequently verified in the GSE50081 GEO cohort. This study revealed the significant clinical utility of the 16-LRG risk signature in the understanding the TME and prognosis of LUAD. The 16-LRG risk signature is conducive to understanding immune cell infiltration in the LUAD TME and contributes to the selection of more effective immunotherapy strategies.", "claims": [{"id": "claim-method", "statement": "LASSO was used to construct a sixteen-gene prognostic signature based on the screened LRGs.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:41266429", "locator": "sections[8] \"Construction and validation of the lactate-related signature\" paragraph 2", "quote": "LASSO was used to construct a sixteen-gene prognostic signature based on the screened LRGs."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets generated during and/or analysed during the current study are available from GEO [https://www.ncbi.nlm.nih.gov/geo/] (https://www.ncbi.nlm.nih.gov/geo) and TCGA official website (https://portal.gdc.cancer.gov/repository).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:41266429", "locator": "sections[26] \"notes\" paragraph 1", "quote": "The datasets generated during and/or analysed during the current study are available from GEO [https://www.ncbi.nlm.nih.gov/geo/] (https://www.ncbi.nlm.nih.gov/geo) and TCGA official website (https://portal.gdc.cancer.gov/repository)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The 16-LRG risk signature achieved an AUC of 0.692 for prognostic prediction.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:41266429", "locator": "sections[14] \"Assessment of the correlation between the prognostic signature and clinical features\" paragraph 2", "quote": "The AUC of the 16-LRG risk signature was 0.692 (Fig. 4A)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The 16-LRG risk signature achieved an AUC of 0.692 for prognostic prediction.", "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:41266429"], "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:41266429", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1038/s41598-025-24770-6", "sha256": 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