Machine learning-based integration of CD8 T cell-related gene signatures for comprehensive prognostic assessment in lung adenocarcinoma.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:39145093", "kind": "science-record", "revision": 3, "content_hash": "sha256:23a9a204ddba6120e39fc177f39ab4b35b295ad31306b2aed331c04f9a04bee3", "created_at": "2026-11-01T20:29:16Z", "updated_at": "2026-11-01T22:12:30Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Machine learning-based integration of CD8 T cell-related gene signatures for comprehensive prognostic assessment in lung adenocarcinoma.", "doi": "10.21037/tcr-23-2332", "pmid": "39145093", "authors": ["Jing Yong", "Dongdong Wang", "Huiming Yu"], "journal": "Translational cancer research", "year": 2024, "abstract": "Background: Lung adenocarcinoma (LUAD) stands as the most prevalent histological subtype of lung cancer, exhibiting heterogeneity in outcomes and diverse responses to therapy. CD8 T cells are consistently present throughout all stages of tumor development and play a pivotal role within the tumor microenvironment (TME). Our objective was to investigate the expression profiles of CD8 T cell marker genes, establish a prognostic risk model based on these genes in LUAD, and explore its relationship with immunotherapy response.\nMethods: By leveraging the expression data and clinical records from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) cohorts, we identified 23 consensus prognostic genes. Employing ten machine-learning algorithms, we generated 101 combinations, ultimately selecting the optimal algorithm to construct an artificial intelligence-derived prognostic signature named riskScore. This selection was based on the average concordance index (C-index) across three testing cohorts.\nResults: RiskScore emerged as an independent risk factor for overall survival (OS), progression-free interval (PFI), disease-free interval (DFI), and disease-specific survival (DSS) in LUAD. Notably, riskScore exhibited notably superior predictive accuracy compared to traditional clinical variables. Furthermore, we observed a positive correlation between the high-risk riskScore group and tumor-promoting biological functions, lower tumor mutational burden (TMB), lower neoantigen (NEO) load, and lower microsatellite instability (MSI) scores, as well as reduced immune cell infiltration and an increased probability of immune evasion within the TME. Of significance, the immunophenoscore (IPS) score displayed significant differences among risk subgroups, and riskScore effectively stratified patients in the IMvigor210 and GSE135222 immunotherapy cohort based on their survival outcomes. Additionally, we identified potential drugs that could target specific risk subgroups.\nConclusions: In summary, riskScore demonstrates its potential as a robust and promising tool for guiding clinical management and tailoring individualized treatments for LUAD patients.", "claims": [{"id": "claim-method", "statement": "We then integrated 10 machine-learning algorithms and 101 algorithm combinations.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39145093", "locator": "sections[5] \"Signature generated from machine-learning-based integrative approaches\" paragraph 2", "quote": "We then integrated 10 machine-learning algorithms and 101 algorithm combinations."}], "state": "complete"}, {"id": "claim-data-access", "statement": "We collected the TCGA-LUAD dataset from TCGA (http://portal.gdc.cancer.gov/), which comprised RNA expression data in transcripts per kilobase million (TPM) format along with corresponding clinical features.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39145093", "locator": "sections[4] \"Data acquisition and preprocessing\" paragraph 2", "quote": "We collected the TCGA-LUAD dataset from TCGA (http://portal.gdc.cancer.gov/), which comprised RNA expression data in transcripts per kilobase million (TPM) format along with corresponding clinical features."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The selected RSF and StepCox[forward] combination achieved the highest average C-index of 0.707 and identified a 23-gene riskScore.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:39145093", "locator": "sections[19] \"Construction of a prognosis signature based on integrative machine-learning\" paragraph 2", "quote": "Based on the results, we selected the combination of RSF and StepCox[forward], which achieved the highest average C-index (0.707). This combination identified a final model named riskScore consisting of 23 CD8TRPGs (Figure 3B)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "The selected RSF and StepCox[forward] combination achieved the highest average C-index of 0.707 and identified a 23-gene riskScore.", "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:39145093"], "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:GSE176021"], "outputs": ["the source data with its repository provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the published analysis", "kind": "analysis", "state": "blocked", "blocked_on": "method:group_comparison_wilcoxon", "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:GSE176021"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": ["step:run_analysis blocked on method:group_comparison_wilcoxon"]}, "data": {"state": "partial", "assets": [{"id": "source:pmid:39145093", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": 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