Identification of costimulatory molecule signatures for evaluating prognostic risk in non-small cell lung cancer.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:39286099", "kind": "science-record", "revision": 3, "content_hash": "sha256:1eb3ff5068a206bc15efed0a70469222145757941e5e909ffe8d1e9b87d63114", "created_at": "2026-11-01T20:29:27Z", "updated_at": "2026-11-01T22:12:42Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of costimulatory molecule signatures for evaluating prognostic risk in non-small cell lung cancer.", "doi": "10.1016/j.heliyon.2024.e36816", "pmid": "39286099", "authors": ["Yan Yang", "Suqiong Lu", "Guomin Gu"], "journal": "Heliyon", "year": 2024, "abstract": "Background: Non-small cell lung cancer (NSCLC) is a leading cause of cancer-related mortality worldwide. Despite advances in treatment, prognosis remains poor, necessitating the identification of reliable prognostic biomarkers. Costimulatory molecules (CMs) have shown to enhance antitumor immune responses. We aimed to explore their prognostic signals in NSCLC.\nMethods: This study is a combination of bioinformatics analysis and laboratory validation. Gene expression profiles from The Cancer Genome Atlas (TCGA), GSE120622, and GSE131907 datasets were collected. NSCLC samples in TCGA were clustered based on CMs using consensus clustering. We used LASSO regression to identify CMs-related signatures and constructed nomogram and risk models. Differences in immune cells and checkpoint expressions between risk models were evaluated. Enrichment analysis was performed for differentially expressed CMs between NSCLC and controls. Key results were validated using qRT-PCR and flow cytometry.\nResults: NSCLC samples in TCGA were divided into two clusters based on CMs, with cluster 1 showing poor overall survival. Ten CMs-related signatures were identified using LASSO regression. NSCLC samples in TCGA were stratified into high- and low-risk groups based on the median risk score of these signatures, revealing differences in survival probability, drug sensitivity, immune cell infiltration and checkpoints expression. The area under the ROC curve values (AUC) for EDA, ICOS, PDCD1LG2, and VTCN1 exceeded 0.7 in both datasets and considered as hub genes. Expression of these hub genes was significance in GSE131907 and validated by qRT-PCR. Macrophage M1 and T cell follicular helper showed high correlation with hub genes and were lower in NSCLC than controls detected by flow cytometry.\nConclusion: The identified hub genes can serve as prognostic biomarkers for NSCLC, aiding in treatment decisions and highlighting potential targets for immunotherapy. This study provides new insights into the role of CMs in NSCLC prognosis and suggests future directions for clinical research and therapeutic strategies.", "claims": [{"id": "claim-method", "statement": "To further selected useful prognostic signatures, the CMs were analyzed using least absolute shrinkage and selector operation (LASSO) regression model by glmnet package [15].", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39286099", "locator": "sections[6] \"Prognostic signature construction\" paragraph 2", "quote": "To further selected useful prognostic signatures, the CMs were analyzed using least absolute shrinkage and selector operation (LASSO) regression model by glmnet package [15]."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The data used to support the findings of this study are available from The Cancer Genome Atlas (TCGA), GSE120622 and GSE131907 datasets.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39286099", "locator": "sections[25] \"Data availability statement\" paragraph 2", "quote": "The data used to support the findings of this study are available from The Cancer Genome Atlas (TCGA), GSE120622 and GSE131907 datasets."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Using LASSO regression analysis, the paper identified 10 regression coefficients as costimulatory-molecule-related prognostic signatures.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:39286099", "locator": "sections[16] \"Prognostic model of costimulatory molecules in NSCLC\" paragraph 2", "quote": "Using LASSO regression analysis, we identified 10 regression coefficients as CMs-related signatures (Fig. 2D and E)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "Using LASSO regression analysis, the paper identified 10 regression coefficients as costimulatory-molecule-related prognostic signatures.", "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:39286099"], "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. 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Results: NSCLC samples in TCGA were divided", "ere lower in NSCLC than controls detected by flow cytometry. Conclusion: The identified hub genes can se", "M1 and Tfh in NSCLC and controls detected by flow cytometry. *P < 0.05, **P < 0.01, ***P < 0.001. Import", "etween NSCLC and controls was examined using flow cytometry (Fig. 10B). The abundances of macrophage M1"], "order_basis": "paper_text_order:1046", "id": "assay:1"}, {"role": "data_generation", "definition_id": "scrna_seq", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "unsupported", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.93, "evidence": ["atures in NSCLC patients. 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