Construction and validation of a TTN mutation associated immune prognostic model for evaluating immune microenvironment and outcomes of gastric cancer: An observational study.

{"sci_version": "1.0", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:39029079", "kind": "science-record", "revision": 2, "content_hash": "sha256:f379b0b62aaa914ffc4abc52ee0f2223bbe1f03e32d35d6eb16efafe4405207d", "created_at": "2026-11-01T20:29:06Z", "updated_at": "2026-11-01T21:33:30Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Construction and validation of a TTN mutation associated immune prognostic model for evaluating immune microenvironment and outcomes of gastric cancer: An observational study.", "doi": "10.1097/MD.0000000000038979", "pmid": "39029079", "authors": ["Ruyue Chen", "Zengwu Yao", "Lixin Jiang"], "journal": "Medicine", "year": 2024, "abstract": "Gastric cancer (GC) is a prevalent form of cancer worldwide, and TTN (titin) mutations are frequently observed in GC. However, the association between TTN mutations and immunotherapy for GC remains unclear, necessitating the development of novel prognostic models. The prognostic value and potential mechanisms of TTN in stomach adenocarcinoma were evaluated by TCGA (The Cancer Genome Atlas)-stomach adenocarcinoma cohort analysis, and an immune prognostic model was constructed based on TTN status. We validated it using the GSE84433 dataset. We performed Gene Set Enrichment Analysis and screened for differentially expressed genes, and used lasso (least absolute shrinkage and selection operator) regression analysis to screen for survival genes to construct a multifactorial survival model. In addition, we evaluated the relative proportions of 22 immune cells using the CIBERSORT algorithm for immunogenicity analysis. Finally, we constructed the nomogram integrating immune prognostic model and other clinical factors. GESA showed enrichment of immune-related phenotypes in patients with TTN mutations. We constructed an immune prognostic model based on 16 genes could identify gastric cancer patients with higher risk of poor prognosis. Immuno-microenvironmental analysis showed increased infiltration of naive B cells, plasma cells, and monocyte in high-risk patients. In addition, Nomo plots predicted the probability of 1-year, 3-year, and 5-year OS (overall survival) in GC patients, showing good predictive performance. In this study, we identified that TTN gene may be a potential clinical biomarker for GC and TTN mutations may be a predictor of immunotherapy in patients. We constructed and validated a new model for prognosis of GC patients based on immune characteristics associated with TTN mutations. This study may provide potential therapeutic strategies for gastric cancer.", "claims": [{"id": "claim-method", "statement": "We performed Gene Set Enrichment Analysis and screened for differentially expressed genes, and used lasso (least absolute shrinkage and selection operator) regression analysis to screen for survival genes to construct a multifactorial survival model.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39029079", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "We performed Gene Set Enrichment Analysis and screened for differentially expressed genes, and used lasso (least absolute shrinkage and selection operator) regression analysis to screen for survival genes to construct a multifactorial survival model."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets generated during and/or analyzed during the current study are publicly available.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39029079", "locator": "sections[19] \"fn-group\" paragraph 1", "quote": "The datasets generated during and/or analyzed during the current study are publicly available."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The AUC results were 0.75, 0.74, and 0.74, respectively (Fig. 3D).", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:39029079", "locator": "sections[12] \"3.2. 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This lane did not download the dataset or read its licence.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE84433"], "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:GSE84433"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:39029079", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1097/MD.0000000000038979", "sha256": 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