Characterization of Tumor Mutation Burden-Based Gene Signature and Molecular Subtypes to Assist Precision Treatment in Gastric Cancer.

{"sci_version": "1.0", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:35601155", "kind": "science-record", "revision": 3, "content_hash": "sha256:62cfea0f25ec7bc1ae071015bfd205c30245f7e0f6c13f7800be189f7e54561b", "created_at": "2026-11-01T18:14:30Z", "updated_at": "2026-11-01T21:27:55Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Characterization of Tumor Mutation Burden-Based Gene Signature and Molecular Subtypes to Assist Precision Treatment in Gastric Cancer.", "doi": "10.1155/2022/4006507", "pmid": "35601155", "authors": ["Cheng Wei", "Minzhe Li", "Shaofeng Lin", "Jun Xiao"], "journal": "BioMed research international", "year": 2022, "abstract": "Objective: Tumor mutation burden (TMB) represents a useful biomarker for predicting survival outcomes and immunotherapy response. Here, we aimed to conduct TMB-based gene signature and molecular subtypes in gastric cancer.\nMethods: Based on differentially expressed genes (DEGs) between high- and low-TMB groups in TCGA, a LASSO model was developed for predicting overall survival (OS) and disease-free survival (DFS). The predictive performance was externally verified in the GSE84437 dataset. Molecular subtypes were conducted via consensus clustering approach based on TMB-related DEGs. The immune microenvironment was estimated by ESTIMATE and ssGSEA algorithms.\nResults: High-TMB patients had prolonged survival duration. TMB-related DEGs were distinctly enriched in cancer- (MAPK, P53, PI3K-Akt, and Wnt pathways) and immune-related pathways (T cell selection and differentiation). The TMB-based gene model was developed (including MATN3, UPK1B, GPX3, and RGS2), and high-risk score was predictive of poor prognosis and recurrence. ROC and multivariate analyses revealed the well predictive performance, which was confirmed in the external cohort. Furthermore, we established the nomogram containing the risk score, age, and stage for personalized prediction of OS and DFS. High-risk score was characterized by high stromal score, increased immune checkpoints, immune cell infiltrations, and enhanced sensitivity to gefitinib, vinorelbine, and gemcitabine. Three TMB-based molecular subtypes were conducted, characterized by distinct prognosis, immune microenvironment, and drug sensitivity.\nConclusion: Collectively, we established a prognostic signature and three distinct molecular subtypes based on TMB features for gastric cancer, which might be beneficial for prognostic prediction and clinical decision-making.", "claims": [{"id": "claim-method", "statement": "Genes with p value < 0.05 were screened for least absolute shrinkage and selection operator (LASSO) analysis. Tenfold cross-verification was employed for acquiring candidate variables.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35601155", "locator": "sections[8] \"2.6. Establishment of a Prognostic Model\" paragraph 2", "quote": "Genes with p value < 0.05 were screened for least absolute shrinkage and selection operator (LASSO) analysis. Tenfold cross-verification was employed for acquiring candidate variables."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The data used to support the findings of this study are included within the supplementary information files.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35601155", "locator": "sections[29] \"Data Availability\" paragraph 2", "quote": "The data used to support the findings of this study are included within the supplementary information files."}], "state": "complete"}, {"id": "claim-target-result", "statement": "A four-gene LASSO model derived from TCGA data predicted overall survival with an AUC of 0.742 and was externally validated in GSE84437 with an AUC of 0.628.", "kind": "quantitative", "target": {"value": 10.0, "unit": "fold change", "tolerance": null, "source": "By applying LASSO method with 10-fold cross-verification followed by 1,000-time iterations, a 4-gene model (MATN3, UPK1B, GPX3, and RGS2) was conducted in TCGA cohort (Figures 4(a) and 4(b))."}, "evidence": [{"asset": "source:pmid:35601155", "locator": "sections[19] \"3.5. A TMB-Related Gene Model for Predicting Gastric Cancer Patients' OS\" paragraph 2", "quote": "By applying LASSO method with 10-fold cross-verification followed by 1,000-time iterations, a 4-gene model (MATN3, UPK1B, GPX3, and RGS2) was conducted in TCGA cohort (Figures 4(a) and 4(b))."}, {"asset": "source:pmid:35601155", "locator": "sections[19] \"3.5. A TMB-Related Gene Model for Predicting Gastric Cancer Patients' OS\" paragraph 2", "quote": "AUC value of OS time was 0.742, indicative of the powerful prognostic prediction usefulness (Figure 4(g))."}, {"asset": "source:pmid:35601155", "locator": "sections[21] \"3.7. Validation of This TMB-Based Gene Signature in an External Cohort\" paragraph 2", "quote": "Following the same formula, we determined the risk score of each specimen."}, {"asset": "source:pmid:35601155", "locator": "sections[21] \"3.7. Validation of This TMB-Based Gene Signature in an External Cohort\" paragraph 2", "quote": "ROC analysis confirmed the accuracy as AUC = 0.628 (Figure 6(e))."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "A four-gene LASSO model derived from TCGA data predicted overall survival with an AUC of 0.742 and was externally validated in GSE84437 with an AUC of 0.628.", "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:35601155"], "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:GSE84437"], "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:GSE84437"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:35601155", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1155/2022/4006507", "sha256": 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