Construction and validation of a tumor mutational burden and immune-related prognostic model for predicting the prognosis of patients with lung squamous cell carcinoma.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:37065576", "kind": "science-record", "revision": 2, "content_hash": "sha256:c38997574b139ecc3da802372fe50cb13493b3e67577126ac1f3df1eb3fae334", "created_at": "2026-11-01T19:01:43Z", "updated_at": "2026-11-01T21:30:34Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Construction and validation of a tumor mutational burden and immune-related prognostic model for predicting the prognosis of patients with lung squamous cell carcinoma.", "doi": "10.21037/jtd-23-103", "pmid": "37065576", "authors": ["Yuting Zhou", "Min Xu", "Kai Zhao", "Baodong Liu"], "journal": "Journal of thoracic disease", "year": 2023, "abstract": "Background: Lung squamous cell carcinoma (LUSC) is a highly malignant tumor with an extremely poor prognosis. Immune checkpoint inhibitors (ICIs) improve survival in some patients with LUSC. Tumor mutation burden (TMB) is a useful biomarker to predict the efficacy of ICIs. However, predictive and prognostic factors related to TMB in LUSC remain elusive. This study aimed to find effective biomarkers based on TMB and immune response and establish a prognostic model of LUSC.\nMethods: We downloaded Mutation Annotation Format (MAF) files from The Cancer Genome Atlas (TCGA) database and identified immune-related differentially expressed genes (DEGs) between high- and low-TMB groups. The prognostic model was established using cox regression. The primary outcome was overall survival (OS). Receiver operating characteristic (ROC) curves and calibration curves were used to verify the accuracy of the model. GSE37745 acted as external validation set. The expression and prognosis of hub genes as well as their correlation with immune cells and somatic copy number variation (sCNA) were analyzed.\nResults: The TMB of patients with LUSC was correlated with prognosis and stage. High TMB group had higher survival rate (P<0.001). Five TMB-related hub immune genes (TINAGL1, FGFR2, CTSE, SFTPA1, and IGHV7-81) were identified and the prognostic model was constructed. The survival time of high-risk group was significantly shorter than that of low-risk group (P<0.001). The validation results of the model were quite stable in different data sets, and the area under curve (AUC) of training set and validation set were 0.658 and 0.644, respectively. Calibration chart, risk curve, and nomogram revealed that the prognostic model was reliable in predicting the prognostic risk of LUSC, and the risk score of the model could be used as an independent prognostic factor for LUSC patients (P<0.001).\nConclusions: Our results show that high TMB is associated with poor prognosis in patients with LUSC. The prognostic model related to TMB and immunity can effectively predict the prognosis of LUSC, and risk score is one of the independent prognostic factors of LUSC. However, this study still has some limitations, which need to be further verified in large-scale and prospective studies.", "claims": [{"id": "claim-method", "statement": "The prognostic model was established using cox regression.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37065576", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "The prognostic model was established using cox regression."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The somatic mutation data of LUSC was obtained from TCGA database.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37065576", "locator": "sections[5] \"Source and processing of somatic mutation data\" paragraph 2", "quote": "The somatic mutation data of LUSC was obtained from TCGA database."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The validation results of the model were quite stable in different data sets, and the area under curve (AUC) of training set and validation set were 0.658 and 0.644, respectively.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:37065576", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "The validation results of the model were quite stable in different data sets, and the area under curve (AUC) of training set and validation set were 0.658 and 0.644, respectively."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The validation results of the model were quite stable in different data sets, and the area under curve (AUC) of training set and validation set were 0.658 and 0.644, respectively.", "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:37065576"], "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:GSE37745"], "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:GSE37745"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:37065576", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.21037/jtd-23-103", "sha256": 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