ArticleScientific reports2021
Tumor mutation burden (TMB)-associated signature constructed to predict survival of lung squamous cell carcinoma patients.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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12 citing papers in PubMed, 18 citations in OpenAlex.
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- Spatial Positioning of Immune Hotspots Reflects the Interplay between B and T Cells in Lung Squamous Cell Carcinoma.Cancer research · 2023Article
- Construction and validation of a tumor mutational burden and immune-related prognostic model for predicting the prognosis of patients with lung squamous cell carcinoma.Journal of thoracic disease · 2023Article
- PSMA1, a Poor Prognostic Factor, Promotes Tumor Growth in Lung Squamous Cell Carcinoma.Disease markers · 2023Article
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- Immune Infiltration Characteristics and a Gene Prognostic Signature Associated With the Immune Infiltration in Head and Neck Squamous Cell Carcinoma.Frontiers in genetics · 2022Article
- A Novel Prognosis Signature Based on Ferroptosis-Related Gene DNA Methylation Data for Lung Squamous Cell Carcinoma.Journal of oncology · 2022Article
- Clinicopathologic Features and Molecular Biomarkers as Predictors of Epidermal Growth Factor Receptor Gene Mutation in Non-Small Cell Lung Cancer Patients.Current oncology (Toronto, Ont.) · 2021Review
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Authors and funding
2 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Lung squamous cell carcinoma (LUSC) is a common type of lung cancer with high incidence and mortality rate. Tumor mutational burden (TMB) is an emerging biomarker for selecting patients with non-small cell lung cancer (NSCLC) for immunotherapy. This study aimed to reveal TMB involved in the mechanisms of LUSC and develop a model to predict the overall survival of LUSC patients. The information of patients with LUSC were obtained from the cancer genome atlas database (TCGA). Differentially expressed genes (DEGs) between low- and the high-TMB groups were identified and taken as nodes for the protein-protein interaction (PPI) network construction. Gene oncology (GO) enrichment analysis and gene set enrichment analysis (GSEA) were used to investigate the potential molecular mechanism. Then, we identified the factors affecting the prognosis of LUSC through cox analysis, and developed a risk score signature. Kaplan-Meier method was conducted to analyze the difference in survival between the high- and low-risk groups. We constructed a nomogram based on the risk score model and clinical characteristics to predict the overall survival of patients with LUSC. Finally, the signature and nomogram were further validated by using the gene expression data downloaded from the Gene Expression Omnibus (GEO) database. 30 DEGs between high- and low-TMB groups were identified. PPI analysis identified CD22, TLR10, PIGR and SELE as the hub genes. Cox analysis indicated that FAM107A, IGLL1, SELE and T stage were independent prognostic factors of LUSC. Low-risk scores group lived longer than that of patients with high-risk scores in LUSC. Finally, we built a nomogram that integrated the clinical characteristics (TMN stage, age, gender) with the three-gene signature to predict the survival probability of LUSC patients. Further verification in the GEO dataset. TMB might contribute to the pathogenesis of LUSC. TMB-associated genes can be used to develope a model to predict the OS of lung squamous cell carcinoma patients.
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