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.
Highlights
Lung cancer is the commonest cancer, and is the main cause of global tumor morbidity and mortality[1]
Many studies suggest that Tumor mutational burden (TMB) is a potential and emerging biomarker for selecting non-small cell lung cancer (NSCLC) patients suitable for immunotherapy, even better than PD-1/PDL-1 expression[6,7]
NSCLC patient prognosis is most often evaluated in light of the American Joint Committee on Cancer (AJCC) staging system (8th edition), with the stage of the cancer being used to guide treatment decision making[9]
Summary
Lung cancer is the commonest cancer, and is the main cause of global tumor morbidity and mortality[1]. Nonsmall cell lung cancer (NSCLC) is a common type of lung cancer, including lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC) and large cell lung carcinoma (LCLC). With the discovery of target genes and the development of targeted therapy and immunotherapy, the survival of patients with NSCLC has been extended[3], especially patients with L UAD4. TMB has a vital role in tumor occurrence and development, and affects the immune response and survival prognosis of LUSC patients[5]. NSCLC patient prognosis is most often evaluated in light of the American Joint Committee on Cancer (AJCC) staging system (8th edition), with the stage of the cancer being used to guide treatment decision making[9]. Several studies have suggested that TMB might be a potentiallyuseful clinical predictor in NSCLC patients undergoing immunotherapy[10] and patients with resected NSCLC8. Using data from the TCGA database, we built a novel predictive model able to predict the survival probability of LUSC patients
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