Department of Respiratory, Jinhua Municipal Central Hospital, Jinhua Hospital of Zhejiang University, No. 365, East Renmin Road, Jinhua, 321000, Zhejiang Province, People's Republic of China.
Department of Hepatology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, People's Republic of China.
Sci Rep. 2021 Apr 27;11(1):9020. doi: 10.1038/s41598-021-88694-7.
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.
肺鳞状细胞癌（LUSC）是一种常见的肺癌类型，具有较高的发病率和死亡率。肿瘤突变负担（TMB）是一种新兴的生物标志物，用于选择非小细胞肺癌（NSCLC）患者进行免疫治疗。本研究旨在揭示 TMB 参与 LUSC 发病机制的途径，并建立预测 LUSC 患者总生存期的模型。从癌症基因组图谱数据库（TCGA）获取 LUSC 患者的信息。低 TMB 组和高 TMB 组之间的差异表达基因（DEGs）被鉴定出来，并作为构建蛋白质-蛋白质相互作用（PPI）网络的节点。通过基因本体（GO）富集分析和基因集富集分析（GSEA），研究潜在的分子机制。然后，我们通过 Cox 分析确定影响 LUSC 预后的因素，并建立风险评分特征。Kaplan-Meier 方法分析高低风险组之间的生存差异。我们基于风险评分模型和临床特征构建了诺莫图，以预测 LUSC 患者的总体生存率。最后，使用从基因表达数据库（GEO）下载的基因表达数据进一步验证了该特征和诺莫图。在高 TMB 组和低 TMB 组之间鉴定出 30 个 DEGs。PPI 分析确定 CD22、TLR10、PIGR 和 SELE 为枢纽基因。Cox 分析表明，FAM107A、IGLL1、SELE 和 T 期是 LUSC 的独立预后因素。低风险评分组的 LUSC 患者比高风险评分组的患者活得更长。最后，我们构建了一个诺莫图，将临床特征（TMN 分期、年龄、性别）与三个基因特征相结合，预测 LUSC 患者的生存概率。在 GEO 数据集的进一步验证。TMB 可能有助于 LUSC 的发病机制。TMB 相关基因可用于开发预测肺鳞状细胞癌患者 OS 的模型。
