Department of Thoracic Surgery, Xiangya Hospital, Central South University, Changsha, Hunan, China.
Hunan Engineering Research Center for Pulmonary Nodules Precise Diagnosis & Treatment, Changsha, Hunan, China.
Sci Rep. 2022 Dec 1;12(1):20737. doi: 10.1038/s41598-022-23140-w.
Lung Squamous Cell Carcinoma (LUSC) is an aggressive malignancy with limited therapeutic options. The response to immune therapy is a determining factor for the prognosis of LUSC patients. This study aimed to develop a reliable immune-related prognostic signature in LUSC. We extracted gene expression and clinical data of LUSC from The Cancer Genome Atlas (TCGA). A total of 502 patients enrolled and were divided into respond and non-responder groups by the TIDE algorithm. The CIBERSORT algorithm and the LM22 gene signature were used to analyze the distribution of immune cells in LUSC. Efficacy and response strength of immunotherapy are calculated by the tumor mutation burden (TMB) and ESTIMATE Score. Differentially expressed genes (DEGs) between the two groups were analyzed. The differential expression genes related to overall survival were pointed as hub DEGs, and a prognostic signature was constructed with lasso regression analysis. LUSC patients were divided into responder and non-responder groups based on the response to immunotherapy. The distribution of immune cells was significantly different between the two groups. Forty-four DGEs were considered as overall survival-related genes. A prognostic signature was constructed, consisting of 11 hub-DGEs, including MMP20, C18orf26, CASP14, FAM71E2, OPN4, CGB5, DIRC1, C9orf11, SPATA8, C9orf144B, and ZCCHC5. The signature can accurately distinguish LUSC patients into high and low-risk groups. Moreover, the high-risk group had a shorter survival time than the low-risk group. The area under the ROC curve was 0.67. The multivariate Cox regression showed that the risk score calculated by the constructed signature was an independent prognostic predictor for LUSC patients. In short, we established a novel immune-related prognostic signature in LUCS, which has significant sensitivity and accuracy in predicting the prognosis of patients. Our research can guide the evaluation of the prognosis of LUSC patients in clinical, and the discovered immune-related genes can provide a theoretical basis for the discovery of new therapeutic targets.
肺鳞状细胞癌(LUSC)是一种侵袭性恶性肿瘤,治疗选择有限。免疫治疗的反应是 LUSC 患者预后的决定因素。本研究旨在开发 LUSC 中可靠的免疫相关预后特征。我们从癌症基因组图谱(TCGA)中提取了 LUSC 的基因表达和临床数据。共纳入 502 例患者,根据 TIDE 算法分为应答组和非应答组。使用 CIBERSORT 算法和 LM22 基因特征分析 LUSC 中免疫细胞的分布。通过肿瘤突变负担(TMB)和 ESTIMATE 评分计算免疫治疗的疗效和反应强度。分析两组间差异表达基因(DEGs)。将与总生存期相关的差异表达基因确定为关键 DEGs,并通过lasso 回归分析构建预后特征。根据免疫治疗的反应将 LUSC 患者分为应答组和非应答组。两组间免疫细胞的分布存在显著差异。筛选出 44 个与总生存期相关的 DEGs,构建预后特征,由 11 个关键 DEGs 组成,包括 MMP20、C18orf26、CASP14、FAM71E2、OPN4、CGB5、DIRC1、C9orf11、SPATA8、C9orf144B 和 ZCCHC5。该特征可以准确地将 LUSC 患者分为高危和低危组。而且,高危组的生存时间短于低危组。ROC 曲线下面积为 0.67。多因素 Cox 回归显示,构建特征计算的风险评分是 LUSC 患者的独立预后预测因子。总之,我们在 LUCS 中建立了一个新的免疫相关预后特征,该特征在预测患者预后方面具有显著的敏感性和准确性。我们的研究可以指导临床评估 LUSC 患者的预后,发现的免疫相关基因可为新的治疗靶点的发现提供理论基础。