Development and validation of a novel immune-related prognostic signature in lung squamous cell carcinoma patients

• Published in: Scientific reports Vol. 12; no. 1; pp. 20737 - 15
• Main Authors: Liu, Xianyu, Zhao, Deze, Shan, Yunhan, Cui, Weifang, Xie, Qun, Jiang, Junjie, Peng, Wei, Zhang, Chunfang, Duan, Chaojun
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 01.12.2022; Nature Publishing Group; Nature Portfolio
• Subjects: 631/67/1612; 631/67/1857; 631/67/580; Algorithms; Carcinoma, Non-Small-Cell Lung; Carcinoma, Squamous Cell - genetics; Gene expression; Genomes; Humanities and Social Sciences; Humans; Immunotherapy; Lung; Lung cancer; Lung carcinoma; Lung Neoplasms - genetics; Malignancy; Medical prognosis; multidisciplinary; Patients; Prognosis; Regression analysis; Risk groups; Science; Science (multidisciplinary); Squamous cell carcinoma; Therapeutic targets; Tumors
• ISSN: 2045-2322; 2045-2322
• 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.