Switched alternative splicing events as attractive features in lung squamous cell carcinoma

Cancer Cell International

Weilin Peng¹  Xiang Wang¹  Shuai Shi¹  Guangxu Tu¹  Zhenyu Zhao¹  Wei Yin¹  Qidong Cai¹  Pengfei Zhang¹  Xiong Peng¹  Yongguang Tao²  Boxue He³  Cong Wei⁴ 

[1] Department of Thoracic Surgery, Second Xiangya Hospital, Central South University, 410011, Changsha, China;Hunan Key Laboratory of Early Diagnosis and Precise Treatment of Lung Cancer, Second Xiangya Hospital, Central South University, 410011, Changsha, China;Department of Thoracic Surgery, Second Xiangya Hospital, Central South University, 410011, Changsha, China;Hunan Key Laboratory of Early Diagnosis and Precise Treatment of Lung Cancer, Second Xiangya Hospital, Central South University, 410011, Changsha, China;Key Laboratory of Carcinogenesis and Cancer Invasion, Ministry of Education, Department of Pathology, Xiangya Hospital, Central South University, 410078, Hunan, China;NHC Key Laboratory of Carcinogenesis (Central South University), Cancer Research Institute and School of Basic Medicine, Central South University, 410078, Changsha, Hunan, China;Department of Thoracic Surgery, Second Xiangya Hospital, Central South University, 410011, Changsha, China;Hunan Key Laboratory of Early Diagnosis and Precise Treatment of Lung Cancer, Second Xiangya Hospital, Central South University, 410011, Changsha, China;Xiangya School of Medicine, Central South University, 410008, Changsha, China;Xiangya School of Medicine, Central South University, 410008, Changsha, China;

关键词: Alternative splicing;    Lung squamous cell carcinoma;    Machine learning algorithms;    Splicing switch;    Biomarkers;   

DOI  :  10.1186/s12935-021-02429-2

来源: Springer

【 摘 要 】

BackgroundAlternative splicing (AS) plays important roles in transcriptome and proteome diversity. Its dysregulation has a close affiliation with oncogenic processes. This study aimed to evaluate AS-based biomarkers by machine learning algorithms for lung squamous cell carcinoma (LUSC) patients.MethodThe Cancer Genome Atlas (TCGA) database and TCGA SpliceSeq database were utilized. After data composition balancing, Boruta feature selection and Spearman correlation analysis were used for differentially expressed AS events. Random forests and a nested fivefold cross-validation were applied for lymph node metastasis (LNM) classifier building. Random survival forest combined with Cox regression model was performed for a prognostic model, based on which a nomogram was developed. Functional enrichment analysis and Spearman correlation analysis were also conducted to explore underlying mechanisms. The expression of some switch-involved AS events along with parent genes was verified by qRT-PCR with 20 pairs of normal and LUSC tissues.ResultsWe found 16 pairs of splicing events from same parent genes which were strongly related to the splicing switch (intrapair correlation coefficient = − 1). Next, we built a reliable LNM classifier based on 13 AS events as well as a nice prognostic model, in which switched AS events behaved prominently. The qRT-PCR presented consistent results with previous bioinformatics analysis, and some AS events like ITIH5-10715-AT and QKI-78404-AT showed remarkable detection efficiency for LUSC.ConclusionAS events, especially switched ones from the same parent genes, could provide new insights into the molecular diagnosis and therapeutic drug design of LUSC.

【 授权许可】

CC BY