2. Academic Validation
  3. Switched alternative splicing events as attractive features in lung squamous cell carcinoma

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

  • Cancer Cell Int. 2022 Jan 5;22(1):5. doi: 10.1186/s12935-021-02429-2.
Boxue He  # 1 2 3,  Cong Wei  # 3,  Qidong Cai 1 2,  Pengfei Zhang 1 2,  Shuai Shi 1 2,  Xiong Peng 1 2,  Zhenyu Zhao 1 2,  Wei Yin 1 2,  Guangxu Tu 1 2,  Weilin Peng 1 2,  Yongguang Tao 1 2 4 5,  Xiang Wang 6 7
Affiliations

Affiliations

  • 1 Department of Thoracic Surgery, Second Xiangya Hospital, Central South University, Changsha, 410011, China.
  • 2 Hunan Key Laboratory of Early Diagnosis and Precise Treatment of Lung Cancer, Second Xiangya Hospital, Central South University, Changsha, 410011, China.
  • 3 Xiangya School of Medicine, Central South University, Changsha, 410008, China.
  • 4 Key Laboratory of Carcinogenesis and Cancer Invasion, Ministry of Education, Department of Pathology, Xiangya Hospital, Central South University, Hunan, 410078, China.
  • 5 NHC Key Laboratory of Carcinogenesis (Central South University), Cancer Research Institute and School of Basic Medicine, Central South University, Changsha, 410078, Hunan, China.
  • 6 Department of Thoracic Surgery, Second Xiangya Hospital, Central South University, Changsha, 410011, China. [email protected].
  • 7 Hunan Key Laboratory of Early Diagnosis and Precise Treatment of Lung Cancer, Second Xiangya Hospital, Central South University, Changsha, 410011, China. [email protected].
  • # Contributed equally.
PMID: 34986865 DOI: 10.1186/s12935-021-02429-2
Abstract

Background: Alternative 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.

Method: The 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.

Results: We 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.

Conclusion: AS events, especially switched ones from the same parent genes, could provide new insights into the molecular diagnosis and therapeutic drug design of LUSC.

Keywords

Alternative splicing; Biomarkers; Lung squamous cell carcinoma; Machine learning algorithms; Splicing switch.

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