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A gastric cancer LncRNAs model for MSI and survival prediction based on support vector machine

Authors
  • Chen, Tao1
  • Zhang, Cangui1
  • Liu, Yingqiao1
  • Zhao, Yuyun2
  • Lin, Dingyi2
  • Hu, Yanfeng1
  • Yu, Jiang1
  • Li, Guoxin1
  • 1 Southern Medical University, Guangdong Provincial Engineering Technology Research Center of Minimally Invasive Surgery, Department of General Surgery, Nanfang Hospital, No.1838, North Guangzhou Avenue, Guangzhou, Guangdong Province, 510515, China , Guangzhou (China)
  • 2 Southern Medical University, School of Biomedical Engineering, Guangzhou, Guangdong Province, 510515, China , Guangzhou (China)
Type
Published Article
Journal
BMC Genomics
Publisher
Springer (Biomed Central Ltd.)
Publication Date
Nov 13, 2019
Volume
20
Issue
1
Identifiers
DOI: 10.1186/s12864-019-6135-x
Source
Springer Nature
Keywords
License
Green

Abstract

BackgroundRecent studies have shown that long non-coding RNAs (lncRNAs) play a crucial role in the induction of cancer through epigenetic regulation, transcriptional regulation, post-transcriptional regulation and other aspects, thus participating in various biological processes such as cell proliferation, differentiation and apoptosis. As a new nova of anti-tumor therapy, immunotherapy has been shown to be effective in many tumors of which PD-1/PD-L1 monoclonal antibodies has been proofed to increase overall survival rate in advanced gastric cancer (GC). Microsatellite instability (MSI) was known as a biomarker of response to PD-1/PD-L1 monoclonal antibodies therapy. The aim of this study was to identify lncRNAs signatures able to classify MSI status and create a predictive model associated with MSI for GC patients.MethodsUsing the data of Stomach adenocarcinoma from The Cancer Genome Atlas (TCGA), we developed and validated a lncRNAs model for automatic MSI classification using a machine learning technology – support vector machine (SVM). The C-index was adopted to evaluate its accuracy. The prognostic values of overall survival (OS) and disease-free survival (DFS) were also assessed in this model.ResultsUsing the SVM, a lncRNAs model was established consisting of 16 lncRNA features. In the training cohort with 94 GC patients, accuracy was confirmed with AUC 0.976 (95% CI, 0.952 to 0.999). Veracity was also confirmed in the validation cohort (40 GC patients) with AUC 0.950 (0.889 to 0.999). High predicted score was correlated with better DFS in the patients with stage I-III and lower OS with stage I-IV.ConclusionThis study identify 16 LncRNAs signatures able to classify MSI status. The correlation between lncRNAs and MSI status indicates the potential roles of lncRNAs interacting in immunotherapy for GC patients. The pathway of these lncRNAs which might be a target in PD-1/PD-L1 immunotherapy are needed to be further study.

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