Cargando…
A gastric cancer LncRNAs model for MSI and survival prediction based on support vector machine
BACKGROUND: Recent 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 proli...
Autores principales: | , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2019
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6854775/ https://www.ncbi.nlm.nih.gov/pubmed/31722674 http://dx.doi.org/10.1186/s12864-019-6135-x |
_version_ | 1783470278343393280 |
---|---|
author | Chen, Tao Zhang, Cangui Liu, Yingqiao Zhao, Yuyun Lin, Dingyi Hu, Yanfeng Yu, Jiang Li, Guoxin |
author_facet | Chen, Tao Zhang, Cangui Liu, Yingqiao Zhao, Yuyun Lin, Dingyi Hu, Yanfeng Yu, Jiang Li, Guoxin |
author_sort | Chen, Tao |
collection | PubMed |
description | BACKGROUND: Recent 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. METHODS: Using 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. RESULTS: Using 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. CONCLUSION: This 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. |
format | Online Article Text |
id | pubmed-6854775 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-68547752019-11-21 A gastric cancer LncRNAs model for MSI and survival prediction based on support vector machine Chen, Tao Zhang, Cangui Liu, Yingqiao Zhao, Yuyun Lin, Dingyi Hu, Yanfeng Yu, Jiang Li, Guoxin BMC Genomics Research Article BACKGROUND: Recent 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. METHODS: Using 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. RESULTS: Using 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. CONCLUSION: This 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. BioMed Central 2019-11-13 /pmc/articles/PMC6854775/ /pubmed/31722674 http://dx.doi.org/10.1186/s12864-019-6135-x Text en © The Author(s). 2019 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Article Chen, Tao Zhang, Cangui Liu, Yingqiao Zhao, Yuyun Lin, Dingyi Hu, Yanfeng Yu, Jiang Li, Guoxin A gastric cancer LncRNAs model for MSI and survival prediction based on support vector machine |
title | A gastric cancer LncRNAs model for MSI and survival prediction based on support vector machine |
title_full | A gastric cancer LncRNAs model for MSI and survival prediction based on support vector machine |
title_fullStr | A gastric cancer LncRNAs model for MSI and survival prediction based on support vector machine |
title_full_unstemmed | A gastric cancer LncRNAs model for MSI and survival prediction based on support vector machine |
title_short | A gastric cancer LncRNAs model for MSI and survival prediction based on support vector machine |
title_sort | gastric cancer lncrnas model for msi and survival prediction based on support vector machine |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6854775/ https://www.ncbi.nlm.nih.gov/pubmed/31722674 http://dx.doi.org/10.1186/s12864-019-6135-x |
work_keys_str_mv | AT chentao agastriccancerlncrnasmodelformsiandsurvivalpredictionbasedonsupportvectormachine AT zhangcangui agastriccancerlncrnasmodelformsiandsurvivalpredictionbasedonsupportvectormachine AT liuyingqiao agastriccancerlncrnasmodelformsiandsurvivalpredictionbasedonsupportvectormachine AT zhaoyuyun agastriccancerlncrnasmodelformsiandsurvivalpredictionbasedonsupportvectormachine AT lindingyi agastriccancerlncrnasmodelformsiandsurvivalpredictionbasedonsupportvectormachine AT huyanfeng agastriccancerlncrnasmodelformsiandsurvivalpredictionbasedonsupportvectormachine AT yujiang agastriccancerlncrnasmodelformsiandsurvivalpredictionbasedonsupportvectormachine AT liguoxin agastriccancerlncrnasmodelformsiandsurvivalpredictionbasedonsupportvectormachine AT chentao gastriccancerlncrnasmodelformsiandsurvivalpredictionbasedonsupportvectormachine AT zhangcangui gastriccancerlncrnasmodelformsiandsurvivalpredictionbasedonsupportvectormachine AT liuyingqiao gastriccancerlncrnasmodelformsiandsurvivalpredictionbasedonsupportvectormachine AT zhaoyuyun gastriccancerlncrnasmodelformsiandsurvivalpredictionbasedonsupportvectormachine AT lindingyi gastriccancerlncrnasmodelformsiandsurvivalpredictionbasedonsupportvectormachine AT huyanfeng gastriccancerlncrnasmodelformsiandsurvivalpredictionbasedonsupportvectormachine AT yujiang gastriccancerlncrnasmodelformsiandsurvivalpredictionbasedonsupportvectormachine AT liguoxin gastriccancerlncrnasmodelformsiandsurvivalpredictionbasedonsupportvectormachine |