Development of a prediction model for radiosensitivity using the expression values of genes and long non-coding RNAs
Journal
Oncotarget
Journal Volume
7
Journal Issue
18
Pages
26739-26750
Date Issued
2016
Author(s)
Abstract
Radiotherapy has become a popular and standard approach for treating cancer patients because it greatly improves patient survival. However, some of the patients receiving radiotherapy suffer from adverse effects and do not obtain survival benefits. This may be attributed to the fact that most radiation treatment plans are designed based on cancer type, without consideration of each individual's radiosensitivity. A model for predicting radiosensitivity would help to address this issue. In this study, the expression levels of both genes and long non-coding RNAs (lncRNAs) were used to build such a prediction model. Analysis of variance and Tukey's honest significant difference tests (P < 0.001) were utilized in immortalized B cells (GSE26835) to identify differentially expressed genes and lncRNAs after irradiation. A total of 41 genes and lncRNAs associated with radiation exposure were revealed by a network analysis algorithm. To develop a predictive model for radiosensitivity, the expression profiles of NCI-60 cell lines along, with their radiation parameters, were analyzed. A genetic algorithm was proposed to identify 20 predictors, and the support vector machine algorithm was used to evaluate their prediction performance. The model was applied to 2 datasets of glioblastoma, The Cancer Genome Atlas and GSE16011, and significantly better survival was observed in patients with greater predicted radiosensitivity.
SDGs
Other Subjects
long untranslated RNA; long untranslated RNA; Article; B lymphocyte; cancer survival; gene expression; gene identification; gene interaction; genetic algorithm; genetic association; glioblastoma; human; immortalized cell line; irradiation; microarray analysis; prediction; radiation exposure; radiosensitivity; support vector machine; algorithm; gene expression profiling; genetics; Kaplan Meier method; mortality; procedures; proportional hazards model; radiation tolerance; treatment outcome; Algorithms; Gene Expression Profiling; Glioblastoma; Humans; Kaplan-Meier Estimate; Proportional Hazards Models; Radiation Tolerance; RNA, Long Noncoding; Treatment Outcome
Publisher
Impact Journals LLC
Type
journal article