A new ten-gene risk fraction model serving as prognostic indicator for clinical outcome of multiple myeloma
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- 作者：Ai-Xin Hu ; Zhi-Yong Huang ; Ping Liu ; Tian Xiang ; Shi Yan ; Li Zhang
- 关键词：Microarray expression profiling ; Genes ; Multiple myeloma ; Survival rates ; Prognostic indicator
- 刊名：Tumor Biology
- 出版年：2016
- 出版时间：December 2016
- 年：2016
- 卷：37
- 期：12
- 页码：15967-15975
- 全文大小：
- 刊物主题：Cancer Research;
- 出版者：Springer Netherlands
- ISSN：1423-0380
- 卷排序：37
文摘
Multiple myeloma (MM) is a kind of aggressive tumor prevalent with high heterogeneity. Abnormal expression of certain genes may lead to the occurrence and development of MM. Nowadays, it is not commonly seen in clinical research to predict the prognostic circumstances of patients with MM by multiple gene expression profiling method. Identification of potential genes in prognostic process could be beneficial for clinical management of MM. Therefore, we aimed to build a risk fraction model to screen out the prognostic indicator for clinical outcome of MM. Microarray data were downloaded from the Genome Expression Omnibus (GEO) datasets with accession numbers GSE24080 and GSE57317. A total of 279 samples were selected out randomly. Besides, a risk formula was constructed and verified in the dataset. Time-dependent receiver operating characteristic (ROC) curve was applied in evaluating the accurate prognostic conditions of patients. Finally, a ten genes model in the training dataset was found to be closely related to the survival condition of MM patients. Patients with MM were divided into two groups, high-risk and low-risk, by the expression of these ten genes, and significant statistical difference was found between the two groups. Furthermore, the result of multivariate cox regression and stratified analysis indicated that this model was independent of other clinical phenotypes. ROC curves also showed its feasibility to predict the survival status of MM patients. Our results demonstrated that the fraction risk model constructed by the selected ten genes could be used to predict survival status of multiple myeloma patients, which could also help in improvement of prognostic and therapeutic tool of MM.