Oncotarget

Research Papers:

Classifying lower grade glioma cases according to whole genome gene expression

Baoshi Chen, Tingyu Liang, Pei Yang, Haoyuan Wang, Yanwei Liu, Fan Yang and Gan You _

PDF  |  HTML  |  Supplementary Files  |  How to cite

Oncotarget. 2016; 7:74031-74042. https://doi.org/10.18632/oncotarget.12188

Metrics: PDF 1670 views  |   HTML 2170 views  |   ?  


Abstract

Baoshi Chen1,*, Tingyu Liang1,2,*, Pei Yang1,2,3,*, Haoyuan Wang4, Yanwei Liu1,2,3, Fan Yang1,2, Gan You1,2,3

1Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China

2Beijing Neurosurgical Institute, Capital Medical University, Beijing, China

3Chinese Glioma Cooperative Group (CGCG), China

4Department of Neurosurgery, Guangdong Zhujiang Hospital, Southern Medical University, Guangzhou, China

*These authors have contributed equally to this work

Correspondence to:

Gan You, email: [email protected]

Keywords: lower grade glioma, risk score, gene signature, prognosis

Received: February 17, 2016     Accepted: August 13, 2016     Published: September 22, 2016

ABSTRACT

OBJECTIVE: To identify a gene-based signature as a novel prognostic model in lower grade gliomas.

RESULTS: A gene signature developed from HOXA7, SLC2A4RG and MN1 could segregate patients into low and high risk score groups with different overall survival (OS), and was validated in TCGA RNA-seq and GSE16011 mRNA array datasets. Receiver operating characteristic (ROC) was performed to show that the three-gene signature was more sensitive and specific than histology, grade, age, IDH1 mutation and 1p/19q co-deletion. Gene Set Enrichment Analysis (GSEA) and GO analysis showed high-risk samples were associated with tumor associated macrophages (TAMs) and highly invasive phenotypes. Moreover, HOXA7-siRNA inhibited migration and invasion in vitro, and downregulated MMP9 at the protein level in U251 glioma cells.

METHODS: A cohort of 164 glioma specimens from the Chinese Glioma Genome Atlas (CGGA) array database were assessed as the training group. TCGA RNA-seq and GSE16011 mRNA array datasets were used for validation. Regression analyses and linear risk score assessment were performed for the identification of the three-gene signature comprising HOXA7, SLC2A4RG and MN1.

CONCLUSIONS: We established a three-gene signature for lower grade gliomas, which could independently predict overall survival (OS) of lower grade glioma patients with higher sensitivity and specificity compared with other clinical characteristics. These findings indicate that the three-gene signature is a new prognostic model that could provide improved OS prediction and accurate therapies for lower grade glioma patients.


Creative Commons License All site content, except where otherwise noted, is licensed under a Creative Commons Attribution 4.0 License.
PII: 12188