A four-gene signature-derived risk score for glioblastoma: prospects for prognostic and response predictive analyses

• Published in: Cancer biology & medicine Vol. 16; no. 3; pp. 595 - 605
• Main Authors: Cao, Mianfu, Cai, Juan, Yuan, Ye, Shi, Yu, Wu, Hong, Liu, Qing, Yao, Yueliang, Chen, Lu, Dang, Weiqi, Zhang, Xiang, Xiao, Jingfang, Yang, Kaidi, He, Zhicheng, Yao, Xiaohong, Cui, Yonghong, Zhang, Xia, Bian, Xiuwu
• Format: Journal Article
• Language: English
• Published: China Institute of Pathology and Southwest Cancer Center, Southwest Hospital, Third Military Medical University (Army Medical University), Key Laboratory of Tumor Immunopathology, Ministry of Education of China, Chongqing 400038, China%Department of Kidney, Southwest Hospital, Third Military Medical University (Army Medical University), Chongqing 400038, China 01.08.2019; Chinese Anti-Cancer Association; China Anti-Cancer Association
• Subjects: Differentially expressed genes; gene set enrichment analysis; glioblastoma prognosis; Original; radiotherapy; temozolomide chemotherapy; radiotherapy; temozolomide chemotherapy; Differentially expressed genes; gene set enrichment analysis; glioblastoma prognosis
• ISSN: 2095-3941; 2095-3941
• DOI: 10.20892/j.issn.2095-3941.2018.0277

Glioblastoma (GBM) is the most common primary malignant brain tumor regulated by numerous genes, with poor survival outcomes and unsatisfactory response to therapy. Therefore, a robust, multi-gene signature-derived model is required to predict the prognosis and treatment response in GBM. Gene expression data of GBM from TCGA and GEO datasets were used to identify differentially expressed genes (DEGs) through DESeq2 or LIMMA methods. The DEGs were then overlapped and used for survival analysis by univariate and multivariate COX regression. Based on the gene signature of multiple survival-associated DEGs, a risk score model was established, and its prognostic and predictive role was estimated through Kaplan-Meier analysis and log-rank test. Gene set enrichment analysis (GSEA) was conducted to explore high-risk score-associated pathways. Western blot was used for protein detection. Four survival-associated DEGs of GBM were identified: OSMR, HOXC10, SCARA3, and SLC39A10. The four-gene signature-derived risk score was higher in GBM than in normal brain tissues. GBM patients with a high-risk score had poor survival outcomes. The high-risk group treated with temozolomide chemotherapy or radiotherapy survived for a shorter duration than the low-risk group. GSEA showed that the high-risk score was enriched with pathways such as vasculature development and cell adhesion. Western blot confirmed that the proteins of these four genes were differentially expressed in GBM cells. The four-gene signature-derived risk score functions well in predicting the prognosis and treatment response in GBM and will be useful for guiding therapeutic strategies for GBM patients.