A Novel Six-mRNA Signature Predicts Survival of Patients With Glioblastoma Multiforme

Frontiers in Genetics

Hongkang Hu¹  Qiang Liang¹  Chengyin Lu¹  Chunhui Wang¹  Lei Jiang¹  Zhentao Liu²  Jun Qian³  Hao Zhang⁴  Zheng Cai⁵ 

[1] Department of Neurosurgery, Changzheng Hospital, Naval Medical University, Shanghai, China;Department of Neurosurgery, No. 988 Hospital of Joint Logistic Support Force, Zhengzhou, China;Department of Neurosurgery, Tongji Hospital, Shanghai Tong Ji University School of Medicine, Shanghai, China;Department of Orthopaedic Oncology, Changzheng Hospital, Naval Medical University, Shanghai, China;Department of Pharmacy, Changhai Hospital, Naval Medical University, Shanghai, China;

关键词: glioblastoma multiforme;    six-mRNA signature;    LASSO;    prognosis;    biomarkers;   

DOI  :  10.3389/fgene.2021.634116

来源: DOAJ

【 摘 要 】

Glioblastoma multiforme (GBM) is a devastating brain tumor and displays divergent clinical outcomes due to its high degree of heterogeneity. Reliable prognostic biomarkers are urgently needed for improving risk stratification and survival prediction. In this study, we analyzed genome-wide mRNA profiles in GBM patients derived from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases to identify mRNA-based signatures for GBM prognosis with survival analysis. Univariate Cox regression model was used to evaluate the relationship between the expression of mRNA and the prognosis of patients with GBM. We established a risk score model that consisted of six mRNA (AACS, STEAP1, STEAP2, G6PC3, FKBP9, and LOXL1) by the LASSO regression method. The six-mRNA signature could divide patients into a high-risk and a low-risk group with significantly different survival rates in training and test sets. Multivariate Cox regression analysis confirmed that it was an independent prognostic factor in GBM patients, and it has a superior predictive power as compared with age, IDH mutation status, MGMT, and G-CIMP methylation status. By combining this signature and clinical risk factors, a nomogram can be established to predict 1-, 2-, and 3-year OS in GBM patients with relatively high accuracy.

【 授权许可】

Unknown