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Expression-Based Survival Signature Proposed for Glioblastoma

NEW YORK (GenomeWeb) – A Chinese research team has narrowed in on a tumor gene expression-signature that appears to distinguish glioblastoma patients with better or worse overall survival times, independent of other potential prognostic predictors such as treatment type. 

Researchers from Huaihe Hospital of Henan University and the Zhengzhou Railway Vocational and Technical College brought together RNA sequence datasets for hundreds of glioblastoma tumors assessed for the Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) projects, searching for gene expression signatures associated with patient survival time or other clinical outcomes. As they reported in Scientific Reports today, their search led to a six-gene prognostic risk score that distinguished high-risk from low-risk glioblastoma cases in validation cohorts.

"Multivariate and stratified analysis demonstrated that the gene panel was independent of other clinical and pathological features, and therefore is a potential prognostic biomarker of glioblastoma," corresponding and lead author Shuguang Zuo, a translational medicine, infection, and immunity researcher with Huaihe Hospital of Henan University, and his co-authors wrote.

Glioblastoma central nervous system tumors are known not only for their aggressiveness, but also for clinical and molecular heterogeneity, even in cases marked by comparable histopathological features, the team noted. That has prompted interest in identifying markers that correspond to survival, treatment response or resistance, and other clinical traits to inform therapeutic strategies and improve patient outcomes.

"A reliable genomic prognostic signature can complement the conventional clinical prognostic factors, and further enable personalized therapy," the authors explained.

For their analysis, the researchers considered Illumina RNA sequencing data for 158 glioblastoma cases (102 male and 56 female) from TCGA and 137 glioblastomas from Chinese individuals (90 men and 47 women) evaluated through the CGGA, focusing on the expression of 22,884 genes and 28,504 genes that passed their filtering steps in the TCGA and CGGA cohorts, respectively.

The team's initial sequential univariate and stepwise multivariate Cox analyses highlighted a 17-gene prognostic model from the TCGA cohort and a dozen genes in a similar model for the CGGA cohort. From genes with informative expression profiles in both cohorts, the team landed on a six-gene prognostic model, based on the expression of the CD79B, MAP2K3, IMPDH1, SLC16A3, MPZL3, and APOBR genes.

When the team assessed that six-gene signature in TCGA and CGGA validation cohorts, it saw a significant rise in expression of all six genes in the high-risk cases with shorter survival times, relative to the lower risk cases in both the TCGA and CGGA groups.

Likewise, the high- and low-risk expression profiles separated patients with better or worse overall survival times in TCGA glioblastoma patients treated with radiation, as well as radiation-or chemotherapy-treated patients from CGGA, though risk stratification appeared most pronounced in the CGGA cases.

The researchers noted that the six-gene expression signature showed independent ties to glioblastoma patient outcomes relative to other clinical features, as did treatment type and MGMT single gene expression. Their preliminary findings indicated that the new risk score could distinguish high- and low-risk cases in both primary and secondary glioblastomas, though it did not show significant ties to survival in individuals with recurrent disease.

The authors noted that although all six genes from the expression-based prognostic signature have been implicated in the development or progression of glioblastoma and/or other cancer types in the past, the new results point to the possibility that the combined expression of these genes "could be regarded as a novel risk factor that might function as a prognosis indicator for glioblastoma patients."