Identification of Prognostic Genes in Gliomas Based on Increased Microenvironment Stiffness

SIMPLE SUMMARY: Glioblastoma multiforme (GBM) is the most aggressive primary brain cancer; less than 50% of patients with GBM survive longer than 15 months. A biomarker for early GBM diagnosis can substantially increase the effectiveness of therapy for glioma patients. Increased stiffness of brain tumors has been reported during the progression of glioma. In this study, we explored the influences of altered tissue stiffness on gene signaling and its prognosis for glioma patients. We identified four stiffness-dependent genes highly associated with poor prognosis by applying bio-informatics analysis through RNA-Seq and The Cancer Genome Atlas glioma database. Based on pathophysiological observation, the stiffness of the brain tumor was introduced as the key criteria in our meta-analysis of glioma. In addition to the pathophysiology-inspired approach for biomarker identification, our findings provide insights into the relationship between glioma stiffness and prognosis as well as identifying potential molecular treatment targets. ABSTRACT: With a median survival time of 15 months, glioblastoma multiforme is one of the most aggressive primary brain cancers. The crucial roles played by the extracellular matrix (ECM) stiffness in glioma progression and treatment resistance have been reported in numerous studies. However, the association between ECM-stiffness-regulated genes and the prognosis of glioma patients remains to be explored. Thus, using bioinformatics analysis, we first identified 180 stiffness-dependent genes from an RNA-Seq dataset, and then evaluated their prognosis in The Cancer Genome Atlas (TCGA) glioma dataset. Our results showed that 11 stiffness-dependent genes common between low- and high-grade gliomas were prognostic. After validation using the Chinese Glioma Genome Atlas (CGGA) database, we further identified four stiffness-dependent prognostic genes: FN1, ITGA5, OSMR, and NGFR. In addition to high-grade glioma, overexpression of the four-gene signature also showed poor prognosis in low-grade glioma patients. Moreover, our analysis confirmed that the expression levels of stiffness-dependent prognostic genes in high-grade glioma were significantly higher than in low-grade glioma, suggesting that these genes were associated with glioma progression. Based on a pathophysiology-inspired approach, our findings illuminate the link between ECM stiffness and the prognosis of glioma patients and suggest a signature of four stiffness-dependent genes as potential therapeutic targets.