Identification of Periostin as a Potential Biomarker in Gliomas by Database Mining

Abstract Introduction Bioinformatics analysis integrating microenvironmental factors and single cell analysis, segregated the glioblastoma (GBM) subtype into three subtypes: proneural, classical and mesenchymal. Mesenchymal GBM tends to have the worst survival rate, but benefits from aggressive treatment protocols. Therefore, it is clinically meaningful to identify relevant biomarkers to distinguish the mesenchymal subtype. Moreover, in developing nations with limited resources, rigorous examinations are costly and inefficient for patient care. Methods In this study, we analyzed The Cancer Genome Atlas-Glioblastoma (TCGA-GBM) and The Cancer Genome Atlas-Low Grade Glioma (TCGA-LGG) Ribonucleic Acid sequencing (RNAseq) cohorts and confirmed that the mesenchymal subtype was associated with the worst prognosis. Results We identified periostin (POSTN) as a mesenchymal subtype biomarker with prognostic value across histological grades, and confirmed the reliability of POSTN by gene-expression meta-analysis combining TCGA, Chinese Glioma Genome Atlas (CGGA) and REpository for Molecular BRAin Neoplasia DaTa (REMBRANDT) GBM cohorts (HR = 1.71 (1.47-2.07), n =693) and LGG cohorts (HR = 2.55 (1.61-4.05), n=1226). Conclusions In summary, by using available online glioma databases, our study provided an insight into the expression of POSTN as an independent predictor for glioma patients (GBM and LGG) and could be useful for diagnostic simplification to identify high-risk groups.