Glioma, one of the most lethal human malignancies, represents almost 80% of malignant brain tumors and exhibits low resection rate and high recurrence risk. With the rapid advancement of sequencing technologies, there is an increasing number of high-throughput studies on glioma, resulting in massive multi-omics multi-cohort data generated from different projects and different laboratories throughout the world.

Therefore, it has become critically important on how to make full use of these valuable data for comprehensive integrative characterization of glioma biomarkers. In this study, we collected a large-scale assemble of multi-omics multi-cohort datasets from public resources, involving a total of 17,022 samples across 19 independent studies. We established a methodological strategy on integrative identification of biomarkers with higher specificity and feasible detectability from periphery and identified that PRKCG (Protein Kinase C Gamma) features higher specificity in brain and detectability in CSF (Cerebrospinal Fluid). 

Through comprehensive molecular characterization of PRKCG based on multi-omics analyses in RNA expression, DNA methylation and copy number variation, we revealed that PRKCG has the significant potential in glioma diagnosis, prognosis and treatment prediction as testified on multiple independent discovery and validation datasets. 

Unlike existing biomarkers that were mostly discovered at single omics level and with limited samples, we found that multi-omics molecular profiles of PRKCG are highly associated with glioma across different populations, bearing great potential for glioma diagnosis, prognosis and therapy.