OpenLB Open Library of Bioscience

Computational identification and characterization of glioma candidate biomarkers through multi-omics integrative profiling.

Lin Liu, Guangyu Wang, Liguo Wang, Chunlei Yu, Mengwei Li, Shuhui Song, Lili Hao, Lina Ma, Zhang Zhang
Author Information
  1. Lin Liu: China National Center for Bioinformation, Beijing, 100101, China.
  2. Guangyu Wang: China National Center for Bioinformation, Beijing, 100101, China.
  3. Liguo Wang: Division of Biomedical Statistics and Informatics, Mayo Clinic College of Medicine, Rochester, MN, 55905, USA.
  4. Chunlei Yu: China National Center for Bioinformation, Beijing, 100101, China.
  5. Mengwei Li: China National Center for Bioinformation, Beijing, 100101, China.
  6. Shuhui Song: China National Center for Bioinformation, Beijing, 100101, China.
  7. Lili Hao: China National Center for Bioinformation, Beijing, 100101, China.
  8. Lina Ma: China National Center for Bioinformation, Beijing, 100101, China. malina@big.ac.cn.
  9. Zhang Zhang: China National Center for Bioinformation, Beijing, 100101, China. zhangzhang@big.ac.cn.

Abstract

BACKGROUND: Glioma is one of the most common malignant brain tumors and exhibits low resection rate and high recurrence risk. Although a large number of glioma studies powered by high-throughput sequencing technologies have led to massive multi-omics datasets, there lacks of comprehensive integration of glioma datasets for uncovering candidate biomarker genes.
RESULTS: In this study, we collected a large-scale assemble of multi-omics multi-cohort datasets from worldwide public resources, involving a total of 16,939 samples across 19 independent studies. Through comprehensive molecular profiling across different datasets, we revealed that PRKCG (Protein Kinase C Gamma), a brain-specific gene detectable in cerebrospinal fluid, is closely associated with glioma. Specifically, it presents lower expression and higher methylation in glioma samples compared with normal samples. PRKCG expression/methylation change from high to low is indicative of glioma progression from low-grade to high-grade and high RNA expression is suggestive of good survival. Importantly, PRKCG in combination with MGMT is effective to predict survival outcomes in a more precise manner.
CONCLUSIONS: PRKCG bears the great potential for glioma diagnosis, prognosis and therapy, and PRKCG-like genes may represent a set of important genes associated with different molecular mechanisms in glioma tumorigenesis. Our study indicates the importance of computational integrative multi-omics data analysis and represents a data-driven scheme toward precision tumor subtyping and accurate personalized healthcare.

Keywords

MeSH Term

Biomarkers, Tumor
Brain Neoplasms
Computational Biology
Gene Expression
Glioma
Humans
Methylation
Protein Kinase C

Chemicals

Biomarkers, Tumor
protein kinase C gamma
Protein Kinase C