Biology Direct (Jun 2020)

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

DOI
https://doi.org/10.1186/s13062-020-00264-5
Journal volume & issue
Vol. 15, no. 1
pp. 1 – 14

Abstract

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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.

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