Computational identification and characterization of glioma candidate biomarkers through multi-omics integrative profiling
Posted on 2020-06-16 - 03:48
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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Liu, Lin; Wang, Guangyu; Wang, Liguo; Yu, Chunlei; Li, Mengwei; Song, Shuhui; et al. (2020). Computational identification and characterization of glioma candidate biomarkers through multi-omics integrative profiling. figshare. Collection. https://doi.org/10.6084/m9.figshare.c.5022716.v1
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AUTHORS (9)
LL
Lin Liu
GW
Guangyu Wang
LW
Liguo Wang
CY
Chunlei Yu
ML
Mengwei Li
SS
Shuhui Song
LH
Lili Hao
LM
Lina Ma
ZZ
Zhang Zhang