Identification of Gender-Specific Molecular Differences in Glioblastoma (GBM) and Low-Grade Glioma (LGG) by the Analysis of Large Transcriptomic and Epigenomic Datasets

Md Tipu Khan; Bharat Prajapati; Simran Lakhina; Mridula Sharma; Sachin Prajapati; Kunzang Chosdol; Subrata Sinha

doi:10.3389/fonc.2021.699594

Identification of Gender-Specific Molecular Differences in Glioblastoma (GBM) and Low-Grade Glioma (LGG) by the Analysis of Large Transcriptomic and Epigenomic Datasets

Front Oncol. 2021 Sep 21:11:699594. doi: 10.3389/fonc.2021.699594. eCollection 2021.

Authors

Md Tipu Khan¹, Bharat Prajapati^{1

2}, Simran Lakhina³, Mridula Sharma³, Sachin Prajapati³, Kunzang Chosdol³, Subrata Sinha^{1

3}

Affiliations

¹ Department of Cellular and Molecular Neuroscience, National Brain Research Centre (NBRC), Manesar, India.
² Department of Medical Biochemistry and Cell Biology, The Sahlgrenska Academy, Institute of Biomedicine, University of Gothenburg, Gothenburg, Sweden.
³ Department of Biochemistry, All India Institute of Medical Science, Delhi, India.

Abstract

Differences in the incidence and outcome of glioma between males and females are well known, being more striking for glioblastoma (GB) than low-grade glioma (LGG). The extensive and well-annotated data in publicly available databases enable us to analyze the molecular basis of these differences at a global level. Here, we have analyzed The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) databases to identify molecular indicators for these gender-based differences by different methods. Based on the nature of data available/accessible, the transcriptomic profile was studied in TCGA by using DeSeq2 and in CGGA by T-test, after correction based. Only IDH1 wild-type tumors were studied in CGGA. Using weighted gene co-expression network analysis (WGCNA), network analysis was done, followed by the assessment of modular differential connectivity. Differentially affected signaling pathways were identified. The gender-based effects of differentially expressed genes on survival were determined. DNA methylation was studied as an indicator of gender-based epigenetic differences. The results clearly showed gender-based differences in both GB and LGG, whatever method or database was used. While there were differences in the results obtained between databases and methods used, some major signaling pathways such as Wnt signaling and pathways involved in immune processes and the adaptive immune response were common to different assessments. There was also a differential gender-based influence of several genes on survival. Also, the autosomal genes NOX, FRG1BP, and AL354714.2 and X-linked genes such as PUDP, KDM6A, DDX3X, and SYAP1 had differential DNA methylation and expression profile in male and female GB, while for LGG, these included autosomal genes such as CNIH3 and ANKRD11 and X-linked genes such as KDM6A, MAOB, and EIF2S3. Some, such as FGF13 and DDX3X, have earlier been shown to have a role in tumor behavior, though their dimorphic effects in males and females have not been identified. Our study thus identifies several crucial differences between male and female glioma, which could be validated further. It also highlights that molecular studies without consideration of gender can obscure critical elements of biology and emphasizes the importance of parallel but separate analyses of male and female glioma.

Keywords: epigenetics; gender-specific analysis; glioblastoma multiforme; low-grade glioma (LGG); pathway analysis; survival analysis; transcriptomics; weighted gene co-expression network analysis (WGCNA).