Identification of genomic biomarkers and their pathway crosstalks for deciphering mechanistic links in glioblastoma

Abstract

Glioblastoma is a grade IV pernicious neoplasm occurring in the supratentorial region of brain. As its causes are largely unknown, it is essential to understand its dynamics at the molecular level. This necessitates the identification of better diagnostic and prognostic molecular candidates. Blood-based liquid biopsies are emerging as a novel tool for cancer biomarker discovery, guiding the treatment and improving its early detection based on their tumour origin. There exist previous studies focusing on the identification of tumour-based biomarkers for glioblastoma. However, these biomarkers inadequately represent the underlying pathological state and incompletely illustrate the tumour because of non-recursive nature of this approach to monitor the disease. Also, contrary to the tumour biopsies, liquid biopsies are non-invasive and can be performed at any interval during the disease span to surveil the disease. Therefore, in this study, a unique dataset of blood-based liquid biopsies obtained primarily from tumour-educated blood platelets (TEP) is utilised. This RNA-seq data from ArrayExpress is acquired comprising human cohort with 39 glioblastoma subjects and 43 healthy subjects. Canonical and machine learning approaches are applied for identification of the genomic biomarkers for glioblastoma and their crosstalks. In our study, 97 genes appeared enriched in 7 oncogenic pathways (RAF-MAPK, P53, PRC2-EZH2, YAP conserved, MEK-MAPK, ErbB2 and STK33 signalling pathways) using GSEA, out of which 17 have been identified participating actively in crosstalks. Using PCA, 42 genes are found enriched in 7 pathways (cytoplasmic ribosomal proteins, translation factors, electron transport chain, ribosome, Huntington's disease, primary immunodeficiency pathways, and interferon type I signalling pathway) harbouring tumour when altered, out of which 25 actively participate in crosstalks. All the 14 pathways foster well-known cancer hallmarks and the identified DEGs can serve as genomic biomarkers, not only for the diagnosis and prognosis of Glioblastoma but also in providing a molecular foothold for oncogenic decision making in order to fathom the disease dynamics. Moreover, SNP analysis for the identified DEGs is performed to investigate their roles in disease dynamics in an elaborated manner. These results suggest that TEPs are capable of providing disease insights just like tumour cells with an advantage of being extracted anytime during the course of disease in order to monitor it.

Keywords: bioinformatics; cancer; genomics.

FIGURE 1

Workflow of the current study protocol. To attain the objectives of this study, raw gene expression data from TEPs in the form of RNA‐seq for glioblastoma is used; followed by its preparation and preprocessing it underwent two distinct approaches, namely GSEA and PCA. GSEA and PCA‐derived DEGs and enriched pathways were subjected to pathway analysis in order to study the involvement of DEGs in crosstalks between enriched pathways that foster cancer hallmarks, followed by SNP analysis of DEGs.

FIGURE 2

Bar charts representing differentially expressed genes from PCA. (a) Log2FC values for PCA‐enriched DEGs, (b) ‐Log10 p‐values for PCA‐enriched DEGs.

FIGURE 3

Gene‐pathway network for PCA‐enriched DEGs. Gene‐pathway network containing differentially expressed genes as genomic biomarkers for glioblastoma using PCA; their involvement in crosstalks; and their variants associated with various brain disorders. 60% of biomarkers are involved in the crosstalks between seven pathways depicted as green diamonds, that is, P1: Cytoplasmic Ribosomal Proteins, P2: Translation Factors, P3: Interferon type I signalling pathways, P4: Electron Transport Chain (OXPHOS system in mitochondria), P5: Ribosome, P6: Huntington disease, P7: Primary immunodeficiency. Pink circles represent unenriched genes, enriched genes are represented by dark pink octagons, purple triangles represent enriched crosstalking genes, whereas big squares of purple and dark pink colour illustrate the genes associated with brain disorders upon SNP analysis.

FIGURE 4

Bar charts representing differentially expressed genes from GSEA. (a) Log2FC values for GSEA enriched DEGs and (b) ‐Log10 p‐values for GSEA‐enriched DEGs.

FIGURE 5

Gene‐pathway network for GSEA enriched DEGs. Gene‐pathway network containing differentially expressed genes as genomic biomarkers for glioblastoma using GSEA; their involvement in crosstalks; and their variants associated with numerous brain disorders. 18% of biomarkers are involved in the crosstalks between seven enriched oncogenic pathways, which abet cancer hallmarks depicted as green diamonds, that is, P1: RAF‐MAPK signalling pathway, P2: P53 signalling pathway, P3: PRC2‐EZH2 signalling pathway, P4: YAP‐conserved (Hippo signalling pathway), P5: MEK‐MAPK signalling pathway, P6: ERBB2 signalling pathway, P7: STK33 pathway. Yellow circles represent unenriched genes, enriched genes are represented by dark pink octagons, purple triangles represent enriched crosstalking genes, whereas big squares of purple and dark pink colour illustrate the genes associated with brain disorders upon SNP analysis.

FIGURE 6

Cancer hallmarks. Enriched signalling pathways from GSEA and PCA alongside their cross‐connection with cancer hallmarks.