Candidate Biomarkers and Molecular Mechanism Investigation for Glioblastoma Multiforme Utilizing WGCNA

Abstract

To reveal the potential molecular mechanism of glioblastoma multiforme (GBM) and provide the candidate biomarkers for GBM gene therapy. Microarray dataset GSE50161 was obtained from GEO database. The differentially expressed genes (DEGs) were identified between GBM samples and control samples, followed by the module partition analysis based on WGCNA. Then, the pathway and functional enrichment analyses of DEGs were performed. The hub genes were further investigated, followed by the survival analysis and data validation. A total of 1913 DEGs were investigated between two groups, followed by analysis of 5 modules using WGCNA. These DEGs were mainly enriched in functions like inflammatory response. The hub genes including upregulated N-Myc and STAT Interactor (NMI), Capping Actin Protein-Gelsolin Like (CAPG), and Proteasome Subunit Beta 8 (PSMB8) were revealed as potential liquid biopsy molecules for GBM diagnose. Moreover, Nucleolar and Spindle Associated Protein 1 (NUSAP1) and G Protein-Coupled Receptor 65 (GPR65) were outstanding genes in survival analysis. Our results suggested that CPNE6, HAPLN2, CMTM3, NMI, CAPG, and PSMB8 might be used as potential molecules for liquid biopsy of GBM. NUSAP1 and GPR65 might be novel prognostic targets for GBM gene therapy. Furthermore, the upregulated NMI might play an important role in GBM progression via inflammatory response.

Peer Review #3 of "Identification of four hub genes associated with adrenocortical carcinoma progression by WGCNA (v0.2)"

“…Only 205 genes were unclassified in any module (in grey), accounting for 10.50% of DEGs. In comparison, previous studies have reported an average gene number in each module of 216 to 336 and percentage of genes not found in any module of 5.67%-33.61% of DEGs (Liu X et al, 2017;Liu Z et al, 2018;Yang Q et al, 2018;Zuo Z et al, 2018). In conclusion, our WGCNA results were comparable.…”

Transcripto‐based network analysis reveals a model of gene activation in tongue squamous cell carcinomas

“…Therefore, WGCNA has been widely applied to screening candidate susceptible genes or therapeutic targets at transcriptional level in previous studies . Abundant tumor‐associated microarray data sets were analyzed by this well‐designed method; thus, several hub genes associated to clinical traits have been investigated in various types of cancers such as breast cancer, osteosarcoma, and glioblastoma multiforme …”

“…11 Abundant tumor-associated microarray data sets were analyzed by this well-designed method; thus, several hub genes associated to clinical traits have been investigated in various types of cancers such as breast cancer, osteosarcoma, and glioblastoma multiforme. [11][12][13] In this study, we constructed a gene co-expression network and identified 15 modules using R package WGCNA based on the data from GSE31056. Key modules associated with tumor site, risk of recurrence, outcome, and survival time of TSCC were identified, and the function enrichment analysis of genes in the pink modules was carried out.…”

Hub Genes of Stroke Identified by Weighted Gene Co-expression Network Analysis

“…Further, thresholding power β based on the criterion of approximate scale-free topology was selected for constructing a weighted gene network. The soft threshold calculates adjacency ranging from 0 to 1, so that the constructed network conforms to the power-law distribution and re ects the real biological network state [28]. In addition, the scale-free gene network was constructed and genes with similar patterns of expression (modules) were identi ed using blockwiseModules function in the WGCNA package.…”

“…Moreover, the area under curve (AUC) was calculated usingpROCR package [33]. Larger AUC value of a gene indicated that it can effectively distinguish stroke from the control samples, and the hub gene with AUC > 0.6 in the three datasets was de ned as a diagnostic e ciency gene [28,34].…”