Construction of co‐expression modules related to survival by WGCNA and identification of potential prognostic biomarkers in glioblastoma

Abstract

Glioblastoma (GBM) is a malignant brain tumour with poor prognosis. The potential pathogenesis and therapeutic target are still need to be explored. Herein, TCGA expression profile data and clinical information were downloaded, and the WGCNA was conducted. Hub genes which closely related to poor prognosis of GBM were obtained. Further, the relationship between the genes of interest and prognosis of GBM, and immune microenvironment were analysed. Patients from TCGA were divided into high‐ and low‐risk group. WGCNA was applied to the high‐ and low‐risk group and the black module with the lowest preservation was identified which could distinguish the prognosis level of these two groups. The top 10 hub genes which were closely related to poor prognosis of patients were obtained. GO analysis showed the biological process of these genes mainly enriched in: Cell cycle, Progesterone‐mediated oocyte maturation and Oocyte meiosis. CDCA5 and CDCA8 were screened out as the genes of interest. We found that their expression levels were closely related to overall survival. The difference analysis resulted from the TCGA database proved both CDCA5 and CDCA8 were highly expressed in GBM. After transfection of U87‐MG cells with small interfering RNA, it revealed that knockdown of the CDCA5 and CDCA8 could influence the biological behaviours of proliferation, clonogenicity and apoptosis of GBM cells. Then, single‐gene analysis was performed. CDCA5 and CDCA8 both had good correlations with genes that regulate cell cycle in the p53 signalling pathway. Moreover, it revealed that high amplification of CDCA5 was correlated with CD8 + T cells while CDCA8 with CD4 + T cells in GBM. These results might provide new molecular targets and intervention strategy for GBM.

Identification of Key LncRNAs and Pathways in Prediabetes and Type 2 Diabetes Mellitus for Hypertriglyceridemia Patients Based on Weighted Gene Co-Expression Network Analysis

“…We selected the 5000 genes using the Median Absolute Deviation (MAD) algorithm to ensure heterogeneity and accuracy of bioinformatics for co‐expression network analysis ( 21 ). Then, to make the constructed network more consistent with the characteristics of scale‐free network and amplify the correlation between genes, an appropriate soft threshold β is selected ( 20 , 22 ). Subsequently, we converted the adjacency matrix into a topological overlap matrix (TOM) to evaluate gene connectivity in the network.…”

“…Meanwhile, WGCNA also alleviates the multiple testing problems inherent in microarray data analysis ( 19 ). In addition, WGCNA focused on the whole genome information to overview of the signature of gene networks in phenotypes which can avoid bias and subject judgement ( 20 ). Based on the above characteristics of WGCNA, we used it to construct a co‐expression network and obtain modules related to blood glucose, thus detecting key genes, and providing a reference for searching potential biomarkers of prediabetes and T2DM in hypertriglyceridemia patients.…”

Low expression of CHRDL1 and SPARCL1 predicts poor prognosis of lung adenocarcinoma based on comprehensive analysis and immunohistochemical validation

“…With the rapid advancement of genomic technology, bioinformatics analyses have been widely used in the analysis of microarray datasets to further study the potential molecular mechanisms of cancers and to identify tumor-specific indicators [ 5 ]. Weighed gene coexpression network analysis (WGCNA) is one of these significant algorithms that provides a better understanding of gene coexpression networks and gene functions [ 6 ]. WGCNA can detect modules of highly correlated genes among samples to relate modules to external sample traits, providing valuable insights into predicting possible functions of coexpressed genes [ 7 ].…”

Gene correlation network analysis to identify regulatory factors in sciatic nerve injury

“…Unlike previous screening out DEGs, WGCNA clusters highly relevant genes into one module and relates it to clinical features, which may be more conductive to identify diagnostic markers and therapeutic targets [ 13 ]. To date, WGCNA has been extensively utilized in genomic research, such as glioblastoma [ 14 ], Kawasaki disease [ 15 ], schizophrenia spectrum [ 16 ], and so on. It is speculated that identification of such co-expression patterns can shed more lights on the disease-related biological pathways.…”