Identification of four hub genes associated with adrenocortical carcinoma progression by WGCNA

Abstract

Background Adrenocortical carcinoma (ACC) is a rare and aggressive malignant cancer in the adrenal cortex with poor prognosis. Though previous research has attempted to elucidate the progression of ACC, its molecular mechanism remains poorly understood. Methods Gene transcripts per million (TPM) data were downloaded from the UCSC Xena database, which included ACC (The Cancer Genome Atlas, n = 77) and normal samples (Genotype Tissue Expression, n = 128). We used weighted gene co-expression network analysis to identify gene connections. Overall survival (OS) was determined using the univariate Cox model. A protein–protein interaction (PPI) network was constructed by the search tool for the retrieval of interacting genes. Results To determine the critical genes involved in ACC progression, we obtained 2,953 significantly differentially expressed genes and nine modules. Among them, the blue module demonstrated significant correlation with the “Stage” of ACC. Enrichment analysis revealed that genes in the blue module were mainly enriched in cell division, cell cycle, and DNA replication. Combined with the PPI and co-expression networks, we identified four hub genes (i.e., TOP2A, TTK, CHEK1, and CENPA) that were highly expressed in ACC and negatively correlated with OS. Thus, these identified genes may play important roles in the progression of ACC and serve as potential biomarkers for future diagnosis.

CCNB2 and AURKA overexpression may cause atypical mitosis in Japanese cortisol-producing adrenocortical carcinoma with TP53 somatic variant

“…The Cancer Genome Atlas (TCGA) project for ACC (ACC-TCGA) is the largest opensource database for comprehensive genomic and molecular analysis of ACC [14], and many studies use the ACC-TCGA dataset for genomic and molecular secondary analyses [19,20]. Those large-scale studies include whole genome or exome sequencing data using next-generation sequencing (NGS) technology.…”

IHS: an integrative method for the identification of network hubs

“…The real-world networks included 196 adjacency matrices compiled by Ghasemian et al (Ghasemian et al, 2019) from the Index of Complex Networks (ICON) (https://icon.colorado.edu) and two of them downloaded from independent biological studies which included a PPI from (Xia et al, 2019) and a miRNA expression dataset from (Yepes et al, 2016). The connectivity matrices were retrieved from different domains to ensure the reliability of our analyses.…”

“…A list of all networks is provided in Table EV1. Additionally, a PPI dataset, as well as its associated co-expression dataset, from (Xia et al, 2019) and three PPI networks from (Liu et al, 2018), (Zhang et al, 2014), and (Nair et al, 2014) were obtained from the corresponding papers to compare and assess the applicability of our method in comparison with other available methods in identification of nodes with the highest impact in the network considering the context of the study of each paper.…”

“…• The "influential" R package for the calculation of IHS and its required centrality metrics: CRAN (https://cran.r-project.org/package=influential) This network has been reconstructed using the interactions presented by the STRING database and based on genes in the blue module provided by (Xia et al, 2019) according to the weighted gene co-expression network analysis (WGCNA) as seed proteins. Red nodes are three examples of nodes with high neighborhood connectivity and low betweenness centrality in the center of the network; while, green nodes are representative of nodes with lower neighborhood connectivity and higher betweenness centrality on the edges of the network.…”

A key genomic signature associated with lymphovascular invasion in head and neck squamous cell carcinoma

“…As method used by previous studies [14,15] , the key gene co-expression module was further explored to predict gene function correlation using STRING database with a confidence score >0.9.…”