Analysis of Transcription Factor-Related Regulatory Networks Based on Bioinformatics Analysis and Validation in Hepatocellular Carcinoma

Abstract

Hepatocellular carcinoma (HCC) accounts for a significant proportion of liver cancer, which has become the second most common cause of cancer-related mortality worldwide. To investigate the potential mechanisms of invasion and progression of HCC, bioinformatics analysis and validation by qRT-PCR were performed. We found 237 differentially expressed genes (DEGs) including EGR1, FOS, and FOSB, which were three cancer-related transcription factors. Subsequently, we constructed TF-gene network and miRNA-TF-mRNA network based on data obtained from mRNA and miRNA expression profiles for analysis of HCC. We found that 42 key genes from the TF-gene network including EGR1, FOS, and FOSB were most enriched in the p53 signaling pathway. The qRT-PCR data confirmed that mRNA levels of EGR1, FOS, and FOSB all were decreased in HCC tissues. In addition, we confirmed that the mRNA levels of CCNB1, CCNB2, and CHEK1, three key markers of the p53 signaling pathway, were all increased in HCC tissues by bioinformatics analysis and qRT-PCR validation. Therefore, we speculated that miR-181a-5p, which was upregulated in HCC tissues, could regulate FOS and EGR1 to promote the invasion and progression of HCC by p53 signaling pathway. Overall, the study provides support for the possible mechanisms of progression in HCC.

Transcriptome analysis revealed key prognostic genes and microRNAs in hepatocellular carcinoma

“…TRRAP/KAT5, which activates TOP2A, has been reported to inhibit HCC cell growth through induction of p53-independent and p21-independent senescence (Kwan et al, 2020). CCNB2, CDC20 and PRC1 are the three most commonly reported upregulated genes in HCC through bioinformatics analyses (Chen et al, 2016a;Gao et al, 2018;Li et al, 2014;Liu et al, 2018;Wang et al, 2019b). CCNB2, as a component of the cell cycle regulatory machinery, is associated with the Golgi region (Draviam et al, 2001;Jackman, Firth & Pines, 1995) and plays an important role in regulating the G2/M transition (Gui & Homer, 2013;Li et al, 2018a).…”

CCNB2, TOP2A, and ASPM Reflect the Prognosis of Hepatocellular Carcinoma, as Determined by Weighted Gene Coexpression Network Analysis

“…The variation in these results could be attributed to the different sample sizes and sequencing databases. A review of the literature revealed that CCNB2 expression is increased in HCC tissues compared to adjacent nontumor tissues [33], and CCNB2 is a target molecule following knockdown of XPOT [34], TPX2 [35], KPNA2 [36], and TMEM9 [37]. The prognostic value and target therapy of the hub genes for HCC patients remain to be confirmed by further clinical studies.…”

Identification of potential hub genes associated with the pathogenesis and prognosis of hepatocellular carcinoma via integrated bioinformatics analysis

“…9 Over the past decades, microarray technology and bioinformatics have been extensively applied to identify the molecular mechanisms of HCC, which provide strong research support for the diagnosis, treatment, and prognosis of HCC. 10 Because of the ability to process a large number of datasets quickly, integrated bioinformatics analysis and microarray technology have allowed researchers to comprehensively identify the functions of numerous differentially expressed genes (DEGs) in HCC, and they help researchers explore the complicated process of HCC occurrence and development. 10,11 A work by He et al 12 identified four hub genes and two important pathways in the development of HCC from cirrhosis from one Gene Expression Omnibus (GEO) dataset using a bioinformatics method, including DEG screening, enrichment analyses, and construction of a protein-protein interaction (PPI) network.…”