Transcriptome analysis revealed key prognostic genes and microRNAs in hepatocellular carcinoma

Abstract

Background. Hepatocellular carcinoma (HCC) is one of the most common cancers worldwide. However, the molecular mechanisms involved in HCC remain unclear and are in urgent need of elucidation. Therefore, we sought to identify biomarkers in the prognosis of HCC through an integrated bioinformatics analysis. Methods. Messenger RNA (mRNA) expression profiles were obtained from the Gene Expression Omnibus database and The Cancer Genome Atlas-Liver Hepatocellular Carcinoma (TCGA-LIHC) for the screening of common differentially expressed genes (DEGs). Function and pathway enrichment analysis, protein-protein interaction network construction and key gene identification were performed. The significance of key genes in HCC was validated by overall survival analysis and immunohistochemistry. Meanwhile, based on TCGA data, prognostic microRNAs (miRNAs) were decoded using univariable and multivariable Cox regression analysis, and their target genes were predicted by miRWalk. Results. Eleven hub genes (upregulated ASPM, AURKA, CCNB2, CDC20, PRC1 and TOP2A and downregulated AOX1, CAT, CYP2E1, CYP3A4 and HP) with the most interactions were considered as potential biomarkers in HCC and confirmed by overall survival analysis. Moreover, AURKA, PRC1, TOP2A, AOX1, CYP2E1, and CYP3A4 were considered candidate liver-biopsy markers for high risk of developing HCC and poor prognosis in HCC. Upregulation of hsa-mir-1269b, hsa-mir-518d, hsa-mir-548aq, hsa-mir-548f-1, and hsa-mir-6728, and downregulation of hsa-mir-139 and hsa-mir-4800 were determined to be risk factors of poor prognosis, and most of these miRNAs have strong potential to help regulate the expression of key genes. Conclusions. This study undertook the first large-scale integrated bioinformatics analysis of the data from Illumina BeadArray platforms and the TCGA database. With a comprehensive analysis of transcriptional alterations, including mRNAs and miRNAs, in HCC, our study presented candidate biomarkers for the surveillance and prognosis of the disease, and also identified novel therapeutic targets at the molecular and pathway levels.How to cite this article Ma X, Zhou L, Zheng S. 2020. Transcriptome analysis revealed key prognostic genes and microRNAs in hepatocellular carcinoma. PeerJ 8:e8930 http://doi.org/10.7717/peerj.8930 Ma et al. (2020), PeerJ, DOI 10.7717/peerj.8930 2/22 Ma et al. (2020), PeerJ, DOI 10.7717/peerj.8930 3/22 PPI network construction and cluster identificationThe PPI network of DEGs was constructed using STRING (version 11.0, http://stringdb.org) and a combined score > 0.7 (high confidence) was set as the cut-off criterion. Cytoscape (version 3.7.2, https://cytoscape.org/), an open source platform, was applied to the visualization of molecular interaction networks based on the results from STRING. The Molecular Complex Detection (MCODE) plugin (version 1.5.1) in Cytoscape was used for identifying densely interconnected clusters. The selection criteria were as follows: degree ≥ 2, node score ≥ 0.2, K-core ≥ 2, and max depth = 100.

Identification of NUF2 and FAM83D as potential biomarkers in triple-negative breast cancer

“…Current researchers are focusing on personalized treatment based on the multi-gene assays ( Pan et al, 2019 ). With the continuous development of high-throughput sequencing technology, bioinformatics analysis plays a key role in the diagnosis, prognosis and screening of tumors ( Goldfeder et al, 2017 ; Ma, Zhou & Zheng, 2020 ). Many genes have been identified as signatures for diagnosis and prognosis of triple negative breast cancer ( Dai et al, 2019 ; Stovgaard et al, 2020 ).…”

Comprehensive Analysis of the Expression and Prognosis of 12 Cytochrome P450s in Human Hepatocellular Carcinoma from the Perspective of Network Pharmacology

“…A high CYP2E1 expression and activity may be a risk factor for HCC induced by diethylnitrosamine [50]. Downregulated CYP2E1 is regarded as a candidate prognostic biomarker in HCC [51]. Few reports are available concerning CYP2J2 expression in patients with HCC; CYP2J2 overexpression remarkably promotes the proliferation of HepG2 cells and affects the resistance to antitumor agents [52].…”

<p>MCM10 Acts as a Potential Prognostic Biomarker and Promotes Cell Proliferation in Hepatocellular Carcinoma: Integrated Bioinformatics Analysis and Experimental Validation</p>

“…For example, Xie et al, 8 analyzed four Gene Expression Omnibus (GEO) datasets including GSE19665, GSE55092, GSE94660, and GSE121248 and identified key genes and pathways involved in HBVrelated HCC. 8 Ma et al, 9 obtained the mRNA expression profiles from the GEO database and the Cancer Genome Atlas-Liver Hepatocellular Carcinoma database, and the results showed that eleven hub genes with the most interactions were suggested as potential biomarkers in HCC. 9 Yan et al, 10 conducted a comprehensive study on bioinformatics, clinical analysis and in vitro validation and found that Misato family member 2, pseudogene had significant diagnostic and prognostic value in HCC.…”

“…8 Ma et al, 9 obtained the mRNA expression profiles from the GEO database and the Cancer Genome Atlas-Liver Hepatocellular Carcinoma database, and the results showed that eleven hub genes with the most interactions were suggested as potential biomarkers in HCC. 9 Yan et al, 10 conducted a comprehensive study on bioinformatics, clinical analysis and in vitro validation and found that Misato family member 2, pseudogene had significant diagnostic and prognostic value in HCC. 10 Due to the rapid increase in the number of microarray datasets, the analysis of these datasets has not been sufficient.…”