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Using Integrated Bioinformatics Analysis to Identify Abnormally Methylated Differentially Expressed Genes in Hepatocellular Carcinoma

Authors Chen QL, Yan Q, Feng KL, Xie CF, Fang CK, Wang JN, Liu LH, Li Y, Zhong C

Received 1 December 2020

Accepted for publication 19 February 2021

Published 10 March 2021 Volume 2021:14 Pages 805—823

DOI https://doi.org/10.2147/IJGM.S294505

Checked for plagiarism Yes

Review by Single anonymous peer review

Peer reviewer comments 2

Editor who approved publication: Dr Scott Fraser


Qing-Lian Chen,1,2,* Qian Yan,1,* Kun-Liang Feng,1,2,* Chun-Feng Xie,1,2,* Chong-Kai Fang,1,2 Ji-Nan Wang,1,2 Li-Hua Liu,2,3 Ya Li,3 Chong Zhong2,3

1Guangzhou University of Chinese Medicine, Guangzhou, 510405, People’s Republic of China; 2Department of Hepatobiliary Surgery, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, 510405, People’s Republic of China; 3Lingnan Medical Research Center, Guangzhou University of Chinese Medicine, Guangzhou, 510405, People’s Republic of China

*These authors contributed equally to this work

Correspondence: Chong Zhong
Department of Hepatobiliary Surgery, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, 16 Airport Road, Guangzhou, 510405, People’s Republic of China
Tel +86-20-36596301
Email [email protected]

Objective: For the identification of abnormally methylated differentially expressed genes (MDEGs) in hepatocellular carcinoma (HCC), this study integrated four microarray datasets to investigate the fundamental mechanisms of tumorigenesis.
Methods: We obtained the expression (GSE76427, GSE57957) and methylation (GSE89852, GSE54503) profiles from Gene Expression Omnibus (GEO). The abnormally MDEGs were identified by using R software. We used the clusterProfiler package for the functional and pathway enrichment analysis. The String database was used to build the protein–protein interaction (PPI) network and visualize it in Cytoscape. MCODE was employed in the module analysis. Additionally, Gene Expression Profiling Interactive Analysis (GEPIA) and The Cancer Genome Atlas (TCGA) were employed to validate results. Lastly, we used cBioPortal software to examine the hub genetic alterations.
Results: We identified 162 hypermethylated, down-regulated genes and 190 hypomethylated, up-regulated genes. Up-regulated genes with low methylation were enriched in biological processes, such as keratinocyte proliferation, and calcium homeostasis. Pathway analysis was enriched in the AMPK and PI3K-Akt signaling pathways. The PPI network identified PTK2, VWF, and ITGA2 as hypomethylated, high-expressing hub genes. Down-regulated genes with high methylation were related to responses to peptide hormones and estradiol, multi-multicellular organism process. Pathway analysis indicated enrichment in camp, oxytocin signaling pathways. The PPI network identified CFTR, ESR1, and CXCL12 as hypermethylated, low-expressing hub genes. Upon verification in TCGA databases, we found that the expression and methylation statuses of the hub genes changed significantly, and it was consistent with our results.
Conclusion: The novel abnormally MDEGs and pathways in HCC were identified. These results helped us further understand the molecular mechanisms underlying HCC invasion, metastasis, and development. Hub genes can serve as biomarkers for an accurate diagnosis and treatment of HCC, and PTK2, VWF, ITGA2, CFTR, ESR1, and CXCL12 are included.

Keywords: methylation, hepatocellular carcinoma, hub genes, gene expression, bioinformatics analysis

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