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. 2021 Mar 10:14:805-823.
doi: 10.2147/IJGM.S294505. eCollection 2021.

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

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 Zhong  2   3
Affiliations

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

Qing-Lian Chen et al. Int J Gen Med. .

Abstract

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: bioinformatics analysis; gene expression; hepatocellular carcinoma; hub genes; methylation.

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Conflict of interest statement

The authors declare that have no competing interests in this work.

Figures

Figure 1
Figure 1
The flow chart of this study.
Figure 2
Figure 2
Identification of abnormally methylated-differentially expressed genes (MDEGs) in mRNA expression profiling datasets (GSE57957, GSE76427) and gene methylation profiling datasets (GSE89852, GSE54503). (A) Hypermethylation and low expression genes. (B) Hypomethylation and high expression genes.
Figure 3
Figure 3
Clustered heat map of the top 50 differentially expressed genes (DEGs) and differentially methylated genes (DMGs). Red: upregulated genes; Blue: downregulated genes. (A and B) The heat map of top 50 DEGs in GSE57959 and GSE76427; (C and D) The heat map of top 50 DMGs in GSE89852 and GSE54503.
Figure 4
Figure 4
Protein–protein interaction (PPI) network of the hypomethylation and high expression genes.
Figure 5
Figure 5
Protein–protein interaction (PPI) network of the hypermethylation and low expression genes.
Figure 6
Figure 6
Module analysis of abnormally methylated differentially expressed genes (MDEGs). (A) The hypomethylation and high expression genes in Module 1; (B) the hypomethylation and high expression genes in Module 2; (C) the hypermethylation and low expression genes in Module 1.
Figure 7
Figure 7
The expression of the six genes in mRNA expression, using data from the TCGA database in GEPIA.
Figure 8
Figure 8
Validation of the six hub genes on a translational level using the Human Protein Atlas database.
Figure 9
Figure 9
Overall survival analyses of ESR1 and ITGA2 in HCC patient. (A) Downregulation of ESR1 was closely associated with worse overall survival; (B) downregulation of ITGA2 resulted in longer overall survival; (C) the network contained our six hub genes and the 50 most frequently altered neighbor genes. The relationship between hub genes and drugs is also exhibited.
Figure 10
Figure 10
Genetic alterations connected with the six genes and the relationship between mRNA expression and DNA methylation in the TCGA HCC study. (A) A visual summary of alteration based on a query of the six hub genes, which were altered in 277 (51%) in the 442 sequenced cases/patients; (B) alteration frequency of hub genes; (C) the correlation between mRNA expression and DNA methylation of the six hub genes in the HCC patient dataset of TCGA.
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References

    1. Siegel RL, Miller KD, Jemal A. Cancer statistics. CA Cancer J Clin. 2019;69(1):7–34. doi:10.3322/caac.21551 - DOI - PubMed
    1. Ferlay J, Soerjomataram I, Dikshit R, et al. Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN 2012. Int J Cancer. 2015;136(5):E359–E386. doi:10.1002/ijc.29210 - DOI - PubMed
    1. Gomaa AI, Khan SA, Toledano MB, Waked I, Taylor-Robinson SD. Hepatocellular carcinoma: epidemiology, risk factors and pathogenesis. World J Gastroenterol. 2008;14(27):4300–4308. doi:10.3748/wjg.14.4300 - DOI - PMC - PubMed
    1. Parikh S, Hyman D. Hepatocellular cancer: a guide for the internist. Am J Med. 2007;120(3):194–202. doi:10.1016/j.amjmed.2006.11.020 - DOI - PubMed
    1. Singal AG, El-Serag HB. Hepatocellular carcinoma from epidemiology to prevention: translating knowledge into practice. Clin Gastroenterol Hepatol. 2015;13(12):2140–2151. doi:10.1016/j.cgh.2015.08.014 - DOI - PMC - PubMed

Grants and funding

Authors would like to thank the National Natural Science Foundation of China (Grant No.81873303), Natural Science Foundation of Guangdong Province, China (2019A1515011013), Key Projects of Educational Commission of Guangdong Province, China (2019KZDXM045), and Medical innovation project of the First Affiliated Hospital of Guangzhou University of Chinese Medicine (2019IIT18) for financial support.

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