Bioinformatic Analysis Identifies Potential Key Genes in the Pathogenesis of Melanoma

Yanjie Han; Xinxin Li; Jiliang Yan; Chunyan Ma; Xin Wang; Hong Pan; Xiaoli Zheng; Zhen Zhang; Biao Gao; Xin-Ying Ji

doi:10.3389/fonc.2020.581985

Bioinformatic Analysis Identifies Potential Key Genes in the Pathogenesis of Melanoma

Front Oncol. 2020 Oct 16:10:581985. doi: 10.3389/fonc.2020.581985. eCollection 2020.

Authors

Yanjie Han¹, Xinxin Li¹, Jiliang Yan¹, Chunyan Ma¹, Xin Wang¹, Hong Pan¹, Xiaoli Zheng², Zhen Zhang¹, Biao Gao¹, Xin-Ying Ji³

Affiliations

¹ Clinical Laboratory, Functional Laboratory and Department of Stomatology, Kaifeng Central Hospital, Kaifeng, China.
² Hospital Infection Control Office, First Affiliated Hospital of Henan University, Kaifeng, China.
³ Kaifeng Key Laboratory for Infectious Diseases and Biosafety, Henan International Joint Laboratory of Nuclear Protein Regulation, Henan School of Basic Medical Sciences, Henan University College of Medicine, Kaifeng, China.

Abstract

Melanoma is the deadliest skin tumor and is prone to distant metastases. The incidence of melanoma has increased rapidly in the past few decades, and current trends indicate that this growth is continuing. This study was aimed to explore the molecular mechanisms of melanoma pathogenesis and discover underlying pathways and genes associated with melanoma. We used high-throughput expression data to study differential expression profiles of related genes in melanoma. The differentially expressed genes (DEGs) of melanoma in GSE15605, GSE46517, GSE7553, and the Cancer Genome Atlas (TCGA) datasets were analyzed. Differentially expressed genes (DEGs) were identified by paired t-test. Then the DEGs were performed cluster and principal component analyses and protein-protein interaction (PPI) network construction. After that, we analyzed the differential genes through bioinformatics and got hub genes. Finally, the expression of hub genes was confirmed in the TCGA databases and collected patient tissue samples. Total 144 up-regulated DEGs and 16 down-regulated DEGs were identified. A total of 17 gene ontology analysis (GO) terms and 11 pathways were closely related to melanoma. Pathway of pathways in cancer was enriched in 8 DEGs, such as junction plakoglobin (JUP) and epidermal growth factor receptor (EGFR). In the PPI networks, 9 hub genes were obtained, such as loricrin (LOR), filaggrin (FLG), keratin 5 (KRT5), corneodesmosin (CDSN), desmoglein 1 (DSG1), desmoglein 3 (DSG3), keratin 1 (KRT1), involucrin (IVL), and EGFR. The pathway of pathways in cancer and its enriched DEGs may play important roles in the process of melanoma. The hub genes of DEGs may become promising melanoma candidate genes. Five key genes FLG, DSG1, DSG3, IVL, and EGFR were identified in the TCGA database and melanoma tissues. The results suggested that FLG, DSG1, DSG3, IVL, and EGFR might play important roles and potentially be valuable in the prognosis and treatment of melanoma. These hub genes might well have clinical significance as diagnostic markers.

Keywords: bioinformatics analysis; differentially expressed genes; hub genes; melanoma; tumor marker.