Bioinformatic Analysis Reveals Hub Genes and Pathways That Promote Melanoma Metastasis

Overview

Journal BMC Cancer

Publisher Biomed Central

Specialty Oncology

Date 2020 Sep 7

PMID 32894090

Citations 9

Authors

Wenxing Su

Yi Guan

Biao Huang

Juanjuan Wang

Yuqian Wei

Ying Zhao

Qingqing Jiao

Jiang Ji

Daojiang Yu

Longjiang Xu

Affiliations

Soon will be listed here.

Abstract

Background: Melanoma has the highest mortality rate of all skin tumors, and metastases are the major cause of death from it. The molecular mechanism leading to melanoma metastasis is currently unclear.

Methods: With the goal of revealing the underlying mechanism, three data sets with accession numbers GSE8401, GSE46517 and GSE7956 were downloaded from the Gene Expression Omnibus (GEO) database. After identifying the differentially expressed gene (DEG) of primary melanoma and metastatic melanoma, three kinds of analyses were performed, namely functional annotation, protein-protein interaction (PPI) network and module construction, and co-expression and drug-gene interaction prediction analysis.

Results: A total of 41 up-regulated genes and 79 down-regulated genes was selected for subsequent analyses. Results of pathway enrichment analysis showed that extracellular matrix organization and proteoglycans in cancer are closely related to melanoma metastasis. In addition, seven pivotal genes were identified from PPI network, including CXCL8, THBS1, COL3A1, TIMP3, KIT, DCN, and IGFBP5, which have all been verified in the TCGA database and clinical specimens, but only CXCL8, THBS1 and KIT had significant differences in expression.

Conclusions: To conclude, CXCL8, THBS1 and KIT may be the hub genes in the metastasis of melanoma and thus may be regarded as therapeutic targets in the future.

Citing Articles

Diagnostic Power of MicroRNAs in Melanoma: Integrating Machine Learning for Enhanced Accuracy and Pathway Analysis.

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Metastatic melanoma: An integrated analysis to identify critical regulators associated with prognosis, pathogenesis and targeted therapies.

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Wen X, Wang R, Yu B, Yang Y, Yang J, Zhang H Sci Rep. 2023; 13(1):15597.

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The Genomic Landscape of Melanoma and Its Therapeutic Implications.

Yang T, Yu S, Ke C, Cheng S Genes (Basel). 2023; 14(5).

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Development and Validation of a Combined Ferroptosis and Immune Prognostic Model for Melanoma.

Tang M, Li Y, Wang F, Han J, Gao Y J Oncol. 2022; 2022:1840361.

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