Prognostic genes of melanoma identified by weighted gene co‑expression network analysis and drug repositioning using a network‑based method

• Published in: Oncology letters 2019-12, Vol.18 (6), p.6066-6078
• Main Authors: Wang, Lu, Wei, Chuan-Yuan, Xu, Yuan-Yuan, Deng, Xin-Yi, Wang, Qiang, Ying, Jiang-Hui, Zhang, Si-Min, Yuan, Xin, Xuan, Tian-Fan, Pan, Yu-Yan, Gu, Jian-Ying
• Format: Article
• Language: English
• Subjects: Biochemistry; Biological markers; Biomarkers; Cancer; Cancer metastasis; Clinical trials; Cluster analysis; Drug approval; Drugs; Gene expression; Genes; Genomes; Genomics; Interleukins; Kinases; Medical prognosis; Medical research; Melanoma; Messenger RNA; Metastasis; Mutation; Oncology; Patients; Phospholipids; Proteins; RNA; Skin; Skin cancer; Tumors
• ISSN: 1792-1074; 1792-1082
• DOI: 10.3892/ol.2019.10961

Melanoma is one of the most malignant types of skin cancer. However, the efficacy and utility of available drug therapies for melanoma are limited. The objective of the present study was to identify potential genes associated with melanoma progression and to explore approved therapeutic drugs that target these genes. Weighted gene co-expression network analysis was used to construct a gene co-expression network, explore the associations between genes and clinical characteristics and identify potential biomarkers. Gene expression profiles of the GSE65904 dataset were obtained from the Gene Expression Omnibus database. RNA-sequencing data and clinical information associated with melanoma obtained from The Cancer Genome Atlas were used for biomarker validation. A total of 15 modules were identified through average linkage hierarchical clustering. In the two significant modules, three network hub genes associated with melanoma prognosis were identified: C-X-C motif chemokine receptor 4 (CXCR4), interleukin 7 receptor (IL7R) and phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit [gamma] (PIK3CG). The receiver operating characteristic curve indicated that the mRNA levels of these genes exhibited excellent prognostic efficiency for primary and metastatic tumor tissues. In addition, the proximity between candidate genes associated with melanoma progression and drug targets obtained from DrugBank was calculated in the protein interaction network, and the top 15 drugs that may be suitable for treating melanoma were identified. In summary, co-expression network analysis led to the selection of CXCR4, IL7R and PIK3CG for further basic and clinical research on melanoma. Utilizing a network-based method, 15 drugs that exhibited potential for the treatment of melanoma were identified.