Weighted correlation network and differential expression analyses identify candidate genes associated with BRAF gene in melanoma

• Published in: BMC medical genetics 2019-03, Vol.20 (1), p.54-10, Article 54
• Main Authors: Zhao, Bin, You, Yanqiu, Wan, Zheng, Ma, Yunhan, Huo, Yani, Liu, Hongyi, Zhou, Yuanyuan, Quan, Wei, Chen, Weibin, Zhang, Xiaohong, Li, Fujun, Zhao, Yilin
• Format: Article
• Language: English
• Subjects: Bioinformatics; BRAF gene; Cancer; Care and treatment; Computational biology; CYR61 protein; Cysteine-Rich Protein 61 - genetics; Data analysis; Datasets; Diagnosis; Differentially expressed genes; Dual Specificity Phosphatase 1 - genetics; Gene expression; Gene Expression Profiling - methods; Gene Expression Regulation, Neoplastic; Gene mutation; Gene Regulatory Networks; Genes; Genetic aspects; Genetic Association Studies - methods; Humans; Identification; Internet server software; Ipilimumab; Kinases; Medical prognosis; Melanoma; Melanoma - genetics; Melanoma, Cutaneous Malignant; Mortality; Mutation; Nucleic acids; Oligonucleotide Array Sequence Analysis - methods; Overall survival; Patient outcomes; Phosphatase; Proto-Oncogene Proteins B-raf - genetics; Ribonucleases - genetics; Risk factors; Skin; Skin cancer; Skin Neoplasms - genetics; Skin tumors; Survival Analysis; Therapeutic applications; Transcription; Transcription (Genetics); Tumors; Vemurafenib; Web sites (World Wide Web); Weighted gene co-expression network analysis
• ISSN: 1471-2350; 1471-2350
• DOI: 10.1186/s12881-019-0791-1

Primary cutaneous malignant melanoma is a cancer of the pigment cells of the skin, some of which are accompanied by BRAF mutation. Melanoma incidence and mortality rates have been rising around the world. As the current knowledge about pathogenesis, clinical and genetic features of cutaneous melanoma is not very clear, we aim to use bioinformatics to identify the potential key genes involved in the expression and mutation status of BRAF. Firstly, we used UCSC public hub datasets of melanoma (Lin et al., Cancer Res 68(3):664, 2008) to perform weighted genes co-expression network analysis (WGCNA) and differentially expressed genes analysis (DEGs), respectively. Secondly, overlapping genes between significant gene modules and DEGs were screened and validated at transcriptional levels and overall survival in TCGA and GTEx datasets. Lastly, the functional enrichment analysis was accomplished to find biological functions on the web-server database. We performed weighted correlation network and differential expression analyses, using gene expression data in melanoma samples. We identified 20 genes whose expression was correlated with the mutation status of BRAF. For further validation, three of these genes (CYR61, DUSP1, and RNASE4) were found to have similar expression patterns in skin tumors from TCGA compared with normal skin samples from GTEx. We also found that weak expression of these three genes was associated with worse overall survival in the TCGA data. These three genes were involved in the nucleic acid metabolic process. In this study, CYR61, DUSP1, and RNASE4 were identified as potential genes of interest for future molecular studies in melanoma, which would improve our understanding of its causes and underlying molecular events. These candidate genes may provide a promising avenue of future research for therapeutic targets in melanoma.