Genetic variants in the integrin signaling pathway genes predict cutaneous melanoma survival

Overexpressed VEPH1 inhibits epithelial-mesenchymal transition, invasion, and migration of human cutaneous melanoma cells through inactivating the TGF-β signaling pathway

Malignant melanoma has a profound influence on populations around the world, with the underlying mechanisms controlling this disease yet to be fully identified. Hence, the current study aimed to investigate effects associated with VEPH1 on epithelial-mesenchymal transition (EMT), proliferation, invasion, migration and the apoptosis of human cutaneous melanoma (CM) cells through the TGF-β signaling pathway. Microarray-based gene analysis was initially performed to screen the CM-related differentially expressed genes. The expression of VEPH1, TGF-β signaling pathway- and EMT-related genes in CM tissues and cell lines was subsequently evaluated. Gain-of- and loss-of-function experiments were conducted to examine the effects of VEPH1 and the TGF-β signaling pathway on the expression of EMT-related genes, cell proliferation, migration, invasion, cell cycle and apoptosis in vitro. Finally, tumor formation in nude mice was conducted. VEPH1 was lowly expressed and regulated the progression of CM with involvement in the TGF-β signaling pathway. Human CM tissues were noted to activate the TGF-β signaling pathway and EMT. A375 cells treated with overexpressed VEPH1 plasmids or/and TGF-β signaling pathway inhibitor SB-431542 displayed diminished TGF-β, SMAD4, Vimentin and N-cadherin expression while the expression of E-cadherin was elevated, accompanied by decreased cell proliferation, migration, invasion, inhibited cell cycle entry. However, si-VEPH1 or TGF-β signaling pathway activator contributed to reverse results. Taken together, the key findings of the current study present evidence suggesting that VEPH1 protects against human CM by inhibiting the activation of the TGF-β signaling pathway, highlighting its potential as a target for the prognosis and diagnosis of CM.

Chromosome-based gene co-expression analysis reveals regions associated with cancers: chromosome 1 as an example

Gene co-expression network analysis has been widely performed in systems biology. Here, I use a chromosome-based strategy to find potential chromosome regions associated with disease, and show an example of cancer. All results are available at http://bioinformatics.fafu.edu.cn/chrom-WGCNA/ .

Genetic variants in the metzincin metallopeptidase family genes predict melanoma survival

Metzincins are key molecules in the degradation of the extracellular matrix and play an important role in cellular processes such as cell migration, adhesion, and cell fusion of malignant tumors, including cutaneous melanoma (CM). We hypothesized that genetic variants of the metzincin metallopeptidase family genes would be associated with CM-specific survival (CMSS). To test this hypothesis, we first performed Cox proportional hazards regression analysis to evaluate the associations between genetic variants of 75 metzincin metallopeptidase family genes and CMSS using the dataset from the genome-wide association study (GWAS) from The University of Texas MD Anderson Cancer Center (MDACC) which included 858 non-Hispanic white patients with CM, and then validated using the dataset from the Harvard GWAS study which had 409 non-Hispanic white patients with invasive CM. Four independent SNPs (MMP16 rs10090371 C>A, ADAMTS3 rs788935 T>C, TLL2 rs10882807 T>C and MMP9 rs3918251 A>G) were identified as predictors of CMSS, with a variant-allele attributed hazards ratio (HR) of 1.73 (1.32-2.29, 9.68E-05), 1.46 (1.15-1.85, 0.002), 1.68 (1.31-2.14, 3.32E-05) and 0.67 (0.51-0.87, 0.003), respectively, in the meta-analysis of these two GWAS studies. Combined analysis of risk genotypes of these four SNPs revealed a decreased CMSS in a dose-response manner as the number of risk genotypes increased (P_trend < 0.001). An improvement was observed in the prediction model (area under the curve [AUC] = 81.4% vs. 78.6%), when these risk genotypes were added to the model containing non-genotyping variables. Our findings suggest that these genetic variants may be promising prognostic biomarkers for CMSS.