Microarray analysis of differentially expressed genes regulating lipid metabolism during melanoma progression.

A new hallmark of cancer involves acquisition of a lipogenic phenotype which promotes tumorigenesis. Little is known about lipid metabolism in melanomas. Therefore, we used BRB (Biometrics Research Branch) class comparison tool with multivariate analysis to identify differentially expressed genes in human cutaneous melanomas, compared with benign nevi and normal skin derived from the microarray dataset (GDS1375). The methods were validated by identifying known melanoma biomarkers (CITED1, FGFR2, PTPRF, LICAM, SPP1 and PHACTR1) in our results. Eighteen genes regulating metabolism of fatty acids, lipid second messengers and gangliosides were 2-9 fold upregulated in melanomas of GDS-1375. Out of the 18 genes, 13 were confirmed by KEGG pathway analysis and 10 were also significantly upregulated in human melanoma cell lines of NCI-60 Cell Miner database. Results showed that melanomas upregulated PPARGC1A transcription factor and its target genes regulating synthesis of fatty acids (SCD) and complex lipids (FABP3 and ACSL3). Melanoma also upregulated genes which prevented lipotoxicity (CPT2 and ACOT7) and regulated lipid second messengers, such as phosphatidic acid (AGPAT-4, PLD3) and inositol triphosphate (ITPKB, ITPR3). Genes for synthesis of pro-tumorigenic GM3 and GD3 gangliosides (UGCG, HEXA, ST3GAL5 and ST8SIA1) were also upregulated in melanoma. Overall, the microarray analysis of GDS-1375 dataset indicated that melanomas can become lipogenic by upregulating genes, leading to increase in fatty acid metabolism, metabolism of specific lipid second messengers, and ganglioside synthesis.

Gene Expression Signatures for Compound Response in Cancers

Recent trends in generating multiple, large-scale datasets provide new challenges to manipulating the relationship of different types of components, such as gene expression and drug response data. Integrative analysis of compound response and gene expression datasets generates an opportunity to capture the possible mechanism of compounds by using signature genes on diverse types of cancer cell lines. Here, we integrated datasets of compound response and gene expression profiles on NCI60 cell lines and constructed a network, revealing the relationship for 801 compounds and 341 gene probes. As examples, obtusol, which shows an exclusive sensitivity on a small number of colon cell lines, is related to a set of gene probes that have unique overexpression in colon cell lines. We also found that the SLC7A11 gene, a direct target of miR-26b, might be a key element in understanding the action of many diverse classes of anticancer compounds. We demonstrated that this network might be useful for studying the mechanisms of varied compound response on diverse cancer cell lines.