Tumor cells rely on aerobic glycolysis as their main energy resource (Warburg effect). Recent research has highlighted the importance of lipid metabolism in tumor progression, and certain cancers even turn to fatty acids as the main fuel. Related studies have identified alterations of fatty acid metabolism in human bladder cancer (BCa). Our microarray analysis showed that fatty acid metabolism was activated in BCa compared with normal bladder. The free fatty acid (FFA) level was also increased in BCa compared with paracancerous tissues. Inhibition of fatty acid oxidation (FAO) with etomoxir caused lipid accumulation, decreased adenosine triphosphate (ATP) and nicotinamide adenine dinucleotide phosphate (NADPH) levels, suppressed BCa cell growth in vitro and in vivo, and reduced motility of BCa cells via affecting epithelial-mesenchymal transition (EMT)-related proteins. Furthermore, etomoxir induced BCa cell cycle arrest at G 0 /G 1 phase through peroxisome proliferator-activated receptor (PPAR) γ-mediated pathway with alterations in fatty acid metabolism associated gene expression. The cell cycle arrest could be reversed by PPARγ antagonist GW9662. Taken together, our results suggest that inhibition of FAO with etomoxir may provide a novel avenue to investigate new therapeutic approaches to human BCa.
Human clear cell renal cell carcinoma (ccRCC) is one of the most common types of malignant adult kidney tumors. We constructed a weighted gene co-expression network to identify gene modules associated with clinical features of ccRCC (n = 97). Six hub genes (CCNB2, CDC20, CEP55, KIF20A, TOP2A and UBE2C) were identified in both co-expression and protein-protein interaction (PPI) networks, which were highly correlated with pathologic stage. The significance of expression of the hub genes in ccRCC was ranked top 4 among all cancers and correlated with poor prognosis. Functional analysis revealed that the hub genes were significantly enriched in cell cycle regulation and cell division. Gene set enrichment analysis suggested that the samples with highly expressed hub gene were correlated with cell cycle and p53 signaling pathway. Taken together, six hub genes were identified to be associated with progression and prognosis of ccRCC, and they might lead to poor prognosis by regulating p53 signaling pathway.
Clear cell renal cell carcinoma (ccRCC) is the most common subtype among renal cancer whose prognostic is affected by the tumor progression associated with complex gene interactions. However, there is currently no available molecular markers associated with ccRCC progression and used or clinical application. In our study, microarray data of 101 ccRCC samples and 95 normal kidney samples were analyzed and 2,425 differentially expressed genes (DEGs) were screened. Weighted gene co-expression network analysis (WGCNA) was then conducted and 11 co-expressed gene modules were identified. Module preservation analysis revealed that two modules (red and black) were found to be most stable. In addition, Pearson's correlation analysis identified the module most relevant to pathological stage(patho-module) (r = 0.44, p = 3e-07). Functional enrichment analysis showed that biological processes of the patho-module focused on cell cycle and cell division related biological process and pathway. In addition, 29 network hub genes highly related to ccRCC progression were identified from the stage module. These 29 hub genes were subsequently validated using 2 other independent datasets including GSE53757 (n = 72) and TCGA (n = 530), and the results indicated that all hub genes were significantly positive correlated with the 4 stages of ccRCC progression. Kaplan-Meier survival curve showed that patients with higher expression of each hub gene had significantly lower overall survival rate and disease-free survival rate, indicating that all hub genes could act as prognosis and recurrence/progression biomarkers of ccRCC. In summary, we identified 29 molecular markers correlated with different pathological stages of ccRCC. They may have important clinical implications for improving risk stratification, therapeutic decision and prognosis prediction in ccRCC patients.
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