The underlying pathophysiology association between the Type 2-diabetic and hepatocellular carcinoma.
Huamei WeiJianchu WangWenchuan LiRihai MaZuoming XuZongjiang LuoYuan LuXiao-Yu ZhangXidai LongJian PuQianli TangPublished in: Journal of cellular physiology (2018)
Type 2-diabetic (T2D) disease has been reported to increase the incidence of liver cancer, however, the underlying pathophysiology is still not fully understood. Here, we aimed to reveal the underlying pathophysiology association between the T2D and hepatocellular carcinoma (HCC) and, therefore, to find the possible therapeutic targets in the occurrence and development of HCC. The methylation microarray data of T2D and HCC were extracted from the Gene Expression Omnibus and The Cancer Genome Atlas. A total of 504 differentially methylated genes (DMGs) between T2D samples and the controls were identified, whereas 6269 DMGs were identified between HCC samples and the control groups. There were 336 DMGs coexisting in diabetes and HCC, among which 86 genes were comethylated genes. These genes were mostly enriched in pathways as glycosaminoglycan biosynthesis, fatty acid, and metabolic pathway as glycosaminoglycan degradation and thiamine, fructose and mannose. There were 250 DMGs that had differential methylation direction between T2D DMGs and HCC DMGs, and these genes were enriched in the Sphingolipid metabolism pathway and immune pathways through natural killer cell-mediated cytotoxicity and ak-STAT signaling pathway. Eight genes were found related to the occurrence and development of diabetes and HCC. Moreover, the result of protein-protein interaction network showed that CDKN1A gene was related to the prognosis of HCC. In summary, eight genes were found to be associated with the development of HCC and CDKN1A may serve as the potential prognostic gene for HCC.
Keyphrases
- genome wide
- genome wide identification
- dna methylation
- bioinformatics analysis
- gene expression
- type diabetes
- copy number
- genome wide analysis
- signaling pathway
- single cell
- fatty acid
- cardiovascular disease
- risk assessment
- transcription factor
- protein protein
- machine learning
- metabolic syndrome
- epithelial mesenchymal transition
- stem cells
- risk factors
- big data
- wound healing
- skeletal muscle
- young adults
- papillary thyroid
- cell wall