Drug-target-disease network analysis of gene-phenotype connectivity for genistein in ovarian cancer

Functional drug-target-disease network analysis of gene-phenotype connectivity for curcumin in hepatocellular carcinoma

Background

The anti-tumor properties of curcumin have been demonstrated for many types of cancer. However, a systematic functional and biological analysis of its target proteins has yet to be fully documented. The aim of this study was to explore the underlying mechanisms of curcumin and broaden the perspective of targeted therapies.

Methods

Direct protein targets (DPTs) of curcumin were searched in the DrugBank database. Using the STRING database, the interactions between curcumin and DPTs and indirect protein targets (IPTs) weres documented. The protein-protein interaction (PPI) network of curcumin-mediated proteins was visualized using Cytoscape. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis was performed for all curcumin-mediated proteins. Furthermore, the cancer targets were searched in the Comparative Toxicogenomics Database (CTD). The overlapping targets were studied using Kaplan-Meier analysis to evaluate cancer survival. Further genomic analysis of overlapping genes was conducted using the cBioPortal database. Lastly, MTT, quantitative polymerase chain reaction (qPCR), and western blot (WB) analysis were used to validate the predicted results on hepatocellular carcinoma (HCC) cells.

Results

A total of five DPTs and 199 IPTs were found. These protein targets were found in 121 molecular pathways analyzed via KEGG enrichment. Based on the anti-tumor properties of curcumin, two pathways were selected, including pathways in cancer (36 genes) and HCC (22 genes). Overlapping with 505 HCC-related gene sets identified in CTD, five genes (TP53, RB1, TGFB1, GSTP1, and GSTM1) were finally identified. High mRNA levels of TP53, RB1, and GSTM1 indicated a prolonged overall survival (OS) in HCC, whereas elevated mRNA levels of TGFB1 were correlated with poor prognosis. The viability of both HepG2 cells and Hep3B cells was significantly reduced by curcumin at concentrations of 20 or 30 μM after 48 or 72 h of culture. At a concentration of 20 μM curcumin cultured for 48 h, the expression of TGFB1 and GSTP1 in Hep3B cells was reduced significantly in qPCR analysis, and reduced TGFB1 protein expression was also found in Hep3B cells.

Integrated Bioinformatics Analysis of DNA Methylation Biomarkers in Thyroid Cancer Based on TCGA Database

Previous studies have reported a cluster of aberrant promoter methylation changes associated with silencing of tumor suppressor genes in thyroid cancer (TC), but these results of individual genes are far from enough. In this work, we aimed to investigate the onset and pattern of methylation changes during the progression of TC by informatics analysis. We downloaded the DNA methylation and RNA sequencing datasets from The Cancer Genome Atlas focusing on TC. Abnormally methylated differentially expressed genes (DEGs) were sorted and pathways were analyzed. The KEGG and GO were then used to perform enrichment and functional analysis of identified pathways and genes. Gene-drug interaction network and human protein atlas were applied to obtain feature DNA methylation biomarkers. In total, we identified 2170 methylation-driven DEGs, including 1054 hypermethylatedlow-expression DEGs and 1116 hypomethylated-high-expression DEGs at the screening step. Further analysis screened total of eight feature DNA methylation biomarkers (RXRG, MET, PDGFRA, FCGR3A, VEGFA, CSF1R, FCGR1A and C1QA). Pathway analysis showed that aberrantly methylated DEGs mainly associated with transcriptional misregulation in cancer, MAPK signaling, and intrinsic apoptotic signaling in TC. Taken together, we have identified novel aberrantly methylated genes and pathways linked to TC, which might serve as novel biomarkers for precision diagnosis and disease treatment.