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

Introduction

Primary liver cancer is the sixth most common cancer and the second largest cause of cancer mortality in the world (Ferlay et al., 2015). Hepatocellular carcinoma (HCC) constitutes approximately 80% of all primary liver cancer (McGlynn, Petrick & London, 2015). The main causes of HCC are hepatitis B and C virus infection, alcoholic liver disease, non-alcoholic fatty liver diseases related to obesity and/or diabetes, aflatoxin B1 contamination of food, and less frequently, genetic predisposition. The incidence of HCC is increasing worldwide, mostly due to the spread of chronic infections with hepatitis B and C viruses. Most of these risk factors cause persistent inflammation in the liver, leading to the formation of liver cirrhosis and then HCC.

Curcumin, also known as diferuloylmethane, is the active ingredient of the dietary spice occurring in the rhizomes of Curcuma longa, a plant belonging to the ginger family (Fig. 2A). Extensive studies investigating curcumin over the past few decades revealed the health benefits of curcumin, including anticancer, anti-inflammatory, antioxidant, and hypoglycemic effects (Prasad et al., 2014). In recent years, curcumin has been increasingly recognized for its anti-tumor and chemopreventive properties, especially in gastrointestinal tumors (Huminiecki, Horbanczuk & Atanasov, 2017). For example, curcumin has shown chemopreventive effects in animal models of colon cancer (Kawamori et al., 1999), stomach cancer (Huang et al., 1994), and HCC (Chuang et al., 2000). Curcumin has been reported to decrease cell growth and induce apoptosis mainly through the inhibition of nuclear factor kappa-B (NFκB) (Ghasemi et al., 2019). Recent reports also indicate the curcumin could decrease tumor cell viability by inhibiting the activity of ATP synthase in isolated mitochondrial membranes which leads to a dramatic drop of ATP and a reduction of oxygen consumption (Bianchi et al., 2018). In addition, DYRK2 has been identified as a direct target of curcumin. By diminishes DYRK2-mediated 26S proteasome phosphorylation in cells, curcumin leading to reduced proteasome activity and inhibited cell proliferation (Banerjee et al., 2018). Several phase I and II clinical trials with curcumin have been conducted for the treatment of different types of cancer (Gupta, Patchva & Aggarwal, 2013; Hatcher et al., 2008).

Recent studies using data mining techniques for the analysis of specific bioinformatics domains have made great progress (Lan et al., 2018). Accordingly, a strategy to systematically explore the rich data publicly available and the underlying connectivity between gene and phenotype mediated by curcumin should be helpful. With advances in genomics, data network analysis has been used to effectively analyze candidate genes linked to experimentally verifiable pathways via mining of web-accessible, open portal databases (Hsieh et al., 2016). In addition, an in-depth analysis of the databases may uncover, previously unknown relationships between drugs, targets, and cancers. These results can be used to generate authentic and rational leads for further investigations. Recently, the combination of related databases including DrugBank, STRING, cBio Cancer Genomics Portal (cBioPortal), and Comparative Toxicogenomics Database (CTD) has been utilized for drug–target–cancer network analysis (Fu et al., 2019; Zhang et al., 2018). In this study, DrugBank was employed to broadly analyze curcumin and drug-target data to obtain related direct protein targets (DPTs). Interactions between DPTs and indirect protein targets (IPTs) were predicted with the STRING database. The readout DPTs and IPTs were further analyzed for functionality via Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis with STRING. The pivotal genes were obtained via intersections of the CTD and STRING databases, and the genomic alterations were investigated using the cBioPortal database. Our findings provide information to obtain a better understanding of the anti-tumor mechanisms of curcumin and identify new targets in the treatment of HCC by curcumin.

Materials and Methods

Results

Genetic alterations connected with curcumin-associated genes in HCC

To further validate the link between curcumin-associated genes and HCC, cBioPortal databases were used to explore the five genes (TP53, RB1, TGFB1, GSTP1, and GSTM1) associated with curcumin in HCC. Seven HCC studies were included in cBioPortal (Ahn et al., 2014; Fujimoto et al., 2012; Harding et al., 2019; Pilati et al., 2014; Schulze et al., 2015; Zheng et al., 2018), and the five selected overlapping genes were queried. The results showed that 363 (33%) of the 1,135 samples in seven studies had alterations in one or more of these genes. Alterations ranged from 0.2% to 29% for gene sets submitted for analysis (Fig. 3A). The five genes (TP53, RB1, TGFB1, GSTP1 and GSTM1) carried five gene pairs showing mutual exclusivity alterations, and another five gene pairs showed concurrent alterations (with TP53 and RB1 showing a statistically significant alteration, Table 4). Furthermore, a liver HCC study (TCGA, Provisional), which carried the largest sample size, was selected to analyze the screened proteins in cBioPortal databases. The results showed that 156 (35%) patients/samples carried an alteration in at least one of the five genes queried using OncoPrint; the frequency of alteration in each gene is shown in Fig. 2G. Most gene alterations included mutations and deletions. Most of the TP53 alterations were classified into missense mutations, truncating mutations, and deep deletions. Gene changes associated with RB1 included truncating mutations and deep deletions, whereas for TGFB1, GSTP1 and GSTM1 amplifications were the most common gene alterations.

Table 4:

Mutual exclusivity analysis of five genes (TP53, RB1, TGFB1, GSTP1, and GSTM1) in seven studies associated with HCC.

A	B	Log 2 odds ratio	p-Value	Tendency
TP53	RB1	0.755	0.024	Co-occurrence
RB1	TGFB1	2.131	0.108	Co-occurrence
TP53	GSTP1	1.045	0.187	Co-occurrence
TP53	TGFB1	0.564	0.421	Co-occurrence
RB1	GSTP1	<−3	0.437	Mutual exclusivity
TP53	GSTM1	1.299	0.495	Co-occurrence
RB1	GSTM1	<−3	0.861	Mutual exclusivity
TGFB1	GSTP1	<−3	0.918	Mutual exclusivity
GSTP1	GSTM1	<−3	0.979	Mutual exclusivity
TGFB1	GSTM1	<−3	0.985	Mutual exclusivity

DOI: 10.7717/peerj.12339/table-4

Curcumin-induced decrease in the activity of HepG2 and Hep3B cells

The test results showed that cell viability appeared dependent on the concentration of curcumin. For Hep3B cells, curcumin (10 μM) significantly decreased cell viability after 48 and 72 h, and 20 and 30 μM of curcumin were found to significantly reduce cell viability after 24, 48, and 72 h (Fig. 4A). For HepG2 cells, curcumin (20 and 30 μM) significantly decreased the cell viability after 48 and 72 h (Fig. 4B). According to these results, the concentration of curcumin was chosen to be 20 μM and the duration of treatment was chosen to be 48 h for subsequent testing.

Discussion

Over the past several years, numerous studies have evaluated the effects of curcumin and its analogs in diverse cancers in vitro and in vivo. According to these studies, the potential use of curcumin as a chemopreventive and therapeutic agent in cancers depends on its potent antioxidant and anti-inflammatory activities as well as its ability to modulate various molecular signaling mechanisms (Gupta, Patchva & Aggarwal, 2013; Hatcher et al., 2008; Huminiecki, Horbanczuk & Atanasov, 2017; Prasad et al., 2014). Nevertheless, the mechanism underlying the wide range of anti-cancer effects of curcumin remains incomplete. Current knowledge about curcumin’s functions and mechanisms is based on conventional experiments, which have yet to be fully integrated and understood. Therefore, new analytical methods are needed to correlate curcumin with its target proteins and the observed biological effects. Functional/activity network (FAN) (Hsieh et al., 2016) is a new analytical method, which elucidates the molecular mechanisms of a drug and its association with clinical outcomes in cancer by using a set of web-based tools, such as DrugBank, STRING, cBioPortal, CTD, or Cytoscape. Using this method, a functional drug–target–cancer network analysis of gene–phenotype connectivity associated with curcumin was conducted in this study. This study bridged curcumin with its primary or secondary targets and illustrated the underlying mechanisms of curcumin and its clinical outcomes in HCC.

The effects of curcumin and its analogs have been a subject of investigation over the past decade in preclinical models of HCC (Darvesh, Aggarwal & Bishayee, 2012), but research in this field is far from complete. A search of PubMed using “curcumin” as the keyword returned more than 13,000 publications, whereas the phrase “curcumin and hepatocellular carcinoma” returned only 192 publications. By using FAN, more direct mechanisms may be mined with reasonable experimental feasibility to validate hypotheses explaining the effects of curcumin in HCC.

In this study, the feasibility of FAN analysis for identifying the underlying connectivity between curcumin and HCC was demonstrated. A network including five genes (TP53, RB1, TGFB1, GSTP1, and GSTM1) as targets of curcumin in HCC was identified via a functional drug–target–cancer network analysis. Among the five genes, TP53 and RB1 are tumor suppressor genes, which is consistent with the finding in this study that their high transcriptional expression is correlated with better OS in HCC. Similar to other cancers, TP53 and RB1 are the most commonly inactivated or mutated genes in the case of HCC. Moreover, mutual exclusivity analysis revealed a tendency toward concurrence between TP53 and RB1. Previous studies indicated that curcumin may inhibit cancer growth and induce apoptosis in colon cancer cells (Dasiram et al., 2017), ameliorate the in vitro efficacy of carfilzomib in human multiple myeloma cells (Allegra et al., 2018), promote apoptosis in non-small cell lung cancer (Ye et al., 2015), and inhibit cell growth in nasopharyngeal carcinoma mediated via the TP53 signaling pathway (Wu et al., 2014). Debata et al. (2013) reported that the sunitinib-curcumin combination was effective in restoring the tumor suppressor activity of the RB gene in renal cancer cells. Furthermore, Su, Wang & Chiu (2010) reported that curcumin significantly enhanced p53 or markedly inhibited the RB pathway by suppressing RB phosphorylation in the signaling pathways of glioblastoma (Su, Wang & Chiu, 2010). These studies demonstrated the anti-tumor activity of curcumin and prompted further investigation of curcumin in HCC.

In the case of HCC, most of the genetic alterations in TGFB1, GSTP1, and GSTM1 were amplifications, which may cause increased expression. However, the prognostic significance varied in this study. The amplification of TGFB1 indicated a worse OS in HCC, whereas amplifications of GSTM1 and GSTP1 (not statistically significant) indicated better prognosis. TGFB1 is a pleiotropic gene with a dual role in hepatocarcinogenesis: apoptosis induction in early phases, but promotion of tumorigenesis in cells with mechanisms to overcome the suppressor effects (Moreno-Caceres et al., 2017). Recent studies found that the expression of TGFB1 genes were downregulated in breast cancer cells treated with curcumin (Calaf & Roy, 2017). Glutathione S-transferases (GSTs) are a family of phase II detoxification enzymes that catalyze the conjugation of a wide variety of endogenous and exogenous toxins. A previous study showed that inter-individual GST variation plays a central role in reducing cell exposure to carcinogens (Di Pietro, Magno & Rios-Santos, 2010). For example, reduced GSTP1 expression may contribute to oxidative stress in HCC (Li et al., 2013). Similar results were observed in breast cancer cells and curcumin-activated GSTP1 expression via antioxidant response element (Nishinaka et al., 2007). However, studies linking curcumin and GST genes in HCC are rare, underscoring the need for further investigation in this field.

These results were partially confirmed by in vitro experiments. HepG2 and Hep3B were used as the experimental objects because of their advantages of fast growth and easy passage. The present study confirmed the inhibitory effect of curcumin on liver cancer cells, and this inhibition increased with time and concentration. In the liver HCC study (TCGA, Provisional), three genes (TGFB1, GSTP1, and GSTM1) showed amplification. For this reason, changes in these amplified genes were more easily detected using PCR. This was confirmed in the present study when it was found that two genes prone to amplification were suppressed in HCC cells treated with curcumin. The other genes, however, did not show changes in expression because the main means of mutation was not amplification mutations but truncating mutations and deep deletions. In terms of the relationship between curcumin and protein expression, only one protein change TGFB1 was detected by western blot. This may be because of the complexity of post-transcriptional regulation of mRNA, meaning that mRNA amplification may not be consistent with protein expression. The results of this study showed that TGFB1 expression was inconsistent at mRNA and protein levels after curcumin treatment, possibly because TGFB1 may be regulated by intracellular activators. In addition, although mRNA concentration is widely used as a surrogate for protein abundance, studies comparing mRNA and protein expression on a global scale indicate that mRNA levels only partly correlate with the corresponding protein concentrations. It has been estimated that protein concentrations are determined by the corresponding mRNA concentrations by only 20–40% (Nie, Wu & Zhang, 2006; Tian et al., 2004).

Furthermore, studies have proved that TGFB1 has a dual role in the development and progression of HCC. In the previous analysis, elevated mRNA levels of TGFB1 were correlated with poor prognosis in HCC patients. This may be because TGFB1 promoted the growth, invasion, and metastasis of HCC cells through the epithelial-mesenchymal transition (Bierie & Moses, 2006; Massagué, 2008; Matsuura et al., 2004). Regarding HCC cell levels, TGFB1 was a potent growth inhibitor and induces apoptosis in these cells, so it is regarded as a tumor-suppressive cytokine (Senturk et al., 2010).Therefore, further studies are needed to validate and establish the specific molecular mechanisms by which curcumin regulates HCC cells.

Conclusions

In summary, by using DrugBank, STRING, CDC, and cBioPortal databases, the connectivity between curcumin and HCC was discovered. Curcumin has the potential to become an alternative chemotherapy or chemoprevention treatment for HCC. The drug–target–cancer network analysis utilized in this study facilitated the testing and validation of reasonable hypotheses explaining curcumin-induced gene alterations in cancers by applying the available biological information in studies from bedside to bench. As advances in curcumin research using traditional experimental approaches continue, additional drug targets will undoubtedly be identified, leading to improved curcumin-related genetic networks, signaling pathways, and cancer types.

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