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 Table of Contents  
Year : 2021  |  Volume : 17  |  Issue : 4  |  Page : 1081-1092

Identification of potential targets with high centrality indicated by diethylnitrosamine + thioacetamide-induced hepatocellular carcinoma model

1 Department of Biotechnology, HIMT Group of Institutions, Greater Noida; Proteomic and Translational Research Laboratory, Centre for Medical Biotechnology, Amity Institute of Biotechnology, Amity University Uttar Pradesh, Noida, Uttar Pradesh, New Delhi, India
2 Department of Biotechnology, School of Chemical and Life Sciences, Jamia Hamdard, Uttar Pradesh, New Delhi, India
3 Department of Biotechnology, School of Chemical and Life Sciences; Hamdard Institute of Medical Sciences and Research, Jamia Hamdard, Uttar Pradesh, New Delhi, India
4 Proteomic and Translational Research Laboratory, Centre for Medical Biotechnology, Amity Institute of Biotechnology, Amity University Uttar Pradesh, Noida, Uttar Pradesh, New Delhi, India

Date of Submission10-Jul-2020
Date of Decision29-Aug-2020
Date of Acceptance07-Oct-2020
Date of Web Publication14-Sep-2021

Correspondence Address:
Deepshikha Pande Katare
Proteomic & Translational, Research Lab, Centre for Medical Biotechnology, Amity Institute of Biotechnology, Amity University, Sector-125, Noida - 201 303, Uttar Pradesh,
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Source of Support: None, Conflict of Interest: None

DOI: 10.4103/jcrt.JCRT_948_20

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 > Abstract 

Background and Aim: Hepatocellular carcinoma (HCC), a primary liver malignancy, represents a continuous challenge to clinicians as it is a leading cause of death due to cancer widely. Early detection is the only hope to cure patients from this deadly disease or possibly increase life expectancy. Mouse models are most acceptable studies as they have ability to manipulate their genome and transcriptome to evaluate mechanistic changes. In addition, system biology can improvise the understanding of molecular mechanism of HCC and also can reveal the protein hub involved in every stage of HCC.
Materials and Methods: Herein, diethylnitrosamine and thioacetamide (TAA) were used to develop stage-specific HCC in Wistar rats. Histopathological changes, biochemical parameters, and the oxidative stress were measured in hepatocytes. We have reanalyzed the microarray dataset to identify the complex signaling pathways involved in hepatocarcinogenesis induced by TAA. GSE45050 dataset was downloaded from Gene Expression Omnibus database, and the gene expression profile of nontumor, cirrhosis, and HCC was compared.
Results: The study reveals stage-specific development of chronic HCC rat model and promising stage-specific targets (EHMT2, GMPS, and SPRY2) of HCC.
Conclusions: EHMT2, GMPS, and SPRY found as high centrality nodes in protein-protein interaction studies using high-throughput microarray data which tend to be present in signaling pathways and co-occur in a biological state of HCC. These genes can be targeted to understand the possible pathology, molecular changes, and target strategy under cirrhosis and HCC condition.

Keywords: Microarray data study, stage-specific hepatocellular carcinoma, system biology, Wistar rat model

How to cite this article:
Hora S, Asad M, Jain SK, Katare DP. Identification of potential targets with high centrality indicated by diethylnitrosamine + thioacetamide-induced hepatocellular carcinoma model. J Can Res Ther 2021;17:1081-92

How to cite this URL:
Hora S, Asad M, Jain SK, Katare DP. Identification of potential targets with high centrality indicated by diethylnitrosamine + thioacetamide-induced hepatocellular carcinoma model. J Can Res Ther [serial online] 2021 [cited 2021 Dec 7];17:1081-92. Available from: https://www.cancerjournal.net/text.asp?2021/17/4/1081/325934

 > Introduction Top

Despite therapeutic advancements in cancer medicine, there is no effective chemotherapeutic protocol or even biomarkers for diagnosis or prognosis of hepatocellular carcinoma (HCC). It still ranks third as lethal cancer worldwide.[1],[2] To understand the disease progression at molecular level, it was imperative to develop the animal model which exhibited all the stages of HCC (fibrosis, cirrhosis, and tumor).[3],[4],[5] System biology can improvise the understanding of molecular mechanism of HCC and also can reveal the protein hub involved in every stage of HCC.

Continuously changing lifestyle is also a big reason to cause HCC; diethylnitrosamine (DEN) and thioacetamide (TAA) both are present in food chain,[6] and hence, both are used as inducer and promoter of HCC.[7],[8],[9],[10],[11] TAA promotes oxidative stress in hepatocytes and causes liver injury due to its oxidation process in the liver [Figure 1].[12],[13] A network analysis approach inclines to be enriched in proteins that are highly connected in the cellular network of different stages involved in HCC.[14] These protein hubs are supposed to play a crucial role in normal cell functioning and during hepatocarcinogenesis.[15] To understand the stage-specific HCC progression, it is important to sort out the interactions of chemically induced HCC with the vast array study of the human cellular proteins present in different stages of hepatocarcinogenesis. Many of these interactions can be a direct link between negative control and DEN + TAA-treated interactions, and many are indirect as well which can alter the human gene expression. A network analysis approach inclines to be enriched in proteins that are highly connected in the cellular network of different stages involved in HCC.[14] These protein hubs are supposed to play a crucial role in normal cell functioning and during hepatocarcinogenesis.[15]
Figure 1: Schematic representation of the mechanism of thioacetamide in the progression of hepatocellular carcinoma

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In the present study, we endeavored to collect published proteins involved in different stages of HCC from Gene Expression Omnibus (GEO) datasets, particularly human proteins associated with fibrosis, cirrhosis, and tumor. Hub proteins involved in every stage have been revealed to enhance insight into hepatocarcinogenesis on a cellular level and to accelerate the development of effective therapeutics. The current work aims to develop the chronic model of HCC by taking a single dose of DEN and multiple low doses of TAA which will mimic the human hepatocarcinogenesis and screen some hub proteins involved in different stages which link all the stages of HCC using computational protein interaction studies.

 > Materials and Methods Top


Adult male Wistar rats (about 325 ± 20 g BW) were obtained from Central Animal House of Jamia Hamdard, New Delhi. They were divided into two groups and kept six rats in a group per cage. The room was maintained at 25°C ± 2°C temperature and standard conditions with 12-h light/dark cycles. Before commencing experiments, rats were allowed to acclimatize under standard laboratory conditions with fresh water and food pellets ad libitum for 2 weeks.

Chemicals and reagents

DEN was purchased from Sigma Chemical Company, USA. TAA was purchased from SRL Pvt. Ltd, India. All liver function tests were analyzed by Autospan kits, India. All other analytical grade chemicals and reagents brought by S. Merck, Germany.

Animal experimental design

Rats were allocated into two groups randomly with six animals in each group. Group I was kept as control and the other group was taken as treated with TAA. Group II was assigned with 200 mg/kg BW of TAA. A single dose of DEN (200 mg/kg BW) was given intraperitoneally to Group II [Figure 2].
Figure 2: Schematic representation of animal model development

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Tissue preparation

Anesthesia was given to rats by ether inhalation. The collection of blood was done from the dorsal aorta. Serum was collected by centrifugation for 5 min at 3500 rpm. Rats were sacrificed at a regular interval, and their livers were removed out in ice-cold phosphate-buffered saline. Postmitochondrial supernatant (PMS) was prepared for several biochemical estimations.

Biochemical estimations

Aspartate transaminase and alanine transaminase activity assay for serum

The serum enzymes were measured using demonstrative units given by Span, and the strategy was taken after, as depicted by Reitman and Frankel (1957).

Alkaline phosphatase activity assay

The alkaline phosphatase (ALP) activity was done by following the procedure as described by Kind and King (1954).

Assay for lipid peroxidation

Lipid peroxidation assay was done by the method described by Ohkawa et al. (1979) with some modifications. In brief, the reaction mixture was made by thiobarbituric acid (1.5 ml, 0.8%), acetic acid (1.5 ml, 20%), sodium dodecyl sulfate (0.2 ml, 8.1%), and PMS (0.1 ml). The reaction mixture was then heated for 1 h at 100°C. The reaction mixture was cooled, and n-butanol (5 ml):pyridine (15:1%, v/v) and distilled water (1 ml) were added to the reaction mixture and shaken vivaciously. The supernatant was taken after centrifugation for 10 min at 4000 rpm. Absorbance was recorded at 532 nm utilizing a BioSpectrometer® basic (Eppendorf). Malondialdehyde (MDA) is the end product of lipid peroxidation, and it was stated at nanomole MDA formed per minute per milligram protein.

Assay for catalase

Catalase assay was done as depicted by Claiborne (1985) with some modifications. The reaction mixture contains phosphate buffer (0.05 M, pH 7.0), hydrogen peroxide (0.019 M), and PMS (0.05 ml) in a total volume of 3.0 ml. Changes were recorded per 30 s at 240 nm. The activity of catalase enzyme was expressed as nanomole H2O2/minute/milligram protein.

Assay for albumin level

Albumin level was measured using a demonstrative kit given by Erba Mannheim, and strategy was followed as depicted by Brutis and Leonard.[16],[17]

Histopathological examinations

At the end of the experiment, the tissue slides were formed by hematoxylin and eosin staining and observed for histopathological changes. The images of histopathological slides were taken by utilizing an Olympus CKX41SF inverted microscope system (Olympus, Japan).

Protein–protein interaction studies

Microarray data

The mRNA expression profile of GSE45050 was downloaded from the GEO database, which was a free and publicly available database. The GSE45050 mRNA profile was deposited by Darpolor et al.[18] In this study, a significant difference in the gene expression of normal, cirrhosis, and HCC liver tissue has been observed. Affymetrix GeneChip was used for microarray gene analysis. We also downloaded the Series Matrix File of GSE45050 from the GEO database. GEO2R tool was used to reanalyze to dataset. In GEO2R analysis, three groups, i.e., nontumor, cirrhosis, and HCC samples, were compared. P value correction was performed by applying Benjamini-Hochberg false discovery rate method. Genes with adjusted P < 0.05 were considered as differentially expressed.[19]

Protein–protein interaction network

Protein–protein interaction (PPI) network was constructed for DE genes in the microarray dataset GSE45050 using STRING and Cytoscape tools. The interactions were retrieved from STRING database with confidence cutoff 0.80. Cytoscape Network Analyzer was used to analyze network topology.[20] The measures of network topology such as betweenness, closeness centrality, and clustering coefficient were calculated.[21]

Pathway enrichment analysis

Pathway enrichment analysis was performed using Cytoscape ClueGO plugin version 2.1.3 (Bindea et al., 2009) and BiNGO. In this analysis, Bonferroni step down was applied for P value adjustment, and pathways with adjusted P < 0.05 were selected.

Statistical analysis

GraphPad Prism 5 software California corperation, US was used to analyze the data. Statistical significance and variance were calculated by two-way ANOVA to evaluate the differences between the control and treated groups. The P value of the data was <0.05 which was statistically significant.

 > Results Top

Morphological changes

Many clinical features such as loss of water intake, anorexia, loss of hair, and loss of appetite were observed in DEN + TAA model. [Figure 3]a shows liver morphology of control liver. At the end of 1st month, no significant morphological changes were observed in treated liver [Figure 3]b. At the end of the 1st month, no significant change was seen in liver morphology [Figure 3]b. Significant changes were observed from the 3rd month onward. At the end of the 3rd month, completely faded and cirrhotic liver was observed in the TAA-treated group [Figure 3]c. Cirrhosis was observed as the longest phase during HCC till the 5th month. At the end of the 6th month, we have observed enlarged decolorized liver with nodules and fully embedded tumors in diseased rats. The physical appearance of the liver was observed as rigid and difficult to resect as compared to the control group [Figure 3]d. Enlarge view of tumor also can be seen in [Figure 3]e.
Figure 3: Morphological analysis of liver (a) Control; (b) 1st month; (c) 2nd month; (d) 3rd month; Control group was showing normal structure of the liver, 1st month showing fibrosis with slightly damage in the liver, 3rd month showing fully faded cirrhotic liver, and 6th month showing liver fully embedded with tumors of different size; (e) enlarge view of maximum tumor size of 5 mm in liver tissue

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Biochemical analysis

Liver function test

Alanine transaminase activity test

Alanine transaminase (ALT) activity was measured in serum samples of both treated and control subjects. The ALT activity found in the control group (Group I) rat was 22.991 U/L. In Group II (200 mg/Kg BW), with every repeated dose of TAA, ALT activity was increased. A significant increase was seen with the progression of disease in treated animals at the 1st, 2nd, 3rd, and 4th months, and the increased level of ALT was 29.172, 47.128 (P < 0.01***), 72.996 (P < 0.001***), and 80.952 (P < 0.001***) U/L, respectively [Figure 4]a.
Figure 4: Biochemical analysis of liver function tests from serum. Alanine transaminase (a), aspartate transaminase (b), and alkaline phosphatase (c) were performed from serum after 1st, 2nd, 3rd, and 4th toxin treatment of the animals of both the groups (*<0.05; **<0.01; ***<0.001)

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Aspartate transaminase activity test

In Group II, the study shows the increased level of aspartate transaminase (AST) in treated animals in comparison to control animals. AST activity in control animals was observed to be 49.6135 U/L. At the 1st, 2nd, 3rd, and 4th months of toxin treatment of 2 months, the AST activity was 65.416, 66.72 (P < 0.05*), 69.256 (P < 0.05*), and 145.324 U/L at the time of the 1st, 2nd, 3rd, and 4th months, respectively. At the 4th month, a sudden increase of AST activity was observed in treated animals [Figure 4]b.

Alkaline phosphatase activity test

The increased level of ALP was found in treated animals than control animals. The ALP level of control subjects was observed 85.107 U/L. In Group II, initially a significant increase was found at the time of the 1st, 2nd, 3rd, and 4th months, i.e., 98.988 (P < 0.05*), 138.312 (P < 0.001***), 154.584 (P < 0.001***), and 160.192 U/L (P < 0.001***) [Figure 4]c.

Albumin level test

Albumin level was measured in the animals of both the groups. It was found to be decreased in the treated group with disease progression in comparison of albumin level of control subjects. The albumin level of control subjects was 8.92 g/dl. A significant decrease was observed in Group II: 5.510, 5.123, 3.284 (*<0.05), and 3.284 (*<0.05) at the time of the 1st, 2nd, 3rd, and 4th months, respectively [Figure 5].
Figure 5: Albumin level test from serum: Albumin test was performed to check albumin level with the progression of disease. Albumin level of both the groups was decreased significantly with disease progression (***<0.001)

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Total glutathione

This study shows the decreased level of glutathione (GSH) of treated animals (Group II) at the end of the 1st month and 6th month as well. The GSH level of control subjects was 0.308 μmole/g tissue/h. At the end of the 1st and 6th months, the GSH level was 0.366 (P <0.001***) and 0.278 (P <0.05*) μmole/g tissue/h, respectively. This parameter decreased significantly in treated animals (Group II) as compared to the control group [Figure 6]a.
Figure 6: Biochemical analysis of antioxidant assays from tissue. Glutathione (a), lipid peroxidation (b), and catalase (c) were performed of the subjects from both the groups to observe the oxidative stress in liver tissue with the progression of disease. Malondialdehyde level is increased and reduced glutathione activity and catalase activity are significantly decreased (*<0.05, ***<0.001)

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Lipid peroxidation

The MDA levels were measured in liver tissue of both treated and control animals. A significant increased MDA level was exhibited in treated (Group II) animals at the end of the 1st month and at the end of the 6th month in comparison to control animals. The MDA level of the control group was 2.416 nmol of MDA formed/h/mg tissue. MDA content was found to be 2.462 and 3.077 (P < 0.001***) nmol formed/h/mg tissue, respectively, in the treated group [Figure 6]b at the end of the 1st month and 6th month, respectively.

Catalase activity

The catalase activity was found to be decreased significantly in toxin-treated animals in comparison to control animals. The catalase activity of control subjects was 28.492 U/mg. In 1 and 6 months, the catalase activity of treated animals (Group II) was 7.94 (P < 0.001***) and 8.089 U/mg (P < 0.001***), respectively [Figure 6]c.

Histopathological studies

Group 2 (200 mg/kg body weight)

Control group

The histopathological examinations of control liver sections showed normal characteristics of hepatoparenchyma and normal portal triad [Figure 7]a and [Figure 7]b.
Figure 7: Histopathological analysis: (a) Control group (H and E, ×100) showing normal hepatoparenchyma, portal triad, and normal central vein, (b) the same section of control group showing normal bile duct proliferation with normal portal triad (H and E, ×400), (c) treated group with thioacetamide (200 mg/kg) at the end of 3rd month showing cirrhotic pattern of liver tissue (H and E, ×100), (d) the same section showing septa formed of mainly inflammatory cells adjoining a hepatocytic nodule (H and E, ×400), (e) liver from the 6th month showing cirrhotic pattern with inflammation and nodule formation also observed (H and E, ×100), (f) high-power photomicrograph (H and E, ×400) from the same section showing a portion of a hepatocytic nodule. The hepatocytes in the nodule show atypical features specific to hepatocellular carcinoma such as anisonucleosis, prominent nucleoli, and presence of very large nuclear profiles (arrows). PT = Portal triad, CV = Central vein, H = Hepatocytic Nodules, S = Septae, PV = Portal vein, BD = Bile duct, F = Fibrous Septae

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Cirrhotic stage

At the end of the 1st month from the first dose of DEN and TAA, inflammation and slight changes in hepatocytes were observed, but at the end of the 3rd month from the first administration of toxin, drastic changes have been observed in hepatocyte structure. Thick inflammatory septae was formed between portal triads and central veins breaking up the liver lobule and creating hepatocytic nodules [Figure 7]c. The hepatocytes in the nodule show atypical features such as anisonucleosis, prominent nucleoli, and presence of very large nuclear profiles (arrows) [Figure 7]d.

Tumor stage

More pronounced cirrhotic pattern was observed in this stage. Thick fibrous septae broke up the liver lobule and create hepatocytic nodules leading to form adenomas [Figure 7]e. Many binucleated cells and clear nodules were surrounded by thick septae [Figure 7]f. A huge change has been observed in the architecture of hepatocyte in the histopathological studies of Group II at the end of the 6th month [Figure 7]e and [Figure 7]f.

Other organ toxicities

The toxicity of DEN and TAA was also analyzed in other organs such as kidney and stomach. The cells of the kidney [Figure 8]a and [Figure 8]b and stomach [Figure 8]c and [Figure 8]d of the treated group did not show any deregulation in comparison to control sections. This stage-specific animal model of HCC induced by DEN and TAA does not induce toxicity to the kidney and stomach. Hence, these carcinogens present in food chain are specific to liver only and cause HCC.
Figure 8: Histopathological analysis diethylnitrosamine and thioacetamide toxicity in (a) cross section of the kidney from control rats is showing normal architecture of cells, (b) cross section of the kidney from treated rats is showing normal architecture of cells, (c) cross section of the stomach from control rats is showing normal cells, and (d) cross section of the stomach from treated rats also showing normal architecture of cells

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All biochemical parameters supported the successful development of HCC chronic animal model using DEN + TAA. The study was further analyzed using microarray dataset to identify potential targets to understand possible pathology, molecular changes, and target strategy under cirrhosis and HCC condition.

Protein–protein interaction analysis

Quality assessment of the microarray dataset and identification of differentially expressed genes

In this study, we re-analyzed the microarray dataset GSE45050 which compares nontumor, cirrhosis, and HCC samples. The quality of microarray dataset was assessed by inbuilt “R” functions and histograms of the array intensities of raw data. The histogram of value distribution showed the median-centered values of the selected samples which indicate the suitability of comparison [Figure 9]. The samples of three groups, i.e., nontumor, cirrhosis, and HCC, were compared by GEO2R which revealed that 82 genes were DE with adjusted P < 0.05 [Table S1]. As different parameters such as the efficiency of RNA extraction and spot detection can influence the validity of microarray experiments, we assessed the suitability of this dataset for further analysis by unsupervised STRING clustering the data of the 82 genes. Both these methods could differentiate samples based on disease state (normal or HCC), indicating the acceptable quality of this dataset.

Figure 9: The quality of microarray dataset (GSE45050)

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Protein–protein interaction network and pathway enrichment analysis

The interaction between 82 selected genes from the microarray dataset was investigated using Cytoscape, BiNGO plugin, and STRING database. Unexpectedly, only a few genes revealed to be interacted. Then, to infer the pathways that are involved in HCC, MCL clustering was performed to analyze the networks. The final PPI interaction network of 40 genes was enriched by BiNGO, and GMPS, EHMT2, and SPRY2 genes were observed as central genes or protein hub that could detect the critical pathways in HCC [Figure 10].
Figure 10: Network construction and pathway enrichment analysis of differentially expressed genes, a. Network construction by Cytoscape using 82 genes. b. MCL clustering using STRING database. c & d: Network enrichment using BiNGO plugin in Cytoscape

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Pathway enrichment of central genes

The topology of this network was assessed by graph theory concepts such as closeness centrality, betweenness centrality, and degree. These parameters were employed to sort the genes and top eight genes with the high rank were selected [Table 1]. Pathway enrichment was then performed, and interestingly, the central gene resulted involved in major pathways that are strongly related to cirrhosis and HCC [Table 2].
Table 1: Central genes in the PPI network. The top eight genes with highest degrees, closeness centrality, and betweenness scores in the enriched network are shown.

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Table 2: Central genes involved in biological pathways related to cirrhosis and HCC

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 > Discussion Top

Animal models have been accepted widely to understand the disease and its progression. A number of animal models have been established to understand the multistage hepatocarcinogenesis using different carcinogens.[22],[23],[24],[25] DEN + 2-AAF-induced model is also one of the animal models of HCC which has already been established by our group.[9] In that model, the most important and longest phase of HCC, i.e., cirrhosis, was not present; at initial stage, fibrosis was observed; and after 4 months, nodules were seen, but the liver was not cirrhotic. To overcome this challenge, TAA was used with DEN to develop the multiple low-dose chronic HCC models to get all the stages of HCC, and from this animal model, stage-specific diagnostic and prognostic biomarkers can be identified.

TAA is one of the known hepatocarcinogens, mostly used to develop acute liver cell damage, fibrosis, or cirrhosis animal models.[26],[27] The present study shows HCC progression (fibrosis, cirrhosis to tumor stage) by administering single dose of DEN and multiple doses of TAA. DEN initiates liver carcinogenesis by producing several mutagenic adducts in the liver, and TAA promotes the growth of tumorigenic hepatocytes. The oxidation properties of TAA result in hepatotoxic action which leads to an increased level of reactive oxygen species and liver damage. Many researchers have reported cirrhotic animal model using TAA and have also tested some drugs for the treatment of the same.[9] However, HCC stage-specific model is not reported yet. The present study shows the initiation of HCC through fibrosis, cirrhosis, and nodule formation leading to adenomas by the administration of TAA.

The successful development of animal model was further supported by histopathological studies. A clear pattern of cirrhotic liver tissue and thick inflammatory septae has been formed between portal triads and central veins breaking up the liver lobule and creating hepatocytic nodules. The hepatocytes in the nodule show atypical features such as anisonucleosis, prominent nucleoli, and presence of very large nuclear profiles, which showed the successful stage-specific HCC progression. In accordance with our study, similar atypical features of hepatocytes after TAA administration were observed in other rat models of HCC using TAA.[28],[29] No such type of cell damage was noticed in the control group rat. Significantly increased levels of LFTs and decreased levels in antioxidant activities supported hepatocarcinogenesis.[30],[31]

DEN is a well-known hepatocarcinogen to increase the level of free radicals in the liver that causes ROS production in liver tissue.[32] In the present study, a significant depletion in CAT and GSH activity was observed.[33],[34],[35] Recently, many studies on serum albumin have reported that albumin level decreases only in the chronic disease of the liver, which leads to cirrhosis or liver damage, and it has also been reported that the lower albumin level is associated with significantly larger HCC tumor diameter.[16],[36],[37] The continuous decrease in albumin level also supports the progression of hepatocarcinogenesis, and albumin can be said as a good prognostic indicator also.

This DEN + TAA stage-specific model can be beneficial to detect the diagnostic and prognostic biomarkers of cirrhosis and HCC. Therefore, the protein–protein interaction studies were performed using microarray dataset (GSE45050) which has been deposited by Darpolor et al. in 2014.[18] In Darpolor's study, DEN and TAA have been used to develop HCC and analysis was performed using samples of three groups: nontumor, cirrhosis, and HCC. PPI network was constructed based on DEG in the microarray experiment. We tried to determine the critical nodes which may be important genes to progress hepatocarcinogenesis. The topology of network and different measures of centrality were analyzed. AURKA and CCMB1 have a higher degree which mean higher connections and are vital for network surveillance. The shortest paths going through nodes are measured by betweenness centrality; hence, ERBB2 and HIST1H3H are the nodes with high betweenness centrality in this network. In addition, GMPS and EHMT2 are the nodes that we have observed with high closeness centrality, which shows that these genes are physically nearest to all nodes. Twenty-four genes were assumed to have high centrality using these parameters in our network.

Pathway enrichment analysis allows the determination of top affected biological processes in a specific disease. Interestingly, we have observed the enriched network of 40 genes that were more informative. According to centrality-lethality rule, it is widely believed that the functional significance of a protein is strongly related to its position in the network, for example; deletion of hub proteins is more lethal than deletion of nonhubs.[38],[39],[40],[41] The present study demonstrates that central nodes (AURKA, CCMB1, ERBB2, HIST1H3H, GMPS, and EHMT2) in the PPI network tend to be present in pathways that are related to cirrhosis and HCC. These pathways co-occur in a given biological state and most probably make pathways cross-link.

Most of the enriched pathways including TGFB, HGF, RAS/RAF, PDGFRA, and MET signaling pathways were previously shown to be associated with cirrhosis and HCC.[42],[43],[44],[45] This pathway enrichment analysis shows the acceptable validity of central nodes. With these analyses, we could determine some pathways which their role in HCC progression remains to be confirmed in future studies. Pathways such as GMP biosynthetic processes, GMP synthase activity pathways, histone lysine methylation, negative regulation of transcription, and peptidyl-lysine dimethylation have a potential role in HCC progression [Figure 11]. These results are concluded by the PPI studies generated by microarray dataset which includes samples of nontumor, cirrhosis, and HCC induced by DEN-TAA and similar analysis depicted by Holzer et al., 2017.[46]
Figure 11: Pathway enrichment by BinGo showing affected biological processes in hepatocarcinogenesis induced by diethylnitrosamine + thioacetamide

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The expressions of GMPS, EHMT2, SPRY2, PDGFRA, and HGF have also been observed by the microarray dataset. The elevated expression of GMPS and EHMT2 were observed in HCC samples as compared to nontumor and cirrhotic samples [Figure 12]a and [Figure 12]b. In a previous study, it has been shown that the enhanced level of EHMT2 is involved in the proliferation of cancer cells.[47] EHMT2 is a histone lysine methyltransferase which is localized in euchromatin regions. It acts as a co-repressor for specific transcription factors. Expression of EHMT2 can be suppressed by interacting with BIX-01294 as it is found as inhibitor of EHMT2. Hence, EHMT2 may play an important role in the cancer cell growth regulation.[48] GMPS (guanine monophosphate synthase) is a direct target of p53-mediated repression in liver cancer.[49] GMP synthase activity involves the conversion of inosine monophosphate to xanthosine monophosphate, and further, it converts into GMPS. The guanosine nucleotides formed by these reactions are essential for energy storage, microtubule assembly, and nuclear transport which are very important in highly proliferative cells.[46] Hence, the high expression of GMPS has been observed in HCC samples. In addition, GMPS can be suppressed by p53 through p21 which is a functional part of p53 mediated program of limiting tumor growth in HCC. On the other hand, SPRY2 is downregulated in HCC condition [Figure 12]c. SPRY2 is an inhibitor of RAS signaling pathway with wild type BRAF. In downregulated condition, SPRY2 is not able to regulate the kinase activity of BRAF which affects extracellular signal-regulated kinase signaling and cell proliferation are increased which promotes carcinogenesis.[50],[51] Similarly, the expression of PDGFRα and HGF get downregulated in HCC condition [Figure 12]d and [Figure 12]e. In the present study of microarray dataset, interestingly, we have observed the overexpression of PDGFRα in cirrhotic samples and decreased level in HCC condition.[52],[53] As described in Brian's study, PDGFRα activates in liver diseases such as fibrosis and cirrhotic but is known to suppress the tumor proliferation.[54] HGF (hepatocyte growth factor) is a cognate ligand of c-MET receptor. The binding of HGF/c-MET activates intracellular signaling pathways such as PI3K/AKT/mTOR and MAP kinase cascades which promote cell proliferation, morphogenesis, and cell survival. Patients with hepatitis B and C viruses have a higher level of HGF, whereas patients without hepatitis C infection have a lower level of HGF.[55]
Figure 12: Expression analysis of (a) GMPS, (b) EHMT2, (c) HGF, (d) PDGFRα, and (e) SPRY2 in nontumor, cirrhosis, and hepatocellular carcinoma samples

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 > Conclusion Top

We have here introduced multiple low-dose stage-specific models of HCC using Den and TAA. This HCC animal model can be used as a mechanistic tool to study hepatocarcinogenesis at genomic and proteomic level. In addition, a system biology approach was opted to reveal some possible genes (EHMT2, GMPS, and SPRY) and signaling pathways which are involved in hepatocarcinogenesis using nontumor, cirrhotic, and HCC samples from microarray dataset. The mRNA expressions deposited in this dataset were of genes identified by DEN + TAA treatment. This animal model and system biology approach can be used to suggest novel therapies for cirrhosis and HCC via generation of a holistic multilevel insight.


This research was supported by Dr. Ashok K. Chauhan, Founder President, Amity University Uttar Pradesh, Noida, India, by providing the infrastructure. We gratefully acknowledge Dr. Ashok Mukherjee for his detailed analysis of histopathological images. We would like to express a special gratitude to our colleagues, Ms. Ruchi Jakhmola Mani, Dr. Khyati Mittal, and Dr. Savita Mishra who provided expertise and greatly assisted to research.

Financial support and sponsorship

Infrastructure is provided by Amity University Uttar Pradesh, Noida, India, and Jamia Hamdard, New Delhi, India.

Conflicts of interest

There are no conflicts of interest.

 > References Top

White DL, Thrift AP, Kanwal F, Davila J, El-Serag HB. Incidence of hepatocellular carcinoma in All 50 United States, from 2000 through 2012. Gastroenterology 2017;152:812-20.  Back to cited text no. 1
Massarweh NN, El-Serag HB. Epidemiology of hepatocellular carcinoma and intrahepatic cholangiocarcinoma. Cancer Control 2017;24:1073274817729245.  Back to cited text no. 2
Shanmugam N, Scott JX, Kumar V, Vij M, Ramachandran P, Narasimhan G, et al. Multidisciplinary management of hepatoblastoma in children: Experience from a developing country. Pediatr Blood Cancer 2017;64:e26249.  Back to cited text no. 3
Karin M, Dhar D. Liver carcinogenesis: From naughty chemicals to soothing fat and the surprising role of NRF2. Carcinogenesis 2016;37:541-6.  Back to cited text no. 4
Kennedy OJ, Roderick P, Buchanan R, Fallowfield JA, Hayes PC, Parkes J. Coffee, including caffeinated and decaffeinated coffee, and the risk of hepatocellular carcinoma: A systematic review and dose-response meta-analysis. BMJ Open 2017;7:e013739.  Back to cited text no. 5
Park DH, Shin JW, Park SK, Seo JN, Li L, Jang JJ, et al. Diethylnitrosamine (DEN) induces irreversible hepatocellular carcinogenesis through overexpression of G1/S-phase regulatory proteins in rat. Toxicol Lett 2009;191:321-6.  Back to cited text no. 6
Frezza EE, Gerunda GE, Farinati F, DeMaria N, Galligioni A, Plebani F, et al. CCL4-induced liver cirrhosis and hepatocellular carcinoma in rats: Relationship to plasma zinc, copper and estradiol levels. Hepato Gastroenterol 1994;41:367-9.  Back to cited text no. 7
De Minicis S, Kisseleva T, Francis H, Baroni GS, Benedetti A, Brenner D, et al. Liver carcinogenesis: Rodent models of hepatocarcinoma and cholangiocarcinoma. Dig Liver Dis 2013;45:450-9.  Back to cited text no. 8
Malik S, Bhatnagar S, Chaudhary N, Katare DP, Jain SK. DEN+2-AAF-induced multistep hepatotumorigenesis in Wistar rats: Supportive evidence and insights. Protoplasma 2013;250:175-83.  Back to cited text no. 9
Ghouri YA, Mian I, Rowe JH. Review of hepatocellular carcinoma: Epidemiology, etiology, and carcinogenesis. J Carcinog 2017;16:1.  Back to cited text no. 10
[PUBMED]  [Full text]  
Qin LX, Tang ZY. The prognostic molecular markers in hepatocellular carcinoma. World J Gastroenterol 2002;8:385.  Back to cited text no. 11
Staňková P, Kučera O, Lotková H, Roušar T, Endlicher R, Cervinková Z. The toxic effect of thioacetamide on rat liver in vitro. Toxicol In Vitro 2010;24:2097-103.  Back to cited text no. 12
Abbasi MH, Akhtar T, Malik IA, Fatima S, Khawar B, Mujeeb KA, et al. Acute and chronic toxicity of thioacetamide and alterations in blood cell indices in rats. J Cancer Ther 2012;4:251.  Back to cited text no. 13
Park NH, Song IH, Chung YH. Chronic hepatitis B in hepatocarcinogenesis. Postgrad Med J 2006;82:507-15.  Back to cited text no. 14
Calderwood MA, Venkatesan K, Xing L, Chase MR, Vazquez A, Holthaus AM, et al. Epstein-Barr virus and virus human protein interaction maps. Proc Natl Acad Sci U S A 2007;104:7606-11.  Back to cited text no. 15
Leonard PJ, Persaud J, Motwani R. The estimation of plasma albumin by BCG RCG binding on the technicon SMA 12/60 analyser and a comparison with the HABA dye binding technique. Clinica Chimica Acta 1971;35:409-12.  Back to cited text no. 16
Burtis CA, Ashwood ER, Bruns DE. Tietz Textbook of Clinical Chemistry and Molecular Diagnostics-E-Book. Boston, USA: Elsevier Health Sciences; 2012.  Back to cited text no. 17
Darpolor MM, Basu SS, Worth A, Nelson DS, Clarke-Katzenberg RH, Glickson JD, et al. The aspartate metabolism pathway is differentiable in human hepatocellular carcinoma: Transcriptomics and 13C-isotope based metabolomics. NMR Biomed 2014;27:381-9.  Back to cited text no. 18
Abedi M, Gheisari Y. Nodes with high centrality in protein interaction networks are responsible for driving signaling pathways in diabetic nephropathy. PeerJ 2015;3:e1284.  Back to cited text no. 19
Assenov Y, Ramírez F, Schelhorn SE, Lengauer T, Albrecht M. Computing topological parameters of biological networks. Bioinformatics 2008;24:282-4.  Back to cited text no. 20
Sharma P, Bhattacharyya DK, Kalita JK. Centrality analysis in PPI networks. In: 2016 International Conference on Accessibility to Digital World (ICADW). Guwahati, India: IEEE; 2016. p. 135-40.  Back to cited text no. 21
Solt DB, Medline A, Farber E. Rapid emergence of carcinogen-induced hyperplastic lesions in a new model for the sequential analysis of liver carcinogenesis. Am J Pathol 1977;88:595-618.  Back to cited text no. 22
Goldsworthy TL, Hanigan MH, Pitot HC, Shinozuka H. Models of hepatocarcinogenesis in the rat Contrasts and comparisons. CRC Crit Rev Toxicol 1986;17:61-89.  Back to cited text no. 23
Dragan YP, Campbell HA, Xu XH, Pitot HC. Quantitative stereological studies of a 'selection' protocol of hepatocarcinogenesis following initiation in neonatal male and female rats. Carcinogenesis 1997;18:149-58.  Back to cited text no. 24
Lto N, Tamano S, Shirai T. A medium-term rat liver bioassay for rapid in vivo detection of carcinogenic potential of chemicals. Cancer Sci 2003;94:3-8.  Back to cited text no. 25
Palacios RS, Roderfeld M, Hemmann S, Rath T, Atanasova S, Tschuschner A, et al. Activation of hepatic stellate cells is associated with cytokine expression in thioacetamide-induced hepatic fibrosis in mice. Lab Invest 2008;88:1192.  Back to cited text no. 26
Liquori GE, Calamita G, Cascella D, Mastrodonato M, Portincasa P, Ferri D. An innovative methodology for the automated morphometric and quantitative estimation of liver steatosis. Histol Histopathol 2009;24:49-60.  Back to cited text no. 27
El-Bahrawy HA, El-Ashmawy NE, Shamloula MM, El-Feky OA. Effect of diet intake imbalance in hepatocellular carcinoma progression. Int J Bio 2014;6:104.  Back to cited text no. 28
Al-Attar AM, Al-Rethea HA. Chemoprotective effect of omega-3 fatty acids on thioacetamide induced hepatic fibrosis in male rats. Saudi J Biol Sci 2017;24:956-65.  Back to cited text no. 29
Gowda S, Desai PB, Hull VV, Math AA, Vernekar SN, Kulkarni SS. A review on laboratory liver function tests. Pan Afr Med J 2009;3:17.  Back to cited text no. 30
Alwahaibi NY, Budin SB, Mohamed J. Biochemical profile of sodium selenite on chemically induced hepatocarcinogenesis in male Sprague-Dawley rats. Afr J Biochem Res 2010;4:273-8.  Back to cited text no. 31
Janani P, Sivakumari K, Parthasarathy C. Hepatoprotective activity of bacoside A against N-nitrosodiethylamine-induced liver toxicity in adult rats. Cell Biol Toxicol 2009;25:425-34.  Back to cited text no. 32
Arslan A, Demir H, Ozbay MF, Arslan H. Evaluation of lipid peroxidation and some antioxidant activities in patients with primary and metastatic liver cancer. J Cancer Ther 2014;5:192.  Back to cited text no. 33
Banakar MC, Paramasivan SK, Chattopadhyay MB, Datta S, Chakraborty P, Chatterjee M, et al. 1α, 25-Dihydroxyvitamin D3 prevents DNA damage and restores antioxidant enzymes in rat hepatocarcinogenesis induced by diethylnitrosamine and promoted by phenobarbital. World J Gastroenterol 2004;10:1268-75.  Back to cited text no. 34
Hassan HA, Ghareb NE, Azhari GF. Antioxidant activity and free radical-scavenging of cape gooseberry (Physalis peruviana L.) in hepatocellular carcinoma rats model. Hepatoma Res 2017;3:27-33.  Back to cited text no. 35
Bağırsakçı E, Şahin E, Atabey N, Erdal E, Guerra V, Carr BI. Role of albumin in growth inhibition in hepatocellular carcinoma. Oncology 2017;93:136-42.  Back to cited text no. 36
Walayat S, Martin D, Patel J, Ahmed UN, Asghar M, Pai AU, et al. Role of albumin in cirrhosis: From a hospitalist's perspective. J Commun Hosp Intern Med Perspect 2017;7:8-14.  Back to cited text no. 37
Hahn MW, Kern AD. Comparative genomics of centrality and essentiality in three eukaryotic protein-interaction networks. Mol Biol Evol 2005;22:803-6.  Back to cited text no. 38
He X, Zhang J. Why do hubs tend to be essential in protein networks? PLoS Genet 2006;2:e88.  Back to cited text no. 39
Jeong H, Mason SP, Barabási AL, Oltvai ZN. Lethality and centrality in protein networks. Nature 2001;411:41-2.  Back to cited text no. 40
Yu H, Greenbaum D, Xin Lu H, Zhu X, Gerstein M. Genomic analysis of essentiality within protein networks. Trends Genet 2004;20:227-31.  Back to cited text no. 41
Dediulia T, Dropmann A, Itzel T, Meindl-Beinker N, Teufel A, Cai S, et al. Screening for TGFB Signaling Regulator expression in human HCC. Zeitschrift Für Gastroenterol 2014;52:4-41.  Back to cited text no. 42
Goyal L, Muzumdar MD, Zhu AX. Targeting the HGF/c-MET pathway in hepatocellular carcinoma. Clin Cancer Res 2013;19:2310-8.  Back to cited text no. 43
Xu Y, Huang J, Ma L, Shan J, Shen J, Yang Z, et al. MicroRNA-122 confers sorafenib resistance to hepatocellular carcinoma cells by targeting IGF-1R to regulate RAS/RAF/ERK signaling pathways. Cancer Lett 2016;371:171-81.  Back to cited text no. 44
Yang S, Liu G. Targeting the Ras/Raf/MEK/ERK pathway in hepatocellular carcinoma. Oncol Lett 2017;13:1041-7.  Back to cited text no. 45
Holzer K, Drucker E, Roessler S, Dauch D, Heinzmann F, Waldburger N, et al. Proteomic analysis reveals GMP synthetase as p53 repression target in liver cancer. Am J Pathol 2017;187:228-35.  Back to cited text no. 46
Cho HS, Kang JG, Lee JH, Lee JJ, Jeon SK, Ko JH, et al. Direct regulation of E-cadherin by targeted histone methylation of TALE-SET fusion protein in cancer cells. Oncotarget 2015;6:23837-44.  Back to cited text no. 47
Cui J, Sun W, Hao X, Wei M, Su X, Zhang Y, et al. EHMT2 inhibitor BIX-01294 induces apoptosis through PMAIP1-USP9X-MCL1 axis in human bladder cancer cells. Cancer Cell Int 2015;15:4.  Back to cited text no. 48
Holzer K, Drucker E, Roessler S, Waldburger N, Eiteneuer E, Herpel E, et al. Guanine monophosphate synthetase (GMPS) is an important target of p53-mediated repression in liver cancer. Zeitschrift Für Gastroenterol 2016;54:A4-23.  Back to cited text no. 49
Tsavachidou D, Coleman ML, Athanasiadis G, Li S, Licht JD, Olsan MF, et al. SPRY2 Is an inhibitor of the ras/extracellular signal-regulated kinase pathway in melanocytes and melanoma cells with wild-type BRAF but not with the V599E Mutant. Cancer Res 2004;64:5556-9.  Back to cited text no. 50
Shukla A, Rai K, Shukla V, Chaturvedi NK, Bociek RG, Pirruccello SJ, et al. Sprouty 2: A novel attenuator of B-cell receptor and MAPK-Erk signaling in CLL. Blood 2016;127:2310-21.  Back to cited text no. 51
Venepalli NK, Goff L. Targeting the HGF-cMET Axis in Hepatocellular Carcinoma. Int J Hepatol 2013;2013:341636.  Back to cited text no. 52
Wei T, Zhang LN, Lv Y, Ma XY, Zhi L, Liu C, et al. Overexpression of platelet-derived growth factor receptor alpha promotes tumor progression and indicates poor prognosis in hepatocellular carcinoma. Oncotarget 2014;5:10307-17.  Back to cited text no. 53
Hayes BJ, Riehle KJ, Shimizu-Albergine M, Bauer RL, Hudkins KL, Johansson F, et al. Activation of platelet-derived growth factor receptor alpha contributes to liver fibrosis. PLoS One 2014;9:e92925.  Back to cited text no. 54
Ang CS, Sun MY, Huitzil-Melendez DF, Chou JF, Capanu M, Jarnagin W, et al. c-MET and HGF mRNA expression in hepatocellular carcinoma: Correlation with clinicopathological features and survival. Anticancer Res 2013;33:3241-5.  Back to cited text no. 55


  [Figure 1], [Figure 2], [Figure 3], [Figure 4], [Figure 5], [Figure 6], [Figure 7], [Figure 8], [Figure 9], [Figure 10], [Figure 11], [Figure 12]

  [Table 1], [Table 2]


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