Exon Array Analysis to Identify Diethyl-nitrosamine Differentially Regulated and Alternately Spliced Genes in Early Liver Carcinogenesis in the Transgenic Mouse ATT-myc Model

Objectives: To identify the regulated genes or the spliced genes of diethylnitorsamine (NDEA) in ATT-myc mice versus control group. Methods: We analysed the 9 hybridizations on the MouseExon10ST array of NDEA treatments and control nontransgenic by application of a mixed model analysis of variance. Results: The 907 genes had regulated significantly between the groups and 916 genes had regulated with a significant exon-group interaction among of them 150 genes had regulated with both gene and possible splicing differences (p<0.01). The 7,618 genes had tested for the alternative gene up-regulation and splicing and compared to the gene-classifications. The genes functions, pathways and geneclassifications in the current study had presented in the contingency table analysis of the set of the regulated genes and alternatively spliced that regulated significantly in the ATT-myc mice treated by diethylnitorsamine versus control nontransgenic. The GOMolFn of gene-classification had 321 groups that had significantly regulated in the set of the regulated genes or differentially spliced. While the GOProcess of gene-classification had 330 groups that had significantly regulated in the set of differentially regulated genes or spliced. Additionally, the CELlLoc of gene-classification had 70 groups that had significantly regulated in the set of differentially regulated genes spliced. Finally, the Pathway geneclassification had 8 groups that had significantly regulated in the set of differentially regulated genes or spliced (p<0.01) in diethylnitorsamine when compared to control group. Conclusion: we summarized the toxicogenomics induced by diethylnitrosamine in early liver carcinogenesis in ATT-myc transgenic mice of liver cancer.


Introduction
100,000 or more new substances, medications, and vaccines are introduced to our population each year, and the FDA and other safety organizations must allow their use for at least 18 months [1]. To protect more people and solve health issues, we need to reduce the time it takes for some vaccines and medicines to be approved in disasters, pandemics, and exotic diseases like corona.

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The use of transgenic models and exon array upregulation could provide quick information on chemical toxicity and carcinogenicity, but it is still expensive. Several experiments and techniques are used to determine the carcinogenicity of chemicals or drugs, which started with two years of testing and has progressed to more sophisticated technology such as microarray, exon array, and models that replicate human tumor genes. The differences between these tests are time consuming during the procedure of each test, and the accuracy of the data results from these projects is questionable. Several alternative research methods are being investigated, such as genetically engineered organisms, in vitro CELl-based assays, and computerized models to overcome the limitations in the cancer testing of chemicals [2][3][4]. Engineered laboratory animals can be useful in identifying liver carcinogens at an early stage, potentially shortening carcinogenicity tests.
To date, several valid transgenic mouse models and hepatocellular carcinoma knock-out mouse models have been published [5] for example the response to the genotoxic carcinogen NDEA was identified in a p53-p53-deficient mouse model [6] as well as in the rasH2 [7][8][9] and ATT-myc [10] transgenic mouse models.
Transgenic models and exon array upregulation could provide quick information on chemical toxicity and carcinogenicity, but the cost is still high for testing of chemical or drug carcinogenicity. To shorten the validation stage time or with higher-state analysis, it is critical to validate the transgenic model by testing the carcinogen such as diethylnitrosamine and comparing the date result with previously published dates of the same compound in old and classical tests [9,11,12].
The aim of this study was to use and analyze up-regulation of the exon array to identify DEN carcinogens using a meta-analysis of genomics function pathways in the ATT-myc transgenic mode

Materials and Methods
The experiment included 48 transgenic ATT-Myc mice of both sexes and 12 non-transgenic mice. All animals were housed on sawdust in groups of 1 to 4 mice per cage in a 12 hour light-dark cycle with 50% relative humidity and an ambient temperature of 22°C. The animals are fed a standardized diet and free access to water (Zucht, ssniff M-/, 10 mm, complete mice diet, ssniff Requirements GmbH, DE-59494, www.ssniff.de). All experiments were managed according to ethical guidelines.

Study Design and Treatment of Animals with NDEA
The mice were divided into three classes: the first and second groups of 48 transgenic mice each had 12 males and 12 females, with 24 non-transgenic mice of both sexes serving as a vehicle control. NDEA (99 percent purity, Sigma Aldrich, Germany) was given to transgenic mice at a dose of 100 mg once a week for 6 weeks, starting at the age of 2 months. Transgenic and non-transgenic control animals were both used to drive the car. Over the course of six weeks, transgenic mice were given a saline injection containing 100 mg/g NDEA once a week, while control mice received only saline injections [11].

Samples Collections and Preparation
Mice were anesthetized with CO2 and the thorax was opened using standard surgical procedures, with the liver being explanted with PBS, at the age of 4 (the end of treatment). Liver tissue was immediately frozen in liquid nitrogen and preserved at -80°C.

Hybridization and RNA isolation
The RNeasy total RNA isolation protocol from QIAGEN was used to separate total RNA from frozen liver tissues. The Target Labelling Assay Manual had to be followed to the letter. Ribosomal RNA reduction, cDNA synthesis, cRNA hydrolysis, fragmentation, terminal labelling, hybridization, cleaning, chip staining, GeneChip scanning, and data interpretation were all part of the assay [13].

Data Analysis, Normalization and Comparison
The current work was performed using a mixed model analysis of variance on 6 hybridizations on the MouseExon10ST array of NDEA treatments and 3 hybridizations of control non-transgenic hybridizations on December 15, 2010 by installer with XRAY (version 3.2) software on 6 hybridizations on the MouseExon10ST array of NDEA treatments and 3 hybridizations of control non-transgenic hybridizations. Both probes' gene expression was normalized against the history (Figures 1 and 2). The fold changes were considered significant at p value ≤ 0.05 and statistical test was done by using student T test. The x axis represented score, and the y axis represented the number of probes with a score in the range divided by the total number of probes.

Figure 2. In the depicted graph, the distribution of scores was summarized for each array
A box plot was used to investigate each array, with the probe score median in the center (joined by lines to aid comparison). The highest and lowest lines in the box represented the 25th and 75th percentiles of probe score, respectively, and the highest and lowest lines represented the 10th and 90th percentiles of probe score, respectively.

Tissue Distribution of Genes Expression
There were 270,096 transcript clusters on the MouseExon10ST array. Following the above-mentioned filters, there were 7,618 applications ranging from 4 to 200 probe-sets. Statistical tests were used to identify gene up-regulation and spliced genes. The following table summarized the number of tested genes (transcript clusters) expressed in each category for the transcript clusters that were tested.

Group
Number of transcript clusters with a significant up-regulation in group ndea_1s_mf 7,122 93.5% of genes tested ntr_4s_mf 6,608 86.7% of genes tested By application of the same test, the following table described all frequencies of pair-wise co-up-regulation between the study groups. The NDEA treatments 1s mf community had 7,122 genes that were substantially controlled above the background level. The following table lists all of the co-regulation trends. Frequency is a term that refers to the number of times

The Gene Upregulation and the Spliced Genes
The statistical analysis showed that 907 genes were significantly expressed with variations between groups, and 916 genes were significantly expressed with exon-group interaction (a symptom of spliced genes), with 150 genes having both gene differences and interaction.
The highest 10-fold changes in genes with significant gene expression are described in Table 1. The fold change was expressed in terms of normalized untransformed results, and Table 2 showed the top 10 spliced genes with a significant fold change.

The Genes Expression According the GOMolFn of Ontology Classification
In the collection of differentially spliced or regulated genes, the GOMolFn gene-classification had 321 groups that had significantly increased (the splicing and gene up-regulation determined as described above). The top 30 groups are listed in Table 3 and Figure 1. The three columns include the number of tested genes found to have significant gene expression (with p-value of regulation), the number of tested genes performed to have significant splicing (with pvalue of regulation), and the group name, respectively.

The Genes Expression According the GOProcess of Ontology Classification
The way to go in the collection of differentially spliced or regulated genes, 330 groups were significantly regulated during the gene-classification process (the splicing and gene up-regulation determined as described above). The top 30 classes were listed in Table 4 and Figure 2. The number of tested genes revealed a significant the gene up-regulation (with p-value of regulation), the number of tested genes found to have a significant the splicing (with p-value of regulation), and the group name were all mentioned in the three columns.

The Genes Expression According the CELlLoc of Ontology Classification
In the collection of differentially spliced or regulated genes, the CELlLoc gene-classification had 70 groups that were significantly expressed (the splicing and gene up-regulation determined as described above). The top 30 classes were shown in Table 5 and Figure 3. The number of tested genes found to have a significant the gene up-regulation (with p-value of regulation), the number of tested genes identified to have a significant the splicing (with p-value of regulation), and the group name were all mentioned in the three columns.

The Genes Expression According the Pathway Ontology Classification
The collection of differentially spliced or regulated genes was divided into eight classes, each of which had a significant expression (the splicing and gene up-regulation determined as described above). The top eight classes are shown in Table 6 and Figure 4. The number of tested genes found to have a significant the gene up-regulation (with pvalue of regulation), the number of tested genes revealed a significant the splicing (with p-value of regulation), and the group name were all mentioned in the three columns.

Discussion
Hepatocellular carcinoma is one of the deadliest and most common cancers in the human population [14]. In comparison to many infrequently mutated genes that may also correlate to tumor biology, there are only a few studies that have well established driver genes that often have a mutation [14,15]. So, there has been more interest in the developing pathway and network analysis methods about group of genes and illuminate the processes of each function group [16]. Animal models of hepato-carcinogenesis have provided reliable evidence for understanding the cellular production of HCC and further developing promising therapies [17][18][19]. The current study explored the function of liver genome at early carcinogenesis induced by NDEA in att-myc mouse model of hepatocellular carcinoma. In terms of gene groups regulated in GOProcess of the GOMolFn, GOProcess, CELlLoc, and Pathway classes, it was discovered that the collection of regulated genes and alternatively spliced genes was significantly regulated in groups of the GOMolFn, GOProcess, CELlLoc, and Pathway classes of NDEA treatment in ATT-myc transgenic model of liver cancer as well as similar study recorded earlier the molecular effect of NDEA in rat hepatocarcinogenesis [20]. Also, compared to usual livers, NDEA therapy increased the up-regulation and down-regulation of genes (DFGs) from progression dysplastic nodules, early tumor nodules, and liver tumor with lung metastasis. The gene ontology (GO) tree was used to categorize the DFGs into functional processes. The GO terms included metabolism, transport, hepatocellular proliferation, apoptosis, angiogenesis, adhesion, and others [15]. Additionally, when compared to liver tissue from normal rats and other one treated by NDEA, the above chosen tissues share 349 upregulated and 345 downregulated genes. Deregulated genes are involved in a variety of processes, including metabolism, transport, cell proliferation, apoptosis, cell adhesion, and angiogenesis. Inflammatory response, immune response, and oxidative stress are all represented by 41 upregulated and 27 downregulated genes [21].
The differentially spliced genes investigated previously in human liver cancer (pharmacological behaviour of berberine) and the possible functional cross-talking between the two sets of genes up-regulation and the spliced genes merits further investigation [22]. In this analysis, we looked at both gene up-regulation and spliced genes in the livers of NDEA-treated mice and found 321 controlled groups in the GOMolFn of early tumorigenesis in att-myc mice treated with NDEA, with the top 30 up-regulation groups discussed in Table 7. The upregulation of structural constituent of ribosome, as mention in the highest 10 best associated gene sets with highest mean minimum Function-1 gene upregulation in HCC [24].

GO:000374translation initiation factor
The upregulation of translation initiation factor, as recorded in GO functional enrichment analysis of DEGs when Aspergillus flavus was treated with, antiaflatoxigenic mechanism, cinnamaldehyde [25].

GO:0004386 helicase activity
The up-regulation of helicase activity, as reported in the GO analysis of predicted target genes of LINC01296 pan-cancers and the molecular regulatory mechanism in hepatocellular carcinoma [26].

GO:000531inorganic phosphate transport
The upregulation of inorganic phosphate transport, as found in Recent studies of mice feed a diet with high in inorganic phosphate lead to increase tumorigenesis in the two-stage skin carcinogenesis model as well as the Kras lung cancer model [27,28] and also the exposure of cancer cell to an environment with increased inorganic phosphate availability could increase the ability of these cells to induce an angiogenic response and/or attract endothelial cell [29].
6 GO:0004449 isocitrate dehydrogenase (NAD dependent) The isocitrate dehydrogenase (NAD dependent) up-regulation in HCC as recorded that the abnormal up-regulation or mutations of human NAD-IDH (also called IDH3) are also found to be associated with the development of cancers and diseases [30].

GO:0017114 wide-spectrum protease inhibition
The up-regulation of wide-spectrum protease inhibition here may be a tool from the body defence against tumor growth as detected in particularly HIV protease inhibitors, for cancer treatment [31].

GO:0016717 oxidoreductase activityactivity
The oxidoreductase activity here could explained as regulated in ovarian and breast cancer regulated by Ets-1 and oxidative stress in tumor environment [32,33].

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GO:0004812 aminoacyl-tRNA ligase activity The up-regulation aminoacyl-tRNA ligase here as reported that the ethology of specific diseases as cancer could connected to specific aminoacyl tRNA synthetases [34]. Three distinct types of methylated arginine residues occur in mammalian cell. The most prevalent is omega-N G ,N G -dimethylarginine [55]. Protein arginine methyltransferases (PRMT1) is responsible for the 85% of total protein arginine methylation activity in cultured RAT1 fibroblast cell and also in the mouse liver [56].

GO:0004576 oligosaccharyl transferase activity
Inhibition the activity of Oligosaccharyltransferase can Overcome the therapeutic Resistance to EGFR Tyrosine Kinase Inhibitors [57].

Conclusion
In conclusions, we summarized the toxicogenomic effects of diethlynitrosamine in att-myc mouse model including 321 groups of GOMolFn of gene-classification that significantly regulated in the set of the regulated genes or differentially spliced. While the GOProcess of gene-classification had 330 groups that had significantly regulated in the set of differentially regulated genes or spliced. Additionally, the CELlLoc of gene-classification had 70 groups that had significantly regulated in the set of differentially regulated genes spliced and induced by diethlynitrosamine in att-myc transgenic mouse of liver cancer in order to understand the biology of liver cancer.

Funding
The authors received no financial support for the research, authorship, and/or publication of this article.

Acknowledgements
We thanked Roman Halter a postdoctoral researcher who helped during the experimental part done in Fraunhofer Institute of Toxicology and Experimental Medicine (Hanover, Germany). Moreover, we had grateful thanks to Tatiana Miere, a postdoctoral researcher, who supported us with the gene expression on array track and the analysis whole liver tumor genome by X-RAY (version 3.2) software.

Ethical Approval
The study was conducted in accordance with the Declaration of Helsinki, and the research protocol was accepted by the animal welfare ethics committee of the city of Hannover, Germany (Tierversuchsvorhaben 33.9-42502-04-08/1619).

Data Availability Statement
The data presented in this study are available in article.

Conflict of Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.