Abstract

   RNA‐sequencing (RNA‐Seq) is a useful method to explore the molecular
   events in cells and tissues at the transcriptional level. However,
   comprehensive transcriptome analysis of hepatocarcinogenesis and
   progression is lacking. In this study, we aimed to characterize a
   dimethylnitrosamine (DEN) and carbon tetrachloride (CCl[4];
   DEN+CCl[4])‐induced hepatocellular carcinoma (HCC) mouse model by
   RNA‐Seq. In total, 2033 genes were up‐regulated and 841 genes were
   down‐regulated after DEN and CCl[4] stimulation. The differentially
   expressed genes were highly enriched for the Gene Ontology terms
   oxoacid metabolic process, carboxylic acid metabolic process, and
   organic acid metabolic process. Kyoto Encyclopedia of Genes and Genomes
   pathway analysis revealed that the top five significantly
   overrepresented pathways were metabolic pathways, chemical
   carcinogenesis, steroid hormone biosynthesis, retinol metabolism and
   metabolism of xenobiotics by cytochrome P450. Moreover, a
   protein‐protein interaction network analysis indicated that Rous
   sarcoma oncogene (Src) may play a key role in DEN+CCl[4]‐induced HCC.
   These results provide a comprehensive overview of transcriptome events
   in DEN+CCl[4]‐induced HCC.

   Keywords: dimethylnitrosamine, hepatocellular carcinoma,
   RNA‐sequencing, tetrachloride‐induced, transcriptome analysis
     __________________________________________________________________

Abbreviations

   DEG
          differentially expressed gene

   CCl[4]
          carbon tetrachloride

   DEN
          dimethylnitrosamine

   GO
          Gene Ontology

   HCC
          hepatocellular carcinoma

   IP
          intraperitoneally

   KEGG
          Kyoto Encyclopedia of Genes and Genomes

   RNA‐Seq
          RNA‐sequencing

   Src
          Rous sarcoma oncogene

   Liver cancer is the fourth major cause of cancer deaths worldwide. In
   China, liver cancer is one of the five most commonly diagnosed cancers
   among males and tops the five leading causes of cancer deaths among
   males and females [34]1. Furthermore, hepatocellular carcinoma (HCC) is
   the main type of liver cancer [35]2. The refractory trait, complicated
   underlying mechanisms and individual differences in HCC are three
   stumbling blocks to the diagnosis and treatment of HCC [36]1.

   Previous research has established chemically induced
   hepatocarcinogenesis rodent models with a multitude of similarities to
   human HCC for studies of the underlying mechanisms [37]3, [38]4.

   However, whole transcriptome analysis of the dimethylnitrosamine (DEN)
   and carbon tetrachloride (CCl[4]; DEN+CCl[4])‐induced HCC mice model is
   lacking. This dissertation aims to unravel the molecular mechanisms of
   underlying DEN+CCl[4]‐induced HCC at the transcriptional level using an
   RNA‐sequencing (RNA‐Seq) approach.

   In brief, 2033 genes were up‐regulated and 841 genes were
   down‐regulated after DEN+CCl[4] stimulation, as identified by RNA‐Seq.
   ‘Gene Ontology’ (GO) and ‘Kyoto Encyclopedia of Genes and Genomes’
   (KEGG) pathway analysis showed that the genes were enriched for the GO
   terms – oxoacid metabolic process, carboxylic acid metabolic process,
   and organic acid metabolic process in both the high‐ and low‐dose
   groups – as well as the key pathways – metabolic pathways, chemical
   carcinogenesis, steroid hormone biosynthesis, retinol metabolism and
   metabolism of xenobiotics by cytochrome P450. A protein‐protein
   interaction network analysis indicated that Rous sarcoma oncogene (Src)
   may play a key role in DEN+CCl[4]‐induced HCC.

Materials and methods

Animals, treatments and tissue collection

   C57BL/6J mice were purchased from the Model Animal Research Center
   (Nanjing University, Nanjing, China). All animals were housed in animal
   facilities at the Anhui Medical University under a standard 12‐h
   light/dark cycle with access to chow and water ad libitum. Experiments
   were conducted in accordance with the ethical statement of the
   Institutional Animal Care and Use Committee of Anhui Medical
   University.

   Fourteen‐day‐old male C57BL/6J mice were treated with DEN
   intraperitoneally (IP; N0258‐1G; Sigma, St. Louis, MO, USA) at a dose
   of 25 mg·kg^−1 (low‐dose group) or 40 mg·kg^−1 (high‐dose group), or
   with vehicle alone (normal saline, IP, referred as control). Four weeks
   after their birth, the mice of the low‐dose group were treated with
   CCl[4] (IP; SINOPHARM, Beijing, China) at 2.5 μL·g^−1 once a week for a
   total of 25 doses. Eight weeks after their birth, the mice of the
   high‐dose group received 5 μL·g^−1 CCl[4] treatment twice a week for a
   total of 39 doses. CCl[4] was diluted 1 : 4 in corn oil (C8267; Sigma).
   The solution was filtered through 0.22‐μm filter (Merck Millipore,
   Billerica, MA, USA). The mice of the control group were treated with
   corn oil alone. Finally, mice were sacrificed, and livers and serum
   samples were collected. Liver and body weights of mice were noted at
   the time of sacrifice and used to determine liver/body weight ratios.

Histology and hematoxylin and eosin staining

   Mouse liver tissues were fixed in 4% paraformaldehyde, embedded in
   paraffin and sectioned. Liver sections (5 μm) were stained with
   hematoxylin and eosin staining reagents. Images were captured using the
   Leica microscope (Wetzlar, Germany).

RNA extraction and whole transcriptome sequencing

   Total RNA was extracted with TRIzol (Invitrogen, Carlsbad, CA, USA)
   according to the manufacturer's protocol. The quality of total RNA was
   assessed by the Agilent Bioanalyzer 2100 (Santa Clara, CA, USA). Equal
   quantities of RNA from three samples in one group were mixed. The mRNA
   was purified and fragmented. Double‐stranded cDNA was synthesized and
   purified before adaptors were ligated to the fragments. PCR enrichment,
   library quality control and cluster generation were performed. Finally,
   the library products were sequenced by synthesis via Illumina HiSeq
   2000 (San Diego, CA, USA).

Analysis of RNA‐Seq data

   Because some adaptor sequences and/or low‐quality reads are present in
   the raw reads, data filtering is carried out to obtain high‐quality
   reads as the clean reads (clean data). Clean reads were mapped to
   reference sequences and/or the reference gene set using SOAP
   aligner/SOAP2 (Short Oligonuclotide Analysis package). No more than two
   mismatches were allowed in the alignment. Then, a comprehensive
   bioinformatics analysis was used to enrich the dataset for genes that
   were most likely to be associated with the development of
   DEN+CCl[4]‐induced HCC, including clustering analysis, GO analysis,
   pathway analysis and dynamic gene network analysis.

Statistical analysis

   Data are expressed as means ± standard error of the mean. Differences
   between groups were tested for statistical significance using Student's
   t‐tests or one‐way ANOVA followed by Newman‐Keuls multiple comparison
   tests using graphpad prism 7.0 (La Jolla, CA, USA). All analyses with
   P‐value < 0.05 were considered significant.

Results

DEN and CCl[4] stimulated HCC in mice

   In the HCC mice model, treatment with both DEN and CCl[4] effectively
   induced liver tumors (Fig. [39]1A,B). Each mouse developed a liver
   tumor; however, a significantly higher number of tumors were detected
   in the high‐dose group than in the low‐dose group, and the tumor cells
   exhibited high‐grade atypia (Fig. [40]1C–E). The observed phenotypes
   indicated that DEN and CCl[4] stimulated HCC successfully.

Figure 1.

   Figure 1
   [41]Open in a new tab

   DEN+CCl[4]‐stimulated HCC in mice. (A, B) Schematic depiction of the
   experimental design of the HCC mouse model induced by DEN and CCl[4].
   (C–E) Tumor and liver samples were identified macroscopically and
   microscopically. Hematoxylin and eosin staining of liver sections from
   treated and control mice (original magnification from left to right:
   ×100, ×200, and ×400; scale bar: 50 μm). Con, control; i.p.,
   intraperitoneally.

Identification of differentially expressed genes

   To investigate differentially expressed genes (DEGs) in the
   DEN+CCl[4]‐induced HCC mouse model, we performed RNA‐Seq. As shown in
   Fig. [42]2A, after filtering primer‐ or adaptor‐contaminated and
   low‐quality reads, clean reads exceeded 94.8% of raw reads in six
   samples.

Figure 2.

   Figure 2
   [43]Open in a new tab

   Distributions of up‐regulated and down‐regulated genes. (A) Composition
   of raw reads after DEN and CCl[4] injection. (B) Bar graph shows the
   number of up‐regulated and down‐regulated genes in the low‐ and
   high‐dose groups compared with the control group. (C) Venn diagram of
   up‐regulated genes in both the high‐ and low‐dose groups. (D) Venn
   diagram of down‐regulated genes in both the high‐ and low‐dose groups.
   P ≤ 0.05; |Log2FC| ≥ 1. (E) Heatmap of DEGs among the three groups.
   Con, control.

   To identify DEGs, we used P ≤ 0.05 and |Log2FC| ≥ 1 as the threshold.
   When compared with genes in the control group, 2914 genes were
   significantly up‐regulated and 1533 genes were down‐regulated in the
   high‐dose group, and 2260 genes were markedly up‐regulated and 1044
   genes were down‐regulated in the low‐dose group (Fig. [44]2B).
   Moreover, 2033 genes were up‐regulated in both the high‐dose and
   low‐dose groups when compared with levels in the control group
   (Fig. [45]2C). In addition, 841 genes were down‐regulated in both the
   high‐dose and low‐dose groups when compared with levels in the control
   group (Fig. [46]2D).

   To investigate the functional correlation of genes with similar
   expression patterns, we performed a cluster analysis of expression
   patterns of DEGs (Fig. [47]2E). Expression differences are shown in
   different colors; green represents relatively low expression, and red
   represents relatively high expression.

GO analysis

   The GO tool was used for the functional classification of DEGs.
   Figure [48]3 summarizes the most significantly overrepresented GO terms
   in the biological process category. When compared with the control
   group, DEGs in the high‐dose group were most highly enriched for the GO
   terms oxoacid metabolic process, carboxylic acid metabolic process,
   organic acid metabolic process, oxidation‐reduction process and small
   molecule metabolic process; DEGs in the low‐dose group were enriched
   for carboxylic acid metabolic process, oxoacid metabolic process,
   organic acid metabolic process, monocarboxylic acid metabolic process
   and single‐organism process. The processes identified in this analysis
   are likely to contribute to the pathobiology of DEN+CCl[4]‐stimulated
   HCC.

Figure 3.

   Figure 3
   [49]Open in a new tab

   GO analysis. Significant GO terms for DEGs between the high‐dose group
   (A) or the low‐dose group (B) and the control group.

KEGG pathway analysis of DEGs

   To further understand the biological functions of genes, we performed a
   KEGG pathway enrichment analysis. The top five significantly
   overrepresented pathways in the high‐dose group were metabolic
   pathways, chemical carcinogenesis, steroid hormone biosynthesis,
   retinol metabolism and metabolism of xenobiotics by cytochrome P450,
   and in the low‐dose group were metabolic pathways, steroid hormone
   biosynthesis, retinol metabolism, chemical carcinogenesis and
   metabolism of xenobiotics by cytochrome P450 (Fig. [50]4). These
   pathways could be important for DEN+CCl[4]‐stimulated HCC.

Figure 4.

   Figure 4
   [51]Open in a new tab

   Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis.
   Significant pathways for DEGs between the high‐dose group (A) or the
   low‐dose group (B) and the control group.

Protein‐protein interaction network analysis

   To investigate the important role of protein interactions in the
   DEN+CCl[4]‐stimulated HCC, we performed a protein‐protein interaction
   network analysis using the STRING online database
   (https://string‐db.org) and cytoscape. Figure [52]5A summarizes the
   correlation results for 759 DEGs between the high‐dose group and the
   control group. Tumor necrosis factor interacted with 108 proteins, Src
   connected with 363 proteins, Rac family small GTPase 2 had connections
   with 282 proteins and topoisomerase (DNA) II alpha had connections with
   250 proteins. Figure [53]5B summarizes the results for 576 DEGs between
   the low‐dose group and the control group. In this analysis, Rac family
   small GTPase 2 interacted with 72 proteins, cyclin‐dependent kinase 1
   interacted with 225 proteins and Src interacted with 363 proteins.
   Taken together, Src interacted with the most genes mostly in both the
   high‐ and low‐dose groups, suggesting that it may play a paramount role
   in DEN+CCl[4]‐stimulated HCC.

Figure 5.

   Figure 5
   [54]Open in a new tab

   Protein‐protein interaction network analysis. (A) Network analysis of
   the DEGs in the high‐dose group versus the control group. (B) Network
   analysis of the DEGs in the low‐dose group versus the control group.
   Cdk1, cyclin‐dependent kinase 1; Rac2, Rac family small GTPase 2; Tnf,
   tumor necrosis factor; Top2a, topoisomerase (DNA) II alpha.

Discussion

   The current study investigated the gene expression characteristics, and
   mechanisms underlying HCC stimulated by DEN and CCl[4] were evaluated
   by RNA‐Seq. A total of 2033 genes were up‐regulated and 841 genes were
   down‐regulated after DEN and CCl[4] stimulation. These genes were
   overrepresented for the GO terms oxoacid metabolic process, carboxylic
   acid metabolic process, and organic acid metabolic process in both the
   high‐ and low‐dose groups, and for the KEGG pathways metabolic
   pathways, chemical carcinogenesis, steroid hormone biosynthesis,
   retinol metabolism, and metabolism of xenobiotics by cytochrome P450 in
   both the high‐ and low‐dose groups. These functions and pathways may be
   important in the pathological process of HCC induced by DEN and CCl[4].
   Furthermore, the results of a gene network analysis indicated that Src
   may play a huge role in the regulation of DEN+CCl[4]‐induced HCC.

   It is well acknowledged that most patients with HCC frequently exhibit
   cirrhosis [55]5. The mouse model induced by DEN and CCl[4] can mimic
   the development from cirrhosis to HCC, similar to human HCC [56]4. It
   is therefore helpful to illustrate the pathophysiology and molecular
   and cellular mechanisms underlying HCC. Although some protocols have
   suggested that tumors might develop in C3H and B6C3F1/J mice earlier
   than in C57BL/6 mice [57]6, tumors developed successfully in all
   C57BL/6 mice in our study. Moreover, the C57BL/6 strain is more
   affordable and accessible.

   Data were collected by RNA‐Seq. The advantages of this approach are as
   follows: first, transcriptome analysis offers complete information of
   tissues or cells at a particular period or time point [58]7. Second,
   RNA‐Seq can detect an undiscovered genome sequence with a low
   background signal and requires fewer RNA samples compared with
   microarrays [59]8. Third, RNA‐Seq has a better accuracy and
   repeatability [60]9, [61]10, [62]11, [63]12. Advances in RNA‐Seq have
   enabled the examination of HCC at high resolution [64]13, [65]14,
   [66]15, [67]16, [68]17.

   Alterations in HCC at the transcriptional level reflect its
   histological and molecular heterogeneity [69]5. Transcriptome analysis
   can provide an overview of gene expression in the physiological state,
   but alternative splicing, microRNAs, long noncoding RNAs, and
   hypomethylation or hypermethylation are involved in carcinogenesis,
   progression and survival in HCC [70]13, [71]14, [72]15, [73]16. Future
   studies on the current topic are suggested to develop a full picture of
   the mechanism underlying HCC.

   In general, we performed a whole transcriptome analysis of the
   DEN+CCl[4]‐induced HCC model by RNA‐Seq analysis. The findings of this
   research provide insights into the pathogenesis and molecular mechanism
   underlying HCC.

Conflict of interest

   The authors declare no conflict of interest.

Author contributions

   JS performed the majority of experiments, analyzed the data and wrote
   the paper. JW performed the molecular investigations and the treatment
   of animals. NZ and RY participated equally in the molecular
   investigations and the treatment of animals. KC provided the
   experimental equipment. DK designed and coordinated the research, as
   well as the critical revision of the manuscript.

Acknowledgements