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Liver cancr is a heterogeneous disease in terms of etiology, biologic and clinical behavior. Very little is known about how many genes concur at the molecular level of tumor development, progression and aggressiveness. To explore the key genes involved in the development of liver cancer, we established a rat model induced by diethylnitrosamine to investigate the gene expression profiles of liver tissues during the transition to cirrhosis and carcinoma.
A rat model of liver cancer induced by diethylnitrosamine was established. The cirrhotic tissue, the dysplasia nodules, the early cancerous nodules and the cancerous nodules from the rats with lung metastasis were chosen to compare with liver tissue of normal rats to investigate the differential expression genes between them. Affymetrix GeneChip Rat 230 2.0 arrays were used throughout. The real-time quantity PCR was used to verify the expression of some differential expression genes in tissues.
The pathological changes that occurred in the livers of diethylnitrosamine-treated rats included non-specific injury, fibrosis and cirrhosis, dysplastic nodules, early cancerous nodules and metastasis. There are 349 upregulated and 345 downregulated genes sharing among the above chosen tissues when compared with liver tissue of normal rats. The deregulated genes play various roles in diverse processes such as metabolism, transport, cell proliferation, apoptosis, cell adhesion, angiogenesis and so on. Among which, 41 upregulated and 27 downregulated genes are associated with inflammatory response, immune response and oxidative stress. Twenty-four genes associated with glutathione metabolism majorly participating oxidative stress were deregulated in the development of liver cancer. There were 19 members belong to CYP450 family downregulated, except CYP2C40 upregulated.
In this study, we provide the global gene expression profiles during the development and progression of liver cancer in rats. The data obtained from the gene expression profiles will allow us to acquire insights into the molecular mechanisms of hepatocarcinogenesis and identify specific genes (or gene products) that can be used for early molecular diagnosis, risk analysis, prognosis prediction, and development of new therapies.
HCC is a heterogeneous disease in terms of etiology, biologic and clinical behavior. Meanwhile, hepatocarcinogenesis is a long-term, multistep process associated with changes in gene expression profiles. In the last several years, there have been important gains in our understanding of the pathogenesis of HCC and our appreciation of the critical oncogenic and tumor suppressor pathways involved in hepatocarcinogenesis [
Molecular profiling has been successfully used to identify candidate genes for HCC in human and animal model systems[
In this study, we established the rat model of liver cancer induced by DEN to explore the processes of initiation and progression of HCC[
Male Wistar rats weighing 120–150 g at the beginning of the experiments were obtained from SLAC Laboratory Animal Co. Ltd. (Shanghai). The animals were acclimatized to standard laboratory conditions (temperature 22–25°C, relative humidity 50–60%, and 12 hour photoperiods (lights on 07:00–19:00 h)) and were housed in stainless steel wire-mesh cages (five rats per cage). During the entire period of study, the rats were supplied with a semi-purified basal diet (Shanghai) and water ad lib. All experiments followed the Guide for the Care and Use of Laboratory Animals.
Briefly, ninety Wistar rats were randomly divided into two groups: the control and DEN group (DEN, Sigma Chemical Co. St Louis, MO; CAS 56-23-5; purity > 98%). After one week on a basal diet, rats belonging to the DEN group underwent intragastric administration of 1% aqueous solution of DEN (70 mg/kg) once a week, consecutively for 14 weeks. Animals that belonged to the control group received distilled water. There were ten rats in the control group. Daily food and water intakes were noted and the body weights of the animals from each group were recorded every second day. The rats were sacrificed with 25% (g/ml) urethane anesthesia (6 ml/kg) by intraperitoneal injection and sacrificed at different time points. At the end of the 2nd, 4th and 6th week after DEN-treatment, 3, 3, and 4 rats were sacrificed respectively at these time periods. From the end of the 8th week until the end of the 20th week after DEN-treatment, ten rats were sacrificed respectively every two weeks. Meanwhile one age-matched control rat was sacrificed. All the age-matched rats from the control group and rats of treatment groups were sacrificed on the same day.
Animals were chosen sequentially for necropsy. The entire liver was observed grossly and weighted. The selected liver tissues were observed for gross changes, divided into pieces of about 0.1 g, snap-frozen directly in liquid nitrogen and stored at -80°C prior to RNA isolation for microarray analysis. The remaining livers were preserved in 10% phosphate-buffered formalin. The liver tissue fixed in neutral formalin was embedded in paraffin, sectioned, and stained with hematoxylin and eosin (H&E). Histopathologic examinations of the liver sections were conducted by a pathologist and peer-reviewed.
Frozen liver sections were ground in a Mixer Mill mm 200 (Retsch GmbH and Co. KG, Haan, Germany) using pre-cooled stainless steel balls. Total RNAs were isolated from livers with Trizol Reagent (Invitrogen, CA) using manufacturer recommended procedures. The ratio of the optical densities from RNA samples measured at 260 and 280 nm was used to evaluate nucleic acid purity, and total RNA concentrations were determined by the absorbance at 260 nm. The quality of total RNA was estimated based on the integrity of 28S and 18S rRNA. RNA was separated using 1% agarose gel electrophoresis. Good RNA quality was indicated by 28S rRNA banding having twice the intensity of the 18S rRNA, without significant smearing of the rRNA bands. Samples of total RNA from livers of rats from the same time points were pooled for subsequent use in the GeneChip analysis. Prior to GeneChip analysis, the pooled total RNA samples were purified using the RNeasy Total RNA Mini Kit (Qiagen, Valencia, CA) according to manufacturer's instructions.
Biotin-labeled cRNA samples were used for hybridization of Affymetrix GeneChip Rat 230 2.0 arrays. The arrays were prepared according to the protocol supplied with the GeneChip Sample Cleanup module (P/N 900371, Affymetrix Inc., Santa Clara, CA). Briefly, 5 μg total RNA was used for cDNA synthesis with the SuperScript Choice System (Invitrogen Life Technologies, Carlsbad, CA) employing a T7-(d7)24 primer. After spin column purification, biotin-labeled cRNA was synthesized from the cDNA using the ENZO RNA Transcript Labeling Kit (Affymetrix Inc.). Spin column-purified cRNA was quality controlled using an Agilent 2100 Bioanalyzer and spectrophotometrically quantified. The cRNA (15 μg) was then fragmented in buffer supplied with the Cleanup Module and hybridized for 16 h at 45°C (Affymetrix Genechip Hybridization Oven 640). The microarrays were washed and stained with streptavidin-phycoerythrin (SAPE, Molecular Probes) on the Affymetrix Fluidics Station 450, including an amplification step according to the manufacturer's instructions. Fluorescent images were read using the Gene Array Scanner 3000. The raw data image files (DAT) were converted into RPT files using Affymetrix Microarray Suite (MAS) 5.0. In RPT files, the scan data from the 36 pixels per oligo set were averaged. Pooled RNAs of cirrhotic tissue, the dysplasia nodules, the early cancerous nodules and the cancerous nodules from the rats with lung metastases at the 12th, 14th, 16th, and 20th week after DEN-treatment and from normal control rat were processed, hybridized and analyzed individually.
The chips were scanned in an Agilent ChipScanner to detect hybridization signals. Average target intensity was set at 500 arbitrary units. Each array was assessed for quality and stability by examining replicated copies of the same gene at different locations on the array. To ensure the quality of the cRNA samples and of the Affymetrix GeneChips, quality control experiments were performed using test chips, and the same cRNA sample used in both the test chip and GeneChip.
With GeneChip Operating Software (GCOS) v1.2, dark and white spots, gradients and distortions were detected and corrected using the RPT file data. The GeneChip Rat Genome 230 2.0 Array provides the entire transcribed rat genome on a single array and enables scientists to obtain the most comprehensive view of the transcribed rat genome in order to make accurate biological conclusions. The Affymetrix Rat Genome 230 2.0 microarrays contain 31,000 probe sets corresponding to about 24,000 annotated rat genes and 6693 expressed sequence tags (ESTs). Each probe set is represented by 11–20 pairs of 25 mer oligonucleotides. Each probe pair consists of a perfect match oligo (PM) complementary to the cRNA target sequence and a mismatched oligo (MM). Using the MAS 5.0 statistical algorithms implemented in the Quality Controller software, the intensities of all 11–20 probe pairs were condensed to one intensity value per probe set associated with a statistical detection
The microarray data were analyzed using the microarray suite 5.0 software. First, using the present genes, those significantly deregulated between the DEN-treated and control groups were selected using a two-sample t-test with a p cutoff value of 0.001 in combination with n-fold regulation/ratio of means. This requires that the mean intensity of a gene has to be at least n times different between the treated and control sample group to be included. The cutoff was set at 2 times.
Secondly, genes designated present in treated samples but absent in controls, or vice versa, were determined, as these could be genes induced from or repressed to background expression levels, respectively, after treatment. From these genes, those discriminating between treated and control samples were again selected with a two-sample t-test (p < 0.001), combined with the requirement of an at least two-fold difference of the mean intensities for a given gene.
Scatter plots were used to visually examine the expressional level of genes between the control and DEN-exposed groups. Hierarchical dendrograms were drawn with the Cluster (2.0). It was created by clustering the genes according to their expression in response to the carcinogenic agent. Genes sharing similar expression profiles tended to be clustered together, and the location of a branch containing the genes can be considered a measure of how similar the gene expression was. Genes were selected for the construction of gene tree if the expression of the gene was two-fold greater or less in the treatments, relative to that in the corresponding control. The horizontal axis shows the clustering of the genes according to their expression across treatments; while the vertical axis showed the clustering according to their expression profile in the treatment.
The genechip probe array system only allows comparison of one treatment hybridizing with the probe set. In a comparison analysis, two samples were hybridized to two genechip probe arrays of the same type, they were compared against each other in order to detect and quantify changes in gene expression. One genechip was for baseline (control) and the other was for the experiment (treatment). Two sets of algorithms were generated and they were used to generate change significance and change quantity metrics for every probe set using Microarray Suite (MAS) version 5.0 (Affymetrix, CA). The change algorithm generated a Change p value and an associated fold-change value. The second algorithm gave a quantitative estimate of the change in gene expression in the form of Signal Log Ratio. In the present study, the level of gene expression can be regarded as increased if its Change p-value was less than 0.002 and the gene expression would be considered to be decreased if its Change p-value was greater than 0.997. This method has been used by other investigators. Fold change could be calculated with the following formula: fold change = 2(signal log ratio).
The differential expression of selected genes was further validated by real-time PCR with SYBR green-based detection (ABI) using gene-specific primer pairs that were run on an ABI 7000 fluorescent sequence detection system (Perkin-Elmer, Foster City, CA). It is known that only the cDNA of each target gene can be the template, owing to the fact all the primer sequences span one or more introns by design (Gene Runner). The rat housekeeping gene β-actin was used as the control. Quantitative values were obtained from the cycle number (Ct value) at which the increase in fluorescent signal (associated with exponential growth of PCR products) starts to be picked up by the laser detector of the detection system. Results, expressed as N-fold differences in target gene expression between the liver tissues of DEN-treated and normal rats and termed 'Ntarget' were determined using the formula: Ntarget = 2ΔCtsample (while ΔCtsample = ΔCtDEN - ΔCtNormal), where the ΔCtDEN and ΔCtnormal values of the sample were determined by subtracting the Ct value of the target gene from the average Ct value of the β-actin gene.
The histological changes of livers of the DEN-treated rats can be divided into three stages. Initially, from the 2nd to 8th week, non-specific injury occurred such as cellular swelling, fatty changes, necrosis, inflammatory infiltration and hepatocyte regeneration. On the 10th to the 14th week, significant liver fibrosis occurred. At the 10th week, the livers showed an quantitative increase in connective tissue, and encapsulation of regenerative nodules, while at the end of the 12th week, nodular cirrhosis could be seen macroscopically. At the 14th week, gray-white nodules, 3 mm to 5 mm in diameter, could be distinguished from the surrounding reddish brown cirrhosis nodules in the livers of 2/10 rats. These were histologically diagnosed as dysplastic nodules. From the 16th to the 20th week the number of nodules increased significantly. At the 16th week, nodules, 5 mm to 1.5 cm in diameter, could be distinguished in the livers of 8/10 rats, while at the 18th and the 20th week, gray-white nodules were present in the livers of all 20 rats. In addition, by the 20th week, abdominal cavity and lung metastases were observed in 2/10 rats. (Figure
Liver tissues were taken from the control and the DEN-treated rats. Total RNA was isolated from theses samples and used to prepare cRNA probes for hybridization with Affymetrix GeneChip Rat 230 2.0 arrays (Figure
The differential expression genes of cirrhotic tissue, dysplastic nodules, early tumors nodules and tumor nodules from rats with lung metastasis compared with the tissue from normal rats were screened and to determine the upregulated and downregulated DEGs. The results are shown in Table
Number of differential expression genes (DEGs) of liver tissues from DEN-treated rats compared with control.
| DEGs | 12th week | 14th week | 16th week | 20th week |
| Up-regulated DEGs | 681 | 857 | 1223 | 999 |
| Down-regulated DEGs | 687 | 732 | 1016 | 906 |
| Total | 1368 | 1589 | 2239 | 1905 |
NOTE: The words '12th week, 14th week, 16th week, 20th week' in the table indicate the cirrhosis tissue, dysplastic nodules, early cancerous nodules and cancerous nodules with metastasis, respectively.
The upregulated and the downregulated DEGs of cirrhosis tissue, dysplastic nodules, early tumor nodules and tumor nodules with lung metastasis vs the normal livers respectively were grouped into functional categories using gene ontology (GO) terms (Table
The deregulated DEGs sharing from cirrhosis to metastasis stage classified by the following screened GO.
| Functional Categories | Number Of Annotated Genes | ||||
|
|
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| 12th week | 14th week | 16th week | 20th week | 4 group | |
| Metabolism | 334/318 | 403/324 | 541/446 | 494/375 | 206/198 |
| Transport | 162/164 | 188/167 | 264/225 | 229/195 | 101/106 |
| Cell Growth | 129/88 | 161/86 | 207/104 | 218/88 | 89/51 |
| Cell Differentiation | 103/57 | 127/67 | 170/69 | 171/69 | 72/35 |
| Apoptosis | 87/50 | 113/48 | 128/62 | 153/46 | 59/28 |
| Angiogenesis | 12/11 | 15/13 | 23/15 | 25/14 | 9/6 |
| Cell Proliferation | 68/51 | 93/57 | 108/57 | 115/54 | 46/36 |
| Cell Migration | 13/12 | 15/15 | 30/13 | 25/13 | 10/8 |
| Cell Adhesion | 62/25 | 76/30 | 106/30 | 94/30 | 40/13 |
| Extracellular Matrix | 41/21 | 48/22 | 61/29 | 73/23 | 26/14 |
| Oxidative Stress | 31/19 | 41/24 | 43/27 | 50/26 | 23/12 |
| Immune Response | 30/25 | 34/23 | 38/35 | 35/28 | 19/16 |
| Inflammatory Response | 12/17 | 18/20 | 17/31 | 18/21 | 7/11 |
| Cytochrome | 19/30 | 23/28 | 29/45 | 25/38 | 11/20 |
| Signal Transduction | 140/106 | 165/111 | 243/129 | 213/115 | 87/59 |
| Protein Kinase | 114/67 | 128/77 | 193/95 | 185/73 | 65/38 |
| Proteasome | 17/6 | 20/8 | 25/7 | 19/6 | 13/4 |
NOTE: The words '12th week, 14th week, 16th week, 20th week' in the table indicate the cirrhosis tissue, dysplastic nodules, early cancerous nodules and cancerous nodules with metastasis, respectively. The word '4 group' means the DEGs sharing for the above 4 stages of liver tissues. The numbers up and down the line indicate the number of up-regulated and down-regulated DEGs respectively.
The histological changes during the hepatocarnogenesis in DEN-treated rat models were similar to those seen in humans, including non-specific damage, fibrosis, cirrhosis, dysplastic nodules, early tumorous nodules, progression and metastasis, which appeared to be sequential events. The processes of chronic inflammation, fibrosis and cirrhosis are closely related to liver cancer, while cirrhosis was considered as the precancerous lesions. Therefore, the co-expression of deregulated genes among these four stages might suggest they play key roles in the development of hepatocellular carcinoma. Among upregulated DEGs sharing from cirrhosis to metastasis, there were 246 known genes, 39 translocation loci, 51 inferred genes and 13 unkown genes; while among downregulated DEGs sharing from cirrhosis to metastasis, there were 215 known genes, 48 translocation loci, 63 inferred genes and 19 unkown genes (see additional file
Cellular proliferation, apoptosis, adhesion, migration and agiogenesis all play important roles in carcinogenesis. Extracellular matrix (ECM) is important for the growth and progression of hepatocarcinogenesis (see additional file
The down-regulated DEGs sharing from cirrhosis to metastasis sorted out by the following GO function.
| Gene Symbol | Gene Title | GO |
| COL18A1 | procollagen, type XVIII, alpha 1 | 1–6 |
| CXCL12 | chemokine (C-X-C motif) ligand 12 | 1,2,4,5 |
| KDR | kinase insert domain protein receptor | 1,4,6 |
| SERPINA3K | serine (or cysteine) peptidase inhibitor, clade A, member 3K | 1,2,5 |
| ANG1 | angiogenin, ribonuclease A family, member 1 | 1,5 |
| RNASE4 | ribonuclease, RNase A family 4 | 1,5 |
| C5 | complement component 5 | 2,4 |
| CML4 | Camello-like 4 | 3 |
| ENPP2 | ectonucleotide pyrophosphatase/phosphodiesterase 2 | 3 |
| GPHN | gephyrin | 3 |
| IGFALS | insulin-like growth factor binding protein, acid labile subunit | 3 |
| LIN7A | lin-7 homolog a (C. elegans) | 3 |
| AZGP1 | alpha-2-glycoprotein 1, zinc | 3,5 |
| PROC | Protein C | 2 |
| PTPRD | protein tyrosine phosphatase, receptor type, D | 3 |
| PVRL3_predicted | poliovirus receptor-related 3 (predicted) | 3 |
| SORL1 | sortilin-related receptor, LDLR class A repeats-containing | 4,5 |
| TGFBI | transforming growth factor, beta induced | 3,4,6 |
| RB1 | retinoblastoma 1 | 2,3,5 |
| EGFR | epidermal growth factor receptor | 2–6 |
| EGF | epidermal growth factor | 2,5,6 |
| IGF1 | Insulin-like growth factor 1 | 2,5,6 |
| HNF4A | Hepatocyte nuclear factor 4, alpha | 2,5 |
| BCL6_PREDICTED | B-cell leukemia 6 (predicted) | 2,5 |
| PEMT | phosphatidylethanolamine N-methyltransferase | 2,5 |
| LRP1 | low density lipoprotein receptor-related protein 1 | 2,5 |
| RGN | regucalcin | 2,5 |
| SGPP1 | sphingosine-1-phosphate phosphatase 1 | 2 |
| NR1D2 | nuclear receptor subfamily 1, group D, member 2 | 2 |
| GHR | Growth hormone receptor | 2 |
| CYP2E1 | cytochrome P450, family 2, subfamily e, polypeptide 1 | 2 |
| C4BPB | complement component 4 binding protein, beta | 2 |
| C6 | complement component 6 | 2 |
| FAAH | fatty acid amide hydrolase | 2 |
| NR0B2 | nuclear receptor subfamily 0, group B, member 2 | 2 |
| PCSK9 | proprotein convertase subtilisin/kexin type 9 | 2 |
| UNG | uracil-DNA glycosylase | 2 |
| CEBPA | CCAAT/enhancer binding protein (C/EBP), alpha | 5 |
| PCAF | p300/CBP-associated factor | 5 |
| CFB | complement factor B | 5 |
| DBP | D site albumin promoter binding protein | 5 |
| ADRA1B | adrenergic receptor, alpha 1b | 5 |
| FABP1 | fatty acid binding protein 1, liver | 5 |
| VIPR1 | vasoactive intestinal peptide receptor 1 | 5 |
| ID4 | Inhibitor of DNA binding 4 | 5 |
| NOX4 | NADPH oxidase 4 | 5 |
| AMY1 | amylase 1, salivary | 6 |
| GPLD1 | glycosylphosphatidylinositol specific phospholipase D1 | 6 |
| SMOC1 | SPARC-related modular calcium binding protein 1 | 6 |
NOTE: The numbers from 1–6 indicate GO terms: angiogenesis, apoptosis, cell adhesion, cell migration, cell proliferation and extracellular matrix, respectively.
The rat models of liver cancer induced by DEN occurred following chronic injury, regenetation, fiborsis and cirrhosis. Elements of the inflammatory response, immune response and oxidative stress were also involved in the process of hepatocarcinogenesis. Tables
The up-regulated DEGs sharing from cirrhosis to metastasis stage relating to the following GO process.
| Gene symbol | Gene name | GO |
| CCL21B | chemokine (C-C motif) ligand 21b (serine) | 1–2 |
| CD276 | CD276 antigen | 1–2 |
| SPP1 | secreted phosphoprotein 1 | 1–2 |
| CD24 | CD24 antigen | 1 |
| C1QG | complement component 1, q subcomponent, gamma polypeptide | 1 |
| CD74 | CD74 antigen | 1 |
| HLA-DMA | major histocompatibility complex, class II, DM alpha | 1 |
| HLA-DMB | major histocompatibility complex, class II, DM beta | 1 |
| DEFB1 | defensin beta 1 | 1 |
| FCGR3 | Fc receptor, IgG, low affinity III | 1 |
| PLSCR1 | phospholipid scramblase 1 | 1 |
| PRNP | prion protein | 1 |
| RT1-BA | RT1 class II, locus Ba | 1 |
| RT1-CE5 | RT1 class I, CE5 | 1 |
| RT1-DA | RT1 class II, locus Da | 1 |
| RT1-DB1 | RT1 class II, locus Db1 | 1 |
| RT1-BB | RT1 class II, locus Bb | 1 |
| ANXA1 | annexin A1 | 2 |
| FABP4 | fatty acid binding protein 4, adipocyte | 2 |
| S100A8 | S100 calcium binding protein A8 | 2 |
| S100A9 | S100 calcium binding protein A9 | 2 |
| CDC2A | cell division cycle 2 homolog A | 3 |
| EGR1 | early growth response 1 | 3 |
| CRYAB | crystallin, alpha B | 3 |
| CCND1 | cyclin D1 | 3 |
| CD36 | cd36 antigen | 3 |
| GCLC | glutamate-cysteine ligase, catalytic subunit | 3 |
| GGT1 | gamma-glutamyltransferase 1 | 3 |
| GPX2 | glutathione peroxidase 2 | 3 |
| GPX3 | glutathione peroxidase 3 | 3 |
| GSR | glutathione reductase | 3 |
| GSS | glutathione synthetase | 3 |
| HSPCB | heat shock 90 kDa protein 1, beta | 3 |
| LAMC1 | laminin, gamma 1 | 3 |
| MTAP2 | microtubule-associated protein 2 | 3 |
| NOL3 | nucleolar protein 3 (apoptosis repressor with CARD domain) | 3 |
| NQO1 | NAD(P)H dehydrogenase, quinone 1 | 3 |
| PDLIM1 | PDZ and LIM domain 1 (elfin) | 3 |
| SLC25A4 | solute carrier family 25 | 3 |
| TXNRD1 | thioredoxin reductase 1 | 3 |
NOTE: The numbers from 1–3 indicate immune response, inflammatory response and oxidative stress, respectively.
The down-regulated DEGs sharing from cirrhosis to metastasis stage relating to the following GO process.
| Gene Symbol | Gene Title | GO |
| C5 | complement component 5 | 1–2 |
| IL4RA | interleukin 4 receptor, alpha | 1–2 |
| MBL2 | mannose binding lectin 2 (protein C) | 1–3 |
| NOX4 | NADPH oxidase 4 | 2–3 |
| ATRN | Attractin | 2–3 |
| C1S | complement component 1, s subcomponent | 1 |
| C4BPB | complement component 4 binding protein, beta | 1 |
| AZGP1 | alpha-2-glycoprotein 1, zinc | 1 |
| C6 | complement component 6 | 1 |
| CXCL12 | chemokine (C-X-C motif) ligand 12 | 1 |
| MX2 | myxovirus (influenza virus) resistance 2 | 1 |
| OAS1 | 2',5'-oligoadenylate synthetase 1, 40/46 kDa | 1 |
| RT1-S3 | RT1 class Ib, locus S3 | 1 |
| VIPR1 | vasoactive intestinal peptide receptor 1 | 1 |
| APOA2 | apolipoprotein A-II | 2 |
| BCL6_predicted | B-cell leukemia/lymphoma 6 (predicted) | 2 |
| KLKB1 | kallikrein B, plasma 1 | 2 |
| PROC | protein C | 2 |
| PTGER3 | Prostaglandin E receptor 3 (subtype EP3) | 2 |
| MEOX2 | mesenchyme homeobox 2 | 3 |
| CA3 | carbonic anhydrase 3 | 3 |
| ABCB11 | ATP-binding cassette, sub-family B (MDR/TAP), member 11 | 3 |
| ALAD | aminolevulinate, delta-, dehydratase | 3 |
| CYP2E1 | cytochrome P450, family 2, subfamily e, polypeptide 1 | 3 |
| EGFR | epidermal growth factor receptor | 3 |
| HAO1 | hydroxyacid oxidase 1 | 3 |
| HNF4A | Hepatocyte nuclear factor 4, alpha | 3 |
NOTE: The numbers from 1–3 indicate immune reponse, inflammatory response and oxidative stress, respectively.
The DEGs commonly present from stages of cirrhosis to metastasis were clustered by Cluster software according to the values of log2 ratio(Figure
The DEGs detected through Affymetrix genechip analysis were confirmed in the selected tissue of DEN-treated and control rats by real-time RT-PCR, as shown in Figure
Primer sequences.
| Primer name | Sequence | Product size (bp) |
| TWEAKR forward | 5' GTGTTGGGATTCGGGTTGGTG 3' | 221 |
| TWEAKR reverse | 5' CTAAGAGCGCCTCCCAGAATGG3' | |
| ARHGAP24 forward | 5' TAGCATCAACTCCTTTCATCCC3' | 232 |
| ARHGAP24 reverse | 5' TCCTTGACAAGGTTTGCCTG3' | |
| RTN4 forward | 5' TGGTGGTTCTGTGCGGTGTG3' | 248 |
| RTN4 reverse | 5' AGCTGTCCTTCACAGGTTC3' | |
| KDR forward | 5'TTTCTGCTCCTTGGTCCTGG3' | 222 |
| KDR reverse | 5' TGTCGCCACACTCAGTCAC 3' | |
| EGFR forward | 5' CCTTAGCCGTCCTGTCCAAC 3' | 235 |
| EGFR reverse | 5' TTGGGACAGCTCGGATCAC 3' | |
| CXCL12 forward | 5' CCCTGCCGATTCTTTGAG3' | 175 |
| CXCL12 reverse | 5' TGTTTAAGGCTTTGTCCAGG3' | |
| Beta-actin forward | 5' AGGGTGTGATGGTGGGTATGGG 3' | 230 |
| Beta-actin reverse | 5' TTCACGGTTGGCCTTAGGGTTC 3' |
Some major drawbacks limit the usefulness of studies regarding molecular pathogenesis in HCC. First, few studies analyze the genetic and genomic alterations that emerge at different time points during the entire progressive process of the disease. Second, the limited size of the studies is often a factor that undermines the capability to provide consistent genomic data[
The annotated information of the DEGs show that extensive and diverse biological processes and molecular functions are involved in hepatocaricnogenesis. Most of the DEGs are involved in metabolism and transport, indicating that significant alterations occurred in the process of metabolism and transport during the developmnet of HCC. For example, tumor cells always perform anaerobic glycolysis, even when there is an adequate oxygen supply[
Fat metabolism have significant changes in the process of tumorigenesis, e.g. a high fat diet was related to the development of many tumors [
In the present study, some enzymes related to the glutathione (GSH) metabolism were found to be significantly altered. For example, the expressional level of Gstm3 (glutathione S-transferase, mu type 3) decreased in all stages of hepatocarcinogenesis, while the expression levels of of enzymes increased, which including Glul (Glutamate-ammonia ligase), Gclc (Glutamate-cysteine ligase, catalytic subunit), GPX2 (Glutathione peroxidase 2), GPX3 (Glutathioneperoxidase 3), GSR (Glutathione reductase), Yc2 (Glutathione S-transferase Yc2 subunit), Gstm5 (Glutathione S-transferase, mu 5), Gstp1 (Glutathione-S-transferase, pi 1) and GSS (Glutathione synthetase). Some studies reported that GSH and the associated enzymes were considered to promot the tumor transformation from dysplastic nodules and take part in the development and progression of hepatocarcinomas[
Many other enzymes associated with metabolism are involved in the defense and stress reaction, such as oxidative stress. For example, AKR1B7 (aldo-keto reductase family 1, member 7) takes part in the detoxification of oxides, such as aldehyde. During the detoxification of aldehyde, the expressional level of AKR1B7 mRNA increased. There are five binding sites with NF-κB at the 5' upstream region of the AKR1B7 gene, and oxidative stress upregulates the expression of AKR1B7 mediated by NF-κB[
Cytochrome P450s (CYPs) are key enzymes in tumorigenesis, taking part in the activation and inactivation of chemotherapeutic agents in tumor tissues[
In addition to the deregulated expression of metabolism associated genes, we noticed that among the DEGs in the hepatocarcinogenesis of rat models, some known tumor-associated genes, such as Rb1 and Myc, showed deregulated expression occurring at all the stages of hepatocarcinogenesis. Their persisting activation or deactivation could induce the tumor phenotype, as well as play roles at the later stage of progression and metastasis. Meanwhile, some known metastasis-associated genes are found deregulated at the promotion stage of tumor development. For example, the expression level of Ndrg2 and Hrasls3 (HRAS like suppressor 3) decreased at all stages compared to the normal livers, while the expression level of Nme1 (expressed in non-metastatic cells 1) increased. Generally, it was thought that genes involved in the development of carcinoma activation participated at the early stage, while genes participating in the metastasis were activated at the latter stage of tumor progression[
As we known, the important characteristic of malignant tumor cells is the capability of invading the vicinity, forming metastasis foci at the remote organ, overcoming the host's control over the microenvironment[
It is worthy of note that the expression of the annexin family members and calcium-binding proteins are deregulated in this rat model of liver cancer. In our study, the expressional level of Annexin A1, A2, A3, A5 and A7 increased compared with the normal liver tissue. Annexins consist of a conserved protein family. Annexin A2 is closely associated with cell division regulation and tumor growth, and is deregulated in many tumors[
The majority of HCCs slowly unfold against a background of chronic hepatitis and cirrhosis, which can be considered as preneoplastic conditions of the liver. Chronic hepatitis is characterized by persistent inflammation, cytokine and oxidative stress-mediated hepatocyte death and active proliferation of residual hepatocytes to replace the lost parenchyma[
In conclusion, analysis of gene expression profile is a useful tool to provide new clues and produce new research targets in the hepatocarcinogenesis field. In the present study, a rat model of liver cancer was established. We have listed the deregulated expression genes in the process from cirrhosis to liver cancer in the DEN-treated rat model. As indicated in the literature, this model shows that cirrhosis is a precancerous lesion of the liver. Although we only discuss some parts of the great quantity of information in this study, much unknown information remains. Functional analysis of these genes revealed discrete expression clusters, including cell proliferation, protein synthesis, and hepatocyte-specific functions. We still need to discern the key genes playing core roles in the promotion and progression of liver cancer. In this article, we focused our attention on the global molecular events that occurred in DEN-treated rats (and probably represent the earliest ones that start the multistep process of hepatocarcinogenesis). Additional information may be mined from this and similar studies to provide clues to many areas including the very important search for diagnostic markers, therapy targets and prognosis prediction markers.
HCC: Hepatocellular carcinoma; DEN: diethylnitrosamine; DEGs: differential expression genes.
The authors declare that they have no competing interests.
YFL wrote the manuscript. BSZ performed the validation of genes. HLZ and XJZ established the animal model. YHL prepared the tissue slides. JZ helped write the manuscript. JPZ, ZQF and XHG participated in the design of the study and helped to draft the manuscript. All authors read and approved the final manuscript.
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This study was supported by a grant from the Science Foundation of the Ministry of Health of China (No. wkj2004 -2-12). The authors thanks for technician Yuhua Li for assistance with preparation of tissue slices. We would like to thank Shanghai Biochip Co., Ltd. And the National Engineering Center for Biochip at Shanghai for the operation of the Affymetrix genechips.