Characterization of hepatocarcinogenesis by transcriptome and exome sequencing via chronic HBV X expression and aflatoxin B1 exposure in immortal hepatic cell

doi:10.21203/rs.3.rs-1747851/v1

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Characterization of hepatocarcinogenesis by transcriptome and exome sequencing via chronic HBV X expression and aflatoxin B1 exposure in immortal hepatic cell

https://doi.org/10.21203/rs.3.rs-1747851/v1

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Backgrounds:

Hepatitis B virus (HBV) and aflatoxin B1 (AFB1) exposure have synergistic effects on the occurrence and progression of hepatocellular carcinoma. In this study, we constructed HBV X (HBX) expression and AFB1 exposure models promoting hepatocarcinogenesis.

Methods

The HBX-overexpressing HL-7702 cell model was constructed by lentivirus transfection. In vitro experiments and transcriptome sequencing determined HBX functions and transcriptional traits. An interventional system containing AFB1, NAPDH, liver microsomes and medium was constructed to mimic AFB1 exposure. Transcriptome and exome sequencing identified transcriptional traits and alterations in exon regions. Differentially expressed (differential) RNAs involved in HBX overexpression, AFB1 exposure and the AFB1 + HBX exposure groups were validated by real-time polymerase chain reaction assays.

Results

HBX overexpressing showed decreased capacity for cellular migration, proliferation, invasion and clonal formation compared with control cells (P ≤ 0.05). Mutational “fingerprints” of AFB1 (TP53-R249S, AGG > AGT) and novel mutations at codons 247, 249 and 250 were unveiled in the model. Transcriptome sequencing identified the following differential RNAs: 285 mRNAs, 9 miRNAs, 78 long non-coding RNAs (lncRNAs) and 117 circular RNAs (circRNAs) in HBX group; 585 mRNAs, 143 miRNAs, 208 lncRNAs and 58 circRNAs in the AFB1 group; and 564 mRNAs, 126 miRNAs, 233 lncRNAs and 61 circRNAs in the AFB1 + HBX group. Enrichment analysis suggested the involvement of differential RNAs in angiogenesis, oxidative phosphorylation and ribosome pathways, etc. Additionally, competing endogenous RNA networks of differential mRNA-miRNA-lncRNA/circRNA cross-talk were constructed in the above groups. Exome sequencing unveiled 1061 (AFB1) and 3601 (AFB1 + HBX) single nucleotide variations, 30 (AFB1) and 40 (AFB1 + HBX) insertion and deletion alterations, 1068 (AFB1) and 138719 (AFB1 + HBX) copy number variations. The number of alterations was consistently higher in the AFB1 + HBX group compared to the AFB1 group.

Conclusions

We successfully constructed a cellular model mimicking HBX expression, AFB1 exposure and provided novel insights for understanding AFB1/HBX-related hepatocarcinogenesis.

HBX

AFB1

transcriptome

exome

hepatocarcinogenesis

Primary liver cancer is one of the most malignant tumors worldwide, with an incidence of 850 000 new cases each year, and hepatocellular carcinoma (HCC) accounts for approximately 90% of all cases (1). HCC ranks as the sixth most common malignancy and the third cause of malignancy-related deaths (2). Well-known risk factors for developing HCC include infection by hepatitis B and C virus (HBV), cirrhosis, excessive alcohol assumption, ingestion of aflatoxin B1 (AFB1), tobacco, metabolic syndrome and diabetes mellitus (1, 3, 4). HBV consists of the family of Hepadnaviridae, which encompasses small, non-cytopathic viruses with related circular DNA (rcDNA) genomes (5). After HBV infection, the rcDNA genome changes into covalently closed circular DNA in the nucleus of infected cells (6). Chronic HBV infections usually endure the whole lifetime and indicates a high risk for hepatic fibrosis, cirrhosis and eventually HCC (7, 8). The HBV genome is composed of four overlapping open reading frames that encode a total of seven proteins via alternative start codons (6). HBV X (HBX) is not only the smallest protein found in mammalian hepadnaviruses, but has also proved to play a variety of functions in the viral life cycle involving interactions with cells and HBV-related HCC (9–12). Lucifora et al. demonstrated that HBX expression positively contributes to HBV transcription and replication in both in vitro and in vivo experiments (13, 14). Nonetheless, several in vitro studies have documented that HBX may act as a tumor suppressor gene (15). Schuster et al. reported that HBX suppressed focus formation by cooperating oncogenes (16).

Approximately 4.6–28.2% of all HCC cases worldwide have been attributed to AFB1 exposure (17). Furthermore, the combination of HBV and AFB1 together leads to over 30 times increased risk for HCC (18). Epoxidation of AFB1 to 8,9-epoxide by CYP450 enzymes plays a crucial role in the progress of HCC, followed by binding of AFB1 to guanine bases especially on the third base of codon 249 of p53 to form AFB1-N7-guanine (17). Previous research has indicated that a G-to-T mis-sense mutation at the third base of codon 249 leads to the substitution of arginine for serine (R249S), which has obvious influence on p53 and therefore prevents the progression of the cell cycle (17). In the past decade, further understanding of the molecular pathogenesis of HCC has been achieved: each HCC has an average of 40 genomic aberrations affecting tumor initiation and progression (19). Common mutations include mutations in telomere reverse transcriptase (TERT), mutations in catenin beta 1, mutations (inactivation) of p53, mutations in mammalian target of rapamycin signaling and oxidative stress pathway activation, and others (1). However, unknown mutation loci relevant to both HBV and AFB1 exposure may still be present.

This study initially constructed a model of HBX overexpression in a human immortalized normal liver cell line. Then, we constructed two additional models: one model used chronic AFB1 exposure (3 months); the second model utilized both HBX overexpression and chronic AFB1 exposure for 3 months. We report in the present study the genetic and transcriptional characteristics of the above groups that mimic HBV-AFB1 relevant hepatocarcinogenesis.

Cell line culture and plasmid transfection

The immortal hepatocyte HL-7702 cell line was purchased from Shanghai Cell Resource Center of the Chinese Academy of Sciences. HL-7702 cells were cultured in DMEM medium with 10% fetal bovine serum (Biological Industries), and 1% penicillin and streptomycin under 5% CO₂ in a 37℃ incubator. The HBX overexpression plasmid was purchased from GeneCopoeia (CS-GS3365-Lv244). Lipo6000 reagent was purchased from Beyotime (C0526). Hygromycin B (H8080, Solarbio) was used for screening of cells stably expressing HBX.

HBX expression validation and in vitro experiments

Primers for HBX were as follows: forward, CCCAGCAATGTCAACGACCGAC; reverse: CCCAGCAATGTCAACGACCGAC. Internal control primers for GAPDH, amplification methods and western blot procedures were previously described (63). Antibody against HBX and secondary antibody were purchased from Abcam (ab39716) and ZSGB-Bio (ZB-2301), respectively.

Cell proliferation assays were performed using the MTT method following the manufacturer’s protocol. Clonal formation assays were performed using 2000 cells per plate of 6-plate incubated for one week. Cell migration was calculated using wound healing assays after 48 h in 6-well plates (Corning). Cell invasion assays were performed using Transwell plates (3422, Corning) and Matrigel matrix (356234, Corning) using 1 x 10⁵ cells/plate.

Intervention system of AFB1 exposure in vitro

An interventional system was constructed to mimic AFB1 exposure in vitro, using the following components: 1200 ml total volume with 10% DMEM medium + AFB1 (A6636, Sigma) + NADPH (6-1910, CHI Scientific) + rat liver microsomes (6-1001, CHI Scientific) in 12-well plates (64, 65). The concentrations of AFB1 used were 2, 1, 0.5, 0.2, 0.1, 0.05, 0.02, 0.01 and 0.005 mmol/L (20 mg/ml by producer), with NADPH 2 times volume (ul) than AFB1. Cells were cultured with 10% DMEM medium after incubation of 10% DMEM medium with intervention system for 24 h.

After exposure to the intervention system for 1, 2 and 3 months, DNA was extracted from the cells for detection of the mutational spectrum of codon 249 and neighboring codons of p53 by Sangon Biotech. Then, whole transcriptome sequencing of the HBX overexpression group, AFB1 intervention group (3 months) and the HBX+AFB1 intervention group (3 months) was performed by Novogene. Exon sequencing was also performed for the AFB1 and HBX+AFB1 intervention groups (3 months). Quality control, library construction, transcriptional sequencing and bioinformatic analysis were also performed by Novogene. Bioinformatic analysis included differential and cluster analysis, gene ontology (GO) terms, including biological process (BP), cellular component (CC) and molecular function (MF), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, and target prediction for microRNAs (miRNAs), long non-coding RNAs (lncRNAs) and circular RNAs (circRNAs). Targets of lncRNAs were predicted on the basis of co-location and co-expression relationships.

Validation of differential mRNAs, miRNAs, lncRNAs and construction of competing endogenous RNA (ceRNA) networks

Some differential RNAs, including mRNAs, miRNAs and lncRNAs in the three groups (HBX overexpression, AFB1 intervention and HBX+AFB1 intervention) were validated by real time polymerase chain reaction (PCR) assays. Primers for these RNAs were designed by Sangon Biotech and are shown in Supplementary Table 1-3. Thereafter, two ceRNA networks, one including differential mRNAs, miRNAs and lncRNAs, the other including differential mRNAs, miRNAs and circRNAs, were constructed for each group based on the following relationships: 1) miRNA negatively associated with mRNA, lncRNA and circRNA, and mRNA positively or negatively associated with lncRNA and circRNA; 2) prediction of potential mRNA targets of miRNAs, lncRNAs and circRNAs.

Statistical analysis

P ≤ 0.05 was considered statistically significant. Unpaired t-tests were used to compare differences between two groups. EdgeR software was used for differential expression analysis; clusterProfiler software was used for enrichment analysis; and miranda software was used for prediction of miRNA targets. Find circ software was used for identification of circRNAs. SAMtools, control-FREEC and muTeck software were used for analysis of single nucleotide polymorphisms (SNPs), somatic single nucleotide variations (SNVs) and variations of insert and deletion (InDel). Sequencing was performed on the Illumina PE150 platform.

Construction of an HBX overexpression cell line and in vitro experiments

The HL-7702 cell line was firstly transfected with HBX lentivirus fluid. Fluorescence microscopy captured fluorescence intensity after Hygromycin B screening (Figure 1A). Then, real time PCR and western blot methods were used to detect HBX expression and found that HL-7702 cells with HBX stable-expression had been successfully constructed (all P<0.05, Figure 1B-C). We then conducted in vitro experiments and found that cellular invasion, clonal formation, proliferation and migratory capacity were reduced compared to the control group (all P<0.05, Figure 1D-G).

Determination of differential RNAs by transcriptional sequencing and enrichment analysis after HBX overexpression

Whole transcriptome sequencing identified 285 differential mRNAs, such as MEMO1, PDE10A and PSMD1, with 101 up-regulated mRNAs and 184 down-regulated mRNAs (Figure 2A), and nine differential miRNAs, such as hsa-miR-625-3p, hsa-miR-3615 and hsa-miR-1268a, with four up-regulated miRNAs and five down-regulated miRNAs (Figure 2B). Enrichment analysis of differential mRNAs in GO terms revealed angiogenesis, blood vessel morphogenesis and extracellular matrix, among others, as enhanced GO terms (Figure 2C), while enriched KEGG pathways included axon guidance, drug metabolism (CYP450 and other enzymes) and steroid hormone biosynthesis (Figure 2D). On the other hand, GO terms in miRNA targets consisted of nervous system development, single-organism process (BP, Figure 2E), intracellular, cytoplasm, organelle (CC, Figure 2F), binding, protein binding (MF, Figure 2G), and others. However, there were no significant KEGG pathways according to Q values (Figure 2H).

Whole transcriptome sequencing identified 78 differential lncRNAs, such as LINC00511, MIR924HG and LINC80, with 39 up-regulated lncRNAs and 39 down-regulated lncRNAs (Figure 3A), and 117 differential circRNAs, such as hsa_circ_0000657, hsa_circ_0000660 and hsa_circ_0007710, with 56 up-regulated circRNAs and 61 down-regulated circRNAs (Figure 3B). Enrichment analysis of co-expression targets in GO terms included wound healing, leukocyte migration, blood vessel development, and others (Figure 3C), and enriched KEGG pathways included pentose and glucuronate interconversions and extracellular matrix-receptor interaction (Figure 3D). On the other hand, GO terms of co-location targets consisted of serotonin receptor activity, response to xenobiotic stimulus and organic cation transmembrane transporter activity (Figure 3E), and KEGG pathways included steroid hormone biosynthesis and drug metabolism (CYP450 and other enzymes, Figure 3F). Meanwhile, GO terms of circRNAs included nuclear export, cellular protein localization, membrane-bounded organelle, and others, while KEGG pathways included only RNA transport (Figure 3G-H). Clustered heatmaps of mRNAs, miRNAs, lncRNAs and circRNAs are shown in Supplementary Figure 1A-D.

Validation of differential RNAs and construction of ceRNA networks associated with HBX expression

Real time PCR results demonstrated that ten differential mRNAs, including ACACA, GIPC1, ITPR3, MEMO1, PLCB1, and others, were consistently validated as showing differential expression in the HBX expression group (all P≤0.05, Supplementary Figure 2A-J). Concerning lncRNAs, all of the five lncRNAs, including LINC0080, LINC00511, MIR924HG, PTK7-215 and SLC2A3-205, were consistently validated with differential expression in the HBX expression group (all P≤0.05, Supplementary Figure 2K-O). In terms of miRNAs, eight miRNAs, such as miR-625-3P, miR-3625 and miR-1268a, were consistently validated with differential expression, except for miR-3180-3P (all P≤0.05, Figure 4A-I).

In the mRNA-miRNA-lncRNA constructed ceRNA network, mRNAs including FAM65A, PDE7A, PAK4, IP6K1, CD99L2 and DNMT3B; miRNAs including miR-625-5P, miR-3180, miR-3180-3P and miR-767-5P; and lncRNAs including AC116366.3, LNC00511, LINC00466 and LINC00670, were enrolled in the network (Figure 4J). At the same time, in the mRNA-miRNA-circRNA constructed ceRNA network, mRNAs including RPL29, RPTOR, IP6K1 and PDCD11; miRNAs including miR-1268a, miR-3180, miR-3180-3P and miR-6842-3P; and circRNAs including circ-0003726, circ-0002153 and circ-0004432, were enrolled in the network (Figure 4K).

Determination of interventional concentrations and sequencing of codon 249 and neighboring codons

Firstly, we set interventional gradients for AFB1 at 2, 1, 0.5, 0.2, 0.1 and 0.05 mmol/L. Then, the growth status of the above groups was assayed 24 h, 48 h and 72 h after intervention for 24 h (Supplementary Figure 3A-F). With the exception of AFB1 at 0.05 mmol/L for 72 h, cells did not present in a proliferative status. Therefore, we selected 0.05, 0.02, 0.01 and 0.005 mmol/L as the interventional gradients to be used in the next experiment. After 1 month of AFB1 intervention, sequencing of codon 249 and neighboring codons did not find any mutations in codons 247–250 in the HBX overexpression group (Figure 5A). In control group, however, AGG>AGA mutations were observed in codon 249 at 0.05 mmol/L, AGG>AGA mutations were found in codon 249, CCC>TCC mutations were found in codon 250 at 0.02 mmol/L, and AGG>AGA mutations were observed in codon 249 at 0.005 mmol/L (Figure 5B). In the HL-7702 group, AGG>AGT mutations were found in codon 249 at AFB1 concentrations of 0.05 and 0.02 mmol/L, while CCC>ACC mutations were observed in codon 250 at 0.01 mmol/L (Figure 5C).

After 2 months of AFB1 intervention, sequencing found that there were consistent AGG>AGC mutations in codon 249 for three groups: 0.005 mmol/L in the HBX overexpression group, 0.05 mmol/L in the control group, and 0.02 mmol/L in the HL-7702 group (Supplementary Figure 4A-C). After 3 months of AFB1 intervention, sequencing found AAC>AGC mutations in codon 247 at 0.02 mmol/L and AGG>AGC mutations in codon 249 at 0.005 mmol/L (Supplementary Figure 5A). In the control group, there were CCC>GCC mutations in codon 250 at 0.05 mmol/L (Supplementary Figure 5B). In the HL-7702 group, there were AGG>AGC mutations in codon 249 at 0.05 mmol/L, AAG>AGC mutations in codon 247 at 0.02 mmol/L, and AGG>AGA mutations in codon 249 at 0.01 mmol/L (Supplementary Figure 5C).

Determination of differential RNAs by transcriptional sequencing and enrichment analysis of differential RNAs after AFB1 intervention

Whole transcriptome sequencing identified 585 differential mRNAs, such as TRPS1, OXR1 and PEPD, with 176 up-regulated mRNAs and 409 down-regulated mRNAs (Figure 6A), and 143 differential miRNAs, such as hsa-miR-122-5p, hsa-miR-122b-3p and hsa-miR-296-3p, with 73 up-regulated miRNAs and 70 down-regulated miRNAs in AFB1 group (Figure 6C). GO terms of differential mRNAs were enriched in single-organism process, regulation of cell proliferation, cytoplasm, protein binding, and others (Figure 6B). GO terms of targets of differential miRNAs were enriched in cellular metabolic process, cell process, intracellular part, protein binding, and others (Figure 6D). Whole transcriptome sequencing identified 564 differential mRNAs, such as FERMT2, XRCC6 and PYGL, with 183 up-regulated mRNAs and 381 down-regulated mRNAs (Figure 6E), and 126 differential miRNAs, such as hsa-let-7b-5p, hsa-let-7f-1-3p and hsa-miR-122-5p, with 64 up-regulated miRNAs and 62 down-regulated miRNAs in the AFB1+HBX group (Figure 6G). GO terms of differential mRNAs were enriched in anatomic structure morphogenesis, developmental process, and others (Figure 6F). GO terms of targets of differential miRNAs were enriched in cellular process, single-organism process, intracellular part, protein binding, and others (Figure 6H). Clustered heatmaps of mRNAs, miRNAs, lncRNAs and circRNAs are shown in Supplementary Figure 1E-H.

Whole transcriptome sequencing identified 208 differential lncRNAs, such as LINC02208, AL592435.1 and MIR924HG, with 86 up-regulated lncRNAs and 122 down-regulated lncRNAs, and 58 differential circRNAs, such as has_circ_0042103, hsa_circ_0000825 and hsa_circ_0000816, with 20 up-regulated circRNAs and 38 down-regulated circRNAs in the AFB1 group (Supplementary Figure 6A, C). GO terms of targets of differential lncRNAs were enriched in mitochondrion organization, mitochondrial envelop, and others. GO terms of targets of differential circRNAs were enriched in regulation of cell cycle, nucleic acid metabolism process, enzyme binding, and others (Supplementary Figure 6B, D). Whole transcriptome sequencing identified 233 differential lncRNAs, such as LINC106, LINC125 and LINC142, with 112 up-regulated lncRNAs and 121 down-regulated lncRNAs, and 61 differential circRNAs, such as hsa_circ_0000117, hsa_circ_0000119 and hsa_circ_0000504, with 21 up-regulated circRNAs and 40 down-regulated circRNAs in the AFB1+HBX group (Supplementary Figure 6E, G). GO terms of targets of differential lncRNAs were enriched in cellular respiration, oxidative phosphorylation, mitochondrion, ribosome, and others. GO terms of targets of differential circRNAs were enriched in cellular response to stress, DNA repair, intracellular organelle, and others (Supplementary Figure 6F, H). Meanwhile, KEGG pathways of these differential RNAs were mainly enriched in non-alcoholic fatty liver diseases, oxidative phosphorylation, ribosome, Parkinson’s diseases, and others (Supplementary Figure 7A-F, H, J). GO terms of targets of differential circRNAs were enriched in nucleic acid metabolic process, regulation of mitosis, membrane-bound organelle, enzyme binding, and others (Supplementary Figure 7G, I).

Validation of differential RNAs and construction of ceRNA networks associated with AFB1 exposure

In AFB1 exposure group, real time PCR results showed that two differential mRNAs, OX4R1 and VAV3, were consistently validated with differential expression (both P≤0.05, Supplementary Figure 8A-D). Concerning miRNAs, two differential mRNAs, miR-122b-3P and miR-122b-5P, were consistently validated with differential expression (both P≤0.05, Supplementary Figure 8E-H). In the mRNA-miRNA-lncRNA constructed ceRNA network, mRNAs including RORC, HADHB and VAV3, miRNAs including miR-99b-5P, miR-100-5P and miR-99a-5P, and lncRNAs including AL592435.1, LINC02356 and LINC01885, were enrolled in the network (Supplementary Figure 8I). At the same time, in the mRNA-miRNA-circRNA constructed ceRNA network, mRNAs includingAGAP3, MED24 and FSD1, miRNAs including miR-193b-5P, miR-6860 and miR-185-3P, and lncRNAs including circ-0000117, circ-0007653 and circ-0001310, were enrolled in the network (Supplementary Figure 8J).

In AFB1+HBX exposure group, real time PCR results showed that two differential mRNAs, XCRR6 and FERMT2, were consistently validated with differential expression, while PYGL and TRIM24 were not validated (P≤0.05, Figure 7A-D). Concerning miRNAs, three differential mRNAs, including miR-122b-3P, miR-122-5P and let-7f-5P, were consistently validated with differential expression while let-7b-5P was not validated (P≤0.05, Figure 7E-H). In the mRNA-miRNA-lncRNA constructed ceRNA network, mRNAs including TNIP1, SPEF2 and HOXA3, miRNAs including miR-4677-3P, miR-486-3P and miR-615-3P, and lncRNAs including LINC115, LINC122 and LINC160, were enrolled in the network (Figure 7I). At the same time, in the mRNA-miRNA-circRNA constructed ceRNA network, mRNAs including MAPT, NEK11 and CTNNA1, miRNAs including miR-2277-5P, miR-671-5P and miR-615-3P, and lncRNAs including circ-0006990, circ-0001467 and circ-0003333, were enrolled in the network (Figure 7J).

Identification of HBX and AFB1-related RNAs

We then performed intersectional analysis to identify HBX expression-related RNAs, which found 32 mRNAs, three miRNAs, eight lncRNAs, and two circRNAs, including APBB2, TNIP1, has-circ_0007108 and has-miR-3615 (Figure 8A-D, Table 1). In addition, intersectional analysis identified AFB1 exposure-related RNAs, including 171 mRNAs, 48 miRNAs, 52 lncRNAs, and 19 circRNAs, such as APBB2, CTSD, has-miR-3615, LINC02211 and has-circ_0007108 (Figure 8A-D, Table 2).

Analysis of exome sequencing results

Exon sequencing results mainly included SNP, SNV, copy number variant (CNV) and InDel results. Visualized results of SNPs and somatic SNVs in coding sequences are shown in Figure 8E-F. Specifically, SNP data of genomic and coding regions in three groups is shown in Table 3. SNP data included downstream, intergenic, intronic, missense SNP, splicing, ncRNA exonic, ncRNA intronic and ncRNA splicing data. Intronic regions included the greatest amount of SNP data, while ncRNA untranslated 3’ and 5’regions did not provide any SNP data. There were 1068 CNV loci in exon regions of the AFB1 group, including PRMT6, AMY1C, AMY2A, OLFM3, and others, and 138719 CNV loci in the exon regions of the AFB1+HBX group, including EIF4G3, HP1BP3, DNAJC6, HFM1, among others.

InDel data from genomic and coding regions in the three groups are shown in Table 4. Groups of frame deletions and insertions, coding sequences, stopgain, stoploss and intronic splicing were included in the groups. Intronic regions showed the highest amount of InDel data, while ncRNA untranslated 3’ and 5’ regions did not provide any InDel data. Somatic SNV and InDel data from genomic regions in the three groups are shown in Table 5-6. Both coding sequence and intronic regions showed the majority of somatic SNV data, but coding sequence regions showed the majority of somatic InDel data. There were 1061 somatic SNV loci in the AFB1 group, including AGHGEF16 (T>C), NBPF1 (rs4127088, T>C), CYP4Z1 (rs868004924, G>A), and others, and 3601 somatic SNV loci in the AFB1+HBX group, including MORN1 (C>A), C1orf158 (G>T), PTCH2 (rs950611083, G>A), and others. Meanwhile, there were 30 InDel loci in the AFB1 group, such as EIF2AK3 (rs143427234, AAT>A), CD96 (rs200152394, GAC>G), DOCK9 (rs774391312, TTTTTG>T), and 40 loci in the AFB1+HBX group, such as TNNT2 (T>TG), CR1L (TTCTACAGCTGTGAGCCCAGCTATGACCTCA GAGGGGCTG CGTC>T) and NOL1 0 (AGGCG>A).

HBX (HBV X, HBxAg), encoded by the X gene, is a trans-activating protein which regulates transcription, signal transduction, the cell cycle as well as genetic stability (15, 20). By integrating its DNA into the host genome, the HBV virus brings about chronic necroinflammatory disease that leads to mutations of liver cells and eventually induces the occurrence of HCC (21, 22). Several reports have shown that HBX antibodies were frequently discovered in HCC patients compared to chronic hepatitis B virus patients without cancer, suggesting a potential relationship between HBX and hepatic tumorigenesis (23, 24). Previous studies have suggested that mice transfected with the X gene were associated with the development of HCC (25, 26). These mice revealed an augmented susceptibility to chemical carcinogens (27) or c-myc-induced hepatocarcinogenesis (28). These studies indicate that HBX at least functions as a cofactor of hepatic tumorigenesis. Given the role of HBX in HCC, we conducted our study by constructing a cell model of HBX overexpression in an immortal liver cell line via lentivirus transfection. On the basis of this model, we performed in vitro experiments and constructed a double exposure model of AFB1 + HBX to mimic human interaction with HBV infection and AFB1 exposure.

Liu et al. found that HBX deletion mutations in HCC were apparently more numerous than in non-tumor tissues (16 out of 19 samples), including eight samples with full-length deletions and eight samples with deletions of four to 150 amino acids (29). They further concluded that deletion of the HBX COOH-terminal was an obvious characteristic of HCC tissues and that mutants with COOH-terminal deletions were significantly linked to the progression of HCC. However, Kumar et al. revealed that mutations of 61, 69 and 137 amino acids led to an apparent loss of the transactivational property (30).

Colony formation is an effective method to determine the overall effects of a gene on cell proliferation (31). Sirma et al. have documented that HBX inhibits clonal growth of cells and leads to apoptosis in a p53-independent manner (31). However, mutations of the HBX gene abrogated the inhibiting effect of wild-type HBX-induced cellular growth and apoptosis. Wang et al. have demonstrated that HBX exhibited an inhibitory effect on tumorigenicity in BALB/c nude mice transplanted with the Chang liver cell line (32). These studies clearly show that hepatocarcinogenesis is always induced by the mutant HBX gene and not by the wild-type HBX (without mutations) (29). By transfecting with the full-length HBX gene, our study found an anti-proliferative effect in the HL-7702 cell line, which is consistent with previous reports. In addition, our RNA-sequencing results based on HBX overexpression found 285 differential mRNAs, nine differential miRNAs, 78 differential lncRNAs and 117 differential circRNAs. Enrichment analysis of these differential RNAs suggests that HBX plays its role in the liver via angiogenesis, drug metabolism (CYP450 and other enzymes), steroid hormone biosynthesis, extracellular matrix-receptor interaction, and other mechanisms. Moreover, we constructed two ceRNA networks, including mRNA-miRNA-lncRNA and mRNA-miRNA-circRNA networks, to unveil potential metabolic pathways of HBX in the liver; however, these differential RNAs and networks have not been independently documented and require further confirmation.

Aflatoxins, generated by fungi of the Aspergillus flavs and parasiticus species, have been reported to contaminate a great deal of foods, including peanuts, maize and tree nuts (33). Of the four types of aflatoxins (B1, B2, G1 and G2), the International Agency for Research on Cancer has determined that AFB1, one of the group 1 human carcinogens, is the most carcinogenic type in HCC occurrence (34). AFB1 exists in many areas, especially in regions with warm and humid environments. Exposure to exogenous reagents may give rise to tumorigenesis and these mutagens always leave a special mutational biomarker as a “fingerprint” of the carcinogenesis process. The hepatocarcinogenesis of the AFB1-induced process has been examined in rodents and non-human primates (35). The most remarkable fingerprint of mutation is R249S (codon 249, AGG > AGT) in the p53 gene (36, 37), and this mutation has been linked to poor prognosis of patients (38). In addition to the fingerprint of AFB1 exposure, other gene mutations have been reported in the literature (39). Jiao et al. have revealed the mutational characteristics of aflatoxin-related HCCs in the Qidong region of China and have suggested novel mutational genes such as ADGRB1, AXIN1 and TERT (19).

Analysis of whole-exome sequencing in over 1150 HCC samples has found some genetic mutations in coding regions and identified six related pathways in HCC. Telomerase is activated in most HCC patients owing to TERT promoter mutations and TERT amplification (40, 41). Consistent with the above studies, our study in the HBX + AFB1 group found an increase in CNVs of the TERT promoter. Mutations of p53 are also documented in approximately 12–48% of HCC patients (36, 37, 42, 43), with no specific p53 hotspot mutations outside the R249S mutation of AFB1 exposure-related HCC (44, 45). Our study found alterations of CNVs in three regions (chromosome 17, 0-53059000, 0-16269000 and 0-21160000) of p53. Additionally, mutations of KIF1B, CTNNB1, APC, CDKN2A, FGF19 and AXIN1 have been identified in HCC samples (1, 46). Consistent with the above mutations, our study demonstrated alterations of CNVs in both AFB1 and HBX + AFB1 groups. However, our study also found the fingerprint of R249S in p53 after 1 month intervention with AFB1 exposure, but not in the AFB1 + HBX group, which is difficult to understand. We would have thought there would be a lower AFB1 intervention frequency in the HBX group compared to the control group, due to the unfavorable environment of AFB1 + HBX for cell growth, compared with only AFB1 intervention.

In terms of carcinogenic property of AFB1 with HCC, a synergistic effect of more than 54-fold combining HBV and AFB1 has been demonstrated, rather than the 6-fold enhancement or 11-fold enhancement reported using HBV alone (47). Genome-wide association studies have determined a great number of the SNPs associated with HCC progression, such as STAT4 (48), TPTE2 (a homolog of PTEN) (49), UBEB4, KIF1B, PGD (50), and others. In addition, the literature has identified some SNPs associated with HCC progression in chronic HBV infection, such as XRCC3 (51), MDM2 (52), MTHFR (53) and TFG-β1 (54). Consistent with the above findings, our study also found these mutations in the HBX + AFB1 group, indicating their potential function in HCC occurrence. Moreover, our exon sequencing also suggested that the HBX + AFB1 group showed significantly more alterations that the AFB1 group in terms of CNVs, SNVs and InDels, which was consistent with previous studies confirming the interplay between AFB1 and HBX. Meanwhile, our RNA-sequencing results based on AFB1 exposure and AFB1 + HBX double exposure also demonstrated the above interaction effects, which were apparent in more differential RNAs (mRNA, miRNAs, lncRNAs, circRNAs) in the AFB1 + HBX group compared to the AFB1 group. Therefore, we constructed two ceRNA networks in both groups to elucidate the potential metabolic pathways of AFB1 and AFB1 + HBX in the liver via these differential RNAs.

The emerging technology of high-throughput RNA sequencing provides us with a novel approach to uncover transcriptional units (55). lncRNA is a type of endogenous and non-protein-coding RNA over 200 nucleotides in length (56). lncRNAs play pivotal roles in a variety of processes, such as chromatin modification, transcription and post-transcriptional regulation, acting as ceRNAs (57, 58). Recent studies have also indicated potentially significant functions for circRNAs in different diseases, including cardiovascular diseases and tumors (59, 60). Aberrant lncRNAs and circRNAs have been reported in HCC functioning as ceRNAs (61, 62). Our study using high-throughput sequencing has identified differential circRNAs, lncRNAs, miRNAs and mRNAs, and we have further constructed four ceRNA networks using these RNAs, including mir-139-5p, miR-95-3p, lncRNA00511, LINC00662, circ-0000378, circ-0003726, and others. Moreover, most of these RNAs were previously reported to be involved in the ceRNA network of HCC. Concerning differential RNAs, our real time PCR assays successfully validated several lncRNAs, miRNAs and mRNAs, but most differential RNAs will require further exploration.

There were some limitations in our study which need to be recognized. First, in vivo experiments and mechanistic explorations are warranted for future studies. Second, differential RNAs and their potential functions in HCC need to be verified in expression assays. Finally, high-throughput sequencing should be performed to identify related molecular mechanisms causing hepatocarcinogenesis of immortal hepatic cells.

We firstly constructed a cell line model of HBX overexpression via lentivirus transfection and found inhibitory effects on cellular abilities caused by wild-type HBX in in vitro experiments. Then, we identified several differentially expressed mRNAs, miRNAs, lncRNAs and circRNAs, as well as metabolic processes and pathways via high-throughput sequencing and constructed two types of ceRNA networks of HBX expression related to these RNAs. We then identified a mutational fingerprint (R249S) after AFB1 exposure as well as mutations of codons neighboring codon 249 via the in vitro model in the AFB1 and AFB1 + HBX groups. Next, transcriptome analysis identified several differential mRNAs, miRNAs, lncRNAs and circRNAs, as well as metabolic processes and pathways. Whole-exome sequencing determined alterations of SNPs, CNVs, SNVs and InDels in the two groups. Moreover, we constructed two types of ceRNA networks using these differential RNAs in two groups. Additionally, real time PCR validated some of the differential RNAs in the HBX overexpression, AFB1 and AFB1 + HBX groups. Finally, we determined HBX-expression and AFB1-exposure-related RNAs by intersectional analysis. Nonetheless, the potential biomarkers of HBX expression and AFB1 exposure identified in the present study will need further validation in future investigations.

Acknowledgement

This work was supported in part by the National Natural Science Foundation of China (No.: 81560535), Research and application demonstration of genome sequencing analysis of intestinal microflora (Genus) markers in liver cancer patients based on high-throughput sequencing (Key research & development program of Science and Technology Bureau of Qingxiu District, Nanning City: 2020056), Key laboratory of High-Incidence-Tumor Prevention & Treatment (Guangxi Medical University), Ministry of Education (GKE2018-01, GKE2019-11, GKE-ZZ202009). As well as, the present study is also partly supported by Research Institute of Innovative Think-tank in Guangxi Medical University (The gene-environment interaction in hepatocarcinogenesis in Guangxi HCCs and its translational applications in the HCC prevention) and the central government in 2019 subsidized Guangxi's major and difficult disease clinical collaboration construction project of Chinese and Western medicine. We would also acknowledge the supported by the Key laboratory of High-Incidence-Tumor Prevention & Treatment (Guangxi Medical University), Ministry of Education.

Conflict of interest

The authors report there are no conflict of interest.

Authors contributions

Xiang-Kun Wang and Tao Peng designed this study; Xiang-Kun Wang wrote the manuscript and Tao Peng guided the revision. Xiang-Kun Wang, Xin Zhou, Ting-Dong Yu, Chuang-Ye Han, Guang-Zhi Zhu, Wei Qin, Jian-Lu Huang, Xi-Wen Liao, Zi-Jun Chen, Cheng-Kun Yang, and Xin-Ping Yeconducted this study.

Availability of data and materials

The datasets analyzed during the current study are available from the corresponding author on reasonable request.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

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Table 1. Differential and intersectional RNAs between HBX and AFB1-HBX groups.

Group	Details
mRNA	APBB2	NFIA	STAG2	NR3C1	YWHAZ	SEC13	NEDD4L	PTK7	ERC2
	CTSD	HADHB	MAP2	HDAC8	ADGRG2	SEC31A	ZFAND6	FAM107B	HNRNPC
	IFT122	SCMH1	SLC33A1	XRCC6	AC092881.1	KANSL1	MELK	CEP57L1	HACD3
	TNIP1	BCAS3	NCOA4	SOX5	RPS6KC1
miRNA	miR-3615	miR-139-5p	miR-625-3p
LncRNA	MIR4435-2HG	AC069277.1	MIR99AHG	LINC00466	PDE10A	CRTC3	MIR100HG	LINC01446
CircRNA	circ_0007108	circ_0016601

Table 2. Differential and intersectional RNAs between AFB1 and AFB1-HBX groups.

Group	Details
mRNA	APBB2	NFIA	STAG2	NR3C1	YWHAZ	SEC13	NEDD4L	PTK7
	CTSD	HADHB	MAP2	HDAC8	ADGRG2	SEC31A	ZFAND6	ERC2
	CD44	TBCB	RNF38	RNF38	ZNF638	ACTR3C	EPB41L5	CALM1
	TTC7B	NOL10	VAV3	DAPK1	COX6C	CCDC126	FAM81A	PGM3
	UHRF1	PARD3	CNNM2	SUCLG2	SGCE	LGALS8	LARP4	AC034102.1
	RORC	USP22	STAMBP	ALG5	LY6E	SLC39A10	UBE2E2	AC004922.1
	SLC35A3	AGAP3	CUL2	NEDD9	MAX	LMTK3	NCALD	CCDC88C
	ST5	SPA17	RSBN1	RUNX2	NFATC2	PKMYT1	EFCAB2	PPP1CB
	HSPH1	COL4A6	RIC8B	RBMS3	ACTG2	IFNGR1	CDC123	PPP2R2A
	RREB1	TXNRD1	FANCI	GOLGA2	TPK1	AKAP9	EFL1	SPOPARHGAP1
	OS9	SDCBP	SCFD2	BPTF	NPEPPS	MTA3	MGA	HNRNPH1
	DIAPH1	GNE	DOCK10	KYAT1	CFL1	CAPN2	AOPEP	TRAPPC3
	FOXP2	MSL2	EFR3B	TDP1	ZNF618	TEAD4	OSBPL6	ITGB3BP
	ZC3H14	NCOA6	PFKM	RSPH3	SP4	CPED1 L	BCL2L13	CACNA1D
	MAP4K5	SH3GL1	AMACR	AADAT	TSPAN5	FSD1	GEMIN6	ATP6V1E1
	SPECC1	GIGYF2	DHTKD1	DGKB	RIF1	HPS1	TANK	MSANTD3-TMEFF1
	PLSCR4	EIF4E2	ITSN1	SUB1	NTN4	PIGG	TTC39C	PARPBP
	DHFR	IQSEC2	SORBS2	NEK6	ARL13B	CCSAP	PHGDH	EPB41L1
	KRT17	UBL7	AAMDC	TENM2	LSM12	NRIP1	TRIP12	SMARCB1
	RAI14	CSDE1	TJAP1	RTN3	RFX3	ACBD5	GON4L	FAM168A
	MYL12A	DDB2	GSS	NR1H3	ACSM3	EIF4G3	EPS8	ADAMTSL1
	SUGCT	AHDC1	LDB3
miRNA	miR-3615	miR-615-3p	miR-320b	miR-576-5p	miR-1246	miR-542-3p	miR-486-5p	miR-99b-5p
	miR-26a-5p	miR-455-5p	miR-224-5p	novel_1002	miR-342-3p	miR-27a-5p	miR-582-5p	miR-92a-3p
	miR-152-3p	miR-7704	miR-345-5p	miR-320a-3p	let-7f-1-3p	miR-122b-3p	miR-486-3p	miR-140-3p
	miR-15b-3p	miR-27b-5p	miR-222-3p	miR-126-3p	miR-20a-5p	miR-340-3p	miR-215-5p	miR-4286
	miR-139-3p	miR-92a-1-5p	miR-25-5p	miR-4677-3p	miR-582-3p	novel_257	miR-6866-5p	miR-4473
	miR-122-5p	miR-450b-5p	miR-331-5p	miR-574-5p	miR-130b-5p	miR-320c	miR-23a-3p	miR-146a-5p
LncRNA	MIR4435-2HG	AC069277.1	MIR99AHG	LINC00466	PDE10A	AL365295.1	AL139260.1	KITLG
	TPT1-AS1	LINC02211	TNPO1	XLOC_133304	AGK	NUTM2G	AL354822.1	PCBP1-AS1
	KLKB1	LINC02208	MBOAT2	NTF3	IGFL2-AS1	MBD5	MICAL2	PPP2R2A
	GNPTG	RPL23AP82	ARL6IP6	FENDRR	MTUS1	PVT1	PSAPL1	AKR1C3
	LINC01572	IWS1	SUN1	FAM157C	LINC01094	STAT4	FASTKD1	LINC00887
	TBC1D5	PATJ	INKA2	SCAPER	OFCC1	MYO1B	AL022068.1	PCAT1
	IFT43	CHD9	LINC01895	SLC10A7
CircRNA	circ_0007108	circ_0016601	circ_0042103	circ_0003296	circ_0001615	circ_0001115	circ_0001310	circ_0003333
	circ_0000847	circ_0008199	circ_0000119	circ_0000117	circ_0007061	circ_0007653	circ_0073371	circ_0002023
	circ_0004243	circ_0006702	circ_0007127

Table 3. SNP data of genomic and coding region in three groups

Sample	AFB1-1	AFB1-2	AFB1-3	HBX_AFB1-1	HBX_AFB1-2	HBX_AFB1-3	c7702_1	c7702_2	c7702_3
CDS	21441	21448	21562	21495	21444	21508	21591	21564	21533
downstream	836	910	1017	843	876	1004	1057	1011	997
intergenic	11629	12355	14965	12218	10714	14966	15820	16765	16046
intronic	69870	71499	80302	70857	72493	79292	81365	77330	77505
missense_SNP	9906	9921	9965	9952	9921	9949	9976	9957	9946
ncRNA_exonic	2020	2095	2241	2072	2080	2190	2256	2234	2206
ncRNA_intronic	3789	3899	4531	3860	3889	4563	4590	4455	4391
ncRNA_splicing	21	21	22	21	22	21	22	22	23
ncRNA_UTR3	0	0	0	0	0	0	0	0	0
ncRNA_UTR5	0	0	0	0	0	0	0	0	0
Others	137	143	165	148	145	153	174	169	159
splicing	472	481	485	485	482	486	486	482	483
stopgain	75	73	74	73	73	71	73	72	72
stoploss	11	10	10	9	10	9	10	10	9
synonymous_SNP	11054	11056	11113	11069	11054	11090	11133	11131	11113
unknown	395	388	400	392	386	389	399	394	393
upstream	1715	1799	2198	1728	1792	2032	2274	2102	2020
UTR3	3859	3948	4429	3947	4045	4341	4484	4257	4281
UTR5	2158	2242	2393	2205	2229	2301	2498	2398	2333
Total	117947	120840	134310	119879	120211	132857	136617	132789	131977

Table 4. InDel data of genomic and coding region in three groups.

Sample	AFB1-1	AFB1-2	AFB1-3	HBX_AFB1-1	HBX_AFB1-2	HBX_AFB1-3	c7702_1	c7702_2	c7702_3
CDS	545	539	553	543	532	537	550	537	528
frameshift_ deletion	78	78	75	83	78	71	72	74	73
frameshift_ insertion	52	51	54	51	52	52	56	55	52
nonframeshift_ deletion	187	187	194	192	178	192	187	183	184
nonframeshift_ insertion	152	147	158	142	147	149	164	150	148
stopgain	3	3	2	3	3	4	2	3	3
stoploss	1	1	1	1	1	1	1	1	1
unknown	72	72	69	71	73	68	68	71	67
intronic	9685	9990	11623	9862	10174	11442	11716	11023	11150
UTR3	574	574	658	568	579	659	657	635	644
UTR5	280	276	312	257	276	305	336	330	301
splicing	136	141	145	147	131	151	147	144	147
ncRNA_exonic	181	189	205	190	197	200	215	206	216
ncRNA_intronic	493	494	614	514	476	618	592	607	585
ncRNA_UTR3	0	0	0	0	0	0	0	0	0
ncRNA_UTR5	0	0	0	0	0	0	0	0	0
ncRNA_splicing	6	5	7	5	5	5	7	8	7
upstream	232	271	314	249	242	294	337	326	287
downstream	119	120	162	127	122	165	165	148	146
intergenic	1166	1272	1658	1254	1079	1646	1683	1806	1691
Others	25	21	30	23	24	30	27	29	24
Total	13442	13892	16281	13739	13837	16052	16432	15799	15726

Table 5. Somatic SNV data of genomic region in three groups

Case	c7702_1_ HBX_AFB1	c7702_2_ HBX_AFB1	c7702_3_ HBX_AFB1	c7702_1_AFB1	c7702_2_ AFB1	c7702_3_ AFB1
CDS	414	422	426	45	57	61
synonymous_SNP	102	110	114	14	9	17
missense_SNP	289	293	294	26	44	41
stopgain	18	15	14	4	3	3
stoploss	2	2	2	0	0	0
unknown	3	2	2	1	1	0
intronic	536	518	517	187	157	168
UTR3	40	34	30	9	6	8
UTR5	25	26	22	4	4	7
splicing	17	17	18	2	1	0
ncRNA_exonic	20	20	21	9	15	8
ncRNA_intronic	45	56	47	23	19	21
ncRNA_UTR3	0	0	0	0	0	0
ncRNA_UTR5	0	0	0	0	0	0
ncRNA_splicing	0	0	0	0	0	2
upstream	14	11	16	2	8	8
downstream	1	0	0	2	5	4
intergenic	87	94	106	70	61	88
Others	0	0	1	0	0	0
Total	1199	1198	1204	353	333	375

Table 6. Somatic InDel data of genomic region in three groups

Case	c7702_1_ HBX_AFB1	c7702_2_ HBX_AFB1	c7702_3_ HBX_AFB1	c7702_1_AFB1	c7702_2_ AFB1	c7702_3_ AFB1
CDS	7	8	4	0	0	0
frameshift_ deletion	3	5	3	0	0	0
frameshift_ insertion	2	2	0	0	0	0
nonframeshift_ deletion	2	1	1	0	0	0
nonframeshift_ insertion	0	0	0	0	0	0
stopgain	0	0	0	0	0	0
stoploss	0	0	0	0	0	0
unknown	0	0	0	0	0	0
intronic	6	4	5	10	6	6
UTR3	0	0	0	0	0	0
UTR5	0	0	0	0	0	0
splicing	0	0	0	0	0	0
ncRNA_exonic	0	0	0	0	0	0
ncRNA_intronic	0	0	0	0	1	2
ncRNA_UTR3	0	0	0	0	0	0
ncRNA_UTR5	0	0	0	0	0	0
ncRNA_splicing	0	0	0	0	0	0
upstream	0	0	0	0	2	2
downstream	0	0	0	0	0	0
intergenic	3	1	2	0	0	1
Others	0	0	0	0	0	0
Total	16	13	11	10	9	11

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Characterization of hepatocarcinogenesis by transcriptome and exome sequencing via chronic HBV X expression and aflatoxin B1 exposure in immortal hepatic cell

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