Abstract

   Activation of the Ras/MAPK pathway is prevalently involved in the
   occurrence and development of hepatocellular carcinoma (HCC). However,
   its effects on the deregulated cellular metabolic processes involved in
   HCC in vivo remain unknown. In this study, a mouse model of HCC induced
   by hepatocyte‐specific expression of the Hras12V oncogene was
   investigated using an integrative analysis of metabolomics and
   transcriptomics data. Consistent with the phenotype of abundant lipid
   droplets in HCC, the lipid biosynthesis in HCC was significantly
   enhanced by (1) a sufficient supply of acetyl‐CoA from enhanced
   glycolysis and citrate shuttle activity; (2) a sufficient supply of
   NADPH from enhanced pentose phosphate pathway (PPP) activity; (3)
   upregulation of key enzymes associated with lipid biosynthesis; and (4)
   downregulation of key enzymes associated with bile acid biosynthesis.
   In addition, glutathione (GSH) was significantly elevated, which may
   result from a sufficient supply of 5‐oxoproline and L‐glutamate as well
   as an enhanced reduction in the process of GSSG being turned into GSH
   by NADPH. The high level of GSH along with elevated Bcl2 and Ucp2
   expression may contribute to a normal level of reactive oxygen species
   (ROS) in HCC. In conclusion, our results suggest that the lipid
   metabolism, glycolysis, PPP, tricarboxylic acid (TCA) cycle, citrate
   shuttle activity, bile acid synthesis, and redox homeostasis in the HCC
   induced by ras oncogene are significantly perturbed, and these altered
   metabolic processes may play crucial roles in the carcinogenesis,
   development, and pathological characteristics of HCC.

   Keywords: Hepatocellular carcinoma, metabolomics, Ras oncogene,
   transcriptomics

Introduction

   Hepatocellular carcinoma (HCC) is a heterogeneous cancer with no
   promising treatments. It remains one of the most prevalent and lethal
   malignancies in the world. Approximately 600,000 new cases are
   diagnosed each year, among which 55% of cases have been identified in
   China, and an increasing incidence has been observed in Western
   countries [44]1. The late diagnosis and consequently poor survival
   associated with the disease are often attributed to a lack of
   pathogenomic symptoms and the limitations of diagnostic modalities.
   Improving both the understanding of HCC etiology and biological
   processes and the early detection of the disease is an important first
   step toward the design of effective prevention strategies aimed at
   early diagnosis and the reduction in HCC.

   Ras is a small signal‐transducing guanosine triphosphatase that plays a
   central role in the control of cell growth and differentiation [45]2.
   Activated mutations in ras have been found in all human tumors, and the
   frequency of ras mutations is the highest among the genes associated
   with human cancers [46]3. In the occurrence and development of human
   HCC, although the mutational activation of Ras protein occurs with an
   incidence of 5%, hyperactivation of the Ras/MAPK pathway is a frequent
   event [47]4. Consistently, murine HCCs also express H‐ras, which is
   activated in 70% of cases. This evidence supports the view that the Ras
   pathway plays crucial roles in hepatocarcinogenesis [48]5. Because
   animal models are useful tools for dissecting etiological factors and
   understanding the process of hepatocarcinogenesis, we established
   Hras12V transgenic (Ras‐Tg) mice lineages in which a
   hepatocyte‐specific expressed ras oncogene under the control of a mouse
   albumin enhancer/promoter could induce multicentric spontaneous hepatic
   tumorigenesis at the appropriate time and with a high level of
   reproducibility and male prevalence [49]5. The histopathological
   characteristics of this disease model are that a nontumor liver shows
   rare inflammation, no signs of fibrosis or cirrhosis, and significantly
   higher levels of apoptosis and cell cycle arrest. Altered foci
   (typically < 2 mm) include the aggregation of atypical hepatocytes with
   basophilic cytoplasm and hyperchromatic nuclei. Phenotypes of hepatic
   adenoma are similar to altered foci but with a larger size (typically
   2–5 mm) and are relatively well circumscribed; HCCs are large in size
   (typically >5 mm), have a trabecular arrangement of tumor cells, and
   have highly anaplastic cells with evidence of necrosis [50]5.
   Specifically, these hepatic alterations are in dominant PCNA‐positive
   cells with abundant droplets but have rare apoptotic signs [51]5.
   Moreover, the hyperactivation of the MEK/ERK and PI3K/AKT/mTOR
   signaling pathways was a marked molecular event in HCC [52]6. Reports
   based on the Ras‐Tg mouse demonstrate that it is a valuable model to
   investigate molecular mechanisms, diagnosis and therapeutic strategies,
   and biomarkers of hepatocarcinogenesis [53]5, [54]6, [55]7, [56]8,
   [57]9, [58]10, [59]11, [60]12, [61]13, [62]14, [63]15, [64]16, [65]17,
   [66]18. For example, by exploring Ras‐Tg mice, we found that
   differences in the molecular responses to the deregulated Ras
   oncoprotein between females and males determine the onset of HCC, which
   may contribute to the striking male prevalence of HCC [67]5, [68]6. In
   addition, we and other researchers found that B lymphocytes, FoxM1, and
   miR‐221 play crucial roles in HCC by using Ras‐Tg mice [69]7, [70]9,
   [71]12.

   Molecular profiling approaches, such as metabolomics and
   transcriptomics to monitor pathological processes of disease, have
   received a great deal of attention. Metabolomic strategies play an
   increasingly important role in clinical and observational studies, and
   they will offer new perspectives not only in understanding the
   processes of disease development but also in the identification of
   diagnostic/prognostic markers and targeted healthcare [72]19. Global
   gene expression studies using RNA‐Seq can provide insights into
   regulatory genes and critical pathways that might lead to HCC [73]20.
   The integrative analysis of metabolomics and transcriptomics data has
   the potential to greatly increase our understanding of metabolic
   networks and biological systems, leading to various potential clinical
   applications [74]21. Recently, several studies integrating metabolomics
   and transcriptomics to uncover the biological processes of tumors have
   been reported and have proven the valuable and powerful applications of
   this approach [75]22. However, an analysis of HCC using this approach
   has rarely been reported.

   In this study, we characterized the metabolite profiles of HCC in
   Ras‐Tg mice by GC‐TOF‐MS analysis to identify the significantly changed
   metabolic pathways in HCC induced by the ras oncogene. In addition,
   metabolomic analysis was integrated with transcriptomic analysis of HCC
   by next‐generation sequencing (NGS) to obtain a better understanding of
   the metabolic profiles in HCC.

Materials and Methods

Experimental animals, sampling, and histopathological examination

   The aim of this study is to investigate the metabolomic and
   transcriptomic differences between HCC and wild‐type liver tissues.
   Because Ras‐Tg males develop HCC at a relatively early stage
   (8–9 months of age) with a high incidence (almost 100%) compared to
   females (over 15 months of age and 30% incidence) [76]5, [77]6, males
   were chosen for this study. Experimental animals and histopathological
   examination procedures for animal handling and tissue sampling were
   conducted in compliance with protocols approved by the Animal Care and
   Use Committee of Dalian Medical University. Ras‐Tg mice (C57BL/6J
   genetic background), which are an animal model of hepatic tumors
   induced by an Hras12V oncogene specifically expressed in hepatocytes
   [78]5, and wild‐type mice (C57BL/6J) were bred and housed in the
   Laboratory Animal Center of Dalian Medical University. Nine‐month‐old
   male mice were selected for this study. Parts of the hepatic tumor
   (over 8 mm in diameter) or wild‐type liver tissues of sacrificed mice
   were removed, rinsed in cold saline, cut into ~ 1 cm^3 tissue blocks,
   and immediately flash frozen in liquid nitrogen. The remaining tissue
   parts were fixed in 10% neutral buffered formalin, embedded in
   paraffin, sectioned, and stained with hematoxylin and eosin using
   standard methods. The histopathology was assessed, and the tissues with
   a confirmed morphological diagnosis were used for subsequent
   experimental procedures.

Sample preparation for GC‐MS analysis

   The histopathologically identified HCC from 9‐month‐old Ras‐Tg males
   and liver tissues from 9‐month‐old wild‐type C57BL/6J males (eight
   individual samples for each group, for a total of over 32 slices with
   at least 2 per individual, identified by a licensed pathologist) were
   subjected to two derivatization steps for GC‐MS analysis. Approximately
   0.05 mg of each sample was collected in 2‐mL EP tubes and extracted
   with 0.4 mL methanol–chloroform (V methanol:V chloroform = 3:1). Then,
   20 μL of L‐2‐chlorophenylalanine (1 mg/mL stock in dH[2]O) was added as
   an internal standard, and the sample was homogenized in a ball mill for
   3 min at 65 Hz. After the samples were centrifuged, the supernatant was
   transferred to a 2‐mL GC‐MS glass vial. An equal volume of 14 μL was
   taken from each sample and placed into the 2‐mL GC‐MS glass vial as a
   mixed sample for quality control. The extracts were dried in a vacuum
   concentrator without heating at 37°C for approximately 2 h. Then, 80 μL
   of methoxylamine hydrochloride (20 mg/mL) was added to the dried
   metabolites and incubated at 80°C for 20 min in an oven after mixing
   and sealing. The lid was opened, and 100 μL of Bis(trimethylsilyl)
   trifluoroacetamide (BSTFA) (containing 1% trimethylchlorosilane (TMCS),
   v/v) was added to each sample, which was sealed again and incubated at
   70°C for an hour. Then, 5 μL of FAMEs (a standard mixture of fatty acid
   methyl esters, C8‐C16: 1 mg/mL; C18‐C24: 0.5 mg/mL in chloroform) was
   added to the mixed sample, cooled to room temperature, and mixed well
   for GC‐MS analysis.

GC‐TOF‐MS analysis

   GC‐TOF‐MS analysis was performed using an Agilent 7890 gas
   chromatograph system coupled with a Pegasus HT time‐of‐flight mass
   spectrometer. The system utilized an Rxi‐5Sil MS column (30 m × 250 μm
   inner diameter, 0.25 μm film thickness; Restek, USA). A 1‐μL aliquot of
   the analyte was injected in splitless mode. Helium was used as the
   carrier gas, the front inlet purge flow was 3 mL/min, and the gas flow
   rate through the column was 20 mL/min. The initial temperature was 50°C
   for 1 min, then it was increased to 330°C at a rate of 10°C/min, and
   then maintained for 5 min at 330°C. The injection, transfer line, and
   ion source temperatures were 280, 280, and 250°C, respectively. The
   energy was −70 eV in electron impact mode. The mass spectrometry data
   were acquired in full‐scan mode with an m/z range 30–600 at a rate of
   20 spectra per second after a solvent delay of 366 sec.

Analysis of GC‐MS data

   First, the chroma TOF4.3X software of the LECO Corporation and
   LECO‐Fiehn Rtx5 database were used for the extraction of raw peaks, the
   filtering of data baselines, and the calibration of the baseline, peak
   alignment, deconvolution analysis, peak identification, and integration
   of the peak area. The RI (retention time index) method was used in peak
   identification, and the RI tolerance was 5000. Then, identified GC‐MS
   data were imported into LineUp (Infometrix, Bothell, WA) and PiroTrans
   (GL Science) to align the chromatograms based on the peak intensity and
   the retention time of the internal standard, 2‐hydroxyundecanoic acid.
   The generated peak lists were imported into Pirouette software
   (Infometrix, Woodinville, WA) for multivariate statistical analysis.
   The following composition analysis of the samples was performed using
   MetaboAnalyst version 3.0 ([79]http://www.metaboanalyst.ca/). The
   analysis methods included Principal Component Analysis (PCA), Partial
   Least Squares‐Discriminant Analysis (PLS‐DA), Orthogonal Projections to
   Latent Structures‐Discriminant Analysis (OPLS‐DA), and model
   validation.

Metabolite set enrichment

   Metabolite set enrichment was investigated using metabolite set
   enrichment analysis (MSEA) and the overrepresentation analysis (ORA)
   tools offered by MetaboAnalyst version 3.0. Briefly, all metabolites
   from the GS‐MS array dataset were entered into MSEA as a list of
   metabolites. The mouse pathway library was chosen, and the ORA
   algorithm was selected using a hypergeometric test. The pathway ORA
   tool then used the proportion of differentially expressed metabolites
   in the whole array to determine whether a particular pathway was
   significant. A list of pathways associated with the uploaded
   metabolites was produced. P < 0.05 was considered to represent
   significant differences.

Different gene expression analysis by NGS

   Different gene expression was detected using NGS analysis. HCC tissues
   from 9‐month‐old Ras‐Tg mice and wild‐type liver tissues from
   9‐month‐old wild‐type C57BL/6J mice (5 for each group) were chosen for
   total RNA extraction using TRIzol reagent (Invitrogen, Grand Island,
   NY). Due to the consistent inbred genetic background, the definite
   etiology (ras oncogene) for HCC development, and the consistent
   pathological features of the samples in each group, the total RNA
   samples from the same group were mixed equivalently to generate two
   composite samples, that is, HCC and wild‐type liver tissues.
   Preparation of the mRNA sample for RNA‐Seq analysis was performed using
   the TruSeq RNA LT Sample Prep Kit v2 (Illumina, San Diego, CA). The
   template molecules were used for cluster generation and sequencing on
   an Illumina HiSeq 2000 (Illumina, San Diego, CA) instrument. One sample
   per lane was used to generate 100‐bp paired‐end reads. Read alignment
   to the UCSC Mouse Reference Genome (mm10, [80]http://genome.ucsc.edu/)
   was performed using Tophat v1.3.2. Transcript assembly and
   quantification were performed by Cufflinks v2.0.2 to process the Tophat
   alignments. The assembled Cufflinks transcripts were processed with
   Cuffcompare analytical strategies to detect differentially expressed
   genes. The database for annotation, visualization, and integrated
   discovery (DAVID) v6.7 was used to interpret the differentially
   expressed gene pools and KEGG pathway enrichment assay.

Metabolite detection

   NADPH: The level of NADPH was detected using a NADP/NADPH
   Quantification Colorimetric Kit (Cat No. k347‐100; BioVision, Milpitas,
   USA) according to the instructions of the manufacturer. Briefly, the
   frozen sample (~50 mg) was washed with ice‐cold 1 × PBS and homogenized
   in 0.5 mL of NADPH extraction buffer. After keeping the samples on ice
   for 10 min, the homogenate supernatant was obtained by centrifugation.
   Two‐hundred microliters of supernatant was transferred into an
   Eppendorf tube and heated at 60°C for 30 min in a water bath. Then, a
   50‐μL aliquot was added to the labeled 96‐well plate in duplicate, and
   the NADPH level was detected by following the instructions for the kit.

Triglyceride and cholesterol

   The frozen samples (~50 mg) were used for lipid extraction. Each frozen
   tissue was added to a 10‐mL heptane–isopropanol–tween mixture (3:2:0.01
   by volume) and homogenized. This homogenate was centrifuged, and the
   supernatants were collected and evaporated with nitrogen flow. The
   triglyceride and cholesterol contents were determined using commercial
   kits (Cat No. A0‐10017 and A0‐10027; Dongou Biological Engineering Co
   Ltd, Wenzhou, China). All samples were measured in duplicate.

Pyruvate, lactate, and bile acid

   The frozen samples (~50 mg) were added to 0.5 mL of 1 × PBS (0°C, pH
   7.2–7.4) and homogenized on ice. The homogenate supernatant was
   obtained by centrifugation, and the levels of pyruvate, lactate, and
   bile acid were detected using commercial kits according to the
   instructions of the manufacturer (Cat No. A081, A019‐2, and E003‐2;
   Nanjing Jiancheng Bioengineering Institute, Nanjing, China). All
   samples were measured in duplicate.

GSH

   Fresh samples (~50 mg) were added to 1 mL of ice‐cold 1 × PBS and
   homogenized with a glass homogenizer on ice. The resulting suspension
   was sonicated with an ultrasonic cell disrupter until the solution was
   clarified. Then, the homogenates were centrifuged for 5 min at
   10,000×g. The supernatant was collected and detected immediately using
   an enzyme‐linked immunosorbent assay kit according to the instructions
   of the manufacturer (Cat No. CEA294Ge; Cloud‐Clone Corp, Wuhan, China).
   All samples were measured in duplicate.

ROS

   The mouse reactive oxygen species (ROS) ELISA Kit (BH8443, BOYAO
   Biotechnology, Shanghai, China) was used to detect the ROS levels, and
   the procedures were performed according to the instructions of the
   manufacturer. Briefly, the fresh tissue samples were cut, weighed, and
   placed into a grinding tube containing ceramic beads and 1 mL of pH 7.4
   PBS. Then, the tissue was homogenized and centrifuged, and the
   supernatant was carefully collected and assayed with an ELISA Kit. The
   final ROS level was calculated by dividing the total ROS content by the
   original tissue weight.

Statistical analysis

   The peak intensities of metabolites and the signal values of gene
   expression were statistically analyzed by a Student's t‐test between
   HCC and wild‐type liver tissues. All the data were presented as the
   mean (
   [MATH: <mover accent="true"><mrow><mi>X</mi></mrow><mrow><mo
   stretchy="false">¯</mo></mrow></mover> :MATH]
   ), with a level of probability of 0.05 as the criterion for
   significance. Fold change is a measure describing how much a quantity
   changes going from the initial to the final value, and we recorded it
   as a log value.

Results

Gross and histological identification

   To investigate the common metabolomics and transcriptomics related to
   HCC induced by the ras oncogene, Ras‐Tg and wild‐type males were
   sacrificed at 9 months of age, and the hepatic tumors and wild‐type
   liver tissues were sampled. The gross and histological findings were
   the same as our previous reports [81]5. The hepatic tumors were well
   developed with visible yellowish‐whitish lesions that were round or
   oval with enriched blood vessels, had soft and fragile qualities, and
   were of various sizes. The lesions were protruding onto or embedded in
   the livers of Ras‐Tg males (Fig. [82]1B). In contrast, the liver
   tissues from wild‐type males were bright red in color and flexible in
   texture with no lesions (Fig. [83]1A). Histopathological diagnosis
   showed that the HCC (typically over 5 mm in diameter) had a trabecular
   arrangement of tumor cells, which were highly anaplastic cells with
   evidence of necrosis and signs of lymphocytic infiltration
   (Fig. [84]1D). In contrast, the wild‐type liver cells were arranged
   neatly with no inflammatory cell infiltration or hepatic necrosis
   (Fig. [85]1C). In all of the sampled mice, lesions were not found in
   any other organs. The histopathologically identified HCC from eight
   individual Ras‐Tg males, and liver tissues from eight individual
   wild‐type males were chosen for further analysis.

Figure 1.

   Figure 1
   [86]Open in a new tab

   Gross anatomic and histopathological analysis of hepatic alterations.
   (A) Representative liver stereogram image of a liver from a 9‐month‐old
   wild‐type male. (B) Representative liver stereogram image of a liver
   carrying multiple tumors from a 9‐month‐old Ras‐Tg male. The red arrows
   indicate the hepatic alterations. (C) Relative histopathological H&E
   staining images (100 × ) showing liver tissue from a wild‐type mouse.
   (D) Relative histopathological H&E staining images (100 × ; upright
   corner: 400 × ) showing HCC from a Ras‐Tg mouse. W: wild‐type liver
   tissue; T: hepatocellular carcinoma (HCC).

GC‐MS chromatograms of HCC and wild‐type liver tissues

   Typical GC‐MS total ion current (TIC) chromatograms of HCC and
   wild‐type liver tissue extracts are shown in Figure [87]S1. The
   retention time of the internal standard was consistent for each run.
   The data showed marked differences in the chromatogram patterns between
   HCC and wild‐type liver tissues.

Clustering of HCC and wild‐type liver tissue samples

   First, the missing values of raw data were calculated by using half of
   the minimum value; then, 631 peaks were detected, and 76 metabolites
   were excluded through the interquartile range denoising method. In
   addition, the internal standard normalization method was employed in
   this data analysis. The resulting two‐dimensional data involving the
   peak number, sample name, and normalized peak area were entered into
   the SIMCA‐P 14.0 software package (Umetrics, Umea, Sweden) for
   principal component analysis (PCA), partial least squares‐discriminant
   analysis (PLS‐DA), and orthogonal projections to latent
   structure‐discriminant analysis (OPLS).

   PCA is an unsupervised analysis method reflecting the original state of
   the data. The PCA algorithm generates a single point that represents
   the metabolites in a sample and their concentrations (each dot in the
   score plots shown in Fig. [88]2A represents a single mouse). The close
   clustering of dots indicates that the samples have similar compositions
   (classification parameters: R2X (cum) = 0.587; Q2 (cum) = 0.221). The
   score plots of samples were clustered into two distinct groups
   (Fig. [89]2A), which indicates that the profiles of the metabolites in
   HCC tissues derived from Ras‐Tg males differed from those in liver
   tissues derived from wild‐type males.

Figure 2.

   Figure 2
   [90]Open in a new tab

   Score plots of PCA, PLS‐DA, and OPLS‐DA based on the metabolite profile
   data of HCC and wild‐type liver tissues. (A) Score plots of PCA based
   on the metabolite profile data for wild‐type liver tissues (black
   circles) and HCC (blue rhombi). The principal components PC1 (t[1]) and
   PC2 (t[2]) described 33.6% and 15.6% of the variation, respectively
   (n = 8). (B) The plot of PLS‐DA scores showing almost complete
   separation of wild‐type liver tissues (black circles) and HCC (blue
   rhombi). The classification parameters were R2X (cum) = 0.539, R2Y
   (cum) = 0.951, and Q2 (cum) = 0.746. (C) Validation model of PLS‐DA for
   wild‐type liver tissues (blue squares) and HCC (green squares) for 200
   permutations of the data showing the degradation of R2 to below 0.515
   and Q2 to below −0.173. (D) OPLS‐DA showing the contribution of
   variables to the difference between wild‐type liver tissues (black
   circles) and HCC (blue rhombi). The classification parameters were R2X
   (cum) = 0.816, R2Y (cum) = 0.996, and Q2 (cum) = 0.874.

   PLS‐DA was applied to obtain a higher level of group separation and
   produce a better understanding of the variables responsible for
   classification (Fig. [91]2B). The classification parameters were R2X
   (cum) = 0.539; R2Y (cum) = 0.951; and Q2 (cum) = 0.746. Seven‐fold
   cross‐validation was used to estimate the robustness and the predictive
   ability of our model, and this permutation test was used to further
   validate the model. The R2 and Q2 intercept values were 0.515 and
   −0.173, respectively, after 200 permutations (Fig. [92]2C). The low
   values of the Q2 intercept indicate the robustness of the models and
   therefore show a low risk of overfitting and reliability.

   OPLS‐DA was applied to show the contribution of variables to the
   difference between the two groups (Fig. [93]2D). The classification
   parameters were R2X (cum) = 0.816; R2Y (cum) = 0.996; and Q2
   (cum) = 0.874. The results also showed important variables that were
   situated far from the origin, but the loading plot was complex because
   of many variables. To refine this analysis, the first‐principal
   component of variable importance projection (VIP) was obtained. The VIP
   values exceeding 1.0 were first selected as altered metabolites. In
   step 2, the remaining variables were then assessed by a Student's
   t‐test (t‐test), P > 0.05, and variables were discarded between the two
   comparison groups. In addition, commercial databases, including KEGG
   ([94]http://www.genome.jp/kegg/) and NIST
   ([95]http://www.nist.gov/index.html), were utilized to search for the
   metabolite pathways.

Metabolite differences between HCC and wild‐type liver tissues screened by
PCA

   In addition to the score plot, which is based on the composition of the
   samples, the PCA algorithm also creates a loading plot, which is based
   on metabolite values. Loading plots identify metabolites that
   contribute to the differential clustering of HCC and wild‐type liver
   tissues in the score plots. The electron impact spectra of these
   discriminatory metabolites were then compared with the NIST 05 Mass
   Spectral Library (NIST). Putatively identified discriminatory
   metabolites with matching NIST spectra are shown in Table [96]1. Fold
   changes and P‐values of these metabolites between HCC and wild‐type
   liver tissues were calculated by the TICs of GC‐MS chromatograms.
   Overall, the metabolic profiles in HCC and wild‐type liver tissues were
   different. Among the detected 555 metabolites, 36 common discriminatory
   metabolites contributed to this difference. However, the array of
   changes in the individual discriminatory metabolites in HCC and
   wild‐type liver tissues was more diverse.

Table 1.

   Fold change and relative P‐value of the discriminatory metabolites
   characterizing hepatic carcinoma and wild‐type liver tissues were
   calculated by the TICs of GC‐MS chromatograms
   Metabolites Retention time (min) W mean T mean Fold change P‐value
   Glycolysis; TCA cycle; Pentose phosphate pathway; Lipid biosynthesis
   Glucose 1‐phosphate (glucose‐1p) 16.7179 1.09E‐01 2.14E‐01 9.73E‐01
   2.81E‐03
   Adenosine 24.0355 6.81E‐03 4.06E‐03 −7.46E‐01 1.17E‐02
   Cytidine diphosphate (CDP) 22.5710 3.47E‐03 1.08E‐03 −1.68E+00 2.81E‐03
   Citrate 17.2200 1.00E‐02 4.55E‐02 2.19E+00 3.18E‐02
   Cytidine monophosphate (CMP) 19.0875 3.49E‐03 1.46E‐03 −1.26E+00
   1.69E‐03
   Deoxycytidine monophosphate (dCMP) 21.3202 7.62E‐04 1.82E‐04 −2.07E+00
   7.87E‐03
   Fructose 17.8171 9.71E‐01 3.30E+00 1.76E+00 2.45E‐02
   Fructose 1,6‐bisphosphate (fructose‐1, 6p2) 11.5276 6.02E+00 1.30E+01
   1.11E+00 1.65E‐02
   Fructose 6‐phosphate (fructose‐6p) 21.2896 3.78E‐03 5.70E‐03 5.93E‐01
   4.18E‐02
   Glucose 6‐phosphate (glucose‐6p) 21.4070 7.04E‐02 7.72E‐01 3.45E+00
   9.13E‐04
   Lactate 8.8119 2.20E‐02 7.75E‐03 −1.51E+00 4.97E‐02
   Malate 13.8313 2.25E+00 5.46E+00 1.28E+00 4.02E‐02
   Oxaloacetate 13.5829 1.22E‐02 2.51E‐02 1.04E+00 4.88E‐02
   Pyruvate 18.9793 2.10E‐01 4.21E‐01 1.00E+00 3.40E‐02
   Succinate 11.8994 1.40E‐01 6.21E‐02 −1.17E+00 2.41E‐03
   Thymine 13.0700 5.41E‐03 1.82E‐02 1.75E+00 4.35E‐03
   Uridine diphosphate (UDP) 21.5776 2.73E‐03 6.28E‐03 1.20E+00 1.15E‐02
   2‐hydroxybutanoic acid 9.5741 4.89E‐02 2.55E‐02 −9.36E‐01 1.81E‐03
   Aspartate 14.2188 1.36E‐01 2.61E‐01 9.40E‐01 4.35E‐02
   Fructose 2,6‐bisphosphate (fructose‐2, 6p2) 20.6541 4.81E‐01 3.42E‐01
   −4.92E‐01 5.43E‐03
   Fucose 16.4754 4.34E‐03 7.03E‐04 −2.63E+00 7.12E‐06
   Fumarate 12.2976 1.56E‐01 2.29E‐01 5.54E‐01 5.66E‐04
   Xylitol 16.1919 3.77E‐01 1.08E‐01 −1.81E+00 7.84E‐05
   Uridine monophosphate (UMP) 24.2838 4.53E‐02 1.26E‐01 1.48E+00 1.97E‐02
   Uracil 12.2391 1.57E‐02 7.11E‐02 2.18E+00 2.94E‐03
   Uridine 22.6655 7.55E‐04 1.53E‐03 1.02E+00 1.06E‐02
   Xanthosine 24.2838 1.07E‐03 1.82E‐04 −2.56E+00 4.95E‐02
   β‐alanine 13.2484 3.79E‐02 1.86E‐02 −1.03E+00 7.11E‐05
   Nicotinamide adenine dinucleotide phosphate (NADP+) 4.7442 4.03E‐01
   2.53E‐01 −6.72E‐01 1.22E‐04
   Glutathione metabolism
   L‐glutamate 15.2758 6.10E‐03 1.55E‐02 1.35E+00 2.02E‐02
   5‐oxoproline 14.4595 3.84E‐01 7.26E‐01 9.19E‐01 1.17E‐02
   Glycine 11.8509 2.22E+00 1.63E+00 −4.46E‐01 2.26E‐02
   Nicotinamide adenine dinucleotide phosphate (NADP+) 4.7442 4.03E‐01
   2.53E‐01 −6.72E‐01 1.22E‐04
   Glutathione (GSH) 17.7547 2.23E‐01 2.95E‐01 4.04E‐01 4.52E‐02
   γ‐aminobutyric acid (GABA) 10.0654 6.19E‐03 1.53E‐02 1.31E+00 4.15E‐02
   Succinate 11.8994 1.40E‐01 6.21E‐02 −1.17E+00 2.41E‐03
   Cholesterol and bile acid biosynthesis
   Glycine 11.8509 2.22E+00 1.63E+00 −4.46E‐01 2.26E‐02
   Cholesterol 10.7894 5.41E‐03 1.82E‐02 1.75E+00 4.35E‐03
   Cholate 24.7399 2.30E+00 3.60E‐01 −2.68E+00 2.68E‐04
   [97]Open in a new tab

   The different cluster of metabolites identified by PCA analysis between
   wild‐type liver tissues (n = 8) and HCC (n = 8) was shown. The
   metabolites were classified into corresponding pathways analyzed in
   Figure [98]4. P < 0.05 means the significant difference. W, wild‐type
   liver; T, hepatocellular carcinoma (HCC); Fold change: log[2] (T mean/W
   mean).

Transcriptional differences between HCC and wild‐type liver tissues screened
by NGS

   Transcriptional data for 28225 well‐identified transcripts in HCC and
   wild‐type liver tissues were analyzed by NGS. Among them, 3747
   transcripts were significantly and differentially expressed between the
   two groups based on P‐values (P < 0.05) (Table [99]S1). Values for
   differentially expressed enzymes involved in the metabolic pathways of
   glycolysis, TCA cycle, PPP, lipid, glutathione, and cholesterol are
   shown in Table [100]2.

Table 2.

   Differently expressed genes identified by Next‐Generation Sequencing
   (NGS) between HCC and wild‐type liver tissues related to the pathways
   analyzed in Figure [101]4
   mRNA accession NO. Gene symbol Full name W value T value Fold change
   P‐value
   Glycolysis; TCA cycle; Pentose phosphate pathway; Lipid biosynthesis
   [102]NM_133904 Acacb Acetyl‐Coenzyme A carboxylase beta 5.65E+00
   2.03E+01 1.28E+00 1.27E‐02
   [103]NM_001199296 Acly ATP‐citrate synthase isoform 1 1.43E+01 1.23E+02
   2.15E+00 2.47E‐02
   [104]NM_019477 Acsl4 Long‐chain fatty‐acid–CoA ligase 4 isoform 2
   7.41E+00 5.07E+01 1.92E+00 6.63E‐04
   [105]NM_028176 Cda Cytidine deaminase 2.51E+00 1.40E+01 1.72E+00
   7.28E‐03
   [106]NM_023203 Dctpp1 dCTP pyrophosphatase 1 5.87E+01 2.50E+01
   −8.54E‐01 3.99E‐02
   [107]NM_007861 Dld Dihydrolipoyl dehydrogenase 9.25E+01 3.90E+01
   −8.64E‐01 4.69E‐02
   [108]NM_170778 Dpyd Dihydropyrimidine dehydrogenase 1.30E+02 2.91E+01
   −1.50E+00 3.64E‐03
   [109]NM_001164466 Dpys Dihydropyrimidinase 7.85E+01 9.94E+00 −2.07E+00
   1.39E‐07
   [110]NM_007988 Fasn Fatty acid synthase 7.90E+01 2.58E+02 1.18E+00
   5.74E‐03
   [111]NM_010209 Fh Fumarate hydratase 1 1.37E+02 5.65E+01 −8.86E‐01
   6.23E‐05
   [112]NM_008061 G6pc Glucose‐6‐phosphatase 1.99E+02 1.47E+01 −2.61E+00
   2.28E‐09
   [113]NM_019468 G6pd2 Glucose‐6‐phosphate 1‐dehydrogenase 2 2.66E‐01
   6.06E+00 3.13E+00 2.63E‐03
   [114]NM_011829 Impdh1 Inosine‐5′‐monophosphate dehydrogenase 1 8.62E‐01
   3.44E+00 1.38E+00 2.29E‐02
   [115]NM_008797 Pcx Pyruvate carboxylase 6.31E+00 5.19E+01 2.11E+00
   1.20E‐04
   [116]NM_008826 Pfkl 6‐phosphofructokinase 4.00E+00 1.30E+01 1.18E+00
   9.30E‐03
   [117]NM_001081274 Pgd 6‐phosphogluconate dehydrogenase 2.31E+01
   6.10E+01 9.71E‐01 3.58E‐02
   [118]NM_011099 Pkm2 Pyruvate kinase isozymes M1/M2 1.09E+01 2.96E+01
   9.99E‐01 2.30E‐02
   [119]NM_011506 Sucla2 Succinyl‐CoA ligase [ADP‐forming] subunit beta
   8.10E+01 3.23E+01 −9.19E‐01 2.64E‐02
   [120]NM_133995 Upb1 Beta‐ureidopropionase 2.14E+02 8.74E+01 −8.95E‐01
   3.23E‐05
   [121]NM_146006 Lss Lanosterol synthase 1.18E+01 6.96E+01 1.77E+00
   1.02E‐03
   [122]NM_007856 Dhcr7 7‐dehydrocholesterol reductase 2.68E+01 9.47E+01
   1.26E+00 2.58E‐02
   [123]NM_138656 Mvd Mevalonate (diphospho) decarboxylase 3.42E+00
   2.57E+01 2.02E+00 6.45E‐05
   [124]NM_145927 Fntb Protein farnesyltransferase subunit beta 1.61E‐01
   1.63E+00 2.31E+00 2.82E‐02
   Glutathione metabolism
   [125]NM_172961 Abat 4‐aminobutyrate aminotransferase 3.77E+01 1.14E+01
   −1.20E+00 3.64E‐03
   [126]NM_019468 G6pd2 Glucose‐6‐phosphate 1‐dehydrogenase 2 2.66E‐01
   6.06E+00 3.13E+00 2.63E‐03
   [127]NM_001081274 Pgd 6‐phosphogluconate dehydrogenase 2.31E+01
   6.10E+01 9.71E‐01 3.58E‐02
   [128]NM_008160 Gpx1 Glutathione peroxidase 1 1.38E+03 1.28E+02
   −2.38E+00 1.35E‐04
   [129]NM_177410 Bcl2 B cell leukemia/lymphoma 2 2.16E‐01 1.03E+00
   1.56E+00 1.01E‐02
   [130]NM_011671 Ucp2 Uncoupling protein 2 3.94E+00 1.89E+01 1.57E+00
   1.79E‐04
   Cholesterol and bile acid biosynthesis
   [131]NM_145364 Akr1d1 3‐oxo‐5‐beta‐steroid 4‐dehydrogenase 1.13E+02
   9.67E+00 −2.46E+00 2.21E‐09
   [132]NM_008537 Amacr Alpha‐methylacyl‐CoA racemase 4.97E+01 2.17E+01
   −8.29E‐01 4.15E‐02
   [133]NM_007519 Baat Bile acid‐CoA:amino acid N‐acyltransferase 1.44E+02
   3.16E+01 −1.52E+00 3.42E‐04
   [134]NM_016668 Bhmt Betaine–homocysteine S‐methyltransferase 1 1.57E+03
   5.90E+01 −3.28E+00 1.29E‐03
   [135]NM_024264 Cyp27a1 Sterol 26‐hydroxylase 1.82E+02 2.49E+01
   −1.99E+00 3.36E‐06
   [136]NM_010010 Cyp46a1 Cholesterol 24‐hydroxylase 1.59E+00 1.28E‐01
   −2.52E+00 6.88E‐04
   [137]NM_007824 Cyp7a1 Cholesterol 7‐alpha‐monooxygenase 6.62E+01
   3.18E+00 −3.04E+00 1.64E‐13
   [138]NM_007825 Cyp7b1 25‐hydroxycholesterol 7‐alpha‐hydroxylase
   1.74E+02 9.14E+00 −2.95E+00 3.37E‐12
   [139]NM_010012 Cyp8b1 7‐alpha‐hydroxycholest‐4‐en‐3‐one 6.44E+01
   1.31E+00 −3.90E+00 0.00E+00
   [140]NM_028772 Dmgdh Dimethylglycine dehydrogenase 2.08E+02 2.70E+01
   −2.04E+00 2.72E‐05
   [141]NM_010321 Gnmt Glycine N‐methyltransferase 1.44E+03 8.06E+01
   −2.88E+00 2.01E‐06
   [142]NM_133943 Hsd3b7 3 beta‐hydroxysteroid dehydrogenase type 7
   7.31E+01 2.82E+01 −9.53E‐01 1.79E‐02
   [143]NM_008952 Pipox Peroxisomal sarcosine oxidase 1.88E+02 5.13E+01
   −1.30E+00 3.65E‐03
   [144]NM_146006 Lss Lanosterol synthase 1.18E+01 6.96E+01 1.77E+00
   1.02E‐03
   [145]NM_007856 Dhcr7 7‐dehydrocholesterol reductase 2.68E+01 9.47E+01
   1.26E+00 2.58E‐02
   [146]NM_138656 Mvd Mevalonate (diphospho) decarboxylase 3.42E+00
   2.57E+01 2.02E+00 6.45E‐05
   [147]NM_145927 Fntb Protein farnesyltransferase subunit beta 1.61E‐01
   1.63E+00 2.31E+00 2.82E‐02
   [148]Open in a new tab

   The mRNA levels of differently expressed genes related to metabolism
   pathways of glycolysis, TCA cycle, pentose phosphate, lipid,
   glutathione, and cholesterol are shown. HCC (n = 5) and wild‐type liver
   (n = 5) and tissues were analyzed. P values were calculated using
   Student's t‐test and P < 0.05 means the significant difference. W,
   wild‐type liver; T, hepatocellular carcinoma (HCC); Fold change: ln (T
   mean/W mean).

Pathway enrichment and metabolite set enrichment analysis

   Although we cannot conclusively determine which of the potential
   metabolic pathways are relevant to a phenotype, KEGG pathway enrichment
   (Fig. [149]3A, B; Tables [150][Link], [151][Link], [152][Link]) and
   metabolite set enrichment analysis (MSEA) (Fig. [153]3C; Tables
   [154][Link], [155][Link]) identified several key metabolic pathways.
   The following pathways were selected from the outputs of the KEGG
   pathway analysis and MSEA based on their biological relevance to HCC.

Figure 3.

   Figure 3
   [156]Open in a new tab

   Pathway enrichment analysis for significantly changed genes and
   metabolites. (A) The significantly upregulated genes and (B)
   downregulated genes in HCC compared to wild‐type liver tissues were
   analyzed by KEGG pathway enrichment assays. (C) Significantly changed
   metabolites in HCC compared to wild‐type liver tissues were analyzed by
   MetaboAnalyst version 3.0 for metabolite set enrichment. The p values
   for the metabolic pathways are color coded, with dark black
   representing the most significant values and white representing the
   least significant values. Detailed information is shown in Tables
   [157][Link], [158][Link], [159][Link], [160][Link]. M.: metabolism.

Alterations of glycolysis, PPP, TCA cycle, and lipid biosynthesis in HCC

   Disordered lipid metabolism has been widely reported in cancer cells.
   Because we observed a large quantity of lipid droplets in HCC cells
   based on gross anatomy and histopathological diagnosis (Fig. [161]1B,
   D), lipid biosynthesis and its closely related pathways, including
   glycolysis, PPP, and the TCA cycle, were comprehensively analyzed based
   on the metabolomics and transcriptomics database.

   For the glycolysis metabolism pathway, we found that mainstream
   metabolites, such as glucose‐1‐phosphate (glucose‐1p),
   glucose‐6‐phosphate (glucose‐6p), fructose‐6‐phosphase (frucotose‐6p),
   fructose‐1,6‐bisphosphage (fructose‐1,6p2), and pyruvate, and the
   related enzymes Pfk1 and Pkm2 were significantly increased. In
   addition, the increased fructose and downregulated G6PC enzyme ensured
   a sufficient supply of raw materials. This outcome indicates that the
   enhanced glycolysis may not only maintain the excessive energy need for
   tumor growth but also provide a large quantity of the final metabolite,
   pyruvate.

   The pyruvate from the glycolysis pathway enters the mitochondria and
   participates in the TCA cycle. Interestingly, in the TCA cycle, parts
   of metabolites, such as malate, oxaloacetate, and citrate, and the
   related enzymes Pcx and Acly were significantly increased, while parts
   of other metabolites, such as succinate, and the related enzymes Dld,
   Fh, and Sucl were significantly decreased. The results indicate that
   the shuttling of citrate and malate across the membrane of mitochondria
   may be enhanced, and therefore, the flow of pyruvate to lipid
   biosynthesis is enhanced by combining TCA and the citrate shuttle, in
   which pyruvate is taken from the cytosol, converted into acetyl‐CoA,
   condensed with oxaloacetate to citrate, and transferred back to the
   cytosol (Fig. [162]4A, Tables [163]1, [164]2).

Figure 4.

   Figure 4
   [165]Open in a new tab

   Schematic representations of the most relevant metabolic and
   transcriptional differences between HCC and wild‐type liver tissues.
   Metabolic pathways related to glycolysis, the TCA cycle, PPP, and lipid
   biosynthesis. (B) Metabolic pathways related to glutathione. (C)
   Metabolic pathways related to cholesterol and bile acid synthesis. Red
   indicates significantly higher concentrations of metabolites,
   expression levels of enzymes, or enhanced pathways in HCC; green
   indicates significantly lower concentrations of metabolites, expression
   levels of enzymes, or attenuated pathways in HCC; and gray indicates
   unchanged or undetermined results. The standard letters indicate
   metabolites, the italic letters indicate enzymes, and the underlined
   italic letters indicate pathways. The metabolites with broken circles
   indicate the central metabolites in HCC. (D) The reduced form of
   nicotinamide adenine dinucleotide phosphate (NADPH), pyruvate, lactate,
   cholesterol (TC), triglyceride (TG), ROS, GSH, and bile acid (BA) was
   detected in wild‐type liver (W) and HCC (T) tissues using the methods
   described in the Materials and Methods section. The data are expressed
   as the mean ± SEM (n = 8). *P < 0.05, **P < 0.01, and ***P < 0.001.

   Another main raw material, hydrogen (H), is dominantly supplied by
   NADPH from the pentose phosphate pathway (PPP). In PPP, the levels of
   enzymes Pgd and G6pd and the concentration of NADPH were significantly
   elevated. Following the PPP, metabolites such as UMP, UDP, uridine,
   uracil, and thymine were significantly increased, while xanthosine,
   adenosine, CMP, CDP, beta‐alanine, and dCMP were significantly
   decreased. In addition, related enzymes, such as Impdh1, Pkm2, and Cda,
   were significantly increased, while Upb1, Dpys, Dpyd, and Dctpp1 were
   significantly decreased (Fig. [166]4A; Tables [167]1, [168]2). This
   outcome indicates that the enhanced PPP may supply not only the NADPH
   but also the raw materials for nucleotide synthesis needed for the
   rapid growth of tumor cells.

   Finally, the enhanced glycolysis pathway and the citrate shuttle
   offered sufficient acetyl‐CoA, and the enhanced PPP offered sufficient
   NADPH. Additionally, increased expression levels of enzymes such as
   Acacb, Acsl, Fasn, Lss, Dhcr7, Mvd, and Fntb stimulated lipid synthesis
   and the accumulation of lipids to form large quantities of lipid
   droplets in HCC cells (Fig. [169]4A; Tables [170]1, [171]2;
   Fig. [172]1B, D).

Alterations of the glutathione metabolism pathway in HCC

   Glutathione (GSH), especially in hepatocytes, plays important roles not
   only as an antioxidant but also in integrated detoxification. The
   increase in GSX (GSH+GSSG) in cancer cells indicates that enhanced de
   novo glutathione synthesis is occurring, which is crucial in cancer
   biology [173]23. In this study, GSH and the substrates for its
   synthesis, such as 5‐oxoproline and L‐glutamate, were significantly
   increased in HCC compared to wild‐type liver tissues (Table [174]1).
   The accumulated GABA resulted from significantly downregulated
   expression of Abat enzymes and subsequently decreased succinate, which
   may contribute to a sufficient L‐glutamate supply (Tables [175]1,
   [176]2). However, glycine, which is a widely used substrate for
   synthesis in cells, was significantly decreased, which may be due to
   its excessive consumption (Table [177]1). In addition, the
   significantly increased NADPH resulted from enhanced PPP via
   significant upregulation of the Pgd and G6pd enzymes, which may
   contribute to the enhanced reduction processes from GSSG to GSH
   (Tables [178]1, [179]2). The decreased Gpx1 level may attenuate the
   oxidation processes from GSH to GSSG. These changes may play crucial
   roles in maintaining the high concentration of GSH and low ROS levels
   in HCC cells. The summary of metabolites and enzymes related to the GSH
   metabolism pathway based on metabolomics and transcriptomics is shown
   in Figure [180]4B.

Alterations of cholesterol and the bile acid metabolism pathway in HCC

   Cholesterol plays an important role not only in the formation of the
   cell membrane but also the synthesis of bile acids, vitamin D, and
   steroid hormones [181]24. Compared to wild‐type liver tissues, in HCC,
   the concentration of cholesterol was significantly increased, while
   glycine and cholate were significantly decreased (Table [182]1).
   Consistently, the transcriptional analysis showed that the enzymes
   related to cholesterol synthesis, such as Lss, Dhcr7, Mvd, and Fntb,
   were significantly upregulated in HCC compared to wild‐type liver
   tissues. However, the enzymes associated with bile acid synthesis from
   cholesterol, such as Cyp7a1, Cyp27a1, Baat, and Cyp8b1, were
   significantly downregulated in HCC (Table [183]2). The summary of
   metabolites and enzymes related to the cholesterol metabolism pathway
   based on metabolomics and transcriptomics is shown in Figure [184]4C.

Discussion

   One of the hallmarks of cancers and precancerous lesions is the
   increase in de novo fatty acid synthesis regardless of the serum lipid
   levels [185]25. The liver is the major organ for regulating lipid
   metabolism, and changes in lipid homeostasis are observed in various
   liver diseases [186]26. Clinically, fat deposition is often (36%) seen
   in well‐differentiated HCCs between 1.1 and 1.5 cm in size [187]27.
   Recent evidence showed that both de novo synthesized and exogenous
   fatty acids support the growth of HCC cells [188]28. Although fatty
   acids are recognized as an important risk factor in
   hepatocarcinogenesis, the detailed mechanisms related to fatty acid
   accumulation remain unknown.

   In this study, we showed that abundant quantities of lipid droplets
   (Fig. [189]1) are a histopathological characteristic of HCC and
   detected significantly elevated levels of triglyceride and cholesterol
   in HCC of Ras‐Tg mice compared to the liver tissues of wild‐type mice
   (Fig. [190]4A). Further integrative analysis of the metabolomic and
   transcriptomic data showed that the key enzymes associated with lipid
   biosynthesis were significantly enhanced at the mRNA level. In
   addition, acetyl‐CoA, which is a basic raw material for lipid de novo
   biosynthesis, is continuously supplied by (1) the enhanced glycolysis
   pathway, which produces pyruvate, and (2) the enhanced citrate shuttle,
   which converts and transfers mitochondrial pyruvate into cytosol
   acetyl‐CoA for further lipid synthesis. Additionally, another raw
   material for lipid synthesis, NADPH, was sufficiently supplied by
   enhanced PPP (Fig. [191]4A, Tables [192]1, [193]2). This evidence
   suggests that there are underlying molecular mechanisms for the
   increase in de novo lipid synthesis in HCC cells. Importantly, the
   combination of TCA and the citrate shuttle to switch mitochondrial
   pyruvate to cytosol acetyl‐CoA may be the key point for lipid synthesis
   and offers possible targets for the treatment of steatosis and HCC.

   The roles of cholesterol in hepatic tumorigenesis and development are
   obscure. Because the liver is the dominant de novo synthesis organ for
   cholesterol [194]29, cholesterol synthesis in the liver is reduced, and
   the serum cholesterol content is gradually depressed along with the
   progression of liver diseases from chronic hepatitis to cirrhosis
   [195]30. Interestingly, several clinical reports showed that the serum
   cholesterol levels increased in cirrhosis patients with HCC compared to
   cirrhosis patients without HCC [196]31, [197]32, [198]33. This evidence
   indicates that the cholesterol levels were elevated in HCC cells
   [199]34, and the relatively lower serum level of cholesterol in HCC
   patients may result from the severity of cirrhosis, which occurred for
   80% of HCCs [200]35. Therefore, the negative correlation of the serum
   cholesterol level to the mortality of HCC patients [201]36 may reflect
   the functional reserve for liver damage [202]37. Furthermore, several
   studies have suggested an important role for cholesterol in liver
   cancer [203]38, [204]39. Intriguingly, this study showed that
   cholesterol was significantly elevated in the HCC cells of Ras‐Tg mice
   (Fig. [205]4A). Apart from the sufficient supply of acetyl‐CoA and
   NADPH and the elevated levels of enzymes related to cholesterol
   biosynthesis, we found that bile acid synthesis pathways were
   attenuated via downregulation of the expression of multiple key enzymes
   that may play important roles in the accumulation of cholesterol in HCC
   of Ras‐Tg mice (Fig. [206]4A, C, Tables [207]1, [208]2). However, the
   detailed mechanisms related to cholesterol accumulation and its roles
   in hepatic tumorigenesis and development need to be investigated
   further.

   Bile acid homeostasis is disrupted in multiple liver diseases, and the
   association between elevated BA levels and liver cancer has been
   demonstrated in both human and animal studies. Cell death and
   inflammation induced by BAs are suggested to be the underlying
   mechanisms [209]40. However, other investigations indicate that bile
   acid tends to be downregulated in clinical human HCC [210]41, and a
   significant negative correlation between bile acid levels and tumor
   burden was also observed [211]42. Consistent with the latter, we find
   that bile acid biosynthesis was substantially attenuated and that bile
   acid levels were significantly decreased in hepatic tumors. ERK is
   especially suggested to play important roles in suppressing Cyp7a1 and
   Cyp8b1, which are the rate‐limiting enzymes for bile acid biosynthesis
   [212]43. Consistently, activated Ras/ERK is a feature of hepatic tumors
   in Ras‐Tg mice, and the expression levels of Cyp7a1 and Cyp8b1 were
   significantly downregulated (Table [213]2). Because the Ras/ERK
   signaling pathway is frequently hyperactivated in human HCC [214]13, we
   suggest that the Ras/ERK pathway may contribute to the accumulation of
   cholesterol in HCC cells by suppressing bile acid biosynthesis.

   High levels of ROS play dual functions not only in damaging biological
   macromolecules and destroying cells but also in promoting tumorigenesis
   [215]44. However, recent evidence indicates that in vivo tumor cells
   showed lower ROS levels, which contributes to tumorigenesis [216]45,
   [217]46. This evidence questions the role of ROS in tumorigenesis and
   development. The expression of the ras oncogene leads to the
   dysfunction of mitochondria and elevation of ROS levels [218]47. This
   phenomenon was also observed in nontumor hepatocytes from Ras‐Tg mice,
   which showed significantly higher ROS levels compared to the
   hepatocytes from wild‐type mice [219]11. However, there was no
   difference in ROS levels between the HCC cells of Ras‐Tg mice and
   hepatocytes of wild‐type mice (Fig. [220]4B). This evidence indicates
   that the roles of high levels of ROS in ras oncogene‐expressing
   hepatocytes in tumorigenesis remain unclear, yet the relatively low
   levels of ROS are essential for HCC development. However, the
   strategies employed by tumor cells to downregulate ROS levels are still
   unknown. Glutathione, which is one of the main ROS‐eliminating
   molecules, has been shown to be elevated in tumor cells [221]48. In HCC
   of Ras‐Tg mice, a significantly elevated quantity of GSH was also
   observed (Fig. [222]4B, Table [223]1). However, Gpx1, which is the main
   enzyme that eliminates ROS by converting GSH into GSSG, was
   significantly decreased in HCC cells (Fig. [224]4B, Table [225]2).
   Therefore, whether the high ratio of GSH/GSSG contributes to low ROS
   levels has yet to be investigated. In addition, Bcl2 and Ucp2, which
   are two molecules that have been recognized to play important roles in
   eliminating ROS [226]49, [227]50, were also significantly overexpressed
   in HCC cells (Table [228]2). Taken together, although the mechanisms
   associated with a normal level of ROS in HCC cells have yet to be
   elucidated, the high ratio of GSH/GSSG and high expression levels of
   Bcl2 and Ucp2 may be involved.

   The interpretation of our findings may be limited to the mouse HCC
   induced by the Hras12V oncogene and the analysis based on static
   metabolic phenotypes. In human HCC, although hyperactivation of
   Ras/MAPK is a frequent event, a portion of patients do not show signs
   of hyperactivation [229]51. In addition, various metabolic profiles
   have been reported due to the complicated causes, different grades, or
   subgroups of human HCC, and there have been different study designs
   [230]41, [231]52, [232]53, [233]54. Similar to the findings on HCC of
   Ras‐Tg mice, it was suggested that there was a high glycolytic
   conversion of glucose into pyruvate in the cytosol followed by
   conversion into acetyl‐CoA and entry into the mitochondrial citric acid
   cycle with little conversion of pyruvate into lactate or alanine based
   on a panel of 31 pairs of human HCC tumors and corresponding nontumor
   liver tissues [234]52. Further ascertaining the metabolic fluxes in HCC
   of Ras‐Tg mice by using dynamic data through isotopic substrate
   labeling experiments will contribute to generalizable knowledge of HCC.

   In addition, several limitations of this study must be mentioned. (1)
   We only presented a snapshot of differences in metabolic pools. Without
   dynamic 13C‐isotopic labeling experiments, it is hard to ascertain
   metabolic fluxes and rates. Therefore, the interpretation offered in
   this study needs further verification. (2) The metabolic profile of the
   nontumor liver harboring the ras oncogene of Ras‐Tg mice was not
   investigated in this study. It will be of great significance to detect
   differences between cells that transformed into malignant tumors and
   those that did not transform while expressing the same ras oncogene
   were detected. (3) The metabolic profile of female HCC was not
   investigated in this study. It will be interesting to compare metabolic
   profiles of hepatic tumorigenesis between males and females, which
   should offer valuable information for determining the source of male
   prevalence in HCC. The investigations addressing these issues are
   ongoing in our laboratory.

   In summary, the mouse HCC induced by the Hras12V oncogene was analyzed
   by integrative analysis of metabolomics and transcriptomics. The
   accumulation of triglycerides and cholesterol in HCC is related to the
   overexpression of lipid biosynthesis‐related enzymes and sufficient
   supply of acetyl‐CoA and NADPH by accelerated glycolysis, the citrate
   shuttle, and PPP. Specifically, the downregulation of bile acid
   synthesis‐associated enzymes may play an important role in cholesterol
   accumulation. In addition, the elevated quantities of GSH, the GSH/GSSG
   ratio, and Bcl2 and Ucp2 may contribute to a normal level of ROS in
   HCC.

Conflict of Interest

   The authors declare no conflicts of interest.

Supporting information

   Figure S1. Typical GC/MS total ion current (TIC) chromatograms of HCC
   of Ras‐Tg mice and liver tissues of wild‐type mice.
   [235]Click here for additional data file.^ (790.6KB, tif)

   Figure S2. Tissue lipids detected by oil red staining.
   [236]Click here for additional data file.^ (8.2MB, tif)

   Table S1. Differentially expressed transcripts between HCC of Ras‐Tg
   mice and liver tissues of wild‐type mice.
   [237]Click here for additional data file.^ (5.1MB, doc)

   Table S2. KEGG pathway enrichment analysis for upregulated genes in HCC
   of Ras‐Tg mice compared to liver tissues of wild‐type mice.
   [238]Click here for additional data file.^ (38.5KB, xls)

   Table S3. KEGG pathway enrichment analysis for downregulated genes in
   HCC of Ras‐Tg mice compared to liver tissues of wild‐type mice.
   [239]Click here for additional data file.^ (41KB, xls)

   Table S4. The number of metabolites in the pathways output by MSEA.
   [240]Click here for additional data file.^ (22.5KB, xls)

   Table S5. Significant metabolites identified in the pathways output by
   MSEA.
   [241]Click here for additional data file.^ (44.5KB, xls)

   Table S6. Overrepresentation analysis of the metabolic pathways output
   by MSEA.
   [242]Click here for additional data file.^ (27.5KB, xls)

   Cancer Medicine 2017; 6(10):2370–2384

Contributor Information

   Jingyu Wang, Email: wangjingyus@163.com.

   Aiguo Wang, Email: wangaiguotl@hotmail.com.

References