The authors have declared that no competing interests exist. Austria Wirtschaftsservice GmbH is a non commercial public funding agency organised as a non profit GmbH. It was neither involved in any decisions on the direction of the funded research nor in the study design; collection, analysis, and interpretation of data; writing of the paper; and decision to submit for publication. Funding through Austria Wirtschaftsservice GmbH does not alter the authors' adherence to all the PLOS ONE policies on sharing data and materials.
Conceived and designed the experiments: JS HS CL. Performed the experiments: JS CL. Analyzed the data: JS MF. Contributed reagents/materials/analysis tools: JS MF WS KLL CL KZ MT HS. Wrote the paper: JS CL MT HS.
Pathogenesis and factors for determining progression of alcoholic and non-alcoholic steatosis to steatohepatitis with risk of further progression to liver cirrhosis and cancer are poorly understood. In the present study, we aimed to identify potential molecular signatures for discrimination of steatohepatitis from steatosis.
Global microarray gene expression analysis was applied to unravel differentially expressed genes between steatohepatitis compared to steatosis and control samples. For functional annotation as well as the identification of disease-relevant biological processes of the differentially expressed genes the gene ontology (GO) database was used. Selected candidate genes (n = 46) were validated in 87 human liver samples from two sample cohorts by quantitative real-time PCR (qRT-PCR). The GO analysis revealed that genes down-regulated in steatohepatitis were mainly involved in metabolic processes. Genes up-regulated in steatohepatitis samples were associated with cancer progression and proliferation. In surgical liver resection samples, 39 genes and in percutaneous liver biopsies, 30 genes were significantly up-regulated in steatohepatitis. Furthermore, immunohistochemical investigation of human liver tissue revealed a significant increase of AKR1B10 protein expression in steatohepatitis.
The development of steatohepatitis is characterized by distinct molecular changes. The most striking examples in this respect were
Fatty liver diseases comprise a spectrum of severity ranging from simple steatosis over steatohepatitis to cirrhosis and hepatocellular cancer (HCC)
While simple steatosis has a relatively benign course and is principally reversible, steatohepatitis carries a poor prognosis and can lead to severe liver damage with progresson to cirrhosis and HCC. Conventional non-invasive markers such as serum transaminases correlate poorly with the risk of development as well as progression of liver disease, and currently available routine liver tests may even be unremarkable in a significant proportion of patients with steatohepatitis
In the present study, we performed microarray-based gene expression profiling analysis of steatosis and steatohepatitis and compared them with normal human liver samples. We focused on analyzing transcriptional changes of genes relevant in steatohepatitis but not in steatosis and normal liver to identify potential signatures as basis for further development of biomarkers in the discrimination of steatohepatitis as a prognostically more relevant disease entity. Notably, we aimed at investigating molecular changes between steatohepatitis and steatosis, rather than to differentiate between disease etiologies of NALFD. Overall, we validated the expression of 46 target genes in human liver samples from two cohorts. We demonstrate a hitherto unknown molecular signature for cancer-related changes in steatohepatitis but not in steatosis. Several genes were highly significantly expressed in steatohepatitis compared to steatosis and normal liver. The associated protein of one gene (
The detailed clinical, biochemical and histological data of the studied patients are given in
Microarray samples | ||||
Steatohepatitis (n = 8) | Steatosis (n = 14) | Controls (n = 10) | p-value | |
|
6∶2 | 8∶6 | 5∶5 | |
|
55 (46–72) | 61.5 (37–78) | 51 (25–73) | 0.37 |
|
25.5 (19.8–39.2) | 31 (21.4–30.8) | 22.8 (18.3–30.1) | 0.17 |
|
34 (16–56) | 25 (12–359) | 20 (5–156) | 0.57 |
|
59 (38–92) | 27 (9–398) | 22 (10–240) | 0.02 a,b |
|
60 (24–804) | 46 (14–136) | 36 (15–146) | 0.26 |
|
88 (60–261) | 88 (42–146) | 77 (45–157) | 0.55 |
|
3.2 (0.8–7.8) | 0.8 (0.3–2.5) | 1 (0.2–2) | 0.01 a,b |
|
112 (57–177) | 226 (117–270) | 174 (44–240) | 0.00 a |
|
97 (57–203) | 159 (117–270) | 97 (32–172) | 0.04 a,c |
|
2/6 | 3/11 | 0/10 | |
|
2/6 | 5/9 | 0/10 | |
|
6/2 | 8/6 | 0/10 | |
|
3 |
0∶10∶3∶1 | 10∶0∶0∶0 | |
|
1∶3∶3∶1 | 1∶7∶5∶1 | 6∶3.1∶0 | |
|
0∶4∶4 | 14∶0∶0 | 10∶0∶0 | |
|
3 (0–4) | 2 (1–2) | n/a | |
|
0∶0∶0∶2∶6 | 9∶4∶1∶0∶0 | 9∶1∶0∶0∶0 | |
|
4∶4∶0 | 7∶5∶2 | 6∶4∶0 | |
|
5∶3 | 0∶14 | n/a |
Samples from the Biobank cohort investigated by microarray analysis. (p-value: a. steatohepatitis vs. steatosis, b. steatohepatitis vs. control, c. steatosis vs. control).
qRTPCR biobank samples | ||||
Steatohepatitis (n = 10) | Steatosis (n = 30) | Controls (n = 18) | p-value | |
|
7∶03 | 14∶16 | 9∶09 | |
|
54 (44–72) | 64 (37–78) | 52.5 (22–73) | 0.02 c |
|
26.1 (19.8–39.2) | 25.7 (21–33.5) | 23.2 (18.3–30.1) | 0.03 c |
|
39 (16–204) | 25 (11–359) | 21.5 (5–311) | 0.33 |
|
65 (38–441) | 28 (9–398) | 23.5 (7–240) | 0.00 a,b |
|
98 (24–804) | 40 (14–286) | 35 (9–223) | 0.03 a,b |
|
132 (60–342) | 87 (42–161) | 77 (45–157) | 0.14 |
|
3.43 (0.8–8.5) | 0.72 (0.3–3.3) | 0.87 (0.2–2.4) | 0.00 a,b |
|
114 (57–180) | 219 (87–301) | 160 (44–240) | 0.00 a,c |
|
103 (57–231) | 149 (54–712) | 95.5 (10–184) | 0.02 c |
|
2/8 | 8/22 | 1/17 | |
|
3/7 | 7/23 | 1/17 | |
|
3/7 | 18/12 | 1/17 | |
|
3 |
0∶19∶8∶3 | 17∶0∶0∶0 | |
|
2∶3∶3∶2 | 2∶18∶9∶1 | 11∶5∶1∶0 | |
|
0∶5∶5 | 30∶0∶0 | 17∶0∶0 | |
|
4 (0–4) | 2 (1–3) | n/a | |
|
0∶0∶0∶2∶8 | 23∶6∶1∶0∶0 | 11∶5∶1∶0∶0 | |
|
4∶6∶0 | 9∶15∶6 | 12∶5∶0 | |
|
7∶3 | 0∶30 | n/a |
Samples from the Biobank cohort analyzed by qRT-PCR (p-value: a. steatohepatitis vs. steatosis, b. steatohepatitis vs. control, c. steatosis vs. control).
qRTPCR biopsy samples | ||||
Steatohepatitis (n = 11) | Steatosis (n = 13) | Controls (n = 5) | p-value | |
|
6∶05 | 9∶04 | 4∶01 | |
|
54 (34–69) | 45 (25–62) | 43 (28–50) | 0.07 |
|
28.8 (25.2–31.6) | 28.3 (22.8–35.5) | 26.7 (24.3–27.7) | 0.19 |
|
83 (43–154) | 75 (21–253) | 66 (39–130) | 0.59 |
|
68 (35–520) | 42 (19–137) | 41 (33–65) | 0.07 |
|
426 (98–2195) | 171 (17–536) | 40 (27–69) | 0.00 a, c |
|
118 (76–267) | 87 (37–224) | 55 (38–69) | 0.01 b |
|
1 (0.4–1.7) | 0.68 (0.3–3.6) | 0.46 (0.4–1.5) | 0.27 |
|
258 (131–326) | 206 (146–259) | 142 (125–177) | 0.04 b,c |
|
185 (127–488) | 161 (51–324) | 43 (20–56) | 0.01 b,c |
|
4/7 | 3/8 | 0/5 | |
|
3/8 | 3/8 | 0/5 | |
|
5/6 | 4/7 | 0/5 | |
|
0∶3∶3∶4 | 0∶5∶3∶3 | 4∶1∶0∶0 | |
|
0∶2∶5∶3 | 1∶5∶5∶0 | n/a | |
|
0∶9∶1 | 11∶0∶0 | 5∶0∶0 | |
|
4 (2–4) | 2 (1–2) | n/a | |
|
0∶5∶1∶0∶4 | 7∶2∶1∶0∶1 | n/a | |
|
4∶6∶0 | 8∶3∶0 | n/a | |
|
3∶8 | 0∶13 | n/a |
Samples from the Biopsy cohort used for the validation by qRT-PCR (p-value: a. steatohepatitis vs. steatosis, b. steatohepatitis vs. control, c. steatosis vs. control).
Eighty-seven samples from two independent cohorts of human liver samples with alcoholic and non-alcoholic fatty liver disease were used for this study (
Microarray screen | Validation of selected target genes | |
|
N = 32 | N = 58 |
|
N = 29 | |
|
|
|
Sample from the screen were also included in the validation.
The second cohort (‘Biopsy cohort’, n = 29) encompassed percutaneous liver biopsy samples including steatosis (n = 13), steatohepatitis (n = 11, of these, 5/6 cirrhotic/non-cirrhotic), and chronic hepatitis C (CHC, n = 5, all genotype 1) as disease controls with absence of steatosis. In the Biopsy cohort, CHC samples were used as controls since patients with normal liver tissue do not undergo percutaneous liver biopsy (
Total RNA was isolated from the samples using TRI Reagent® (Molecular Research Center, Cincinnati, OH, USA) according to the manufacturer's protocols. The RNA quality was analyzed using microcapillary electrophoresis (Agilent 2100 Bioanalyzer, Agilent Technologies, Böblingen, Germany). Only samples with RIN (RNA integrity number) of 5.0 or higher were subjected to gene expression array analysis.
Global gene expression screenings were performed using Biobank samples only (steatohepatitis n = 8 (6/2 cirrhotic/non-cirrhotic), steatosis n = 14 and controls n = 10). For gene expression analysis, we used whole genome expression microarray Sentrix® Human-6 v3 expression bead chips (Illumina®, San Diego, CA, USA) encompassing 49577 features. The experiments were performed at the Genomics and Proteomics Core Facility of DKFZ Heidelberg using 300 ng/µl RNA and protocols recommended by the supplier.
The raw data was log2-transformed and quantile-normalized. Principal Components Analysis (PCA) was performed to investigate the internal data structure in a way which best explained the variance in the data and to identify potential outliers. The R-package “limma" was used to identify differentially expressed genes
The data discussed in this publication have been deposited in NCBI's Gene Expression Omnibus
Quantitative gene expression analysis was performed with 87 human liver tissue samples (Biobank samples: 10 steatohepatitis, 8/2 cirrhotic/non-cirrhotic; 30 steatosis and 18 controls; Biopsy samples: 11 steatohepatitis, 5/6 cirrhotic/non-cirrhotic; 13 steatosis; 5 disease controls from patients with chronic hepatitis C) using real-time PCR (LightCycler®480 Real-Time PCR System, Roche Applied Science, Mannheim, Germany). We applied gene specific primer and probe TaqMan® gene expression assays (Applied Biosystems, Weiterstadt, Germany) and performed relative quantification of all genes.
For immunohistochemical analysis 3 µm thick paraffin sections of liver tissue of chronic hepatitis C, NAFLD associated steatosis (NAFL) and steatohepatitis were dewaxed and rehydrated following standard procedures. For antigen retrieval the sections were microwaved in Target Retrieval Solution, pH 9.0 (Dako REAL ™ S2367; Dako, Glostrup, Denmark), for 40 min at 160 W followed by cooling down for 20 min at RT. The sections were then washed in water and PBS. Blocking was carried out with Dako REAL ™ Blocking Solution for 10 min prior to incubation with antibodies against the aldose reductase AKR1B10 (Novus Biologicals, Littleton, CO, USA) diluted 1∶500 in Dako REAL ™ Antibody Diluent for 60 min at RT. Binding of the antibodies was detected with the Dako REAL ™ EnVision ™ Detection System Peroxidase/DAB+, Rabbit/Mouse leading to a reddish-brown reaction product.
AKR1B10 protein expression detected in the cytoplasm of hepatocytes was assessed semiquantitatively as outlined below. The intensity of immunostaining was classified as mild to moderate (score A) or marked (score B), and the amount of positive hepatocytes was estimated by application of numerical scores which were defined as:
Score A or B 0: – AKR1B10 immunostaining in hepatocytes not detected
Score A or B 1: – AKR1B10 positive hepatocytes comprise less than 10% of liver parenchyma
ScoreA or B 2: – AKR1B10 positive hepatocytes comprise between 10–30% of liver parenchyma
Score A or B 3: – AKR1B10 positive hepatocytes comprise more than 30% of liver parenchyma
The AKR1B10 score was then derived from the sum of scores A and B and represents an estimate of the amount and the intensity of immunohistochemically detectable AKR1B10 protein expression in liver parenchyma (
Histological diagnosis | Score A |
Score B |
AKR1B10 score (A+B) | M-W-U test | |
|
Median | 2 | 2 | 4 | |
Range | 1–3 | 1–5 | 2–5 | ||
|
Median | 1 | 1 | 2 | SH vs.S: |
Range | 0–2 | 0–2 | 0–4 | ||
|
Median | 1 | 0 | 1 | S vs. CHC: |
Range | 0–3 | 0–1 | 0–4 |
Weak and moderate AKR1B10 expression (% of parenchymal area): 0: no expression; 1: 1–10%; 2:>10–30%; 3:>30%.
Marked AKR1B10 expression (% of parenchymal area): 0: no expression; 1: 1–10%; 2:>10–30%; 3:>30%.
To screen for changes in hepatic gene expression distinguishing steatohepatitis from steatosis, liver samples from patients with both alcoholic and nonalcoholic fatty liver disease obtained by surgery (Biobank) were subjected to Illumina gene expression bead chip-based analysis. A total of 32 Biobank samples were investigated (
Hierarchical clustering was used to visualize the 1931 common genes in a heat map (
A. Common differentially expressed genes were used for supervised clustering. B. Unsupervised clustering was performed with the 1000 most variable genes in the three groups of liver samples (steatohepatitis, red; steatosis, orange; controls, green).
The 1000 genes with the highest variance were used for an unsupervised clustering and a visualization of the results in a heat map (
Gene ontology
GO.ID | Term | pvalue | |
|
GO:0006805 | xenobiotic metabolic process | 3.0e-07 |
2 | GO:0055114 | oxidation-reduction process | 3.7e-07 |
3 | GO:0032787 | monocarboxylic acid metabolic process | 7.1e-07 |
4 | GO:0006569 | tryptophan catabolic process | 1.9e-05 |
5 | GO:0042559 | pteridine-containing compound biosynthetic process | 7.5e-05 |
6 | GO:0009437 | carnitine metabolic process | 8.9e-05 |
7 | GO:0046874 | quinolinate metabolic process | 0.0002 |
8 | GO:0070646 | protein modification by small protein removal | 0.0005 |
9 | GO:0016098 | monoterpenoid metabolic process | 0.0008 |
10 | GO:0006542 | glutamine biosynthetic process | 0.0012 |
Over-represented GO terms for down regulated genes in the common gene list in steatohepatitis samples (
GO.ID | Term | pvalue | |
1 | GO:0007155 | cell adhesion | 1.7e-15 |
2 | GO:0006415 | translational termination | 1.7e-10 |
3 | GO:0006414 | translational elongation | 1.8e-10 |
4 | GO:0006935 | chemotaxis | 2.7e-10 |
5 | GO:0007409 | axonogenesis | 4.4e-10 |
6 | GO:0030198 | extracellular matrix organization | 8.4e-09 |
7 | GO:0019083 | viral transcription | 2.5e-08 |
8 | GO:0016477 | cell migration | 1.0e-07 |
9 | GO:0031018 | endocrine pancreas development | 1.0e-07 |
10 | GO:0042060 | wound healing | 1.1e-06 |
Over-represented GO terms for up regulated genes in common gene list in steatohepatitis samples (
A total of 87 human liver samples were used for PCR-based validation of the array data (
The expression of selected target genes was determined in surgically collected samples (A) and in liver samples obtained by biopsy (B). The fold change was calculated by comparing the three groups of liver samples against each other.
Biobank samples | |||||||||
|
Reference | Chip/FC (SH/ctrl) | Chip/FC (SH/steatosis) | FC q-RT-PCR (SH/ctrl) | p-value (SH/ctrl) | FC q-RT-PCR (SH/steatosis) | p-value (SH/steatosis) | FC q-RT-PCR (steatosis/ctrl) | p-value (steatosis/ctrl) |
|
This study | 1.261 | 1.253 | 460.482 | 2.29E-10 | 155.130 | 9.57E-09 | 2.968 | 0.011 |
|
IPA biomarker | 4.473 | 4.123 | 331.812 | 2.12E-11 | 121.404 | 4.29E-11 | 2.733 | 0.079 |
|
This study | 1.427 | 1.331 | 48.235 | 9.56E-11 | 20.135 | 9.44E-10 | 2.396 | 0.025 |
|
This study | 3.503 | 2.564 | 40.122 | 7.16E-08 | 20.196 | 1.50E-06 | 1.987 | 0.018 |
|
IPA biomarker | 2.683 | 2.347 | 33.277 | 8.17E-10 | 18.341 | 2.13E-08 | 1.814 | 0.061 |
|
This study | 1.590 | 1.439 | 32.522 | 1.17E-10 | 13.147 | 2.02E-08 | 2.474 | 3.54E-04 |
|
This study | 0.804 | 0.926 | 30.678 | 4.88E-07 | 14.726 | 1.86E-05 | 2.083 | 0.044 |
|
1.993 | 1.825 | 28.509 | 1.16E-08 | 13.526 | 3.57E-07 | 2.108 | 0.029 | |
|
IPA biomarker | 2.587 | 2.177 | 24.562 | 1.62E-10 | 10.147 | 1.75E-08 | 2.421 | 0.004 |
|
IPA biomarker | 1.699 | 1.690 | 20.043 | 5.11E-08 | 12.173 | 1.88E-06 | 1.646 | 0.064 |
|
This study | 2.257 | 1.661 | 19.309 | 4.88E-11 | 8.200 | 8.81E-09 | 2.355 | 0.001 |
|
IPA biomarker | 2.509 | 2.262 | 19.256 | 7.43E-08 | 13.272 | 2.20E-07 | 1.451 | 0.061 |
|
This study |
0.676 | 0.758 | 17.483 | 8.48E-06 | 13.658 | 2.18E-05 | 1.280 | 0.494 |
|
This study | 0.677 | 0.586 | 15.864 | 3.57E-09 | 8.378 | 2.06E-07 | 1.893 | 0.013 |
|
IPA biomarker | 2.076 | 1.673 | 14.207 | 4.24E-07 | 9.669 | 1.16E-06 | 1.469 | 0.069 |
|
This study | 2.046 | 1.949 | 12.954 | 1.72E-08 | 12.109 | 1.32E-08 | 1.070 | 0.803 |
|
IPA biomarker | 2.277 | 2.068 | 12.309 | 1.26E-07 | 11.841 | 6.90E-08 | 1.040 | 0.887 |
|
This study | 1.208 | 1.224 | 12.061 | 5.28E-08 | 4.938 | 1.90E-05 | 2.442 | 0.002 |
|
This study | 1.456 | 1.083 | 10.156 | 3.48E-06 | 5.372 | 1.32E-04 | 1.891 | 0.026 |
|
IPA biomarker | 1.406 | 1.588 | 9.552 | 2.28E-07 | 8.076 | 6.84E-07 | 1.183 | 0.482 |
|
IPA biomarker | 2.038 | 2.095 | 8.593 | 4.03E-06 | 8.380 | 5.31E-06 | 1.025 | 0.919 |
|
This study | - | - | 8.164 | 1.99E-07 | 5.632 | 2.21E-06 | 1.450 | 0.133 |
|
IPA biomarker | 1.457 | 1.246 | 7.418 | 9.03E-07 | 4.747 | 1.87E-05 | 1.563 | 0.053 |
|
This study | 0.157 | 0.231 | 6.710 | 5.51E-06 | 4.423 | 9.26E-05 | 1.517 | 0.032 |
|
This study | 0.244 | 0.260 | 6.068 | 3.56E-06 | 3.324 | 2.68E-04 | 1.825 | 0.002 |
|
This study | 0.587 | 0.539 | 5.236 | 9.67E-06 | 3.789 | 9.27E-05 | 1.382 | 0.143 |
|
This study | 1.171 | 1.659 | 5.206 | 6.46E-05 | 5.174 | 7.44E-05 | 1.006 | 0.968 |
|
This study | 0.400 | 0.389 | 3.610 | 1.10E-05 | 2.476 | 3.66E-04 | 1.458 | 0.010 |
|
This study | - | - | 2.993 | 1.50E-04 | 2.260 | 0.002 | 1.324 | 0.130 |
|
|
- | - | 2.782 | 0.001 | 1.768 | 0.104 | 1.573 | 0.174 |
|
This study |
- | - | 2.776 | 0.008 | 1.223 | 0.531 | 2.270 | 0.001 |
|
This study | - | - | 2.689 | 0.001 | 1.628 | 0.049 | 1.652 | 0.041 |
|
This study | −0.398 | −0.535 | 2.665 | 9.07E-05 | 1.482 | 0.037 | 1.799 | 0.005 |
|
This study | - | - | 1.917 | 0.029 | 0.894 | 0.641 | 2.145 | 0.002 |
|
This study | - | - | 1.434 | 0.030 | 1.218 | 0.209 | 1.178 | 0.306 |
|
IPA biomarker |
−1.389 | - | 1.406 | 0.079 | 1.948 | 0.197 | 0.722 | 0.521 |
|
This study | −0.896 | −0.983 | 0.780 | 0.282 | 0.565 | 0.026 | 1.380 | 0.049 |
|
This study | - | - | 0.715 | 0.324 | 0.544 | 0.074 | 1.313 | 0.231 |
|
IPA biomarker | −1.245 | −1.007 | 0.556 | 0.012 | 0.650 | 0.051 | 0.855 | 0.321 |
|
This study | −2.113 | −1.499 | 0.435 | 0.000 | 0.560 | 0.060 | 0.777 | 0.405 |
|
This study | - | - | 0.301 | 0.018 | 0.288 | 0.009 | 1.043 | 0.916 |
|
This study | - | - | 0.296 | 0.021 | 0.213 | 0.006 | 1.388 | 0.051 |
|
This study | 0.740 | 0.710 | - | - | - | - | - | - |
|
IPA biomarker | 3.408 | 3.158 | - | - | - | - | - | - |
|
This study | - | - | - | - | - | - | - | - |
|
IPA biomarker | 2.581 | 2.249 | - | - | - | - | - | - |
FCs (fold changes) of selected target genes were calculated from gene expression array data for the Biobank cohort. Gene expression from selected target genes was validated by qRT-PCR in the Biobank cohort.. FC was calculated between steatohepatitis (SH) and controls (ctrl) as well as between SH and steatosis.
biopsy samples | ||||||
|
FC q-RT-PCR (SH/ctrl) | p-value (SH/ctrl) | FC q-RT-PCR (SH/steatosis) | p-value (SH/steatosis) | FC q-RT-PCR (steatosis/ctrl) | p-value (steatosis/ctrl) |
|
1095.838 | 4.98E-10 | 27.173 | 6.79E-05 | 40.328 | 1.23E-05 |
|
39.302 | 0.001 | 14.809 | 0.006 | 2.654 | 0.049 |
|
9.060 | 0.010 | 8.043 | 0.004 | 1.126 | 0.862 |
|
7.444 | 0.001 | 3.491 | 0.004 | 2.132 | 0.103 |
|
6.939 | 1.58E-05 | 5.274 | 0.001 | 1.316 | 0.449 |
|
6.362 | 1.94E-05 | 4.852 | 1.67E-04 | 1.311 | 0.408 |
|
6.251 | 0.001 | 2.968 | 0.003 | 2.106 | 0.078 |
|
4.862 | 3.18E-04 | 5.130 | 0.001 | 0.948 | 0.894 |
|
4.794 | 8.99E-06 | 1.767 | 0.066 | 2.713 | 0.001 |
|
4.786 | 2.50E-04 | 4.478 | 2.93E-04 | 1.069 | 0.846 |
|
4.735 | 1.54E-04 | 7.045 | 4.00E-05 | 0.672 | 0.213 |
|
4.650 | 0.001 | 4.661 | 4.00E-04 | 0.998 | 0.995 |
|
4.037 | 0.014 | 2.291 | 0.119 | 1.762 | 0.283 |
|
3.986 | 7.93E-05 | 2.337 | 0.006 | 1.706 | 0.075 |
|
3.909 | 0.009 | 2.170 | 0.046 | 1.801 | 0.221 |
|
3.860 | 0.005 | 2.389 | 0.003 | 1.616 | 0.208 |
|
3.841 | 1.11E-04 | 1.591 | 0.139 | 2.415 | 0.010 |
|
3.386 | 2.98E-04 | 1.240 | 0.454 | 2.729 | 0.005 |
|
3.039 | 2.74E-06 | 2.291 | 0.003 | 1.326 | 0.215 |
|
2.732 | 0.005 | 1.603 | 0.138 | 1.704 | 0.051 |
|
2.694 | 0.044 | 3.364 | 0.001 | 0.801 | 0.599 |
|
2.519 | 0.014 | 1.694 | 0.201 | 1.487 | 0.310 |
|
2.472 | 0.012 | 2.148 | 0.003 | 1.151 | 0.632 |
|
2.456 | 5.48E-05 | 1.913 | 0.013 | 1.284 | 0.266 |
|
2.442 | 0.007 | 2.146 | 0.013 | 1.138 | 0.637 |
|
2.207 | 0.019 | 1.823 | 0.041 | 1.211 | 0.504 |
|
2.161 | 0.063 | 1.138 | 0.750 | 1.900 | 0.125 |
|
2.085 | 0.001 | 2.089 | 0.001 | 0.998 | 0.991 |
|
1.821 | 0.001 | 1.664 | 0.025 | 1.094 | 0.655 |
|
1.725 | 0.016 | 1.055 | 0.810 | 1.635 | 0.034 |
|
1.599 | 0.035 | 1.098 | 0.684 | 1.456 | 0.046 |
|
1.443 | 0.304 | 0.932 | 0.805 | 1.548 | 0.177 |
|
1.329 | 0.389 | 0.682 | 0.233 | 1.950 | 0.003 |
|
1.295 | 0.539 | 2.826 | 0.035 | 0.458 | 0.094 |
|
1.216 | 0.502 | 1.037 | 0.906 | 1.173 | 0.296 |
|
1.178 | 0.397 | 1.101 | 0.646 | 1.070 | 0.719 |
|
1.114 | 0.723 | 1.188 | 0.392 | 0.938 | 0.835 |
|
0.956 | 0.875 | 0.708 | 0.264 | 1.351 | 0.045 |
|
0.916 | 0.903 | 1.451 | 0.616 | 0.632 | 0.318 |
|
0.765 | 0.557 | 1.097 | 0.757 | 0.698 | 0.432 |
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0.643 | 0.348 | 0.859 | 0.709 | 0.749 | 0.466 |
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0.634 | 0.385 | 3.689 | 0.032 | 0.172 | 0.002 |
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Gene expression from selected target genes was validated in the Biopsy cohort. FC was calculated between steatohepatitis (SH) and controls (ctrl) as well as between SH and steatosis.
The genes
The major finding supports the notion of a striking alteration of molecular programs in steatohepatitis when compared to steatosis and control samples. The most strikingly deregulated genes in this respect were
Weak to moderate or marked cytoplasmic and sometimes nuclear reactivity with antibodies against AKR1B10 was detected in hepatocytes of almost all of the liver tissue samples of patients with steatosis and steatohepatitis of both, alcoholic and non alcoholic etiology, as well as chronic hepatitis C controls (
Only representative areas are shown. (A) Case of chronic hepatitis C with an inflamed portal tract with lymphocytic infiltrates and mild interphase hepatitis (central vein marked by asterisk, H&E stained section). (B) Consecutive section of the area shown in (A). Only a group of few centrilobular hepatocytes exhibit cytoplasmic and nuclear AKR1B10 immunostaining (central vein marked by asterisk). (C) Case of fatty liver with marked macro- and mediovacuolar steatosis predominantly of centrilobular and mid-zonal hepatocytes (central vein marked by asterisk; H&E stained section). (D) Consecutive section of the area shown in (C) of the hepatocytes with fatty change show staining of the rim of cytoplasm not occupied by fat with AKR1B10 antibodies (central vein marked by asterisk). (E) Case of steatohepatitis in a cirrhotic liver with parenchymal nodule abuting a fibrous septum with mild ductular reaction. Many hepatocytes show fatty change and some of them are characterized by an enlarged, lightly stained cytoplasm (ballooned hepatocytes) and irregular eosinophilic cytoplasmic inclusions (Mallory-Denk bodies, MDBs; inset with higher magnification showing ballooned hepatocytes containing MDBs; H&E stained section). (F) Consecutive section of the area shown in (E). Many of the normal-sized as well as the ballooned hepatocytes show moderate cytoplasmic immunostaining with AKR1B10 antibodies whereas the MDBs remain unstained (inset with higher magnification shows ballooned hepatocytes with MDB).
The present study unravels gene expression signatures profoundly distinguishing steatohepatitis from steatosis and normal liver. Notably, normal tissue and steatosis clustered more closely together when compared to the steatohepatitis samples. Since the hierarchical clustering clearly demonstrated common expression profiles for NASH and ASH samples, no further distinction was made between the different etiologies. Furthermore, both etiologies manifest with a broadly overlapping spectrum of histological key features (e.g. centrilobular based features of steatosis, inflammation and hepatocellular ballooning as well as pericellular fibrosis)
The gene expression data suggests that steatohepatitis exhibits molecular profiles which are characteristic for processes relevant in malignant tumors and may therefore reflect a premalignant state of liver disease already at the precirrhotic stage (
The gene expression of the 46 selected genes was validated by qRT-PCR for both of the tested sample cohorts. Although the analyzed samples were collected by different approaches, and for the biopsy samples chronic hepatitis C samples were used as controls (since percutaneous liver biopsy is not performed in individuals with normal liver), many significantly deregulated genes were found in all three tested groups of liver samples in both independent sample cohorts (
Genes involved in lipid partitioning by keeping potentially lipotoxic fatty acids stored in neutral triglycerides (TG) may counteract/protect from lipotoxicity as a potentially important factor in progression of NAFLD to steatohepatitis
Furthermore, we describe the highly significant overexpression of
In summary, we propose that
We thank Dr. G. Bernhardt and Prof. H.J. Mischinger for their support with providing clinical patient data and help with obtaining surgical samples. For excellent technical assistance, we thank Sabrina Balaguer Puig and Simon John Ogrodnik.