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Novel Serum Markers of Fibrosis Progression for the Follow-Up of Hepatitis C Virus-Infected Patients

2009; Elsevier BV; Volume: 175; Issue: 1 Linguagem: Inglês

10.2353/ajpath.2009.080850

1525-2191

Frédérique Caillot, Martine Hiron, Odile Goria, Marie Gueudin, Arnaud François, Michel Scotté, Maryvonne Daveau, Jean‐Philippe Salier,

Hepatitis B Virus Studies

Liver biopsy is considered the gold-standard method for the assessment of liver fibrosis during follow-up of hepatitis C virus-infected patients, but this invasive procedure is not devoid of complications. The aim of the present study was to identify novel non-invasive markers of fibrosis progression. By microarray analysis, we compared transcript levels in two extreme stages of fibrosis from 16 patients. Informative transcripts were validated by real-time PCR and used for the assessment of fibrosis in 23 additional patients. Sixteen transcripts were found to be dysregulated during the fibrogenesis process. Among them, some were of great interest because their corresponding proteins could be serologically measured. Thus, the protein levels of inter-α inhibitor H1, serpin peptidase inhibitor clade F member 2, and transthyretin were all significantly different according to the four Metavir stages of fibrosis. In conclusion, we report here that dysregulation, at both the transcriptional and protein levels, exists during the fibrogenesis process. Our description of three novel serum markers and their potential use as serological tests for the non-invasive diagnosis of liver fibrosis open new opportunities for better follow-up of hepatitis C virus-infected patients. Liver biopsy is considered the gold-standard method for the assessment of liver fibrosis during follow-up of hepatitis C virus-infected patients, but this invasive procedure is not devoid of complications. The aim of the present study was to identify novel non-invasive markers of fibrosis progression. By microarray analysis, we compared transcript levels in two extreme stages of fibrosis from 16 patients. Informative transcripts were validated by real-time PCR and used for the assessment of fibrosis in 23 additional patients. Sixteen transcripts were found to be dysregulated during the fibrogenesis process. Among them, some were of great interest because their corresponding proteins could be serologically measured. Thus, the protein levels of inter-α inhibitor H1, serpin peptidase inhibitor clade F member 2, and transthyretin were all significantly different according to the four Metavir stages of fibrosis. In conclusion, we report here that dysregulation, at both the transcriptional and protein levels, exists during the fibrogenesis process. Our description of three novel serum markers and their potential use as serological tests for the non-invasive diagnosis of liver fibrosis open new opportunities for better follow-up of hepatitis C virus-infected patients. Liver fibrosis results from chronic injury of the liver with an excessive deposition of extracellular matrix (ECM) proteins such as glycoproteins, collagens, and proteoglycans. In industrialized countries, the main causes of liver fibrosis include chronic hepatitis C virus (HCV) infection, alcohol abuse, and non-alcoholic steatohepatitis. The accumulation of ECM proteins distorts the hepatic architecture by forming ECM complexes and a fibrous scar. In addition, the development of regenerating nodules results in progression to cirrhosis, which induces hepatocellular dysfunctions and can lead to clinical complications such as hepatic insufficiency, portal hypertension, and hepatocellular carcinoma (HCC) occurrence.1Bataller R Brenner DA Liver fibrosis.J Clin Invest. 2005; 115: 209-218Crossref PubMed Scopus (4060) Google Scholar, 2Rockey DC Bissell DM Noninvasive measures of liver fibrosis.Hepatology. 2006; 43: S113-S120Crossref PubMed Scopus (266) Google Scholar In the majority of HCV-infected patients, progression to cirrhosis occurs after an interval of 15 to 20 years,1Bataller R Brenner DA Liver fibrosis.J Clin Invest. 2005; 115: 209-218Crossref PubMed Scopus (4060) Google Scholar can be asymptomatic and then unobserved. In this context, it is very important to identify markers for the different stages of fibrosis. Hitherto liver biopsy is considered as the gold-standard method for the establishment of liver disease diagnosis and for the assessment of liver fibrosis during the follow-up of patients. Histological examination is useful for assessing the stage of fibrosis and the necroinflammatory grade,3Bedossa P Poynard T An algorithm for the grading of activity in chronic hepatitis C.The METAVIR Cooperative Study Group Hepatology. 1996; 24: 289-293Crossref PubMed Google Scholar, 4Goodman ZD Grading and staging systems for inflammation and fibrosis in chronic liver diseases.J Hepatol. 2007; 47: 598-607Abstract Full Text Full Text PDF PubMed Scopus (575) Google Scholar but liver biopsy is an invasive procedure, with possible pain and major complications. Furthermore, sampling variations can occur and not exactly predict fibrosis progression because the effectiveness of fibrosis determination varies according to the length of biopsy sample.5Bedossa P Dargere D Paradis V Sampling variability of liver fibrosis in chronic hepatitis C.Hepatology. 2003; 38: 1449-1457Crossref PubMed Scopus (1970) Google Scholar Therefore, there is an urgent need for reliable and non-invasive methods for assessing liver fibrosis. Scores that include routine laboratory tests have been proposed to assess fibrosis in chronic HCV infection. Among these, we can quote some scores, which are correlated with the degree of fibrosis: aspartate aminotransferase-to-platelet ratio index6Lackner C Struber G Liegl B Leibl S Ofner P Bankuti C Bauer B Stauber RE Comparison and validation of simple noninvasive tests for prediction of fibrosis in chronic hepatitis C.Hepatology. 2005; 41: 1376-1382Crossref PubMed Scopus (284) Google Scholar, 7Wai CT Greenson JK Fontana RJ Kalbfleisch JD Marrero JA Conjeevaram HS Lok AS A simple noninvasive index can predict both significant fibrosis and cirrhosis in patients with chronic hepatitis C.Hepatology. 2003; 38: 518-526Crossref PubMed Scopus (3092) Google Scholar; Fibrometer (BBL Fibro System) calculated with platelet count, prothrombine time, aspartate aminotransferase, serum concentration of α2-macroglobulin, hyaluronate, urea, and age of patient 8Calès P Oberti F Michalak S Hubert-Fouchard I Rousselet MC Konaté A Gallois Y Ternisien C Chevaillier A Lunel F A novel panel of blood markers to assess the degree of liver fibrosis.Hepatology. 2005; 42: 1373-1381Crossref PubMed Scopus (442) Google Scholar; Fibrotest (Biopredictive) combines serum concentrations of α2-macroglobulin, haptoglobin, γ-glutamyltransferase, bilirubin, and apolipoprotein A1; MP3 score combines procollagen type III N-terminal peptide, a marker of fibrogenesis, and the matrix metalloproteinase 1.9Leroy V Hilleret MN Sturm N Trocme C Renversez JC Faure P Morel F Zarski JP Prospective comparison of six non-invasive scores for the diagnosis of liver fibrosis in chronic hepatitis C.J Hepatol. 2007; 46: 775-782Abstract Full Text Full Text PDF PubMed Scopus (198) Google Scholar Diagnostic performance of various paired combination scores, has been evaluated but the best combinations could only select one-third of patients for whom either absence or presence of extensive fibrosis could be predicted with more than 90% of certainty.9Leroy V Hilleret MN Sturm N Trocme C Renversez JC Faure P Morel F Zarski JP Prospective comparison of six non-invasive scores for the diagnosis of liver fibrosis in chronic hepatitis C.J Hepatol. 2007; 46: 775-782Abstract Full Text Full Text PDF PubMed Scopus (198) Google Scholar Another non-invasive method used for the diagnostic of cirrhosis is the Fibroscan (Echosens, Paris), which is related to assessment of the tissue stiffness and is a valuable method for the evaluation of mild fibrosis or cirrhosis in HCV-infected patients.10Kettaneh A Marcellin P Douvin C Poupon R Ziol M Beaugrand M de Ledinghen V Features associated with success rate and performance of FibroScan measurements for the diagnosis of cirrhosis in HCV patients: a prospective study of 935 patients.J Hepatol. 2007; 46: 628-634Abstract Full Text Full Text PDF PubMed Scopus (219) Google Scholar In conclusion, most of these non-invasive methods are useful for detecting mild or advanced fibrosis, but are not effective for differentiating the intermediate stages of fibrosis.11Shaheen AA Wan AF Myers RP FibroTest and FibroScan for the prediction of hepatitis C-related fibrosis: a systematic review of diagnostic test accuracy.Am J Gastroenterol. 2007; 102: 2589-2600Crossref PubMed Scopus (307) Google Scholar In HCC, numerous genome-wide analyses of abnormal gene expression have been performed and have shown transcript deregulations during its development and especially between early HCC and dysplastic nodules, with the description of specific markers for early HCC development.12Chuma M Sakamoto M Yamazaki K Ohta T Ohki M Asaka M Hirohashi S Expression profiling in multistage hepatocarcinogenesis: identification of HSP70 as a molecular marker of early hepatocellular carcinoma.Hepatology. 2003; 37: 198-207Crossref PubMed Scopus (273) Google Scholar, 13Coulouarn C Derambure C Lefebvre G Daveau R Hiron M Scotte M Francois A Daveau M Salier JP Global gene repression in hepatocellular carcinoma and fetal liver, and suppression of dudulin-2 mRNA as a possible marker for the cirrhosis-to-tumor transition.J Hepatol. 2005; 42: 860-869Abstract Full Text Full Text PDF PubMed Scopus (27) Google Scholar, 14Iizuka N Oka M Yamada-Okabe H Mori N Tamesa T Okada T Takemoto N Sakamoto K Hamada K Ishitsuka H Miyamoto T Uchimura S Hamamoto Y Self-organizing-map-based molecular signature representing the development of hepatocellular carcinoma.FEBS Lett. 2005; 579: 1089-1100Abstract Full Text Full Text PDF PubMed Scopus (35) Google Scholar, 15Llovet JM Chen Y Wurmbach E Roayaie S Fiel MI Schwartz M Thung SN Khitrov G Zhang W Villanueva A Battiston C Mazzaferro V Bruix J Waxman S Friedman SL A molecular signature to discriminate dysplastic nodules from early hepatocellular carcinoma in HCV cirrhosis.Gastroenterology. 2006; 131: 1758-1767Abstract Full Text Full Text PDF PubMed Scopus (325) Google Scholar We have previously observed transcripts whose expression significantly differs between HCC-free and HCC-associated cirrhosis and among them, some have a prognostic interest.16Caillot F Derambure C Bioulac-Sage P Francois A Scotte M Goria O Hiron M Daveau M Salier JP Transient and etiology-related transcription regulation in cirrhosis prior to hepatocellular carcinoma occurrence.World J Gastroenterol. 2009; 15: 300-309Crossref PubMed Scopus (8) Google Scholar In contrast, the number of comparative studies devoted to only fibrosis progression was still scarce. In an HCV-related fibrosis context, studies have underlined transcript regulation differences between normal liver, mild and severe fibrosis.17Asselah T Bieche I Laurendeau I Paradis V Vidaud D Degott C Martinot M Bedossa P Valla D Vidaud M Marcellin P Liver gene expression signature of mild fibrosis in patients with chronic hepatitis C.Gastroenterology. 2005; 129: 2064-2075Abstract Full Text Full Text PDF PubMed Scopus (149) Google Scholar, 18Smith MW Walters KA Korth MJ Fitzgibbon M Proll S Thompson JC Yeh MM Shuhart MC Furlong JC Cox PP Thomas DL Phillips JD Kushner JP Fausto N Carithers Jr, RL Katze MG Gene expression patterns that correlate with hepatitis C and early progression to fibrosis in liver transplant recipients.Gastroenterology. 2006; 130: 179-187Abstract Full Text Full Text PDF PubMed Scopus (74) Google Scholar, 19Shao RX Hoshida Y Otsuka M Kato N Tateishi R Teratani T Shiina S Taniguchi H Moriyama M Kawabe T Omata M Hepatic gene expression profiles associated with fibrosis progression and hepatocarcinogenesis in hepatitis C patients.World J Gastroenterol. 2005; 11: 1995-1999PubMed Google Scholar Likewise, studies have shown a dysregulation in the transcriptional network regulated by interferons in the first stage of HCV-induced liver fibrosis.18Smith MW Walters KA Korth MJ Fitzgibbon M Proll S Thompson JC Yeh MM Shuhart MC Furlong JC Cox PP Thomas DL Phillips JD Kushner JP Fausto N Carithers Jr, RL Katze MG Gene expression patterns that correlate with hepatitis C and early progression to fibrosis in liver transplant recipients.Gastroenterology. 2006; 130: 179-187Abstract Full Text Full Text PDF PubMed Scopus (74) Google Scholar, 20Bieche I Asselah T Laurendeau I Vidaud D Degot C Paradis V Bedossa P Valla DC Marcellin P Vidaud M Molecular profiling of early stage liver fibrosis in patients with chronic hepatitis C virus infection.Virology. 2005; 332: 130-144Crossref PubMed Scopus (141) Google Scholar So, the aim of the present study was to identify specific transcripts whose expression could be differentially regulated during the fibrogenesis process in an HCV context. We now report that such transcript dysregulations do exist according to the different stages of fibrosis and some of their related-proteins could be used as novel serum markers of fibrosis progression. Needle liver biopsy specimens (n = 51) were obtained from HCV-infected patients and histology for fibrotic staging (F) and inflammatory process (A) was determined by the department of pathology according to the METAVIR score 3Bedossa P Poynard T An algorithm for the grading of activity in chronic hepatitis C.The METAVIR Cooperative Study Group Hepatology. 1996; 24: 289-293Crossref PubMed Google Scholar: A0, no activity; A1, mild; A2, moderate; A3, marked; F0, no fibrosis; F1, portal fibrosis without septa; F2 portal fibrosis with few septa; F3, septal fibrosis without cirrhosis; and F4, cirrhosis. Resting samples not used by the pathologist were then used for RNA extraction. Patients with an HCC-associated cirrhosis or hepatitis B virus (HBV)-infected were excluded from this study. HBV and HCV infections were serologically determined in every patient as previously described.21Derambure C Coulouarn C Caillot F Daveau R Hiron M Scotte M Francois A Duclos C Goria O Gueudin M Cavard C Terris B Daveau M Salier JP Genome-wide differences in hepatitis C- vs alcoholism-associated hepatocellular carcinoma.World J Gastroenterol. 2008; 14: 1749-1758Crossref PubMed Scopus (5) Google Scholar A standard normal liver reference was pooled from eight samples of normal, uninfected human liver tissue obtained from patients operated on for a benign liver tumor or a metastasis of a non-hepatic cancer. Liver fragments were obtained under strict anonymity from Charles Nicolle Hospital (Rouen, France). According to the current French rules and ethical guidelines, neither an informed consent nor an advice from an ethical committee are requested before RNA analysis in these liver resting fragments which would otherwise be discarded. Serum samples (n = 100) collected from an independent group of other HCV-infected patients were obtained under strict anonymity from the virology unit of Charles Nicolle Hospital (Rouen, France) and were also resting samples, which had been previously analyzed for HCV antibodies and detectable serum HCV RNA levels. A pool of normal serum samples was obtained from 10 non-infected patients. Biological and clinical data from the 51 liver fragments and 100 serum samples are summarized in Table 1.Table 1Biological and Clinical Data from Patients with Different Stages of FibrosisA. Liver samples for mRNA quantificationPatients*F1, fibrosis METAVIR stage 1; F2, fibrosis METAVIR stage 2; F3, fibrosis METAVIR stage 3; F4, fibrosis METAVIR stage 4.nMale/femaleAge†Mean ± SD.Inflammatory score‡In parenthesis, number of patients with their inflammatory score, from A0 to A3, in each subgroup.F1–1 to F1–884/447.6 ± 11.6(2 A0/5 A1/1 A2/0 A3)F1–9 to F1–20125/749.6 ± 10(1 A0/6 A1/4 A2/1 A3)F2–1 to F2–663/354.8 ± 14.8(0 A0/1 A1/3 A2/2 A3)F3–1 to F3–665/149.5 ± 11.7(0 A0/4 A1/1 A2/1 A3)F4–1 to F4–886/253.9 ± 16.3(0 A0/2 A1/5 A2/1 A3)F4–9 to F4–19117/458.1 ± 11.3(0 A0/5 A1/4 A2/2 A3)B. Serum samples for proteins quantificationPatients*F1, fibrosis METAVIR stage 1; F2, fibrosis METAVIR stage 2; F3, fibrosis METAVIR stage 3; F4, fibrosis METAVIR stage 4.nMale/femaleAge†Mean ± SD.Inflammatory score‡In parenthesis, number of patients with their inflammatory score, from A0 to A3, in each subgroup.F1–21 to F1–462612/1446.0 ± 10.5(3 A0/9 A1/9 A2/5 A3)F2–7 to F2–302413/1151.2 ± 11.9(1 A0/8 A1/12 A2/3 A3)F3–7 to F3–292318/552.5 ± 10.9(0 A0/8 A1/11 A2/4 A3)F4–20 to F1–462716/1153.3 ± 10.6(0 A0/4 A1/13 A2/10 A3)A: Underlined samples were studied by microarray and qRT-PCR; no underlined samples were only studied by qRT-PCR. B: These samples correspond to sera obtained from HCV-infected patients.* F1, fibrosis METAVIR stage 1; F2, fibrosis METAVIR stage 2; F3, fibrosis METAVIR stage 3; F4, fibrosis METAVIR stage 4.† Mean ± SD.‡ In parenthesis, number of patients with their inflammatory score, from A0 to A3, in each subgroup. Open table in a new tab A: Underlined samples were studied by microarray and qRT-PCR; no underlined samples were only studied by qRT-PCR. B: These samples correspond to sera obtained from HCV-infected patients. RNA extraction from tissues was done with MiniRNA isolation I kit (Zymo Research) then was amplified with MessageAmp II aRNA Amplification Kit (Ambion). Our set of human cDNA probes dubbed Liverpool that is tailored to a complete coverage of the human liver transcriptome under healthy or pathological conditions (ca. 104 genes), the associated LiverTools database, as well as the procedures from array preparation to data handling have all been detailed.22Coulouarn C Lefebvre G Derambure C Lequerre T Scotte M Francois A Cellier D Daveau M Salier JP Altered gene expression in acute systemic inflammation detected by complete coverage of the human liver transcriptome.Hepatology. 2004; 39: 353-364Crossref PubMed Scopus (30) Google Scholar In brief, every antisense (a)RNA sample was subjected to three rounds of hybridization and the resulting signals were normalized from the average signal of every spot (mean gray) on the matching hybridization image. The mean signal per transcript was used for selections of significantly regulated transcripts. Probe re-sequencing was done with an ABI3100 capillary sequencer (Applied Biosystems, Foster City, CA). Real-time quantitative reverse transcription (qRT)-PCR of non-amplified transcripts was done with a Light Cycler (Roche Diagnostics, Manheim, Germany). Transcript normalization was done with the 18S RNA. The primers designed with the Primer3 software () were: acetyl-Coenzyme A acyltransferase 2 (ACAA2), forward 5′-CATAAAACCTTCCCTGAAGTGC-3′, reverse 5′-AATTTTCAGGCCCATTTGGA-3′ (100 bp product size); alcohol dehydrogenase 4 (class II), pi polypeptide (ADH4), forward 5′-GTCTGCTTGGATGTGGGTTT-3′, reverse 5′-TGATTCTGGAAGCTCCTGCT-3′ (150 bp product size); acireductone dioxygenase 1 (ADI1), forward 5′-GGAGAAGGGAGACATGGTGA-3′, reverse 5′-ACGAGGCACGTGTTAGTTCC-3′ (216 bp product size); aldehyde dehydrogenase 2 family (ALDH2), forward 5′-GTTGGGAGAGCCAACAATTC-3′, reverse 5′-ACTCCCCGACATCTTGTAGC-3′ (171 bp product size); brain and reproductive organ-expressed (TNFRSF1A modulator) (BRE), forward 5′-AAGTATGCCACCTGCTCACC-3′, reverse 5′-TCTTTCCACATCAGCAGCAG-3′ (160 bp product size); Cell division cycle associated 2 (CDCA2), forward 5′-GGCTCTCCTGAAACAAACCA-3′, reverse 5′-CGCTGAGACCTTCCTTTCTG-3′ (271 bp product size); eukaryotic translation initiation factor 2B subunit 1 alpha (EIF2B1), forward 5′-GTGCCAAAGCACAGAACAAA-3′, reverse 5′-TGATTAAGGAAGGGGCAGTG-3′ (192 bp product size); hemopexin (HPX), forward 5′-TGTGGATGCGGCCTTTATCT-3′, reverse 5′-GGCCAAGGGACTTTTCCATA-3′ (167 bp product size); inter-alpha (globulin) inhibitor H1 (ITIH1), forward 5′-GTGAATGGACAGCTCATTGG-3′, reverse 5′-CCACCAGGTTCCTCTTCTTG-3′ (232 bp product size); KIAA1949, forward 5′-GGGACTCTCGGGATTTAAGC-3′, reverse 5′-TGTAAACCAGGCTGTGGTCA-3′ (141 bp product size); metallothionein 1H (MT1H), forward 5′-ACGTGTTCCACTGCCTCTTC-3′, reverse 5′-CTTCTTGCAGGAGGTGCATT-3′ (152 bp product size); REV1 homolog (S. cerevisiae), forward 5′-ACCGAAGAGGAGCACAAAGA-3′, reverse 5′-CCATTCCATTTCCCTGAAGA-3′ (152 bp product size); ribosomal protein S26 (RPS26), forward 5′-CAGCCTATTCGCTGCACTAAC-3′, reverse 5′-CATACAGCTTGGGAAGCACA-3′ (151 bp product size); serpin peptidase inhibitor, clade F (alpha-2 antiplasmin), member 2 (SERPINF2), forward 5′-CAAGTTTGACCCGAGCCTTA-3′, reverse 5′-TACCTGGGACACGTTCCATT-3′ (211 bp product size); signal transducer and activator of transcription 3 interacting protein 1 (STATIP1), forward 5′-AAGACTCTGCTTGCCTCAGC-3′, reverse 5′-TGCTTTTTCCACAATGACCA-3′ (197 bp product size); transthyretin (prealbumin, amyloidosis type I)(TTR), forward 5′-CAGAAAGGCTGCTGATGACA-3′, reverse 5′-ATGCCAAGTGCCTTCCAGTA-3′ (153 bp product size); and 18S, forward 5′-GTGGAGCGATTTGTCTGGTT-3′, reverse 5′-CGCTGAGCCAGTCAGTGTAG-3′ (200 bp product size). Our raw data were deposited in the GEO repository under accession GSE 11536. The TIGR Multiexperiment viewer (Tmev version 2.2, ) was used for i) unsupervised hierarchical clustering using the Pearson correlation and complete linkage options, ii) supervised classifications such as Significance Analysis of Microarrays with parameters adjusted to an estimated false discovery rate (FDR) <1% or K-nearest neighbor classification, and iii) evaluation of sample re-assignment by a jacknife procedure (106 iterations). Another, supervised classification was done by Support Vector Machine (). Detailed functions were retrieved with the SOURCE () and/or OMIM () tools. Statistics were performed with the GraphPad Instat software, version 3 (GraphPad Software, Inc. La Jolla, CA). Relative plasmatic concentration of ITIH1 protein was measured using Western blot. In brief, plasma aliquots were mixed with gel loading buffer and separated on NuPAGE Novex 4 to 12% Bis-Tris Mini Gels (Invitrogen). After electrophoresis, proteins were transferred out of the gels onto nylon membranes (Hybond Amersham) blocked with 5% dry milk in PBS containing 0.5% Tween 20, and then probed with goat polyclonal antibody raised against human ITIH1 (1 μg/ml, Tebu-Bio) in milk at 4°C overnight. The membranes were washed with PBS Tween 0.5% and incubated with an appropriate Alexa Fluor 680-labeled secondary antibody (2 μg/ml, Invitrogen) for 1 hour. Finally, proteins on the membranes were detected using the LI-COR Odyssey and the use of a standard (pool of normal serum samples obtained from 10 non-infected patients) allowed to calculate the relative amount of ITIH1 present in all serum samples. The concentration of SERPINF2 in plasma was measured with a sandwich-type immunoassay. A rabbit polyclonal antibody anti-SERPINF2 (1/500, Abcam) was first immobilized on a Nunc 96-well immunoplate (Fisher-bioblock) by incubating overnight in PBS. After washing four times with PBS containing 0.5% Tween 20, wells were blocked with 3% bovine serum albumin in PBS. After 1 hour-incubation, plates were washed, and serum samples diluted 20-fold were incubated for 2 hours. In the same time, a recombinant human SERPINF2 (R&D Systems) was used at serial concentrations (31.25 ng/ml, 62.5 ng/ml, 125 ng/ml, 250 ng/ml, 500 ng/ml, 1 μg/ml, 2 μg/ml). Subsequently, 100 μl of goat polyclonal antibody anti-SERPINF2 (2 μg/ml, Euromedex) was added and incubated 2 hours. After washing, TMB single solution chromogen (Invitrogen) was added and incubated 10 minutes then the reaction was stopped by addition of 50 μl of HCl (1 M/L). Finally, the intensity of the yellow color was read at OD 450 nm using Metertech ∑960 (Bioblock). A scale of increasing concentrations of recombinant the protein was used to determine the SERPINF2 concentration in the samples. The concentration of TTR protein in serum was determined by nephelometry using the BN Systems. The N antiserum to Human prealbumin kit (Dade Behring) was used according to the manufacturer instructions. To define which genes were able to be discriminating between different stages of fibrosis, we have analyzed microarray data obtained from the 16 underlined amplified fibrosis samples (F1–1 to F1–8 and F4–1 to F4–8 see Table 1). We selected by Significance Analysis of Microarrays (FDR set to <1%) 16 transcripts whose levels significantly differed between F1 vs F4 fibrosis stage. As shown in Figure 1A, when comparing these 16 transcript levels between F1 vs F4, their mean levels in F4 fibrosis stage were mainly down-regulated (green dots) with only one transcript level up-regulated (red dot). Furthermore, levels of these 16 transcripts completely separated by unsupervised hierarchical clustering two major clusters comprised of i) the fibrosis METAVIR stage F1, and ii) the fibrosis METAVIR stage F4 (Figure 1B). This was supported by a jacknife procedure (100% success) and, in addition, no liver sample was misclassified. We next validated the above differences by real-time qRT-PCR in our entire population of 39 non-amplified fibrotic samples (20 F1 and 19 F4). The median levels of most transcripts (81%: ADH4, ADI1, ALDH2, BRE, EIF2B1, HPX, ITIH1, KIAA1949, MT1H, RPS26, SERPINF2, STATIP1, TTR) significantly varied according the fibrosis stage F1 vs the fibrosis stage F4. In addition, the median levels of these transcripts were significantly correlated with the fibrosis progression (Spearman rank correlation, P < 0.05) but not with the inflammation process (except MT1H). Furthermore, when used unsupervised (unsupervised hierarchical clustering) or supervised training/testing procedures (K-nearest neighbor classification and Support Vector Machine) for classifying fibrosis stage, the 16 above identified transcripts resulted in a proper classification of 87% to 91% test samples (Table 2).Table 2Performance of Unsupervised or Supervised Classification Tools for Fibrotic Samples from Quantitative PCR Analysis of mRNATool*Unsupervised hierarchical clustering (UHC) was done with the 39 fibrotic samples (20F1 and 19F4). Supervised training/testing procedures (KNNC; SVM) were each done by separating these 39 samples into 16 training (F1–1 to F1–8, F4–1 to F4–8) and 23 test samples (F1–9 to F1–20, F4–9 to F4–19).Samples†mRNA expression of the 16 defined transcripts (ACAA2, ADH4, ADI1, ALDH2, BRE, CDCA2, EIF2B1, HPX, ITIH1, KIAA1949, MT1H, REV1L, RPS26, SERPINF2, STATIP1, and TTR) were measured from every sample by qRT-PCR.n‡Number of samples for each group.UHCSVMKNNCF12083%§Percentage of properly classified test samples.100%100%F41991%82%82%all test samples3987%91%91%* Unsupervised hierarchical clustering (UHC) was done with the 39 fibrotic samples (20F1 and 19F4). Supervised training/testing procedures (KNNC; SVM) were each done by separating these 39 samples into 16 training (F1–1 to F1–8, F4–1 to F4–8) and 23 test samples (F1–9 to F1–20, F4–9 to F4–19).† mRNA expression of the 16 defined transcripts (ACAA2, ADH4, ADI1, ALDH2, BRE, CDCA2, EIF2B1, HPX, ITIH1, KIAA1949, MT1H, REV1L, RPS26, SERPINF2, STATIP1, and TTR) were measured from every sample by qRT-PCR.‡ Number of samples for each group.§ Percentage of properly classified test samples. Open table in a new tab We have researched if these transcripts could also separate the four stages of fibrosis. All of the identified transcripts showed significant differences of expression levels in at least one comparison between two groups of fibrosis stages. To not overload the Figure 2, only two comparisons of transcript levels have been represented: mild (F1) vs moderate fibrosis (F2–F3) and moderate (F2–F3) vs advanced (F4) fibrosis. The two intermediate stages (F2 and F3) have been gathered because the number of samples in each group was too small. Thus, most of the transcripts showed an up-regulation during the F1 to F2-F3 progression (transcripts with a diamond in down left, 9/16, 56%), followed by a down-regulation during the F2-F3 to F4 progression (transcripts with a diamond in up right, 14/16, 88%). Furthermore, most of these transcripts showed expression levels significantly different between mild or moderate fibrosis and cirrhosis (F1 vs F4 : 81%, F2 vs F4 : 88% and F3 vs F4 : 81%). So, we can hypothesize that these transcripts could allow a better identification of the four stages of fibrosis. Among the 16 above-mentioned transcripts, only four coded for serum proteins, which could be thus serologically measured and become very interesting for a diagnosis purpose. Among these, ITIH1, SERPINF2, and TTR have been measured in the serum samples listed in Table 1 but we failed to quantify the HPX protein level (data not shown). These 100 serum samples were divided in four groups, which corresponded to the four stages of fibrosis. The level of these three proteins was significantly different in the four groups of fibrosis (P < 0.01; Kruskall-Wallis test). Moreover, as shown in Figure 3A,B and Table 3, this level was significantly different according to the stage of fibrosis. Indeed, the ITIH1 protein level increased during the fibrogenic process and strongly decreased at stage F4 (P < 0.005, Mann-Whitney test between F3 and F4).Table 3Significant Differences of Proteins Quantification According to Fibrosis StagesProteinF1 vs F2*Different comparisons between two fibrosis stages.F1 vs F3*Different comparisons between two fibrosis stages.F1 vs F4*Different comparisons between two fibrosis stages.F2 vs F3*Different comparisons between two fibrosis stages.F2 vs F4*Different comparisons between two fibrosis stages.F3 vs F4*Different comparisons between two fibrosis stages.ITIH1NS†P value calculated by Mann-Whitney test; NS, non significant.0.030.04NS0.0050.005SERPINF20.00070.00010.0010.05NS0.006TTR0.0040.0060.0001NS0.0050.02* Different comparisons between two fibrosis stages.† P value calculated by Mann-Whitney test; NS, non significant. Open table in a new tab In contrast, SERPINF2 and TTR protein levels did not increased during the fibrotic process. The SERPINF2 protein level significantly decrea