Quantitative analysis of proteome dynamics in a mouse model of asthma.

BACKGROUND: Asthma is an inflammatory disease of the respiratory system, and a major factor of increasing health care costs worldwide. The molecular actors leading to the development of chronic asthma are not fully understood and require further investigation. OBJECTIVE: The aim of this study was to monitor the proteome dynamics during asthma development from early inflammatory to late fibrotic stages. METHODS: A mouse asthma model was used to analyse the lung proteome at four time points during asthma development (0 weeks = control, 5, 8 and 12 weeks of treatment, n = 6 each). The model was analysed using lung function tests, immune cell counting and histology. Furthermore, a multi-fraction mass spectrometry-based proteome analysis was performed to achieve a comprehensive coverage and quantification of the lung proteome. RESULTS: At early stages, the mice showed predominant eosinophilic inflammation of the airways, which disappeared at later stages and was replaced by marked airway hyper-reactivity and fibrosis of the airways. 3325 proteins were quantified with 435 proteins found to be significantly differentially abundant between the experimental groups (ANOVA p-value ≤.05, maximum fold change ≥1.5). We applied hierarchical clustering to identify common protein abundance profiles along the asthma development and analysed these clusters using gene ontology annotation and enrichment analysis. We demonstrate the correlation of protein clusters with the course of asthma development, that is eosinophilic inflammation and fibrotic remodelling of the airways. CONCLUSIONS AND CLINICAL RELEVANCE: Proteome analysis revealed proteins that were previously described to be important during asthma chronification. Moreover, we identified additional proteins previously not described in the context of asthma. We provide a comprehensive data set of a long-term mouse model of asthma that may contribute to a better understanding and allow new insights into the progression and development of chronic asthma. Data are available via ProteomeXchange with identifier PXD011159.

Noninvasive diagnosis of urothelial cancer in urine using DNA hypermethylation signatures-Gender matters.

Urothelial cancer (UCa) is the most predominant cancer of the urinary tract and noninvasive diagnosis using hypermethylation signatures in urinary cells is promising. Here, we assess gender differences in a newly identified set of methylation biomarkers. UCa-associated hypermethylated sites were identified in urine of a male screening cohort (n = 24) applying Infinium-450K-methylation arrays and verified in two separate mixed-gender study groups (n = 617 in total) using mass spectrometry as an independent technique. Additionally, tissue samples (n = 56) of mixed-gender UCa and urological controls (UCt) were analyzed. The hypermethylation signature of UCa in urine was specific and sensitive across all stages and grades of UCa and independent on hematuria. Individual CpG sensitivities reached up to 81.3% at 95% specificity. Albeit similar methylation differences in tissue of both genders, differences were less pronounced in urine from women, most likely due to the frequent presence of squamous epithelial cells and leukocytes. Increased repression of methylation levels was observed at leukocyte counts ≥500/µl urine which was apparent in 30% of female and 7% of male UCa cases, further confirming the significance of the relative amounts of cancerous and noncancerous cells in urine. Our study shows that gender difference is a most relevant issue when evaluating the performance of urinary biomarkers in cancer diagnostics. In case of UCa, the clinical benefits of methylation signatures to male patients may outweigh those in females due to the general composition of women's urine. Accordingly, these markers offer a diagnostic option specifically in males to decrease the number of invasive cystoscopies.

Direct-acting antivirals-based therapy decreases hepatic fibrosis serum biomarker microfibrillar-associated protein 4 in hepatitis C patients.

BACKGROUND/AIMS: An estimated 80 million people worldwide are infected with viremic hepatitis C virus (HCV). Even after eradication of HCV with direct acting antivirals (DAAs), hepatic fibrosis remains a risk factor for hepatocarcinogenesis. Recently, we confirmed the applicability of microfibrillar-associated protein 4 (MFAP4) as a serum biomarker for the assessment of hepatic fibrosis. The aim of the present study was to assess the usefulness of MFAP4 as a biomarker of liver fibrosis after HCV eliminating therapy with DAAs. METHODS: MFAP4 was measured using an immunoassay in 50 hepatitis C patients at baseline (BL), the end-of-therapy (EoT), and the 12-week follow-up visit (FU). Changes in MFAP4 from BL to FU and their association with laboratory parameters including alanine aminotransferase (ALT), aspartate aminotransferase (AST), platelets, the AST to platelet ratio index (APRI), fibrosis-4 score (FIB-4), and albumin were analyzed. RESULTS: MFAP4 serum levels were representative of the severity of hepatic fibrosis at BL and correlated well with laboratory parameters, especially APRI (Spearman correlation, R²=0.80). Laboratory parameters decreased significantly from BL to EoT. MFAP4 serum levels were found to decrease from BL and EoT to FU with high statistical significance (Wilcoxon P<0.001 for both). CONCLUSION: Our findings indicate that viral eradication resulted in reduced MFAP4 serum levels, presumably representing a decrease in hepatic fibrogenesis or fibrosis. Hence, MFAP4 may be a useful tool for risk assessment in hepatitis C patients with advanced fibrosis after eradication of the virus.

Landscape of pain in Parkinson's disease: impact of gender differences.

Background/Aims: Pain is a non-motor symptom of Parkinson's disease (PD). Few systematic studies have been carried out and there are still no guidelines on pain therapy in PD. Additionally, within the studies that do exist, gender-specific differences in pain perception are often the focus, though no consistent results have to date been obtained. The first main aim of our study was therefore to map pain in the largest PD study group to date, with the second being the analysis of the impact of different pain therapies in PD. The third and main aim was to correlate the obtained results with gender. Methods: A structured questionnaire with questions focusing on pain was sent to PD patients, with a subsequent statistical analysis correlating the data on pain features and pain therapy with gender. Results: The study included 1204 female and 1610 male PD patients. Spinal-paravertebral pain emerged as the dominant form of pain. A significant correlation was further demonstrated between gender and pain localization, pain intensity (p-value < 0.05), and pain as impairment to quality of life (p-value < 0.05). Nonsteroidal anti-inflammatory drugs were the painkillers most frequently used by the patients. Aside from non-opioid analgesics (p-value < 0.05), there was no demonstrated significant correlation between pain treatments and gender. Conclusion: This study found that gender influenced pain perception in the PD patients tested but did not impact the approach to pain therapy.

BioInfra.Prot: A comprehensive proteomics workflow including data standardization, protein inference, expression analysis and data publication.

The analysis of high-throughput mass spectrometry-based proteomics data must address the specific challenges of this technology. To this end, the comprehensive proteomics workflow offered by the de.NBI service center BioInfra.Prot provides indispensable components for the computational and statistical analysis of this kind of data. These components include tools and methods for spectrum identification and protein inference, protein quantification, expression analysis as well as data standardization and data publication. All particular methods of the workflow which address these tasks are state-of-the-art or cutting edge. As has been shown in previous publications, each of these methods is adequate to solve its specific task and gives competitive results. However, the methods included in the workflow are continuously reviewed, updated and improved to adapt to new scientific developments. All of these particular components and methods are available as stand-alone BioInfra.Prot services or as a complete workflow. Since BioInfra.Prot provides manifold fast communication channels to get access to all components of the workflow (e.g., via the BioInfra.Prot ticket system: bioinfraprot@rub.de) users can easily benefit from this service and get support by experts.

Annexin A10 optimally differentiates between intrahepatic cholangiocarcinoma and hepatic metastases of pancreatic ductal adenocarcinoma: a comparative study of immunohistochemical markers and panels.

Discriminating intrahepatic cholangiocarcinoma (ICC) from hepatic metastases of pancreatic ductal adenocarcinoma (mPDAC) can be challenging. While pathologists might depend on clinical information regarding a primary tumor, their diagnosis will lead the patient either to potentially curative surgery (for ICC) or to palliation (for mPDAC). Beyond the validation of recently published potential biomarkers for PDAC (primary or metastatic) in a large cohort, we assessed diagnostic performance of the most promising candidates in the challenging task of discriminating metastatic PDAC (mPDAC) from ICC. In a training set of 87 ICC and 88 pPDAC, our previously identified biomarkers Annexin A1 (ANXA1), ANXA10, and ANXA13 were tested and compared with 11 published biomarkers or panels (MUCIN 1, Agrin, S100P, MUC5 AC, Laminin, VHL, CK 17, N-Cadherin, ELAC2, PODXL and HSPG2). Biomarkers with best results were further tested in an independent series of biopsies of 27 ICC and 36 mPDAC. Highest AUC values (between 0.72 and 0.84) for the discrimination between ICC and pPDAC were found in the training set for Annexin A1, Annexin A10, MUC5 AC, CK17, and N-Cadherin. These markers were further tested on an independent series of liver biopsies containing ICC or mPDAC. Diagnostic characteristics were evaluated for individual markers as well as for 3× panels. ANXA 10 showed the highest diagnostic potential of all single markers, correctly classifying 75% of mPDAC and 85% of ICC. Our results suggest that ANXA10 may be useful to differentiate between ICC and mPDAC, when only a tissue specimen is available.

Comprehensive proteomic analysis of Penicillium verrucosum.

Mass spectrometric identification of proteins in species lacking validated sequence information is a major problem in veterinary science. In the present study, we used ochratoxin A producing Penicillium verrucosum to identify and quantitatively analyze proteins of an organism with yet no protein information available. The work presented here aimed to provide a comprehensive protein identification of P. verrucosum using shotgun proteomics. We were able to identify 3631 proteins in an "ab initio" translated database from DNA sequences of P. verrucosum. Additionally, a sequential window acquisition of all theoretical fragment-ion spectra analysis was done to find differentially regulated proteins at two different time points of the growth curve. We compared the proteins at the beginning (day 3) and at the end of the log phase (day 12).

Spatial and molecular resolution of diffuse malignant mesothelioma heterogeneity by integrating label-free FTIR imaging, laser capture microdissection and proteomics.

Diffuse malignant mesothelioma (DMM) is a heterogeneous malignant neoplasia manifesting with three subtypes: epithelioid, sarcomatoid and biphasic. DMM exhibit a high degree of spatial heterogeneity that complicates a thorough understanding of the underlying different molecular processes in each subtype. We present a novel approach to spatially resolve the heterogeneity of a tumour in a label-free manner by integrating FTIR imaging and laser capture microdissection (LCM). Subsequent proteome analysis of the dissected homogenous samples provides in addition molecular resolution. FTIR imaging resolves tumour subtypes within tissue thin-sections in an automated and label-free manner with accuracy of about 85% for DMM subtypes. Even in highly heterogeneous tissue structures, our label-free approach can identify small regions of interest, which can be dissected as homogeneous samples using LCM. Subsequent proteome analysis provides a location specific molecular characterization. Applied to DMM subtypes, we identify 142 differentially expressed proteins, including five protein biomarkers commonly used in DMM immunohistochemistry panels. Thus, FTIR imaging resolves not only morphological alteration within tissue but it resolves even alterations at the level of single proteins in tumour subtypes. Our fully automated workflow FTIR-guided LCM opens new avenues collecting homogeneous samples for precise and predictive biomarkers from omics studies.

Tissue-based quantitative proteome analysis of human hepatocellular carcinoma using tandem mass tags.

CONTEXT AND OBJECTIVE: Human hepatocellular carcinoma (HCC) is a severe malignant disease, and accurate and reliable diagnostic markers are still needed. This study was aimed for the discovery of novel marker candidates by quantitative proteomics. METHODS AND RESULTS: Proteomic differences between HCC and nontumorous liver tissue were studied by mass spectrometry. Among several significantly upregulated proteins, translocator protein 18 (TSPO) and Ras-related protein Rab-1A (RAB1A) were selected for verification by immunohistochemistry in an independent cohort. For RAB1A, a high accuracy for the discrimination of HCC and nontumorous liver tissue was observed. CONCLUSION: RAB1A was verified to be a potent biomarker candidate for HCC.

Quantitative Secretome Analysis of Activated Jurkat Cells Using Click Chemistry-Based Enrichment of Secreted Glycoproteins.

Quantitative secretome analyses are a high-performance tool for the discovery of physiological and pathophysiological changes in cellular processes. However, serum supplements in cell culture media limit secretome analyses, but serum depletion often leads to cell starvation and consequently biased results. To overcome these limiting factors, we investigated a model of T cell activation (Jurkat cells) and performed an approach for the selective enrichment of secreted proteins from conditioned medium utilizing metabolic marking of newly synthesized glycoproteins. Marked glycoproteins were labeled via bioorthogonal click chemistry and isolated by affinity purification. We assessed two labeling compounds conjugated with either biotin or desthiobiotin and the respective secretome fractions. 356 proteins were quantified using the biotin probe and 463 using desthiobiotin. 59 proteins were found differentially abundant (adjusted p-value ≤0.05, absolute fold change ≥1.5) between inactive and activated T cells using the biotin method and 86 using the desthiobiotin approach, with 31 mutual proteins cross-verified by independent experiments. Moreover, we analyzed the cellular proteome of the same model to demonstrate the benefit of secretome analyses and provide comprehensive data sets of both. 336 proteins (61.3%) were quantified exclusively in the secretome. Data are available via ProteomeXchange with identifier PXD004280.

Shotgun proteomic analysis of Yersinia ruckeri strains under normal and iron-limited conditions.

Yersinia ruckeri is the causative agent of enteric redmouth disease of fish that causes significant economic losses, particularly in salmonids. Bacterial pathogens differentially express proteins in the host during the infection process, and under certain environmental conditions. Iron is an essential nutrient for many cellular processes and is involved in host sensing and virulence regulation in many bacteria. Little is known about proteomics expression of Y. ruckeri in response to iron-limited conditions. Here, we present whole cell protein identification and quantification for two motile and two non-motile strains of Y. ruckeri cultured in vitro under iron-sufficient and iron-limited conditions, using a shotgun proteomic approach. Label-free, gel-free quantification was performed using a nanoLC-ESI and high resolution mass spectrometry. SWATH technology was used to distinguish between different strains and their responses to iron limitation. Sixty-one differentially expressed proteins were identified in four Y. ruckeri strains. These proteins were involved in processes including iron ion capture and transport, and enzymatic metabolism. The proteins were confirmed to be differentially expressed at the transcriptional level using quantitative real time PCR. Our study provides the first detailed proteome analysis of Y. ruckeri strains, which contributes to our understanding of virulence mechanisms of Y. ruckeri, and informs development of novel control methods for enteric redmouth disease.

Quantitative proteome analysis reveals the correlation between endocytosis-associated proteins and hepatocellular carcinoma dedifferentiation.

The majority of poorly differentiated hepatocellular carcinomas (HCCs) develop from well-differentiated tumors. Endocytosis is a cellular function which is likely to take part in this development due to its important role in regulating the abundances of vital signaling receptors. Here, we aimed to investigate the abundance of endocytosis-associated proteins in HCCs with various differentiation grades. Therefore, we analyzed 36 tissue specimens from HCC patients via LC-MS/MS-based label-free quantitative proteomics including 19 HCC tissue samples with different degrees of histological grades and corresponding non-tumorous tissue controls. As a result, 277 proteins were differentially regulated between well-differentiated tumors and controls. In moderately and poorly differentiated tumors, 278 and 1181 proteins, respectively, were significantly differentially regulated compared to non-tumorous tissue. We explored the regulated proteins based on their functions and identified thirty endocytosis-associated proteins, mostly overexpressed in poorly differentiated tumors. These included proteins that have been shown to be up-regulated in HCC like clathrin heavy chain-1 (CLTC) as well as unknown proteins, such as secretory carrier-associated membrane protein 3 (SCAMP3). The abundances of SCAMP3 and CLTC were immunohistochemically examined in tissue sections of 84 HCC patients. We demonstrate the novel association of several endocytosis-associated proteins, in particular, SCAMP3 with HCC progression.

Evaluation of the biomarker candidate MFAP4 for non-invasive assessment of hepatic fibrosis in hepatitis C patients.

BACKGROUND: The human microfibrillar-associated protein 4 (MFAP4) is located to extracellular matrix fibers and plays a role in disease-related tissue remodeling. Previously, we identified MFAP4 as a serum biomarker candidate for hepatic fibrosis and cirrhosis in hepatitis C patients. The aim of the present study was to elucidate the potential of MFAP4 as biomarker for hepatic fibrosis with a focus on the differentiation of no to moderate (F0-F2) and severe fibrosis stages and cirrhosis (F3 and F4, Desmet-Scheuer scoring system). METHODS: MFAP4 levels were measured using an AlphaLISA immunoassay in a retrospective study including n = 542 hepatitis C patients. We applied a univariate logistic regression model based on MFAP4 serum levels and furthermore derived a multivariate model including also age and gender. Youden-optimal cutoffs for binary classification were determined for both models without restrictions and considering a lower limit of 80 % sensitivity (correct classification of F3 and F4), respectively. To assess the generalization error, leave-one-out cross validation (LOOCV) was performed. RESULTS: MFAP4 levels were shown to differ between no to moderate fibrosis stages F0-F2 and severe stages (F3 and F4) with high statistical significance (t test on log scale, p value <2.2·10(-16)). In the LOOCV, the univariate classification resulted in 85.8 % sensitivity and 54.9 % specificity while the multivariate model yielded 81.3 % sensitivity and 61.5 % specificity (restricted approaches). CONCLUSIONS: We confirmed the applicability of MFAP4 as a novel serum biomarker for assessment of hepatic fibrosis and identification of high-risk patients with severe fibrosis stages in hepatitis C. The combination of MFAP4 with existing tests might lead to a more accurate non-invasive diagnosis of hepatic fibrosis and allow a cost-effective disease management in the era of new direct acting antivirals.

Flow cytometric characterization of microglia in the offspring of PolyI:C treated mice.

The neuropathology of schizophrenia has been reported to be closely associated with microglial activation. In a previous study, using the prenatal PolyI:C schizophrenia animal model, we showed an increase in cell numbers and a reduction in microglial branching in 30-day-old PolyI:C descendants, which suggests that there is microglial activation during adolescence. To provide more information about the activation state, we aimed to examine the expression levels of Iba1, which was reported to be up-regulated in activated microglia. We used a flow cytometric approach and investigated CD11b and CD45, two additional markers for the identification of microglial cells. We demonstrated that intracellular staining against Iba1 can be used as a reliable flow cytometric method for identification of microglial cells. Prenatal PolyI:C treatment had long-term effects on CD11b and CD45 expression. It also resulted in a trend towards increased Iba1 expression. Imbalance in CD11b, CD45, and Iba1 expression might contribute to impaired synaptic surveillance and enhanced activation/inflammatory activity of microglia in adult offspring.

Novel immunohistochemical markers differentiate intrahepatic cholangiocarcinoma from benign bile duct lesions.

AIMS: The distinction between intrahepatic cholangiocarcinoma (ICC) and benign bile duct lesions can be challenging. Using our previously identified potential biomarkers for ICC, we examined whether these are useful for the differential diagnosis of ICC, bile duct adenoma and reactive bile duct proliferations in an immunohistochemical approach and identified a diagnostic marker panel including known biomarkers. METHODS: Subjects included samples from 77 patients with ICC, 33 patients with bile duct adenoma and 47 patients with ductular reactions in liver cirrhosis. Our previously identified biomarkers (stress-induced phosphoprotein 1 (STIP1), SerpinH1, 14-3-3Sigma) were tested immunohistochemically following comparison with candidates from the literature (cluster of differentiation 56, heat shock protein (HSP)27, HSP70, B-cell-lymphoma2, p53, ki67). RESULTS: The expression of SerpinH1 and 14-3-3Sigma was significantly higher in ICC than in bile duct adenomas and ductular reactions (p<0.05), whereas STIP1 expression was significantly higher (p<0.05) in ICC than in ductular reactions, but the difference to the bile duct adenoma group was not significant. A panel of the biomarker SerpinH1, 14-3-3Sigma and ki67 (≥2 marker positive) showed a high diagnostic accuracy (sensitivity 87.8%, specificity 95.9%, accuracy 91.8%) in the differential diagnosis of ICC versus non-malignant bile duct lesions. CONCLUSIONS: This suggests that 14-3-3Sigma and SerpinH1 may be useful in the differential diagnosis of malignant, benign and reactive bile duct lesions in addition to ki67 where a cut-off of >5% might be used for the distinction of malignant and non-malignant lesions.

PAA: an R/bioconductor package for biomarker discovery with protein microarrays.

UNLABELLED: The R/Bioconductor package Protein Array Analyzer (PAA) facilitates a flexible analysis of protein microarrays for biomarker discovery (esp., ProtoArrays). It provides a complete data analysis workflow including preprocessing and quality control, uni- and multivariate feature selection as well as several different plots and results tables to outline and evaluate the analysis results. As a main feature, PAA's multivariate feature selection methods are based on recursive feature elimination (e.g. SVM-recursive feature elimination, SVM-RFE) with stability ensuring strategies such as ensemble feature selection. This enables PAA to detect stable and reliable biomarker candidate panels. AVAILABILITY AND IMPLEMENTATION: PAA is freely available (BSD 3-clause license) from http://www.bioconductor.org/packages/PAA/ CONTACT: michael.turewicz@rub.de or martin.eisenacher@rub.de.

Immunohistochemical Markers Distinguishing Cholangiocellular Carcinoma (CCC) from Pancreatic Ductal Adenocarcinoma (PDAC) Discovered by Proteomic Analysis of Microdissected Cells.

Cholangiocellular carcinoma (CCC) and pancreatic ductal adenocarcinoma (PDAC) are two highly aggressive cancer types that arise from epithelial cells of the pancreatobiliary system. Owing to their histological and morphological similarity, differential diagnosis between CCC and metastasis of PDAC located in the liver frequently proves an unsolvable issue for pathologists. The detection of biomarkers with high specificity and sensitivity for the differentiation of these tumor types would therefore be a valuable tool. Here, we address this problem by comparing microdissected CCC and PDAC tumor cells from nine and eleven cancer patients, respectively, in a label-free proteomics approach. The novel biomarker candidates were subsequently verified by immunohistochemical staining of 73 CCC, 78 primary, and 18 metastatic PDAC tissue sections. In the proteome analysis, we found 180 proteins with a significantly differential expression between CCC and PDAC cells (p value < 0.05, absolute fold change > 2). Nine candidate proteins were chosen for an immunohistochemical verification out of which three showed very promising results. These were the annexins ANXA1, ANXA10, and ANXA13. For the correct classification of PDAC, ANXA1 showed a sensitivity of 84% and a specificity of 85% and ANXA10 a sensitivity of 90% at a specificity of 66%. ANXA13 was higher abundant in CCC. It presented a sensitivity of 84% at a specificity of 55%. In metastatic PDAC tissue ANXA1 and ANXA10 showed similar staining behavior as in the primary PDAC tumors (13/18 and 17/18 positive, respectively). ANXA13, however, presented positive staining in eight out of eighteen secondary PDAC tumors and was therefore not suitable for the differentiation of these from CCC. We conclude that ANXA1 and ANXA10 are promising biomarker candidates with high diagnostic values for the differential diagnosis of intrahepatic CCC and metastatic liver tumors deriving from PDAC.

Circulating U2 small nuclear RNA fragments as a diagnostic and prognostic biomarker in lung cancer patients.

PURPOSE: Lung cancer accounts for one in five cancer deaths. Broad screening strategies for high-risk populations are unavailable, and the validation of biomarkers for early cancer detection remains a prime interest. Therefore, we investigated the value of circulating U2 small nuclear RNA fragments (RNU2-1f) as a biomarker for diagnosis, prognosis estimation and treatment monitoring in a large lung cancer cohort. METHODS: We determined RNU2-1f abundance in sera of patients with treatment-naive lung cancer (n = 211, 25.6 % early stage), chronic lung disease (n = 56) and healthy controls (n = 58) by reverse transcription quantitative PCR. Initial levels and changes after one chemotherapy cycle were correlated with treatment outcomes in patient subsets. RESULTS: Relative serum RNU2-1f expression levels (REL) were elevated in lung cancer patients compared with patients with chronic lung disease and healthy controls (p < 0.0001). The area under the receiver operating characteristic curve for the complete data set (lung cancer vs. healthy) was 0.91 (95 % CI 0.87-0.95). By applying a REL of -4.505 as diagnostic cutoff (Youden's criterion), sensitivity and specificity reached 0.86 and 0.81, respectively. To determine the generalization error, in a subsampling study, sensitivity and specificity were estimated as 0.82 and 0.77 for the application to future, independent samples. High initial RNU2-1f REL were associated with shorter median survival in stage IIIB/IV disease (RNU2-1fhigh = 228 days/RNU2-1flow = 484 days; p = 0.009, log-rank test, HR1.43 95 % CI 1.23-1.66). Multivariate analysis confirmed RNU2-1f as an independent prognostic factor. Patients with subsequent RNU2-1f reduction had a trend toward better treatment outcome. CONCLUSIONS: Serum RNU2-1f may serve as a biomarker for lung cancer detection, prognosis prediction and treatment monitoring.

Differential proteomic and tissue expression analyses identify valuable diagnostic biomarkers of hepatocellular differentiation and hepatoid adenocarcinomas.

The exact discrimination of lesions with true hepatocellular differentiation from secondary tumours and neoplasms with hepatocellular histomorphology like hepatoid adenocarcinomas (HAC) is crucial. Therefore, we aimed to identify ancillary protein biomarkers by using complementary proteomic techniques (2D-DIGE, label-free MS). The identified candidates were immunohistochemically validated in 14 paired samples of hepatocellular carcinoma (HCC) and non-tumourous liver tissue (NT). The candidates and HepPar1/Arginase1 were afterwards tested for consistency in a large cohort of hepatocellular lesions and NT (n = 290), non-hepatocellular malignancies (n = 383) and HAC (n = 13). Eight non-redundant, differentially expressed proteins were suitable for further immunohistochemical validation and four (ABAT, BHMT, FABP1, HAOX1) for further evaluation. Sensitivity and specificity rates for HCC/HAC were as follows: HepPar1 80.2%, 94.3% / 80.2%, 46.2%; Arginase1 82%, 99.4% / 82%, 69.2%; BHMT 61.4%, 93.8% / 61.4%, 100%; ABAT 84.4%, 33.7% / 84.4%, 30.8%; FABP1 87.2%, 95% / 87.2%, 69.2%; HAOX1 95.5%, 36.3% / 95.5%, 46.2%. The best 2×/3× biomarker panels for the diagnosis of HCC consisted of Arginase1/HAOX1 and BHMT/Arginase1/HAOX1 and for HAC consisted of Arginase1/FABP1 and BHMT/Arginase1/FABP1. In summary, we successfully identified, validated and benchmarked protein biomarker candidates of hepatocellular differentiation. BHMT in particular exhibited superior diagnostic characteristics in hepatocellular lesions and specifically in HAC. BHMT is therefore a promising (panel based) biomarker candidate in the differential diagnostic process of lesions with hepatocellular aspect.

Analysis of disease-associated protein expression using quantitative proteomics­fibulin-5 is expressed in association with hepatic fibrosis.

Hepatic fibrosis and cirrhosis are major health problems worldwide. Until now, highly invasive biopsy remains the diagnostic gold standard despite many disadvantages. To develop noninvasive diagnostic assays for the assessment of liver fibrosis, it is urgently necessary to identify molecules that are robustly expressed in association with the disease. We analyzed biopsied tissue samples from 95 patients with HBV/HCV-associated hepatic fibrosis using three different quantification methods. We performed a label-free proteomics discovery study to identify novel disease-associated proteins using a subset of the cohort (n = 27). Subsequently, gene expression data from all available clinical samples were analyzed (n = 77). Finally, we performed a targeted proteomics approach, multiple reaction monitoring (MRM), to verify the disease-associated expression in samples independent from the discovery approach (n = 68). We identified fibulin-5 (FBLN5) as a novel protein expressed in relation to hepatic fibrosis. Furthermore, we confirmed the altered expression of microfibril-associated glycoprotein 4 (MFAP4), lumican (LUM), and collagen alpha-1(XIV) chain (COL14A1) in association to hepatic fibrosis. To our knowledge, no tissue-based quantitative proteomics study for hepatic fibrosis has been performed using a cohort of comparable size. By this means, we add substantial evidence for the disease-related expression of the proteins examined in this study.