iTRAQ Quantitative Proteomic Comparison of Metastatic and Non-Metastatic Uveal Melanoma Tumors

Background

Uveal melanoma is the most common malignancy of the adult eye. The overall mortality rate is high because this aggressive cancer often metastasizes before ophthalmic diagnosis. Quantitative proteomic analysis of primary metastasizing and non-metastasizing tumors was pursued for insights into mechanisms and biomarkers of uveal melanoma metastasis.

Methods

Eight metastatic and 7 non-metastatic human primary uveal melanoma tumors were analyzed by LC MS/MS iTRAQ technology with Bruch’s membrane/choroid complex from normal postmortem eyes as control tissue. Tryptic peptides from tumor and control proteins were labeled with iTRAQ tags, fractionated by cation exchange chromatography, and analyzed by LC MS/MS. Protein identification utilized the Mascot search engine and the human Uni-Prot/Swiss-Protein database with false discovery ≤ 1%; protein quantitation utilized the Mascot weighted average method. Proteins designated differentially expressed exhibited quantitative differences (p ≤ 0.05, t-test) in a training set of five metastatic and five non-metastatic tumors. Logistic regression models developed from the training set were used to classify the metastatic status of five independent tumors.

Results

Of 1644 proteins identified and quantified in 5 metastatic and 5 non-metastatic tumors, 12 proteins were found uniquely in ≥ 3 metastatic tumors, 28 were found significantly elevated and 30 significantly decreased only in metastatic tumors, and 31 were designated differentially expressed between metastatic and non-metastatic tumors. Logistic regression modeling of differentially expressed collagen alpha-3(VI) and heat shock protein beta-1 allowed correct prediction of metastasis status for each of five independent tumor specimens.

Conclusions

The present data provide new clues to molecular differences in metastatic and non-metastatic uveal melanoma tumors. While sample size is limited and validation required, the results support collagen alpha-3(VI) and heat shock protein beta-1 as candidate biomarkers of uveal melanoma metastasis and establish a quantitative proteomic database for uveal melanoma primary tumors.     

Quantitative Proteomic Analysis of Serum from Pregnant Women Carrying a Fetus with Conotruncal Heart Defect Using Isobaric Tags for Relative and Absolute Quantitation (iTRAQ) Labeling

Objective

To identify differentially expressed proteins from serum of pregnant women carrying a conotruncal heart defects (CTD) fetus, using proteomic analysis.

Methods

The study was conducted using a nested case-control design. The 5473 maternal serum samples were collected at 14–18 weeks of gestation. The serum from 9 pregnant women carrying a CTD fetus, 10 with another CHD (ACHD) fetus, and 11 with a normal fetus were selected from the above samples, and analyzed by using isobaric tags for relative and absolute quantitation (iTRAQ) coupled with two-dimensional liquid chromatography-tandem mass spectrometry(2D LC-MS/MS). The differentially expressed proteins identified by iTRAQ were further validated with Western blot.

Results

A total of 105 unique proteins present in the three groups were identified, and relative expression data were obtained for 92 of them with high confidence by employing the iTRAQ-based experiments. The downregulation of gelsolin in maternal serum of fetus with CTD was further verified by Western blot.

Conclusions

The identification of differentially expressed protein gelsolin in the serum of the pregnant women carrying a CTD fetus by using proteomic technology may be able to serve as a foundation to further explore the biomarker for detection of CTD fetus from the maternal serum.     

Proteomic Profiling of Bronchoalveolar Lavage Fluid in Critically Ill Patients with Ventilator-Associated Pneumonia

Rationale

Ventilator-associated pneumonia (VAP) is a common complication in patients with acute lung injury (ALI) and can lead to increased morbidity and mortality. Identifying protein profiles specific to VAP in bronchoalveolar lavage fluid (BALF) may aid in earlier diagnosis, elucidate mechanisms of disease, and identify putative targets for therapeutic intervention.

Methods

BALF was obtained from 5 normal subjects and 30 ALI patients: 14 with VAP (VAP⁺) and 16 without VAP (VAP^–). Each sample underwent shotgun proteomic analysis based on tandem mass spectrometry. Differentially expressed proteins between the groups were identified using statistical methods based on spectral counting. Mechanisms of disease were explored using functional annotation and protein interaction network analysis. Supervised classification algorithms were implemented to discover a proteomic classifier for identifying critically ill patients with VAP.

Results

ALI patients had distinct BALF proteomic profiles compared to normal controls. Within the ALI group, we identified 76 differentially expressed proteins between VAP⁺ and VAP^–. Functional analysis of these proteins suggested activation of pro-inflammatory pathways during VAP. We identified and validated a limited proteomic signature that discriminated VAP⁺ from VAP^– patients comprised of three proteins: S100A8, lactotransferrin (LTF), and actinin 1 (ACTN1).

Conclusions

Combining proteomic with computational analyses is a powerful approach to study the BALF proteome during lung injury and development of VAP. This integrative methodology is a promising strategy to differentiate clinically relevant subsets of ALI patients, including those suffering from VAP.     

Proteins from formalin-fixed paraffin-embedded prostate cancer sections that predict the risk of metastatic disease

Background

Prostate cancer is the most frequently diagnosed cancer in men and the third leading cause of cancer related deaths among men living in developed countries. Biomarkers that predict disease outcome at the time of initial diagnosis would substantially aid disease management.

Results

Proteins extracted from formalin-fixed paraffin-embedded tissue were identified using nanoflow liquid chromatography-MALDI MS/MS or after separation by one- or two-dimensional electrophoresis. The proteomics data have been deposited to the ProteomeXchange with identifier PXD000963. A list of potential biomarker candidates, based on proposed associations with prostate cancer, was derived from the 320 identified proteins. Candidate biomarkers were then examined by multiplexed Western blotting of archival specimens from men with premetastatic disease and subsequent disease outcome data. Annexin A2 provided the best prediction of risk of metastatic disease (log-rank Chi squared p = 0. 025). A tumor/control tissue >2-fold relative abundance increase predicted early biochemical failure, while <2-fold change predicted late or no biochemical failure.

Conclusions

This study confirms the potential for use of archival FFPE specimens in the search for prognostic biomarkers for prostate cancer and suggests that annexin A2 abundance in diagnostic biopsies is predictive for metastatic potential. Protein profiling each cancer may lead to an overall reduction in mortality from metastatic prostate cancer as well as reduced treatment associated morbidity.

Electronic supplementary material

The online version of this article (doi:10.1186/s12014-015-9096-3) contains supplementary material, which is available to authorized users.     

Novel Multiple Markers to Distinguish Melanoma from Dysplastic Nevi

Background

Distinguishing melanoma from dysplastic nevi can be challenging.

Objective

To assess which putative molecular biomarkers can be optimally combined to aid in the clinical diagnosis of melanoma from dysplastic nevi.

Methods

Immunohistochemical expressions of 12 promising biomarkers (pAkt, Bim, BRG1, BRMS1, CTHRC1, Cul1, ING4, MCL1, NQO1, SKP2, SNF5 and SOX4) were studied in 122 melanomas and 33 dysplastic nevi on tissue microarrays. The expression difference between melanoma and dysplastic nevi was performed by univariate and multiple logistic regression analysis, diagnostic accuracy of single marker and optimal combinations were performed by receiver operating characteristic (ROC) curve and artificial neural network (ANN) analysis. Classification and regression tree (CART) was used to examine markers simultaneous optimizing the accuracy of melanoma. Ten-fold cross-validation was analyzed for estimating generalization error for classification.

Results

Four (Bim, BRG1, Cul1 and ING4) of 12 markers were significantly differentially expressed in melanoma compared with dysplastic nevi by both univariate and multiple logistic regression analysis (p < 0.01). These four combined markers achieved 94.3% sensitivity, 81.8% specificity and attained 84.3% area under the ROC curve (AUC) and the ANN classified accuracy with training of 83.2% and testing of 81.2% for distinguishing melanoma from dysplastic nevi. The classification trees identified ING4, Cul1 and BRG1 were the most important classification parameters in ranking top-performing biomarkers with cross-validation error of 0.03.

Conclusions

The multiple biomarkers ING4, Cul1, BRG1 and Bim described here can aid in the discrimination of melanoma from dysplastic nevi and provide a new insight to help clinicians recognize melanoma.     

Proteomic Interactions in the Mouse Vitreous-Retina Complex

Purpose

Human vitreoretinal diseases are due to presumed abnormal mechanical interactions between the vitreous and retina, and translational models are limited. This study determined whether nonstructural proteins and potential retinal biomarkers were expressed by the normal mouse vitreous and retina.

Methods

Vitreous and retina samples from mice were collected by evisceration and analyzed by liquid chromatography-tandem mass spectrometry. Identified proteins were further analyzed for differential expression and functional interactions using bioinformatic software.

Results

We identified 1,680 unique proteins in the retina and 675 unique proteins in the vitreous. Unbiased clustering identified protein pathways that distinguish retina from vitreous including oxidative phosphorylation and neurofilament cytoskeletal remodeling, whereas the vitreous expressed oxidative stress and innate immunology pathways. Some intracellular protein pathways were found in both retina and vitreous, such as glycolysis and gluconeogenesis and neuronal signaling, suggesting proteins might be shuttled between the retina and vitreous. We also identified human disease biomarkers represented in the mouse vitreous and retina, including carbonic anhydrase-2 and 3, crystallins, macrophage inhibitory factor, glutathione peroxidase, peroxiredoxins, S100 precursors, and von Willebrand factor.

Conclusions

Our analysis suggests the vitreous expresses nonstructural proteins that functionally interact with the retina to manage oxidative stress, immune reactions, and intracellular proteins may be exchanged between the retina and vitreous. This novel proteomic dataset can be used for investigating human vitreoretinopathies in mouse models. Validation of vitreoretinal biomarkers for human ocular diseases will provide a critical tool for diagnostics and an avenue for therapeutics.     

Proteomic Analysis of Kidney in Rats Chronically Exposed to Monosodium Glutamate

Background

Chronic monosodium glutamate (MSG) intake causes kidney dysfunction and renal oxidative stress in the animal model. To gain insight into the renal changes induced by MSG, proteomic analysis of the kidneys was performed.

Methods

Six week old male Wistar rats were given drinking water with or without MSG (2 mg/g body weight, n = 10 per group) for 9 months. Kidneys were removed, frozen, and stored at –75°C. After protein extraction, 2-D gel electrophoresis was performed and renal proteome profiles were examined with Colloidal Coomassie Brilliant Blue staining. Statistically significant protein spots (ANOVA, p<0.05) with 1.2-fold difference were excised and analyzed by LC-MS. Proteomic data were confirmed by immunohistochemistry and Western blot analyses.

Results

The differential image analysis showed 157 changed spots, of which 71 spots were higher and 86 spots were lower in the MSG-treated group compared with those in the control group. Eight statistically significant and differentially expressed proteins were identified: glutathione S-transferase class-pi, heat shock cognate 71 kDa, phosphoserine phosphatase, phosphoglycerate kinase, cytosolic glycerol-3-phosphate dehydrogenase, 2-amino-3-carboxymuconate-6-semialdehyde decarboxylase, α-ketoglutarate dehydrogenase and succinyl-CoA ligase.

Conclusion

The identified proteins are mainly related to oxidative stress and metabolism. They provide a valuable clue to explore the mechanism of renal handling and toxicity on chronic MSG intake.     

Proteomic profiling of serum samples from chikungunya-infected patients provides insights into host response

Background

Chikungunya is a highly debilitating febrile illness caused by Chikungunya virus, a single-stranded RNA virus, which is transmitted by Aedes aegypti or Aedes albopictus mosquito species. The pathogenesis and host responses in individuals infected with the chikungunya virus are not well understood at the molecular level. We carried out proteomic profiling of serum samples from chikungunya patients in order to identify molecules associated with the host response to infection by this virus.

Results

Proteomic profiling of serum obtained from the infected individuals resulted in identification of 569 proteins. Of these, 63 proteins were found to be differentially expressed (≥ 2-fold) in patient as compared to control sera. These differentially expressed proteins were involved in various processes such as lipid metabolism, immune response, transport, signal transduction and apoptosis.

Conclusions

This is the first report providing a global proteomic profile of serum samples from individuals infected with the chikungunya virus. Our data provide an insight into the proteins that are involved as host response factors during an infection. These proteins include clusterin, apolipoproteins and S100A family of proteins.     

Identification of Novel Biomarkers for Sepsis Prognosis via Urinary Proteomic Analysis Using iTRAQ Labeling and 2D-LC-MS/MS

Objectives

Sepsis is the major cause of death for critically ill patients. Recent progress in proteomics permits a thorough characterization of the mechanisms associated with critical illness. The purpose of this study was to screen potential biomarkers for early prognostic assessment of patients with sepsis.

Methods

For the discovery stage, 30 sepsis patients with different prognoses were selected. Urinary proteins were identified using isobaric tags for relative and absolute quantitation (iTRAQ) coupled with LC-MS/MS. Mass spec instrument analysis were performed with Mascot software and the International Protein Index (IPI); bioinformatic analyses were used by the algorithm of set and the Gene Ontology (GO) Database. For the verification stage, the study involved another 54 sepsis-hospitalized patients, with equal numbers of patients in survivor and non-survivor groups based on 28-day survival. Differentially expressed proteins were verified by Western Blot.

Results

A total of 232 unique proteins were identified. Proteins that were differentially expressed were further analyzed based on the pathophysiology of sepsis and biomathematics. For sepsis prognosis, five proteins were significantly up-regulated: selenium binding protein-1, heparan sulfate proteoglycan-2, alpha-1-B glycoprotein, haptoglobin, and lipocalin; two proteins were significantly down-regulated: lysosome-associated membrane proteins-1 and dipeptidyl peptidase-4. Based on gene ontology clustering, these proteins were associated with the biological processes of lipid homeostasis, cartilage development, iron ion transport, and certain metabolic processes. Urinary LAMP-1 was down-regulated, consistent with the Western Blot validation.

Conclusion

This study provides the proteomic analysis of urine to identify prognostic biomarkers of sepsis. The seven identified proteins provide insight into the mechanism of sepsis. Low urinary LAMP-1 levels may be useful for early prognostic assessment of sepsis.

Trial Registration

ClinicalTrial.gov {"type":"clinical-trial","attrs":{"text":"NCT01493492","term_id":"NCT01493492"}}NCT01493492     

Proteomic Analysis of Urine to Identify Breast Cancer Biomarker Candidates Using a Label-Free LC-MS/MS Approach

Introduction

Breast cancer is a complex heterogeneous disease and is a leading cause of death in women. Early diagnosis and monitoring progression of breast cancer are important for improving prognosis. The aim of this study was to identify protein biomarkers in urine for early screening detection and monitoring invasive breast cancer progression.

Method

We performed a comparative proteomic analysis using ion count relative quantification label free LC-MS/MS analysis of urine from breast cancer patients (n = 20) and healthy control women (n = 20).

Results

Unbiased label free LC-MS/MS-based proteomics was used to provide a profile of abundant proteins in the biological system of breast cancer patients. Data analysis revealed 59 urinary proteins that were significantly different in breast cancer patients compared to the normal control subjects (p<0.05, fold change >3). Thirty-six urinary proteins were exclusively found in specific breast cancer stages, with 24 increasing and 12 decreasing in their abundance. Amongst the 59 significant urinary proteins identified, a list of 13 novel up-regulated proteins were revealed that may be used to detect breast cancer. These include stage specific markers associated with pre-invasive breast cancer in the ductal carcinoma in-situ (DCIS) samples (Leucine LRC36, MAST4 and Uncharacterized protein CI131), early invasive breast cancer (DYH8, HBA, PEPA, uncharacterized protein C4orf14 (CD014), filaggrin and MMRN2) and metastatic breast cancer (AGRIN, NEGR1, FIBA and Keratin KIC10). Preliminary validation of 3 potential markers (ECM1, MAST4 and filaggrin) identified was performed in breast cancer cell lines by Western blotting. One potential marker MAST4 was further validated in human breast cancer tissues as well as individual human breast cancer urine samples with immunohistochemistry and Western blotting, respectively.

Conclusions

Our results indicate that urine is a useful non-invasive source of biomarkers and the profile patterns (biomarkers) identified, have potential for clinical use in the detection of BC. Validation with a larger independent cohort of patients is required in the following study.     

Mass spectrometry assays of plasma biomarkers to predict radiographic progression of knee osteoarthritis

Introduction

Biomarkers to identify osteoarthritis (OA) patients at risk for disease progression are needed. As part of a proteomic analysis of knee synovial fluid from normal and OA patients, differentially expressed proteins were identified that could represent potential biomarkers for OA. This study aimed to use mass spectrometry assays to identify representative peptides from several proteins in synovial fluid and peripheral blood, and assess their levels as biomarkers of OA progression.

Methods

Multiplexed high throughput selected reaction monitoring (SRM) assays were developed to measure tryptic peptides representative of 23 proteins in matched serum and synovial fluid samples from late OA subjects at the time of joint replacement. Subsequently plasma samples from the baseline visit of 173 subjects in an observational OA cohort were tested by SRM for peptides from nine of these proteins: afamin, clusterin, cartilage oligomeric matrix protein, hepatocyte growth factor, kallistatin, insulin-like growth factor binding protein, acid labile subunit, lubricin, lumican, and pigment epithelium-derived factor. Linear regression was used to determine the association between the peptide biomarker level at baseline and change in joint space width (ΔJSW) from baseline to 30 months, adjusting for age and sex.

Results

In the matched cohort, 17 proteins could be identified in synovial fluid and 16 proteins were detected in serum. For the progression cohort, the average age was 62 and average ΔJSW over 30 months was 0.68 mm. A high correlation between different peptides from individual proteins was observed, indicating our assays correctly measured their target proteins. Peptides representative of clusterin, lumican and lubricin showed statistically significant associations with joint space narrowing after adjustment for age and sex. Partial R² values showed clusterin FMETVAEK and lubricin LVEVNPK peptide biomarkers explains about 2 to 3% of the variability of ΔJSW, similar to that explained by age. A biomarker score combining normalized data for both lubricin and clusterin peptides increased the model R² to 0.079.

Conclusions

Our results suggest that when combined, levels of peptides representative of clusterin and lubricin in plasma are as predictive of OA progression as age. Replication of these findings in other prospective OA cohorts is planned.

Electronic supplementary material

The online version of this article (doi:10.1186/s13075-014-0456-6) contains supplementary material, which is available to authorized users.     

Proteomic analysis of human follicular fluid from fertile women

Background

Follicular fluid is a unique biological fluid in which the critical events of oocyte and follicular maturation and somatic cell-germ cell communication occur. Because of the intimate proximity of follicular fluid to the maturing oocyte, this fluid provides a unique window into the processes occurring during follicular maturation. A thorough identification of the specific components within follicular fluid may provide a better understanding of intrafollicular signaling, as well as reveal potential biomarkers of oocyte health for women undergoing assisted reproductive treatment. In this study, we used high and low pH HPLC peptide separations followed by mass spectrometry to perform a comprehensive proteomic analysis of human follicular fluid from healthy ovum donors. Next, using samples from a second set of patients, an isobaric mass tagging strategy for quantitative analysis was used to identify proteins with altered abundances after hCG treatment.

Results

A total of 742 follicular fluid proteins were identified in healthy ovum donors, including 413 that have not been previously reported. The proteins belong to diverse functional groups including insulin growth factor and insulin growth factor binding protein families, growth factor and related proteins, receptor signaling, defense/immunity, anti-apoptotic proteins, matrix metalloprotease related proteins, and complement activity. In a quantitative analysis, follicular fluid samples from age-matched women undergoing in vitro fertilization oocyte retrieval were compared and 17 follicular fluid proteins were found at significantly altered levels (p < 0.05) between pre-hCG and post-hCG samples. These proteins belong to a variety of functional processes, including protease inhibition, inflammation, and cell adhesion.

Conclusions

This database of FF proteins significantly extends the known protein components present during the peri-ovulatory period and provides a useful basis for future studies comparing follicular fluid proteomes in various fertility, disease, and environmental exposure conditions. We identified 17 differentially expressed proteins after hCG treatment and together these data showed the feasibility for defining biomarkers that illuminate how the ovarian follicle microenvironment is altered in various infertility-related conditions.

Electronic supplementary material

The online version of this article (doi:10.1186/s12014-015-9077-6) contains supplementary material, which is available to authorized users.     

MicroRNAs associated with metastatic prostate cancer

Objective

Metastasis is the most common cause of death of prostate cancer patients. Identification of specific metastasis biomarkers and novel therapeutic targets is considered essential for improved prognosis and management of the disease. MicroRNAs (miRNAs) form a class of non-coding small RNA molecules considered to be key regulators of gene expression. Their dysregulation has been shown to play a role in cancer onset, progression and metastasis, and miRNAs represent a promising new class of cancer biomarkers. The objective of this study was to identify down- and up-regulated miRNAs in prostate cancer that could provide potential biomarkers and/or therapeutic targets for prostate cancer metastasis.

Methods

Next generation sequencing technology was applied to identify differentially expressed miRNAs in a transplantable metastatic versus a non-metastatic prostate cancer xenograft line, both derived from one patient''s primary cancer. The xenografts were developed via subrenal capsule grafting of cancer tissue into NOD/SCID mice, a methodology that tends to preserve properties of the original cancers (e.g., tumor heterogeneity, genetic profiles).

Results

Differentially expressed known miRNAs, isomiRs and 36 novel miRNAs were identified. A number of these miRNAs (21/104) have previously been reported to show similar down- or up-regulation in prostate cancers relative to normal prostate tissue, and some of them (e.g., miR-16, miR-34a, miR-126*, miR-145, miR-205) have been linked to prostate cancer metastasis, supporting the validity of the analytical approach.

Conclusions

The use of metastatic and non-metastatic prostate cancer subrenal capsule xenografts derived from one patient''s cancer makes it likely that the differentially expressed miRNAs identified in this study include potential biomarkers and/or therapeutic targets for human prostate cancer metastasis.     

Proteomic Identification of Novel Differentiation Plasma Protein Markers in Hypobaric Hypoxia-Induced Rat Model

Background

Hypobaric hypoxia causes complex changes in the expression of genes, including stress related genes and corresponding proteins that are necessary to maintain homeostasis. Whereas most prior studies focused on single proteins, newer methods allowing the simultaneous study of many proteins could lead to a better understanding of complex and dynamic changes that occur during the hypobaric hypoxia.

Methods

In this study we investigated the temporal plasma protein alterations of rat induced by hypobaric hypoxia at a simulated altitude of 7620 m (25,000 ft, 282 mm Hg) in a hypobaric chamber. Total plasma proteins collected at different time points (0, 6, 12 and 24 h), separated by two-dimensional electrophoresis (2-DE) and identified using matrix assisted laser desorption ionization time of flight (MALDI-TOF/TOF). Biological processes that were enriched in the plasma proteins during hypobaric hypoxia were identified using Gene Ontology (GO) analysis. According to their properties and obvious alterations during hypobaric hypoxia, changes of plasma concentrations of Ttr, Prdx-2, Gpx -3, Apo A-I, Hp, Apo-E, Fetub and Nme were selected to be validated by Western blot analysis.

Results

Bioinformatics analysis of 25 differentially expressed proteins showed that 23 had corresponding candidates in the database. The expression patterns of the eight selected proteins observed by Western blot were in agreement with 2-DE results, thus confirming the reliability of the proteomic analysis. Most of the proteins identified are related to cellular defense mechanisms involving anti-inflammatory and antioxidant activity. Their presence reflects the consequence of serial cascades initiated by hypobaric hypoxia.

Conclusion/Significance

This study provides information about the plasma proteome changes induced in response to hypobaric hypoxia and thus identification of the candidate proteins which can act as novel biomarkers.     

Cytoplasmic Skp2 expression is increased in human melanoma and correlated with patient survival

Background

S-phase kinase protein 2 (Skp2), an F-box protein, targets cell cycle regulators via ubiquitin-mediated degradation. Skp2 is frequently overexpressed in a variety of cancers and associated with patient survival. In melanoma, however, the prognostic significance of subcellular Skp2 expression remains controversial.

Methods

To investigate the role of Skp2 in melanoma development, we constructed tissue microarrays and examined Skp2 expression in melanocytic lesions at different stages, including 30 normal nevi, 61 dysplastic nevi, 290 primary melanomas and 146 metastatic melanomas. The TMA was assessed for cytoplasmic and nuclear Skp2 expression by immunohistochemistry. The Kaplan-Meier method was used to evaluate the patient survival. The univariate and multivariate Cox regression models were performed to estimate the harzard ratios (HR) at five-year follow-up.

Results

Cytoplasmic but not nuclear Skp2 expression was gradually increased from normal nevi, dysplastic nevi, primary melanomas to metastatic melanomas. Cytoplasmic Skp2 expression correlated with AJCC stages (I vs II–IV, P<0.001), tumor thickness (≤2.00 vs >2.00 mm, P<0.001) and ulceration (P = 0.005). Increased cytoplasmic Skp2 expression was associated with a poor five-year disease-specific survival of patients with primary melanoma (P = 0.018) but not metastatic melanoma (P>0.05).

Conclusion

This study demonstrates that cytoplasmic Skp2 plays an important role in melanoma pathogenesis and its expression correlates with patient survival. Our data indicate that cytoplasmic Skp2 may serve as a potential biomarker for melanoma progression and a therapeutic target for this disease.     

Proteomic profiling and functional characterization of early and late shoulder osteoarthritis

Introduction

The development of effective treatments for osteoarthritis (OA) has been hampered by a poor understanding of OA at the cellular and molecular levels. Emerging as a disease of the ''whole joint’, the importance of the biochemical contribution of various tissues, including synovium, bone and articular cartilage, has become increasingly significant. Bathing the entire joint structure, the proteomic analysis of synovial fluid (SF) from osteoarthritic shoulders offers a valuable ''snapshot’ of the biologic environment throughout disease progression. The purpose of this study was to identify differentially expressed proteins in early and late shoulder osteoarthritic SF in comparison to healthy SF.

Methods

A quantitative ¹⁸O labeling proteomic approach was employed to identify the dysregulated SF proteins in early (n = 5) and late (n = 4) OA patients compared to control individuals (n = 5). In addition, ELISA was used to quantify six pro-inflammatory and two anti-inflammatory cytokines.

Results

Key results include a greater relative abundance of proteins related to the complement system and the extracellular matrix in SF from both early and late OA. Pathway analyses suggests dysregulation of the acute phase response, liver x receptor/retinoid x receptor (LXR/RXR), complement system and coagulation pathways in both early and late OA. The network related to lipid metabolism was down-regulated in both early and late OA. Inflammatory cytokines including interleukin (IL) 6, IL 8 and IL 18 were up-regulated in early and late OA.

Conclusions

The results suggest a dysregulation of wound repair pathways in shoulder OA contributing to the presence of a ''chronic wound’ that progresses irreversibly from early to later stages of OA. Protease inhibitors were downregulated in late OA suggesting uncontrolled proteolytic activity occurring in late OA. These results contribute to the theory that protease inhibitors represent promising therapeutic agents which could limit proteolytic activity that ultimately leads to cartilage destruction.     

An Attempt at a Molecular Prediction of Metastasis in Patients with Primary Cutaneous Melanoma

Background

Current prognostic clinical and morphological parameters are insufficient to accurately predict metastasis in individual melanoma patients. Several studies have described gene expression signatures to predict survival or metastasis of primary melanoma patients, however the reproducibility among these studies is disappointingly low.

Methodology/Principal Findings

We followed extended REMARK/Gould Rothberg criteria to identify gene sets predictive for metastasis in patients with primary cutaneous melanoma. For class comparison, gene expression data from 116 patients with clinical stage I/II (no metastasis) and 72 with III/IV primary melanoma (with metastasis) at time of first diagnosis were used. Significance analysis of microarrays identified the top 50 differentially expressed genes. In an independent data set from a second cohort of 28 primary melanoma patients, these genes were analyzed by multivariate Cox regression analysis and leave-one-out cross validation for association with development of metastatic disease. In a multivariate Cox regression analysis, expression of the genes Ena/vasodilator-stimulated phosphoprotein-like (EVL) and CD24 antigen gave the best predictive value (p = 0.001; p = 0.017, respectively). A multivariate Cox proportional hazards model revealed these genes as a potential independent predictor, which may possibly add (both p = 0.01) to the predictive value of the most important morphological indicator, Breslow depth.

Conclusion/Significance

Combination of molecular with morphological information may potentially enable an improved prediction of metastasis in primary melanoma patients. A strength of the gene expression set is the small number of genes, which should allow easy reevaluation in independent data sets and adequately designed clinical trials.