Excavation of diagnostic biomarkers and construction of prognostic model for clear cell renal cell carcinoma based on urine proteomics

Purpose

Clear cell renal cell carcinoma (ccRCC) is the most common pathology type in kidney cancer. However, the prognosis of advanced ccRCC is unsatisfactory. Thus, early diagnosis becomes one of the most important research priorities of ccRCC. However, currently available studies about ccRCC lack urine-related further studies. In this study, we applied proteomics to search urinary biomarkers to assist early diagnosis of ccRCC. In addition, we constructed a prognostic model to assist judge patients' prognosis.

Materials and methods

Urine which was used to perform 4D label-free quantitative proteomics was collected from 12 ccRCC patients and 11 non-tumor patients with no urinary system diseases. The urine of 12 patients with ccRCC confirmed by pathological examination after surgery was collected before operatoin. Bioinformatics analysis was used to describe the urinary proteomics landscape of these patients with ccRCC. The top ten proteins with the highest expression content were selected as the basis for subsequent validation. Urine from 46 ccRCC patients and 45 control patients were collected to use for verification by enzyme linked immunosorbent assay (ELISA). In order to assess the prognostic value of urine proteomics, a prognostic model was constructed by COX regression analysis on the intersection of RNA-sequencing data in The Cancer Genome Atlas (TCGA) database and our urine proteomic data.

Results

133 proteins differentially expressed in the urinary samples were found and 85 proteins (Fold Change, FC>1.5) were identified up-regulated while 48 down-regulated (FC<0.5). Top 10 proteins including S100A14, PKHD1L1, FABP4, ITIH2, C3, C8G, C2, ATF6, ANGPTL6, F13B were performed ELISA to verify. The results showed that PKHD1L1, ANGPTL6, FABP4 and C3 were statistically significant (P<0.05). We performed multivariate logistic regression analysis and plotted a nomogram. Receiver operating characteristic (ROC) curve indicted that the diagnostic efficiency of combined indicators is satisfactory (Aare under curve, AUC=0.835). Furthermore, the prognostic value of the urine proteomics was explored through the intersection between urine proteomics and TCGA RNA-seq data. Thus, COX regression analysis showed that VSIG4, HLA-DRA, SERPINF1, and IGLV2-23 were statistically significant (P<0.05).

Conclusion

Our study indicated that the application of urine proteomics to explore diagnostic biomarkers and to construct prognostic models of renal clear cell carcinoma is of certain clinical value. PKHD1L1, ANGPTL6, FABP4 and C3 can assist to diagnose ccRCC. The prognostic model constituted of VSIG4, HLA-DRA, SERPINF1, and IGLV2-23 can significantly predict the prognosis of ccRCC patients, but this still needs more clinical trials to verify.

Review and Comparison of Cancer Biomarker Trends in Urine as a Basis for New Diagnostic Pathways

Cancer is one of the major causes of mortality worldwide and its already large burden is projected to increase significantly in the near future with a predicted 22 million new cancer cases and 13 million cancer-related deaths occurring annually by 2030. Unfortunately, current procedures for diagnosis are characterized by low diagnostic accuracies. Given the proved correlation between cancer presence and alterations of biological fluid composition, many researchers suggested their characterization to improve cancer detection at early stages. This paper reviews the information that can be found in the scientific literature, regarding the correlation of different cancer forms with the presence of specific metabolites in human urine, in a schematic and easily interpretable form, because of the huge amount of relevant literature. The originality of this paper relies on the attempt to point out the odor properties of such metabolites, and thus to highlight the correlation between urine odor alterations and cancer presence, which is proven by recent literature suggesting the analysis of urine odor for diagnostic purposes. This investigation aims to evaluate the possibility to compare the results of studies based on different approaches to be able in the future to identify those compounds responsible for urine odor alteration.

Development of biomarkers of genitourinary cancer using mass spectrometry-based clinical proteomics

Prostate, bladder and kidney cancer are the three most common types of genitourinary cancer in the world. Of these, prostate and bladder cancers are within the top 10 most common cancers in men. Notably, kidney cancer causes no obvious symptoms in the early stages. To satisfy clinical-management requirements, researchers have developed numerous biomarkers by applying proteomic approaches using clinical serum, urine and tissue specimens, as well as cell and animal models. Through application of biomarker pipeline protocols, including discovery, verification and validation phases, and mass-spectrometric based proteomic platforms coupled with multiplexed quantification assays, these studies have led to recent rapid progress in this area. With improvements in mass-spectrometric based proteomic techniques, numerous promising biomarker candidates and marker panels for various clinical purposes have been proposed. Verification of novel protein biomarker candidates is very resource demanding (e.g. on the clinical and laboratory sides). With the support of national consortia, it is now possible to investigate the future clinical use of such biomarker strategies and assess their cost-effectiveness in personalized medicine.

The proteomic landscape of renal tumors

INTRODUCTION

Renal cell carcinoma (RCC) is the most fatal of the common urologic cancers, with approximately 35% of patients dying within 5 years following diagnosis. Therefore, there is a need for non-invasive markers that are capable of detecting and determining the severity of small renal masses at an early stage in order to tailor treatment and follow-up. Proteomic studies have proved to be very useful in the study of tumors. Areas covered: In this review, we will detail the current knowledge obtained by the different proteomic approaches, focusing on MS-based strategies, used to investigate RCC biology in order to identify diagnostic, prognostic and predictive biomarkers on tissue, cultured cells and biological fluids. Expert commentary: Currently, no reliable biomarkers or targets for RCC have been translated into the clinical setting. Moreover, despite the efforts of proteomics and other -omics disciplines, only a small number of them have been observed as shared targets between the different analytical platforms and biological specimens. The difficulty to define a specific molecular pattern for RCC and its subtypes highlights a peculiar profile and a heterogeneity that must be taken into account in future studies.

The Three-Gene Signature in Urinary Extracellular Vesicles from Patients with Clear Cell Renal Cell Carcinoma

Renal cell carcinoma (RCC) accounts for more than 2% of neoplasias in humans worldwide. Renal biopsy is the gold standard among the diagnostic procedures, but it is invasive and not suitable for all patients. Therefore, new reliable and non-invasive biomarkers for RCC are required. Secretion of extracellular vesicles (EVs), containing RNA molecules that can be transferred between cells, appears to be a common feature of neoplasia. Consistently, cancer-derived EVs are increased in blood and urine. Therefore, urinary samples may be a non-invasive approach for discovering new diagnostic biomarkers. We enrolled 46 patients of whom 33 with clear cell renal cell carcinoma (ccRCC) and 22 healthy subjects (HS). Urinary EVs were isolated by differential centrifugation. Microarray analysis led to the identification of RNA molecules that were validated using RT-qPCR. We found that urinary exosomal shuttle RNA (esRNA) pattern was significantly different in ccRCC patients compared to HS and to non-clear cell RCC (non-ccRCC) and we identified three esRNAs involved in the tumor biology that may be potentially suitable as non-invasive gene signature. GSTA1, CEBPA and PCBD1 esRNA levels were decreased in urine of patients compared with HS. This pattern was specific of the ccRCC and one month after partial or radical nephrectomy the esRNA levels increased to reach the normal level. This study suggests, for the first time, the potential use of the RNA content of urinary EVs to provide a non-invasive first step to diagnose the ccRCC.

Identification of candidate biomarker mass (m/z) ranges in serous ovarian adenocarcinoma using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry profiling

OBJECTIVE

To differentiate plasma from ovarian cancer and healthy individuals using MALDI-TOF mass spectroscopy.

MATERIALS AND METHODS

MALDI-TOF was used to generate profiles of immuno-depleted plasma samples (89 cancers and 199 healthy individuals) that were fractionated using three types of magnetic beads (HIC8, WCX and IMAC-Cu).

RESULTS

Differentially expressed mass ranges showing >1.5-2-fold change in expression from HIC8 (30), WCX (12) and IMAC-Cu (6) fractions were identified. Cross validation and recognition capability scores for the models indicated discrimination between the classes.

CONCLUSIONS

Spectral profiles can differentiate plasma samples of ovarian cancer patients from healthy individuals.

Tumor size, stage and grade alterations of urinary peptidome in RCC

Background

Several promising biomarkers have been found for RCC, but none of them has been used in clinical practice for predicting tumour progression. The most widely used features for predicting tumour aggressiveness still remain the cancer stage, size and grade. Therefore, the aim of our study is to investigate the urinary peptidome to search and identify peptides whose concentrations in urine are linked to tumour growth measure and clinical data.

Methods

A proteomic approach applied to ccRCC urinary peptidome (n = 117) based on prefractionation with activated magnetic beads followed by MALDI-TOF profiling was used. A systematic correlation study was performed on urinary peptide profiles obtained from MS analysis. Peptide identity was obtained by LC–ESI–MS/MS.

Results

Fifteen, twenty-six and five peptides showed a statistically significant alteration of their urinary concentration according to tumour size, pT and grade, respectively. Furthermore, 15 and 9 signals were observed to have urinary levels statistically modified in patients at different pT or grade values, even at very early stages. Among them, C1RL, A1AGx, ZAG2G, PGBM, MMP23, GP162, ADA19, G3P, RSPH3, DREB, NOTC2 SAFB2 and CC168 were identified.

Conclusions

We identified several peptides whose urinary abundance varied according to tumour size, stage and grade. Among them, several play a possible role in tumorigenesis, progression and aggressiveness. These results could be a useful starting point for future studies aimed at verifying their possible use in the managements of RCC patients.

Electronic supplementary material

The online version of this article (doi:10.1186/s12967-015-0693-8) contains supplementary material, which is available to authorized users.

Proteomic analysis reveals differentially secreted proteins in the urine from patients with clear cell renal cell carcinoma

OBJECTIVE

The aim of this study was to evaluate the differentially secreted protein profile in the urine from patients with clear cell renal cell carcinoma (ccRCC) using mass spectrometry-based methods. Urine composition can reflect kidney physiology and can be used to detect markers for renal diseases. Moreover, characterization of the secretome is likely to assist in the investigation of new drugs for biological targets and diagnose the ccRCC at an early stage.

METHODS AND MATERIALS

Urine samples from patients were divided according to Fuhrman degree (FI-IV), which was associated with the cellular differentiation as good prognosis (GP) and poor prognosis (PP). Healthy individuals were used as the control group (CG). We used both qualitative and quantitative mass spectrometry-based analyses that involved the following approaches: 1-dimensional gel electrophoresis combined with liquid chromatography mass spectrometry in tandem (1DE LC-MS/MS), in-solution digestion combined with label-free 1-dimensional LC-MS(E) (1D LC-MS(E)), and bidimensional gel electrophoresis combined with matrix-assisted laser desorption/ionization time of flight in tandem (2DE MALDI-TOF/TOF) or combined with LC-MS/MS.

RESULTS

All the strategies allowed the identification of 354 proteins from the CG, GP, and PP groups. Qualitative experiments using 1DE LC-MS/MS analysis detected different protein profiles, and 224 proteins were identified in all groups. The label-free MS(E) quantitative analysis identified 113 proteins and generated novel information on secreted protein profiles, including 49 up-secreted proteins in the urine from patients with ccRCC and 40 down-secreted proteins related to the CG. Proteins such as kininogen-1, uromodulin, apolipoprotein D, polyubiquitin, and CD59 glycoprotein were down secreted according to the groups CG>GP>PP. In contrast, apolipoprotein A, fibrinogen, and haptoglobin were up secreted in patient groups. The same expression profile observed for kininogen-1, apolipoprotein D, fibrinogen, and haptoglobin was corroborated by 2DE LC-MS/MS or 2DE MALDI-TOF/TOF analyses. These 2 strategies also showed 13 differentially secreted proteins among the 3 groups.

CONCLUSIONS

The proteins kininogen-1, apolipoprotein D, fibrinogen, and haptoglobin presented similar quantitative protein profiles according to MS(E) and 2DE approaches. The latter proteins were up secreted and the former ones were down-regulated. The strategies used proved to be valuable in identifying proteins that were differentially secreted in urine from patients with RCC.

Identification of Protein Markers Specific for Papillary Renal Cell Carcinoma Using Imaging Mass Spectrometry

Since the emergence of proteomics methods, many proteins specific for renal cell carcinoma (RCC) have been identified. Despite their usefulness for the specific diagnosis of RCC, such proteins do not provide spatial information on the diseased tissue. Therefore, the identification of cancer-specific proteins that include information on their specific location is needed. Recently, matrix-assisted laser desorption ionization (MALDI) mass spectrometry (MS) based imaging mass spectrometry (IMS) has emerged as a new tool for the analysis of spatial distribution as well as identification of either proteins or small molecules in tissues. In this report, surgical tissue sections of papillary RCC were analyzed using MALDI-IMS. Statistical analysis revealed several discriminative cancer-specific m/z-species between normal and diseased tissues. Among these m/z-species, two particular proteins, S100A11 and ferritin light chain, which are specific for papillary RCC cancer regions, were successfully identified using LC-MS/MS following protein extraction from independent RCC samples. The expressions of S100A11 and ferritin light chain were further validated by immunohistochemistry of human tissues and tissue microarrays (TMAs) of RCC. In conclusion, MALDI-IMS followed by LC-MS/MS analysis in human tissue identified that S100A11 and ferritin light chain are differentially expressed proteins in papillary RCC cancer regions.

Frontiers in clinical and molecular diagnostics and staging of metastatic clear cell renal cell carcinoma

The last few years have brought advances in the understanding of the molecular biology of metastatic clear cell renal cell carcinoma (RCC). Both preclinical research and clinical trials brought together results from the latest advancements in RCC diagnostic and staging. Understanding of the complex molecular alterations involved in the development and progression of RCC enables development of immunohistochemical and genetic diagnostic tools and is also opening the doors for experimental targeted therapies. At the same time, improvements of medical and molecular imaging improves the sensitivity and specificity of metastatic disease diagnosis. Moreover, independent validation of molecular profiles across high-throughput platforms, methods, laboratories and cancer populations has recently been successfully performed in RCC. Generation of informative, clinical diagnostic tools is likely to contribute to development of novel personalized diagnostic and treatment protocols and ensure prolonged survival of RCC patient in the near future.

Urinary signatures of Renal Cell Carcinoma investigated by peptidomic approaches

Renal Cell Carcinoma (RCC) is typically asymptomatic and surgery usually increases patient's lifespan only for early stage tumours. Moreover, solid renal masses cannot be confidently differentiated from RCC. Therefore, markers to distinguish malignant kidney tumours and for their detection are needed. Two different peptide signatures were obtained by a MALDI-TOF profiling approach based on urine pre-purification by C8 magnetic beads. One cluster of 12 signals could differentiate malignant tumours (n = 137) from benign renal masses and controls (n = 153) with sensitivity of 76% and specificity of 87% in the validation set. A second cluster of 12 signals distinguished clear cell RCC (n = 118) from controls (n = 137) with sensitivity and specificity values of 84% and 91%, respectively. Most of the peptide signals used in the two models were observed at higher abundance in patient urines and could be identified as fragments of proteins involved in tumour pathogenesis and progression. Among them: the Meprin 1α with a pro-angiogenic activity, the Probable G-protein coupled receptor 162, belonging to the GPCRs family and known to be associated with several key functions in cancer, the Osteopontin that strongly correlates to tumour stages and invasiveness, the Phosphorylase b kinase regulatory subunit alpha and the SeCreted and TransMembrane protein 1.

The urinary proteome and peptidome of renal cell carcinoma patients: a comparison of different techniques

Renal cell carcinomas, originating from the renal cortex, account for about 80% of kidney primary malignancies. Small localized tumors rarely produce symptoms and diagnosis is often delayed until the disease is advanced. In contrast to other urological cancers, renal cell carcinomas are associated with a high degree of metastases and a low 5-year survival rate. The identification of diagnostic and prognostic markers, especially in the urine, remains an area of intense investigation. Different proteomic strategies have been applied so far to biomarker discovery in urine at the proteome or the peptidome level. Gel-based and gel-free strategies combined with mass spectrometry are the most-used strategies, have different success rates, and will be depicted here. We also prefigure a scenario in which the limitations of a single approach are overcome by applying new and complementary research strategies, relying on the excellent availability coupled to the intrinsic richness typical of urine samples.

Non-invasively collected amniotic fluid as a source of possible biomarkers for premature rupture of membranes investigated by proteomic approach

PURPOSE

Preterm delivery is one of the main causes of perinatal morbidity and mortality and it accounts for 75 % of perinatal mortality and more than half of the long-term morbidity. We applied a proteomic approach based on mass spectrometry (MS) for biomarkers discovery of preterm premature rupture of membranes (pPROM) by investigating amniotic fluid (AF) invasively and non-invasively collected.

METHODS

Amniotic fluid was obtained from vagina of women with pPROM (group 1), PROM at term (group 2) and by genetic amniocentesis (group 3). Pre-fractionated AF proteome was analyzed through matrix assisted laser desorption ionization-time of flight (MALDI-TOF) MS. The characterization of proteins/peptides of interest was obtained by high performance liquid chromatography-electrospray tandem MS.

RESULTS

Three peptides overexpressed in pPROM and able to discriminate the groups 1 and 2 were detected. One peptide was identified as the fragment Gly452LAVPDGPLGLPPKPro466 of the protein KIAA1522, expressed by fetal brain and liver. This peptide was overexpressed in a patient of the group 3, completely asymptomatic at the time of the amniocentesis, who later developed pPROM.

CONCLUSION

Amniotic fluid invasively and non-invasively collected can be analyzed by MALDI-TOF MS to obtain proteomic profiles. Proteomic analysis identified a peptide with promising diagnostic capability for pPROM.

Proteomic studies of urinary biomarkers for prostate, bladder and kidney cancers

Urine is an ideal body fluid for the detection of protein markers produced by urological cancers as it can be sampled noninvasively and contains secreted and directly shed proteins from the prostate, bladder and kidney. Major challenges of working with urine include high inter-individual and intra-individual variability, low protein concentration, the presence of salts and the dynamic range of protein expression. Despite these challenges, significant progress is being made using modern proteomic methods to identify and characterize protein-based markers for urological cancers. The development of robust, easy-to-use clinical tests based on novel biomarkers has the potential to impact upon diagnosis, prognosis and monitoring and could revolutionize the treatment and management of these cancers.

Mutual information optimization for mass spectra data alignment

"Signal" alignments play critical roles in many clinical setting. This is the case of mass spectrometry data, an important component of many types of proteomic analysis. A central problem occurs when one needs to integrate (mass spectrometry) data produced by different sources, e.g., different equipment and/or laboratories. In these cases some form of "data integration'" or "data fusion'" may be necessary in order to discard some source specific aspects and improve the ability to perform a classification task such as inferring the "disease classes'" of patients. The need for new high performance data alignments methods is therefore particularly important in these contexts. In this paper we propose an approach based both on an information theory perspective, generally used in a feature construction problem, and on the application of a mathematical programming task (i.e. the weighted bipartite matching problem). We present the results of a competitive analysis of our method against other approaches. The analysis was conducted on data from plasma/ethylenediaminetetraacetic acid (EDTA) of "control" and Alzheimer patients collected from three different hospitals. The results point to a significant performance advantage of our method with respect to the competing ones tested.

Proteomic analysis in clear cell renal cell carcinoma: identification of differentially expressed protein by 2-D DIGE

Renal cell carcinoma (RCC), the most common neoplasm affecting the adult kidney, is characterised by heterogeneity of histological subtypes, drug resistance, and absence of molecular markers. Two-dimensional difference gel electrophoresis (2-D DIGE) technology in combination with mass spectrometry (MS) was applied to detect differentially expressed proteins in 20 pairs of RCC tissues and matched adjacent normal kidney cortex (ANK), in order to search for RCC markers. After gel analysis by DeCyder 6.5 and EDA software, differentially expressed protein spots were excised from Deep Purple stained preparative 2DE gel. A total of 100 proteins were identified by MS out of 2500 spots, 23 and 77 of these were, respectively, over- and down-expressed in RCC. The Principal Component Analysis applied to gels and protein spots exactly separated the two sample classes in two groups: RCC and ANK. Moreover, some spots, including ANXA2, PPIA, FABP7 and LEG1, resulted highly differential. The DIGE data were also confirmed by immunoblotting analysis for these proteins. In conclusion, we suggest that applying 2-D DIGE to RCC may provide the basis for a better molecular characterization and for the discovery of candidate biomarkers.

A proposed new technique in prostate cancer tissue bio-banking: our experience with a new protocol

The aim of our study, beyond validating a method of collecting and storing biological samples from patients with prostate cancer, was to validate an innovative biopsy method for the creation of a biobank of prostatic frozen tissues. Patients referred to our hospital between November 2008 and March 2010 to undergo radical prostatectomy were invited to participate in the study. Each patient's data were stored in two databases (personal information and clinical database) while samples of urine, blood and its derivatives, fresh material and formalin-processed tissue were stored in a correlated biobank. The proposed method for collecting fresh material was to take samples of the neoplastic tissue by carrying out targeted biopsies in the area indicated by the biopsy mapping as the site of the malignancy, under manual palpation to identify the neoplastic nodule. The site of sampling was marked by an injection of India ink. 55 patients agreed to participate in the study. In 43 cases biopsies were correct, with a mean of 48% of core involved by tumour (range, 10-90%). Overall the tumour detection rate was 78.2%. The protocol for collecting biological material and the new method for collecting fresh tissue reduce internal steps and staff involved, thereby reducing all those variables that cause heterogeneity of material and changes in its quality. This process provides high quality, low cost material for research on prostate cancer. The features of the collection protocol mean that the protocol can also be used in non-academic centres with only limited research funds.

Assessment of high-throughput high-resolution MALDI-TOF-MS of urinary peptides for the detection of muscle-invasive bladder cancer

PURPOSE

There is a need for better biomarkers to both detect bladder cancer and distinguish muscle-invasive (stage T2+) from non-invasive (stage Ta/T1) disease. We assess whether MALDI-TOF-MS of the urine peptidome can achieve this.

EXPERIMENTAL DESIGN

We analysed urine from 751 patients with bladder cancer and 127 patients without bladder cancer. Endogenous peptide profiling was performed using a Bruker Ultraflextreme MALDI-TOF-MS.

RESULTS

Significant differences were seen between the spectra of urine from patients with and without T2+ disease. Albumin, total protein and haematuria were also elevated in T2+ patients. Haematuria was detected in 39% of patients with Ta/T1 disease and in 77% of patients with T2+ disease. Class prediction models based on MALDI data produced areas under receiver-operator characteristic curves of up to 0.76 but did not significantly outperform a model based on total protein alone. Many peptides significantly associated with invasive disease are fragments of abundant blood proteins and are also associated with haematuria.

CONCLUSIONS AND CLINICAL RELEVANCE

Microscopic haematuria is strongly associated with invasive disease; even traces of blood/plasma strongly influence the urinary peptidome. This needs to be taken into consideration when using 'omic' methods to search for urinary biomarkers as blood proteins may give false-positive results.

Serum biomarkers of renal cell carcinoma assessed using a protein profiling approach based on ClinProt technique

OBJECTIVES

To investigate the possibility of using the ClinProt technique to find serum cancer related diagnostic markers that are able to better discriminate healthy subjects from patients affected by renal cell carcinoma (ccRCC). Renal cell carcinoma is the most common malignancy of the kidney. Biomarkers for early detection, prognosis, follow-up, and differential diagnosis of ccRCC from benign renal lesions are needed in daily clinical practice when imaging is not helpful.

METHODS

Serum of 29 healthy subjects and 33 ccRCC patients was analyzed by the ClinProt/MALDI-ToF technique.

RESULTS

A cluster of 3 peptides (A = m/z 1083 +/- 8 Da, B = m/z 1445 +/- 8 Da and C = m/z 6879 +/- 8 Da) was able to discriminate patients from control subjects. Cross-validation analysis using the whole casistic showed 88% and 96% of sensitivity and specificity, respectively. Moreover, the cluster showed 100% sensitivity for the identification of patients at pT2 (n = 5) and pT3 (n = 8) and 85% for pT1 patients (n = 20). The intensity of peaks A and C continuously decreased from pT1 to pT3, whereas peak B increased in pT1 and pT2.

CONCLUSIONS

These results may be useful to set up new diagnostic or prognostic tools.

SELDI-TOF as a method for biomarker discovery in the urine of aristolochic-acid-treated mice

Aristolochic acids (AAs) present in Aristolochia plants are substances responsible for Chinese herbs nephropathy. Recently, strong indications have also been presented, which dietary poisoning with AA is responsible for endemic (Balkan) nephropathy (EN), an enigmatic renal disease that affects rural population living in some countries in Southeastern Europe. A mouse model was applied to follow the effects of two forms of AA, AAI and AAII. SDS-PAGE and SELDI-TOF mass spectrometry with normal phase chips were used to evaluate changes in the urine of treated animals. These two methods are demonstrated to be comparable. The use of SELDI-TOF MS for rapid analysis of a large number of samples and the combination of this method with nano-LC-ESI MS/MS for protein identification were demonstrated. Biomarker discovery after analysis of large cohort of EN patients will be the final aim of these investigations.

Different expression of fibrinopeptide A and related fragments in serum of type 1 diabetic patients with nephropathy

Type 1 diabetes (insulin-dependent diabetes mellitus, IDDM) is an autoimmune disease affecting about 0.12% of the world's population. Diabetic nephropathy (DN) is a major long-term complication of both types of diabetes and retains a high human, social and economic cost. Thus, the identification of markers for the early detection of DN represents a relevant target of diabetic research. The present work is a pilot study focused on proteomic analysis of serum of controls (n=9), IDDM patients (n=10) and DN patients (n=4) by the ClinProt profiling technology based on mass spectrometry. This approach allowed to identify a pattern of peptides able to differentiate the studied populations with sensitivity and specificity close to 100%. Variance of the results allowed to estimate the sample size needed to keep the expected False Discovery Rate low. Moreover, three peptides differentially expressed in the serum of patients as compared to controls were identified by LC-ESI MS/MS as the whole fibrinopeptide A peptide and two of its fragments, respectively. The two fragments were under-expressed in diabetic patients, while Fibrinopeptide A was over-expressed, suggesting that anomalous turnover of Fibrinopeptide A could be involved in the pathogenesis of DN.

Serum biomarkers of vascular cognitive impairment evaluated by bead-based proteomic technology

To investigate the biomarkers of vascular cognitive impairment (VCI), we analyzed 30 VCI patients and 30 healthy controls using proteome technology. Peptides in sera were purified using magnetic beads, and analyzed by matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF-MS) and ClinProTool software. Twenty-four peptides were significantly differently expressed between VCI patients and controls. There were seven peptides selected for our diagnostic model. The sensitivity of the model measured by internal and external validation was 95% and 80% respectively, while the specificity was 100% for both. A peptide peak at 4963 m/z was chosen for identification by nano-liquid chromatography-electrospray ionization-tandem mass spectrometry (nano-LC-ESI-MS/MS). The amino acid sequence was consistent with the fragment of the trace-amine associated receptor 6 (TAAR6) in the human international protein index database. In conclusion, the specific fragment of TAAR6 may be important for clinical biomarkers in the diagnosis of VCI. The high specificity and sensitivity of the model may provide an appropriate method for screening VCI patients.