Targeted Lipid Profiling Discovers Plasma Biomarkers of Acute Brain Injury

Prior efforts to identify a blood biomarker of brain injury have relied almost exclusively on proteins; however their low levels at early time points and poor correlation with injury severity have been limiting. Lipids, on the other hand, are the most abundant molecules in the brain and readily cross the blood-brain barrier. We previously showed that certain sphingolipid (SL) species are highly specific to the brain. Here we examined the feasibility of using SLs as biomarkers for acute brain injury. A rat model of traumatic brain injury (TBI) and a mouse model of stroke were used to identify candidate SL species though our mass-spectrometry based lipid profiling approach. Plasma samples collected after TBI in the rat showed large increases in many circulating SLs following injury, and larger lesions produced proportionately larger increases. Plasma samples collected 24 hours after stroke in mice similarly revealed a large increase in many SLs. We constructed an SL score (sum of the two SL species showing the largest relative increases in the mouse stroke model) and then evaluated the diagnostic value of this score on a small sample of patients (n = 14) who presented with acute stroke symptoms. Patients with true stroke had significantly higher SL scores than patients found to have non-stroke causes of their symptoms. The SL score correlated with the volume of ischemic brain tissue. These results demonstrate the feasibility of using lipid biomarkers to diagnose brain injury. Future studies will be needed to further characterize the diagnostic utility of this approach and to transition to an assay method applicable to clinical settings.     

Plasma Protein Profiling Reveal Osteoprotegerin as a Marker of Prognostic Impact for Colorectal Cancer

BACKGROUND: Due to difficulties in predicting recurrences in colorectal cancer stages II and III, reliable prognostic biomarkers could be a breakthrough for individualized treatment and follow-up. OBJECTIVE: To find potential prognostic protein biomarkers in colorectal cancer, using the proximity extension assays. METHODS: A panel of 92 oncology-related proteins was analyzed with proximity extension assays, in plasma from a cohort of 261 colorectal cancer patients with stage II-IV. The survival analyses were corrected for disease stage and age, and the recurrence analyses were corrected for disease stage. The significance threshold was adjusted for multiple comparisons. RESULTS: The plasma proteins expression levels had a greater prognostic relevance in disease stage III colorectal cancer than in disease stage II, and for overall survival than for time to recurrence. Osteoprotegerin was the only biomarker candidate in the protein panel that had a statistical significant association with overall survival (P = .00029). None of the proteins were statistically significantly associated with time to recurrence. CONCLUSIONS: Of the 92 analyzed plasma proteins, osteoprotegerin showed the strongest prognostic impact in patients with colorectal cancer, and therefore osteoprotegerin is a potential predictive marker, and it also could be a target for treatments.     

Serum Biomarkers of Keshan Disease Assessed Using a Protein Profiling Approach Based on ClinProt Technique

The etiology of Keshan disease (KD), an endemic myocardiopathy in regions of China, is largely unknown. To show the protein changes in serum from KD patients versus controls and idiopathic dilated cardiomyopathy (IDCM) and to search specific biological markers for differential diagnosis for KD. Serum of 65 patients with KD was compared with 29 patients with IDCM, 62 controls from KD areas and 28 controls from non-KD areas by ClinProt/MALDI-ToF technique. The genetic algorithm, quick classifier algorithm and supervised neural network algorithm methods were used to screen marker proteins and establish diagnostic model. Thirty-four differential peaks were identified in KD patients compared with the healthy controls from non-KD areas. Thirty-eight differentially peaks were identified in KD patients and controls from KD areas; and sixty-seven differentially peaks were identified in patients with KD and patients with IDCM. We believe that marker protein peaks screened in KD patients, healthy controls and IDCM patients may provide clues for the differential diagnosis and treatment of KD.     

Plasma Protein Biomarkers of Hepatocellular Carcinoma in HCV-Infected Alcoholic Patients with Cirrhosis

Hepatocellular carcinoma (HCC) is one of the most common and lethal cancers in the world, with limited options for treatment unless timely diagnosed. Chronic hepatitis C virus (HCV) infection and persistent heavy alcohol consumption are independent risk factors for HCC development, which may induce a specific protein expression pattern different from those caused separately. The aim of the study was to identify protein biomarkers for the detection of HCC in HCV-infected alcoholic patients with cirrhosis in order to improve survival. We compared protein expression profiles of plasma samples from 52 HCV-infected alcoholic patients with and without HCC, using 2-D DIGE coupled with MALDI-TOF/TOF mass spectrometry. The 2-D DIGE results were analyzed statistically using Decyder software, and verified by western-blot and ELISA. In plasma samples from HCV-infected alcoholic patients, we found significantly differential expression profiles of carboxypeptidase-N, ceruloplasmin (CP), complement component 4a (C4a), fibrinogen-alpha (FGA), immunoglobulin mu chain C region, serum albumin, and serum paraoxonase/arylesterase 1 (PON1). Deregulation of plasma/serum levels of the identified proteins was associated to HCV, ethanol consumption, and/or HCC progression. In the validation through ELISA, C4a serum concentration was increased in HCC patients (2.4±1 ng/mg vs 1.8±0.6 ng/mg; p = 0.029), being the only independent predictor of HCC in the multivariate analysis (OR = 2.15; p = 0.015), with an AUROC = 0.70. The combination of C4a, FGA, CP and PON1 improved slightly the predictive ability of C4a alone (AUROC 0.81). In conclusion, we identified proteins related to acute-phase response, oxidative stress, or immune response, whose differential expression in plasma may be attributed to the presence of HCC. Among them, C4a, and its combination with CP, FGA and PON1, could be considered as potentially reliable biomarkers for the detection of HCC in HCV-infected alcoholic patients.     

Identification of Plasma Lipid Biomarkers for Prostate Cancer by Lipidomics and Bioinformatics

Background

Lipids have critical functions in cellular energy storage, structure and signaling. Many individual lipid molecules have been associated with the evolution of prostate cancer; however, none of them has been approved to be used as a biomarker. The aim of this study is to identify lipid molecules from hundreds plasma apparent lipid species as biomarkers for diagnosis of prostate cancer.

Methodology/Principal Findings

Using lipidomics, lipid profiling of 390 individual apparent lipid species was performed on 141 plasma samples from 105 patients with prostate cancer and 36 male controls. High throughput data generated from lipidomics were analyzed using bioinformatic and statistical methods. From 390 apparent lipid species, 35 species were demonstrated to have potential in differentiation of prostate cancer. Within the 35 species, 12 were identified as individual plasma lipid biomarkers for diagnosis of prostate cancer with a sensitivity above 80%, specificity above 50% and accuracy above 80%. Using top 15 of 35 potential biomarkers together increased predictive power dramatically in diagnosis of prostate cancer with a sensitivity of 93.6%, specificity of 90.1% and accuracy of 97.3%. Principal component analysis (PCA) and hierarchical clustering analysis (HCA) demonstrated that patient and control populations were visually separated by identified lipid biomarkers. RandomForest and 10-fold cross validation analyses demonstrated that the identified lipid biomarkers were able to predict unknown populations accurately, and this was not influenced by patient''s age and race. Three out of 13 lipid classes, phosphatidylethanolamine (PE), ether-linked phosphatidylethanolamine (ePE) and ether-linked phosphatidylcholine (ePC) could be considered as biomarkers in diagnosis of prostate cancer.

Conclusions/Significance

Using lipidomics and bioinformatic and statistical methods, we have identified a few out of hundreds plasma apparent lipid molecular species as biomarkers for diagnosis of prostate cancer with a high sensitivity, specificity and accuracy.     

Neurobiological Signatures of Alcohol Dependence Revealed by Protein Profiling

Alcohol abuse causes dramatic neuroadaptations in the brain, which contribute to tolerance, dependence, and behavioral modifications. Previous proteomic studies in human alcoholics and animal models have identified candidate alcoholism-related proteins. However, recent evidences suggest that alcohol dependence is caused by changes in co-regulation that are invisible to single protein-based analysis. Here, we analyze global proteomics data to integrate differential expression, co-expression networks, and gene annotations to unveil key neurobiological rearrangements associated with the transition to alcohol dependence modeled by a Chronic Intermittent Ethanol (CIE), two-bottle choice (2BC) paradigm. We analyzed cerebral cortices (CTX) and midbrains (MB) from male C57BL/6J mice subjected to a CIE, 2BC paradigm, which induces heavy drinking and represents one of the best available animal models for alcohol dependence and relapse drinking. CIE induced significant changes in protein levels in dependent mice compared with their non-dependent controls. Multiple protein isoforms showed region-specific differential regulation as a result of post-translational modifications. Our integrative analysis identified modules of co-expressed proteins that were highly correlated with CIE treatment. We found that modules most related to the effects of CIE treatment coordinate molecular imbalances in endocytic- and energy-related pathways, with specific proteins involved, such as dynamin-1. The qRT-PCR experiments validated both differential and co-expression analyses, and the correspondence among our data and previous genomic and proteomic studies in humans and rodents substantiates our findings. The changes identified above may play a key role in the escalation of ethanol consumption associated with dependence. Our approach to alcohol addiction will advance knowledge of brain remodeling mechanisms and adaptive changes in response to drug abuse, contribute to understanding of organizational principles of CTX and MB proteomes, and define potential new molecular targets for treating alcohol addiction. The integrative analysis employed here highlight the advantages of systems approaches in studying the neurobiology of alcohol addiction.     

Serum Circulating microRNA Profiling for Identification of Potential Type 2 Diabetes and Obesity Biomarkers

Background and Aim

MicroRNAs are small non-coding RNAs that play important regulatory roles in a variety of biological processes, including complex metabolic processes, such as energy and lipid metabolism, which have been studied in the context of diabetes and obesity. Some particular microRNAs have recently been demonstrated to abundantly and stably exist in serum and to be potentially disease-specific. The aim of this profiling study was to characterize the expression of miRNA in serum samples of obese, nonobese diabetic and obese diabetic individuals to determine whether miRNA expression was deregulated in these serum samples and to identify whether any observed deregulation was specific to either obesity or diabetes or obesity with diabetes.

Patients and Methods

Thirteen patients with type 2 diabetes, 20 obese patients, 16 obese patients with type 2 diabetes and 20 healthy controls were selected for this study. MiRNA PCR panels were employed to screen serum levels of 739 miRNAs in pooled samples from these four groups. We compared the levels of circulating miRNAs between serum pools of each group. Individual validation of the twelve microRNAs selected as promising biomarkers was carried out using RT-qPCR.

Results

Three serum microRNAs, miR-138, miR-15b and miR-376a, were found to have potential as predictive biomarkers in obesity. Use of miR-138 or miR-376a provides a powerful predictive tool for distinguishing obese patients from normal healthy controls, diabetic patients, and obese diabetic patients. In addition, the combination of miR-503 and miR-138 can distinguish diabetic from obese diabetic patients.

Conclusion

This study is the first to show a panel of serum miRNAs for obesity, and compare them with miRNAs identified in serum for diabetes and obesity with diabetes. Our results support the use of some miRNAs extracted from serum samples as potential predictive tools for obesity and type 2 diabetes.     

Let-7, Mir-98 and Mir-181 as Biomarkers for Cancer and Schizophrenia

Recent evidence supports a role of microRNAs in cancer and psychiatric disorders such as schizophrenia and bipolar disorder, through their regulatory role on the expression of multiple genes. The rather rare co-morbidity of cancer and schizophrenia is an old hypothesis which needs further research on microRNAs as molecules that might exert their oncosuppressive or oncogenic activity in the context of their role in psychiatric disorders. The expression pattern of a variety of different microRNAs was investigated in patients (N = 6) suffering from schizophrenia termed control, patients with a solid tumor (N = 10) and patients with both schizophrenia and tumor (N = 8). miRNA profiling was performed on whole blood samples using the miRCURY LNA microRNA Array technology (6th & 7th generation). A subset of 3 microRNAs showed a statistically significant differential expression between the control and the study groups. Specifically, significant down-regulation of the let-7p-5p, miR-98-5p and of miR-183-5p in the study groups (tumor alone and tumorand schizophrenia) was observed (p<0.05). The results of the present study showed that let-7, miR-98 and miR-183 may play an important oncosuppressive role through their regulatory impact in gene expression irrespective of the presence of schizophrenia, although a larger sample size is required to validate these results. Nevertheless, further studies are warranted in order to highlight a possible role of these and other micro-RNAs in the molecular pathways of schizophrenia.     

Identification of Cryptic Anopheles Mosquito Species by Molecular Protein Profiling

Vector control is the mainstay of malaria control programmes. Successful vector control profoundly relies on accurate information on the target mosquito populations in order to choose the most appropriate intervention for a given mosquito species and to monitor its impact. An impediment to identify mosquito species is the existence of morphologically identical sibling species that play different roles in the transmission of pathogens and parasites. Currently PCR diagnostics are used to distinguish between sibling species. PCR based methods are, however, expensive, time-consuming and their development requires a priori DNA sequence information. Here, we evaluated an inexpensive molecular proteomics approach for Anopheles species: matrix assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS). MALDI-TOF MS is a well developed protein profiling tool for the identification of microorganisms but so far has received little attention as a diagnostic tool in entomology. We measured MS spectra from specimens of 32 laboratory colonies and 2 field populations representing 12 Anopheles species including the A. gambiae species complex. An important step in the study was the advancement and implementation of a bioinformatics approach improving the resolution over previously applied cluster analysis. Borrowing tools for linear discriminant analysis from genomics, MALDI-TOF MS accurately identified taxonomically closely related mosquito species, including the separation between the M and S molecular forms of A. gambiae sensu stricto. The approach also classifies specimens from different laboratory colonies; hence proving also very promising for its use in colony authentication as part of quality assurance in laboratory studies. While being exceptionally accurate and robust, MALDI-TOF MS has several advantages over other typing methods, including simple sample preparation and short processing time. As the method does not require DNA sequence information, data can also be reviewed at any later stage for diagnostic or functional patterns without the need for re-designing and re-processing biological material.     

Proteomic Profiling of Exosomes Leads to the Identification of Novel Biomarkers for Prostate Cancer

Background

Current markers for prostate cancer, such as PSA lack specificity. Therefore, novel biomarkers are needed. Unfortunately, the complexity of body fluids often hampers biomarker discovery. An attractive alternative approach is the isolation of small vesicles, i.e. exosomes, ∼100 nm, which contain proteins that are specific to the tissue from which they are derived and therefore can be considered as treasure chests for disease-specific biomarker discovery.

Materials and Methods

Exosomes were isolated from 2 immortalized primary prostate epithelial cells (PNT2C2 and RWPE-1) and 2 PCa cell lines (PC346C and VCaP) by ultracentrifugation. After tryptic digestion, proteomic analyses utilized a nanoLC coupled with an LTQ-Orbitrap operated in tandem MS (MS/MS) mode. Accurate Mass and Time (AMT) tag approach was employed for peptide identification and quantitation. Candidate biomarkers were validated by Western blotting and Immunohistochemistry.

Results

Proteomic characterization resulted in the identification of 248, 233, 169, and 216 proteins by at least 2 peptides in exosomes from PNT2C2, RWPE-1, PC346C, and VCaP, respectively. Statistical analyses revealed 52 proteins differently abundant between PCa and control cells, 9 of which were more abundant in PCa. Validation by Western blotting confirmed a higher abundance of FASN, XPO1 and PDCD6IP (ALIX) in PCa exosomes.

Conclusions

Identification of exosomal proteins using high performance LC-FTMS resulted in the discovery of PDCD6IP, FASN, XPO1 and ENO1 as new candidate biomarkers for prostate cancer.     

Potential Biomarkers of Fatigue Identified by Plasma Metabolome Analysis in Rats

In the present study, prior to the establishment of a method for the clinical diagnosis of chronic fatigue in humans, we validated the utility of plasma metabolomic analysis in a rat model of fatigue using capillary electrophoresis-mass spectrometry (CE-MS). In order to obtain a fatigued animal group, rats were placed in a cage filled with water to a height of 2.2 cm for 5 days. A food-restricted group, in which rats were limited to 10 g/d of food (around 50% of the control group), was also assessed. The food-restricted group exhibited weight reduction similar to that of the fatigued group. CE-MS measurements were performed to evaluate the profile of food intake-dependent metabolic changes, as well as the profile in fatigue loading, resulting in the identification of 48 metabolites in plasma. Multivariate analyses using hierarchical clustering and principal component analysis revealed that the plasma metabolome in the fatigued group showed clear differences from those in the control and food-restricted groups. In the fatigued group, we found distinctive changes in metabolites related to branched-chain amino acid metabolism, urea cycle, and proline metabolism. Specifically, the fatigued group exhibited significant increases in valine, leucine, isoleucine, and 2-oxoisopentanoate, and significant decreases in citrulline and hydroxyproline compared with the control and food-restricted groups. Plasma levels of total nitric oxide were increased in the fatigued group, indicating systemic oxidative stress. Further, plasma metabolites involved in the citrate cycle, such as cis-aconitate and isocitrate, were reduced in the fatigued group. The levels of ATP were significantly decreased in the liver and skeletal muscle, indicative of a deterioration in energy metabolism in these organs. Thus, this comprehensive metabolic analysis furthered our understanding of the pathophysiology of fatigue, and identified potential diagnostic biomarkers based on fatigue pathophysiology.     

Depression of Neuronal Protein Synthesis Initiation by Protein Tyrosine Kinase Inhibitors

Abstract— Growth factors stimulate cellular protein synthesis, but the intracellular signaling mechanisms that regulate initiation of mRNA translation in neurons have not been clarified. A rate-limiting step in the initiation of protein synthesis is the formation of the ternary complex among GTP, eukaryotic initiation factor 2 (elF-2), and the initiator tRNA. Here we report that genistein, a specific tyrosine kinase inhibitor, decreases tyrosine kinase activity and the content of phosphotyrosine proteins in cultured primary cortical neurons. Genistein inhibits protein synthesis by >80% in a dose-dependent manner (10–80 μg/ml) and concurrently decreases ternary complex formation by 60%. At the doses investigated, genistein depresses tyrosine kinase activity and concomitantly stimulates PKC activity. We propose that a protein tyrosine kinase participates in the initiation of protein synthesis in neurons, by affecting the activity of elF-2 directly or through a protein kinase cascade.     

New Biomarkers of Coffee Consumption Identified by the Non-Targeted Metabolomic Profiling of Cohort Study Subjects

Coffee contains various bioactives implicated with human health and disease risk. To accurately assess the effects of overall consumption upon health and disease, individual intake must be measured in large epidemiological studies. Metabolomics has emerged as a powerful approach to discover biomarkers of intake for a large range of foods. Here we report the profiling of the urinary metabolome of cohort study subjects to search for new biomarkers of coffee intake. Using repeated 24-hour dietary records and a food frequency questionnaire, 20 high coffee consumers (183–540 mL/d) and 19 low consumers were selected from the French SU.VI.MAX2 cohort. Morning spot urine samples from each subject were profiled by high-resolution mass spectrometry. Partial least-square discriminant analysis of multidimensional liquid chromatography-mass spectrometry data clearly distinguished high consumers from low via 132 significant (p-value<0.05) discriminating features. Ion clusters whose intensities were most elevated in the high consumers were annotated using online and in-house databases and their identities checked using commercial standards and MS-MS fragmentation. The best discriminants, and thus potential markers of coffee consumption, were the glucuronide of the diterpenoid atractyligenin, the diketopiperazine cyclo(isoleucyl-prolyl), and the alkaloid trigonelline. Some caffeine metabolites, such as 1-methylxanthine, were also among the discriminants, however caffeine may be consumed from other sources and its metabolism is subject to inter-individual variation. Receiver operating characteristics curve analysis showed that the biomarkers identified could be used effectively in combination for increased sensitivity and specificity. Once validated in other cohorts or intervention studies, these specific single or combined biomarkers will become a valuable alternative to assessment of coffee intake by dietary survey and finally lead to a better understanding of the health implications of coffee consumption.     

Plasma Autoantibodies against Heat Shock Protein 70, Enolase 1 and Ribonuclease/Angiogenin Inhibitor 1 as Potential Biomarkers for Cholangiocarcinoma

The diagnosis of cholangiocarcinoma (CCA) is often challenging, leading to poor prognosis. CCA arises via chronic inflammation which may be associated with autoantibodies production. This study aims to identify IgG antibodies directed at self-proteins and tumor-associated antigens. Proteins derived from immortalized cholangiocyte cell line (MMNK1) and CCA cell lines (M055, M214 and M139) were separated using 2-dimensional electrophoresis and incubated with pooled plasma of patients with CCA and non-neoplastic controls by immunoblotting. Twenty five immunoreactive spots against all cell lines-derived proteins were observed on stained gels and studied by LC-MS/MS. Among these, heat shock protein 70 (HSP70), enolase 1 (ENO1) and ribonuclease/angiogenin inhibitor 1 (RNH1) obtained the highest matching scores and were thus selected for further validation. Western blot revealed immunoreactivity against HSP70 and RNH1 in the majority of CCA cases and weakly in healthy individuals. Further, ELISA showed that plasma HSP70 autoantibody level in CCA was significantly capable to discriminate CCA from healthy individuals with an area under the receiver operating characteristic curve of 0.9158 (cut-off 0.2630, 93.55% sensitivity and 73.91% specificity). Plasma levels of IgG autoantibodies against HSP70 were correlated with progression from healthy individuals to cholangitis to CCA (r = 0.679, P<0.001). In addition, circulating ENO1 and RNH1 autoantibodies levels were also significantly higher in cholangitis and CCA compared to healthy controls (P<0.05). Moreover, the combinations of HSP70, ENO1 or RNH1 autoantibodies positivity rates improved specificity to over 78%. In conclusion, plasma IgG autoantibodies against HSP70, ENO1 and RNH1 may represent new diagnostic markers for CCA.     

Proteomic Profiling of Ovarian Cancer Plasma using Immunoaffinity Depleted Plasma and Two-Dimensional PAGE

Objective

The aim of this study was to evaluate a multiple immunoaffinity protein depletion (multiple affinity removal system, MARS) pre-treatment strategy with subsequent two-dimensional polyacrylamide gel electrophoresis (2D PAGE) and peptide mass finger printing analysis for the detection of ovarian cancer-associated plasma proteins.

Materials and Methods

Following immunoaffinity depletion, total plasma protein content was reduced by 84.2?±?1.8% (mean?±?SE, n?=?32). The number of proteins detected in the control and ovarian cancer groups was 349 and 357, respectively. This represented an increase in spot detection of almost twofold when compared to 2D PAGE displays of untreated plasma (174 spots). Of the proteins displayed, post-depletion, 300 (control) and 302 (ovarian cancer, OC) were common within each group. PDQuest analysis indicated that 109 protein spots were statistically different between the two groups and, of these, 59 exhibited greater than or equal to twofold difference in spot density (Student’s t test, p?=?0.01). Thirty-nine of these proteins were successfully identified with reliable confidence.

Results and Discussion

The data obtained in this study demonstrates that immunodepletion of plasma before 2D PAGE profiling have generated identifiable plasma proteins that are differentially expressed in the high-grade ovarian cancer sample set compared to controls. This approach, therefore, may be useful in identifying candidate biomarkers for inclusion in multi-marker tests for ovarian cancer that may exhibit greater sensitivity and specificity than those currently available. It was evident, however, from the predominant identification of host response proteins that immunodepletion did not generate sufficient levels of enrichment of lower abundance tumor-specific proteins to facilitate detection.     

Multi Label Learning for Prediction of Human Protein Subcellular Localizations

Predicting protein subcellular locations has attracted much attention in the past decade. However, one of the most challenging problems is that many proteins were found simultaneously existing in, or moving between, two or more different cell components in a eukaryotic cell. Seldom previous predictors were able to deal with such multiplex proteins although they have extremely important implications in future drug discovery in terms of their specific subcellular targeting. Approximately 20% of the human proteome consists of such multiplex proteins with multiple sample labels. In order to efficiently handle such multiplex human proteins, we have developed a novel multi-label (ML) learning and prediction framework called ML-PLoc, which decomposes the multi-label prediction problem into multiple independent binary classification problems. ML-PLoc is constructed based on support vector machine (SVM) and sequential evolution information. Experimental results show that ML-PLoc can achieve an overall accuracy 64.6% and recall ratio 67.2% on a benchmark dataset consisting of 14 human subcellular locations, and is very powerful for dealing with multiplex proteins. The current approach represents a new strategy to deal with the multi-label biological problems. ML-PLoc software is freely available for academic use at: http://www.csbio.sjtu.edu.cn/bioinf/ML-PLoc.     

Identification of Protein Biomarkers for Cervical Cancer Using Human Cervicovaginal Fluid

Objectives

Cervicovaginal fluid (CVF) can be considered as a potential source of biomarkers for diseases of the lower female reproductive tract. The fluid can easily be collected, thereby offering new opportunities such as the development of self tests. Our objective was to identify a CVF protein biomarker for cervical cancer or its precancerous state.

Methods

A differential proteomics study was set up using CVF samples from healthy and precancerous women. Label-free spectral counting was applied to quantify protein abundances.

Results

The proteome analysis revealed 16 candidate biomarkers of which alpha-actinin-4 (p = 0.001) and pyruvate kinase isozyme M1/M2 (p = 0.014) were most promising. Verification of alpha-actinin-4 by ELISA (n = 28) showed that this candidate biomarker discriminated between samples from healthy and both low-risk and high-risk HPV-infected women (p = 0.009). Additional analysis of longitudinal samples (n = 29) showed that alpha-actinin-4 levels correlated with virus persistence and clearing, with a discrimination of approximately 18 pg/ml.

Conclusions

Our results show that CVF is an excellent source of protein biomarkers for detection of lower female genital tract pathologies and that alpha-actinin-4 derived from CVF is a promising candidate biomarker for the precancerous state of cervical cancer. Further studies regarding sensitivity and specificity of this biomarker will demonstrate its utility for improving current screening programs and/or its use for a cervical cancer self-diagnosis test.