Salivary MicroRNA in Pancreatic Cancer Patients

Background

Pancreatic cancer is the fourth leading cause of cancer death in Western countries, with the lowest 1-year survival rate among commonly diagnosed cancers. Reliable biomarkers for pancreatic cancer diagnosis are lacking and are urgently needed to allow for curative surgery. As microRNA (miRNA) recently emerged as candidate biomarkers for this disease, we explored in the present pilot study the differences in salivary microRNA profiles between patients with pancreatic tumors that are not eligible for surgery, precancerous lesions, inflammatory disease or cancer-free patients as a potential early diagnostic tool.

Methods

Whole saliva samples from patients with pancreatic cancer (n = 7), pancreatitis (n = 4), IPMN (n = 2), or healthy controls (n = 4) were obtained during endoscopic examination. After total RNA isolation, expression of 94 candidate miRNAs was screened by q(RT)PCR using Biomark Fluidgm. Human-derived pancreatic cancer cells were xenografted in athymic mice as an experimental model of pancreatic cancer.

Results

We identified hsa-miR-21, hsa-miR-23a, hsa-miR-23b and miR-29c as being significantly upregulated in saliva of pancreatic cancer patients compared to control, showing sensitivities of 71.4%, 85.7%, 85,7% and 57%, respectively and excellent specificity (100%). Interestingly, hsa-miR-23a and hsa-miR23b are overexpressed in the saliva of patients with pancreatic cancer precursor lesions. We found that hsa-miR-210 and let-7c are overexpressed in the saliva of patients with pancreatitis as compared to the control group, with sensitivity of 100% and 75%, and specificity of 100% and 80%, respectively. Last hsa-miR-216 was upregulated in cancer patients as compared to patients diagnosed with pancreatitis, with sensitivity of 50% and specificity of 100%. In experimental models of PDAC, salivary microRNA detection precedes systemic detection of cancer cells markers.

Conclusions

Our novel findings indicate that salivary miRNA are discriminatory in pancreatic cancer patients that are not eligible for surgery. In addition, we demonstrate in experimental models that salivary miRNA detection precedes systemic detection of cancer cells markers. This study stems for the use of salivary miRNA as biomarker for the early diagnosis of patients with unresectable pancreatic cancer.     

Metabolic Profiles are Principally Different between Cancers of the Liver,Pancreas and Breast

Molecular profiling of primary tumors may facilitate the classification of patients with cancer into more homogenous biological groups to aid clinical management. Metabolomic profiling has been shown to be a powerful tool in characterizing the biological mechanisms underlying a disease but has not been evaluated for its ability to classify cancers by their tissue of origin. Thus, we assessed metabolomic profiling as a novel tool for multiclass cancer characterization. Global metabolic profiling was employed to identify metabolites in paired tumor and non-tumor liver (n=60), breast (n=130) and pancreatic (n=76) tissue specimens. Unsupervised principal component analysis showed that metabolites are principally unique to each tissue and cancer type. Such a difference can also be observed even among early stage cancers, suggesting a significant and unique alteration of global metabolic pathways associated with each cancer type. Our global high-throughput metabolomic profiling study shows that specific biochemical alterations distinguish liver, pancreatic and breast cancer and could be applied as cancer classification tools to differentiate tumors based on tissue of origin.     

Risk Prediction for Breast,Endometrial, and Ovarian Cancer in White Women Aged 50 y or Older: Derivation and Validation from Population-Based Cohort Studies

Background

Breast, endometrial, and ovarian cancers share some hormonal and epidemiologic risk factors. While several models predict absolute risk of breast cancer, there are few models for ovarian cancer in the general population, and none for endometrial cancer.

Methods and Findings

Using data on white, non-Hispanic women aged 50+ y from two large population-based cohorts (the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial [PLCO] and the National Institutes of Health–AARP Diet and Health Study [NIH-AARP]), we estimated relative and attributable risks and combined them with age-specific US-population incidence and competing mortality rates. All models included parity. The breast cancer model additionally included estrogen and progestin menopausal hormone therapy (MHT) use, other MHT use, age at first live birth, menopausal status, age at menopause, family history of breast or ovarian cancer, benign breast disease/biopsies, alcohol consumption, and body mass index (BMI); the endometrial model included menopausal status, age at menopause, BMI, smoking, oral contraceptive use, MHT use, and an interaction term between BMI and MHT use; the ovarian model included oral contraceptive use, MHT use, and family history or breast or ovarian cancer. In independent validation data (Nurses'' Health Study cohort) the breast and ovarian cancer models were well calibrated; expected to observed cancer ratios were 1.00 (95% confidence interval [CI]: 0.96–1.04) for breast cancer and 1.08 (95% CI: 0.97–1.19) for ovarian cancer. The number of endometrial cancers was significantly overestimated, expected/observed = 1.20 (95% CI: 1.11–1.29). The areas under the receiver operating characteristic curves (AUCs; discriminatory power) were 0.58 (95% CI: 0.57–0.59), 0.59 (95% CI: 0.56–0.63), and 0.68 (95% CI: 0.66–0.70) for the breast, ovarian, and endometrial models, respectively.

Conclusions

These models predict absolute risks for breast, endometrial, and ovarian cancers from easily obtainable risk factors and may assist in clinical decision-making. Limitations are the modest discriminatory ability of the breast and ovarian models and that these models may not generalize to women of other races. Please see later in the article for the Editors'' Summary     

Global Metabolomic Analysis of Human Saliva and Plasma from Healthy and Diabetic Subjects,with and without Periodontal Disease

Recent studies suggest that periodontal disease and type 2 diabetes mellitus are bi-directionally associated. Identification of a molecular signature for periodontitis using unbiased metabolic profiling could allow identification of biomarkers to assist in the diagnosis and monitoring of both diabetes and periodontal disease. This cross-sectional study identified plasma and salivary metabolic products associated with periodontitis and/or diabetes in order to discover biomarkers that may differentiate or demonstrate an interaction of these diseases. Saliva and plasma samples were analyzed from 161 diabetic and non-diabetic human subjects with a healthy periodontium, gingivitis and periodontitis. Metabolite profiling was performed using Metabolon''s platform technology. A total of 772 metabolites were found in plasma and 475 in saliva. Diabetics had significantly higher levels of glucose and α-hydroxybutyrate, the established markers of diabetes, for all periodontal groups of subjects. Comparison of healthy, gingivitis and periodontitis saliva samples within the non-diabetic group confirmed findings from previous studies that included increased levels of markers of cellular energetic stress, increased purine degradation and glutathione metabolism through increased levels of oxidized glutathione and cysteine-glutathione disulfide, markers of oxidative stress, including increased purine degradation metabolites (e.g. guanosine and inosine), increased amino acid levels suggesting protein degradation, and increased ω-3 (docosapentaenoate) and ω-6 fatty acid (linoleate and arachidonate) signatures. Differences in saliva between diabetic and non-diabetic cohorts showed altered signatures of carbohydrate, lipid and oxidative stress exist in the diabetic samples. Global untargeted metabolic profiling of human saliva in diabetics replicated the metabolite signature of periodontal disease progression in non-diabetic patients and revealed unique metabolic signatures associated with periodontal disease in diabetics. The metabolites identified in this study that discriminated the periodontal groups may be useful for developing diagnostics and therapeutics tailored to the diabetic population.     

A Review of the Role of ERp57 in Cancerous and Non-Cancerous Cell Physiology and its Potential as a Therapeutic Target

The protein ERp57 is a stress-responsive protein, mainly exists in the endoplasmic reticulum (ER), and a small amount in the cell membrane, cytoplasm, nucleus and mitochondria, which is involved in the signal transduction from the cell surface, the regulation process that occurs in the nucleus, and the formation of polymer protein complexes involved in DNA repair. Various degrees of ERp57 dysregulation has been observed in many types of non-communicable diseases especially in cancers. Previous studies showed that the expression of ERp57 could play a key role in occurrence and development of cancers such as breast cancer, gastric cancer, ovarian cancer, etc.; in addition, it has been suggested to play a pivotal role in disease progression of non-cancerous diseases such as neurodegeneration, liver disease, kidney disease, intestinal irritability syndrome and airway hypersensitivity. Thus, abnormal expression of ERp57 could be used as promising biomarker for cancer diagnosis and prognosis based on the previous studies. In this regard, current study was aimed to review the literature, which have been elucidate the role of ERp57 protein expression in both non-cancer and cancer disease. Overall, most studies have shown that inhibiting/knocking out of ERp57 could inhibit the cell proliferation and also induce apoptosis in both human cancerous and non-cancerous cells. Also, it has been suggested that the overexpression of ERp57 could intensify the cancer development. Therefore, it could be hypothesized that targeting of ERp57 might be a potential treatment in cancerous and non-cancerous diseases.     

Ratio of Pro-Resolving and Pro-Inflammatory Lipid Mediator Precursors as Potential Markers for Aggressive Periodontitis

Aggressive periodontitis (AgP) is a rapidly progressing type of periodontal disease in otherwise healthy individuals which causes destruction of the supporting tissues of the teeth. The disease is initiated by pathogenic bacteria in the dental biofilm, and the severity of inflammation and attachment loss varies with the host response. Recently, there has been an increased interest in determining the role of lipid mediators in inflammatory events and the concept of pro-inflammatory and pro-resolution lipid mediators has been brought into focus also in periodontal disease. The present study aimed to determine the profile of omega-3 or n3- as well as omega-6 or n6- polyunsaturated fatty acids (PUFAs) and PUFA-metabolites of linoleic acid, arachidonic acid (AA), eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) in gingival crevicular fluid (GCF), saliva and serum in AgP patients and healthy controls. In total, 60 selected n3- and n6-PUFAs and various PUFA metabolites were measured using high performance liquid chromatography-tandem electrospray ionisation mass spectrometry (HPLC-ESI-MS-MS). Of these, 51 could be quantified in this study. The concentrations of the majority were low in saliva samples compared with serum and GCF, but were mainly higher in AgP patients compared with healthy controls in all three kinds of sample. Ratios of n3- to n6-PUFAs (DHA + EPA)/AA were significantly lower in the GCF of AgP patients than in the healthy controls. Furthermore, various ratios of the direct precursors of the pro-resolution lipid mediators (precursors of resolvins and protectins) were calculated against the precursors of mainly pro-inflammatory lipid mediators. These ratios were mainly lower in GCF and saliva of AgP patients, compared with healthy controls, but only reached significance in GCF (P<0.05). To conclude, the ratios of precursors of pro-resolution/pro-inflammatory lipid mediators seem to be more relevant for describing the disease status of AgP than the concentration of specific lipid mediators.     

Metabolic profiling by <Superscript>1</Superscript>H NMR spectroscopy of saliva shows clear distinction between control and diseased case of periodontitis

Introduction

Periodontitis is a chronic, non-reversible inflammatory disease of the oral cavity leading to destruction of periodontal tissues. Thus, the estimation of bacterial metabolite, tissue damage and secretory metabolites of the triggered inflammatory cells likely to yield results. It may be of value for understanding the pathophysiology of the disease by metabolic profiling of saliva samples using high-resolution NMR spectroscopy.

Objective

The study will evaluate the difference in salivary metabolites in healthy and periodontal condition along with fetching of possible biomarkers in case of chronic periodontitis.

Methods

¹H- NMR spectroscopy has been employed in 114 saliva samples in search of distinctive differences and spectral data were further subjected to multivariate analysis.

Result

One-hundred metabolites were characterised and assigned in the ¹H NMR spectra of saliva. The statistical analysis of control (Healthy subjects) and diseased (Periodontal subjects) using PLS-DA model resulted in R² of 0.84 and Q² of 0.79. There was an elevation in the concentration of statistically discriminant metabolites. The twenty newly identified metabolites in saliva indicates bacterial population shift along with change in homeostasis. These disturbs the biofilm, a real protector against any possible bio-damage on tooth surface. These newly identified metabolites could define better geographically diversified periodontal condition.

Conclusion

Analysis clearly differentiates healthy subjects from the diseased ones. Few newly identified metabolites along with the pool of metabolites may serve as biomarkers for distinguishing the severity and complexity of periodontitis.

     

Plasma Metabolomic Profiles in Breast Cancer Patients and Healthy Controls: By Race and Tumor Receptor Subtypes

BACKGROUND: A few studies in the last several years have shown that metabolomics, the study of metabolites and small intermediate molecules, may help better understand the breast carcinogenesis. However, breast cancer is a heterogeneous disease with different subtypes. Additionally, there is a significant racial difference in terms of breast cancer incidence and mortality. Few, if any, metabolomics studies in breast cancer have considered race and tumor subtypes in the study design. METHODS: We performed a global metabolomic profiling using mass spectrometry and samples from 60 breast cancer cases and 60 matched controls. RESULTS: A total of 375 named metabolites were observed, with 117 metabolites whose levels were significantly different between African American and Caucasian American women (P < .05 and q < 0.10) and 78 that differed between breast cancer cases and healthy controls (P < .05 and q < 0.10). Most of those differentiated metabolites belong to amino acids, fatty acids, and lysolipids. In the pathway-based analysis, we found that plasma levels of many amino acids were statistically significantly lower in patients with breast cancer, especially those with triple-negative breast cancer, than healthy controls. However, plasma levels of many FAs related to β-oxidation were statistically significantly higher in patients with breast cancer than healthy controls, suggesting the possibility of altered FA β-oxidation in patients with breast cancer. CONCLUSIONS: Because of small sample size, the clinical usage of the metabolites from this study is unclear. Further validation of those significant metabolites is warranted, especially with the consideration of racial difference.     

Application of quantitative proteomic analysis using tandem mass tags for discovery and identification of novel biomarkers in periodontal disease

Periodontal disease is a bacterial infection that destroys the gingiva and surrounding tissues of the oral cavity. Gingival crevicular fluid (GCF) is extracted from the gingival sulcus and pocket. Analysis of biochemical markers in GCF, which predict the progression of periodontal disease, may facilitate disease diagnosis. However, no useful GCF biochemical markers with high sensitivity for detecting periodontal disease have been identified. Thus, the search for biochemical markers of periodontal disease is of continued interest in experimental and clinical periodontal disease research. Using tandem mass tag (TMT) labeling, we analyzed GCF samples from healthy subjects and patients with periodontal disease, and identified a total of 619 GCF proteins based on proteomic analysis. Of these, we focused on two proteins, matrix metalloproteinase (MMP)‐9 and neutrophil gelatinase‐associated lipocalin (LCN2), which are involved in the progression of periodontal disease. Western blot analysis revealed that the levels of MMP‐9 and LCN2 were significantly higher in patients with periodontal disease than in healthy subjects. In addition, ELISA also detected significantly higher levels of LCN2 in patients with periodontal disease than in healthy subjects. Thus, LC‐MS/MS analyses of GCF using TMT labeling led to the identification of LCN2, which may be a promising GCF biomarker for the detection of periodontal disease.     

Synthesis and biological evaluation of water-soluble progesterone-conjugated probes for magnetic resonance imaging of hormone related cancers

Progesterone receptor (PR) is strongly associated with disease prognosis and therapeutic efficacy in hormone-related diseases such as endometriosis and breast, ovarian, and uterine cancers. Receptor status is currently determined by immunohistochemistry assays. However, noninvasive PR imaging agents could improve disease detection and help elucidate pathological molecular pathways, leading to new therapies and animal disease models. A series of water-soluble PR-targeted magnetic resonance imaging (MRI) probes were synthesized using Cu(I)-catalyzed click chemistry and evaluated in vitro and in vivo. These agents demonstrated activation of PR in vitro and preferential accumulation in PR(+) compared to PR(-) human breast cancer cells with low toxicity. In xenograft tumor models, the agents demonstrated enhanced signal intensity in PR(+) tumors compared to PR(-) tumors. The results suggest that these agents may be promising MRI probes for PR(+) diseases.     

The scientific exploration of saliva in the post-proteomic era: from database back to basic function

The proteome of human saliva can be considered as being essentially completed. Diagnostic markers for a number of diseases have been identified among salivary proteins and peptides, taking advantage of saliva as an easy-to-obtain biological fluid. Yet, the majority of disease markers identified so far are serum components and not intrinsic proteins produced by the salivary glands. Furthermore, despite the fact that saliva is essential for protecting the oral integuments and dentition, little progress has been made in finding risk predictors in the salivary proteome for dental caries or periodontal disease. Since salivary proteins, and in particular the attached glycans, play an important role in interactions with the microbial world, the salivary glycoproteome and other post-translational modifications of salivary proteins need to be studied. Risk markers for microbial diseases, including dental caries, are likely to be discovered among the highly glycosylated major protein species in saliva. This review will attempt to raise new ideas and also point to under-researched areas that may hold promise for future applicability in oral diagnostics and prediction of oral disease.     

Presence of Candida albicans in the oral cavity of healthy subjects

The influence of possible changes in bacterial flora in the etiology of periodontal disease assessed during a study of saprophitism of Candida albicans in the oral cavity is reported. Three saliva samples, each from each of 50 healthy subjects not on drugs, taken prior to breakfast and after careful cleaning of the oral cavity the previous evening were subjected to bacterioscopic examination both immediately and after culturing on Sabouraud medium. pH levels were also determined. 17 subjects, of which females predominated, (12 cases compared to 5 male cases) were found to be C.a carriers.     

Significance of pregnancy-associated alpha 2-glycoprotein (alpha 2-PAG) in patients with breast, gastric and colorectal cancer

Pregnancy-associated alpha 2-glycoprotein (alpha 2-PAG) levels were measured in human sera by a modification of Laurell's electroimmunoassay using rabbit anti-alpha 2-PAG serum. Sera were obtained from healthy controls (32 males and 46 females), patients with benign breast diseases (55 cases), and patients with breast (82 cases), gastric (89 cases), or colorectal (22 cases) cancers. In healthy controls, the mean alpha 2-PAG value for females was higher than that for males (p less than 0.05), so alpha 2-PAG values for males and females were considered separately in this study. Serum alpha 2-PAG levels in patients with benign breast tumors were almost the same as those of controls. In patients with primary breast and gastric cancer, alpha 2-PAG levels were higher than those of controls (p less than 0.005) and tended to increase with progress of the disease. Raised alpha 2-PAG levels decreased in these patients after curative surgery. These results show that serum alpha 2-PAG is useful as a marker in both the initial diagnosis and follow-up of breast and gastric cancer. The reliability of alpha 2-PAG as a tumor-associated marker was reinforced by comparison of the positive rates of the three parameters alpha 2-PAG, carcinoembryonic antigen (CEA), and immunosuppressive acidic protein (IAP) in patients with breast and gastric cancer.     

Proteomic analysis of human saliva from lung cancer patients using two-dimensional difference gel electrophoresis and mass spectrometry

Lung cancer is often asymptomatic or causes only nonspecific symptoms in its early stages. Early detection represents one of the most promising approaches to reduce the growing lung cancer burden. Human saliva is an attractive diagnostic fluid because its collection is less invasive than that of tissue or blood. Profiling of proteins in saliva over the course of disease progression could reveal potential biomarkers indicative of oral or systematic diseases, which may be used extensively in future medical diagnostics. There were 72 subjects enrolled in this study for saliva sample collection according to the approved protocol. Two-dimensional difference gel electrophoresis combined with MS was the platform for salivary proteome separation, quantification, and identification from two pooled samples. Candidate proteomic biomarkers were verified and prevalidated by using immunoassay methods. There were 16 candidate protein biomarkers discovered by two-dimensional difference gel electrophoresis and MS. Three proteins were further verified in the discovery sample set, prevalidation sample set, and lung cancer cell lines. The discriminatory power of these candidate biomarkers in lung cancer patients and healthy control subjects can reach 88.5% sensitivity and 92.3% specificity with AUC = 0.90. This preliminary data report demonstrates that proteomic biomarkers are present in human saliva when people develop lung cancer. The discriminatory power of these candidate biomarkers indicate that a simple saliva test might be established for lung cancer clinical screening and detection.     

Plasma free amino acid profiling of five types of cancer patients and its application for early detection

Background

Recently, rapid advances have been made in metabolomics-based, easy-to-use early cancer detection methods using blood samples. Among metabolites, profiling of plasma free amino acids (PFAAs) is a promising approach because PFAAs link all organ systems and have important roles in metabolism. Furthermore, PFAA profiles are known to be influenced by specific diseases, including cancers. Therefore, the purpose of the present study was to determine the characteristics of the PFAA profiles in cancer patients and the possibility of using this information for early detection.

Methods and Findings

Plasma samples were collected from approximately 200 patients from multiple institutes, each diagnosed with one of the following five types of cancer: lung, gastric, colorectal, breast, or prostate cancer. Patients were compared to gender- and age- matched controls also used in this study. The PFAA levels were measured using high-performance liquid chromatography (HPLC)–electrospray ionization (ESI)–mass spectrometry (MS). Univariate analysis revealed significant differences in the PFAA profiles between the controls and the patients with any of the five types of cancer listed above, even those with asymptomatic early-stage disease. Furthermore, multivariate analysis clearly discriminated the cancer patients from the controls in terms of the area under the receiver-operator characteristics curve (AUC of ROC >0.75 for each cancer), regardless of cancer stage. Because this study was designed as case-control study, further investigations, including model construction and validation using cohorts with larger sample sizes, are necessary to determine the usefulness of PFAA profiling.

Conclusions

These findings suggest that PFAA profiling has great potential for improving cancer screening and diagnosis and understanding disease pathogenesis. PFAA profiles can also be used to determine various disease diagnoses from a single blood sample, which involves a relatively simple plasma assay and imposes a lower physical burden on subjects when compared to existing screening methods.     

Predicting microRNA-disease association based on microRNA structural and functional similarity network

Background: Increasing evidences indicate that microRNAs (miRNAs) are functionally related to the development and progression of various human diseases. Inferring disease-related miRNAs can be helpful in promoting disease biomarker detection for the treatment, diagnosis, and prevention of complex diseases. Methods: To improve the prediction accuracy of miRNA-disease association and capture more potential disease-related miRNAs, we constructed a precise miRNA global similarity network (MSFSN) via calculating the miRNA similarity based on secondary structures, families, and functions. Results: We tested the network on the classical algorithms: WBSMDA and RWRMDA through the method of leave-one-out cross-validation. Eventually, AUCs of 0.8212 and 0.9657 are obtained, respectively. Also, the proposed MSFSN is applied to three cancers for breast neoplasms, hepatocellular carcinoma, and prostate neoplasms. Consequently, 82%, 76%, and 82% of the top 50 potential miRNAs for these diseases are respectively validated by the miRNA-disease associations database miR2Disease and oncomiRDB. Conclusion: Therefore, MSFSN provides a novel miRNA similarity network combining precise function network with global structure network of miRNAs to predict the associations between miRNAs and diseases in various models.     

益生菌辅助治疗牙周病的研究进展

牙周病是累及牙周支持组织的慢性感染性疾病。牙菌斑生物膜中的微生物及其代谢产物是其必不可少的始动因素,可导致口腔微生态失衡和宿主免疫反应,最终引起牙周病的发生发展。目前,牙周病的基础治疗主要是机械清除牙菌斑生物膜和牙石,但治疗效果具有局限性。益生菌通过产生抑菌物质、刺激局部免疫反应、与致病菌争夺黏膜受体和营养物质,从而改善口腔微生态平衡,促进牙周病的治疗。本文就近年来益生菌在牙周病治疗上的实验和临床研究、作用机制、安全性等进行综述,为将来益生菌辅助治疗牙周病的应用提供参考。     

Salivary MicroRNAs as Promising Biomarkers for Detection of Esophageal Cancer

Background and Purpose

Tissue microRNAs (miRNAs) can detect cancers and predict prognosis. Several recent studies reported that tissue, plasma, and saliva miRNAs share similar expression profiles. In this study, we investigated the discriminatory power of salivary miRNAs (including whole saliva and saliva supernatant) for detection of esophageal cancer.

Materials and Methods

By Agilent microarray, six deregulated miRNAs from whole saliva samples from seven patients with esophageal cancer and three healthy controls were selected. The six selected miRNAs were subjected to validation of their expression levels by RT-qPCR using both whole saliva and saliva supernatant samples from an independent set of 39 patients with esophageal cancer and 19 healthy controls.

Results

Six miRNAs (miR-10b*, miR-144, miR-21, miR-451, miR-486-5p, and miR-634) were identified as targets by Agilent microarray. After validation by RT-qPCR, miR-10b*, miR-144, and miR-451 in whole saliva and miR-10b*, miR-144, miR-21, and miR-451 in saliva supernatant were significantly upregulated in patients, with sensitivities of 89.7, 92.3, 84.6, 79.5, 43.6, 89.7, and 51.3% and specificities of 57.9, 47.4, 57.9%, 57.9, 89.5, 47.4, and 84.2%, respectively.

Conclusions

We found distinctive miRNAs for esophageal cancer in both whole saliva and saliva supernatant. These miRNAs possess discriminatory power for detection of esophageal cancer. Because saliva collection is noninvasive and convenient, salivary miRNAs show great promise as biomarkers for detection of esophageal cancer in areas at high risk.     

Computational Prediction of Human Salivary Proteins from Blood Circulation and Application to Diagnostic Biomarker Identification

Proteins can move from blood circulation into salivary glands through active transportation, passive diffusion or ultrafiltration, some of which are then released into saliva and hence can potentially serve as biomarkers for diseases if accurately identified. We present a novel computational method for predicting salivary proteins that come from circulation. The basis for the prediction is a set of physiochemical and sequence features we found to be discerning between human proteins known to be movable from circulation to saliva and proteins deemed to be not in saliva. A classifier was trained based on these features using a support-vector machine to predict protein secretion into saliva. The classifier achieved 88.56% average recall and 90.76% average precision in 10-fold cross-validation on the training data, indicating that the selected features are informative. Considering the possibility that our negative training data may not be highly reliable (i.e., proteins predicted to be not in saliva), we have also trained a ranking method, aiming to rank the known salivary proteins from circulation as the highest among the proteins in the general background, based on the same features. This prediction capability can be used to predict potential biomarker proteins for specific human diseases when coupled with the information of differentially expressed proteins in diseased versus healthy control tissues and a prediction capability for blood-secretory proteins. Using such integrated information, we predicted 31 candidate biomarker proteins in saliva for breast cancer.