Biomarkers For Disease Prediction Research Articles

MiR‐181b expression as a potential biomarker in Alzheimer’s Disease

AbstractBackgroundAlzheimer’s Disease (AD) is a multifactorial neurodegenerative disorder with complex molecular pathophysiology at the cellular level. Despite intensive research to decipher genetic and molecular players, non‐invasive markers of early diagnosis and progression are yet to be discovered. MicroRNAs (miRNAs) regulate post‐transcriptional gene expression and hold the potential to act as biomarkers for disease prediction, diagnosis and prognosis. Identification of differentially expressed circulating miRNAs in late‐onset AD will serve for the establishment of non‐invasive biomarker panels for disease prediction at earlier ages. In this regard, miR‐181b may serve as a candidate molecule. Here we aimed to evaluate the expression pattern of hsa‐miR‐181b in the serum samples of late‐onset AD patients and healthy controls.MethodBlood samples were collected from 33 late‐onset AD patients and 34 healthy individuals. Samples were processed immediately for plasma isolation and kept at ‐80°C. Following total RNA extraction and cDNA synthesis, expression levels of hsa‐miR‐181b‐5p were determined in all samples via qRT‐PCR using SyberGreen. Cel‐miR39 was used as a spike‐in control and all samples were processed in duplicates. Data were analyzed by the ∆∆CT method. p<0,05 was considered to be significant.ResultThe average ages of AD patients and healthy controls were 82 and 59 years, respectively. Females constituted 60% of the samples in both groups. Patients were being treated with Donepezil or Donepezil and Memantine. According to the qPCR results, miR181b levels were found to be significantly reduced by 3 folds in AD patients, compared to controls.ConclusionThere are conflicting data in the literature regarding up or down regulation of miR181b in AD. This may be due to different experimental set‐ups, ethnicities of the study populations, sample characteristics or other confounding factors. To the best of our knowledge, this is the first study evaluating the expression pattern of miR181b in the plasma samples of Turkish AD patients. Our results indicate that hsa‐miR‐181b‐5p can be regarded as a potential blood biomarker for the prediction of AD. miR181 was shown to be involved in immune response regulation and neuroinflammation in the literature, therefore it is of high importance in AD.

Genome-wide DNA methylation analysis of extreme phenotypes in the identification of novel epigenetic modifications in diabetic retinopathy

BackgroundAberrant epigenetic modifications such as DNA methylation may contribute to the pathogenesis of DR. We aimed at elucidating the role of novel DNA methylation modifications in diabetic retinopathy (DR) in patients with type 2 diabetes mellitus (T2DM) using an extreme phenotypic design.Methods/resultsTwo consecutive studies were conducted. A cross-sectional study using an extreme phenotypic design was conducted to identify rare methylation modifications that might contribute to DR pathogenesis. A 2-year longitudinal nested case–control study was conducted to validate the results and assess whether these novel methylation modifications could be used as biomarkers for predicting DR onset. A large number of differentially methylated CpG sites were identified in the cross-sectional study, and two (cg12869254 and cg04026387) corresponding to known genes were replicated in the longitudinal study. Higher methylation of cg12869254 significantly correlated with macular RNFL thinning in the superior and nasal subregions, and that of cg04026387 correlated with reduced deep capillary plexus VD in the superior and inferior subregions after adjusting for covariates.ConclusionsCg12869254 and cg04026387 hypermethylation may complement the known risk factors that contribute to the pathogenesis of DR and as novel biomarkers for disease prediction.

Evidence for TET-mediated DNA demethylation as an epigenetic alteration in cumulus granulosa cells of women with polycystic ovary syndrome

Peripheral and tissue-specific alterations in global DNA methylation (5-methylcytosine (5mC)) and DNA hydroxymethylation (5-hydroxymethylcytosine (5hmC)) profiles have been identified as both biomarkers for disease prediction and as hallmarks of dysregulated localized gene networks. Global and gene-specific epigenetic alterations in the 5mC profiles have shown widespread implications in the etiology of polycystic ovary syndrome (PCOS). However, there has been no study in PCOS that integrates the quantification of 5mC and 5hmC signatures alongside the expression levels of DNA methylating and demethylating enzymes as respective indicators of methylation and demethylation pathways. Having previously shown that the 5mC signatures are not substantially altered in PCOS, we assessed the global 5hmC levels in peripheral blood leukocytes and cumulus granulosa cells (CGCs) of 40 controls and 40 women with PCOS. This analysis revealed higher 5hmC levels in CGCs of PCOS women, indicating a more dominant demethylation pathway. Furthermore, we assessed the transcript and protein expression levels of DNA demethylating and methylating enzymes, i.e. ten-eleven translocation methylcytosine dioxygenases (TET1, TET2, TET3) and DNA methyltransferases (DNMT1, DNMT3A and DNMT3B), respectively, in CGCs. The relative transcript and protein expression levels of all three TETs were found to be higher in women with PCOS, and the TET mRNA expression profiles were positively correlated with 5hmC levels in CGCs. Also, all three DNMT genes showed altered transcript expression in PCOS, although only the downregulated DNMT3A transcript was correlated with decreasing 5mC levels. At the protein level, the expression of DNMT1 (maintenance methylation enzyme) was higher, while that of DNMT3A (de novo methylation enzyme) was found to be lower in PCOS compared to controls. Overall, these results indicate that DNA methylation changes in CGCs of PCOS women may arise partly due to intrinsic alterations in the transcriptional regulation of TETs and DNMT3A.

Altered DNA methylation pattern reveals epigenetic regulation of Hox genes in thoracic aortic dissection and serves as a biomarker in disease diagnosis

BackgroundThoracic aortic dissection (TAD) is a severe disease with limited understandings in its pathogenesis. Altered DNA methylation has been revealed to be involved in many diseases etiology. Few studies have examined the role of DNA methylation in the development of TAD. This study explored alterations of the DNA methylation landscape in TAD and examined the potential role of cell-free DNA (cfDNA) methylation as a biomarker in TAD diagnosis.ResultsAscending aortic tissues from TAD patients (Stanford type A; n = 6) and healthy controls (n = 6) were first examined via whole-genome bisulfite sequencing (WGBS). While no obvious global methylation shift was observed, numerous differentially methylated regions (DMRs) were identified, with associated genes enriched in the areas of vasculature and heart development. We further confirmed the methylation and expression changes in homeobox (Hox) clusters with 10 independent samples using bisulfite pyrosequencing and quantitative real-time PCR (qPCR). Among these, HOXA5, HOXB6 and HOXC6 were significantly down-regulated in TAD samples relative to controls. To evaluate cfDNA methylation pattern as a biomarker in TAD diagnosis, cfDNA from TAD patients (Stanford type A; n = 7) and healthy controls (n = 4) were examined by WGBS. A prediction model was built using DMRs identified previously from aortic tissues on methylation data from cfDNA. Both high sensitivity (86%) and specificity (75%) were achieved in patient classification (AUC = 0.96).ConclusionsThese findings showed an altered epigenetic regulation in TAD patients. This altered epigenetic regulation and subsequent altered expression of genes associated with vasculature and heart development, such as Hox family genes, may contribute to the loss of aortic integrity and TAD pathogenesis. Additionally, the cfDNA methylation in TAD was highly disease specific, which can be used as a non-invasive biomarker for disease prediction.

Proximity Extension Assay in Combination with Next-Generation Sequencing for High-throughput Proteome-wide Analysis

Understanding the dynamics of the human proteome is crucial for developing biomarkers to be used as measurable indicators for disease severity and progression, patient stratification, and drug development. The Proximity Extension Assay (PEA) is a technology that translates protein information into actionable knowledge by linking protein-specific antibodies to DNA-encoded tags. In this report we demonstrate how we have combined the unique PEA technology with an innovative and automated sample preparation and high-throughput sequencing readout enabling parallel measurement of nearly 1500 proteins in 96 samples generating close to 150,000 data points per run. This advancement will have a major impact on the discovery of new biomarkers for disease prediction and prognosis and contribute to the development of the rapidly evolving fields of wellness monitoring and precision medicine.

Interpretable Log Contrasts for the Classification of Health Biomarkers: a New Approach to Balance Selection

Since the turn of the century, technological advances have made it possible to obtain the molecular profile of any tissue in a cost-effective manner. Among these advances are sophisticated high-throughput assays that measure the relative abundances of microorganisms, RNA molecules, and metabolites. While these data are most often collected to gain new insights into biological systems, they can also be used as biomarkers to create clinically useful diagnostic classifiers. How best to classify high-dimensional -omics data remains an area of active research. However, few explicitly model the relative nature of these data and instead rely on cumbersome normalizations. This report (i) emphasizes the relative nature of health biomarkers, (ii) discusses the literature surrounding the classification of relative data, and (iii) benchmarks how different transformations perform for regularized logistic regression across multiple biomarker types. We show how an interpretable set of log contrasts, called balances, can prepare data for classification. We propose a simple procedure, called discriminative balance analysis, to select groups of 2 and 3 bacteria that can together discriminate between experimental conditions. Discriminative balance analysis is a fast, accurate, and interpretable alternative to data normalization.IMPORTANCE High-throughput sequencing provides an easy and cost-effective way to measure the relative abundance of bacteria in any environmental or biological sample. When these samples come from humans, the microbiome signatures can act as biomarkers for disease prediction. However, because bacterial abundance is measured as a composition, the data have unique properties that make conventional analyses inappropriate. To overcome this, analysts often use cumbersome normalizations. This article proposes an alternative method that identifies pairs and trios of bacteria whose stoichiometric presence can differentiate between diseased and nondiseased samples. By using interpretable log contrasts called balances, we developed an entirely normalization-free classification procedure that reduces the feature space and improves the interpretability, without sacrificing classifier performance.

Targeted therapies and precision medicine for inflammatory skin diseases.

Over the last years, several immune pathways in the skin have been identified. These discoveries have been translated into targeted therapies for inflammatory skin diseases, which have revolutionized our clinical practice. The perturbed molecular network in the skin may serve as a biomarker for disease prediction and as a source of targets for early therapeutic intervention or prevention. With rising costs, this disease-focused healthcare model will become unsustainable, therefore next-generation healthcare must not focus solely on treatment personalisation (the right treatment for the right patient), but also needs to shift its focus towards disease prevention and early therapeutic intervention prior to disease manifestation.

Linking of metabolomic biomarkers with cardiometabolic health in Chinese population

Due to rapid nutrition transitions, the prevalence of cardiometabolic diseases, such as metabolic syndrome, type 2 diabetes, and cardiovascular diseases, has been increasing at an alarming rate in the Chinese population. Moreover, Asians, including Chinese, have been hypothesized to have a higher susceptibility to cardiometabolic diseases than Caucasians. Early prediction and prevention are key to controlling this epidemic trend; to this end, the identification of novel biomarkers is critical to reflect environmental exposure, as well as to reveal endogenous metabolic and pathophysiologic mechanisms. The emerging "omics" technologies, especially metabolomics, offer a unique opportunity to provide novel signatures or fingerprints to understand the effects of genetic and non-genetic factors on cardiometabolic health. During the past two decades, metabolomic approaches have been increasingly used in various epidemiological studies, primarily in Western populations. Although the field is still in its early stages, some studies have tried to identify novel compounds or confirm their metabolites and associations with cardiometabolic diseases in Chinese populations, including amino acids, fatty acids, acylcarnitines and other metabolites. Despite major efforts to discover novel biomarkers for disease prediction or intervention, the limits in current study design, analytical platforms, and data processing approaches are challenges in metabolomic research worldwide. Therefore, future research with more advanced technologies, rigorous study designs, standardized detection and analytic approaches, and integrated data from multiomics approaches are essential to evaluate the feasibility of using metabolomics in clinical settings. Finally, the functional roles and underlying biological mechanisms of metabolomic biomarkers should be elucidated by future mechanistic research.

Biomarker microRNAs for prostate cancer metastasis: screened with a network vulnerability analysis model

BackgroundProstate cancer (PCa) is a fatal malignant tumor among males in the world and the metastasis is a leading cause for PCa death. Biomarkers are therefore urgently needed to detect PCa metastatic signature at the early time. MicroRNAs are small non-coding RNAs with the potential to be biomarkers for disease prediction. In addition, computer-aided biomarker discovery is now becoming an attractive paradigm for precision diagnosis and prognosis of complex diseases.MethodsIn this study, we identified key microRNAs as biomarkers for predicting PCa metastasis based on network vulnerability analysis. We first extracted microRNAs and mRNAs that were differentially expressed between primary PCa and metastatic PCa (MPCa) samples. Then we constructed the MPCa-specific microRNA-mRNA network and screened microRNA biomarkers by a novel bioinformatics model. The model emphasized the characterization of systems stability changes and the network vulnerability with three measurements, i.e. the structurally single-line regulation, the functional importance of microRNA targets and the percentage of transcription factor genes in microRNA unique targets.ResultsWith this model, we identified five microRNAs as putative biomarkers for PCa metastasis. Among them, miR-101-3p and miR-145-5p have been previously reported as biomarkers for PCa metastasis and the remaining three, i.e. miR-204-5p, miR-198 and miR-152, were screened as novel biomarkers for PCa metastasis. The results were further confirmed by the assessment of their predictive power and biological function analysis.ConclusionsFive microRNAs were identified as candidate biomarkers for predicting PCa metastasis based on our network vulnerability analysis model. The prediction performance, literature exploration and functional enrichment analysis convinced our findings. This novel bioinformatics model could be applied to biomarker discovery for other complex diseases.

Circulating microRNAs and diabetes mellitus: a novel tool for disease prediction, diagnosis, and staging?

Diabetes is a complex, multifactorial group of metabolic diseases characterized by chronic hyperglycaemia due to pancreatic beta-cell dysfunction and/or loss. It is characterized by an asymptomatic and highly variable prodromic phase, which renders diabetes mellitus difficult to be predicted with sufficient accuracy. Despite several efforts in the identification and standardization of newly trustable. Biomarkers able to predict and follow-up diabetes and to specifically subtype its different forms, few of them have proven of clinical utility. Recently, a new class of endogenous non-coding small RNAs, namely microRNAs, have been indicated as putative biomarkers, being released by cells and tissues and found in a cell-free circulating form in many biological fluids, including serum and/or plasma. MicroRNAs have been initially identified as promising biomarkers in cancer, and nowadays their application has been extended to other diseases, including diabetes. Although an increasing number of studies focused on the evaluation of circulating microRNAs in diabetes, few reproducibly identified microRNAs as biomarkers for disease prediction or follow-up. Technological problems as well as the need to obtain highly standardized operating procedures and methods are still an issue in such research field. In this review, we comprehensively resume the main and most recent findings on circulating microRNAs, and their possible use as biomarkers to predict and follow-up diabetes and its complications, as well as the methodological challenges to standardize accurate operating procedures for their analysis.

Multiplex Biomarker Approaches in Type 2 Diabetes Mellitus Research.

Type 2 diabetes mellitus is a multifactorial condition resulting in high fasting blood glucose levels. Although its diagnosis is straightforward, there is not one set of biomarkers or drug targets that can be used for classification or personalized treatment of individuals who suffer from this condition. Instead, the application of multiplex methods incorporating a systems biology approach is essential in order to increase our understanding of this disease. This chapter reviews the state of the art in biomarker studies of human type 2 diabetes from a proteomic and metabolomic perspective. Our main focus was on biomarkers for disease prediction as these could lead to early intervention strategies for the best possible patient outcomes.

FRI0030 Cxcl4 and CXCL7 Expression on Macrophages: A Potential Predictor of Disease Outcome in Patients Presenting with Early Synovitis?

BackgroundWithin the context of a comprehensive study of the pathology of the early stages of rheumatoid arthritis (RA) we compared cytokine mRNA expression in the synovium of patients with early...

Global miRNA expression profiles uncover serum miRNAs as novel biomarkers for diabetes staging in NOD mice (P3267)

Abstract Type 1 diabetes (T1D) is an autoimmune diseases resulting from T cell-mediated pancreatic beta cell destruction. New biomarkers are urgently needed for earlier T1D risk prediction and progression. MicroRNAs (miRNAs) regulate beta cell and immune cell development and function, and are involved in T1D development. Recently, it has been discovered that serum contains large amounts of stable miRNAs derived from immune cells and other tissues/cells, and that serum miRNAs might serve as biomarkers for disease prediction and progression. Yet little is known about serum miRNAs in T1D. We hypothesize that serum miRNAs could be a novel class of blood-based biomarker for diabetes staging. Serum RNAs were isolated from nonobese diabetic (NOD) mice at different stages (3-4, 7-8, 16-19 weeks, and newly diagnosed diabetes) during diabetes development. Serum miRNA expression profiles were performed by real-time RT-PCR MicroRNA Arrays. Twenty-three miRNAs appeared to have specific expression patterns during diabetes development. Single qRT-PCR further confirmed that serum miR-150, miR-146b, and miR-215 are significantly upregulated starting at the stage of insulitis, destructive insulitis and diabetes, respectively. Our data highly suggest that serum miRNAs are potential novel biomarkers for diabetes prediction and staging, and that miR-150, miR-146b and miR-215 could be used as serum biomarkers for diabetes staging or therapy response in the NOD mouse.

Evaluation of a type 1 diabetes serum cohort by SELDI‐TOF MS protein profiling

Proteomics analysis of serum from patients with type 1 diabetes (T1D) may lead to novel biomarkers for prediction of disease and for patient monitoring. However, the serum proteome is highly sensitive to sample processing and before proteomics biomarker research serum cohorts should preferably be examined for potential bias between sample groups. SELDI-TOF MS protein profiling was used for preliminary evaluation of a biological-bank with 766 serum samples from 270 patients with T1D, collected at 18 different paediatric centers representing 15 countries in Europe and Japan over 2 years (2000-2002). Samples collected 1 (n = 270), 6 (n = 248), and 12 (n = 248) months after T1D diagnosis were grouped across centers and compared. The serum protein profiles varied with collection site and day of analysis; however, markers of sample processing were not systematically different between samples collected at different times after diagnosis. Three members of the apolipoprotein family increased with time in patient serum collected 1, 6, and 12 months after diagnosis (ANOVA, p<0.001). These results support the use of this serum cohort for further proteomic studies and illustrate the potential of high-throughput MALDI/SELDI-TOF MS protein profiling for evaluation of serum cohorts before proteomics biomarker research.