Edge biomarkers for classification and prediction of phenotypes

In general, a disease manifests not from malfunction of individual molecules but from failure of the relevant system or network, which can be considered as a set of interactions or edges among molecules. Thus, instead of individual molecules, networks or edges are stable forms to reliably characterize complex diseases. This paper reviews both traditional node biomarkers and edge biomarkers, which have been newly proposed. These biomarkers are classified in terms of their contained information. In particular, we show that edge and network biomarkers provide novel ways of stably and reliably diagnosing the disease state of a sample. First, we categorize the biomarkers based on the information used in the learning and prediction steps. We then briefly introduce conventional node biomarkers, or molecular biomarkers without network information, and their computational approaches. The main focus of this paper is edge and network biomarkers, which exploit network information to improve the accuracy of diagnosis and prognosis. Moreover, by extracting both network and dynamic information from the data, we can develop dynamical network and edge biomarkers. These biomarkers not only diagnose the immediate pre-disease state but also detect the critical molecules or networks by which the biological system progresses from the healthy to the disease state. The identified critical molecules can be used as drug targets, and the critical state indicates the critical point of disease control. The paper also discusses representative biomarker-based methods.     

Pathway mapping and development of disease‐specific biomarkers: protein‐based network biomarkers

It is known that a disease is rarely a consequence of an abnormality of a single gene, but reflects the interactions of various processes in a complex network. Annotated molecular networks offer new opportunities to understand diseases within a systems biology framework and provide an excellent substrate for network‐based identification of biomarkers. The network biomarkers and dynamic network biomarkers (DNBs) represent new types of biomarkers with protein–protein or gene–gene interactions that can be monitored and evaluated at different stages and time‐points during development of disease. Clinical bioinformatics as a new way to combine clinical measurements and signs with human tissue‐generated bioinformatics is crucial to translate biomarkers into clinical application, validate the disease specificity, and understand the role of biomarkers in clinical settings. In this article, the recent advances and developments on network biomarkers and DNBs are comprehensively reviewed. How network biomarkers help a better understanding of molecular mechanism of diseases, the advantages and constraints of network biomarkers for clinical application, clinical bioinformatics as a bridge to the development of diseases‐specific, stage‐specific, severity‐specific and therapy predictive biomarkers, and the potentials of network biomarkers are also discussed.     

Exosomes in tuberculosis: Still terra incognita?

Today, diagnosis, vaccination, and treatment of tuberculosis (TB) remain major clinical challenges. Therefore, an introduction of new diagnostic measures and biomarkers is necessary to improve infection control. The ideal biomarker for TB infection can be defined as a host or pathogen-derived biomolecule, which is potent for identifying infection and determining its clinical stage. Exosomes, defined as cell-derived nanovesicles released into biological fluids, are involved in cell–cell communication and immune modulation. These vesicles have emerged as a new platform for improving the clinical diagnosis and prognosis of different infectious diseases and cancers. The role of these nanovehicles, as alternative biomarkers for the improvement of TB diagnosis and treatment, has been demonstrated in a significant body of literature. In this review, we summarized recent progress in the clinical application of exosome-based biomarkers in TB infection.     

Long noncoding RNAs biomarker-based cancer assessment

Cancer diagnosis have mainly relied on the incorporation of molecular biomarkers as part of routine diagnostic tool. The molecular alteration ranges from those involving DNA, RNA, noncoding RNAs (microRNAs and long noncoding RNAs [lncRNAs]) and proteins. lncRNAs are recently discovered noncoding endogenous RNAs that critically regulates the development, invasion, and metastasis of cancer cells. They are dysregulated in different types of malignancies and have the potential to serve as diagnostic markers for cancer. The expression of noncoding RNAs is altered following many diseases, and besides, some of them can be secreted from the cells into the circulation following the apoptotic and necrotic cell death. These secreted noncoding RNAs are known as cell free RNA. These RNAs can be secreted from the cell through the apoptotic body, extracellular vesicles including microvesicle and exosome, and bind to proteins. Since, lncRNAs display high organ and cell specificity, can be found in the blood, urine, tumor tissue, or other tissues or bodily fluids of some patients with cancer, this review summarizes the most significant and up-to-date findings of research on lncRNAs involvement in different cancers, focusing on the potential of cancer-related lncRNAs as biomarkers for diagnosis, prognosis, and therapy.     

阿尔茨海默病体外诊断纳米技术

阿尔茨海默病(Alzheimer's disease,AD)是一种最常见的神经退行性疾病.AD的精准诊断技术,特别是早期诊断技术是临床亟需的.近年来,以生物标志物为基础的非侵入性体外诊断技术发展迅速,特别是利用纳米材料和纳米技术的高表面活性、独特的光电特性、生物相容性好、易于表面修饰、小型化、集成化等特点,发展了新型的...     

DNA repair and neurological disease: From molecular understanding to the development of diagnostics and model organisms

In both replicating and non-replicating cells, the maintenance of genomic stability is of utmost importance. Dividing cells can repair DNA damage during cell division, tolerate the damage by employing potentially mutagenic DNA polymerases or die via apoptosis. However, the options for accurate DNA repair are more limited in non-replicating neuronal cells. If DNA damage is left unresolved, neuronal cells die causing neurodegenerative disorders. A number of pathogenic variants of DNA repair proteins have been linked to multiple neurological diseases. The current challenge is to harness our knowledge of fundamental properties of DNA repair to improve diagnosis, prognosis and treatment of such debilitating disorders. In this perspective, we will focus on recent efforts in identifying novel DNA repair biomarkers for the diagnosis of neurological disorders and their use in monitoring the patient response to therapy. These efforts are greatly facilitated by the development of model organisms such as zebrafish, which will also be summarised.     

Dependence network modeling for biomarker identification

MOTIVATION: Our purpose is to develop a statistical modeling approach for cancer biomarker discovery and provide new insights into early cancer detection. We propose the concept of dependence network, apply it for identifying cancer biomarkers, and study the difference between the protein or gene samples from cancer and non-cancer subjects based on mass-spectrometry (MS) and microarray data. RESULTS: Three MS and two gene microarray datasets are studied. Clear differences are observed in the dependence networks for cancer and non-cancer samples. Protein/gene features are examined three at one time through an exhaustive search. Dependence networks are constructed by binding triples identified by the eigenvalue pattern of the dependence model, and are further compared to identify cancer biomarkers. Such dependence-network-based biomarkers show much greater consistency under 10-fold cross-validation than the classification-performance-based biomarkers. Furthermore, the biological relevance of the dependence-network-based biomarkers using microarray data is discussed. The proposed scheme is shown promising for cancer diagnosis and prediction. AVAILABILITY: See supplements: http://dsplab.eng.umd.edu/~genomics/dependencenetwork/     

Banking of biological fluids for studies of disease-associated protein biomarkers

With the increasing demand of providing personalized medicine the need for biobanking of biological material from individual patients has increased. Such samples are essential for molecular research aimed at characterizing diseases at several levels ranging from epidemiology and diagnostic and prognostic classification to prediction of response to therapy. Clinically validated biomarkers may provide information to be used for diagnosis, screening, evaluation of risk/predisposition, assessment of prognosis, monitoring (recurrence of disease), and prediction of response to treatment and as a surrogate response marker. Many types of biological fluids or tissues can be collected and stored in biorepositories. Samples of blood can be further processed into plasma and serum, and tissue pieces can be either frozen or fixed in formalin and then embedded into paraffin. The present review focuses on biological fluids, especially serum and plasma, intended for study of protein biomarkers. In biomarker studies the process from the decision to take a sample from an individual to the moment the sample is safely placed in the biobank consists of several phases including collection of samples, transport of the samples, and handling and storage of samples. Critical points in each step important for high quality biomarker studies are described in this review. Failure to develop and adhere to robust standardized protocols may have significant consequences as the quality of the material stored in the biobank as well as conclusions and clinical recommendations based on analysis of such material may be severely affected.     

Personalized medicine using DNA biomarkers: a review

Biomarkers are of increasing importance for personalized medicine, with applications including diagnosis, prognosis, and selection of targeted therapies. Their use is extremely diverse, ranging from pharmacodynamics to treatment monitoring. Following a concise review of terminology, we provide examples and current applications of three broad categories of biomarkers-DNA biomarkers, DNA tumor biomarkers, and other general biomarkers. We outline clinical trial phases for identifying and validating diagnostic and prognostic biomarkers. Predictive biomarkers, more generally termed companion diagnostic tests predict treatment response in terms of efficacy and/or safety. We consider suitability of clinical trial designs for predictive biomarkers, including a detailed discussion of validation study designs, with emphasis on interpretation of study results. We specifically discuss the interpretability of treatment effects if a large set of DNA biomarker profiles is available and the number of therapies is identical to the number of different profiles.     

Thematic review series: systems biology approaches to metabolic and cardiovascular disorders. Reverse engineering gene networks to identify key drivers of complex disease phenotypes

Diseases such as obesity, diabetes, and atherosclerosis result from multiple genetic and environmental factors, and importantly, interactions between genetic and environmental factors. Identifying susceptibility genes for these diseases using genetic and genomic technologies is accelerating, and the expectation over the next several years is that a number of genes will be identified for common diseases. However, the identification of single genes for disease has limited utility, given that diseases do not originate in complex systems from single gene changes. Further, the identification of single genes for disease may not lead directly to genes that can be targeted for therapeutic intervention. Therefore, uncovering single genes for disease in isolation of the broader network of molecular interactions in which they operate will generally limit the overall utility of such discoveries. Several integrative approaches have been developed and applied to reconstructing networks. Here we review several of these approaches that involve integrating genetic, expression, and clinical data to elucidate networks underlying disease. Networks reconstructed from these data provide a richer context in which to interpret associations between genes and disease. Therefore, these networks can lead to defining pathways underlying disease more objectively and to identifying biomarkers and more-robust points for therapeutic intervention.     

Clinical application of quantitative glycomics

ABSTRACT

Introduction: Aberrant glycosylation has been associated with many diseases. Decades of research activities have reported many reliable glycan biomarkers of different diseases which enable effective disease diagnostics and prognostics. However, none of the glycan markers have been approved for clinical diagnosis. Thus, a review of these studies is needed to guide the successful clinical translation.

Area covered: In this review, we describe and discuss advances in analytical methods enabling clinical glycan biomarker discovery, focusing only on studies of released glycans. This review also summarizes the different glycobiomarkers identified for cancers, Alzheimer’s disease, diabetes, hepatitis B and C, and other diseases.

Expert commentary: Along with the development of techniques in quantitative glycomics, more glycans or glycan patterns have been reported as better potential biomarkers of different diseases and proved to have greater diagnostic/diagnostic sensitivity and specificity than existing markers. However, to successfully apply glycan markers in clinical diagnosis, more studies and verifications on large biological cohorts need to be performed. In addition, faster and more efficient glycomic strategies need to be developed to shorten the turnaround time. Thus, glycan biomarkers have an immense chance to be used in clinical prognosis and diagnosis of many diseases in the near future.     

Next-generation biomarkers based on 100-parameter functional super-resolution microscopy TIS

Functional super-resolution (fSR) microscopy is based on the automated toponome imaging system (TIS). fSR-TIS provides insight into the myriad of different cellular functionalities by direct imaging of large subcellular protein networks in morphologically intact cells and tissues, referred to as the toponome. By cyclical fluorescence imaging of at least 100 molecular cell components, fSR-TIS overcomes the spectral limitations of fluorescence microscopy, which is the essential condition for the detection of protein network structures in situ/in vivo. The resulting data sets precisely discriminate between cell types, subcellular structures, cell states and diseases (fSR). With up to 16 bits per protein, the power of combinatorial molecular discrimination (PCMD) is at least 2(100) per subcellular data point. It provides the dimensionality necessary to uncover thousands of distinct protein clusters including their subcellular hierarchies controlling protein network topology and function in the one cell or tissue section. Here we review the technology and findings showing that functional protein networks of the cell surface in different cancers encompass the same hierarchical and spatial coding principle, but express cancer-specific toponome codes within that scheme (referred to as TIS codes). Findings suggest that TIS codes, extracted from large-scale toponome data, have the potential to be next-generation biomarkers because of their cell type and disease specificity. This is functionally substantiated by the observation that blocking toponome-specific lead proteins results in disassembly of molecular networks and loss of function.     

Multiclass cancer classification and biomarker discovery using GA-based algorithms

MOTIVATION: The development of microarray-based high-throughput gene profiling has led to the hope that this technology could provide an efficient and accurate means of diagnosing and classifying tumors, as well as predicting prognoses and effective treatments. However, the large amount of data generated by microarrays requires effective reduction of discriminant gene features into reliable sets of tumor biomarkers for such multiclass tumor discrimination. The availability of reliable sets of biomarkers, especially serum biomarkers, should have a major impact on our understanding and treatment of cancer. RESULTS: We have combined genetic algorithm (GA) and all paired (AP) support vector machine (SVM) methods for multiclass cancer categorization. Predictive features can be automatically determined through iterative GA/SVM, leading to very compact sets of non-redundant cancer-relevant genes with the best classification performance reported to date. Interestingly, these different classifier sets harbor only modest overlapping gene features but have similar levels of accuracy in leave-one-out cross-validations (LOOCV). Further characterization of these optimal tumor discriminant features, including the use of nearest shrunken centroids (NSC), analysis of annotations and literature text mining, reveals previously unappreciated tumor subclasses and a series of genes that could be used as cancer biomarkers. With this approach, we believe that microarray-based multiclass molecular analysis can be an effective tool for cancer biomarker discovery and subsequent molecular cancer diagnosis.     

Towards precise reconstruction of gene regulatory networks by data integration

Background: More and more high-throughput datasets are available from multiple levels of measuring gene regulations. The reverse engineering of gene regulatory networks from these data offers a valuable research paradigm to decipher regulatory mechanisms. So far, numerous methods have been developed for reconstructing gene regulatory networks. Results: In this paper, we provide a review of bioinformatics methods for inferring gene regulatory network from omics data. To achieve the precision reconstruction of gene regulatory networks, an intuitive alternative is to integrate these available resources in a rational framework. We also provide computational perspectives in the endeavors of inferring gene regulatory networks from heterogeneous data. We highlight the importance of multi-omics data integration with prior knowledge in gene regulatory network inferences. Conclusions: We provide computational perspectives of inferring gene regulatory networks from multiple omics data and present theoretical analyses of existing challenges and possible solutions. We emphasize on prior knowledge and data integration in network inferences owing to their abilities of identifying regulatory causality.     

The Use of Biomarker Expression to Characterize Neoplastic Processes

Biomarkers have been used by pathologists to aid the diagnosis of tumors for almost three decades. Their use has resulted in the re-evaluation and reclassification of several types of tumors. Currently, biomarkers are required to differentiate certain specific tumors with similar histologic patterns. Additional uses of biomarkers in the characterization of neoplastic processes are discussed including their use in prognosis, detecting early neoplastic processes, identifying tumor recurrence, measuring the effectiveness of various therapies (surrogate end point biomarkers), and identifying targets for novel therapies including immunotherapy and gene therapy. We propose that these newer uses of biomarkers will be just as important to pathology in the future as the uses of biomarkers in diagnosis have been over the past two decades.     

表观遗传生物标志物在人类疾病早期诊治中的研究进展

表观遗传修饰异常见于人类的多种疾病(如肿瘤、老年性疾病、发育源性疾病等),影响着这些疾病的发生发展。已有的研究表明,异常表观遗传改变可以作为疾病状态和疾病预测的生物标志物。表观遗传修饰改变的可逆性和可控性也为疾病早期的预防和治疗提供了新策略。本文对DNA甲基化修饰、组蛋白共价修饰、非编码RNA等三种表观遗传方式在肿瘤、老年性疾病和发育源性疾病的研究,以及三者作为表遗传生物标志物在疾病早期诊断和治疗的应用展开介绍,以期为肿瘤、老年性和发育源性相关疾病的诊断与治疗提供借鉴和 参考。     

Molecular targets of HPV oncoproteins: Potential biomarkers for cervical carcinogenesis

Cervical cancer is the second most common cancer among women worldwide and is responsible for 275,000 deaths each year. Persistent infection with high-risk human papillomavirus (HR-HPV) is an essential factor for the development of cervical cancer. Although the process is not fully understood, molecular mechanisms caused by HPV infection are necessary for its development and reveal a large number of potential biomarkers for diagnosis and prognosis. These molecules are host genes and/or proteins, and cellular microRNAs involved in cell cycle regulation that result from disturbed expression of HR-HPV E5, E6 and E7 oncoproteins. One of the current challenges in medicine is to discover potent biomarkers that can correctly diagnose cervical premalignant lesions and standardize clinical management. Currently, studies are showing that some of these molecules are potential biomarkers of cervical carcinogenesis, and it is possible to carry out a more accurate diagnosis and provide more appropriate follow-up treatment for women with cervical dysplasia. In this paper, we review recent research studies on cell cycle molecules deregulated by HPV infections, as well as their potential use for cervical cancer screening.     

Epigenetic deregulations in chordoma

Chordoma is a rare type of malignant bone tumour arising from remnant notochord and prognosis of patients with it remains poor as its molecular and genetic mechanisms are not well understood. Increasing evidence has demonstrated that epigenetic mechanisms (DNA methylation, histone modification and nucleosome remodelling), play a crucial role in the pathogenesis of many diseases. Aberrant epigenetic patterns are present in patients with chordoma, indicating a potential role for epigenetic mechanisms inthis malignancy. Furthermore, epigenetic alterations may provide novel biomarkers for diagnosis and prognosis as well as therapeutic targets for treatment. In this review, we discuss relevant epigenetic findings associated with chordoma, and their potential application for diagnosis, prognosis and treatment.