Metabolomics – an overview. From basic principles to potential biomarkers (part 2)

The metabolome, which represents the complete set of molecules (metabolites) of a biological sample (cell, tissue, organ, organism), is the final downstream product of the metabolic cell process that involves the genome and exogenous sources. The metabolome is characterized by a large number of small molecules with a huge diversity of chemical structures and abundances. Exploring the metabolome requires complementary analytical platforms to reach its extensive coverage. The metabolome is continually evolving, reflecting the continuous flux of metabolic and signaling pathways. Metabolomic research aims to study the biochemical processes by detecting and quantifying metabolites to obtain a metabolic picture able to give a functional readout of the physiological state. Recent advances in mass spectrometry (one of the mostly used technologies for metabolomics studies) have given the opportunity to determine the spatial distribution of metabolites in tissues. In a two-part article, we describe the usual metabolomics technologies, workflows and strategies leading to the implementation of new clinical biomarkers. In this second part, we first develop the steps of a metabolomic analysis from sample collection to biomarker validation. Then with two examples, autism spectrum disorders and Alzheimer's disease, we illustrate the contributions of metabolomics to clinical practice. Finally, we discuss the complementarity of in vivo (positron emission tomography) and in vitro (metabolomics) molecular explorations for biomarker research.     

The effects of age and dietary restriction on the tissue‐specific metabolome of Drosophila

Dietary restriction (DR) is a robust intervention that extends lifespan and slows the onset of age‐related diseases in diverse organisms. While significant progress has been made in attempts to uncover the genetic mechanisms of DR, there are few studies on the effects of DR on the metabolome. In recent years, metabolomic profiling has emerged as a powerful technology to understand the molecular causes and consequences of natural aging and disease‐associated phenotypes. Here, we use high‐resolution mass spectroscopy and novel computational approaches to examine changes in the metabolome from the head, thorax, abdomen, and whole body at multiple ages in Drosophila fed either a nutrient‐rich ad libitum (AL) or nutrient‐restricted (DR) diet. Multivariate analysis clearly separates the metabolome by diet in different tissues and different ages. DR significantly altered the metabolome and, in particular, slowed age‐related changes in the metabolome. Interestingly, we observed interacting metabolites whose correlation coefficients, but not mean levels, differed significantly between AL and DR. The number and magnitude of positively correlated metabolites was greater under a DR diet. Furthermore, there was a decrease in positive metabolite correlations as flies aged on an AL diet. Conversely, DR enhanced these correlations with age. Metabolic set enrichment analysis identified several known (e.g., amino acid and NAD metabolism) and novel metabolic pathways that may affect how DR effects aging. Our results suggest that network structure of metabolites is altered upon DR and may play an important role in preventing the decline of homeostasis with age.     

Mass spectrometry-based expression profiling of clinical prostate cancer

The maturation of MS technologies has provided a rich opportunity to interrogate protein expression patterns in normal and disease states by applying expression protein profiling methods. Major goals of this research strategy include the identification of protein biomarkers that demarcate normal and disease populations, and the identification of therapeutic biomarkers for the treatment of diseases such as cancer (Celis, J. E., and Gromov, P. (2003) Proteomics in translational cancer research: Toward an integrated approach. Cancer Cell 3, 9-151). Prostate cancer is one disease that would greatly benefit from implementing MS-based expression profiling methods because of the need to stratify the disease based on molecular markers. In this review, we will summarize the current MS-based methods to identify and validate biomarkers in human prostate cancer. Lastly, we propose a reverse proteomic approach implementing a quantitative MS research strategy to identify and quantify biomarkers implicated in prostate cancer development. With this approach, the absolute levels of prostate cancer biomarkers will be identified and quantified in normal and diseased samples by measuring the levels of native peptide biomarkers in relation to a chemically identical but isotopically labeled reference peptide. Ultimately, a centralized prostate cancer peptide biomarker expression database could function as a repository for the identification, quantification, and validation of protein biomarker(s) during prostate cancer progression in men.     

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.     

Untargeted metabolome quantitative trait locus mapping associates variation in urine glycerate to mutant glycerate kinase

With successes of genome-wide association studies, molecular phenotyping systems are developed to identify genetically determined disease-associated biomarkers. Genetic studies of the human metabolome are emerging but exclusively apply targeted approaches, which restricts the analysis to a limited number of well-known metabolites. We have developed novel technical and statistical methods for systematic and automated quantification of untargeted NMR spectral data designed to perform robust and accurate quantitative trait locus (QTL) mapping of known and previously unreported molecular compounds of the metabolome. For each spectral peak, six summary statistics were calculated and independently tested for evidence of genetic linkage in a cohort of F2 (129S6xBALB/c) mice. The most significant evidence of linkages were obtained with NMR signals characterizing the glycerate (LOD10-42) at the mutant glycerate kinase locus, which demonstrate the power of metabolomics in quantitative genetics to identify the biological function of genetic variants. These results provide new insights into the resolution of the complex nature of metabolic regulations and novel analytical techniques that maximize the full utilization of metabolomic spectra in human genetics to discover mappable disease-associated biomarkers.     

Discovering oxysterols in plasma: a window on the metabolome

While the proteome defines the expressed gene products, the metabolome results from reactions controlled by such gene products. Plasma represents an accessible "window" to the metabolome both in regard of availability and content. The wide range of the plasma metabolome, in terms of molecular diversity and abundance, makes its comprehensive analysis challenging. Here we demonstrate an analytical method designed to target one region of the metabolome, that is, oxysterols. Since the discovery of their biological activity as ligands to nuclear receptors there has been a reawakening of interest in oxysterols and their analysis. In addition, the oxysterols, 24S- and 27-hydroxycholesterol, are currently under investigation as potential biomarkers associated with neurodegenerative disorders such as Alzheimer's disease and multiple sclerosis; widespread analysis of these lipids in clinical studies will require the development of robust, sensitive and rapid analytical techniques. In this communication we present results of an investigation of the oxysterols content of human plasma using a newly developed high-performance liquid chromatography-mass spectrometry (HPLC-MS) method incorporating charge-tagging and high-resolution MS. The method has allowed the identification in plasma of monohydroxylated cholesterol molecules, 7alpha-, 24S-, and 27-hydroxycholesterol; the cholestenetriol 7alpha,27-dihydroxycholesterol; and 3beta-hydroxycholest-5-en-27-oic acid and its metabolite 3beta,7alpha-dihydroxycholest-5-en-27-oic acid. The methodology described is also applicable for the analysis of other sterols in plasma, that is, cholesterol, 7-dehydrocholesterol, and desmosterol, as well as cholesterol 5,6- seco-sterols and steroid hormones. Although involving derivatization, sample preparation is straightforward and chromatographic analysis rapid (17 min), while the MS method offers high sensitivity (ng/mL of sterol in plasma, or pg on-column) and specificity. The methodology is suitable for targeted metabolomic analysis of sterols, oxysterols, and steroid hormones opening a "window" to view this region of the metabolome.     

Release of Small RNA-containing Exosome-like Vesicles from the Human Filarial Parasite Brugia malayi

Lymphatic filariasis (LF) is a socio-economically devastating mosquito-borne Neglected Tropical Disease caused by parasitic filarial nematodes. The interaction between the parasite and host, both mosquito and human, during infection, development and persistence is dynamic and delicately balanced. Manipulation of this interface to the detriment of the parasite is a promising potential avenue to develop disease therapies but is prevented by our very limited understanding of the host-parasite relationship. Exosomes are bioactive small vesicles (30–120 nm) secreted by a wide range of cell types and involved in a wide range of physiological processes. Here, we report the identification and partial characterization of exosome-like vesicles (ELVs) released from the infective L3 stage of the human filarial parasite Brugia malayi. Exosome-like vesicles were isolated from parasites in culture media and electron microscopy and nanoparticle tracking analysis were used to confirm that vesicles produced by juvenile B. malayi are exosome-like based on size and morphology. We show that loss of parasite viability correlates with a time-dependent decay in vesicle size specificity and rate of release. The protein cargo of these vesicles is shown to include common exosomal protein markers and putative effector proteins. These Brugia-derived vesicles contain small RNA species that include microRNAs with host homology, suggesting a potential role in host manipulation. Confocal microscopy shows J774A.1, a murine macrophage cell line, internalize purified ELVs, and we demonstrate that these ELVs effectively stimulate a classically activated macrophage phenotype in J774A.1. To our knowledge, this is the first report of exosome-like vesicle release by a human parasitic nematode and our data suggest a novel mechanism by which human parasitic nematodes may actively direct the host responses to infection. Further interrogation of the makeup and function of these bioactive vesicles could seed new therapeutic strategies and unearth stage-specific diagnostic biomarkers.     

Network biology methods integrating biological data for translational science

The explosion of biomedical data, both on the genomic and proteomic side as well as clinical data, will require complex integration and analysis to provide new molecular variables to better understand the molecular basis of phenotype. Currently, much data exist in silos and is not analyzed in frameworks where all data are brought to bear in the development of biomarkers and novel functional targets. This is beginning to change. Network biology approaches, which emphasize the interactions between genes, proteins and metabolites provide a framework for data integration such that genome, proteome, metabolome and other -omics data can be jointly analyzed to understand and predict disease phenotypes. In this review, recent advances in network biology approaches and results are identified. A common theme is the potential for network analysis to provide multiplexed and functionally connected biomarkers for analyzing the molecular basis of disease, thus changing our approaches to analyzing and modeling genome- and proteome-wide data.     

Metabolic response to low-level toxicant exposure in a novel renal tubule epithelial cell system

Toxicity testing is vital to protect human health from exposure to toxic chemicals in the environment. Furthermore, combining novel cellular models with molecular profiling technologies, such as metabolomics can add new insight into the molecular basis of toxicity and provide a rich source of biomarkers that are urgently required in a 21st Century approach to toxicology. We have used an NMR-based metabolic profiling approach to characterise for the first time the metabolome of the RPTEC/TERT1 cell line, an immortalised non-tumour human renal epithelial cell line that recapitulates phenotypic characteristics that are absent in other in vitro renal cell models. RPTEC/TERT1 cells were cultured with either the dosing vehicle (DMSO) or with exposure to one of six compounds (nifedipine, potassium bromate, monuron, D-mannitol, ochratoxin A and sodium diclofenac), several of which are known to cause renal effects. Aqueous intracellular and culture media metabolites were profiled by (1)H NMR spectroscopy at 6, 24 and 72 hours of exposure to a low effect dose (IC(10)). We defined the metabolome of the RPTEC/TERT1 cell line and used a principal component analysis approach to derive a panel of key metabolites, which were altered by chemical exposure. By considering only major changes (±1.5 fold change from control) across this metabolite panel we were able to show specific alterations to cellular processes associated with chemical treatment. Our findings suggest that metabolic profiling of RPTEC/TERT1 cells can report on the effect of chemical exposure on multiple cellular pathways at low-level exposure, producing different response profiles for the different compounds tested with a greater number of major metabolic effects observed in the toxin treated cells. Importantly, compounds with established links to chronic renal toxicity produced more diverse and severe perturbations to the cellular metabolome than non-toxic compounds in this model. As these changes can be rationalised with the different pharmacological and toxicity profiles of the chemicals it is suggested that metabolic profiling in the RPTEC/TERT1 model would be useful in investigating the mechanism of action of toxins at a low dose.     

Development of biomarkers based on diet-dependent metabolic serotypes: concerns and approaches for cohort and gender issues in serum metabolome studies

Mathematical models that reflect the effects of dietary restriction (DR) on the sera metabolome may have utility in understanding the mechanisms of DR and in applying this knowledge to human epidemiological studies. Previous studies demonstrated both the feasibility of identifying biomarkers through metabolome analysis and the validity of our approach in independent cohorts of 6-month-old male and female ad libitum fed or DR rats. Cross-cohort studies showed that cohort-specific effects distorted the dataset. The present study extends these observations across the entire sample set, thereby validating our markers independently of specific cohorts. Metabolites originally identified in males were examined in females and vice-versa. DR's effect on the metabolome is partially gender-specific and is modulated by environmental factors. DR reduces inter-gender differences in the metabolome. Univariate statistical methods showed that 56/93 metabolites in the female samples and 39/93 metabolites in the male samples were significantly altered (using our previous cut-off criteria of p < or = 0.2) by DR. The metabolites modulated by DR present a wide spectrum of concentration, redox reactivity and hydrophilicity, suggesting that our serotype is broadly representative of the metabolome and that DR has broad effects on the metabolome. These studies, coupled with those in the preceding and following reports, also highlight the utility for consideration of the metabolome as a network of metabolites using appropriate data analysis approaches. The inter-cohort and inter-gender differences addressed herein suggest potential cautions, and potential approaches, for identification of multivariate biomarker profiles that reflect changes in physiological status, such as a metabolism that predisposes to increased risk of neoplasia.     

MiMeNet: Exploring microbiome-metabolome relationships using neural networks

The advance in microbiome and metabolome studies has generated rich omics data revealing the involvement of the microbial community in host disease pathogenesis through interactions with their host at a metabolic level. However, the computational tools to uncover these relationships are just emerging. Here, we present MiMeNet, a neural network framework for modeling microbe-metabolite relationships. Using ten iterations of 10-fold cross-validation on three paired microbiome-metabolome datasets, we show that MiMeNet more accurately predicts metabolite abundances (mean Spearman correlation coefficients increase from 0.108 to 0.309, 0.276 to 0.457, and -0.272 to 0.264) and identifies more well-predicted metabolites (increase in the number of well-predicted metabolites from 198 to 366, 104 to 143, and 4 to 29) compared to state-of-art linear models for individual metabolite predictions. Additionally, we demonstrate that MiMeNet can group microbes and metabolites with similar interaction patterns and functions to illuminate the underlying structure of the microbe-metabolite interaction network, which could potentially shed light on uncharacterized metabolites through “Guilt by Association”. Our results demonstrated that MiMeNet is a powerful tool to provide insights into the causes of metabolic dysregulation in disease, facilitating future hypothesis generation at the interface of the microbiome and metabolomics.     

Multiomics metabolic and epigenetics regulatory network in cancer: A systems biology perspective

Genetic, epigenetic, and metabolic alterations are all hallmarks of cancer. However, the epigenome and metabolome are both highly complex and dynamic biological networks in vivo. The interplay between the epigenome and metabolome contributes to a biological system that is responsive to the tumor microenvironment and possesses a wealth of unknown biomarkers and targets of cancer therapy. From this perspective, we first review the state of high-throughput biological data acquisition(i.e. multiomics data)and analysis(i.e. computational tools) and then propose a conceptual in silico metabolic and epigenetic regulatory network(MER-Net) that is based on these current high-throughput methods. The conceptual MER-Net is aimed at linking metabolomic and epigenomic networks through observation of biological processes, omics data acquisition, analysis of network information, and integration with validated database knowledge. Thus, MER-Net could be used to reveal new potential biomarkers and therapeutic targets using deep learning models to integrate and analyze large multiomics networks. We propose that MER-Net can serve as a tool to guide integrated metabolomics and epigenomics research or can be modified to answer other complex biological and clinical questions using multiomics data.     

Physiological conditions can be reflected in human urine proteome and metabolome

Biomarkers are the measurable changes associated with physiological or pathophysiological processes. Urine, unlike blood, lacks mechanisms for maintaining homeostasis: it is therefore an ideal source of biomarkers that can reflect systemic changes. Urinary proteome and metabolome have been studied for their diagnostic capabilities, ability to monitor disease and prognostic utility. In this review, the effects of common physiological conditions such as gender, age, diet, daily rhythms, exercise, hormone status, lifestyle and extreme environments on human urine are discussed. These effects should be considered when biomarker studies of diseases are conducted. More importantly, if physiological changes can be reflected in urine, we have reason to expect that urine will become widely used to detect small and early changes in pathological and/or pharmacological conditions.     

The role of reactive oxygen species and oxidative stress in environmental carcinogenesis and biomarker development

Although we have greatly benefited from the use of traditional epidemiological approaches in linking environmental exposure to human disease, we are still lacking knowledge in to how such exposure participates in disease development. However, molecular epidemiological studies have provided us with evidence linking oxidative stress with the pathogenesis of human disease and in particular carcinogenesis. To this end, oxidative stress-based biomarkers have proved to be essential in revealing how oxidative stress may be mediating toxicity induced by many known carcinogenic environmental agents. Therefore, throughout this review article, we aim to address the current state of oxidative stress-based biomarker development with major emphasis pertaining to biomarkers of DNA, lipid and protein oxidation.     

Urinary Biomarkers of Brain Diseases

Biomarkers are the measurable changes associated with a physiological or pathophysiological process. Unlike blood, urine is not subject to homeostatic mechanisms. Therefore, greater fluctuations could occur in urine than in blood, better reflecting the changes in human body. The roadmap of urine biomarker era was proposed. Although urine analysis has been attempted for clinical diagnosis, and urine has been monitored during the progression of many diseases, particularly urinary system diseases, whether urine can reflect brain disease status remains uncertain. As some biomarkers of brain diseases can be detected in the body fluids such as cerebrospinal fluid and blood,there is a possibility that urine also contain biomarkers of brain diseases. This review summarizes the clues of brain diseases reflected in the urine proteome and metabolome.     

Detecting microRNAs of high influence on protein functional interaction networks: a prostate cancer case study

ABSTRACT: BACKGROUND: The use of biological molecular network information for diagnostic and prognostic purposes and elucidation of molecular disease mechanism is a key objective in systems biomedicine. The network of regulatory miRNA-target and functional protein interactions is a rich source of information to elucidate the function and the prognostic value of miRNAs in cancer. The objective of this study is to identify miRNAs that have high influence on target protein complexes in prostate cancer as a case study. This could provide biomarkers or therapeutic targets relevant for prostate cancer treatment. RESULTS: Our findings demonstrate that a miRNA's functional role can be explained by its target protein connectivity within a physical and functional interaction network. To detect miRNAs with high influence on target protein modules, we integrated miRNA and mRNA expression profiles with a sequence based miRNA-target network and human functional and physical protein interactions (FPI). miRNAs with high influence on target protein complexes play a role in prostate cancer progression and are promising diagnostic or prognostic biomarkers. We uncovered several miRNA-regulated protein modules which were enriched in focal adhesion and prostate cancer genes. Several miRNAs such as miR-96, miR-182, and miR-143 demonstrated high influence on their target protein complexes and could explain most of the gene expression changes in our analyzed prostate cancer data set. CONCLUSIONS: We describe a novel method to identify active miRNA-target modules relevant to prostate cancer progression and outcome. miRNAs with high influence on protein networks are valuable biomarkers that can be used in clinical investigations for prostate cancer treatment.     

Development of biomarkers based on diet-dependent metabolic serotypes: characteristics of component-based models of metabolic serotypes

Our research seeks to identify a serum profile, or serotype, that reflects the systemic physiologic modifications resultant from dietary restriction (DR), in part such that this knowledge can be applied for biomarker studies. Direct comparison suggests that component-based classification algorithms consistently out-perform distance-based metrics for studies of nutritional modulation of metabolic serotype, but are subject to over-fitting concerns. Intercohort differences in the sera metabolome could partially obscure the effects of DR. Further analysis now shows that implementation of component-based approaches (also called projection methods) optimized for class separation and controlled for over-fitting have >97% accuracy for distinguishing sera from control or DR rats. DR's effect on the metabolome is shown to be robust across cohorts, but differs in males and females (although some metabolites are affected in both). We demonstrate the utility of projection-based methods for both sample and variable diagnostics, including identification of critical metabolites and samples that are atypical with respect to both class and variable models. Inclusion of non-statistically different variables enhances classification models. Variables that contribute to these models are sharply dependent on mathematical processing techniques; some variables that do not contribute under one paradigm are powerful under alternative mathematical paradigms. In practical terms, this information may find purpose in other endeavors, such as mechanistic studies of DR. Application of these approaches confirms the utility of megavariate data analysis techniques for optimal generation of biomarkers based on nutritional modulation of physiological processes.     

Proteomic Analysis of C2C12 Myoblast and Myotube Exosome-Like Vesicles: A New Paradigm for Myoblast-Myotube Cross Talk?

Exosomes are nanometer-sized microvesicles formed in multivesicular bodies (MVBs) during endosome maturation. Exosomes are released from cells into the microenvironment following fusion of MVBs with the plasma membrane. During the last decade, skeletal muscle-secreted proteins have been identified with important roles in intercellular communications. To investigate whether muscle-derived exosomes participate in this molecular dialog, we determined and compared the protein contents of the exosome-like vesicles (ELVs) released from C2C12 murine myoblasts during proliferation (ELV-MB), and after differentiation into myotubes (ELV-MT). Using a proteomic approach combined with electron microscopy, western-blot and bioinformatic analyses, we compared the protein repertoires within ELV-MB and ELV-MT. We found that these vesicles displayed the classical properties of exosomes isolated from other cell types containing components of the ESCRT machinery of the MVBs, as well as numerous tetraspanins. Specific muscle proteins were also identified confirming that ELV composition also reflects their muscle origin. Furthermore quantitative analysis revealed stage-preferred expression of 31 and 78 proteins in ELV-MB and ELV-MT respectively. We found that myotube-secreted ELVs, but not ELV-MB, reduced myoblast proliferation and induced differentiation, through, respectively, the down-regulation of Cyclin D1 and the up-regulation of myogenin. We also present evidence that proteins from ELV-MT can be incorporated into myoblasts by using the GFP protein as cargo within ELV-MT. Taken together, our data provide a useful database of proteins from C2C12-released ELVs throughout myogenesis and reveals the importance of exosome-like vesicles in skeletal muscle biology.     

Bayesian personalized treatment selection strategies that integrate predictive with prognostic determinants

The evolution of “informatics” technologies has the potential to generate massive databases, but the extent to which personalized medicine may be effectuated depends on the extent to which these rich databases may be utilized to advance understanding of the disease molecular profiles and ultimately integrated for treatment selection, necessitating robust methodology for dimension reduction. Yet, statistical methods proposed to address challenges arising with the high‐dimensionality of omics‐type data predominately rely on linear models and emphasize associations deriving from prognostic biomarkers. Existing methods are often limited for discovering predictive biomarkers that interact with treatment and fail to elucidate the predictive power of their resultant selection rules. In this article, we present a Bayesian predictive method for personalized treatment selection that is devised to integrate both the treatment predictive and disease prognostic characteristics of a particular patient's disease. The method appropriately characterizes the structural constraints inherent to prognostic and predictive biomarkers, and hence properly utilizes these complementary sources of information for treatment selection. The methodology is illustrated through a case study of lower grade glioma. Theoretical considerations are explored to demonstrate the manner in which treatment selection is impacted by prognostic features. Additionally, simulations based on an actual leukemia study are provided to ascertain the method's performance with respect to selection rules derived from competing methods.     

Inconvenient truth: Cancer biomarker development by using proteomics

A biomarker is a crucial tool for measuring the progress of disease and the effects of treatment for better clinical outcomes in cancer patients. Diagnostic, predictive, and prognostic biomarkers are required in various clinical settings. The proteome, a functional translation of the genome, is considered a rich source of biomarkers; therefore, sizable time and funding have been spent in proteomics to develop biomarkers. Although significant progress has been made in technologies toward comprehensive protein expression profiling, and many biomarker candidates published, none of the reported biomarkers have proven to be beneficial for cancer patients. The present deceleration in biomarker research can be attributed to technical limitations. Additional efforts are required to further technical progress; however, there are many examples demonstrating that problems in biomarker research are not so much with the technology but in the study design. In the study of biomarkers for early diagnosis, candidates are screened and validated by comparing cases and controls of similar sample size, and the low prevalence of disease is often ignored. Although it is reasonable to take advantage of multiple rather than single biomarkers when studying diverse disease mechanisms, the annotation of individual components of reported multiple biomarkers does not often explain the variety of molecular events underlying the clinical observations. In tissue biomarker studies, the heterogeneity of disease tissues and pathological observations are often not considered, and tissues are homogenized as a whole for protein extraction. In addition to the challenge of technical limitations, the fundamental aspects of biomarker development in a disease study need to be addressed. This article is part of a Special Issue entitled: Biomarkers: A Proteomic Challenge.