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PubMed2005

Biomarkers for Alzheimer's disease--clinical needs and application.

Biomarkers for Alzheimer's disease have many diagnostic and therapeutic uses. They can be used to facilitate diagnosis, particularly early diagnosis and possibly presymptomatic diagnosis. In evaluating treatment aimed at modifying the course of AD, biomarkers can help to assess whether the drug is hitting a target or influencing disease mechanisms in the brain, and can help with dose-finding. In definitive clinical trails, biomarkers can be used as surrogate outcome measures. Progress has been made in investigating candidate biomarkers in cerebrospinal fluid and plasma. At present, cerebrospinal fluid levels of amyloid-beta, tau and phospho-tau come closest to meeting criteria proposed for useful biomarkers for diagnosis. Other biomarkers can provide insight into processes such as inflammation and oxidative damage. Unbiased and broad-based searches for novel biomarkers using proteomics have much potential. As efforts to diagnose and treat AD move towards earlier intervention and even towards prevention, biomarkers will gain significance as tools to aid research and ultimately clinical practice.

Alzheimer Disease↗
PubMed2001

Biomarkers and surrogacy: relevance to chemoprevention.

Clinical cancer prevention trials that use disease as the end-point are of necessity large, lengthy and costly. While such trials will always remain the 'gold standard' for establishing efficacy, they are unwieldy and inefficient for the rapid translation of our accelerating understanding of the molecular basis of cancer into preventive strategies. The inclusion of biomarkers in the process of chemopreventive agent development is crucial for the advancement of the field. This overview highlights the types of approach that are being used in the development and application of biomarkers in chemoprevention studies. Biomarkers, which measure exposure, susceptibility or risk factors, can be used in selecting study cohorts, assessing participant compliance and/or determining agent efficacy. Key features of biomarkers include reliability, precision, accuracy and validity. Not all biomarkers are suitable for all purposes and are likely to be imperfect in any single setting. Judicious selection and matching of biomarkers with agents and study cohorts is required for their effective utilization. A critical but non-dichotomous element of risk biomarkers is their degree of surrogacy. A classification scheme is provided that relates the degree of surrogacy of risk biomarkers to their utility in preventive interventions.

Animals↗
PubMed2002

Cancer biomarkers: easier said than done.

BACKGROUND: Biological and technical advances have led to greatly increased research and development of cancer biomarkers. This overview lists some of the challenges and barriers to developing novel effective cancer biomarkers and enablers to facilitate cancer biomarker development. METHODS: Current scientific literature regarding development of biomarkers for cancer and other diseases was reviewed. RESULTS: Challenges to developing cancer biomarkers include better understanding of biological heterogeneity, including host/tumor heterogeneity; analytical factors, such as interferences and analytical sensitivity; clinical pathologic factors, such as current histopathologic standards; and health service and market factors. More standardized biomarker definitions, standardization of cancer biology terminology, and high-quality reference materials (specimen and clinical data repositories) were identified as factors required to support advances in cancer biomarkers. CONCLUSIONS: With the above enablers, novel cancer biomarkers may be useful, both for assessing early and established neoplasia more precisely and for contributing data toward development of novel practical concepts regarding cancer biology.

Biomarkers, Tumor↗
PubMed2003

Validation and evaluation of biomarkers in workers exposed to benzene in China.

This study was conducted to validate biomarkers for early detection of benzene exposure and effect in 2 phases. The main purpose of phase 1 was to determine whether these biomarkers could reliably detect differences between workers with high exposure levels and unexposed subjects, which is the minimal screening criterion for a biomarker assay. Phase 2 of the study mainly focused on evaluating the exposure-response relation, confounding factors, and sensitivities of biomarkers for low benzene exposures. The Chinese occupational population studied had a broad range of benzene exposures. On the day of biological sample collection, exposures ranged from 0.06 to 122 ppm with a median exposure of 3.2 ppm. The median of the 4-week mean benzene exposures was 3.8 ppm, and the median lifetime cumulative exposure was 51.1 ppm-years. Compared with benzene levels in collected samples, toluene levels were relatively high, with a median of 12.6 ppm (mean, 26.3 ppm), but xylene levels were low, with a median of 0.30 ppm (mean, 0.40 ppm). The biomarkers evaluated were urinary metabolites S-phenylmercapturic acid (S-PMA*), trans,trans-muconic acid (t,t-MA), hydroquinone (HQ), catechol (CAT), and phenol; albumin adducts of benzene oxide and 1,4-benzoquinone (BO-Alb and 1,4-BQ-Alb, respectively) in blood; blood cell counts; and chromosomal aberrations. Blood cell counts in this population, including red blood cells (RBCs), white blood cells (WBCs), and neutrophils, decreased significantly with increased exposures but remained in normal ranges. Chromosomal aberration data showed significant increases of chromatid breaks and total chromosomal aberrations in exposed subjects compared with unexposed subjects. Among the urinary metabolites, the levels of S-PMA and t,t-MA were significantly elevated after benzene exposures. Both markers showed significant exposure-response trends even over the exposure range from 0 to 1 ppm. However, HQ, CAT, and phenol showed significant increases only for benzene exposure levels above 5 ppm. Multiple regression analyses of these urinary metabolites on benzene exposure indicated that toluene exposure, smoking status, and cotinine levels had no significant effects on urinary metabolite levels. A time-course study estimated the half-lives of S-PMA, t,t-MA, HQ, CAT, and phenol to be 12.8, 13.7, 12.7, 15.0, and 16.3 hours, respectively. Both BO-Alb and 1,4-BQ-Alb showed strong exposure-response associations with benzene. Regression analyses showed that after adjustment for potential confounding by smoking, there was still a strong association between benzene exposure and these markers. Furthermore, the analyses for correlations among biomarkers revealed that the urinary metabolites correlated substantially with each other. The albumin adducts also correlated well with the urinary biomarkers, especially with S-PMA. BO-Alb and 1,4-BQ adducts also correlated well with each other (r = 0.74). For benzene exposure monitoring, both S-PMA and t,t-MA were judged to be good and sensitive markers, which detected benzene exposures at around 0.1 ppm and 1 ppm, respectively. But S-PMA was clearly superior to t,t-MA as a biomarker for low levels of benzene exposure.

Adult↗
PubMed1998

Oltipraz chemoprevention trial in Qidong, People's Republic of China: modulation of serum aflatoxin albumin adduct biomarkers.

In 1995, 234 adults from Qidong, People's Republic of China, were enrolled and followed in a Phase IIa 4-methyl-5-(N-2-pyrazinyl)-1,2-dithiole-3-thione (oltipraz) chemoprevention trial. Residents of this area are at high risk for development of hepatocellular carcinoma, in part due to consumption of aflatoxin-contaminated foods. The intervention was a randomized, placebo-controlled, double-blind study. Elements of the study design and clinical outcomes have been recently published (Jacobson et al, Cancer Epidemiol. Biomark. Prev., 6: 257-265, 1997). The primary objective was to conduct a preliminary assessment of the ability of oltipraz to modulate levels of a validated biomarker of aflatoxin exposure and of the risk of hepatocellular carcinoma by determining levels of aflatoxin-albumin adducts in sera. Healthy eligible individuals were randomized into three arms to receive p.o. 125 mg of oltipraz daily, 500 mg of oltipraz weekly, or placebo for 8 weeks. There were no consistent changes in biomarker levels in the placebo arm over the 16-week observation period, nor was any apparent effect observed in the arm receiving 125 mg of oltipraz each day. However, individuals receiving 500 mg of oltipraz once a week for 8 weeks showed a triphasic response to oltipraz. No effect was observed during the 1st month of the intervention, whereas a significant (P = 0.001) diminution in adduct levels was observed during the 2nd month of active intervention and during the lst month of follow-up. A partial rebound in adduct levels toward baseline values was observed during the 2nd month postintervention. Linear regression models up to week 13 confirmed a significant (P = 0.008) weekly decline of biomarker levels in the group receiving 500 mg of oltipraz once a week. However, despite these effects relative to baseline values within the 500-mg weekly arm, there were no statistically significant differences in biomarker trajectories between treatment arms. The genotype for glutathione S-transferase M1, an oltipraz-inducible isoform involved in the detoxification of aflatoxin B1, did not appear to affect either baseline levels or rates of decline in the biomarker. A follow-up Phase IIb trial with a longer intervention period will be necessary to determine the full extent to which aflatoxin biomarker burden can be reduced and whether diminution of biomarkers can be sustained over the long term.

Adult↗
PubMed1997

Breast cytology and biomarkers obtained by random fine needle aspiration: use in risk assessment and early chemoprevention trials.

In a prospective pilot study, we performed breast fine needle aspirations (FNAs) on 224 high-risk and 30 low-risk women and analyzed these aspirates for cytologic changes and biomarker abnormalities of aneuploidy and overexpressed estrogen receptor (ER), epidermal growth factor receptor (EGFR), p53 and HER-2/neu. High-risk women had a first-degree relative with breast cancer (74%), prior biopsy indicating premalignant breast disease (25%), a history of breast cancer (13%), or some multiple of these risk factors (12%). Median ages of the high- and low-risk groups were 44 and 42, respectively. Seventy percent of high-risk and 17% of low-risk women had cytologic evidence of hyperplasia with or without atypia (P < .0001). Aneuploidy and overexpression of EGFR and p53 occurred in 27, 37, and 29% of high-risk subjects but only 0, 3, and 3% of low-risk subjects (P < .0023). Overexpression of ER and HER-2/neu occurred in 7 and 20% of high-risk women but in none of the low-risk subjects. Biomarker abnormalities were more frequent with increasing cytologic abnormality. Restricting the analysis to those 3 biomarkers most frequently overexpressed in the high-risk group (ploidy, EGFR, p53), 13% of high-risk women with normal cytology, 19% of high-risk women with epithelial hyperplasia, and 49% of high-risk women with hyperplasia with atypia had abnormalities of 2 or more of these 3 biomarkers (P = .00004). At a median follow-up of 32 months, four women have been diagnosed with invasive cancer and two with ductal carcinoma in situ (DCIS). Later detection of these neoplastic conditions was associated (P < or = .016) by univariate analysis with prior FNA evidence of hyperplasia with atypia; overexpression of p53 and EGFR; the modified Gail risk of breast cancer development at 10 years; and multiple biomarker abnormalities. By multivariate analysis, later detection of cancer was primarily predicted by the number of biomarker abnormalities in the 3-test battery (P = .0005) and secondarily by the Gail risk at 10 years (P = .0049). In turn, hyperplasia with atypia was associated with multiple biomarker abnormalities, particularly p53 and EGFR overexpression. Thus, hyperplasia with atypia and cytologic markers in breast FNAs have promise as risk predictors and as surrogate endpoint biomarkers for breast cancer chemoprevention trials.

Adult↗
PubMed2000

Biomarkers in zebra mussels (Dreissena polymorpha) for the assessment and monitoring of water quality of the St Lawrence River (Canada).

Five biomarkers (MT: metallothionein-like proteins, EROD: ethoxyresorufin ortho-dééthylase, DNA strand breaks, LPO: peroxidation of lipids, VG: vitellogenin-like protiens) were measured in the soft tissues of zebra mussels (Dreissena polymorpha) in order to assess the spatial variation of exposure to contaminants along the St Lawrence River (Canada). Fifteen mussels >25 mm shell length were analyzed from each of the 13 sampling sites. Significant differences between sites were noted for all biomarkers, but the general level of variability was low. Three biomarkers (DNA, LPO and VG) exhibited a similar pattern of spatial variation while MT and EROD had distinct and specific patterns. MT had the strongest discriminating power and EROD showed the largest range of variation among sites. Highest biomarker responses were measured in specimens from local contaminated sites such as harbors and industrial sectors. A positive relationship was found between MT and copper (Cu), but no significant correlation was observed between other biomarker responses and the levels of ten trace metals bioaccumulating in the zebra mussels tissues. Results indicate that the measurement of biomarker responses is technically feasible. The performance of each biomarker is assessed in the context of the role and advantages of selecting a battery of biomarkers for detecting contamination problems. The use of zebra mussels as a sentinel species for biomonitoring potential toxic effects in situ is discussed.

Journal Article↗
PubMed2026

Validation and refinement of a biomarker panel for frailty assessment and prediction of muscle weakness in older adults.

Frailty is a complex geriatric syndrome characterized by age-related declines in physiological function and cognitive reserve. To promote early prevention and intervention, minimally invasive and objective biomarkers that can detect frailty progression are required. We aimed to identify biomarkers associated with frailty progression and to elucidate their relevance to the Japanese version of the Cardiovascular Health Study (J-CHS) criteria, consist of five components (unintentional weight loss, self-reported exhaustion, muscle weakness, slow walking speed, and low physical activity). A total of 168 individuals (61 robust, 25 pre-frail, and 82 frail) enrolled in the NCGG (National Center for Geriatrics and Gerontology) Biobank were analyzed. Clinical information, blood-test data, aging-related factors, and gene-expression data were integrated for the analysis. First, linear regression identified one clinical factor, five aging-related factors, and 251 gene-expression factors associated with frailty. Subsequent logistic regression analyses examining each J-CHS components highlighted six candidate biomarkers. Cross-validation further suggested that three of these biomarkers-SMI, apelin, and GDF15-may represent potential biomarkers. Finally, retrospective and prospective analyses further demonstrated that those biomarkers were predictive of future muscle weakness, yielding a concordance index of 0.70. In conclusion, we validated and refined a biomarker panel consisting of SMI, apelin, and GDF15 that is associated with frailty, particularly muscle weakness (a major J-CHS component). These biomarkers may be useful for frailty assessment. Longitudinal analyses further suggested that they may be associated with the future development of muscle weakness in initially robust older adults, although validation in larger prospective cohorts is warranted.

Journal Article↗
PubMed2026

Progress towards a biotypic biomarker profile for amyotrophic lateral sclerosis-frontotemporal spectrum disorders.

Determining the optimal timing of disease-modifying therapies for neurodegenerative disorders will necessitate identification of when the underlying pathobiological process becomes active, well in advance of the point at which clinical manifestions appear. Phenoconversion, the emergence of clinically manifest syndomes, may be preceded by years to decades of silent pathobiological activity that can only be mapped by an array of biomarkers. ALS and FTD, traditionally identified as distinct clinical syndromes, are increasingly recognized to exist along a spectrum of clinical syndromes with shared genetic risk and shared underlying pathology. This clinicopathological spectrum is underpinned by cytoplasmic aggregation of TAR DNA-binding protein 43 (TDP-43) as the common neuropathological hallmark. In contrast, the majority of neuropathologically-defined frontotemporal lobar degeneration (FTLD) is associated with alterations in either TDP-43 metabolism (FTLD-TDP) or of the microtubule associated protein tau (FTLD-tau), with a smaller percentage associated with either autosomal dominant genetic mutations or impairments in the ubiquitin proteasome system. As the field of neurodegenerative disorders increasingly shifts towards the frameworks of a pathobiological definition of disease, there is a growing imperative to develop biomarkers that reflect the varied pathobiologies that underly these disorders, and to determine the sensitivity of such biomarkers to detect the presence of these pathobiologies before phenoconversion. To that end, an international workshop was convened in London, Canada in 2025 to review the evidence for existing or evolving biomarkers suitable for (1) the detection of either ALS or FTD pathobiology prior to phenoconversion and/or (2) predict phenoconversion in at risk individuals. Such biomarkers might be conceptualized as "biotypic biomarkers", capturing their ability to describe an underlying pathophysiology whilst being agnostic to the emergent clinical manifestations. Whereas no single biotypic marker is yet able to predict the emergence of ALS, FTD or their intersection, a multimodal approach to developing a biotypic biomarker profile holds promise for the detection of relevant pathobiological processes. The strength of such an approach would be augmented by also addressing issues of resiliency/susceptibility both in terms of genetic risk susceptibility profiles and developing sensitive biomarkers of genomic and cellular aging. By including such nontraditional markers of disease, a more robust picture of not only the degenerative process but also of those factors that might potentially mitigate or drive a heightened probability of disease can be derived.

cryptic exons↗
PubMed2026

cfMethDB: A Comprehensive cfDNA Methylation Data Resource for Cancer Biomarkers.

Cancer is a major global health threat, and early detection is crucial for improving patient outcomes. DNA methylation in circulating cell-free DNA (cfDNA) has emerged as a promising biomarker for non-invasive cancer diagnosis. However, the integration and utilization of existing cfDNA methylation data have been limited, hindering comprehensive research efforts, particularly in the discovery of cfDNA methylation biomarkers. To address this challenge, we introduced cfMethDB, a comprehensive database dedicated to cfDNA methylation in cancer that encompasses 4828 publicly available datasets. Through standardized analysis, we identified 1,048,770 differentially methylated cytosines (DMCs) as candidate biomarkers across seven cancer types. With cfMethDB, we not only identified known cfDNA methylation biomarkers, but also discovered several genes, such as ZIC4, that could be novel biomarkers. Moreover, cfMethDB offers a suite of user-friendly tools, including biomarker evaluation, pan-cancer search, and end motif analysis. We hope that cfMethDB will serve as a valuable platform for the discovery of novel cancer cfDNA methylation biomarkers and facilitate cancer research and clinical applications. cfMethDB is publicly available at https://cfmethdb.hzau.edu.cn/home.

Humans↗
PubMed2025

Developing a disease-specific accessible transcriptional signature as a biomarker for ataxia with oculomotor apraxia type 2.

BACKGROUND: Genetic ataxias are clinically heterogenous neurodegenerative conditions often involving rare or private mutations and it is often difficult to assign pathogenicity to rare gene variants solely based on DNA sequencing. An effective functional assay from an easy-to-obtain biospecimen would aid this assessment and be of high clinical value. SETX encodes a ubiquitous DNA/RNA helicase crucial for resolving R-loops and maintaining genome stability. Loss-of-function mutations cause a recessive disorder, Ataxia with Oculomotor Apraxia Type 2 (AOA2). METHODS: Here we utilize Weighted Gene Co-expression Network Analysis (WGCNA) from patient blood to construct an AOA2-specific transcriptomic signature as a biomarker to evaluate SETX variants in patients clinically suspected of having AOA2. RESULTS: WGCNA from peripheral blood RNA of 11 AOA2 patients from 7 families initially identified a single gene module that was modestly effective in distinguishing individuals with AOA2 from controls (sensitivity 73%, specificity 97%) and was able to robustly differentiate AOA2 patients from those with genetically distinct, yet phenotypically similar, neurological disorders (sensitivity 100%, specificity 100%). An independent derivation of the transcriptional biomarker identified a dual module model that was able to better distinguish individuals with AOA2 from controls (sensitivity 100%, specificity 97%). As validation, we examined a second cohort of 21 patients from 13 families and demonstrate that this dual module transcriptional biomarker could discriminate patients clinically suspected of AOA2 from controls (57%, 95%CI: 34%-78%). Overall, the transcriptional biomarker was able to separate AOA2 subjects (n&#x2009;=&#x2009;32) from controls (n&#x2009;=&#x2009;35) with 72% sensitivity and 97% specificity. Notably, this transcriptomic biomarker enabled verification of the first pathogenic SETX mutation found in a non-canonical transcript, expanding the spectrum of mutations that contribute to AOA2. CONCLUSIONS: Our study identified a transcriptional biomarker that was able to differentiate AOA2 from controls and from other related neurological disorders, consequently expanding the spectrum of known pathogenic mutations. This proof-of-concept study illustrates that transcriptional biomarkers may be used to validate variants of uncertain significance in known genetic diseases.

Humans↗
PubMed2025

Identification of potential biomarkers and mechanisms for keloid disorder based on comprehensive bioinformatics analysis and machine learning algorithms.

BACKGROUND: Keloid disorder (KD) encompasses a spectrum of fibroproliferative dermal conditions, the pathogenesis remains complex and incompletely understood. This study sought to identify biomarkers and potential therapeutic targets for KD through an integrative bioinformatics approach and machine learning analysis of RNA sequencing data. METHODS: RNA sequencing was performed on skin tissue samples from 13 patients with KD and 14 healthy controls. Using weighted gene co-expression network analysis and differential expression analysis revealed differentially expressed key module genes, and the CytoHubba plugin identified candidate genes. Subsequently analyzed using least absolute shrinkage and selection operator (LASSO) and support vector machine recursive feature elimination (SVM-RFE) methods to pinpoint feature genes associated with KD. Following this, biomarkers were determined through expression level validation, enrichment analysis, and immune infiltration analysis. RESULTS: A total of 420 differentially expressed key module genes were identified, and the top 10 genes with DMNC values were selected as candidate genes. Five feature genes were selected through LASSO and SVM-RFE, with NID2, MFAP2, COL8A1, and P4HA3 showing significant expression differences between KD and control samples, along with consistent expression patterns across datasets, identified as potential biomarkers. These four biomarkers were proved to possess high diagnostic potential, and they were found to exhibit significant positive correlations with one another. Functional enrichment analysis indicated that the primary KEGG pathways associated with these biomarkers included "steroid hormone biosynthesis" and "cytokine-cytokine receptor interaction." Moreover, immune infiltration analysis revealed that the four biomarkers were negatively correlated with type 17 T helper cells and positively correlated with 15 immune cell types, including activated B cells and central memory CD4 T cells. CONCLUSION: In conclusion, NID2, MFAP2, COL8A1, and P4HA3 were identified as key biomarkers for KD, offering new avenues for more targeted and effective diagnostic and therapeutic strategies for managing this condition.

Humans↗
PubMed2006

Biomarker discovery by CE-MS enables sequence analysis via MS/MS with platform-independent separation.

CE-MS is a successful proteomic platform for the definition of biomarkers in different body fluids. Besides the biomarker defining experimental parameters, CE migration time and molecular weight, especially biomarker's sequence identity is an indispensable cornerstone for deeper insights into the pathophysiological pathways of diseases or for made-to-measure therapeutic drug design. Therefore, this report presents a detailed discussion of different peptide sequencing platforms consisting of high performance separation method either coupled on-line or off-line to different MS/MS devices, such as MALDI-TOF-TOF, ESI-IT, ESI-QTOF and Fourier transform ion cyclotron resonance, for sequencing indicative peptides. This comparison demonstrates the unique feature of CE-MS technology to serve as a reliable basis for the assignment of peptide sequence data obtained using different separation MS/MS methods to the biomarker defining parameters, CE migration time and molecular weight. Discovery of potential biomarkers by CE-MS enables sequence analysis via MS/MS with platform-independent sample separation. This is due to the fact that the number of basic and neutral polar amino acids of biomarkers sequences distinctly correlates with their CE-MS migration time/molecular weight coordinates. This uniqueness facilitates the independent entry of different sequencing platforms for peptide sequencing of CE-MS-defined biomarkers from highly complex mixtures.

Amino Acid Sequence↗
PubMed1998

Uncertainty analysis methods for comparing predictive models and biomarkers: A case study of dietary methyl mercury exposure.

Biologically based markers (biomarkers) are currently used to provide information on exposure, health effects, and individual susceptibility to chemical and radiological wastes. However, the development and validation of biomarkers are expensive and time consuming. To determine whether biomarker development and use offer potential improvements to risk models based on predictive relationships or assumed values, we explore the use of uncertainty analysis applied to exposure models for dietary methyl mercury intake. We compare exposure estimates based on self-reported fish intake and measured fish mercury concentrations with biomarker-based exposure estimates (i.e., hair or blood mercury concentrations) using a published data set covering 1 month of exposure. Such a comparison of exposure model predictions allowed estimation of bias and random error associated with each exposure model. From these analyses, both bias and random error were found to be important components of uncertainty regarding biomarker-based exposure estimates, while the diary-based exposure estimate was susceptible to bias. Application of the proposed methods to a simple case study demonstrates their utility in estimating the contribution of population variability and measurement error in specific applications of biomarkers to environmental exposure and risk assessment. Such analyses can guide risk analysts and managers in the appropriate validation, use, and interpretation of exposure biomarker information.

Animals↗
PubMed2007

The applications of biomarkers in early clinical drug development to improve decision-making processes.

Selecting and evaluating biomarkers in drug discovery and early drug development can substantially shorten clinical development time or the time to reach a critical decision point in exploratory drug development. Critical decisions such as candidate selection, early proof of concept/principle, dose ranging, development risks, and patient stratification are based on the appropriate measurements of biomarkers that are biologically and/or clinically validated. The use of biomarkers helps to streamline clinical development by determining whether the drug is reaching and affecting the molecular target in humans, delivering findings that are comparable to preclinical data, and by providing a measurable endpoint that predicts desired or undesired clinical effects and will increase the success rate in the confirmatory stage of clinical development. Appropriateness of biomarkers depends on the stage of development, development strategy, and the nature of the medical indication. Even if a biomarker fails in the validation process there may be still a benefit of having used it. More knowledge about pathophysiology of the disease and the drug has been obtained. Different levels of validation exist at different development phases. Biomarkers are perhaps most useful in the early phase of clinical development when measurement of clinical endpoints may be too time-consuming or cumbersome to provide timely proof of concept or dose-ranging information. Examples of biomarkers are illustrated for the development of new drugs in variant cardiovascular, pulmonary, and CNS diseases.

Animals↗
PubMed2003

New cancer biomarkers deriving from NCI early detection research.

Cancer is not a single disease but an accumulation of several events, genetic and epigenetic, arising in a single cell over a long time interval. A high priority in the cancer field is to identify these events. This can be achieved by characterizing cancer-associated genes and their protein products. Identifying the molecular alterations that distinguish any particular cancer cell from a normal cell will ultimately help to define the nature and predict the pathologic behavior of that cancer cell. It will also indicate the responsiveness to treatment of that particular tumor. Understanding the profile of molecular changes in any particular cancer will be extremely useful as it will become possible to correlate the resulting phenotype of that cancer with molecular events. Achieving these goals and knowledge will provide an opportunity for discovering new biomarkers for early cancer detection and developing prevention approaches. This will also help us identify new targets for therapeutic development. Advancement in technology includes methods and tools that enable research including, but not limited to, instrumentation, techniques, devices, and analysis tools (e.g., computer software). Resources such as databases, reagents, and tissue repositories are different than technologies. The identification and definition of the molecular profiles of cancer will require the development and dissemination of high-throughput molecular analysis technologies, as well as elucidation of all of the molecular species embedded in the genome of cancer and normal cells. The main challenge in cancer control and prevention is to detect the cancer early. This could then enable effective interventions and therapies contributing to reduction in mortality and morbidity. At a specific time, biomarkers serve as molecular signposts of the physiologic state of a cell. These signposts are the result of genes, their products (proteins) and other organic chemicals made by the cell. Biomarkers could prove to be vital for the identification of early cancer and subjects at risk of developing cancer as a normal cell progresses through the complex process of transformation to a cancerous state. This chapter discusses ongoing research in genetic and proteomic approaches to identify molecular signatures such as protein profiles, microsatellite instability, hypermethylation, and single nucleotide polymorphisms. Other topics covered here include the use of genomics and proteomics as high-throughput technology platforms to facilitate biomarker-aided detection of early cancer. Other areas covered include issues surrounding the analysis, validation, and predictive value of biomarkers using such technologies. Recent advances in noninvasive techniques, such as buccal cell isolates serving as viable sources of biomarkers, complementary to traditional sources such as serum or plasma, are also presented. The review also brings attention to the efforts of the Early Detection Research Network (EDRN) at the National Cancer Institute (NCI), in bringing together scientific expertise from leading national and international institutions, to identify and validate biomarkers for the detection of precancerous and cancerous cells in determining risk for developing cancer. The network's serious determined efforts in linking discovery to process development, resulting in early detection tests and clinical assessment, are also discussed.

Biomarkers, Tumor↗
PubMed2026

Circulating Biomarkers Related to Mitral Valve Prolapse: Current Evidence and Mechanistic Perspective.

PURPOSE OF REVIEW: Although imaging remains central to diagnosis and risk stratification, circulating biomarkers provide complementary information reflecting myocardial stress, fibrosis, extracellular matrix remodeling, inflammation, and metabolic dysregulation. The purpose of this review is to critically evaluate current and emerging circulating biomarkers in MVP and to assess whether these biomarkers may improve individualized management of this disease. RECENT FINDINGS: Natriuretic peptides are the most validated biomarkers in MVP, consistently predicting adverse outcomes and providing complementary prognostic information that may help inform the timing of surgical intervention, particularly in asymptomatic patients with significant mitral regurgitation. In contrast, evidence supporting fibrosis and inflammation mediators, proteomics, metabolomics, and circulating microRNAs remains emerging. In arrhythmic MVP, no circulating biomarker is currently recommended for routine risk stratification. Future research should prioritize phenotype-specific MVP registries, biomarker-CMR integration, multi-omics profiling, and biomarker-guided interventional trials.

Humans↗
PubMed2006

Inflammatory biomarkers in stable atherosclerosis.

Inflammation is a key mechanism in atherosclerotic progression and destabilization that precedes clinical events such as myocardial infarction. The inflammatory biomarkers provide a window into many of these inflammatory processes. In research studies, average levels of these biomarkers in groups of subjects are often related to the risk of clinical events, and modification of risk factors can change the plasma concentrations of many biomarkers, reflecting suppression of inflammation. More evidence exists for C-reactive protein (CRP) than for other inflammatory biomarkers, and the discussion of the clinical value of plasma levels of these markers is focused mainly on CRP. Although the inflammatory biomarkers are useful research tools, their value as a clinical instrument for assessment of cardiovascular risk and/or treatment efficacy is still widely debated. Each biomarker has passionate supporters who advocate these applications, but, at this stage, no inflammatory biomarker has universal support for clinical use and their potential for fulfilling this role requires further study.

Biomarkers↗