A biomarker, short for biological marker, is a measurable indicator that reflects biological processes occurring in the body.

Biomarkers offer valuable insights into normal bodily functions, the presence or progression of a disease, or how the body responds to treatment or intervention. Examples include blood sugar levels, cholesterol, and specific proteins or genes linked to certain conditions.

An ideal biomarker should exhibit properties like binary ie, it can be present or absent or quantifiable without any subjective assessments. It should be specific and sensitive, or it can be easily detected using accessible specimens.

Biomarkers enable scientists to study the biological mechanisms underlying diseases, helping to identify potential targets for new therapies. In clinical practice, they aid healthcare providers in diagnosing diseases, monitoring disease progression, and evaluating the effectiveness of treatments, leading to more personalized and effective patient care¹.

Types of biomarkers

Biomarkers are classified into various types, each serving a unique role in healthcare, from diagnosing diseases to personalizing treatment and optimizing drug development.

Diagnostic biomarkers

Diagnostic biomarkers are measurable indicators used to confirm the presence of a disease, enabling accurate identification of specific medical conditions. For example, elevated blood sugar levels serve as a diagnostic marker for identifying Type 2 diabetes². Hemoglobin A1c (HbA1c) is an important biomarker in diagnosing and managing Type 2 diabetes mellitus. It reflects the average blood glucose levels over the preceding two to three months by measuring the percentage of glycated hemoglobin in the blood³.

An HbA1c level of ≥6.5% is indicative of diabetes, while levels between 5.7% and 6.4% suggest a high risk of developing the disease. These thresholds are based on the correlation between HbA1c levels and the risk of diabetes-related complications³.

Prognostic biomarkers

Prognostic biomarkers are tools that help predict the future progression of a disease, including the likelihood of recurrence. In gastrointestinal cancers, for example, KRAS and BRAF mutations serve as prognostic biomarkers by indicating poorer outcomes in colorectal cancer⁴. A meta-analysis of adjuvant phase III trials involving stage II and III colon cancer patients demonstrated that KRAS mutations were associated with decreased disease-free survival (DFS) and overall survival (OS)⁵.

Predictive biomarkers

Predictive biomarkers assess the likelihood of a patient responding to a specific treatment, allowing for personalized therapy approaches. For example, HER2 status in gastric cancer is a predictive biomarker that determines if a patient is likely to benefit from anti-HER2 therapy, such as trastuzumab; the use of trastuzumab in chemotherapy improved overall survival from 11.1 to 13.1 months⁴.

Pharmacodynamic and pharmacokinetic biomarkers

Pharmacodynamic biomarkers measure a drug’s effect on its intended target, helping to assess its therapeutic impact within the body. These biomarkers are vital for optimizing dosage and understanding the biological response to treatment during cancer drug development⁶. For example, an inflammatory marker, C-reactive protein (CRP) levels, can indicate the body’s response to therapeutic interventions, especially in inflammatory conditions⁷.

Pharmacokinetic biomarkers track a drug's absorption, distribution, metabolism, and excretion, providing insights into how the drug behaves within the body. They support evaluating drug safety, dosage, and timing, enhancing the design of clinical trials and treatment strategies⁶. For example, serum creatinine (sCr) is used to evaluate renal function. sCr levels can influence drug dosing and clearance assessments. However, sCr measurements may be less reliable in patients with low muscle mass or fluid overload⁸.

The biomarker discovery workflow

The biomarker discovery process is a multi-stage approach to identifying, testing, and implementing biological markers to enhance disease diagnosis, prognosis, and treatment strategies.

Sample collection and preparation

The initial step in biomarker discovery involves collecting biological samples, such as blood, urine, or tissue, from relevant patient groups. Proper handling and storage protocols are essential to maintain the integrity and quality of these samples for further analysis⁹.

High-throughput screening and data generation

High-throughput screening technologies, such as genomics and proteomics, analyze large volumes of biological data. These techniques help identify potential biomarkers by revealing patterns and variations across numerous samples.

Data analysis and candidate selection

In this phase, bioinformatics and statistical tools process and interpret the data to identify promising biomarker candidates. Researchers focus on markers that distinguish between diseased and healthy samples or indicate specific disease characteristics.

Validation and verification of biomarkers

Selected biomarkers undergo rigorous testing to ensure their accuracy, reliability, and clinical relevance. This includes analytical and clinical validation to confirm the biomarkers’ utility in diagnosis, prognosis, or treatment response.

Clinical implementation

Once validated, biomarkers are integrated into clinical practice to support diagnostics, therapeutic monitoring, or personalized treatment approaches. Continuous monitoring and updates ensure biomarker efficacy and safety in real-world applications.

Biomarker discovery methods

Biomarker discovery employs a variety of molecular approaches, each uniquely designed to enhance our understanding and detection of disease indicators at the cellular and molecular levels.

Genomic approaches

Genomic approaches in biomarker discovery are transforming the understanding and identification of disease indicators at the molecular level.

DNA sequencing

• Genomic approaches to biomarker discovery utilize DNA sequencing to identify specific genetic variations linked to diseases.
• By analyzing the complete genetic sequence, researchers can pinpoint mutations, gene expression patterns, or alterations that may serve as potential biomarkers for early detection and disease management¹⁰.

Gene expression profiling

• Gene expression allows identification of biomarkers by analyzing gene activity patterns, revealing potential indicators for disease states.
• High-throughput technologies, such as RNA sequencing and DNA microarrays, provide large datasets, facilitating data-driven biomarker discovery¹¹.

Proteomic approaches

Proteomics employs various analytical approaches and tools to study protein structures, functions, and interactions, each offering unique advantages for specific research objectives.

Top-down approach

The top-down approach involves analyzing intact proteins without prior digestion.

• It allows examination of protein structures, including post-translational modifications, and is beneficial for identifying and characterizing intact proteoforms.
• This approach is suitable for high-resolution mass spectrometry, where the entire protein is ionized and fragmented for analysis, providing detailed structural information¹².

Bottom-up approach

• The bottom-up approach is the most used in proteomics. It involves enzymatically digesting proteins into peptides, which are then analyzed by mass spectrometry.
• This approach is more scalable and practical for complex samples, allowing for high-throughput analysis.
• It is particularly useful for quantifying proteins and identifying sequences, although it may miss certain structural modifications due to the fragmentation process¹².

Mass spectrometry

Mass spectrometry is central to both top-down and bottom-up proteomic approaches.

• Top-down proteomics enables the detection of entire proteins, providing insights into proteoform-specific variations.
• Bottom-up proteomics involves analysis of peptides, facilitating protein identification, quantification, and sequence analysis across complex samples¹².

Protein arrays

Protein array-based approaches are effective tools in biomarker discovery, offering high-throughput capabilities and precise detection.

• These arrays are generally classified into analytical arrays for detecting proteins in complex samples, functional arrays for studying protein interactions and modifications, and reverse-phase arrays for analyzing cell or tissue lysates.
• Each array type provides unique insights, enhancing researchers’ ability to identify biomarkers for early disease detection and targeted treatment strategies. For example, protein arrays are instrumental in cancer biomarker research, providing detailed protein profiles to improve diagnosis and prognosis¹³.

Metabolomic, lipidomic, and glycomic approaches

Metabolomics focuses on profiling small molecules and metabolites involved in cellular processes. It provides insights into metabolic pathway disruptions that occur in various diseases, aiding in biomarker discovery. For example, cancer metabolomics can identify unique metabolic patterns associated with cancer cells, such as altered glucose utilization, which serves as a valuable biomarker.

Lipidomics examines lipid profiles to understand lipid-related alterations in cellular processes. Lipids play roles in cell signaling, membrane structure, and energy storage. Changes in lipid composition are often linked to diseases like metabolic disorders and cancer, where certain lipid markers can indicate disease presence or progression¹⁴.

Glycomic approaches offer promising methods for discovering biomarkers that can differentiate between healthy and diseased individuals by identifying changes in glycosylation patterns. However, traditional methods are often too complex and costly for clinical application. To address these challenges, researchers developed a high-throughput, quantitative assay for glycan biomarker validation, reducing preparation and instrumentation demands while achieving single-digit picomole detection limits.

This new method streamlines glycan analysis for potential clinical use, with a proof of concept demonstrated by measuring sialic acid changes in fetuin, showing its potential for disease biomarker analysis¹⁵.

Integrative multi-omics approaches

Integrative multi-omics approaches in biomarker discovery leverage diverse data types, such as genomics, transcriptomics, proteomics, and metabolomics, to provide a comprehensive view of disease mechanisms.

Integrative multi-omics approaches in biomarker discovery leverage diverse data types, such as genomics, transcriptomics, proteomics, and metabolomics, to provide a comprehensive view of disease mechanisms.

Researchers can identify unique biomarkers that reflect complex biological interactions and disease progression by analyzing multiple molecular layers. This approach enables the classification of diseases into specific subtypes, improves diagnosis accuracy, and aids in personalized therapeutic strategies. In cancer research, multi-omics studies facilitate the discovery of predictive biomarkers, potentially enhancing early detection, treatment efficacy, and patient outcomes¹⁶.

Our guide on cancer biomarker antibody validation for IHC provides essential steps and in-lab validation tips for accurate cancer biomarker analysis.

Technologies for biomarker discovery

Advances in various technologies have significantly accelerated biomarker discovery, enabling more precise, early diagnosis and personalized treatment approaches across various diseases.

Next-generation sequencing

Next-generation sequencing has revolutionized biomarker discovery by enabling high-throughput DNA sequencing, which allows researchers to rapidly analyze entire genomes.

For example, in colorectal cancer (CRC), it is being used to identify genetic mutations and patterns linked to disease progression and treatment responses. This technology’s ability to capture detailed genetic information holds promise for advancing personalized medicine by tailoring therapies based on an individual’s unique genetic profile.

A study involving 526 CRC patients utilized NGS to profile mutations across 22 cancer-related genes. The findings revealed that patients with wild-type profiles in all these genes experienced longer progression-free survival when treated with cetuximab, an anti-EGFR therapy. Conversely, mutations in genes such as KRAS, PIK3CA, AKT1, and FBXW7 were associated with poorer responses, highlighting the utility of NGS in guiding targeted therapies¹⁷.​

Mass spectrometry-based proteomics

Mass spectrometry-based proteomics is advancing biomarker discovery by enabling the precise identification and quantification of proteins linked to diseases. Analyzing proteins in body fluids helps pinpoint biomarkers for early diagnosis and monitoring of conditions like cancer and cardiovascular diseases. This technology’s sensitivity allows the detection of even low-abundance proteins and provides insights into functional protein changes relevant to disease progression.

While challenges remain in data handling and standardization, mass spectrometry-based proteomics shows great promise for personalized medicine and improved disease management¹⁸.

Microarray technologies

Microarray technologies allow for the simultaneous measurement of thousands of gene expressions, enabling the identification of disease-related biomarkers. This approach is especially useful for cancer research, as it helps detect specific genetic changes associated with various stages and types of cancer.

Despite challenges like data variability and standardization issues, microarrays remain a valuable tool for discovering potential biomarkers. By analyzing gene expression patterns, microarrays provide insights that can aid early diagnosis, prognosis, and treatment planning in clinical research¹⁹.

Bioinformatics and machine learning tools

Bioinformatics and machine learning tools are transforming biomarker discovery by analyzing large-scale gene expression data. These tools enable the identification of disease-related molecular markers, which are vital for diagnosis and treatment. Machine learning techniques, like feature selection and classification, help isolate the most informative genes, improving prediction accuracy and stability.

By combining multiple analytical methods, researchers enhance the reliability of biomarker identification, advancing personalized medicine and toxicology studies¹¹.

ChIP

Chromatin immunoprecipitation (ChIP) is a technique used to analyze protein-DNA interactions, providing insights into gene regulation and identifying potential biomarkers. By targeting specific proteins, such as transcription factors or modified histones, ChIP enables researchers to determine which genomic regions are associated with these proteins in various cellular states.

This technique involves crosslinking proteins to DNA, fragmenting the chromatin, and using antibodies to precipitate the protein-DNA complexes, which are then analyzed to pinpoint DNA binding sites. ChIP data can help discover epigenetic markers linked to diseases, enhancing our understanding of disease mechanisms and guiding therapeutic development^20,21,22.

Methylation arrays are valuable tools for biomarker discovery, enabling the identification of cancer-specific methylation patterns that can be used in diagnostic assays. These arrays facilitate the detection of abnormal DNA methylation in cancer cells, a hallmark of various cancers, by analyzing methylation patterns across the genome. This approach has shown potential in distinguishing specific cancer types, such as central nervous system lymphomas, from other brain tumors, aiding in accurate cancer diagnosis and targeted treatment²³.

CRISPR screening

CRISPR screening has become a powerful tool in biomarker discovery, particularly for identifying genetic markers that indicate resistance or sensitivity to cancer treatments. By using CRISPR technology, researchers can induce loss-of-function or gain-of-function changes across the genome, enabling the identification of genes that impact the response to drugs, especially those targeting DNA repair mechanisms like PARP and ATR inhibitors.

This approach helps uncover genetic dependencies that can guide personalized cancer therapies, as these biomarkers provide insights into which genetic backgrounds may respond better to specific treatments. CRISPR screens are widely utilized to tailor therapeutic approaches, enhancing the precision and effectiveness of cancer treatment plans²⁴.

Epigenomic profiling

Epigenomic profiling explores changes in DNA methylation, histone modifications, and chromatin structure to identify biomarkers linked to various diseases. Advances in profiling techniques have enabled large-scale, high-resolution analysis of patient samples, even at single-cell levels, for more precise disease diagnosis, prognosis, and treatment stratification.

Techniques like bisulfite sequencing, ChIP-sequencing, and ATAC-sequencing are commonly used to investigate epigenetic patterns in preserved clinical samples, making biomarker discovery more feasible. The ability to profile these changes at a genome-wide scale holds promise for personalized medicine, enabling more accurate and early disease detection²⁵.

Biomarker discovery in different biological samples

Biomarkers are measurable biological indicators that offer insights into health and disease, allowing for early detection, monitoring, and personalized treatment across various medical conditions.

Blood-based biomarkers

Blood-based biomarkers are emerging as a transformative tool in clinical diagnostics, offering a less invasive, cost-effective alternative to traditional methods like positron emission tomography (PET) and cerebrospinal fluid (CSF) testing. These biomarkers hold promise for early and accurate disease detection, particularly for neurodegenerative conditions like Alzheimer’s. Detection of these biomarkers may improve patient outcomes by enabling timely diagnosis and personalized treatment strategies²⁶.

For example, neurofilament light chain (NfL) is a promising blood-based biomarker for neurodegenerative diseases. As a structural protein in neurons, NfL provides stability to axons. When neurons are damaged, NfL is released into cerebrospinal fluid (CSF) and, subsequently, into the bloodstream. Elevated blood NfL levels correlate with disease activity and progression in MS patients, making it a valuable marker for monitoring treatment efficacy²⁷.

Urine biomarkers

Urine biomarkers are measurable biological molecules found in urine. They provide valuable insights for diagnosing and monitoring various diseases, such as cancer, cardiovascular diseases, and kidney diseases. Due to the non-invasive nature of urine collection, these biomarkers are ideal for frequent health monitoring, allowing for early detection and personalized disease management²⁸.

For example, nuclear matrix protein 22 (NMP22) is a protein associated with the nuclear mitotic apparatus, and its elevated levels in urine have been linked to bladder cancer. A systematic review and meta-analysis reported a pooled sensitivity of 56% and specificity of 88% for an NMP22 test in detecting bladder cancer. The diagnostic performance was notably higher for advanced-stage and high-grade tumors²⁹.

Tissue biomarkers

Tissue biomarkers are biological indicators found in tissue samples, including nucleic acids and proteins, and are used in clinical pathology for diagnosing, prognosticating, and guiding targeted therapies. Commonly identified through techniques like immunohistochemistry (IHC) and in situ hybridization (ISH), these biomarkers are essential for precision medicine, as they provide insights into disease characteristics and potential treatment responses. Digital image analysis has enhanced their quantification, ensuring greater accuracy and reproducibility in pathology assessments³⁰.

For example, the presence of estrogen receptors (ER) and progesterone receptors (PR) in breast cancer tissues is a well-established biomarker profile. Assessing the expression of these receptors helps in selecting appropriate hormonal therapies and predicting patient outcomes. This approach can be beneficial in tailoring treatments and improving prognostic accuracy for breast cancer patients³¹.

Cerebrospinal fluid biomarkers

Cerebrospinal fluid (CSF) biomarkers are valuable in diagnosing Alzheimer’s disease (AD) by reflecting amyloid and tau pathologies associated with the condition. These biomarkers can differentiate between normal cognitive function, mild cognitive impairment, and AD, aiding in early diagnosis and classification. CSF analysis also offers advantages over amyloid PET, including accessibility, cost-effectiveness, and providing a comprehensive view of neurodegenerative changes in the brain³².

For example, neurogranin is a postsynaptic protein expressed in neurons within the hippocampus and cerebral cortex. Studies have consistently demonstrated increased levels of neurogranin in the CSF of individuals with AD compared to cognitively normal controls. This elevation reflects synaptic damage and loss, correlating with the severity of cognitive deficits observed in patients. Higher CSF neurogranin concentrations have been associated with accelerated cognitive decline and greater brain atrophy, particularly in AD-related regions³³.

Saliva biomarkers

Saliva biomarkers are measurable molecules in saliva, such as proteins, enzymes, cytokines, and DNA, that reflect physiological or pathological states. These biomarkers offer a non-invasive, accessible method to assist in diagnosing and monitoring various diseases, including oral conditions, cardiovascular disease, and diabetes. They provide real-time insights into disease presence and progression, making them valuable for early diagnosis and personalized healthcare³⁴.

For example, aberrant methylation of specific genes in salivary DNA has been investigated as a biomarker for the early detection of oral cancers. Mutations in exon 4 and codon 63 of the p53 gene have been detected in the saliva of a significant percentage of oral cancer patients. Additionally, autoantibodies against p53, an aberrantly expressed protein in oral cancer, have been found in both saliva and serum. These findings suggest that p53 mutations and autoantibodies could serve as potential biomarkers for oral cancer detection³⁵.

Biomarkers in stool samples

Biomarkers in stool samples, such as calprotectin and lactoferrin, provide a non-invasive way to detect and monitor gastrointestinal inflammation, particularly in inflammatory bowel disease (IBD). These biomarkers originate from neutrophils in the intestinal mucosa, allowing clinicians to gauge bowel inflammation and assess disease activity without invasive procedures. Their high negative predictive value helps rule out bowel lesions, though they are not exclusively specific to IBD³⁶.

For example, elevated fecal calprotectin levels are indicative of inflammatory bowel diseases (IBD) such as Crohn’s disease and ulcerative colitis. This biomarker helps differentiate between IBD and non-inflammatory disorders like irritable bowel syndrome (IBS). Studies have demonstrated that fecal calprotectin correlates well with endoscopic disease activity, making it a reliable marker for assessing mucosal inflammation³⁷.

Biomarkers in breath

Breath biomarkers, particularly volatile organic compounds (VOCs), offer a non-invasive way to gather biological information from exhaled breath, reflecting underlying metabolic processes in the body. By analyzing these VOCs, breath biopsy can aid in early disease diagnosis, monitor treatment responses, and identify exposure to environmental hazards, with applications across various medical fields, including cancer, respiratory, and infectious diseases³⁸.

For example, nitric oxide (NO) is a gaseous molecule produced in the respiratory tract, playing a significant role in airway inflammation. Measuring fractional exhaled nitric oxide (FeNO) levels can help measure inflammation. Regular FeNO assessments can help monitor airway inflammation, allowing for tailored adjustments in anti-inflammatory therapy. Elevated FeNO levels indicate eosinophilic inflammation, which is responsive to corticosteroid therapy. Thus, FeNO measurement assists in identifying patients who may benefit from inhaled corticosteroids³⁹.

Sweat biomarkers

Sweat biomarkers are molecules found in sweat that provide valuable insights into physiological and metabolic states. These include electrolytes (like sodium and potassium), metabolites (such as glucose and lactate), and trace elements (like zinc and copper), which can indicate hydration levels, metabolic function, and even stress response, making them useful for non-invasive health monitoring⁴⁰.

For example, lactate levels in sweat can indicate muscle metabolism and are used to monitor physical exertion and fatigue. Elevated sweat lactate concentrations may reflect increased anaerobic metabolism during intense exercise. In patients with cardiovascular diseases, monitoring sweat lactate can aid in assessing exercise tolerance and tailoring rehabilitation programs⁴¹.

Biomarker validation

In biomarker research, validation ensures biomarkers are accurate, reliable, and clinically meaningful for guiding healthcare decisions.

Importance of validation in biomarker discovery

Validation in biomarker discovery is essential for ensuring that biomarkers reliably indicate specific biological states or disease conditions, allowing them to be confidently used in clinical settings. Without proper validation, biomarkers may provide inaccurate or misleading results, which can lead to improper diagnosis or ineffective treatments.

Validation helps verify that a biomarker is specific to the condition it is intended to detect, minimizing false positives and negatives. Additionally, it confirms the reproducibility of biomarker results across different laboratories, assays, and patient populations, ensuring broad applicability⁴².

Phases of biomarker validation

Biomarker validation typically follows several phases, beginning with analytical validation, which ensures the test’s accuracy, precision, and reproducibility. Following analytical validation, clinical validation assesses whether the biomarker is indeed associated with the disease or outcome of interest in clinical settings⁴².

Analytical validation

Analytical validation is the foundational step in biomarker validation, focusing on establishing the test’s reliability and accuracy under controlled laboratory conditions. This phase assesses parameters such as sensitivity, specificity, accuracy, and precision, ensuring the biomarker can consistently detect or measure the intended biological marker.

Reproducibility is also important, as analytical validation must confirm that different laboratories, instruments, and operators can yield the same results. Additionally, this phase evaluates factors like sample stability, matrix effects, and assay interferences, ensuring the biomarker’s robustness in real-world conditions. Completing analytical validation is necessary before a biomarker can progress to clinical validation, as it establishes the test’s technical reliability.

Clinical validation

Clinical validation aims to confirm that the biomarker is meaningfully associated with the disease or outcome it’s intended to predict in a clinical context. This phase often involves retrospective and prospective studies to verify that the biomarker can distinguish between diseased and non-diseased individuals accurately.

Clinical validation tests the biomarker across diverse patient populations, enhancing its generalizability and confirming its relevance in real-world applications. Moreover, this stage assesses the biomarker’s predictive value, examining whether it can provide helpful information regarding disease progression, treatment response, or patient prognosis.

Validation study design and statistical considerations

Designing a validation study requires careful planning to ensure the study accurately reflects the biomarker’s performance and minimizes biases. A prospective-specimen-collection, retrospective-blinded-evaluation (PRoBE) design is often recommended, where samples are collected without knowing patient outcomes, and biomarker analysis is performed blind to clinical data.

Statistical methods, such as cross-validation and bootstrapping, are essential for internal validation, helping to assess the biomarker’s stability within the study data. External validation, requiring an independent dataset, is important for confirming generalizability across settings. Additionally, managing factors like batch effects, randomization, and multiple comparisons is essential to avoid overfitting and ensure accurate performance estimates, maximizing the reliability of validation results⁴².

Challenges in biomarker discovery and validation

Biomarker discovery and validation involve complex challenges that must be addressed to ensure reliable and clinically valuable outcomes.

Biological and technical variability

Biomarker discovery faces challenges due to biological variability, where differences between individuals, such as genetic diversity and environmental influences, can affect biomarker expression. Technical variability arising from differences in sample collection, handling, and analysis can further complicate consistent biomarker identification and validation across studies⁴³.

Reproducibility issues

Reproducibility is a challenge in biomarker research, as results need to be consistent across different studies and laboratories to ensure reliability. Inconsistent results can arise from variations in experimental design, data handling, or measurement techniques, making it difficult to achieve consensus on biomarker validity.

Sample size and power considerations

Adequate sample size and statistical power are essential for robust biomarker validation, as insufficient sample sizes can lead to unreliable results or false discoveries. In biomarker studies, large sample sizes are often needed to capture variability and confirm findings, but obtaining enough samples can be logistically and financially challenging.

Translating discovery to clinical application

The transition from biomarker discovery to clinical application is hindered by regulatory, technical, and economic barriers that must be overcome to prove clinical utility. Demonstrating that a biomarker provides actionable insights for patient care requires extensive validation and often substantial investments, delaying the path from bench to bedside.

Applications of biomarker discovery

Biomarkers are indispensable tools in modern healthcare, providing insights into disease detection, progression, treatment response, and environmental exposure, thus enhancing precision in diagnostics, therapeutics, and preventive care.

Disease diagnostics and prognosis

Biomarker discovery is vital in disease diagnostics, as it enables the early detection and accurate identification of various diseases, including cancer and infectious diseases, often through non-invasive methods like blood tests. In prognosis, biomarkers provide insights into disease progression, helping to predict outcomes and tailor treatment approaches based on the likely course of the disease, ultimately supporting personalized medicine and improving patient care⁴⁴.

Personalized medicine

In personalized medicine, biomarkers are essential for selecting targeted treatments tailored to an individual’s unique biological characteristics. They enable healthcare providers to identify patients who are most likely to benefit from a specific therapy, optimizing drug efficacy and reducing adverse effects by ensuring the right treatment is given to the right patient at the right dose⁴⁵.

Drug development and clinical trials

Biomarkers are important indicators that provide insights into disease detection, progression, and treatment response across various health conditions, enabling personalized and precise medical interventions.

Cancer

Biomarkers play a vital role in cancer drug discovery by identifying molecular targets specific to an individual’s cancer type, which enables the development of targeted therapies. They assist in determining drug efficacy, patient selection, and dosing, ultimately enhancing the precision and effectiveness of cancer treatments⁴⁶.

The following guides can assist researchers in identifying the appropriate cancer biomarkers for studies across different cancer types⁴⁶:

• The guide on lung cancer biomarkers explores essential lung cancer biomarkers, including AHR, PD-L1, SPA, SOX2, MET, FAS, Ki67, and ASCL1, used in immunohistochemistry for diagnosing, prognosis, and guiding targeted therapies in various lung cancer types.
• The colorectal cancer markers guide focuses on key colorectal cancer biomarkers, including NOTUM, Calretinin, MUC2, COX-2, LI Cadherin, GPA33, EGFR, VEGF, p53, Ki67, IGFBP2, PKM2, CDX2, Ep-CAM, Villin, and CK7/CK20.
• The guide on breast cancer markers provides information on essential biomarkers, including ALDH1A1, SLFN11, Estrogen receptor alpha, Progesterone receptor, ErbB2/HER2, p53, BRCA1, EGFR, CK7/CK20, Cyclin D1, Ki67, COX-2, Caveolin-1, FOXA1, GATA3, SMMHC, E-cadherin, ACTA2, Calponin, Delta-1-Catenin, and CK14.
• The ovarian cancer biomarkers guide explores key ovarian cancer biomarkers, including CD9, PAX8, MUC16, WT1, p53, HE4, CDKN2A/p16INK4a, Estrogen and Progesterone Receptors, HER2, Osteopontin, PD1, IGFBP2, Kallikrein 8, CK7, CK20, Pan-cytokeratin, Vimentin, FOXL2, and ARID1A.

Cardiovascular diseases

Cardiac biomarker tests measure enzymes, proteins, and hormones in the blood to assess heart health, especially after a heart attack. Elevated levels, particularly of troponin, can indicate heart muscle damage, helping diagnose and evaluate the severity of a cardiac event⁴⁷.

Biomarkers in cardiovascular drug discovery help streamline clinical trials by providing insights into disease progression, patient selection, and drug efficacy. Their use can accelerate therapy development by offering surrogate endpoints, reducing trial duration, and enhancing precision in patient stratification⁴⁸.

Neurodegenerative disorders

Biomarkers are essential for early detection and personalized treatment of neurodegenerative diseases like Alzheimer’s and Parkinson’s. Key types include miRNAs (eg, miR-342-5p in Alzheimer’s), lncRNAs, and proteins like neurofilament light (NFL), which help track disease progression and cognitive decline⁴⁹.

Biomarkers are important in Alzheimer’s disease drug development, aiding in diagnosis, target engagement, and safety monitoring, while the A-T-N framework (amyloid, tau, neurodegeneration) helps characterize Alzheimer’s and optimize clinical trials. Biomarkers offer the potential to de-risk Alzheimer’s drug trials, attract sponsors, and accelerate the approval of new therapies⁵⁰.

Our guide on biomarkers in traumatic brain injury explores key biomarkers, highlighting their roles in neuron components and protein pathways, with the potential to predict outcomes and improve diagnosis and management.

Infectious diseases

Biomarkers play a vital role in infectious disease management by helping to diagnose infections, monitor the response to treatment, and guide antibiotic use. Pathogen-specific biomarkers, like antigen tests, assist in identifying infections quickly, while host-response biomarkers, such as procalcitonin and C-reactive protein, provide insight into the body’s inflammatory response, helping clinicians adjust treatments effectively. However, these biomarkers should always be used alongside clinical evaluation for accurate patient care⁵¹.

Aging

Biomarkers in aging help identify physiological and molecular changes associated with the aging process, providing insights into health and predicting disease onset. These biomarkers, including DNA damage, telomere length, and inflammation markers, aim to measure biological aging accurately and can indicate susceptibility to age-related conditions, guiding interventions to promote healthy aging⁵².

Studies show that knocking out telomerase subunits leads to shortened lifespan, rapid organ failure, and reduced telomere length. Overexpressing telomerase reverse transcriptase (TERT) in deficient cells, or reactivating telomerase in TERT fused with estrogen receptor (TERT-ER) mice, restores telomere length, reduces DNA damage, and reverses degenerative changes. These animal models confirm the essential role of telomeres and telomerase in aging and related dysfunctions⁵³.

Metabolic disorders

Biomarkers in metabolic diseases enable early detection, diagnosis, and management by indicating specific physiological and biochemical changes associated with disease progression. These biomarkers, such as adipokines, cytokines, and lipid peroxidation markers, help stratify patient risk, personalize treatments, and monitor therapeutic outcomes in conditions like obesity, diabetes, and cardiovascular disease⁵⁴.

For example, leptin has been studied as a biomarker for metabolic syndrome across various populations, consistently showing elevated levels in affected individuals. High leptin levels are linked to obesity, insulin resistance, heart disease, and heart failure. Studies have shown leptin to be a sensitive predictor of metabolic syndrome and cardiovascular risk in both children and postmenopausal women, correlating with abdominal obesity and the number of metabolic syndrome components⁵⁴.

Biomarkers for exposure to environmental pollutants and toxins

Biomarkers for exposure to environmental pollutants and toxins help assess the impact of toxicants on human health. They can identify specific exposures, track the body’s response, and reveal underlying biological changes, such as DNA damage or protein alterations, which may be linked to disease risk. Advanced methods, like mass spectrometry, enable accurate biomonitoring, providing valuable data for preventive health strategies and risk assessment⁵⁵.

Emerging trends in biomarker discovery

Emerging trends in biomarker discovery are advancing through innovative technologies that enable more precise, non-invasive, and comprehensive approaches across various medical fields.

Liquid biopsies

Emerging trends in biomarker discovery through liquid biopsies include the use of advanced sequencing technologies like next-generation sequencing (NGS) and long-read sequencing to detect and analyze cancer-specific biomarkers in bodily fluids. Techniques such as fragmentomics, DNA methylation profiling, and single-cell sequencing are enhancing the sensitivity and specificity of liquid biopsies, offering a non-invasive method to monitor disease progression and personalize treatment for conditions like non-small-cell lung cancer⁵⁶.

Single-cell technologies

Biomarker discovery using single-cell technologies focuses on analyzing individual cells to identify unique biomarker patterns in cancer, immune responses, and disease progression. Techniques such as single-cell RNA sequencing and single-cell proteomics allow researchers to detect rare cell populations and heterogeneity within tumors, offering insights into treatment resistance and potential therapeutic targets⁵⁶.

Microbiome analysis

Trends in biomarker discovery are focusing on microbiome analysis, particularly using advanced machine learning and omics technologies to identify potential biomarkers. These approaches allow for a detailed examination of the microbiome’s role in various diseases and conditions, such as inflammatory bowel disease, type 2 diabetes, and autism spectrum disorder. With improved reproducibility methodologies, researchers can more accurately identify microbiome-based biomarkers, opening new pathways for diagnosis and personalized medical treatments⁵⁷.

Multimodal biomarker panels

Multimodal biomarker panels integrate various biomarker types, such as imaging, genetic, and molecular markers, to improve the detection, prediction, and monitoring of central nervous system disorders. By combining different modalities, these panels offer a more comprehensive view of disease processes, potentially enhancing diagnostic accuracy, and supporting the development of targeted therapies⁵⁸.

Outlook on biomarker discovery and validation

Future directions in biomarker discovery and validation aim to enhance diagnostic precision and accessibility by leveraging innovations in data integration, personalized testing, point-of-care technologies, and real-time monitoring.

Integration of multi-omics data

Integrating multi-omics data offers a holistic approach to biomarker discovery, enabling insights that extend beyond single-dimensional analyses by combining genomic, transcriptomic, proteomic, and metabolomic data. This approach allows for a more comprehensive understanding of complex biological interactions, particularly in cancer treatment prediction. Advanced data analytics, including artificial intelligence, are important in processing this multidimensional data, improving predictive accuracy and supporting personalized treatment strategies⁵⁹.

Personalized biomarker panels

The focus is on developing personalized biomarker panels that can more precisely assess individual risk, prognosis, and therapeutic response. With the integration of multi-omics data and advanced computational methods, these panels can improve the sensitivity and specificity of clinical tests, especially for multifactorial diseases. Continued advancements in machine learning and systems biology will support the creation and validation of these personalized panels, paving the way for more targeted and effective medical treatments^60,61.

Point-of-care biomarker testing

Creating low-cost, point-of-care (POC) biomarker testing tools can provide fast and accurate results in diverse settings, including low-resource areas. Technological advances, such as smartphone integration and modular biosensors, are paving the way for quantitative biomarker measurements at the POC, enhancing diagnostic accessibility and impact on global health. The modularity and affordability of these POC devices hold promise for widespread adoption and effective disease monitoring in real-time⁶².

Continuous monitoring of biomarkers

Continuous biomarker monitoring is a promising advancement in biomarker discovery and validation, enabling real-time tracking of molecular changes in patients and biological systems. With innovative sensor technologies like free-diffusion-based biosensing, continuous monitoring can detect biomarkers at extremely low concentrations, enhancing diagnostics and treatment monitoring over long periods without the need for repeated reagent usage. This approach holds significant potential for critical care, personalized medicine, and environmental monitoring⁶³.

Ethical and regulatory considerations

Establishing ethical, regulatory, and methodological frameworks is important in biomarker research for advancing reliable and responsible clinical applications.

Informed consent and privacy issues

Informed consent and privacy are considerations in biomarker research for AD/Pso, particularly given the sensitive nature of health data and the potential for incidental findings. Ensuring that participants fully understand the implications of data usage, including potential future research applications, is essential for maintaining trust and ethical integrity in these studies. Additionally, robust privacy protections are required to safeguard against unauthorized data access, especially in big data and advanced analytics contexts⁶⁴.

Regulatory pathways for biomarker approval

The regulatory pathway for biomarker approval involves a structured three-stage process managed by the FDA’s biomarker qualification program, submitting a letter of intent, a qualification plan, and a complete qualification package. This pathway enables biomarker qualification for specific contexts of use in drug development, supporting regulatory decisions and fostering innovation through collaborative efforts with multiple stakeholders under the 21st Century Cures Act⁶⁵.

Standardization and quality control

Standardization and quality control in biomarker development are essential for ensuring reliability and reproducibility in clinical applications. Establishing consistent protocols involves rigorous analytical validation and standardized testing to ensure biomarkers effectively predict disease outcomes across diverse clinical settings⁶⁶.

FAQs

How does affinity proteomics contribute to the biomarker discovery process?

Affinity proteomics facilitates biomarker discovery by allowing the detection and quantification of low-abundance proteins in complex samples through highly sensitive and specific assays. This approach, particularly through techniques like immunoassays and aptamer-based methods, enables researchers to identify potential biomarkers with high throughput, aiding in the early diagnosis and monitoring of diseases⁶⁷.

What role do high-throughput omics technologies play in biomarker identification?

High-throughput omics technologies, such as genomics, proteomics, and metabolomics, are important for biomarker identification because they allow comprehensive analysis of molecular profiles across numerous biological samples, providing insights into disease mechanisms and potential diagnostic or therapeutic targets. These technologies, paired with bioinformatics tools, facilitate the discovery and validation of biomarkers at multiple molecular levels, which are particularly valuable in complex diseases like cancer⁶⁸.

What are the key steps in the biomarker discovery workflow?

The biomarker discovery workflow involves stages from sample collection and high-throughput data generation to rigorous validation and clinical implementation, ensuring biomarkers are reliable for disease diagnosis, prognosis, or treatment. This multi-step process combines bioinformatics and statistical analysis to identify, verify, and apply biomarkers in clinical settings, ultimately improving personalized healthcare strategies⁶⁸.

References

1. Strimbu, K., Tavel, J.A. What are biomarkers? Current Opinion in HIV and AIDS. 5(6), 463–466 (2010).
2. McBride, B. Examples of biomarkers and biomarker data analysis. Fios Genomics. (2022).
3. Owora, A.H. Commentary: Diagnostic Validity and Clinical Utility of HbA1c Tests for Type 2 Diabetes Mellitus. Current Diabetes Reviews. 14(2), 196–199 (2018).
4. Verdaguer, H., Saurí, T., Macarulla, T. Predictive and prognostic biomarkers in personalized gastrointestinal cancer treatment. Journal of Gastrointestinal Oncology. 8(3), 405–417 (2017).
5. Formica, V., Sera, F., Cremolini, C., et al. KRAS and BRAF mutations in stage II/III colon cancer: A systematic review and meta-analysis. JNCI: Journal of the National Cancer Institute. (2021).
6. Sarker, D., Pacey, S., Workman, P. Use of pharmacokinetic/pharmacodynamic biomarkers to support rational cancer drug development. Biomarkers in Medicine. 1(3), 399–417 (2007).
7. Netterberg, I., Li, C., Molinero, L., et al. A PK/PD analysis of circulating biomarkers and their relationship to tumor response in atezolizumab‐treated non‐small cell lung cancer patients. Clinical Pharmacology & Therapeutics. 105(2), 486–495 (2018).
8. Sanz Codina, M., Zeitlinger, M. Biomarkers predicting tissue pharmacokinetics of antimicrobials in sepsis: A review. Clinical Pharmacokinetics. 61(5), 593–617 (2022).
9. Wisecube AI. Knowledge graphs in biomarker discovery & validation. Wisecube.ai. (2023).
10. Apeltsin, L., Wang, S., von Büdingen, H.C., et al. A haystack heuristic for autoimmune disease biomarker discovery using next-gen immune repertoire sequencing data. Scientific Reports. 7(1), (2017).
11. Zhang, X., Jonassen, I., Goksøyr, A. Machine learning approaches for biomarker discovery using gene expression data. PubMed. (2021).
12. Feala, J.D., Diwan, M., Yu, C., et al. Systems biology approaches for discovering biomarkers for traumatic brain injury. Journal of Neurotrauma. 30(13), 1101–1116 (2013).
13. Huang, Y., Zhu, H. Protein array-based approaches for biomarker discovery in cancer. Genomics, Proteomics & Bioinformatics. 15(2), 73–81 (2017).
14. Shahid, M., Yeon, A., Kim, J. Metabolomic and lipidomic approaches to identify biomarkers for bladder cancer and interstitial cystitis (Review). Molecular Medicine Reports. 22(6), 5003–5011 (2020).
15. Shipman, J.T., Nguyen, H.T., Desaire, H. So you discovered a potential glycan-based biomarker; now what? We developed a high-throughput method for quantitative clinical glycan biomarker validation. ACS Omega. 5(12), 6270–6276 (2020).
16. Sun, Y.V., Hu, Y.J. Integrative analysis of multi-omics data for discovery and functional studies of complex human diseases. Advances in Genetics. 93, 147–190 (2016).
17. Li, W., Qiu, T., Guo, L., et al. NGS-based oncogenic mutations analysis in advanced colorectal cancer patients improves targeted therapy prediction. Pathology, Research and Practice. 215(3), 483–489 (2019).
18. Birhanu, A.G. Mass spectrometry-based proteomics as an emerging tool in clinical laboratories. Clinical Proteomics. 20(1), (2023).
19. Sánchez-Peña, M.L., Isaza, C.E., Pérez-Morales, J., et al. Identification of potential biomarkers from microarray experiments using multiple criteria optimization. Cancer Medicine. 2(2), 253–265 (2013).
20. Wiehle, L., Breiling, A. Chromatin immunoprecipitation. Methods in Molecular Biology. 7–21 (2016).
21. C. Dictionary of Toxicology. 65–104 (2015).
22. Trier Maansson, C., Meldgaard, P., Stougaard, M., et al. Cell-free chromatin immunoprecipitation can determine tumor gene expression in lung cancer patients. Molecular Oncology. 17(5), 722–736 (2023).
23. Technology. Novel biomarker discovery program to identify methylation markers in cancer. Technologypublisher.com. (2020).
24. Schleicher, E.M., Moldovan, G. CRISPR screens guide the way for PARP and ATR inhibitor biomarker discovery. The FEBS Journal. (2021).
25. Dirks, R.A.M., Stunnenberg, H.G., Marks, H. Genome-wide epigenomic profiling for biomarker discovery. Clinical Epigenetics. 8, 122 (2016).
26. Angioni, D., Delrieu, J., Hansson, O., et al. Blood biomarkers from research use to clinical practice: What must be done? A report from the EU/US CTAD Task Force. The Journal of Prevention of Alzheimer’s Disease. 9(4) (2022).
27. Gaetani, L., Blennow, K., Calabresi, P., et al. Neurofilament light chain as a biomarker in neurological disorders. Journal of Neurology, Neurosurgery & Psychiatry. 90(8), 870–81 (2019).
28. Sequeira-Antunes, B., Ferreira, H.A. Urinary biomarkers and point-of-care urinalysis devices for early diagnosis and management of disease: A review. Biomedicines. 11(4), 1051 (2023).
29. Wang, Z., Que, H., Suo, C., et al. Evaluation of the NMP22 BladderChek test for detecting bladder cancer: a systematic review and meta-analysis. Oncotarget. 8, 100648–100656 (2017).
30. Li, Z., Bui, M. M., Pantanowitz, L., et al. Clinical tissue biomarker digital image analysis: A review of current applications. Human Pathology Reports. 28, 300633 (2022).
31. Freelander, A., Brown, L. J., Parker, A., et al. Molecular biomarkers for contemporary therapies in hormone receptor-positive breast cancer. Genes. 12(2), 285 (2021).
32. Lee, J., Jang, H., Kang, S. H., et al. Cerebrospinal fluid biomarkers for the diagnosis and classification of Alzheimer’s disease spectrum. Journal of Korean Medical Science. 35(44) (2020).
33. Agnello, L., Lo Sasso, B., Vidali, M., et al. Neurogranin as a reliable biomarker for synaptic dysfunction in Alzheimer’s disease. Diagnostics. 11(12) (2021).
34. Melguizo-Rodríguez, L., Costela-Ruiz, V. J., Manzano-Moreno, F. J., et al. Salivary biomarkers and their application in the diagnosis and monitoring of the most common oral pathologies. International Journal of Molecular Sciences. 21(14) (2020).
35. Shah, F. D., Begum, R., Vajaria, B. N., et al. A review on salivary genomics and proteomics biomarkers in oral cancer. Indian Journal of Clinical Biochemistry. 26(4), 326–334 (2011).
36. Van Assche, G. Fecal biomarkers for the diagnosis and management of inflammatory bowel disease. Gastroenterology & Hepatology. 7(6), 396 (2011).
37. Kopylov, U., Rosenfeld, G., Bressler, B., et al. Clinical utility of fecal biomarkers for the diagnosis and management of inflammatory bowel disease. Inflammatory Bowel Diseases. 20(4), 742–756 (2014).
38. Breath Biopsy. A new diagnostic modality. Owlstone Medical.
39. Dweik, R.A., Boggs, P.B., Erzurum, S.C., et al. An official ATS clinical practice guideline: interpretation of exhaled nitric oxide levels (FENO) for clinical applications. American Journal of Respiratory and Critical Care Medicine. 184(5), 602–615 (2011).
40. Gao, F., Liu, C., Zhang, L., et al. Wearable and flexible electrochemical sensors for sweat analysis: a review. Microsystems & Nanoengineering. 9(1), 1–21 (2023).
41. Cai, J., Cao, M., Bai, J., et al. Flexible epidermal wearable sensor for Athlete’s sweat biomarkers monitoring. Talanta. 282, 126986–6 (2024).
42. Ou, F.S., Michiels, S., Shyr, Y., et al. Biomarker Discovery and Validation: Statistical Considerations. Journal of Thoracic Oncology. 16(4), 537–45 (2021).
43. Gupta, S., Venkatesh, A., Ray, S., et al. Challenges and prospects for biomarker research: A current perspective from the developing world. Biochemical Pharmacology. 1844(5), 899–908 (2014).
44. Das, S., Dey, M. K., Devireddy, R., et al. Biomarkers in cancer detection, diagnosis, and prognosis. Sensors. 24(1), 37–7 (2023).
45. Landeck, L., Kneip, C., Reischl, J., et al. Biomarkers and personalized medicine: current status and further perspectives with special focus on dermatology. Experimental Dermatology. 25(5), 333–9 (2016).
46. Hodgson, D.R., Whittaker, R.D., Herath, A., et al. Biomarkers in oncology drug development. Molecular Oncology. 3(1), 24–32 (2008).
47. University of Rochester Medical Center. Cardiac Biomarkers (Blood) - Health Encyclopedia - University of Rochester Medical Center. University of Rochester Medical Center. (2024).
48. Januzzi, J.L., Canty, J.M., Das, S., et al. Gaining Efficiency in Clinical Trials With Cardiac Biomarkers. Journal of the American College of Cardiology. 77(15), 1922–33 (2021).
49. Selvam, S., Ayyavoo, V. Biomarkers in neurodegenerative diseases: a broad overview. Exploration of Neuroprotective Therapy. 119–47 (2024).
50. Cummings, J. The Role of Biomarkers in Alzheimer’s Disease Drug Development. Advances in Experimental Medicine and Biology. 29–61 (2019).
51. Póvoa, P., Coelho, L., Dal-Pizzol, F., et al. How to use biomarkers of infection or sepsis at the bedside: guide to clinicians. Intensive Care Medicine. 49(2), 142–53 (2023).
52. Wagner, K.H., Cameron-Smith, D., Wessner, B., et al. Biomarkers of Aging: From Function to Molecular Biology. Nutrients. 8(6), 338 (2016).
53. Biomarkers of aging and relevant evaluation techniques: a comprehensive review. Aging and Disease. 15(1), (2024).
54. Srikanthan, K., Feyh, A., Visweshwar, H., et al. Systematic Review of Metabolic Syndrome Biomarkers: A Panel for Early Detection, Management, and Risk Stratification in the West Virginian Population. International Journal of Medical Sciences. 13(1), 25–38 (2016).
55. Turesky, R.J., Lu, K. Biomarkers of Environmental Toxicants: Exposure and Biological Effects. Toxics. 8(2), 37 (2020).
56. Brockley, L.J., Vanessa, Forder, A., Pewarchuk, M.E., Erkan, M., Telkar, N., et al. Sequence-Based Platforms for Discovering Biomarkers in Liquid Biopsy of Non-Small-Cell Lung Cancer. Cancers. 15(8), 2275 (2023).
57. Rojas-Velazquez, D., Kidwai, S., Kraneveld, A.D., Tonda, A., Oberski, D., Garssen, J., et al. Methodology for biomarker discovery with reproducibility in microbiome data using machine learning. BMC Bioinformatics. 25(1), (2024).
58. E, Posey, S.M., Childers, E., Carter, S. Exploring Opportunities to Move Forward. National Academies Press (US). (2025).
59. Yang, Z., Guan, F., Bronk, L., et al. Multi-omics approaches for biomarker discovery in predicting the response of esophageal cancer to neoadjuvant therapy: A multidimensional perspective. Pharmacology & Therapeutics. (108591), (2024).
60. Fahrmann, J.F., Marsh, T., Irajizad, E., et al. Blood-Based Biomarker Panel for Personalized Lung Cancer Risk Assessment. Journal of Clinical Oncology. 40(8), 876-883 (2022).
61. Smith, B.J., Silva-Costa, L.C., Martins-de-Souza, D. Human disease biomarker panels through systems biology. Biophysical Reviews. 13(6), 1179-1190 (2021).
62. Smedemark, S.A., Aabenhus, R., Llor, C., et al. Biomarkers as point-of-care tests to guide prescription of antibiotics in people with acute respiratory infections in primary care. Cochrane Database of Systematic Reviews. (10), (2022).
63. Buskermolen, A.D., Lin, Y.T., van Smeden, L., et al. Continuous biomarker monitoring with single molecule resolution by measuring free particle motion. Nature Communications. 13(1) (2022).
64. Fritzsche, M.-Ch., Buyx, A.M., Hangel, N. Mapping ethical and social aspects of biomarker research and its application in atopic dermatitis and psoriasis: a systematic review of reason. Journal of the European Academy of Dermatology and Venereology. 36(8), 1201–1213 (2022).
65. Research C for DE and. About Biomarkers and Qualification. FDA. (2021).
66. Veltri, R. W., Miller, M. C., An, G. Standardization, analytical validation, and quality control of intermediate endpoint biomarkers. Urology. 57(4), 164–170 (2001).
67. Smith, J. G., Gerszten, R. E. Emerging Affinity-Based Proteomic Technologies for Large-Scale Plasma Profiling in Cardiovascular Disease. Circulation. 135(17), 1651–1664 (2017).
68. Hu, Z. Z., Huang, H., Wu, C. H., et al. Omics-Based Molecular Target and Biomarker Identification. Methods in Molecular Biology (Clifton, NJ). 719, 547–571 (2011).