diagnostic biomarkers

Overview

Diagnostic biomarkers are measurable biological features used to detect the presence of a disease, support diagnosis, or help distinguish one clinical state from another. They may include nucleic acids, proteins, Metabolites, cells, imaging features, or composite molecular signatures derived from multi-omics data. In biomedical research, diagnostic biomarkers are especially important because they can improve early detection, reduce reliance on invasive procedures, and help stratify patients by disease subtype or risk.

In practice, diagnostic biomarkers are often studied alongside related concepts such as prognostic biomarkers, predictive biomarkers, and therapeutic targets. Their development increasingly relies on artificial intelligence, machine learning, proteomics, metabolomics, bulk and single-cell RNA-seq, and other analytical methodologies. Across many diseases, including cancer, cardiovascular disease, kidney disorders, autoimmune conditions, and transplant-related complications, the central challenge is to identify biomarkers that are both biologically informative and clinically validated.

Focus of Latest Publications

Recent studies have used diagnostic biomarkers as a central framework for disease detection, monitoring, and stratification across a wide range of conditions.

In infectious disease, a multi-omics and artificial intelligence study on Mycobacterium avium infection sought to identify novel diagnostic biomarkers and to screen host-directed drug candidates. This reflects a growing strategy in which biomarker discovery is paired with host-directed drug screening to connect disease signatures with potential interventions.

In transplant medicine, gut dysbiosis was proposed as a potential source of diagnostic biomarkers and therapeutic targets in post-liver transplant acute lung injury. This work linked the gut-lung axis, immune gene expression, and lung inflammation, suggesting that microbiome-associated signals may help characterize transplant-associated complications. A separate study in kidney transplant recipients identified urinary exosomal miR-7975 as a promising noninvasive biomarker for diagnosing and monitoring subclinical acute T-cell-mediated rejection, highlighting the value of extracellular vesicle-derived microRNA in transplant surveillance.

Several studies focused on endocrine and metabolic disease. In children with congenital hypothyroidism, serum metabolomic profiling combined with machine learning identified Metabolites that may serve as potential diagnostic biomarkers. In diabetic nephropathy, chemerin was evaluated as an early biomarker, with the study suggesting that this adipokine may be useful in diagnosis. In allergy, integrated multi-omics and network pharmacology analysis indicated that active Metabolites could serve as critical factors and diagnostic biomarkers in allergic responses.

Neurologic and functional disorders were also represented. A study on functional motor disorders used multimodal behavioral, neurophysiological, and imaging assessment with explainable machine learning to identify diagnostic biomarkers, addressing the problem that reliable biomarkers remain limited and heterogeneous. In stroke research, imaging and genomics were combined to define quantifiable and heritable biomarkers through imaging endophenotypes, which were described as potentially more mechanistic than conventional clinical measures. Another study examined suspected non-Alzheimer pathophysiology and discussed amyloid and tau biomarkers in relation to survival outcomes, underscoring the broader role of biomarker interpretation in neurodegenerative and related syndromes.

In oncology, biomarker discovery remained a major theme. Studies in hepatocellular carcinoma noted that reliable biomarkers are still lacking for immunotherapy selection, while biomarker analysis in recurrent/metastatic adenoid cystic carcinoma aimed to identify biomarkers predictive of clinical benefit from axitinib plus avelumab. In muscle-invasive bladder cancer, identifying novel biomarkers was described as crucial for improving prognosis and optimizing treatment strategies. In lung cancer, smoking-dependent circulating non-coding biomarkers were investigated to clarify when biomarkers are informative in a disease context. Reviews on cancer dormancy and lineage plasticity also emphasized that biomarkers and biomarker-guided therapies remain incompletely implemented. In addition, a bibliometric analysis of oncolytic virus therapy for hepatocellular carcinoma highlighted biomarker-based personalization, including immune cell subsets.

Cardiovascular and inflammatory phenotypes were another major area. A comparative evaluation study in people with HIV used recursive feature addition to refine biomarker-based clustering of cardiovascular inflammatory phenotypes, aiming to determine which biomarker combinations best predict clinical outcomes. Another study identified mitochondrial signature biomarkers in atherosclerotic tissues and blood using combined single-cell and bulk RNA sequencing, linking these markers to atherosclerosis biology. Testosterone-related biomarker clustering was also used to define high-risk aging profiles associated with inflammation and renal function.

In immune-mediated and reproductive disorders, biomarker discovery was similarly prominent. In pregnant women with inflammatory bowel disease, blood-based biomarkers were assessed for clinical utility because clinicians lack reliable minimally invasive tools to monitor disease activity and pregnancy-related risk. In recurrent pregnancy loss, integrated proteomic and transcriptomic analysis of endometrium was used to identify biomarkers relevant to diagnosis and treatment. In psoriasis, machine learning methods including LASSO regression, random forest, and artificial neural network approaches were used to screen diagnostic biomarkers linked to cell-type-specific causal effects in immune cell populations.

Other studies emphasized the technical and translational challenges of biomarker development. Urinary proteomics was described as a rapidly advancing platform for identifying non-invasive biomarkers and monitoring disease. Dried blood spot sampling was evaluated as a scalable and accessible alternative to conventional blood collection for proteomics and metabolomics profiling in clinical trials, supporting novel biomarker discovery. In atherosclerosis, mitochondrial biomarkers were derived from combined single-cell and bulk RNA sequencing. In osteoporosis, plasma exosomal miR-106b-5p was investigated as a novel diagnostic biomarker and therapeutic target. In uterine fibroids, a systematic review of multi-omics approaches concluded that clinically validated biomarkers for stratification, monitoring, or therapeutic targeting are still lacking.

Across these studies, diagnostic biomarkers were repeatedly framed as tools for early detection, disease classification, monitoring, and risk stratification. The recurring themes were heterogeneity of disease, need for minimally invasive sampling, and the use of computational tools, artificial intelligence, and multi-omics approaches to improve biomarker robustness and clinical relevance.

Key Publications

• Jun Identification of Novel Diagnostic Biomarkers and Host-Directed Drug Screening for Mycobacterium avium Infection: A Multi-Omics and Artificial Intelligence Study. (SLAS discovery : advancing life sciences R & D, 2026, PMID 42276326): "Identification of Novel Diagnostic Biomarkers and Host-Directed Drug Screening for Mycobacterium avium Infection: A Multi-Omics and Artificial Intelligence Study."
• Jun Exploring the gut-lung axis in post-liver transplant acute lung injury: A multi-omics approach. (Acta microbiologica et immunologica Hungarica, 2026, PMID 42258310): "Gut dysbiosis modulates immune gene expression and lung inflammation, suggesting that the microbiome serves as a potential source of diagnostic biomarkers and therapeutic targets in transplant-associated lung injury."
• May Identification of Urinary Exosomal microRNAs Associated With Subclinical Acute T-Cell-Mediated Rejection in Kidney Transplant Recipients. (Clinical and translational science, 2026, PMID 42067915): "Collectively, these findings suggest that urinary exosomal miR-7975 may serve as a promising noninvasive biomarker for diagnosing and monitoring sc-TCMR, offering valuable insights for future research and clinical applications."
• May Exploring Metabolic Changes in Children with Congenital Hypothyroidism: A Serum Metabolomic Study Combined by Machine Learning. (Journal of proteome research, 2026, PMID 41973905): "These findings suggest that the identified metabolites can be used as potential diagnostic biomarkers for CH in children."
• May Correlation Between Chemerin Levels and Diagnosis of Diabetic Nephropathy: A Case-Control Study. (La Clinica terapeutica, 2026, PMID 42047143): "Chemerin, an adipokine involved in inflammation and endothelial dysfunction, may serve as an early biomarker for DN."
• May Plasma Exosomal miR-106b-5p Is Associated With Osteoporosis by Targeting SMAD5, BMP2, and MAPK1 Genes. (In vivo (Athens, Greece), 2026, PMID 42049413): "This study aimed to explore the expression, function, and regulatory mechanism of exosomal miR-106b-5p in osteoporosis (OP), to identify a novel diagnostic biomarkers and therapeutic targets."
• May Identifying diagnostic biomarkers in functional motor disorders through multimodal behavioral, neurophysiological, and imaging assessment using explainable machine learning. (Journal of neurology, 2026, PMID 42060125): "The lack of reliable diagnostic biomarkers and their heterogeneity might affect diagnosis."
• May Outcome associations of CSF total tau in suspected non-Alzheimer pathophysiology. (Journal of neurology, 2026, PMID 42060129): "...associations of amyloid and tau biomarkers with survival outcomes in the SNAP context remain unclear."
• Jan Implications of acute change in estimated Glomerular Filtration Rate (eGFR) for the effect of sodium-glucose cotransporter-2 inhibitors (SGLT-2i) on long-term endpoints. (PloS one, 2026, PMID 42054403): "Some treatments also lead to early changes in a biomarker that is prognostic for the clinical endpoint, prompting investigators to explore how these acute biomarker changes might inform the treatment's effect on long-term clinical outcomes."
• Apr Refining biomarker-based clustering of cardiovascular inflammatory phenotypes in HIV using Recursive Feature Addition: A comparative evaluation approach. (PLoS computational biology, 2026, PMID 42044089): "While prior studies have identified inflammatory biomarker patterns linked to CVD in people with HIV, it remains unclear which combinations of biomarkers most effectively predict clinical outcomes."