A scoping review on emerging biomarkers in inflammatory bowel disease: Towards precision medicine in diagnosis and therapeutic management

Inflammatory Biomarkers for Assessing Treatment Response in Psoriasis: A Narrative Review

Psoriasis vulgaris is a chronic immune-mediated inflammatory disease in which treatment response is assessed using clinical indices such as the Psoriasis Area and Severity Index (PASI) and Dermatology Life Quality Index (DLQI), despite their limited ability to capture systemic inflammation and underlying immunological activity. This narrative review aims to summarize current evidence on inflammatory biomarkers for monitoring treatment response and to evaluate their potential clinical utility. A structured, non-systematic literature search was performed in April–May 2026 across PubMed, Cochrane Library, Scopus, and ClinicalTrials.gov, focusing primarily on literature published during the preceding 10 years, with selected earlier studies being retained when directly relevant. Emerging data indicate that multiple biomarker domains may reflect therapeutic outcomes, including cytokines and chemokines, acute-phase proteins, complete-blood-count (CBC)-derived inflammatory indices, genetic markers, micro(mi)RNAs, metabolomic and lipidomic profiles, and tissue-based markers. These biomarkers may serve as severity-associated, baseline-predictive, pharmacodynamic, or prognostic markers, and these roles should not be interpreted interchangeably. Nevertheless, discrepancies between biomarker dynamics and clinical improvement occur, reflecting the partial dissociation between local and systemic inflammation, disease heterogeneity, and differences in response kinetics. Although inflammatory biomarkers provide a biologically grounded framework for assessing treatment response, their clinical implementation remains limited. Further large-scale, standardized studies are required to validate candidate markers and support the development of integrated, multi-omics approaches for personalized management.

Immunoediting and precision biomarkers in prostate cancer: The emerging role of liquid biopsy, immune contexture and HLA genotype (Review)

Prostate cancer (PCa) remains a leading cause of cancer-related mortality in men despite advances in screening and localized treatment. The clinical heterogeneity of PCa, ranging from indolent disease to aggressive, lethal phenotypes, underscores the urgent need for reliable biomarkers that improve diagnosis, prognostication and therapeutic decision-making. While prostate-specific antigen testing has reduced mortality, its limited specificity has resulted in overdiagnosis and overtreatment. The present review provides a comprehensive overview of contemporary and emerging biomarkers that support a precision-medicine approach to PCa management. The present review summarizes established and novel diagnostic, prognostic and predictive biomarkers, including serum- and urine-based assays, genomic and transcriptomic signatures and multiparametric imaging. Particular emphasis is placed on liquid biopsy technologies (circulating tumor cells, circulating tumor DNA and extracellular vesicles), which offer minimally invasive, real-time insights into tumor burden, molecular evolution and treatment resistance, although their clinical implementation remains context-dependent and is currently most established in advanced disease settings rather than routine early-stage management. The present review discusses the strengths and limitations of these platforms, highlighting disease-stage dependency, technical variability and sensitivity constraints. Beyond tumor-intrinsic markers, tissue-based immune biomarkers that capture the tumor immune microenvironment, including immune cell density, spatial organization, checkpoint expression and immune-related gene signatures, are explored. Evidence indicates that 'immune-hot' tumors characterized by CD8+ T-cell infiltration and interferon-γ signaling are associated with improved outcomes, whereas immunosuppressive macrophage- or regulatory T-cell-dominant profiles predict poor prognosis. However, these associations are not uniform across studies and PCa remains largely resistant to immunotherapy, underscoring the need to improve the understanding of immune evasion mechanisms and the contextual limitations of immune biomarkers. Furthermore, the present review examines the emerging role of germline human leukocyte antigen class I genotype as a prognostic and predictive biomarker, explicitly integrating it with tissue-based immune contexture and liquid biopsy readouts by proposing immunoediting as a unifying mechanistic framework that links allele-specific antigen presentation to immune infiltration. Finally, the present review highlights the prognostic significance of preexisting tumor-antigen-specific CD8+ T cells, which reflect an active antitumor immune response and predict a favorable progression-free survival and responsiveness to immunotherapeutic strategies. Collectively, the present review underscores the need for standardized, multimodal biomarker integration and prospective validation to enable personalized, immune-aware management of PCa.

Precision periodontology in clinical practice: bridging omics and clinical decision-making

AI/ML-Enabled Multi-Omics Integration of Host Genetics, Immunity, and the Gut Microbiome in Crohn's Disease: From Diagnosis to Theranostics.

Crohn's disease is a long-term inflammatory disorder arising from the interaction of genetic risk factors, immune system dysfunction, and alterations in gut microbiota. Variability in clinical phenotypes and lack of biomarker specificity hinder the efficiency of current traditional diagnostic and treatment approaches. This review aims to assess how AI- and ML-driven multi-omics offer comprehensive insights into pathogenicity, thereby enhancing diagnostic techniques and personalized therapeutic approaches in CD. Current studies employ integration of multi-omics like genomics, proteomics, transcriptomics, metabolomics, and microbiome analysis in CD with AI and ML for significant advancement of biomarker discovery and clinical applications. Emerging evidence reveals that CD is a multi-factorial disorder involving host genetics, immune dysfunction, and microbiome shifts. Integration of advanced AI/ML models with multi-omics data can predict disease-specific biomarkers for easy diagnosis and facilitate precision medicine to enhance therapies. For a successful clinical implementation of an AI/ML model with multi-omics in CD, a standardized data framework and large-scale validation are needed. Additionally, future research should focus on developing interpretable AI models, real-time monitoring systems, and theranostic platforms to enhance precision healthcare delivery.

Biomarkers of treatment response in rheumatoid arthritis: from conventional markers to tissue immunophenotype

Rheumatoid arthritis (RA) is a biologically heterogeneous immune-mediated disease characterized by substantial variability in therapeutic response. Despite the availability of multiple conventional synthetic, biologic, and targeted synthetic disease-modifying antirheumatic drugs (DMARDs), many patients fail to achieve adequate disease control or experience secondary loss of efficacy, underscoring the need for predictive biomarkers that can guide treatment selection. This narrative review was based on a structured literature search of PubMed/MEDLINE, Scopus, Web of Science, and Google Scholar, covering publications from January 2000 to June 2026, with earlier landmark studies included when relevant. Literature selection followed PRISMA-informed principles, although the review was not designed as a formal systematic review. Unlike previous reviews that mainly catalogue RA biomarkers by analytical platform, drug class, or clinical use, this review integrates conventional and emerging biomarkers within a tissue-immunophenotype-centered framework. We critically evaluate clinical, serological, pharmacological, molecular, imaging, and tissue-based biomarkers according to biological plausibility, reproducibility, level of validation, clinical actionability, and translational readiness. Established markers such as rheumatoid factor, anti-citrullinated protein antibodies, acute-phase reactants, drug concentrations, and anti-drug antibodies remain clinically useful but provide incomplete insight into mechanism-specific therapeutic response. In contrast, synovial pathotypes, fibroblast and macrophage subsets, B-cell niches, tertiary lymphoid structures, single-cell and spatial omics, and ligand–receptor interaction networks offer a mechanistically richer view of treatment response and resistance. We conclude that precision medicine in RA will require integrated biomarker panels combining clinical, pharmacological, molecular, and synovial tissue data. The key future direction is the development of scalable, externally validated, and clinically interpretable models capable of assigning synovial endotypes and supporting mechanism-based therapeutic selection.

Predictive biomarkers for immunotherapy response in renal cell carcinoma: why have they failed?

PURPOSE OF REVIEW The heterogeneity of response to immunotherapies in renal cell carcinoma has created a strong need for predictive biomarkers to guide treatment decisions. However, no robust predictive biomarker has been adopted in clinical practice. This review aims to outline current limitations in the development of consistent predictive biomarkers. RECENT FINDINGS Current biomarkers, including programmed cell death 1 ligand 1 expression, tumor mutational burden, blood-based biomarkers, and histopathological and molecular features, are primarily prognostic rather than predictive. Spatial and temporal tumor heterogeneity, methodological limitations, and trial design constraints are increasingly recognized as key factors limiting progress in the field. In addition, combination therapies further complicate biomarker interpretation. To better reflect the complexity and dynamic nature of renal cell carcinoma, biomarker development is increasingly exploring multiomics approaches, longitudinal assessments, and artificial intelligence-driven composite models. SUMMARY Establishing predictive biomarkers in clinical practice has not yet succeeded due to biological complexity, methodological and trial design limitations, and lack of sufficient validation. Single-biomarker approaches are insufficient to capture spatial and temporal heterogeneity and the impact of combination therapies. Future progress will require biomarker-stratified prospective trial designs with prespecified endpoints and rigorous external validation to establish clinically meaningful biomarkers.