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V. Bogdanov

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Review Open access Jul 2026

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.

N. A. Batashkov, E. Gerasimova, D. Gerasimova et al. · 0 citations
Open access Aug 2026

Expanding the Genomic Spectrum of NHLRC2-Associated FINCA Disease: Integrated Bioinformatic Characterization of a Novel Deep Intronic Variant Predicted to Activate a Pseudoexon

NHLRC2-associated FINCA disease is an ultra-rare autosomal recessive multisystem disorder caused by biallelic pathogenic variants in NHLRC2. Its mutational spectrum and genotype–phenotype correlations remain incompletely defined, and the contribution of non-coding variants is poorly understood. Here, we report a male infant with a severe FINCA-like phenotype, including early-onset hemolytic anemia, pulmonary involvement, neurodevelopmental impairment, growth failure, recurrent infections, and fatal progression at 8.5 months. Whole-genome sequencing identified a compound heterozygous NHLRC2 genotype comprising the previously reported pathogenic missense variant c.442G>T (p.Asp148Tyr) and a novel deep intronic variant, c.331+6863A>G. Segregation analysis confirmed inheritance from different parents. Integrated genomic and splicing analysis predicted that c.331+6863A>G creates a strong cryptic donor splice site and supports pseudoexon inclusion. Reconstruction of the predicted aberrant transcript indicated premature termination and potential susceptibility to nonsense-mediated mRNA decay. To our knowledge, this is the first reported deep intronic NHLRC2 variant predicted to activate pseudoexon inclusion. Although experimental validation was unavailable, convergent clinical, segregation, population, and computational evidence supports c.331+6863A>G as the most plausible second disease-associated allele. This case expands the genomic spectrum of NHLRC2-associated FINCA disease and highlights the diagnostic value of phenotype-driven whole-genome sequencing.

A. Rozhkova, Anton Esibov, A. Borkovskaia et al. · 0 citations