INTERGENIC INTERACTIONS OF TUMOR NECROSIS FACTOR ALPHA, INTERLEUKIN 17, AND OSTEOPROTEGERIN IN THE IMMUNOPATHOGENESIS OF RHEUMATOID ARTHRITIS IN THE RUSSIAN POPULATION OF THE CHELYABINSK REGION

Abstract

Rheumatoid arthritis (RA) is a chronic systemic autoimmune disease that predominantly affects small joints, causing persistent pain, functional impairment, and a marked reduction in patients’ quality of life. The pathological process is characterized by ongoing synovial inflammation, destruction of cartilage and subchondral bone, and extra-articular manifestations involving the cardiovascular, pulmonary, and nervous systems. A key pathogenetic element is the imbalance between pro- and anti-inflammatory mediators, among which tumour necrosis factor-α (TNFα), interleukins IL-17A and IL-17F, and osteoprotegerin (TNFRSF11B), a regulator of osteoclast differentiation, play central roles.

The present study aimed to assess the contribution of polymorphisms in the TNFA, IL-17A, IL-17F, and TNFRSF11B genes to individual susceptibility to RA in the Russian population of the Chelyabinsk Region. We hypothesised that the major genetic impact on disease development derives not from single nucleotide variations in isolation but from their combined multilocus configurations. Consequently, special attention was given to inter-genic interactions, which are often under-appreciated in conventional association studies.

Genotyping was performed by polymerase chain reaction (PCR). To analyse the data we applied the multifactor dimensionality reduction (MDR) algorithm, which constructs predictive case–control models and evaluates their robustness through ten-fold cross-validation and permutation testing.

The algorithm identified three most informative combinations comprising four to six SNPs each; every combination showed statistical significance and high predictive accuracy. Cross-validation consistency values exceeded 9/10, indicating excellent reproducibility of the models.

These findings confirm that a comprehensive multilocus genotype analysis is more informative than examining individual markers alone and can be used for patient risk stratification, early diagnosis, and the development of personalised preventive strategies based on targeted anti-cytokine therapies. Further studies in larger cohorts are needed to validate these results.

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