Introduction Rheumatoid arthritis (RA) is a chronic, multifactorial autoimmune disease characterized by symmetrical polyarthritis, joint pain, and swelling, which can lead to disability and premature death. Increasing attention has focused on epigenetic mechanisms, including non-coding RNAs in circular and linear forms, in RA pathogenesis. Purpose This study aimed to identify novel supportive RNA-based biomarkers associated with RA disease activity. Patients and methods The discovery cohort included 18 RA patients and 10 healthy controls (HCs). Peripheral blood mononuclear cells were analyzed using targeted RNA sequencing, encompassing both linear (LINOUT) and circular (CIRC) RNA forms, to assess differences between RA patients and HCs, as well as between high (HDA; DAS28 5.1; n = 10) and low disease activity/remission (LDA/REM; DAS28 ≤ 3.2; n = 8) disease activity groups. The results were validated in cohort of 45 patients with RA, divided into a high disease activity group (HDA; DAS28 5.1; n = 22) and a non-high disease activity group (non-HDA; DAS28 ≤ 5.1; n = 23) along with 24 control subjects, using quantitative PCR (qPCR). Results LMTK2 LINOUT expression correlated negatively with disease activity ( r s = -0.30) and distinguished RF-negative patients (n = 17) from HCs (p = 0.027). Expression was significantly lower in the high activity group (n = 22) versus the non-high activity group (n = 23; p = 0.022). Post-hoc ANOVA showed significant differences among HDA, non-HDA, and HCs (p = 0.001), with reduced expression in HDA versus non-HDA (p = 0.038) and increased expression in non-HDA versus controls (p = 0.001). EML6 LINOUT expression exhibited activity-dependent changes (p = 0.034) and positively correlated with disease activity ( r s = 0.303). Integration with erythrocyte sedimentation rate (ESR) improved discriminative performance. Combining LMTK2 LINOUT + EML6 LINOUT + ESR yielded the highest accuracy (AUC = 0.923). Conclusion LMTK2 and EML6 show disease activity-dependent expression in RA and provide complementary information to conventional inflammatory parameters such as ESR. Their integration may improve diagnostic performance, highlighting their potential as novel supportive molecular biomarkers in RA.
Kubis et al. (Wed,) studied this question.