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Background <p>Targeted synthetic therapeutics, such as Janus kinase (JAK) inhibitors, have opened up a new platform for the treatment of various diseases, including rheumatoid arthritis (RA). As with all drugs, the efficacy of different medicine may vary from patient to patient, and there may be different side effects if the expected effect is not achieved. In addition to the uncertain success of treatments, they also place a heavy burden on the health care system, making the identification of potential predictive biomarkers for drug optimisation highly valuable.</p> Methods <p>In order to identify potential predictive biomarkers, 28 patients with RA were recruited and blood samples were taken twice–before medical treatment and after 6&#xa0;months of continuous therapy. Peripheral blood mononuclear cells (PBMCs) were isolated from blood samples and after RNA isolation, RNA sequencing was performed using high throughput sequencing technology to generate global gene expression data. Validation of target genes was conducted using real-time quantitative PCR methods.</p> Results <p>After data analyses we examined the gene expression changes between the two sampling time points and responder versus non-responder groups. 225 genes showed significantly different expression between T6 and T0 samples, while 60 and 66 genes showed differential expression between the responder and non-responder patients at T0 and T6 sampling points, respectively. 13 differentially expressed genes were common between two time points and showed the same direction in regulation.</p> Conclusions <p>Based on our results, several RA-relevant genes were identified, as a result of the JAK-inhibitor treatments in comparison of T6 versus T0 samples. At both time points, the differentially expressed genes between responder and non-responder groups could separate the samples, however, the separation was not clear. The identified 13 common genes could also partially separate the responder and non-responder groups from each other. These sets of genes could be the source of potential biomarkers, which could help predict the responsiveness of patients to JAK inhibitor therapy.</p>

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Identification of potential predictive biomarkers during JAK-inhibitor therapies in rheumatoid arthritis

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月23日

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  • János Rózsa,
  • Dóra Csige,
  • Monika Bodoki,
  • Zsófia Hagymási-Szabó,
  • Ferenc Tóth,
  • Szilvia Szamosi,
  • Ágnes Horváth,
  • Nóra Bodnár,
  • Edit Végh,
  • Sándor Szántó,
  • Gabriella Szűcs,
  • Zsófia Pethő,
  • Zsuzsanna Gyetkó,
  • Levente Bodoki,
  • János Kádas,
  • Zoltán Szekanecz,
  • Szilárd Póliska

摘要

Background

Targeted synthetic therapeutics, such as Janus kinase (JAK) inhibitors, have opened up a new platform for the treatment of various diseases, including rheumatoid arthritis (RA). As with all drugs, the efficacy of different medicine may vary from patient to patient, and there may be different side effects if the expected effect is not achieved. In addition to the uncertain success of treatments, they also place a heavy burden on the health care system, making the identification of potential predictive biomarkers for drug optimisation highly valuable.

Methods

In order to identify potential predictive biomarkers, 28 patients with RA were recruited and blood samples were taken twice–before medical treatment and after 6 months of continuous therapy. Peripheral blood mononuclear cells (PBMCs) were isolated from blood samples and after RNA isolation, RNA sequencing was performed using high throughput sequencing technology to generate global gene expression data. Validation of target genes was conducted using real-time quantitative PCR methods.

Results

After data analyses we examined the gene expression changes between the two sampling time points and responder versus non-responder groups. 225 genes showed significantly different expression between T6 and T0 samples, while 60 and 66 genes showed differential expression between the responder and non-responder patients at T0 and T6 sampling points, respectively. 13 differentially expressed genes were common between two time points and showed the same direction in regulation.

Conclusions

Based on our results, several RA-relevant genes were identified, as a result of the JAK-inhibitor treatments in comparison of T6 versus T0 samples. At both time points, the differentially expressed genes between responder and non-responder groups could separate the samples, however, the separation was not clear. The identified 13 common genes could also partially separate the responder and non-responder groups from each other. These sets of genes could be the source of potential biomarkers, which could help predict the responsiveness of patients to JAK inhibitor therapy.