類風濕性關節炎（RA）是常見的一種自身免疫性疾病，其特徵為自體抗體的產生以及關節發炎和腫脹。儘管目前已有多種生物製劑（bioDMARDs）可供RA患者使用，但生物製劑對於許多患者治療效果仍有限。因此，識別不同生物製劑治療的預後因素有助於更高效地使用這些藥物。然而，目前關於這些預後因素的研究仍然有限。因此，在本研究中，我們旨於發掘與RA疾病嚴重程度相關的反應標誌物，並致力於發現與不同生物製劑（包括TNF抑制劑、IL-6抑制劑、CTLA4-Ig和抗CD20療法）有良好預後相關的治療標誌物。我們採用大腸桿菌（E. coli）蛋白質組微陣列作為抗原發現工具，以探索不同RA治療組別中的潛在生物標誌物。我們比較生物製劑治療組與csDMARDs對照組，篩選潛在生物標誌物。接著，分析四個生物製劑治療組的數據，以識別與良好預後相關的標誌物，並整合數據，探索與DAS28得分相關的反應標誌物，評估其與RA嚴重程度的關係。最終，我們將篩選具臨床潛力的標誌物，但仍需進一步驗證與探討。

Rheumatoid arthritis (RA) is an autoimmune disease characterized with joint inflammation and swelling. Although there are a lot of treatment options for RA patients such as bioDMARDs. But there are still many patients who do not respond well to bioDMARDs treatment. Therefore, identifying the prognostic factors of different bioDMARDs can better apply these drugs. However, the current exploration of these prognostic factors is still limited. So, this study aimed to identify response markers related to the severity of RA and is dedicated to discovering treatment markers associated with related to favorable prognoses for different biologic DMARD treatments, including TNF inhibitors, IL-6 inhibitors, CTLA4-Ig, and anti-CD20 therapies. We employed an Escherichia coli (E. coli) proteome microarray as an antigen discovery tool to explore potential biomarkers across various treatment groups for RA. Initially, we compared the bioDMARDs treatment groups to a control group of RA patients who received conventional synthetic DMARDs (csDMARDs). This comparison aimed to observe the differences and establish a threshold for screening potential biomarkers. We then collected candidate treatment markers from the four bioDMARDs groups, analyzing them to identify biomarkers associated with a favorable prognosis for each treatment individually. Subsequently, we integrated data from the four bioDMARDs treatment groups to analyze response markers correlated with Disease Activity Score 28 (DAS28) scores, thereby assessing their relationship with the severity of RA. Ultimately, both analyses allow us to identify potential biomarkers in clinical applications. Further validation and exploration are needed for those biomarkers.

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