{"database":"iProX","file_versions":[],"scores":null,"additional":{"omics_type":["Proteomics"],"submitter":["Fen Li"],"species":["Homo Sapiens"],"full_dataset_link":["http://www.iprox.org/page/project.html?id=IPX0015384000"],"submitter_email":["lifen0731@csu.edu.cn"],"submitter_affiliation":["The Second Xiangya Hospital of Central South University"],"sample_protocol":[""],"repository":["iProX"],"data_protocol":[""],"pubmed_abstract":["<h4>Background</h4>The pathogenesis of systemic lupus erythematosus (SLE) is closely associated with abnormal activation of B lymphocytes. Telitacicept simultaneously blocks B-cell stimulating factors and proliferation-inducing ligands, thereby inhibiting B-cell proliferation and differentiation, demonstrating favorable therapeutic efficacy in the majority of SLE patients. However, there is a lack of reliable biomarkers of efficacy and systematic elucidation of its mechanism of action.<h4>Methods</h4>The study employed proteomics and metabolomics analysis to explore biomarkers and mechanisms underlying therapeutic response variability to Telitacicept in SLE patients. Twenty-five SLE patients were enrolled and divided into the responder group and non-responder group based on the SLE Response Index 4 to identify key proteins, metabolites, and mechanisms associated with treatment response.<h4>Results</h4>Proteomics results revealed XPNPEP3, SRSF5, SRSF6, WARS1, IDH1, and ITLN1 as protein biomarkers correlated with Telitacicept efficacy in SLE patients. Metabolomics results indicated that pyruvate was a potential metabolic biomarker for responder group, while gamma-aminobutyric acid (GABA) was a potential biomarker for non-responder group. The combined analysis revealed that both pyruvate and IDH1 participate in the citric acid cycle. GABA showed a negative correlation with XPNPEP3.<h4>Conclusions</h4>The above results reveal biomarkers related to the differential efficacy of Telitacicept in treating SLE patients and potential mechanisms underlying these differences, which may provide a reference for personalized treatment and mechanistic research in SLE."],"pubmed_title":["Unveiling biomarkers of telitacicept's efficacy in SLE treatment through proteomics and metabolomics."],"pubmed_authors":["Nie Huiyu H, Chang Siyuan S, Chen Hanhan H, Shi Jiahui J, Li Shu S, Peng Xiaofei X, Cheng Wei W, Wang Jia J, Tang Qi Q, Ge Yan Y, Xie Xi X, Li Fen F"],"additional_accession":[]},"is_claimable":false,"name":"Unveiling Biomarkers of Telitacicept’s Efficacy in SLE Treatment  Through Proteomics","description":"Our study is highly relevant to the fields of Medical Bioinformatics and Personalised Medicine, as it  employs high-throughput omics technologies and bioinformatics analyses to uncover personalized  biomarkers predictive of treatment response, thereby paving the way for tailored therapeutic strategies  in SLE management. By  proteomic datasets, we provide a comprehensive  view of the molecular alterations associated with Telitacicept treatment, offering novel insights into the  pathogenesis of SLE and the mechanisms of action of this promising biologic agent.","dates":{"publication":"Mon Feb 02 00:00:00 GMT 2026"},"accession":"PXD073923","cross_references":{"TAXONOMY":["9606"],"pubmed":["41859090"]}}