Dataset Information

Tandem Mass Tag (TMT)-coupled LC-MS/MS in OA and RA patients synovial tissues

ABSTRACT: All the synovial tissue specimens for TMT relative quantitative proteomics and further experiments were obtained from the patients with RA or OA undergoing surgical joint replacement at the clinical of joint surgery (Xi'an Hong Hui Hospital, Xi'an Jiaotong University, China). The diagnosis of the patients were accorded to the criteria of the American College of Rheumatology (ACR) and European League Against Rheumatism (EULAR) in 2010.a quantitative proteomic profiling of synovial tissue obtained from RA and OA patients was carried out by using TMT labeling followed by high resolution mass spectrometry analysis. We have identified 4822 proteins out of which 510 proteins were found to be differentially expressed by ≥1.2 fold change in the synovial tissue from RA verses OA patients.

INSTRUMENT(S):

ORGANISM(S): Homo Sapiens (human)

TISSUE(S): Fibroblast

SUBMITTER: Peng Xu

LAB HEAD: Peng Xu

PROVIDER: PXD027703 | Pride | 2021-12-30

REPOSITORIES: Pride

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Publications

Quantitative Proteomic Analysis of Synovial Tissue Reveals That Upregulated OLFM4 Aggravates Inflammation in Rheumatoid Arthritis.

Ren Xiaoyu X Geng Manman M Xu Ke K Lu Chao C Cheng Yuanyuan Y Kong Linbo L Cai Yongsong Y Hou Weikun W Lu Yufeng Y Aihaiti Yirixiati Y Xu Peng P

Journal of proteome research 20210909 10

Tandem mass tag (TMT)-coupled liquid chromatography coupled with tandem mass spectrometry is a powerful method to investigate synovial tissue protein profiles in patients with rheumatoid arthritis (RA) and osteoarthritis (OA). Protein was isolated from synovial tissue samples of 22 patients and labeled with a TMT kit. Over 500 proteins were identified as the differential expression protein on comparing RA and OA synovial tissue, including 239 upregulated and 271 downregulated proteins. Data are ...[more]

PMID: 34496567

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Project description:Discrimination of rheumatoid arthritis (RA) patients from patients with other inflammatory/degenerative joint diseases or healthy individuals purely on the basis of genes differentially expressed in high-throughput data has proven very difficult. Thus, the present study sought to achieve such discrimination by employing a novel unbiased approach using rule-based classifiers. Three multi-center genome-wide transcriptomic data sets (Affymetrix HG- U133 A/B) from a total of 79 individuals, including 20 healthy controls (control group - CG), as well as 26 osteoarthritis (OA) and 33 RA patients, were used to infer rule- based classifiers to discriminate the disease groups. The rules were ranked with respect to Kiendl’s statistical relevance index, and the resulting rule set was optimized by pruning. The rule sets were inferred separately from data of one of three centers and applied to the two remaining centers for validation. All rules from the optimized rule sets of all centers were used to analyze their biological relevance applying the software Pathway Studio. The optimized rule sets for the three centers contained a total of 29, 20, and 8 rules (including 10, 8, and 4 rules for ‘RA’), respectively. The mean sensitivity for the prediction of RA based on six center-to-center tests was 96% (range 90% to 100%), that for OA 86% (range 40% to 100%). The mean specificity for RA prediction was 94% (range 80% to 100%), that for OA 96% (range 83.3% to 100%). The average overall accuracy of the three different rule-based classifiers was 91% (range 80% to 100%). Unbiased analyses by Pathway Studio of the gene sets obtained by discrimination of RA from OA and CG with rule-based classifiers resulted in the identification of the pathogenetically and/or therapeutically relevant interferon-gamma and GM-CSF pathways. First-time application of rule-based classifiers for the discrimination of RA resulted in high performance, with means for all assessment parameters close to or higher than 90%. In addition, this unbiased, new approach resulted in the identification not only of pathways known to be critical to RA, but also of novel molecules such as serine/threonine kinase 10. Synovial tisssue from healthy joints, OA joints, and RA joints.

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