Platelets-related signature based diagnostic model in rheumatoid arthritis using WGCNA and machine learning

Yuchen Liu; Haixu Jiang; Tianlun Kang; Xiaojun Shi; Xiaoping Liu; Chen Li; Xiujuan Hou; Meiling Li

doi:10.3389/fimmu.2023.1204652

Platelets-related signature based diagnostic model in rheumatoid arthritis using WGCNA and machine learning

Front Immunol. 2023 Jun 23:14:1204652. doi: 10.3389/fimmu.2023.1204652. eCollection 2023.

Authors

Yuchen Liu^{1

2}, Haixu Jiang³, Tianlun Kang³, Xiaojun Shi³, Xiaoping Liu³, Chen Li^{2

4}, Xiujuan Hou³, Meiling Li^{5

6}

Affiliations

¹ School of Clinical Medicine, Peking Union Medical College, Beijing, China.
² Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
³ Department of Rheumatology, Dongfang Hospital, Beijing University of Chinese Medicine, Beijing, China.
⁴ Department of Rheumatology, Fangshan Hospital Beijing University of Chinese Medicine, Beijing, China.
⁵ Department of Rheumatology, Fuyang Hospital of Anhui Medical University, Fuyang, Anhui, China.
⁶ Department of Rheumatology, The Second Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China.

Abstract

Background and aim: Rheumatoid arthritis (RA) is an autoinflammatory disease that may lead to severe disability. The diagnosis of RA is limited due to the need for biomarkers with both reliability and efficiency. Platelets are deeply involved in the pathogenesis of RA. Our study aims to identify the underlying mechanism and screening for related biomarkers.

Methods: We obtained two microarray datasets (GSE93272 and GSE17755) from the GEO database. We performed Weighted correlation network analysis (WGCNA) to analyze the expression modules in differentially expressed genes identified from GSE93272. We used KEGG, GO and GSEA enrichment analysis to elucidate the platelets-relating signatures (PRS). We then used the LASSO algorithm to develop a diagnostic model. We then used GSE17755 as a validation cohort to assess the diagnostic performance by operating Receiver Operating Curve (ROC).

Results: The application of WGCNA resulted in the identification of 11 distinct co-expression modules. Notably, Module 2 exhibited a prominent association with platelets among the differentially expressed genes (DEGs) analyzed. Furthermore, a predictive model consisting of six genes (MAPK3, ACTB, ACTG1, VAV2, PTPN6, and ACTN1) was constructed using LASSO coefficients. The resultant PRS model demonstrated excellent diagnostic accuracy in both cohorts, as evidenced by area under the curve (AUC) values of 0.801 and 0.979.

Conclusion: We elucidated the PRSs occurred in the pathogenesis of RA and developed a diagnostic model with excellent diagnostic potential.

Keywords: bioinformatics analysis; diagnostic model; machine learning (ML); platelet; rheumatoid arthritis.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Algorithms
Arthritis, Rheumatoid* / diagnosis
Arthritis, Rheumatoid* / genetics
Blood Platelets*
Humans
Machine Learning
Reproducibility of Results

Grants and funding

This work was supported by the Beijing Fengtai District Key Clinical Specialties (Rheumatology) Project and Guangxi Medical and health appropriate technology application project of development and promotion, (S2021116)and Research project funded by the Administration of Traditional Chinese Medicine of Guangxi Zhuang Autonomous Region(GXZYA2022024).