Identification of four-gene signature to diagnose osteoarthritis through bioinformatics and machine learning methods

Ziyi Chen; Wenjuan Wang; Yuwen Zhang; Xiao'ao Xue; Yinghui Hua

doi:10.1016/j.cyto.2023.156300

Identification of four-gene signature to diagnose osteoarthritis through bioinformatics and machine learning methods

Cytokine. 2023 Sep:169:156300. doi: 10.1016/j.cyto.2023.156300. Epub 2023 Jul 14.

Authors

Ziyi Chen¹, Wenjuan Wang¹, Yuwen Zhang², Xiao'ao Xue¹, Yinghui Hua³

Affiliations

¹ Department of Sports Medicine, Huashan Hospital, Fudan University, Shanghai, China.
² Department of Sports Medicine, Huashan Hospital, Fudan University, Shanghai, China; Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.
³ Department of Sports Medicine, Huashan Hospital, Fudan University, Shanghai, China. Electronic address: hua_cosm@aliyun.com.

PMID: 37454542
DOI: 10.1016/j.cyto.2023.156300

Abstract

Background: Although osteoarthritis (OA) is one of the most prevalent joint disorders, effective biomarkers to diagnose OA are still unavailable. This study aimed to acquire some key synovial biomarkers (hub genes) and analyze their correlation with immune infiltration in OA.

Methods: Gene expression profiles and clinical characteristics of OA and healthy synovial samples were retrieved from the Gene Expression Omnibus (GEO) database. Hub genes for OA were mined based on a combination of weighted gene co-expression network analysis (WGCNA), the least absolute shrinkage and selection operator (LASSO), support vector machine recursive feature elimination (SVM-RFE), and random forest (RF) algorithms. A diagnostic nomogram model for OA prediction was developed based on the hub genes. Receiver operating characteristic curves (ROC) were performed to confirm the abnormal expression of hub genes in the experimemtal and validation datasets. qRT-PCR using patients' samples were conducted as well. In addition, the infiltration level of 28 immune cells in the expression profile and their relationship with hub genes were analyzed using single-sample GSEA (ssGSEA).

Results: 4 hub genes (ZBTB16, TNFSF11, SCRG1 and KDELR3) were obtained by WGCNA, lasso, SVM-RFE, RF algorithms as potential biomarkers for OA. The immune infiltration analyses revealed that hub genes were most correlated with regulatory T cell and natural killer cell.

Conclusion: A machine learning model to diagnose OA based on ZBTB16, TNFSF11, SCRG1 and KDELR3 using synovial tissue was constructed, providing theoretical foundation and guideline for diagnostic and treatment targets in OA.

Keywords: Diagnostic biomarker; Immune cell infiltration; Machine learning; Osteoarthritis.

Publication types

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

MeSH terms

Computational Biology
Databases, Factual
Gene Expression Profiling
Humans
Machine Learning
Osteoarthritis* / diagnosis
Osteoarthritis* / genetics