Identification of immune-associated genes in diagnosing osteoarthritis with metabolic syndrome by integrated bioinformatics analysis and machine learning

Front Immunol. 2023 Apr 17:14:1134412. doi: 10.3389/fimmu.2023.1134412. eCollection 2023.

Abstract

Background: In the pathogenesis of osteoarthritis (OA) and metabolic syndrome (MetS), the immune system plays a particularly important role. The purpose of this study was to find key diagnostic candidate genes in OA patients who also had metabolic syndrome.

Methods: We searched the Gene Expression Omnibus (GEO) database for three OA and one MetS dataset. Limma, weighted gene co-expression network analysis (WGCNA), and machine learning algorithms were used to identify and analyze the immune genes associated with OA and MetS. They were evaluated using nomograms and receiver operating characteristic (ROC) curves, and finally, immune cells dysregulated in OA were investigated using immune infiltration analysis.

Results: After Limma analysis, the integrated OA dataset yielded 2263 DEGs, and the MetS dataset yielded the most relevant module containing 691 genes after WGCNA, with a total of 82 intersections between the two. The immune-related genes were mostly enriched in the enrichment analysis, and the immune infiltration analysis revealed an imbalance in multiple immune cells. Further machine learning screening yielded eight core genes that were evaluated by nomogram and diagnostic value and found to have a high diagnostic value (area under the curve from 0.82 to 0.96).

Conclusion: Eight immune-related core genes were identified (FZD7, IRAK3, KDELR3, PHC2, RHOB, RNF170, SOX13, and ZKSCAN4), and a nomogram for the diagnosis of OA and MetS was established. This research could lead to the identification of potential peripheral blood diagnostic candidate genes for MetS patients who also suffer from OA.

Keywords: differentially expressed genes; immune infiltration; machine learning; metabolic syndrome; osteoarthritis.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Autoantigens
  • Computational Biology
  • Humans
  • Machine Learning
  • Metabolic Syndrome* / diagnosis
  • Metabolic Syndrome* / genetics
  • Osteoarthritis* / diagnosis
  • Osteoarthritis* / genetics
  • SOXD Transcription Factors
  • Ubiquitin-Protein Ligases

Substances

  • SOX13 protein, human
  • Autoantigens
  • SOXD Transcription Factors
  • RNF170 protein, human
  • Ubiquitin-Protein Ligases

Grants and funding

The State Administration of Traditional Chinese Medicine Construction Project of Inheritance Studio of National Famous Traditional Chinese Medicine Experts (No. 2021-2024). Youth Qihuang Scholar Support Project of the State Administration of Troditional Chinese Medicine (No. 2021-2023). Key Laboratory of Basic and Clinical Research of Osteonecrosis in Heilongjiang Province.