Applying 12 machine learning algorithms and Non-negative Matrix Factorization for robust prediction of lupus nephritis

Front Immunol. 2024 Aug 19:15:1391218. doi: 10.3389/fimmu.2024.1391218. eCollection 2024.

Abstract

Lupus nephritis (LN) is a challenging condition with limited diagnostic and treatment options. In this study, we applied 12 distinct machine learning algorithms along with Non-negative Matrix Factorization (NMF) to analyze single-cell datasets from kidney biopsies, aiming to provide a comprehensive profile of LN. Through this analysis, we identified various immune cell populations and their roles in LN progression and constructed 102 machine learning-based immune-related gene (IRG) predictive models. The most effective models demonstrated high predictive accuracy, evidenced by Area Under the Curve (AUC) values, and were further validated in external cohorts. These models highlight six hub IRGs (CD14, CYBB, IFNGR1, IL1B, MSR1, and PLAUR) as key diagnostic markers for LN, showing remarkable diagnostic performance in both renal and peripheral blood cohorts, thus offering a novel approach for noninvasive LN diagnosis. Further clinical correlation analysis revealed that expressions of IFNGR1, PLAUR, and CYBB were negatively correlated with the glomerular filtration rate (GFR), while CYBB also positively correlated with proteinuria and serum creatinine levels, highlighting their roles in LN pathophysiology. Additionally, protein-protein interaction (PPI) analysis revealed significant networks involving hub IRGs, emphasizing the importance of the interleukin family and chemokines in LN pathogenesis. This study highlights the potential of integrating advanced genomic tools and machine learning algorithms to improve diagnosis and personalize management of complex autoimmune diseases like LN.

Keywords: NMF; PPI; immune-related genes; lupus nephritis; machine learning; prediction model; scRNA-seq; systemic lupus erythematosus.

MeSH terms

  • Adult
  • Algorithms*
  • Biomarkers
  • Computational Biology / methods
  • Female
  • Gene Expression Profiling
  • Humans
  • Lupus Nephritis* / diagnosis
  • Lupus Nephritis* / immunology
  • Machine Learning*
  • Male
  • Protein Interaction Maps
  • Single-Cell Analysis / methods

Substances

  • Biomarkers

Grants and funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This research was funded by The Shenzhen Science and Technology Program (grant numbers JCYJ20200109140412476, JCYJ20190809095811254, GCZX2015043017281705), Team-based Medical Science Research Program (2024YZZ06), Shenzhen High-level Hospital Construction Fund (2024), the Clinical Research Project in Shenzhen (grant numbers 20213357002 and 20213357028).