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ORIGINAL RESEARCH article

Front. Immunol.
Sec. Autoimmune and Autoinflammatory Disorders : Autoimmune Disorders
Volume 15 - 2024 | doi: 10.3389/fimmu.2024.1391218
This article is part of the Research Topic Community Series in Emerging Insights in Controlling Autoimmunity: Volume II View all 5 articles

Applying 12 Machine Learning Algorithms and Non-negative Matrix Factorization for Robust Prediction of Lupus Nephritis

Provisionally accepted
Lisha Mou Lisha Mou 1Ying Lu Ying Lu 2Zijing Wu Zijing Wu 3Zuhui Pu Zuhui Pu 3*
  • 1 Shenzhen Second People’s Hospital, Shenzhen, China
  • 2 Department of Biology, Skidmore College, New York, American Samoa
  • 3 Shenzhen Second People's Hospital, Shenzhen, Guangdong Province, China

The final, formatted version of the article will be published soon.

    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: Lupus Nephritis, ScRNA-seq, immune-related genes, NMF, machine learning, Prediction model, PPI

    Received: 25 Feb 2024; Accepted: 23 Jul 2024.

    Copyright: © 2024 Mou, Lu, Wu and Pu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Zuhui Pu, Shenzhen Second People's Hospital, Shenzhen, Guangdong Province, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.