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ORIGINAL RESEARCH article

Front. Immunol.
Sec. Alloimmunity and Transplantation
Volume 15 - 2024 | doi: 10.3389/fimmu.2024.1479166
This article is part of the Research Topic Community Series in The future direction toward immunological issues of allo-and xeno-islet transplantation: Volume II View all articles

Deciphering the Role of Metal Ion Transport-Related Genes in T2D Pathogenesis and Immune Cell Infiltration via scRNA-Seq and Machine Learning

Provisionally accepted
Zuhui Pu Zuhui Pu 1Tony Bowei Wang Tony Bowei Wang 2Ying Lu Ying Lu 1Zijing Wu Zijing Wu 1Yuxian Chen Yuxian Chen 1Ziqi Luo Ziqi Luo 1Xinyu Wang Xinyu Wang 1Lisha Mou Lisha Mou 1*
  • 1 Shenzhen Second People’s Hospital, Shenzhen, China
  • 2 Skidmore College, Saratoga Springs, New York, United States

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

    Introduction: Type 2 diabetes (T2D) is a complex metabolic disorder with significant global health implications. Understanding the molecular mechanisms underlying T2D is crucial for developing effective therapeutic strategies. This study employs single-cell RNA sequencing (scRNA-seq) and machine learning to delve into the pathogenesis of T2D, with a particular focus on immune cell infiltration.We analyzed scRNA-seq data from islet cells of T2D and nondiabetic (ND) patients, identifying differentially expressed genes (DEGs), especially those related to metal ion transport (RMITRGs). We employed 12 machine learning algorithms to develop predictive models and assessed immune cell infiltration using single-sample gene set enrichment analysis (ssGSEA). Correlations between immune cells and key RMITRGs were investigated, and the interactions among these genes were explored through proteinprotein interaction (PPI) network analysis. Additionally, we performed a detailed cell-cell communication analysis to identify significant signaling pathways in T2D.Stepglm[backward] plus GBM model demonstrating high predictive accuracy and identifying 13 hub RMITRGs. Twelve protein structures were predicted using AlphaFold 3, revealing potential functional conformations. We observed a strong correlation between hub RMITRGs and immune cells, and PPI network analysis revealed key interactions.Cell-cell communication analysis highlighted 16 active signaling pathways, with CXCL, MIF, and COMPLEMENT linked to immune and inflammatory responses, and WNT, KIT, LIFR, and HGF pathways uniquely activated in T2D. Discussion: Our analysis identified genes crucial for T2D, emphasizing ion transport, signaling, and immune cell interactions. These findings suggest therapeutic potential to enhance T2D management. The identified pathways and genes provide valuable insights into the disease mechanisms and potential targets for intervention.

    Keywords: type 2 diabetes, Immune Cell Infiltration, single-cell RNA sequencing, machine learning, protein-protein interaction, therapeutic targets

    Received: 11 Aug 2024; Accepted: 30 Dec 2024.

    Copyright: © 2024 Pu, Wang, Lu, Wu, Chen, Luo, Wang and Mou. 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: Lisha Mou, Shenzhen Second People’s Hospital, Shenzhen, China

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