Integrated transcriptomic and single-cell RNA sequencing identifies lysosomal ion channel genes as potential biomarkers for Alzheimer’s disease

Previous research has highlighted lysosomal ion channel-related genes (LICRGs) as promising therapeutic targets for neurodegenerative diseases. This study aimed to identify and analyze LICRG-associated biomarkers for Alzheimer's disease (AD), elucidating their underlying biological mechanisms. Three datasets (GSE63061, GSE63060, GSE181279) were analyzed. In GSE63061, intersecting genes were identified by integrating differentially expressed genes (DEGs) from differential expression analysis with key module genes from Weighted Gene Co-expression Network Analysis (WGCNA). Candidate biomarkers were then selected using the MCODE plugin for PPI analysis (top 30 genes), two machine learning approaches, and cross-validation of gene expression profiles in GSE63061 and GSE63060. Single-cell RNA sequencing (scRNA-seq) analysis of GSE181279 identified key biomarkers and cell populations, followed by pseudo-temporal analysis of these cells. Nomogram construction, functional enrichment analysis, immune infiltration assessment, and RT-qPCR analysis were subsequently performed. scRNA-seq analysis revealed that <i>SRP14</i>, <i>EIF3E</i>, and <i>COX7C</i> were prominently expressed across most cell types, particularly in CD4<sup>+</sup> T cells, which were identified as key cells in AD. Pseudo-temporal analysis indicated that CD4<sup>+</sup> T cells from control subjects primarily resided in early differentiation stages, whereas those from patients with AD were predominantly found in later stages. The reduced expression of these biomarkers in AD CD4<sup>+</sup> T cells was consistent with transcriptomic data and further validated by RT-qPCR. A nomogram incorporating these biomarkers demonstrated strong predictive power for AD risk. Functional analysis linked the biomarkers to pathways such as "ribosome" and "oxidative phosphorylation." Immune infiltration analysis revealed 23 differentially abundant immune cell types, with significant correlations between all three biomarkers and memory CD4<sup>+</sup> T cells, mesangial cells, and other immune cell types. This study identified <i>SRP14</i>, <i>EIF3E</i>, and <i>COX7C</i> as novel biomarkers, underscoring CD4<sup>+</sup> T cells as pivotal in AD pathogenesis. These findings offer new mechanistic insights and potential therapeutic strategies for AD.

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Publication Details

Journal
Frontiers in Genetics
Published
2025-10-08
DOI
https://doi.org/10.3389/fgene.2025.1676565
Primary Topic
Calcium signaling and nucleotide metabolism
Type
article
Field-Weighted Citation Impact
0.00

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article

Integrated transcriptomic and single-cell RNA sequencing identifies lysosomal ion channel genes as potential biomarkers for Alzheimer’s disease

Xinran Zhao, Juan Lin, Fang‐Jie Zhao, Shengjuan Wei et al.
Frontiers in Genetics
Calcium signaling and nucleotide metabolism
article

Integrated transcriptomic and single-cell RNA sequencing identifies lysosomal ion channel genes as potential biomarkers for Alzheimer’s disease

Xinran Zhao, Juan Lin, Fang‐Jie Zhao, Shengjuan Wei, Xin Wang, Xiaolei Liu, Zelin Wu
article en

Abstract

Previous research has highlighted lysosomal ion channel-related genes (LICRGs) as promising therapeutic targets for neurodegenerative diseases. This study aimed to identify and analyze LICRG-associated biomarkers for Alzheimer's disease (AD), elucidating their underlying biological mechanisms. Three datasets (GSE63061, GSE63060, GSE181279) were analyzed. In GSE63061, intersecting genes were identified by integrating differentially expressed genes (DEGs) from differential expression analysis with key module genes from Weighted Gene Co-expression Network Analysis (WGCNA). Candidate biomarkers were then selected using the MCODE plugin for PPI analysis (top 30 genes), two machine learning approaches, and cross-validation of gene expression profiles in GSE63061 and GSE63060. Single-cell RNA sequencing (scRNA-seq) analysis of GSE181279 identified key biomarkers and cell populations, followed by pseudo-temporal analysis of these cells. Nomogram construction, functional enrichment analysis, immune infiltration assessment, and RT-qPCR analysis were subsequently performed. scRNA-seq analysis revealed that <i>SRP14</i>, <i>EIF3E</i>, and <i>COX7C</i> were prominently expressed across most cell types, particularly in CD4<sup>+</sup> T cells, which were identified as key cells in AD. Pseudo-temporal analysis indicated that CD4<sup>+</sup> T cells from control subjects primarily resided in early differentiation stages, whereas those from patients with AD were predominantly found in later stages. The reduced expression of these biomarkers in AD CD4<sup>+</sup> T cells was consistent with transcriptomic data and further validated by RT-qPCR. A nomogram incorporating these biomarkers demonstrated strong predictive power for AD risk. Functional analysis linked the biomarkers to pathways such as "ribosome" and "oxidative phosphorylation." Immune infiltration analysis revealed 23 differentially abundant immune cell types, with significant correlations between all three biomarkers and memory CD4<sup>+</sup> T cells, mesangial cells, and other immune cell types. This study identified <i>SRP14</i>, <i>EIF3E</i>, and <i>COX7C</i> as novel biomarkers, underscoring CD4<sup>+</sup> T cells as pivotal in AD pathogenesis. These findings offer new mechanistic insights and potential therapeutic strategies for AD.

Frontiers in GeneticsVol. 16
Shanxi Medical University (CN), Shanxi Academy of Medical Sciences (CN)
National Natural Science Foundation of China, Natural Science Foundation for Young Scientists of Shanxi Province
Openalex Percentile: Top 72%
Calcium signaling and nucleotide metabolism
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