Identification and validation of biomarkers associated with mitochondrial dynamics dysfunction in Parkinson’s disease based on GEO datasets
4 affiliations
- 1Department of Neurosurgery, The Second Hospital of Nanjing, Affiliated to Nanjing University of Chinese Medicine, Nanjing, China
- 2Department of Neurology, The Second Hospital of Nanjing, Affiliated to Nanjing University of Chinese Medicine, Nanjing, China
- 3Clinical Research Center, The Second Hospital of Nanjing, Affiliated to Nanjing University of Chinese Medicine, Nanjing, China
- 4Department of Neurology, Nanjing BenQ Medical Center, The Affiliated BenQ Hospital of Nanjing Medical University, Nanjing, China
Abstract
Background: Parkinson’s disease (PD), the second most prevalent neurodegenerative disorder, is characterized by an unclear etiology and a lack of specific early diagnostic biomarkers. This study investigated novel biomarkers associated with mitochondrial dynamics (MD) dysfunction in PD. Methods: Transcriptomic data from the Gene Expression Omnibus database (training set: GSE6613, n = 72; validation set: GSE99039, n = 438) and 23 MD-related genes (MDRGs) were integrated. Differentially expressed genes were screened using the limma package. Three machine learning algorithms (random forest, support vector machine, and generalized linear model) were employed to construct diagnostic models. Feature importance analysis identified key candidate biomarkers, which were preliminarily validated in clinical samples via reverse transcription quantitative PCR (RT-qPCR). Functional enrichment, immune infiltration, and compound prediction analyses were subsequently performed. Results: Cysteinyl leukotriene receptor 2 (CYSLTR2) and alpha‑tocopherol transfer protein‑like gene (TTPAL) were identified as MDRG score-correlated biomarkers. In the validation set, CYSLTR2 was significantly downregulated (log2FC = −1.25, P < 0.001) and participated in neuroactive ligand–receptor interactions. Conversely, TTPAL was significantly upregulated (log2FC = 1.52, P < 0.001) and associated with the pathogenic Escherichia coli infection pathway. Immune analysis revealed six differentially infiltrated immune cell types. Notably, TTPAL expression was positively correlated with keratinocytes (cor = 0.30, P = 0.031). Four potential therapeutic compounds were predicted. RT-qPCR suggested the downregulation of CYSLTR2 and upregulation of TTPAL in patients with PD (P < 0.0001). Conclusion: This study identified CYSLTR2 and TTPAL as novel candidate biomarkers associated with MD dysfunction in PD, providing novel targets for mechanistic investigation and therapeutic exploration.
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BibTeX
@article{gong2026identification,
title = {{Identification and validation of biomarkers associated with mitochondrial dynamics dysfunction in Parkinson’s disease based on GEO datasets}},
author = {Dawei Gong and Guanyu Li and Ming Li and Bo Zhang and Weixiao Wang and Jinjin Chen and Yanyan Zhao},
journal = {PLOS One},
year = {2026},
month = nov,
doi = {10.1371/journal.pone.0362081},
url = {https://doi.org/10.1371/journal.pone.0362081},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Identification and validation of biomarkers associated with mitochondrial dynamics dysfunction in Parkinson’s disease based on GEO datasets
AU - Dawei Gong
AU - Guanyu Li
AU - Ming Li
AU - Bo Zhang
AU - Weixiao Wang
AU - Jinjin Chen
AU - Yanyan Zhao
PY - 2026
DA - 2026/11/04
JO - PLOS One
DO - 10.1371/journal.pone.0362081
UR - https://doi.org/10.1371/journal.pone.0362081
AB - Background: Parkinson’s disease (PD), the second most prevalent neurodegenerative disorder, is characterized by an unclear etiology and a lack of specific early diagnostic biomarkers. This study investigated novel biomarkers associated with mitochondrial dynamics (MD) dysfunction in PD. Methods: Transcriptomic data from the Gene Expression Omnibus database (training set: GSE6613, n = 72; validation set: GSE99039, n = 438) and 23 MD-related genes (MDRGs) were integrated. Differentially expressed genes were screened using the limma package. Three machine learning algorithms (random forest, support vector machine, and generalized linear model) were employed to construct diagnostic models. Feature importance analysis identified key candidate biomarkers, which were preliminarily validated in clinical samples via reverse transcription quantitative PCR (RT-qPCR). Functional enrichment, immune infiltration, and compound prediction analyses were subsequently performed. Results: Cysteinyl leukotriene receptor 2 (CYSLTR2) and alpha‑tocopherol transfer protein‑like gene (TTPAL) were identified as MDRG score-correlated biomarkers. In the validation set, CYSLTR2 was significantly downregulated (log2FC = −1.25, P < 0.001) and participated in neuroactive ligand–receptor interactions. Conversely, TTPAL was significantly upregulated (log2FC = 1.52, P < 0.001) and associated with the pathogenic Escherichia coli infection pathway. Immune analysis revealed six differentially infiltrated immune cell types. Notably, TTPAL expression was positively correlated with keratinocytes (cor = 0.30, P = 0.031). Four potential therapeutic compounds were predicted. RT-qPCR suggested the downregulation of CYSLTR2 and upregulation of TTPAL in patients with PD (P < 0.0001). Conclusion: This study identified CYSLTR2 and TTPAL as novel candidate biomarkers associated with MD dysfunction in PD, providing novel targets for mechanistic investigation and therapeutic exploration.
SN - 1932-6203
ER -