Exploring the mechanism of metabolic cell death-related genes AKR1C2 and MAP1LC3A as biomarkers in Parkinson's disease.

Journal: Scientific reports
Published Date: Jul 15, 2025

Abstract

There is a strong relationship between metabolic cell death (MCD) and neurodegenerative diseases. However, the involvement of metabolic cell death (MCD)-related genes (MCDRGs) in Parkinson's disease (PD) pathogenesis remains poorly analyzed. Integrating PD-associated differentially expressed genes (DEGs) from GSE7621 with MCDRGs, we identified key biomarkers through protein-protein interaction networks and machine learning. Diagnostic performance was validated through nomogram analysis. Subsequent analyses included functional enrichment, immune profiling, drug prediction, and single-cell RNA sequencing. AKR1C2 and MAP1LC3A were identified as potential biomarkers. A nomogram with superior diagnostic performance was constructed. Gene set enrichment analysis indicated that both biomarkers were linked to the "Parkinson's disease". Further, immune infiltration revealed that AKR1C2 had the remarkably strongest positive correlation with M2 macrophages. Moreover, benzo[a]pyrene-1,6-dione, mestranol, and paraoxon-methyl might be potential therapeutic agents for PD. Single-cell RNA-seq analysis demonstrated endothelial-specific expression, with MAP1LC3A and AKR1C2 exhibiting distinct temporal regulation during differentiation. AKR1C2 and MAP1LC3A were identified as potential biomarkers associated with MCD in PD. These results offer fresh concepts for PD prevention and diagnosis.

Authors

  • Jia Fu
  • Jing Zhao
    Department of Pharmacy, Pharmacoepidemiology and Drug Safety Research Group, Faculty of Mathematics and Natural Sciences, University of Oslo, Oslo, Norway.
  • Xue Zhao
    Fujian Key Laboratory of Precision Diagnosis and Treatment in Breast Cancer, Xiang'an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China; National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, China; Department of Breast-Thyroid-Surgery and Cancer Center, Xiang'an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China; Xiamen Research Center of Clinical Medicine in Breast & Thyroid Cancers, Xiang'an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China; Xiamen Key Laboratory of Endocrine-Related Cancer Precision Medicine, Xiang'an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China.
  • Na Mi
    Department of Neurology, Chi Feng Municipal Hospital, Chi Feng City, 024000, Inner Mongolia Autonomous Region, China.
  • Chao Zhang
    School of Information Engineering, Suqian University, Suqian, Jiangsu, China.
  • Xueying Li
    Geriatrics Division, Department of Medicine, Peking University First Hospital, Beijing 100034, China.
  • Lei Wu
    Advanced Photonics Center, Southeast University, Nanjing, 210096, China.
  • Lige Han
    The First Affiliated Hospital of Harbin Medical University, Harbin, 150080, Heilongjiang, China.
  • Yali Zhang
    College of Food Science and Technology, Nanjing Agricultural University, Nanjing 210095, China.
  • Lifen Yao
    State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China; College of Water Sciences, Beijing Normal University, Beijing 100875, China.

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