Identification of Gene Signatures and Molecular Mechanisms for Diagnosing Parkinson's Disease and Nonalcoholic Fatty Liver Disease Using Machine Learning.

Overview

Authors: Xuan Chen^1,2, Yulin Wu³, Yonglai Zhang⁴, Yuli Hou¹

ORCID iDs: Xuan Chen, Yulin Wu, Yonglai Zhang, Yuli Hou

Department of Neurology, First Hospital of Shanxi Medical University, Taiyuan, Shanxi, China, sxmu.edu.cn
Department of Neurology, Taiyuan City Central Hospital, Peking University First Hospital Taiyuan Branch, The Ninth Clinical Medical College of Shanxi Medical University, Taiyuan, Shanxi, China
Changzhi Medical College, Changzhi, Shanxi, China, czmc.com
School of Software, North University of China, Taiyuan, Shanxi, China, nuc.edu.cn

Institutions: Shanxi Medical University (China); First Hospital of Shanxi Medical University (China); Peking University First Hospital (China); Taiyuan Central Hospital (China); Changzhi Medical College (China); North University of China (China)

Journal: Parkinson's disease, volume 2026, issue 1, article 8731032

Dates: received 27 January 2026; accepted 22 April 2026; published online 26 May 2026

Type: Research article · Language: English

License: CC BY

Identifiers: DOI 10.1155/padi/8731032 · PMID 42205493 · PMCID PMC13202728 · OpenAlex W7162424933

Open access: gold, a free copy (OpenAlex)

Status: data only

Categories: genetics / omics (modality), Parkinson's (population), cellular / molecular (subfield)

Methods: Smoothing, state filtering, decompositions, Machine learning, Statistics, Connectivity

Keywords: CASP1, CCNA2, immune regulation, INHBE, machine learning, NAFLD, Parkinson’s disease

Topic: Parkinson's Disease Mechanisms and Treatments (Neurology, Medicine), according to OpenAlex

Funding: Taiyuan Bureau of Science and Technology (202209)

Citations: not cited yet (Europe PMC); 57 references in the paper

Abstract

Background: Parkinson’s disease (PD) is a prevalent neurodegenerative disease, whereas nonalcoholic fatty liver disease (NAFLD) is a common metabolic liver disorder. Growing evidence suggests that NAFLD may affect the central nervous system through the liver–brain axis, potentially contributing to PD, although the underlying molecular mechanisms remain unclear.

Methods: To identify differentially expressed genes (DEGs), transcriptomic data for NAFLD and PD were sourced from the GEO database. Key common candidate genes were screened using protein–protein interaction (PPI) networks, machine learning approaches (LASSO, neural networks, and random forest), and functional enrichment analyses, including GO, KEGG, GSEA, and GSVA. Immune infiltration, TF‐miRNA regulatory networks, and single‐cell RNA sequencing analyses were applied to investigate gene function, immune regulation, and cellular distribution. Candidate drugs were predicted using bioinformatic approaches and validated through molecular docking.

Results: CASP1, CCNA2, and INHBE were identified as three core common candidate genes that may be associated with NAFLD and PD. Involvement of these genes includes inflammatory responses, regulation of the cell cycle, metabolic pathways, and immune microenvironment remodeling. The analysis of the TF‐miRNA network suggested possible regulation by transcription factors CEBPB, BRD4, FOS, and miRNAs such as hsa‐miR‐29b‐1‐5p and hsa‐miR‐128‐3p. Drug prediction and molecular docking identified ethinyl estradiol, mesalamine, and seliciclib as candidate therapeutics, showing strong binding affinity to the core targets.

Conclusion: This study offers a comprehensive elucidation of the molecular ties between NAFLD and PD. The identified core genes and candidate drugs offer theoretical support for potential candidate biomarkers and therapeutic targets in comorbid NAFLD and PD.

Reproduced under the paper's license (CC BY), from the paper cited above.

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Data

Data links

ncbi.nlm.nih.gov/geo — NCBI; found in the text, “2.1. The Data Source”

Data Availability Statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 2, 28 September 2026

Issue: — → 1

Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 4 authors, 7 keywords, 1 funder, 57 references.

Cite

This paper

Chen, X., Wu, Y., Zhang, Y., & Hou, Y. (2026). Identification of Gene Signatures and Molecular Mechanisms for Diagnosing Parkinson's Disease and Nonalcoholic Fatty Liver Disease Using Machine Learning. Parkinson's disease, 2026(1), 8731032. https://doi.org/10.1155/padi/8731032

BibTeX

@article{chen2026identification, author = {Chen, Xuan and Wu, Yulin and Zhang, Yonglai and Hou, Yuli}, title = {{Identification of Gene Signatures and Molecular Mechanisms for Diagnosing Parkinson's Disease and Nonalcoholic Fatty Liver Disease Using Machine Learning}}, journal = {Parkinson's disease}, year = {2026}, month = may, volume = {2026}, number = {1}, pages = {8731032}, publisher = {Wiley}, issn = {2090-8083}, doi = {10.1155/padi/8731032}, url = {https://doi.org/10.1155/padi/8731032}, pmid = {42205493}, pmcid = {PMC13202728} }

RIS

TY - JOUR AU - Chen, Xuan AU - Wu, Yulin AU - Zhang, Yonglai AU - Hou, Yuli TI - Identification of Gene Signatures and Molecular Mechanisms for Diagnosing Parkinson's Disease and Nonalcoholic Fatty Liver Disease Using Machine Learning T2 - Parkinson's disease J2 - Parkinsons Dis PY - 2026 DA - 2026/05/26 VL - 2026 IS - 1 SP - 8731032 SN - 2090-8083 PB - Wiley DO - 10.1155/padi/8731032 UR - https://doi.org/10.1155/padi/8731032 LA - en ER -

CSL-JSON

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