Evidence map›Paper›PMID 42497137›Full record

ArticlePloS one2026

Research advances in key genes and regulatory mechanisms of posttranslational modifications in Parkinson's disease.

Bangzhi Wang, Zhuo Huang, Rong Wang, Chaolin Zhu, Shijiang Ma, Minghong Wang

Abstract read
In one paragraph

Article in PloS one, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Bangzhi WangYunnan University of Chinese Medicine, Kunming, Yunnan, China.
Zhuo HuangYunnan University of Chinese Medicine, Kunming, Yunnan, China.
Rong WangYunnan University of Chinese Medicine, Kunming, Yunnan, China.
Chaolin ZhuYunnan University of Chinese Medicine, Kunming, Yunnan, China.
Shijiang MaYunnan University of Chinese Medicine, Kunming, Yunnan, China.
Minghong WangYunnan Provincial Hospital of Chinese Medicine, Kunming, Yunnan, China.ORCID https://orcid.org/0009-0000-2677-8206

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe genesis of Parkinson's disease (PD), a common central neurodegenerative disorder, involves dysregulation of protein posttranslational modifications (PTM). The primary objective of this study was to screen key PTM-associated genes (PTMGs) serving as diagnostic indicators and potential therapeutic targets in PD.

methodsPeripheral blood transcriptomic data for PD cohorts and healthy controls were retrieved from publicly accessible repositories. Candidate genes were identified by overlapping differentially expressed genes (DEGs) with established PTMGs via differential expression profiling. Machine learning-based screening approaches were used for the selection of feature genes. Key genes were validated via receiver operating characteristic curve assessment combined with verification of expression levels. Subsequently, enrichment analysis, immune infiltration assessment, chromosome mapping, prediction of ribonucleic acid (RNA) modification sites, and compound screening were further explored.

resultsA total of 404 DEGs were identified, 19 of which overalpped with PTMGs and were thus selected as candidate genes. ML-based analysis narrowed these to eight feature genes, among which those coding for beta-1,4-galactosyltransferase 3 (B4GALT3), ring finger and FYVE-like domain-containing E3 ubiquitin protein ligase (RFFL), and GABA type A receptor-associated protein (GABARAP) were validated as key genes based on their diagnostic performance and consistent downregulation in the PD group (p < 0.05). Gene set enrichment analysis demonstrated significant enrichment within immune signaling cascades. Analysis of immune cell infiltration revealed diminished populations of activated B lymphocytes, activated CD4-positive T cells, and natural killer T cell subsets in the PD group, which exhibited predominantly positive associations with the identified key genes (p < 0.05). Chromosome mapping localized B4GALT3 to chromosome 1 and RFFL/GABARAP to chromosome 17. High-confidence m6A methylation sites were predicted for B4GALT3 and RFFL. Compound screening identified 34 potential compounds targeting these genes, including valproic acid and phenobarbital.

conclusionThis study identified B4GALT3, RFFL, and GABARAP as key PTMGs in PD, highlighting their roles in PD genesis and potential as diagnostic biomarkers.

Indexed as

Parkinson DiseaseProtein Processing, Post-TranslationalGene Expression ProfilingHumansTranscriptome

Identifiers

PMID42497137
PMCPMC13399316

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.