Evidence map›Paper›PMID 42142278›Full record

ArticleJournal of molecular neuroscience : MN2026

Transcriptomic Analysis and Multiple Machine Learning Approaches Identify ZDHHC20 and Its Highly Correlated Gene AK5 as Biomarkers in Multiple System Atrophy.

Zhipeng Lu, Zhongqi Li, Zhibiao Yin, Jialong Liu, Pu Fang

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Article in Journal of molecular neuroscience : MN, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Zhipeng LuDepartment of Neurology, The First Affiliated Hospital of Nanchang University, Nanchang, China.
Zhongqi LiJiangxi Provincial Institute of Urology, The First Affiliated Hospital of Nanchang University, Nanchang, China.
Zhibiao YinDepartment of Neurology, The First Affiliated Hospital of Nanchang University, Nanchang, China.
Jialong LiuDepartment of Cardiology, Nanchang University Second Affiliated Hospital, Nanchang, China.
Pu FangDepartment of Neurology, The First Affiliated Hospital of Nanchang University, Nanchang, China. fangpu1972@163.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Multiple system atrophy (MSA) is a fatal neurodegenerative disorder lacking effective diagnostic tools. While protein palmitoylation is crucial for neuronal function, its specific role in MSA pathogenesis remains unexplored. We integrated bulk and single-nucleus RNA sequencing (snRNA-seq) data from postmortem MSA brain tissues. Eight machine learning algorithms were utilized to screen palmitoylation-related genes. Downstream analyses, including functional enrichment, cellular deconvolution, and pseudotime trajectory inference, were then conducted. ZDHHC20 and its highly correlated gene, AK5, were identified as hub genes. Both demonstrated significant downregulation in MSA, particularly within the cerebellar white matter. Functional enrichment analysis linked this expression pattern to mitochondrial dysfunction and impaired energy metabolism. Furthermore, snRNA-seq revealed that ZDHHC20 and AK5 are predominantly expressed in oligodendrocytes and exhibit lower expression levels along the developmental trajectory in MSA compared to healthy controls. ZDHHC20 and AK5 represent promising biomarkers for MSA. These findings provide new insights into the diagnosis and treatment of MSA.

Indexed as

AcyltransferasesMachine LearningMultiple System AtrophyBiomarkersHumansOligodendrogliaAcyltransferasesBiomarkersEnergy metabolismMachine learningMultiple System Atrophy (MSA)OligodendrocytesPalmitoylation

Identifiers

What OpenQuestion holds

Textmetadata
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Registered trials

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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.