ArticleMolecular biology reports2026
Identification and experimental validation of candidate biomarkers for Parkinson's Disease through integrated bioinformatics, Mendelian randomization, and machine learning.
Article in Molecular biology reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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.
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.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundIdentifying reliable biomarkers and therapeutic targets is important for improving the early diagnosis and treatment of Parkinson's disease (PD). This study aimed to identify PD-related core genes, validate their expression, and explore potential therapeutic drugs.
methodsPD transcriptomic datasets were analyzed using differential expression analysis, Mendelian randomization, and machine learning. Candidate genes were validated using quantitative PCR (qPCR) in an MPTP-induced PD mouse model. Potential drugs were screened using the DSigDB database, followed by molecular docking and 100-ns molecular dynamics simulations to evaluate protein-ligand binding and stability.
resultsSeven potential core genes-AMIGO2, ATG7, CDK2AP2, CYP27A1, MT1X, NIPSNAP1, and TTC19-were identified. AMIGO2, ATG7, NIPSNAP1, and TTC19 were downregulated, whereas CDK2AP2, CYP27A1, and MT1X were upregulated. The results of animal experiments validated the abnormal expression of ATG7, NIPSNAP1, TTC19, CYP27A1, and MT1X. Drug screening identified 239 candidate compounds, five of which met the screening criteria. Molecular docking and molecular dynamics simulations provided computational support for relatively stable interactions between the top three drug-target complexes.
conclusionThis study identified several PD-related core genes and experimentally validated the expression of selected genes in an MPTP-induced PD mouse model. In addition, several potential therapeutic compounds were identified through computational analyses, providing candidates for further experimental investigation.
Indexed as
Identifiers
42927520What OpenQuestion holds
Registered trials
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.