ArticleMolecular neurobiology2026
Identification of Succinylation-Related Transcriptional Signatures in Alzheimer's Disease: Integrated Bioinformatics Analysis and Clinical Validation.
Article in Molecular neurobiology, 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
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Alzheimer's disease (AD) is a progressive neurodegenerative disorder involving mitochondrial dysfunction, oxidative stress, proteostasis imbalance, and immune-inflammatory alterations. Protein succinylation is closely linked to mitochondrial metabolic status, but peripheral blood transcriptomic signatures related to succinylation in AD remain insufficiently characterized. This study aimed to identify peripheral blood transcriptional candidate biomarkers associated with a predefined succinylation-related gene set in AD. The GSE63061 dataset was used as the discovery cohort, and GSE63060 served as an external validation cohort. Differential expression analysis, ssGSEA-derived succinylation scoring, and WGCNA were integrated with a predefined succinylation-related gene set. Candidate biomarkers were then refined through four feature selection approaches: LASSO, random forest, SVM-RFE, and Boruta, and their discriminatory performance was evaluated by ROC analysis, nomogram construction, calibration analysis, decision curve analysis, and SHAP analysis. Functional enrichment, GSVA/GSEA, GeneMANIA, immune-cell signature analysis, and single-cell analyses were used to explore biological relevance and cellular distribution. qRT-PCR was performed using peripheral blood-derived PBMC samples for experimental validation. Five genes, NDUFA1, NDUFS5, RPS27A, AIF1, and PRDX1, were identified as candidate biomarkers associated with a succinylation-related transcriptional signature in AD. These genes were mainly involved in mitochondrial complex I activity, oxidative phosphorylation, ribosomal function, protein homeostasis, redox balance, and immune inflammation. Immune-cell signatures and single-cell analyses further suggested that the signature was associated with T cells, B cells, NK cells, and monocytes/macrophages. qRT-PCR confirmed significant downregulation of all five genes in AD PBMC samples (P < 0.05). These findings suggest that peripheral blood transcriptional signatures associated with succinylation-related genes may provide candidate biomarkers and hypothesis-generating clues for AD.
Indexed as
Identifiers
42897366What 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.