ArticleScientific reports2026
Identification of small ubiquitin-related modifier (SUMO)-related genes-based biomarkers in Alzheimer's disease based on bioinformatics analysis.
Article in Scientific 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
7 authors.
Funding
Abstract
To investigate the role of small ubiquitin-like modification (SUMO) in Alzheimer's disease (AD) and its pathogenesis, this study employed bioinformatics methods to identify diagnostic biomarkers for AD based on SUMO-related genes (SRGs). It further conducted preliminary explorations into their role in AD pathogenesis and potential therapeutic targets. Datasets related to AD (GSE140831, GSE63060), a dementia dataset (GSE140830), and 189 SRGs were retrieved from public databases. Candidate genes were identified by intersecting differentially expressed genes (DEGs) with SRGs. A protein-protein interaction (PPI) network was constructed to select the top 15 core genes, and the support vector machine-recursive feature elimination (SVM-RFE), least absolute shrinkage and selection operator (LASSO) and boruta random forest (Boruta-RF) identified feature genes. Validation was done using the GSE140831 and GSE63060 datasets, and the nomogram model was assessed by receiver operating characteristic (ROC) curve analysis. Gene set enrichment analysis (GSEA) and other analyses were performed. Reverse transcription quantitative polymerase chain reaction (RT-qPCR) was used for further validation. Overlapping 189 SRGs and 12,853 DEGs identified 107 candidate genes. Six overlapping genes were selected. CREBBP, PIAS1, and TRIM28 were confirmed as AD biomarkers due to their increased expression in AD and strong ROC performance. GSEA highlighted their involvement in pathways such as olfactory transduction, lysosome, and spliceosome. Immune infiltration analysis suggested immune cell involvement in AD progression. Additionally, 21 potential drugs for AD therapy were predicted. RT-qPCR confirmed the over-expression of CREBBP and TRIM28 in AD samples, consistent with dataset trends. CREBBP, PIAS1, and TRIM28 were identified as SRG-based biomarkers for AD diagnosis, providing new insights into AD pathogenesis.
Indexed as
Identifiers
What 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.