ArticleFrontiers in immunology2024
Unveiling Immune-related feature genes for Alzheimer's disease based on machine learning.
Article in Frontiers in immunology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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
2 citing papers in PubMed.
- RHBDF2 governs microglial neuroinflammation during cerebral ischemia-reperfusion injury and is positively regulated by the m6A reader YTHDF1.Molecular medicine (Cambridge, Mass.) · 2025Article
- Integrating AI/ML and multi-omics approaches to investigate the role of TNFRSF10A/TRAILR1 and its potential targets in pancreatic cancer.Computers in biology and medicine · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The identification of diagnostic and therapeutic biomarkers for Alzheimer's Disease (AD) remains a crucial area of research. In this study, utilizing the Weighted Gene Co-expression Network Analysis (WGCNA) algorithm, we identified RHBDF2 and TNFRSF10B as feature genes associated with AD pathogenesis. Analyzing data from the GSE33000 dataset, we revealed significant upregulation of RHBDF2 and TNFRSF10B in AD patients, with correlations to age and gender. Interestingly, their expression profile in AD differs notably from that of other neurodegenerative conditions. Functional analysis unveiled their involvement in immune response and various signaling pathways implicated in AD pathogenesis. Furthermore, our study demonstrated the potential of RHBDF2 and TNFRSF10B as diagnostic biomarkers, exhibiting high discrimination power in distinguishing AD from control samples. External validation across multiple datasets confirmed the robustness of the diagnostic model. Moreover, utilizing molecular docking analysis, we identified dinaciclib and tanespimycin as promising small molecule drugs targeting RHBDF2 and TNFRSF10B for potential AD treatment. Our findings highlight the diagnostic and therapeutic potential of RHBDF2 and TNFRSF10B in AD management, shedding light on novel strategies for precision medicine in AD.
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.