Evidence map›Paper›PMID 39433822›Full record

SynthesisScientific reports2024

A meta-analysis of bulk RNA-seq datasets identifies potential biomarkers and repurposable therapeutics against Alzheimer's disease.

Anika Bushra Lamisa, Ishtiaque Ahammad, Arittra Bhattacharjee, Mohammad Uzzal Hossain, Ahmed Ishtiaque, Zeshan Mahmud Chowdhury, Keshob Chandra Das, Md Salimullah, Chaman Ara Keya

Erratum issuedAbstract readMeta-Analysis
In one paragraph

Synthesis in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
–field-weighted citation impact
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

9 citing papers in PubMed.

  1. Article
  2. Wild-type C9orf72 drives proteasomal dysfunction and mutant aggregates via a Stat1-Isg15 axis in Huntington's disease.Neurotherapeutics : the journal of the American Society for Experimental NeuroTherapeutics · 2026
    Article
  3. Searching the druggable genome using large language models.Bioinformatics (Oxford, England) · 2026
    Article
  4. Searching the Druggable Genome using Large Language Models.bioRxiv : the preprint server for biology · 2026
    Article
  5. Article
  6. Article
  7. Neuroprotective Potential ofPharmaceuticals (Basel, Switzerland) · 2025
    Article
  8. Article
  9. Review
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

9 authors.

Anika Bushra Lamisa *Department of Biochemistry and Microbiology, North South University, Bashundhara, Dhaka, 1229, Bangladesh.
Ishtiaque Ahammad *Bioinformatics Division, National Institute of Biotechnology, Ganakbari, Savar, Dhaka, 1349, Ashulia, Bangladesh.
Arittra Bhattacharjee *Bioinformatics Division, National Institute of Biotechnology, Ganakbari, Savar, Dhaka, 1349, Ashulia, Bangladesh.
Mohammad Uzzal HossainBioinformatics Division, National Institute of Biotechnology, Ganakbari, Savar, Dhaka, 1349, Ashulia, Bangladesh.
Ahmed IshtiaqueDepartment of Biochemistry and Microbiology, North South University, Bashundhara, Dhaka, 1229, Bangladesh.
Zeshan Mahmud ChowdhuryBioinformatics Division, National Institute of Biotechnology, Ganakbari, Savar, Dhaka, 1349, Ashulia, Bangladesh.
Keshob Chandra DasMolecular Biotechnology Division, National Institute of Biotechnology, Ganakbari, Savar, Dhaka, 1349, Ashulia, Bangladesh.
Md SalimullahMolecular Biotechnology Division, National Institute of Biotechnology, Ganakbari, Savar, Dhaka, 1349, Ashulia, Bangladesh.
Chaman Ara KeyaDepartment of Biochemistry and Microbiology, North South University, Bashundhara, Dhaka, 1229, Bangladesh. chaman.keya@northsouth.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Alzheimer's disease (AD) poses a major challenge due to its impact on the elderly population and the lack of effective early diagnosis and treatment options. In an effort to address this issue, a study focused on identifying potential biomarkers and therapeutic agents for AD was carried out. Using RNA-Seq data from AD patients and healthy individuals, 12 differentially expressed genes (DEGs) were identified, with 9 expressing upregulation (ISG15, HRNR, MTATP8P1, MTCO3P12, DTHD1, DCX, ST8SIA2, NNAT, and PCDH11Y) and 3 expressing downregulation (LTF, XIST, and TTR). Among them, TTR exhibited the lowest gene expression profile. Interestingly, functional analysis tied TTR to amyloid fiber formation and neutrophil degranulation through enrichment analysis. These findings suggested the potential of TTR as a diagnostic biomarker for AD. Additionally, druggability analysis revealed that the FDA-approved drug Levothyroxine might be effective against the Transthyretin protein encoded by the TTR gene. Molecular docking and dynamics simulation studies of Levothyroxine and Transthyretin suggested that this drug could be repurposed to treat AD. However, additional studies using in vitro and in vivo models are necessary before these findings can be applied in clinical applications.

Indexed as

Alzheimer DiseaseBiomarkersDrug RepositioningPrealbuminRNA-SeqGene Expression RegulationHumansMolecular Docking SimulationThyroxineTranscriptomeBiomarkersPrealbuminThyroxineTTR protein, humanAlzheimer’s diseaseBiomarkerDrug discoveryRNA-Seq

Identifiers

PMID39433822
PMCPMC11494203

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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 Socratic graph.