Evidence map›Paper›PMID 40419517›Full record

ArticleScientific reports2025

Uncovering dendritic cell specific biomarkers for diagnosis and prognosis of cardiomyopathy using single cell RNA sequencing and comprehensive bioinformatics analysis.

Md Mizanur Rahman, Md Habibur Rahman, Md Arju Hossain, Kh Mujahidul Islam, Prosenjit Saha Apu, Mahfuj Khan, Md Golam Kibria, Siddique Akber Ansari, Mahammad Humayoo

Abstract read
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Md Mizanur Rahman *Department of Computer Science and Engineering, Islamic University, Kushtia, 7003, Bangladesh.
Md Habibur Rahman *Department of Computer Science and Engineering, Islamic University, Kushtia, 7003, Bangladesh. habib@iu.ac.bd.
Md Arju HossainDepartment of Microbiology, Primeasia University, Banani, Dhaka, 1213, Bangladesh.
Kh Mujahidul IslamDepartment of Computer Science and Engineering, Islamic University, Kushtia, 7003, Bangladesh.
Prosenjit Saha ApuDepartment of Computer Science and Engineering, Islamic University, Kushtia, 7003, Bangladesh.
Mahfuj KhanDepartment of Computer Science and Engineering, Islamic University, Kushtia, 7003, Bangladesh.
Md Golam KibriaDepartment of Chemical and Petroleum Engineering, Schulich School of Engineering, University of Calgary, Calgary, Alberta, Canada.
Siddique Akber AnsariDepartment of Pharmaceutical Chemistry, College of Pharmacy, King Saud University, P.O Box 2457, Riyadh, 11451, Saudi Arabia.
Mahammad HumayooSchool of Engineering, Pokhara University, Lekhnath, Kaski, 427, Nepal. mahammad.humayoo@pu.edu.np.

Funding

King Saud University, Riyadh, Saudi Arabia RSPD2024R744
6 · The paper itself

Abstract

Cardiomyopathy is a type of cardiovascular disorder that is a primary cause of death globally, killing millions of people each year. Cardiomyopathy detection and early diagnosis are crucial in reducing negative health effects. Thus, this study aims to use single cell RNA sequencing, and bioinformatics analysis to uncover dendritic cell-specific biomarkers, gene ontology, pathways, regulatory interaction networks, and protein-chemical compounds related to the molecular mechanism of cardiomyopathy progression. Two RNAseq datasets GSE65446 and GSE155495 also were evaluated to identify significant biomarkers in cardiomyopathy, and 123 mutual DEGs appeared between scRNAseq and RNAseq datasets. In addition, the DAVID online platform and FunRich software were utilized to detect cell communication in innate immune responses, type 1 IFN, antigen processing and presentation, allograft rejection and viral infection significant gene ontology and metabolic pathways in cardiomyopathy. The protein-protein interaction (PPI) network revealed five key hub proteins (ITGAX, IRF7, MX1, HLA-B, and IRF1). Following that, several transcription factors (GATA2, FOXC1, SREBF1, STAT3, and NFKB1) as well as microRNA (hsa-mir-26a-5p, hsa-mir-129-2-3p, etc.) were predicted. Prospective chemical substances such as tretinoin, valproic acid, and arsenic trioxide have been predicted to be linked to cardiomyopathy treatment. The acceptable value of receiver operating characteristic (ROC) curve analysis revealed that biomarkers play critical roles in cardiomyopathy. This study identifies molecular indicators at the RNA and protein levels that may be useful in improving understanding of molecular causes, early diagnosis, and devising favorable cardiomyopathy treatment. More research will be needed to validate our predicted findings as future clinical biomarkers.

Indexed as

BiomarkersCardiomyopathiesComputational BiologyDendritic CellsSingle-Cell AnalysisGene Expression ProfilingGene OntologyGene Regulatory NetworksHumansPrognosisProtein Interaction MapsSequence Analysis, RNABiomarkersAnd BioinformaticsBiomarkersCardiomyopathyNetworking analysisSingle-cell RNA-sequencing

Identifiers

PMID40419517
PMCPMC12106800

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.