ArticlePeerJ2026
Exploratory bioinformatics analysis for identifying candidate biomarkers of type 1 diabetes mellitus.
Article in PeerJ, 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
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Type 1 diabetes mellitus (T1DM) is a chronic disease that significantly impacts patients' quality of life. Its prevalence is rising globally each year. This study aims to identify potential biomarkers associated with T1DM through comprehensive bioinformatics analysis, further enhancing T1DM early diagnosis and treatment. Methods: Transcriptome datasets from T1DM patients and the control group were from the Gene Expression Omnibus (GEO) database. Differentially Expressed Genes (DEGs) were identified and subsequently analyzed using Gene Ontology (GO) enrichment, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment, and protein-protein interaction (PPI) network analysis. Hub genes were identified using Enzyme-Linked Immunosorbent Assay (ELISA) on clinical samples comprising 17 T1DM patients and 19 controls. Immune cell infiltration was estimated using the Cell-type Identification By Estimating Relative Subsets Of RNA Transcripts (CIBERSORT) algorithm, while the diagnostic performance of the hub genes was evaluated Results: A total of 20 up-regulated and eight down-regulated DEGs were identified in the GEO database. Functional enrichment analysis showed that immune activation played an important role in T1DM. The expression levels of the hub genes, Conclusions: The results indicate that
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.