ArticleHeliyon2024
Screening and validation of differentially expressed genes in polymyositis.
Article in Heliyon, 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.
- Energy metabolism dysregulation in idiopathic inflammatory myopathies: mechanisms and therapeutic implications.Frontiers in immunology · 2026Review
- Association of FANCD2, TFPI, and CD33 genes with prognosis of lung adenocarcinoma: a bioinformatics study.Discover oncology · 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
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Polymyositis (PM), a prevalent inflammatory myopathy, currently lacks defined pathogenic mechanism. To illuminate its pathogenesis, we integrated bioinformatics and clinical specimens to examine potential aberrant gene expression patterns and their localization. Methods: We obtained GSE128470 and GSE3112 dataset from the Gene Expression Omnibus, performed Gene Set Enrichment Analysis (GSEA) and immune infiltration analysis using CiberSort, identified differentially expressed genes with Limma, conducted functional annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, constructed a Protein-Protein Interaction network, and identified hub genes using Cytoscape. ROC analysis evaluated hub gene diagnostic accuracy for PM, validated their expression levels with clinical specimens. Results: DEG analysis revealed 51 upregulated and 779 downregulated genes. Gene Ontology (GO) analysis implicated Type I interferon (IFN-Ⅰ) signaling, while KEGG pointed to cell adhesion molecule activation and oxidative phosphorylation inhibition. Protein-Protein Interaction (PPI) analysis identified 8 diagnosffftic hub genes. Clinical samples confirmed their upregulation in PM, especially IRF1 and IRF9 between muscle fibers. Different immune cell infiltrations were observed in PM patients versus controls. Conclusions: Our study explores potential pathogenic factors, diagnostic markers, and immune cells in PM, with a focus on verifying IRF1 and IRF9 upregulation in the IFN-I signaling pathway. These findings bear significance for PM diagnosis and treatment.
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.