ArticleScientific reports2025
Identification of potential pathogenic genes associated with the comorbidity of rheumatoid arthritis and renal fibrosis using bioinformatics and machine learning.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- Use of Artificial Intelligence in Rheumatoid Arthritis: Advancements and Novel Perspectives.Journal of clinical medicine · 2026Review
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
Abstract
This study aimed to identify the potential pathogenic genes associated with the comorbidity of rheumatoid arthritis (RA) and renal fibrosis (RF). Transcriptomic data related to RA and RF were retrieved from the GEO database. Differential expression gene analysis (DEGs) and weighted gene co-expression network analysis (WGCNA) were carried out to identify the RA-RF-DEGs. Subsequently, functional enrichment analysis was performed to clarify the biological functions of these genes. Machine learning algorithms were used to screen for the hub RA-RF differential expression genes, and then a Logistic Regression (LR) model was constructed. The accuracy of the model was evaluated using the ROC curve. At the same time, single-sample gene set enrichment analysis (ssGSEA) was applied to conduct immune infiltration analysis on the RF dataset. Gene set enrichment analysis (GSEA) was further performed on the hub genes to explore their underlying mechanisms in RF. Finally, a miRNA-TF-mRNA regulatory network centered around the hub genes was constructed.The results showed that 10 RA-RF-DEGs were identified through a comprehensive screening process. Enrichment analysis indicated that these differential expression genes were mainly involved in inflammatory responses and immune regulation. Subsequently, two hub genes, namely BIRC3 and PSMB9, were identified. A LR model was developed, and its predictive accuracy was verified using the ROC curve derived from an external independent dataset. Immune infiltration analysis revealed a significant correlation between the two hub genes and immune dysregulation in RF. Gene set enrichment analysis (GSEA) clarified the potential biological pathways through which BIRC3 and PSMB9 might function in RF. The constructed miRNA-TF-mRNA regulatory network provided a comprehensive overview of the post-transcriptional and transcriptional regulatory mechanisms. In conclusion, this study identified two candidate risk genes for RA-RF, providing new insights for the early diagnosis and treatment of RA complicated with RF.
Indexed as
Identifiers
What Socratic 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 Socratic graph.