ArticleClinical rheumatology2026
Integrated bioinformatics and machine learning for shared diagnostic genes and mechanisms between periodontitis and psoriasis.
Article in Clinical rheumatology, 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
2 authors.
Funding
Abstract
backgroundPeriodontitis and psoriasis are two prevalent conditions that are bidirectionally associated. However, the molecular basis remains poorly understood. This study utilized bioinformatics approaches to investigate the common diagnostic genes and shared mechanisms of periodontitis and psoriasis.
methodsClassical datasets for periodontitis and psoriasis were sourced from the GEO database. Differentially expressed genes (DEGs) analysis, weighted gene co-expression network analysis (WGCNA), protein-protein interaction (PPI) network analysis, and two machine learning algorithms were used to screen common biomarkers. Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) enrichment analyses were utilized to explore biological functions. CIBERSO\ RT was used to assess the immune microenvironment. Transcription factor (TF)-gene and gene-miRNA regulatory networks were analyzed using NetworkAnalyst.
results24 DEGs and 333 disease-related genes were identified. Next, three biomarkers (CXCR4, SASH3, and LYN) were identified among the 12 genes shared between DEGs and WGCNA using machine learning. RT-qPCR analysis confirmed the elevated expression of the three shared genes in both conditions. We further constructed a nomogram model and validated it using ROC curves. Immune infiltration analysis revealed a significant association between the three common biomarkers and cellular immune dysregulation.
conclusionCXCR4, SASH3, and LYN are common biomarkers for psoriasis and periodontitis. Additionally, we proposed immune patterns, TF-gene, and gene-miRNA regulatory networks between the two diseases, which could provide new insights for future studies. Key Points • CXCR4, SASH3, and LYN were identified as shared diagnostic biomarkers for periodontitis and psoriasis, validated through bioinformatics and machine learning approaches. • A robust diagnostic model using the three biomarkers demonstrated high accuracy. • Regulatory networks involving key TFs and miRNAs suggest similar mechanisms between periodontitis and psoriasis.
Indexed as
Identifiers
42313235What 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.