Background: Increasing evidence has proven that rheumatoid arthritis (RA) can aggravate atherosclerosis (AS), and we aimed to explore potential diagnostic genes for patients with AS and RA.
Methods: We obtained the data from public databases, including Gene Expression Omnibus (GEO) and STRING, and obtained the differentially expressed genes (DEGs) and module genes with Limma and weighted gene co-expression network analysis (WGCNA). Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) enrichment analysis, the protein–protein interaction (PPI) network, and machine learning algorithms [least absolute shrinkage and selection operator (LASSO) regression and random forest] were performed to explore the immune-related hub genes. We used a nomogram and receiver operating characteristic (ROC) curve to assess the diagnostic efficacy, which has been validated with GSE55235 and GSE57691. Finally, immune infiltration was developed in AS.
Results: The AS dataset included 5,322 DEGs, while there were 1,439 DEGs and 206 module genes in RA. The intersection of DEGs for AS and crucial genes for RA was 53, which were involved in immunity. After the PPI network and machine learning construction, six hub genes were used for the construction of a nomogram and for diagnostic efficacy assessment, which showed great diagnostic value (area under the curve from 0.723 to 1). Immune infiltration also revealed the disorder of immunocytes.
Conclusion: Six immune-related hub genes (NFIL3, EED, GRK2, MAP3K11, RMI1, and TPST1) were recognized, and the nomogram was developed for AS with RA diagnosis.
In recent years, diagnostic and therapeutic approaches for rheumatoid arthritis (RA) have continued to improve. However, in the advanced stages of the disease, patients are unable to achieve long-term clinical remission and often suffer from systemic multi-organ damage and severe complications. Patients with RA usually have no overt clinical manifestations in the early stages, and by the time a definitive diagnosis is made, the disease is already at an advanced stage. RA is diagnosed clinically and with laboratory tests, including the blood markers C-reactive protein (CRP) and erythrocyte sedimentation rate (ESR) and the autoantibodies rheumatoid factor (RF) and anticitrullinated protein antibodies (ACPA). However, the presence of RF and ACPA autoantibodies is associated with aggravated disease, joint damage, and increased mortality, and these autoantibodies have low specificity and sensitivity. The etiology of RA is unknown, with the pathogenesis involving multiple factors and clinical heterogeneity. The early diagnosis, subtype classification, and prognosis of RA remain challenging, and studies to develop minimally invasive or non-invasive biomarkers in the form of biofluid biopsies are becoming more common. Non-coding RNA (ncRNA) molecules are composed of long non-coding RNAs, small nucleolar RNAs, microRNAs, and circular RNAs, which play an essential role in disease onset and progression and can be used in the early diagnosis and prognosis of RA. In this review of the diagnostic and prognostic approaches to RA disease, we provide an overview of the current knowledge on the subject, focusing on recent advances in mRNA–ncRNA as diagnostic and prognostic biomarkers from the biofluid to the tissue level.
Background: An epidemiological link between celiac disease (CeD) and inflammatory bowel disease (IBD) has been well established recently. In this study, Mendelian randomization (MR) analysis was performed employing pooled data of publicly available genome-wide association studies (GWAS) to determine the causal relationship between CeD and IBD, encompassing ulcerative colitis (UC) and Crohn’s disease (CD).
Methods: Dataset of CeD was acquired from GWAS for 12,041 cases and 12,228 controls. A GWAS of more than 86,000 patients and controls was used to identify genetic variations underlying IBD. MR analyses were performed with an inverse-variance-weighted approach, an MR-Egger regression, a weighted-mode approach, a weighted-median method, and sensitivity analyses of MR pleiotropy residual sum and outlie (MR-PRESSO).
Results: MR demonstrated that genetic predisposition to CeD was linked to a augmented risk of IBD (OR: 1.1408; 95% CI: 1.0614-1.2261; P = 0.0003). In the analysis of the two IBD subtypes, genetic predisposition to CeD was also linked to increased risks of UC (OR: 1.1646; 95% CI: 1.0614-1.2779; P = 0.0012) and CD (OR: 1.1865; 95% CI: 1.0948-1.2859; P = 3.07E-05). Reverse MR analysis results revealed that genetic susceptibility to IBD and CD was correlated with an augmented risk of CeD. However, there was no genetic correlation between UC and CeD. All of the above results were validated with other GWAS databases.
Conclusion: There is a bidirectional causal relationship of CeD with IBD and CD. However, UC only augments the risk of developing CeD.