Bioinformatics analyses of gene expression profile to identify pathogenic mechanisms for COVID-19 infection and cutaneous lupus erythematosus

ObjectiveThe global mortality rates have surged due to the ongoing coronavirus disease 2019 (COVID-19), leading to a worldwide catastrophe. Increasing incidents of patients suffering from cutaneous lupus erythematosus (CLE) exacerbations after either contracting COVID-19 or getting immunized against it, have been observed in recent research. However, the precise intricacies that prompt this unexpected complication are yet to be fully elucidated. This investigation seeks to probe into the molecular events inciting this adverse outcome.MethodGene expression patterns from the Gene Expression Omnibus (GEO) database, specifically GSE171110 and GSE109248, were extracted. We then discovered common differentially expressed genes (DEGs) in both COVID-19 and CLE. This led to the creation of functional annotations, formation of a protein-protein interaction (PPI) network, and identification of key genes. Furthermore, regulatory networks relating to these shared DEGs and significant genes were constructed.ResultWe identified 214 overlapping DEGs in both COVID-19 and CLE datasets. The following functional enrichment analysis of these DEGs highlighted a significant enrichment in pathways related to virus response and infectious disease in both conditions. Next, a PPI network was constructed using bioinformatics tools, resulting in the identification of 5 hub genes. Finally, essential regulatory networks including transcription factor-gene and miRNA-gene interactions were determined.ConclusionOur findings demonstrate shared pathogenesis between COVID-19 and CLE, offering potential insights for future mechanistic investigations. And the identification of common pathways and key genes in these conditions may provide novel avenues for research

Machine-learning-based models assist the prediction of pulmonary embolism in autoimmune diseases: A retrospective, multicenter study

Abstract. Background:. Pulmonary embolism (PE) is a severe and acute cardiovascular syndrome with high mortality among patients with autoimmune inflammatory rheumatic diseases (AIIRDs). Accurate prediction and timely intervention play a pivotal role in enhancing survival rates. However, there is a notable scarcity of practical early prediction and risk assessment systems of PE in patients with AIIRD. Methods:. In the training cohort, 60 AIIRD with PE cases and 180 age-, gender-, and disease-matched AIIRD non-PE cases were identified from 7254 AIIRD cases in Tongji Hospital from 2014 to 2022. Univariable logistic regression (LR) and least absolute shrinkage and selection operator (LASSO) were used to select the clinical features for further training with machine learning (ML) methods, including random forest (RF), support vector machines (SVM), neural network (NN), logistic regression (LR), gradient boosted decision tree (GBDT), classification and regression trees (CART), and C5.0 models. The performances of these models were subsequently validated using a multicenter validation cohort. Results:. In the training cohort, 24 and 13 clinical features were selected by univariable LR and LASSO strategies, respectively. The five ML models (RF, SVM, NN, LR, and GBDT) showed promising performances, with an area under the receiver operating characteristic (ROC) curve (AUC) of 0.962–1.000 in the training cohort and 0.969–0.999 in the validation cohort. CART and C5.0 models achieved AUCs of 0.850 and 0.932, respectively, in the training cohort. Using D-dimer as a pre-screening index, the refined C5.0 model achieved an AUC exceeding 0.948 in the training cohort and an AUC above 0.925 in the validation cohort. These results markedly outperformed the use of D-dimer levels alone. Conclusion:. ML-based models are proven to be precise for predicting the onset of PE in patients with AIIRD exhibiting clinical suspicion of PE. Trial Registration:. Chictr.org.cn: ChiCTR2200059599