Development of an immunoinflammatory indicator-related dynamic nomogram based on machine learning for the prediction of intravenous immunoglobulin-resistant Kawasaki disease patients.

BACKGROUND: Approximately 10-20% of Kawasaki disease (KD) patients suffer from intravenous immunoglobulin (IVIG) resistance, placing them at higher risk of developing coronary artery aneurysms. Therefore, we aimed to construct an IVIG resistance prediction tool for children with KD in Shanghai, China. METHODS: Retrospective analysis was conducted on data from 1271 patients diagnosed with KD and the patients were randomly divided into a training set and a validation set in a 2:1 ratio. Machine learning algorithms were employed to identify important predictors associated with IVIG resistance and to build a predictive model. The best-performing model was used to construct a dynamic nomogram. Moreover, receiver operating characteristic curves, calibration plots, and decision-curve analysis were utilized to measure the discriminatory power, accuracy, and clinical utility of the nomogram. RESULTS: Six variables were identified as important predictors, including C-reactive protein, neutrophil ratio, procalcitonin, CD3 ratio, CD19 count, and IgM level. A dynamic nomogram constructed with these factors was available at https://hktk.shinyapps.io/dynnomapp/. The nomogram demonstrated good diagnostic performance in the training and validation sets (area under the receiver operating characteristic curve = 0.816 and 0.800, respectively). Moreover, the calibration curves and decision curves analysis indicated that the nomogram showed good consistency between predicted and actual outcomes and had good clinical benefits. CONCLUSION: A web-based dynamic nomogram for IVIG resistance was constructed with good predictive performance, which can be used as a practical approach for early screening to assist physicians in personalizing the treatment of KD patients in Shanghai.

Identification of key signaling pathways and hub genes related to immune infiltration in Kawasaki disease with resistance to intravenous immunoglobulin based on weighted gene co-expression network analysis.

Background: Kawasaki disease (KD) is an acute vasculitis, that is, the leading cause of acquired heart disease in children, with approximately 10%-20% of patients with KD suffering intravenous immunoglobulin (IVIG) resistance. Although the underlying mechanism of this phenomenon remains unclear, recent studies have revealed that immune cell infiltration may associate with its occurrence. Methods: In this study, we downloaded the expression profiles from the GSE48498 and GSE16797 datasets in the Gene Expression Omnibus database, analyzed differentially expressed genes (DEGs), and intersected the DEGs with the immune-related genes downloaded from the ImmPort database to obtain differentially expressed immune-related genes (DEIGs). Then CIBERSORT algorithm was used to calculate the immune cell compositions, followed by the WGCNA analysis to identify the module genes associated with immune cell infiltration. Next, we took the intersection of the selected module genes and DEIGs, then performed GO and KEGG enrichment analysis. Moreover, ROC curve validation, Spearman analysis with immune cells, TF, and miRNA regulation network, and potential drug prediction were implemented for the finally obtained hub genes. Results: The CIBERSORT algorithm showed that neutrophil expression was significantly higher in IVIG-resistant patients compared to IVIG-responsive patients. Next, we got differentially expressed neutrophil-related genes by intersecting DEIGs with neutrophil-related module genes obtained by WGCNA, for further analysis. Enrichment analysis revealed that these genes were associated with immune pathways, such as cytokine-cytokine receptor interaction and neutrophil extracellular trap formation. Then we combined the PPI network in the STRING database with the MCODE plugin in Cytoscape and identified 6 hub genes (TLR8, AQP9, CXCR1, FPR2, HCK, and IL1R2), which had good diagnostic performance in IVIG resistance according to ROC analysis. Furthermore, Spearman's correlation analysis confirmed that these genes were closely related to neutrophils. Finally, TFs, miRNAs, and potential drugs targeting the hub genes were predicted, and TF-, miRNA-, and drug-gene networks were constructed. Conclusion: This study found that the 6 hub genes (TLR8, AQP9, CXCR1, FPR2, HCK, and IL1R2) were significantly associated with neutrophil cell infiltration, which played an important role in IVIG resistance. In a word, this work rendered potential diagnostic biomarkers and prospective therapeutic targets for IVIG-resistant patients.

Metabolomics analysis of the serum from children with urolithiasis using UPLC-MS.

Pediatric urolithiasis is a common urologic disease with high morbidity and recurrence rates. Recent studies have shown that metabolic dysfunction plays a vital role in the pathogenesis of urolithiasis, especially in children, but the specific mechanism is still unclear. Metabolomics is an ideal technology for exploring the mechanism of metabolic disorders in urolithiasis. In the present study, a serum metabolomics based on ultra-performance liquid chromatography mass spectrometry was performed. A total of 50 children subjects were recruited for the study, including 30 patients with kidney stones and 20 normal controls (NCs). Principal component analysis and orthogonal partial least-squares determinant analysis were carried, and 40 metabolites were found to be significantly altered in patients with kidney stones, mainly involving retinol metabolism, steroid hormone biosynthesis, and porphyrin and chlorophyll metabolism. The kidney stone group appeared to have a lower serum level of bilirubin, but a relative higher level of retinal, all-transretinoic acid, progesterone, and prostaglandin E2 compared with those of the NC group. All the findings suggest that patients with urolithiasis have several metabolic characteristics, which are related to stone formation or compensation. These metabolites and pathways are very likely associated with development of kidney stones and should be considered as potential novel targets for treatment and prevention.

Polycomb chromobox (Cbx) 7 modulates activation-induced CD4+ T cell apoptosis.

CD4(+) T cell polarization plays a critical role in a number of immune disorders; the pathogenesis is unclear. Chromobox homolog 7 (Cbx7) is involved in the gene transcription of several cell types. This study aims to investigate the mechanism by which Cbx7 modulates the CD4(+) T cell polarization. Expression of Cbx7 was assessed by quantitative RT-PCR and Western blotting. Apoptosis of CD4(+) T cell was analyzed by flow cytometry. The FasL promoter methylation was evaluated by the methylation specific PCR. The results showed that CD4(+) CD25(-) T cells express Cbx7 that was increased significantly after activation by exposing to anti-CD3/CD28 Ab, but suppressed by exposing to specific antigens. More apoptotic cells were detected in CD4(+) T cells with the Cbx7 gene knockdown. Exposure to insulin-like growth factor-1 up regulated the expression of Cbx7 in CD4(+) T cells. After antigen-specific TCR activation, Cbx7-deficient CD4(+) T cells expressed more FasL and showed the FasL gene promoter hyper demethylation than wild CD4(+) T cells. In addition, CD4(+) T cells with overexpression of Cbx7 showed lower levels of FasL gene promoter demethylation. We conclude that CD4(+) T cells express Cbx7; the latter prevents FasL expression and the activation-induced CD4(+) T cell apoptosis.