Background: Systemic Lupus Erythematosus (SLE), a prototypical autoimmune disorder, presents a challenge due to the absence of reliable biomarkers for discerning organ-specific damage within SLE. A growing body of evidence underscores the pivotal involvement of N6-methyladenosine (m6A) in the etiology of autoimmune conditions. Methods: The datasets, which primarily encompassed the expression profiles of m6A regulatory genes, were retrieved from the Gene Expression Omnibus (GEO) repository. The optimal model, selected from either Random Forest (RF) or Support Vector Machine (SVM), was employed for the development of a predictive nomogram model. To identify pivotal genes associated with SLE, a comprehensive screening process was conducted utilizing LASSO, SVM-RFE, and RF techniques. Within the realm of SLE susceptibility, Weighted Gene Co-expression Network Analysis (WGCNA) was harnessed to delineate relevant modules and hub genes. Additionally, MeRIP-qPCR assays were performed to elucidate key genes correlated with m6A targets. Furthermore, a Mendelian randomization study was conducted based on genome-wide association studies to assess the causative influence of MMP9 on ischemic stroke (IS), which is not only a severe cerebrovascular event but also a common complication of SLE. Results: Twelve m6A regulatory genes was identified, demonstrating statistical significance (p < 0.05) and utilized for constructing a nomogram model using the RF algorithm. EPSTI1, USP18, HP, and MMP9, as the hub genes, were identified. MMP9 uniquely correlates with m6A modification and was causally linked to an increased risk of IS, as indicated by our inverse variance weighting analysis showing an odds ratio of 1.0134 (95% CI=1.0004– 1.0266, p = 0.0440). Conclusion: Our study identified twelve m6A regulators, shedding light on the molecular mechanisms underlying SLE risk genes. Importantly, our analysis established a causal relationship between MMP9, a key m6A-related gene, and ischemic stroke, a common complication of SLE, thereby providing critical insights for presymptomatic diagnostic approaches.

Background: Invasive Trichosporon asahii ( T. asahii ) infection frequently occurs with a high mortality in immunodeficient hosts, but the pathogenesis of T. asahii infection remains elusive. Circular RNAs (circRNAs) are a type of endogenous noncoding RNA that participate in various disease processes. However, the mechanism of circRNAs in T. asahii infection remains completely unknown. Methods: RNA sequencing (RNA-seq) was performed to analyze the expression profiles of circRNAs, microRNAs (miRNAs), and mRNAs in THP-1 cells infected with T. asahii or uninfected samples. Some of the RNA-seq results were verified by RT-qPCR. Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were used to analyze the differentially expressed mRNAs. A circRNA-miRNA-mRNA network was constructed and verified by dual-luciferase reporter assay and overexpression experiments. Results: A total of 46 circRNAs, 412 mRNAs and 47 miRNAs were differentially expressed at 12 h after T. asahii infection. GO and KEGG analyses showed that the differentially expressed mRNAs were primarily linked to the leukocyte migration involved in the inflammatory response, the Toll-like receptor signaling pathway, and the TNF signaling pathway. A competing endogenous RNA (ceRNA) network was constructed with 5 differentially expressed circRNAs, 5 differentially expressed miRNAs and 42 differentially expressed mRNAs. Among them, hsa_circ_0065336 was found to indirectly regulate PTPN11 expression by sponging miR-505-3p. Conclusions: These data revealed a comprehensive circRNA-associated ceRNA network during T. asahii infection, thus providing new insights into the pathogenesis of the T. asahii -host interactions.

Background: Solar lentigines (SLs), serving as a prevalent characteristic of skin photoaging, present as cutaneous aberrant pigmentation. However, the underlying pathogenesis remains unclear and there is a dearth of reliable diagnostic biomarkers. Objective: The aim of this study was to identify diagnostic biomarkers for SLs and reveal its immunological features. Methods: In this study, gene expression profiling datasets (GSE192564 and GSE192565) of SLs were obtained from the GEO database. The GSE192564 was used as the training group for screening of differentially expressed genes (DEGs) and subsequent depth analysis. Gene set enrichment analysis (GSEA) was employed to explore the biological states associated with SLs. The weighted gene co-expression network analysis (WGCNA) was employed to identify the significant modules and hub genes. Then, the feature genes were further screened by the overlapping of hub genes and up-regulated differential genes. Subsequently, an artificial neural network was constructed for identifying SLs samples. The GSE192565 was used as the test group for validation of feature genes expression level and the model’s classification performance. Furthermore, we conducted immune cell infiltration analysis to reveal the immune infiltration landscape of SLs. Results: The 9 feature genes were identified as diagnostic biomarkers for SLs in this study. And an artificial neural network based on diagnostic biomarkers was successfully constructed for identification of SLs. GSEA highlighted potential role of immune system in pathogenesis of SLs. SLs samples had a higher proportion of several immune cells, including activated CD8 T cell, dendritic cell, myeloid-derived suppressor cell and so on. And diagnostic biomarkers exhibited a strong relationship with the infiltration of most immune cells. Conclusion: Our study identified diagnostic biomarkers for SLs and explored its immunological features, enhancing the comprehension of its pathogenesis.