MiR-522-3p inhibits proliferation and activation by regulating the expression of SLC31A1 in T cells

The intricate dance of non-coding RNAs in myasthenia gravis pathogenesis and treatment

Myasthenia gravis (MG) stands as a perplexing autoimmune disorder affecting the neuromuscular junction, driven by a multitude of antibodies targeting postsynaptic elements. However, the mystery of MG pathogenesis has yet to be completely uncovered, and its heterogeneity also challenges diagnosis and treatment. Growing evidence shows the differential expression of non-coding RNAs (ncRNAs) in MG has played an essential role in the development of MG in recent years. Remarkably, these aberrantly expressed ncRNAs exhibit distinct profiles within diverse clinical subgroups and among patients harboring various antibody types. Furthermore, they have been implicated in orchestrating the production of inflammatory cytokines, perturbing the equilibrium of T helper 1 cells (Th1), T helper 17 cells (Th17), and regulatory T cells (Tregs), and inciting B cells to generate antibodies. Studies have elucidated that certain ncRNAs mirror the clinical severity of MG, while others may hold therapeutic significance, showcasing a propensity to return to normal levels following appropriate treatments or potentially foretelling the responsiveness to immunosuppressive therapies. Notably, the intricate interplay among these ncRNAs does not follow a linear trajectory but rather assembles into a complex network, with competing endogenous RNA (ceRNA) emerging as a prominent hub in some cases. This comprehensive review consolidates the landscape of dysregulated ncRNAs in MG, briefly delineating their pivotal role in MG pathogenesis. Furthermore, it explores their promise as prospective biomarkers, aiding in the elucidation of disease subtypes, assessment of disease severity, monitoring therapeutic responses, and as novel therapeutic targets.

Identification and immuno-infiltration analysis of cuproptosis regulators in human spermatogenic dysfunction

Introduction: Cuproptosis seems to promote the progression of diverse diseases. Hence, we explored the cuproptosis regulators in human spermatogenic dysfunction (SD), analyzed the condition of immune cell infiltration, and constructed a predictive model.

Methods: Two microarray datasets (GSE4797 and GSE45885) related to male infertility (MI) patients with SD were downloaded from the Gene Expression Omnibus (GEO) database. We utilized the GSE4797 dataset to obtain differentially expressed cuproptosis-related genes (deCRGs) between SD and normal controls. The correlation between deCRGs and immune cell infiltration status was analyzed. We also explored the molecular clusters of CRGs and the status of immune cell infiltration. Notably, weighted gene co-expression network analysis (WGCNA) was used to identify the cluster-specific differentially expressed genes (DEGs). Moreso, gene set variation analysis (GSVA) was performed to annotate the enriched genes. Subsequently, we selected an optimal machine-learning model from four models. Finally, nomograms, calibration curves, decision curve analysis (DCA), and the GSE45885 dataset were utilized to verify the predictions’ accuracy.

Results: Among SD and normal controls, we confirmed that there are deCRGs and activated immune responses. Through the GSE4797 dataset, we obtained 11 deCRGs. ATP7A, ATP7B, SLC31A1, FDX1, PDHA1, PDHB, GLS, CDKN2A, DBT, and GCSH were highly expressed in testicular tissues with SD, whereas LIAS was lowly expressed. Additionally, two clusters were identified in SD. Immune-infiltration analysis showed the existing heterogeneity of immunity at these two clusters. Cuproptosis-related molecular Cluster2 was marked by enhanced expressions of ATP7A, SLC31A1, PDHA1, PDHB, CDKN2A, DBT, and higher proportions of resting memory CD4+ T cells. Furthermore, an eXtreme Gradient Boosting (XGB) model based on 5-gene was built, which showed superior performance on the external validation dataset GSE45885 (AUC = 0.812). Therefore, the combined nomogram, calibration curve, and DCA results demonstrated the accuracy of predicting SD.

Conclusion: Our study preliminarily illustrates the relationship between SD and cuproptosis. Moreover, a bright predictive model was developed.

A Review on the Role of Non-Coding RNAs in the Pathogenesis of Myasthenia Gravis

Myasthenia gravis (MG) is an autoimmune condition related to autoantibodies against certain proteins in the postsynaptic membranes in the neuromuscular junction. This disorder has a multifactorial inheritance. The connection between environmental and genetic factors can be established by epigenetic factors, such as microRNAs (miRNAs) and long non-coding RNAs (lncRNAs). XLOC_003810, SNHG16, IFNG-AS1, and MALAT-1 are among the lncRNAs with a possible role in the pathoetiology of MG. Moreover, miR-150-5p, miR-155, miR-146a-5p, miR-20b, miR-21-5p, miR-126, let-7a-5p, and let-7f-5p are among miRNAs whose roles in the pathogenesis of MG has been assessed. In the current review, we summarize the impact of miRNAs and lncRNAs in the development or progression of MG.