Clinical practice guidance for childhood-onset systemic lupus erythematosus-secondary publication

Macrophage Activation Syndrome as an Atypical Manifestation of Mixed Connective Tissue Disease in a 14-Year-Old Girl: A Case Report

Macrophage activation syndrome (MAS) is a subtype of secondary hemophagocytic lymphohistiocytosis (HLH) associated with rheumatic diseases. It is a life-threatening condition characterized by hypercytokinemia due to uncontrolled T-cell and macrophage activation. MAS is an uncommon complication in patients with mixed connective tissue disease (MCTD), particularly in those without a prior diagnosis or treatment. We report the case of a previously healthy 14-year-old Japanese girl admitted to our hospital with severe headache and persistent fever. Despite an initially unremarkable examination, she subsequently developed altered mental status, arthralgia, and a small erythema. Laboratory findings revealed cytopenia and coagulation abnormalities, along with elevated levels of C-reactive protein, liver enzymes, triglycerides, and ferritin. Although she did not meet the HLH-2004 diagnostic criteria established by the Histiocyte Society, MAS was suspected based on early diagnostic criteria for MAS associated with other rheumatic diseases. Prompt initiation of steroid pulse therapy led to rapid clinical improvement. Further serological testing revealed strongly positive anti-U1-ribonucleoprotein (RNP) antibodies, confirming a diagnosis of MCTD. Over one year of follow-up, the patient remained well-controlled on a tapered dose of prednisolone, with no serological relapse. This case highlights the importance of early diagnosis and treatment of MAS, even when the underlying disease is unknown. Additionally, cytokine profiling may provide valuable insights into understanding the pathogenesis of diseases that cause hypercytokinemia.

An artificial neural network model based on autophagy-related genes in childhood systemic lupus erythematosus

Background

Childhood systemic lupus erythematosus (cSLE) is a multisystemic, life-threatening autoimmune disease. Compared to adults, SLE in childhood is more active, can cause multisystem involvement including renal, neurological and hematological, and can cause cumulative damage across systems more rapidly. Autophagy, one of the core functions of cells, is involved in almost every process of the immune response and has been shown to be associated with many autoimmune diseases, being a key factor in the interplay between innate and adaptive immunity. Autophagy influences the onset, progression and severity of SLE. This paper identifies new biomarkers for the diagnosis and treatment of childhood SLE based on an artificial neural network of autophagy-related genes.

Methods

We downloaded dataset GSE100163 from the Gene Expression Omnibus database and used Protein–protein Interaction Network (PPI) and Least Absolute Shrinkage and Selection Operator (LASSO) to screen the signature genes of autophagy-related genes in cSLE. A new artificial neural network model for cSLE diagnosis was constructed using the signature genes. The predictive efficiency of the model was also validated using the dataset GSE65391. Finally, "CIBERSORT" was used to calculate the infiltration of immune cells in cSLE and to analyze the relationship between the signature genes and the infiltration of immune cells.

Results

We identified 37 autophagy-related genes that differed in cSLE and normal samples, and finally obtained the seven most relevant signature genes for cSLE (DDIT3, GNB2L1, CTSD, HSPA8, ULK1, DNAJB1, CANX) by PPI and LASOO regression screening, and constructed an artificial neural network diagnostic model for cSLE. Using this model, we plotted the ROC curves for the training and validation group diagnoses with the area under the curve of 0.976 and 0.783, respectively. Finally, we performed immunoassays on cSLE samples, and the results showed that Plasma cells, Macrophages M0, Dendritic cells activated and Neutrophils were significantly infiltrated in cSLE.

Conclusion

We constructed an artificial neural network diagnostic model of seven autophagy-related genes that can be used for the diagnosis of cSLE. Meanwhile, the characteristic genes affect the immune infiltration of cSLE, which may provide new perspectives for the exploration of cSLE treatment and related mechanisms.