Investigation of ferroptosis-associated molecular subtypes and immunological characteristics in lupus nephritis based on artificial neural network learning

• Published in: Arthritis research & therapy Vol. 26; no. 1; pp. 126 - 16
• Main Authors: Zhang, Li, Yan, Qing, Lin, Miao, He, Juanjuan, Tian, Jie, Chen, Zhihan, Hong, Fuyuan
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 03.07.2024; BioMed Central; BMC
• Subjects: Adult; Analysis; Care and treatment; Cell death; Complications and side effects; Diagnosis; Female; Ferroptosis; Ferroptosis - genetics; Ferroptosis - immunology; Health aspects; Humans; Immune; Lupus nephritis; Lupus Nephritis - genetics; Lupus Nephritis - immunology; Machine Learning; Male; Methods; Middle Aged; Molecular subtype; Nephritis; Neural networks; Neural Networks, Computer; Prognosis; Protective model; Risk factors; Systemic lupus erythematosus; China; Protective model; Ferroptosis; Lupus nephritis; Molecular subtype; Immune; Machine learning
• ISSN: 1478-6362; 1478-6354; 1478-6362
• DOI: 10.1186/s13075-024-03356-z

Lupus nephritis (LN) is a severe complication of systemic lupus erythematosus (SLE) with poor treatment outcomes. The role and underlying mechanisms of ferroptosis in LN remain largely unknown. We aimed to explore ferroptosis-related molecular subtypes and assess their prognostic value in LN patients. Molecular subtypes were classified on the basis of differentially expressed ferroptosis-related genes (FRGs) via the Consensus ClusterPlus package. The enriched functions and pathways, immune infiltrating levels, immune scores, and immune checkpoints were compared between the subgroups. A scoring algorithm based on the subtype-specific feature genes identified by artificial neural network machine learning, referred to as the NeuraLN, was established, and its immunological features, clinical value, and predictive value were evaluated in patients with LN. Finally, immunohistochemical analysis was performed to validate the expression and role of feature genes in glomerular tissues from LN patients and controls. A total of 10 differentially expressed FRGs were identified, most of which showed significant correlation. Based on the 10 FRGs, LN patients were classified into two ferroptosis subtypes, which exhibited significant differences in immune cell abundances, immune scores, and immune checkpoint expression. A NeuraLN-related protective model was established based on nine subtype-specific genes, and it exhibited a robustly predictive value in LN. The nomogram and calibration curves demonstrated the clinical benefits of the protective model. The high-NeuraLN group was closely associated with immune activation. Clinical specimens demonstrated the alterations of ALB, BHMT, GAMT, GSTA1, and HAO2 were in accordance with bioinformatics analysis results, GSTA1 and BHMT were negatively correlated with the severity of LN. The classification of ferroptosis subtypes and the establishment of a protective model may form a foundation for the personalized treatment of LN patients.