Objective:

This study aimed to explore potential molecular targets and pathways of Hyp in LN using integrative bioinformatics and network pharmacology, and to provide in vitro validation of key mechanistic hypotheses in an IFN-α-induced mesangial-cell injury model.

Methods:

Differential expression analysis was performed on multiple datasets to identify LN-related target genes. Integrative approaches including machine learning algorithms, network pharmacology, and molecular docking were employed to explore the binding interactions between Hyp and target proteins. In vitro experiments were conducted to validate the mechanism by which Hyp intervenes in glomerular mesangial cell apoptosis.

Results:

A total of 18 genes were identified as potential targets involved in Hyp-induced modulation of LN progression. Machine learning SHAP analysis identified 5 core genes (STAT1, RSAD2, OAS3, GBP1, XAF1) as key regulators. Molecular docking simulations revealed specific binding between Hyp and each target protein, with particularly strong binding affinity between Hyp and XAF1. Cellular experimental results demonstrated that Hyp could inhibit the P53/XAF1 signaling pathway, downregulate the expression of apoptosis-related proteins, and thereby alleviate glomerular mesangial cell apoptosis.

Conclusion:

Hyp attenuated IFN-α-induced glomerular mesangial cell apoptosis by suppressing the P53/XAF1 signaling pathway, suggesting a potential therapeutic mechanism in LN. These integrative bioinformatics and in vitro findings provide a rationale for future in vivo validation and clinical translation.