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Abstract: FR-OR84

A Novel Bioinformatics Strategy to Explore Key Biomarkers in IgA Nephropathy Using Network Biomarkers and Machine Learning

Session Information

Category: Pathology and Lab Medicine

  • 1800 Pathology and Lab Medicine

Authors

  • Sun, Jiantong, West China Hospital of Sichuan University, Chengdu, Sichuan, China
  • Tan, Jiaxing, West China Hospital of Sichuan University, Chengdu, Sichuan, China
  • Qin, Wei, West China Hospital of Sichuan University, Chengdu, Sichuan, China
Background

Serum metabolites have been established as pivotal biomarkers for a multitude of diseases. This study explored serum metabolic differences between IgAN patients and healthy controls, aiming to identify potential biomarkers for IgAN.

Methods

Serum from 31 healthy individuals and 65 IgAN patients was analyzed using ultra-performance liquid chromatography-mass spectrometry. Machine learning algorithms combined with traditional molecular/ network biomarkers differentiated IgAN from controls, identifying key metabolites and interaction networks. All subjects were followed for >3 years to assess their prognostic value, with mechanisms explored by Mendelian Randomization(MR), network pharmacology, and molecular docking.

Results

Volcano plot and PLS-DA analyses showed distinct serum metabolite profiles in IgAN patients compared to healthy controls, correlating with kidney damage. A machine learning model utilizing network biomarkers also surpassed traditional biomarkers in performance. SHAP analysis recognized Dehydroepiandrosterone sulfate (DHEA-S) as a crucial differentiator, and networks differed significantly between IgAN cases and controls, supporting its strong linkage to IgAN. MR analysis established a causal link between reduced DHEA-S levels and IgAN onset. Long-term follow-up revealed higher DHEA-S levels correlated with better renal outcomes. Network pharmacology identified 42 potential targets and a protein-protein interaction network pinpointed 6 central targets; molecular docking showed stable binding of DHEA-S with 4 proteins, suggesting its involvement in signaling pathways and mechanisms in IgAN.

Conclusion

The network structure of serum metabolites in IgAN was significantly different from that of healthy controls, with changes of DHEA-S closely related to the progression of IgAN.