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Purpose <p>This study aimed to elucidate the pharmacological mechanisms of Sijing Pill (SJP) in treating postmenopausal osteoporosis (PMOP) through an integrative strategy combining UHPLC-MS, bioinformatics, and machine learning.</p> Methods <p>Active compounds in SJP were identified by UHPLC-MS, and potential targets were retrieved from public databases. PMOP-related genes were intersected with SJP targets to construct a protein–protein interaction (PPI) network, and core genes were screened using topological analysis, LASSO regression, random forest, and PPI ranking. Gene Ontology (GO), KEGG, and GSEA analyses were performed to explore functional pathways. GEO dataset GSE230665 was analyzed for differentially expressed genes using “limma” and WGCNA. Diagnostic value was assessed using ROC curves and a nomogram. Immune cell infiltration was evaluated using the CIBERSORT algorithm. Molecular docking and molecular dynamics (MD) simulations were used to validate key compound-target interactions.</p> Results <p>A total of 22 active compounds and 1,175 targets were identified, with 314 overlapping PMOP-related genes. Enrichment analysis highlighted immune-inflammatory-bone pathways, including IL-17 signaling and Th17 differentiation. IL6, IL1B, and TNF were identified as key targets, while FANCA, IL17A, and RET were potential diagnostic markers. Immune cell analysis showed correlations with naïve CD4⁺ T cells, eosinophils, and memory B cells. Docking and MD simulations confirmed stable binding of IL6-skimmin, IL17A-baicalein, and TNF-cryptotanshinone.</p> Conclusion <p>SJP exerts therapeutic effects on PMOP by modulating immune and inflammatory pathways through multi-target interactions, offering mechanistic insights for future pharmacological studies.</p> Graphical Abstract <p></p>

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Revealing the Immunomodulatory Targets and Mechanisms of Sijing Pill for Postmenopausal Osteoporosis: A UHPLC-MS-Based Bioinformatics and Machine Learning Study

  • 期刊论文

  • 发表日期：

    2026年3月6日

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  • Xiaojuan Zhu,
  • Xianting Meng,
  • Yufen Li,
  • Chenhao Li,
  • Xiujing Zhu,
  • Zixin Yin,
  • Tao Jiang,
  • Xin Su

摘要

Purpose

This study aimed to elucidate the pharmacological mechanisms of Sijing Pill (SJP) in treating postmenopausal osteoporosis (PMOP) through an integrative strategy combining UHPLC-MS, bioinformatics, and machine learning.

Methods

Active compounds in SJP were identified by UHPLC-MS, and potential targets were retrieved from public databases. PMOP-related genes were intersected with SJP targets to construct a protein–protein interaction (PPI) network, and core genes were screened using topological analysis, LASSO regression, random forest, and PPI ranking. Gene Ontology (GO), KEGG, and GSEA analyses were performed to explore functional pathways. GEO dataset GSE230665 was analyzed for differentially expressed genes using “limma” and WGCNA. Diagnostic value was assessed using ROC curves and a nomogram. Immune cell infiltration was evaluated using the CIBERSORT algorithm. Molecular docking and molecular dynamics (MD) simulations were used to validate key compound-target interactions.

Results

A total of 22 active compounds and 1,175 targets were identified, with 314 overlapping PMOP-related genes. Enrichment analysis highlighted immune-inflammatory-bone pathways, including IL-17 signaling and Th17 differentiation. IL6, IL1B, and TNF were identified as key targets, while FANCA, IL17A, and RET were potential diagnostic markers. Immune cell analysis showed correlations with naïve CD4⁺ T cells, eosinophils, and memory B cells. Docking and MD simulations confirmed stable binding of IL6-skimmin, IL17A-baicalein, and TNF-cryptotanshinone.

Conclusion

SJP exerts therapeutic effects on PMOP by modulating immune and inflammatory pathways through multi-target interactions, offering mechanistic insights for future pharmacological studies.

Graphical Abstract