Data mining and experimental validation identify a core herb combination as a therapeutic agent for malignant...

Area of Science:

• Integrative Oncology
• Pharmacology
• Computational Biology

Background:

• Malignant lymphoma (ML) presents treatment challenges, necessitating novel therapeutic strategies.
• Refractory and relapsed cases of ML have a poor prognosis, underscoring the need for new treatments.
• Traditional Chinese Medicine (TCM) offers a potential source for novel therapeutic agents.

Purpose of the Study:

• To identify potential TCM therapeutic agents for ML using data mining and experimental validation.
• To explore the underlying mechanisms of TCM in ML treatment.
• To identify a core herb combination for ML therapy.

Main Methods:

• Data mining of TCM prescriptions for ML, including association rule mining.
• In vivo experiments using an ML xenograft tumor model.
• Network pharmacology and molecular docking for mechanism exploration.
• Western blot analysis to validate pathway involvement.

Main Results:

• 118 TCM prescriptions and 213 herbs were identified; a core combination of Glycyrrhiza uralensis, Prepared Rehmannia Root, and Astragalus membranaceus was determined.
• The identified herb combination significantly inhibited tumor growth in the ML xenograft model.
• Functional analysis revealed the PI3K-Akt signaling pathway as a key target, with suppressed activation observed in treated tumors.

Conclusions:

• Data mining successfully identified a core TCM herb combination for ML treatment.
• The identified combination demonstrated anti-tumor efficacy in vivo, linked to PI3K-Akt pathway inhibition.
• These findings support TCM as a viable approach for ML therapy, warranting further research and clinical application.