Integrative Transcriptomics, Machine Learning, and Molecular Dynamics Reveal Honghua Longdan (Gentiana...

Area of Science:

• Oncology
• Pharmacology
• Bioinformatics

Background:

• Bladder cancer (BC) presents high recurrence rates and molecular heterogeneity, with limited effective biomarkers and therapeutic targets.
• The traditional Chinese medicine Honghua Longdan (GR) shows antitumor effects, but its mechanisms in BC are unclear.

Purpose of the Study:

• To identify novel biomarkers and therapeutic targets for bladder cancer using integrated bioinformatics and network pharmacology.
• To investigate the potential of Honghua Longdan-derived compounds in targeting identified bladder cancer pathways.

Main Methods:

• Integrated analysis of TCGA-BLCA and GEO datasets with WGCNA to identify BC-related gene modules.
• Machine learning algorithms for hub gene identification and diagnostic model development.
• Molecular docking and simulations to validate drug-target interactions.

Main Results:

• Eight core drug-disease genes linked to cell-cycle pathways were identified.
• CCNB1 was identified as a hub gene, significantly upregulated in BC and associated with poorer overall survival.
• In silico analysis predicted a stable complex between the GR-derived compound swertiamarin and CCNB1.

Conclusions:

• CCNB1 is a promising biomarker for bladder cancer diagnosis and prognosis.
• The interaction between swertiamarin and CCNB1 suggests CCNB1 as a potential therapeutic target for GR-mediated bladder cancer treatment.
• This study supports the rational development of traditional Chinese medicine-derived therapies for bladder cancer.