Integrative Transcriptomics, Machine Learning, and Molecular Dynamics Reveal Honghua Longdan (Gentiana rhodantha)‑Modulated Therapeutic Targets in Bladder Cancer

TL;DR

This study combines transcriptomics, machine learning, and molecular simulations to identify CCNB1 as a potential biomarker and therapeutic target in bladder cancer, using compounds from Honghua Longdan.

Contribution

The study introduces an integrative approach combining transcriptomics, machine learning, and molecular dynamics to identify CCNB1 as a GR-modulated therapeutic target in bladder cancer.

Findings

Eight core drug–disease genes were identified, enriched in cell-cycle-related pathways.

CCNB1 high expression is significantly associated with poorer overall survival in bladder cancer.

Swertiamarin from Honghua Longdan forms a stable complex with CCNB1 in molecular simulations.

Abstract

Bladder cancer (BC) has a high recurrence rate and marked
molecular
heterogeneity, yet effective biomarkers and druggable targets remain
scarce. Honghua Longdan (Gentiana rhodantha Franch., GR), a traditional Chinese medicine, exhibits antitumor
activity, but its therapeutic targets and mechanisms in BC remain
poorly defined. Here, differentially expressed genes (DEGs) in BC
from TCGA-BLCA and GEO were integrated with weighted gene coexpression
network analysis (WGCNA) to identify BC-related gene modules. Active
GR-derived compounds were obtained from HERB v 2.0 and SymMap. Three
machine-learning algorithms were employed to identify hub genes and
build a neural-network diagnostic model. Functional enrichment, survival,
and gene set enrichment analyses together with immunohistochemistry
(IHC) were used to characterize key genes. Molecular docking and all-atom
molecular dynamics simulations were performed to validate interactions
between hub proteins and GR-derived compounds. Integrated analyses
identified eight core drug–disease genes enriched in cell-cycle-related
pathways. Six hub genes were consistently selected by the three algorithms,
and the neural-network model showed excellent diagnostic performance.
High CCNB1 expression was significantly associated with poorer overall
survival (OS), and its expression is markedly upregulated in BC tissues.
In silico simulations suggested that the GR-derived iridoid glycoside
swertiamarin forms a stable complex with CCNB1. This study integrates
network pharmacology, machine learning, and computational biophysics
to identify CCNB1 as a biomarker with diagnostic and prognostic value
in BC. The predicted interaction between swertiamarin and CCNB1 suggests
that CCNB1 is a GR-mediated therapeutic target and supports the rational
discovery of traditional Chinese medicine-derived therapeutics.

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