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Updated: May 10, 2025
Culture of Bladder Cancer Organoids as Precision Medicine Tools
Published on: December 28, 2021
Integrating machine learning models with multi-omics analysis to decipher the prognostic significance of mitotic
Haojie Dai1,2, Zijie Yu1,2,3, You Zhao1
1Liyang Branch of the First Affiliated Hospital of Nanjing Medical University, The Affliated Liyang People's Hospital of Kangda College of Nanjing Medical University, Changzhou, Jiangsu, China.
Background:
Mitotic catastrophe is well-known as a major pathway of endogenous tumor death, but the prognostic significance of its heterogeneity regarding bladder cancer (BLCA) remains unclear.
Methods:
Our study focused on digging deeper into the TCGA and GEO databases. Through differential expression analysis as well as Weighted Gene Co-expression Network Analysis (WGCNA), we identified dysregulated mitotic catastrophe-associated genes, followed by univariate cox regression as well as ten machine learning algorithms to construct robust prognostic models. Based on prognostic stratification, we revealed intergroup differences by enrichment analysis, immune infiltration assessment, and genomic variant analysis. Subsequently by multivariate cox regression as well as survshap(t) model we screened core prognostic gene and identified it by Mendelian randomization. Integration of qRT-PCR, immunohistochemistry, and single-cell analysis explored the core gene expression landscape. In addition, we explored the ceRNA axis containing upstream non-coding RNAs after detailed analysis of pathway activation, immunoregulation, and methylation functions of the core genes. Finally, we performed drug screening and molecular docking experiments based on the core gene in the DSigDB database.
Results:
Our efforts culminated in the establishment of an accurate prognostic model containing 16 genes based on Coxboost as well as the Random Survival Forest (RSF) algorithm. Detailed analysis from multiple perspectives revealed a strong link between model scores and many key indicators: pathway activation, immune infiltration landscape, genomic variant landscape, and personalized treatment. Subsequently ANLN was identified as the core of the model, and prognostic analysis revealed that it portends a poor prognosis, further corroborated by Mendelian randomization analysis. Interestingly, ANLN expression was significantly upregulated in cancer cells and specifically clustered in epithelial cells and provided multiple pathways to mediate cell division. In addition, ANLN regulated immune infiltration patterns and was also inseparable from overall methylation levels. Further analysis revealed potential regulation of the MIR4435-2HG, hsa-miR-15a-5p, ANLN axis and highlighted a range of potential therapeutic agents including Phytoestrogens.
Conclusion:
The model we developed was a powerful predictive tool for BLCA prognosis and revealed the impact of mitotic catastrophe heterogeneity on BLCA in multiple dimensions, which then guided clinical decision-making. Furthermore, we highlighted the potential of ANLN as a BLCA target.
