Integrating machine learning models with multi-omics analysis to decipher the prognostic significance of mitotic catastrophe heterogeneity in bladder cancer

Abstract

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.

Keywords: ANLN; Bladder cancer; Machine learning; Mendelian randomization; Mitotic catastrophe; Prognosis; Single cell.

Fig. 1

Key components of the research

Fig. 2

Construction of prognostic model. (A) C-index heatmap of machine learning algorithms (B) Scatterplot of median stratification (C) Risk score-survival time scatterplot (D-F) KM curves of OS, DFS, PFS in TCGA cohort (G) ROC curves in TCGA cohort (H-J) KM curves of OS in GEO cohorts (K-M) ROC curves in GEO cohorts

Fig. 3

Independent prognosis for risk stratification. (A) Pie charts of the distribution of different clinical characteristics in the high and low risk groups (B) Comparison of risk scores in the context of various clinical characteristics (C) KM survival curves in the context of various clinical characteristics (D) Forest plot of cox regression for risk stratification versus clinical characteristics in each cohort

Fig. 4

Comprehensive enrichment analysis. (A) Bubble diagram of GO enrichment (B) Circle diagram of GO enrichment (C) Bubble diagram of KEGG enrichment (D) Hallmark-based GSVA heatmap (E–F) GSEA in the context of GO (E) and KEGG (F)

Fig. 5

Immunoinfiltration profiling. (A) Histogram of the percentage of immunocytes (B) Box plots comparing immune cell abundance between high and low risk groups (C) Correlation of risk score with immune cells (D) Box plots of differences in immune function between high and low risk groups (E) Differential expression of HLA-related genes (F) Correlation of risk score with immune checkpoint gene expression (G) KM curves for Imvigor 210 cohort (H) Comparison of Risk Score for Immunotherapy Response Subgroups

Fig. 6

Malignancy analysis and mutational landscapes. (A) Differences in malignancy scores between high and low risk groups (B) Correlation of risk scores with malignancy scores (C) Differences in TMB between high and low risk groups (D) Correlation of risk scores with TMB (E) KM survival curves based on the median TMB (F) TMB-Risk stratified survival curves (G) Chromosomal localization of model genes (H) CNV of model genes (I) Mutation waterfall plot for model genes (J-K) Mutation waterfall plots for high (J) and low (K) risk groups

Fig. 7

Screening for model core gene. (A) SurvSHAP (t) model feature importance ranking (B) Aggregated SurvSHAP (t) values (C) Differences in ANLN expression between tumor and normal samples (D) Differential ANLN expression in TCGA paired samples (E) Diagnostic ROC curves of ANLN (F-G) Differences in ANLN expression in patients with different Stages (F) and T-stages (G) (H) Differences in ANLN expression between dead and surviving patients (I) OS, DSS, PFS curves for ANLN stratification in TCGA cohort (J) OS curves for ANLN stratification in each GEO cohort

Fig. 8

MR analysis. (A) Impact of ANLN-related SNPs on BLCA outcomes (B) Forest plot of ANLN-related SNPs on BLCA outcomes (C) funnel plot (D) leave-one-out sensitivity test (E–H) Reverse Mendelian randomization with BLCA-associated SNPs as exposure and ANLN as outcome

Fig. 9

Exploration of expression modes. (A) Immunohistochemical profiles of ANLN (B) qRT-PCR of ANLN in cell lines (C) qRT-PCR of ANLN in tissues

Fig. 10

Comprehensive single-cell analysis. (A) Cluster distribution of GSE135337 (B) Cell type annotation of GSE135337 (C) Expression landscape of ANLN in GSE135337 (D) Disease Ontology enrichment (E) DisGeNET enrichment (F) GO enrichment (G) KEGG enrichment (H) Clusters of GSE130001 (I) Cell type of GSE130001 (J) Expression landscape of ANLN in GSE130001 (K) Expression of ANLN in different clusters (L) Intercellular communication of C1 and C3 (M) GSEA between clusters (N) Display of E2 F_Targets score and G2M_checkpoint score

Fig. 11

Comprehensive functional analysis of ANLN. (A) GSEA between high and low expression groups (B) PPI network around ANLN (C) Differences in immune cell abundance between high and low expression groups (D) Correlation of Tregs abundance with ANLN (E) Differences in immune function between high and low expression groups (F) Correlation of partial immune function with ANLN (G) Survival curves stratified by ANLN in Imvigor 210 cohort (H) Differences in ANLN expression between immunotherapy effect groups (I) Differences in TMB between ANLN groups (J) Differences in ICD score between ANLN groups (K) Differences in methylation-related gene expression between high and low expression groups (L) Methylation-related gene expression correlates with ANLN

Fig. 12

ceRNA network. (A) Identification of potential upstream miRNAs (B) Correlation between hsa-mir-15a-5p and ANLN (C) Differential expression of hsa-mir-15a-5p (D) Stratified survival curves of hsa-mir-15a-5p (E) Binding site of hsa-mir-15a-5p with ANLN (F) Correlation of MIR4435-2HG with hsa-mir-15a-5p (G) Correlation of MIR4435-2HG with ANLN (H) Differential expression of MIR4435-2HG (I) Stratified survival curves of MIR4435-2HG (J) Correlation of MIR4435-2HG with EMT score (K) Correlation of MIR4435-2HG with EMT markers (L) Model diagram of the regulatory network

Fig. 13

Drug prediction and molecular docking. (A) Potential drugs (B-F) Molecular docking site demonstration