A novel spatial framework to validate arsenic exposure gene expression profiling in bladder cancer using multiplex...

Area of Science:

• Oncology
• Genomics
• Pathology

Background:

• Bladder cancer displays significant spatial heterogeneity in gene expression and immune cell infiltration.
• A validated three-gene arsenic-responsive risk model (NKIRAS2, AKTIP, HLA-DQA1) was previously identified in arsenic-exposed individuals and linked to bladder cancer risk.

Purpose of the Study:

• To integrate multiplex fluorescence in situ hybridization (mFISH) with AI-assisted digital pathology.
• To characterize the spatial distribution of the three-gene arsenic-responsive risk model in bladder tumors.
• To explore the relationship between gene expression, tumor grade, and immune infiltration.

Main Methods:

• Whole-slide mFISH imaging of five bladder tumor specimens.
• AI-driven (HoverNet) nuclear segmentation for single-cell gene expression quantification.
• Spatial profiling and correlation analysis with tumor grade and tumor-infiltrating lymphocyte (TIL) density.

Main Results:

• Elevated expression of the three-gene panel was observed in tumor-adjacent regions, correlating strongly with higher tumor grade (r=0.83).
• Gene-enriched regions showed spatial clustering of tumor cells.
• Tumor-infiltrating lymphocyte density was inversely correlated with tumor grade, indicating immune exclusion in high-grade tumors.

Conclusions:

• The combination of spatial transcriptomics and AI pathology is feasible for biomarker validation.
• This integrative approach provides a foundation for population-scale studies on arsenic-associated gene signatures in bladder cancer.
• The findings support the potential of spatial omics in bladder cancer risk stratification and understanding disease progression.