# AI/ML-Assisted Detection of HMGA2 RNA Isoforms in Prostate Cancer Patient Tissue

## TL;DR

This study uses AI/ML to analyze RNA in prostate cancer tissue, finding that a specific HMGA2 isoform is more common in tumors from men of African descent and linked to cancer severity.

## Contribution

The study introduces an AI/ML pipeline for RISH quantification and identifies racial disparities in HMGA2 isoform expression in prostate cancer.

## Key findings

- Wild-type HMGA2 is more abundant in tumors from men of African descent.
- Wild-type HMGA2 levels correlate with higher Gleason grades, indicating cancer aggressiveness.
- Truncated HMGA2 shows inconsistent expression across racial groups.

## Abstract

RNA In Situ Hybridization (RISH) is a powerful tool for spatial gene expression analysis, yet its quantitative use remains limited by the high cost and inaccessibility of commercial software, particularly in under-resourced settings. This study developed an Artificial Intelligence/Machine Learning (AI/ML)-assisted RISH quantification pipeline to evaluate expression patterns of High Mobility Group AT Hook-2 (HMGA2) in prostate cancer (PCa), focusing on racial disparities. We created a machine learning model capable of analyzing RISH images. Expressions of full-length (wild-type) and truncated HMGA2 isoforms were assessed in tissues from 85 men of African descent, European American, and Asian descent. A training dataset was generated for supervised learning analysis of the full cohort. RISH findings revealed that the wild-type HMGA2 isoform was significantly more abundant in tumors from men of African descent and positively correlated with increasing Gleason grade. The truncated isoform was less abundant and did not display a consistent expression pattern across racial groups. These results demonstrate the feasibility of AI/ML-based RISH quantification and suggest that elevated wild-type HMGA2 expression may represent a biomarker linked to prostate cancer aggressiveness and racial disparities. These findings highlight the importance of interdisciplinary collaboration and equitable computational tools in advancing biomarker discovery and addressing cancer health inequities.