Oxidative stress is increased in several cancers including prostate cancer, and is currently being exploited in cancer therapy to induce ferroptosis, a novel nonapoptotic form of cell death. High mobility group A2 (HMGA2), a non-histone protein up-regulated in several cancers, can be truncated due to chromosomal rearrangement or alternative splicing of HMGA2 gene. The purpose of this study is to investigate the role of wild-type vs. truncated HMGA2 in prostate cancer (PCa). We analyzed the expression of wild-type vs. truncated HMGA2 and showed that prostate cancer patient tissue and some cell lines expressed increasing amounts of both wild-type and truncated HMGA2 with increasing tumor grade, compared to normal epithelial cells. RNA-Seq analysis of LNCaP prostate cancer cells stably overexpressing wild-type HMGA2 (HMGA2-WT), truncated HMGA2 (HMGA2-TR) or empty vector (Neo) control revealed that HMGA2-TR cells exhibited higher oxidative stress compared to HMGA2-WT or Neo control cells, which was also confirmed by analysis of basal reactive oxygen species (ROS) levels using 2′, 7′-dichlorofluorescin diacetate (DCFDA) dye, the ratio of reduced glutathione/oxidized glutathione (GSH/GSSG) and NADP/NADPH using metabolomics. This was associated with increased sensitivity to RAS-selective lethal 3 (RSL3)-induced ferroptosis that could be antagonized by ferrostatin-1. Additionally, proteomic and immunoprecipitation analyses showed that cytoplasmic HMGA2 protein interacted with Ras GTPase-activating protein-binding protein 1 (G3BP1), a cytoplasmic stress granule protein that responds to oxidative stress, and that G3BP1 transient knockdown increased sensitivity to ferroptosis even further. Endogenous knockdown of HMGA2 or G3BP1 in PC3 cells reduced proliferation which was reversed by ferrostatin-1. In conclusion, we show a novel role for HMGA2 in oxidative stress, particularly the truncated HMGA2, which may be a therapeutic target for ferroptosis-mediated prostate cancer therapy.

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.