: Chronic Myeloid Leukemia (CML) progresses through chronic, accelerated, and blast crisis phases, posing challenges for disease stratification and predicting therapeutic response. IGF2BP3 (Insulin-like Growth Factor 2 mRNA Binding Protein 3) has recently gained attention as a potential prognostic biomarker due to its role in RNA stabilization and oncogenic signaling.

: This study employed a multi-platform approach, utilizing immunohistochemistry (IHC), ELISA, qRT-PCR, and Western blotting to assess IGF2BP3 expression in 121 CML patient samples across various disease phases. Statistical modeling (R-Studio) followed by Advanced artificial intelligence (ChatGPT 4.0) was employed to correlate IGF2BP3 expression with clinical parameters and therapeutic response outcomes.

: IGF2BP3 expression showed a stepwise increase from the chronic to blast crisis phase, correlating with disease severity and therapeutic non-responsiveness. Both IHC staining intensity and serum IGF2BP3 levels were highest in blast crisis patients, findings further validated by qRT-PCR and Western blot analyses. Statistical model (Chat GPT & R-Studio) based regression modeling confirmed a strong association between IGF2BP3 levels, P210 translocation percentage, and blast count. Notably, patients who were non-responsive to therapy exhibited significantly elevated IGF2BP3 expression compared to responders.

: IGF2BP3 serves as a robust biomarker for disease progression and therapeutic resistance in CML. Elevated IGF2BP3 expression may identify patients at higher risk of poor treatment response and relapse, making it valuable for risk stratification and longitudinal disease monitoring. While this study does not address therapeutic targeting of IGF2BP3, its strong association with resistance phenotypes supports further exploration of IGF2BP3 as a predictive biomarker in precision hematologic oncology.