Background: Prostate cancer is a prevalent malignancy with a wide range of clinical behaviors. Alpha-Methylacl-CoA Racemase (AMACR) has emerged as a potential biomarker for prostate cancer due to its altered expression patterns. Understanding the association between AMACR expression and histomorphological parameters, particularly Gleason Grade, can contribute to improved prognostication and treatment strategies. Aim: This study aims to investigate the expression of AMACR in prostate cancer tissues and explore its association with Gleason Grade and other histomorphological parameters using immunohistochemically analysis. Methods: The study was conducted in Jinnah Medical College Peshawar during Feb 2021 to Jan 2022. Archival prostate cancer tissue specimens from a cohort of patients were subjected to immunohistochemically staining for AMACR expression. Gleason Grade and additional histomorphological parameters, such as tumor size, lymph node involvement, and perineural invasion, were assessed. Statistical analyses were employed to determine the correlation between AMACR expression and these histomorphological features. Results: Our findings revealed a significant correlation between AMACR expression and Gleason Grade in prostate cancer tissues. Additionally, AMACR expression demonstrated associations with other histomorphological parameters, including tumor size, lymph node involvement, and perineural invasion. The immunohistochemically analysis provided valuable insights into the role of AMACR as a potential marker for prostate cancer aggressiveness. Conclusion: The observed correlation between AMACR expression and Gleason Grade, along with other histomorphological parameters, underscores the potential utility of AMACR as a prognostic biomarker in prostate cancer. These findings may have implications for refining risk stratification and guiding personalized treatment approaches in prostate cancer patients.
Keywords: Prostate cancer, Alpha-Methylacl-CoA Racemase (AMACR), Gleason Grade, Immunohistochemistry, Histomorphological parameters, Biomarker, Prognostication, Personalized treatment.