Baqiyatallah University of Medical Sciences Journal of Applied Biotechnology Reports 2322-1186 13 1 2026 03 30 SATB2–Ki67 Axis: Toward Artificial Intelligence-Enhanced Prognostic Models in Colorectal Cancer 1990 1992 242590 10.30491/jabr.2025.557020.1938 EN Piruz Shadbash Basic and Molecular Epidemiology of Gastrointestinal Disorders Research Center, Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran Department of Microbiology and Microbial Biotechnology, Faculty of Life Sciences and Biotechnology, Shahid Beheshti University, Tehran, Iran 0009-0008-1328-482X Marzieh Bahari Babadi Department of Biochemistry, Medical School, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran 0009-0002-9280-389X Journal Article 2025 11 02 <span class="fontstyle0">The convergence of molecular pathology and artificial intelligence (AI) has begun redefining prognostic assessment in colorectal cancer (CRC). The recent study by Kareem et al. (2025) in the Journal of Applied Biotechnology Reports underscores the prognostic potential of combined SATB2 and Ki67 expression in predicting progression-free survival among CRC patients. Beyond its biomarker significance, the SATB2–Ki67 axis embodies a paradigm shift in digital oncology, linking chromatin architecture and cellular proliferation with image-based analytics and computational modeling. This commentary discusses how integrating immunohistochemical (IHC) signatures of SATB2 and Ki67 into AI-driven histopathological platforms could transform CRC prognostication, enabling precision risk stratification, digital biomarker scoring, and personalized therapeutic guidance. We further explore how deep learning algorithms, multiplex IHC, and radiogenomic data fusion could optimize SATB2–Ki67–based predictive models for next-generation oncology practice.</span>  https://www.biotechrep.ir/article_242590_4a3614fc4d0dc24daee73c258db9bb3a.pdf