SATB2–Ki67 Axis: Toward Artificial Intelligence-Enhanced Prognostic Models in Colorectal Cancer

Shadbash, Piruz; Bahari Babadi, Marzieh

doi:10.30491/jabr.2025.557020.1938

SATB2–Ki67 Axis: Toward Artificial Intelligence-Enhanced Prognostic Models in Colorectal Cancer

Document Type : Commentary

Authors

Piruz Shadbash ¹^{, 2}

Marzieh Bahari Babadi ³

¹ Basic and Molecular Epidemiology of Gastrointestinal Disorders Research Center, Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran

² Department of Microbiology and Microbial Biotechnology, Faculty of Life Sciences and Biotechnology, Shahid Beheshti University, Tehran, Iran

³ Department of Biochemistry, Medical School, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran

10.30491/jabr.2025.557020.1938

Abstract

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.

Keywords

Colorectal Cancer

SATB2, Ki67

Artificial Intelligence

Digital Oncology