In Vivo Modelling of ACTN4 in Oral Cancer: Tracking Tumor Progression and Metastasis

Abstract

Oral squamous cell carcinoma (OSCC) is marked by its aggressive clinical behavior and high propensity for metastasis, necessitating deeper insights into the molecular drivers of tumor progression. ACTN4, an actin-binding protein, has been independently linked to increased cell motility, invasion, and metastatic potential in OSCC, with gene amplification and overexpression serving as prognostic indicators of poor patient outcomes. Recent investigations highlight ACTN4 as a critical modulator in epithelial–mesenchymal transition, extracellular matrix remodeling, and cytoskeletal reorganization, thereby directly promoting both local invasion and distant dissemination of cancer cells. To unravel the mechanistic underpinnings and functional impact of ACTN4 in OSCC, advanced in vivo models—utilizing gene-editing, orthotopic implantation, and metastatic tracking protocols—are now employed to dynamically monitor tumor growth and metastatic events. Integration of artificial intelligence-assisted histopathology, leveraging machine learning algorithms on digitized tissue sections, enables robust quantitative assessments of cellular architecture, invasion patterns, and molecular marker distribution, thus enhancing the precision of tissue phenotyping and metastatic burden analysis. Collectively, these approaches provide a transformative framework for elucidating ACTN4’s pro-metastatic role and identifying actionable therapeutic targets in OSCC.