AI-Driven Scientific Prompting and Sequential Discovery Pipeline for STAT3-Targeted Predictive Modeling and Therapeutic Insights in Oral Cancer Using Betanin from Beetroot

Abstract

Oral cancer represents one of the most aggressive malignancies affecting the head and neck region and continues to pose significant challenges for global healthcare systems. Despite advances in surgical interventions, radiation therapy, and chemotherapy, survival rates for advanced oral squamous cell carcinoma remain unsatisfactory due to tumor recurrence, metastasis, and resistance to therapeutic agents. Recent molecular studies have revealed that dysregulation of intracellular signaling networks plays a fundamental role in oral cancer progression. Among these signaling mechanisms, the signal transducer and activator of transcription 3 (STAT3) pathway has emerged as a key regulator of tumor cell proliferation, immune evasion, angiogenesis, and metastasis. Parallel to advances in molecular oncology, artificial intelligence has begun to transform biomedical research by enabling rapid analysis of large biological datasets and facilitating the discovery of novel therapeutic molecules. AI-driven scientific prompting represents a computational strategy in which structured instructions guide artificial intelligence systems to perform complex analytical tasks such as target identification, compound screening, predictive modeling, and hypothesis generation. Natural phytochemicals have attracted increasing interest as potential anticancer agents due to their diverse biological activities and relatively low toxicity. Betanin, a betalain pigment extracted from beetroot (Beta vulgaris), has demonstrated strong antioxidant, antiinflammatory, and anticancer properties. Emerging evidence suggests that betanin can modulate multiple signaling pathways involved in cancer development, including pathways related to oxidative stress and inflammation.

This review explores the potential of integrating artificial intelligence with natural product research to identify novel therapeutic strategies targeting STAT3 in oral cancer. The article examines the biological significance of STAT3 signaling in tumor progression, the pharmacological properties of betanin, and the emerging role of AI-driven computational pipelines in predicting molecular interactions between phytochemicals and oncogenic proteins.
Through the integration of computational biology, molecular modeling, and systems pharmacology, AI-guided discovery frameworks offer promising opportunities for accelerating the development of plant-derived therapeutics for oral cancer.