Convergent Mechanisms in Virus-Induced Cancers: A Perspective on Classical Viruses, SARS-CoV-2, and AI-Driven Solutions.

Abstract

This perspective examines the potential oncogenic mechanisms of SARS-CoV-2 through comparative analysis with established cancer-causing viruses, integrating classical virological approaches with artificial intelligence (AI)-driven analysis. The paper explores four key themes: shared oncogenic mechanisms between classical viruses and SARS-CoV-2 (including cell cycle dysregulation, inflammatory signaling, immune evasion, and metabolic reprogramming); the application of AI in understanding viral oncogenesis; the integration of neuroimaging evidence; and future research directions. The author presents novel hypotheses regarding SARS-CoV-2's potential oncogenic mechanisms, supported by recent PET/FDG imaging studies showing persistent metabolic alterations. The manuscript emphasizes the transformative potential of combining traditional virological methods with advanced AI technologies for better understanding and preventing virus-induced cancers, while highlighting the importance of long-term monitoring of COVID-19 survivors for potential oncogenic developments.