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Updated: May 10, 2025

Paramyxoviruses for Tumor-targeted Immunomodulation: Design and Evaluation Ex Vivo
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Convergent Mechanisms in Virus-Induced Cancers: A Perspective on Classical Viruses, SARS-CoV-2, and AI-Driven

Thorsten Rudroff1

  • 1Turku PET Centre, University of Turku, Turku University Hospital, 20520 Turku, Finland.

Infectious Disease Reports
|April 25, 2025
PubMed

Insights

This study explores how SARS-CoV-2 may cause cancer by comparing it to known cancer-causing viruses. It highlights the use of artificial intelligence (AI) and suggests long-term monitoring for COVID-19 survivors.

Area of Science:

  • Virology
  • Oncology
  • Artificial Intelligence

Background:

  • Emerging viruses like SARS-CoV-2 (Severe Acute Respiratory Syndrome Coronavirus 2) raise concerns about potential long-term health effects, including oncogenesis.
  • Established oncoviruses provide a framework for understanding viral mechanisms that can lead to cancer development.
  • The intersection of virology and artificial intelligence (AI) offers new avenues for investigating complex biological processes like viral oncogenesis.

Purpose of the Study:

  • To examine potential oncogenic mechanisms of SARS-CoV-2 by comparing it with known cancer-causing viruses.
  • To explore the role of artificial intelligence (AI) in deciphering viral oncogenesis.
  • To integrate neuroimaging findings and propose future research directions for virus-induced cancers.

Main Methods:

  • Comparative analysis of SARS-CoV-2 with established oncoviruses.
  • Integration of classical virological approaches with AI-driven data analysis.
  • Review of PET/FDG imaging studies to assess metabolic alterations.

Main Results:

  • Identified shared oncogenic mechanisms including cell cycle dysregulation, inflammatory signaling, immune evasion, and metabolic reprogramming.
  • Proposed novel hypotheses for SARS-CoV-2's oncogenic potential, supported by metabolic imaging evidence.
  • Demonstrated the utility of AI in analyzing viral oncogenesis.

Conclusions:

  • Combining traditional virology with AI can enhance understanding and prevention of virus-induced cancers.
  • Persistent metabolic alterations observed in COVID-19 survivors warrant further investigation for oncogenic risks.
  • Long-term monitoring of COVID-19 survivors is crucial for identifying potential cancer development.
Keywords:
SARS-CoV-2artificial intelligencemetabolic programmingneuroimagingviral oncogenesis

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