From multi-omics to predictive biomarker: AI in tumor microenvironment

Area of Science:

• Oncology
• Bioinformatics
• Artificial Intelligence in Medicine

Background:

• Tumors pose a significant global health challenge.
• The tumor microenvironment (TME) critically influences tumor progression and cell fate.
• Recent AI advancements, especially large language models, are transforming medical research.

Purpose of the Study:

• To review the application of AI algorithms in tumor metabolism studies.
• To explore expression differences between tumor and normal cells using AI.
• To examine AI's role in understanding TME interactions and cytokine functions in cancer.

Main Methods:

• Analysis of metabolomics data.
• Investigation of tumor microenvironment (TME) interactions.
• Application of artificial intelligence (AI) algorithms, including large language models.
• Review of existing literature on AI in cancer metabolism.

Main Results:

• AI algorithms can identify and analyze expression differences between tumor and normal cells.
• AI facilitates the study of metabolic pathways within the tumor microenvironment (TME).
• AI provides insights into the roles of cytokines in tumor progression.

Conclusions:

• AI offers powerful tools for advancing tumor metabolism research.
• AI-driven analysis of metabolomics and TME interactions deepens understanding of cancer pathology.
• This review highlights AI's potential to uncover novel therapeutic strategies by elucidating tumor mechanisms.