Deciphering gastric inflammation-induced tumorigenesis through multi-omics data and AI methods.

Area of Science:

• Oncology
• Gastroenterology
• Systems Biology

Background:

• Gastric cancer (GC) is a leading cause of cancer mortality globally.
• Inflammation-driven tumorigenesis is a primary mechanism in GC development.
• Understanding these mechanisms is crucial for improving GC prevention and treatment.

Purpose of the Study:

• To summarize biological knowledge on gastric inflammation-induced tumorigenesis.
• To characterize multi-omics data and systems biology methods for GC research.
• To propose translational applications for early GC detection and precision therapy.

Main Methods:

• Review of biological knowledge on inflammation-induced GC.
• Characterization of multi-omics data relevant to GC.
• Application of network-based algorithms and systems biology approaches.

Main Results:

• Integration of multi-omics data reveals key features of inflammation-driven GC.
• Network-based algorithms dissect complex biological interactions in GC development.
• Identification of potential early warning biomarkers and precise treatment strategies.

Conclusions:

• A comprehensive understanding of inflammation's role in GC is essential.
• Multi-omics and systems biology offer powerful tools for GC research.
• This review provides insights for advancing GC precision oncology and prevention strategies.