Unravelling key genes and molecular pathways in gastric inflammation-to-cancer transition through causal discovery: implications for early diagnosis and therapy.

Objective: Gastric cancer remains a major global health concern. This study aimed to identify key genes involved in the inflammation-to-cancer transition in the stomach using an integrative framework combining graph neural networks and causal discovery. Methods: In retrospective study gene expression data from two gastric cancer-related datasets were categorised into two stages: gastritis to precancerous lesions and precancerous lesions to gastric cancer. Differentially expressed genes were identified and analysed for functional enrichment. A relevance network was constructed using Pearson's correlations. Graph sample and aggregate was then applied to the expression matrix, using this network for training. Node embeddings were generated via neighbourhood aggregation, and causal regulatory relationships were inferred using a constraint-based algorithm. Genes with the highest degrees in the causal network were assessed for prognostic relevance using Kaplan-Meier analysis. Results: A total of 857 differentially expressed genes were identified in the gastritis-to-precancerous transition and 337 in the precancerous-to-gastric cancer transition, with 83 differentially expressed genes shared. Enrichment analysis highlighted pathways linked to bacterial responses, especially Conclusion: This study integrates graph neural networks and causal inference to identify critical genes involved in gastric inflammation-cancer progression, providing novel insights into the pathogenesis of gastric cancer and potential biomarkers for validation in future studies.