Identification of metabolism-immune crosstalk biomarker candidates in Helicobacter pylori infection using integrated bioinformatics and machine learning

Helicobacter pylori ( H. pylori ) is a gram-negative pathogen that can cause various digestive system diseases. The role of metabolic reprogramming in its infection is still unknown. By integrating transcriptome data from the GEO database with metabolic gene sets from GeneCards, we identified core metabolism-related differentially expressed genes (MR-DEGs). Three machine learning algorithms (SVM, LASSO, RF) were used to screen key diagnostic biomarkers. Finally, immune association and binding affinity were evaluated through immune infiltration analysis and molecular docking. A total of 119 MR-DEGs associated with H. pylori infection were identified. Functional enrichment analysis revealed significant involvement in key pathways such as cholesterol metabolism, HIF-1 signaling pathway, and arachidonic acid metabolism, along with significant enrichment in biological processes including small molecule metabolism, inflammatory response, organic acid biosynthetic process, and carboxylic acid biosynthetic process. Based on integrated machine learning algorithms, four core biomarkers (LCN2, ICAM1, SAA1, and CXCL2) with significant diagnostic value were screened, all exhibiting area under the receiver operating characteristic (ROC) curve (AUC) values exceeding 0.7. The CIBERSORT algorithm revealed significant changes in the distribution of immune cell subsets in the H. pylori -infected group. Molecular docking results indicated strong binding affinity (binding energy < -5.0 kcal/mol) between the identified biomarkers and drugs such as carisoprodol, D-Norepinephrine d-bitartrate, and trimethoprim. Finally, we integrated cellular models and clinical samples for validation. Using qRT-PCR, Western blot, and immunohistochemistry, we found that the expression level of LCN2 was significantly upregulated following H. pylori infection. In this study, we identified four potential biomarkers and evaluated the potential pathogenic role of LCN2 in H. pylori infection. These findings provide candidate biomarkers for further mechanistic investigation and offer a foundation for hypothesis-driven studies on therapeutic targeting in H. pylori infection.