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This research aims to identify metabolism-related biomarkers that have diagnostic potential for gastric cancer by analyzing transcriptomic data.

April 22, 2026Open Access

Machine Learning Identification of Metabolism-Related Biomarkers with Diagnostic Potential for Gastric Cancer: Multi-Dimensional Transcriptomic Validation

Key Points

This research aims to identify metabolism-related biomarkers that have diagnostic potential for gastric cancer by analyzing transcriptomic data.
Analyzed transcriptomic data from GEO, TCGA, and GTEx databases to identify differential genes.
Employed Limma and WGCNA algorithms for gene identification, followed by Lasso and SVM for hub gene screening.
Conducted single-cell and spatial transcriptomic analyses to explore gene distribution and expression differences between cancerous and normal tissues.
Identified CSE1L, IL32, and CCDC86 as hub genes related to both metabolic syndrome and gastric cancer.
These hub genes showed significant associations with immune cell infiltration and dysregulated metabolic pathways.
qPCR confirmed higher expression levels of these genes in gastric cancer tissues compared to normal tissues.

Abstract

Background: An increasing body of evidence suggests an association between metabolic syndrome and gastric cancer. However, the shared genetic signatures and underlying molecular mechanisms between them remain to be elucidated. Methods: We obtained transcriptomic data for gastric cancer and metabolic syndrome from the GEO, TCGA, and GTEx databases. Using the Limma and WGCNA algorithms respectively, we identified differential genes and co-expression module genes related to metabolic syndrome and gastric cancer. Lasso and SVM were employed to further screen for hub genes, while XGBoost was utilized to enhance the diagnostic value of these hub genes. CIBERSORT and GSVA were applied to assess the correlation among hub genes for immune infiltration and metabolic scores. Single-cell and spatial transcriptomic analyses were conducted to explore cell subpopulations and tissue distribution of hub genes in gastric cancer. We used qPCR experiments to detect expression differences of hub genes between gastric cancer tissues and normal tissues. Results: CSE1L, IL32, and CCDC86 were identified as shared hub genes between metabolic syndrome and gastric cancer. These genes were significantly associated with immune cell infiltration and dysregulated metabolic pathways. Single-cell analysis revealed elevated glycolysis across gastric cancer cell subpopulations, accompanied by enhanced cell–cell interactions. Spatial transcriptomic analysis confirmed the upregulation of hub genes in tumor regions. qPCR further verified significantly higher mRNA expression levels of these genes in gastric cancer tissues than in adjacent normal tissues. Conclusion: CSE1L, IL32, and CCDC86 may represent potential metabolism-related biomarkers associated with gastric cancer and metabolic syndrome. These findings provide additional insight into the molecular links between the two conditions and may support future mechanistic studies and larger-scale clinical validation. Keywords: metabolic syndrome, gastric cancer, diagnosis, immune infiltration

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