Integrating bioinformatics and machine learning identifies HMGA2 as a potential common biomarker for Gastric Cancer and sarcopenia

Han Pengyu; Zheng Jin; Han Xiao; Liu Donghu; Zhou Xingyu; Zhang Jin

doi:10.16689/j.cnki.cn11-9349/r.2026.01.014

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Electronic Journal of Metabolism and Nutrition of Cancer ›› 2026, Vol. 13 ›› Issue (1) : 98-109. DOI: 10.16689/j.cnki.cn11-9349/r.2026.01.014

Original Articles

Integrating bioinformatics and machine learning identifies HMGA2 as a potential common biomarker for Gastric Cancer and sarcopenia

¹Han Pengyu, ¹Zheng Jin, ²Han Xiao, ¹Liu Donghu, ¹Zhou Xingyu, ¹Zhang Jin

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Abstract

Objective To integrate bioinformatics technology and machine learning strategies to screen for common potential biomarkers of gastric cancer and sarcopenia, and to explore Chinese herbal medicines with potential therapeutic value. Method RNA-seq datasets related to gastric cancer and sarcopenia were retrieved from TCGA and GEO databases; the limma software package was used to screen for differentially expressed genes, and weighted gene co-expression network analysis (WGCNA) was combined to identify common targets of the two diseases. Three machine learning algorithms were applied to further screen and determine the core targets; GO functional enrichment analysis, KEGG pathway enrichment analysis, and GSEA enrichment analysis were performed on the core targets, and CIBERSORT algorithm was used to conduct immune infiltration analysis. Potential Chinese herbal medicines targeting the core targets were predicted through Coremine and TCMSP databases, and molecular docking technology was used to verify the binding ability between the active components of herbal medicines and the core targets. Result HMGA2 was identified as the key core target. This gene was significantly highly expressed in gastric cancer tissues (P<0.001) but significantly lowly expressed in sarcopenia tissues (P<0.001). The area under the receiver operating characteristic (ROC) curve (AUC) of HMGA2 was 0.714, indicating a moderate degree of discriminative ability. The results of GO, KEGG, and GSEA functional enrichment analyses showed that HMGA2 was mainly involved in regulating biological processes such as mitochondrial function, inflammatory response, and cell proliferation (P<0.05). Immune infiltration analysis indicated that the expression level of HMGA2 was significantly correlated with the infiltration degree of T cells and NK cells (P<0.05). Molecular docking results confirmed that the active components of 6 Chinese herbal medicines, including Salvia miltiorrhiza, Carthamus tinctorius, and Scutellaria baicalensis, had good binding ability with HMGA2 (binding energy <-5.0 kcal/mol). Conclusion HMGA2 can be used as a common potential biomarker for gastric cancer and sarcopenia; the active components of Chinese herbal medicines targeting HMGA2 can provide new ideas and clues for subsequent experimental research and clinical treatment.

Key words

Gastric-cancer / Sarcopenia / Bioinformatics / Biomarker / Machine learning / Molecular docking / Herb / Pharmaceutical ingredients

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Han Pengyu, Zheng Jin, Han Xiao, Liu Donghu, Zhou Xingyu, Zhang Jin. Integrating bioinformatics and machine learning identifies HMGA2 as a potential common biomarker for Gastric Cancer and sarcopenia[J]. Electronic Journal of Metabolism and Nutrition of Cancer. 2026, 13(1): 98-109 https://doi.org/10.16689/j.cnki.cn11-9349/r.2026.01.014

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