目的 整合生物信息学技术与机器学习策略,筛选胃癌与肌少症的共同潜在生物标志物,并挖掘具有潜在治疗价值的中草药。方法 检索TCGA及GEO数据库,获取胃癌与肌少症相关的RNA-seq数据集;采用limma软件包进行差异表达基因筛选,结合加权基因共表达网络分析(WGCNA)鉴定两者的共同靶点。应用三种机器学习算法进一步筛选并确定核心靶点;对核心靶点进行GO富集分析、KEGG富集分析及GSEA富集分析,同时采用CIBERSORT算法开展免疫浸润分析。通过Coremine数据库及TCMSP数据库预测靶向核心靶点的潜在中草药,利用分子对接技术验证中草药活性成分与核心靶点的结合能力。结果 筛选获得HMGA2作为关键核心靶点,该基因在胃癌组织中呈显著高表达(P<0.001),而在肌少症组织中呈显著低表达(P<0.001)。HMGA2的受试者操作特征(ROC)曲线下面积(AUC)为0.714,提示其具有中等程度的区分能力。GO、KEGG及GSEA富集分析结果显示,HMGA2主要参与调控线粒体功能、炎症反应及细胞增殖等生物学过程(P<0.05)。免疫浸润分析表明,HMGA2的表达水平与T细胞、NK细胞的浸润程度呈显著相关性(P<0.05)。分子对接结果证实,丹参、红花、黄芩等6种中草药的活性成分与HMGA2具有良好的结合能力(结合分数<-5.0 kcal/mol)。结论 HMGA2可作为胃癌与肌少症共有的潜在生物标志物;靶向HMGA2的中草药活性成分可为后续相关实验研究及临床治疗提供新的思路与线索。
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.
关键词
胃癌 /
肌少症 /
生物信息学 /
生物标志物 /
机器学习 /
分子对接 /
中草药 /
药物成分
Key words
Gastric-cancer /
Sarcopenia /
Bioinformatics /
Biomarker /
Machine learning /
Molecular docking /
Herb /
Pharmaceutical ingredients
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基金
陕西省中医药管理局项目(SZY-KJCYC-2025-LC-003);陕西省自然科学基础研究计划项目基金(2025JC-YBMS-868)
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