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基于GEO数据库的胃癌差异表达基因系统分析及潜在中药靶向筛选

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中草药 | 数据挖掘与循证医学 2026,57(8): 3099-3109

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中草药 |数据挖掘与循证医学 2026 , 57 (8) : 3099 -3109

基于GEO数据库的胃癌差异表达基因系统分析及潜在中药靶向筛选

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沈翊康¹, 庞华鑫¹, 刘明睿¹, 刘海煜¹, 马鹏珍¹, 李雅宁¹, 王麒皓¹, 谢晓霞¹, 张小平¹, 赵玉凤¹

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通讯作者:

赵玉凤

作者简介:

沈翊康: 沈翊康(1995-),男,汉族,江苏无锡人,博士研究生,研究方向为真实世界中医药大数据分析与应用。

Systematic analysis of differentially expressed genes in gastric cancer based on GEO database and targeted screening of potential traditional Chinese medicines

SHEN Yikang, PANG Huaxin, LIU Mingrui, LIU Haiyu, Ma Pengzhen, LI Yaning, WANG Qihao, XIE Xiaoxia, ZHANG Xiaoping, ZHAO Yufeng

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doi: 10.7501/j.issn.0253-2670.2026.08.022

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摘要

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目的基于基因表达综合数据库（Gene Expression Omnibus，GEO）整合胃癌差异表达基因，系统鉴定肿瘤进展相关核心靶点，并通过网络距离预测具有治疗潜力的中药，为胃癌中西医结合精准干预提供分子依据。方法从GEO下载21个胃癌数据集（胃癌2 125例、正常367例），构建表达矩阵。采用limma包筛选差异表达基因（differentially expressed genes，DEGs）；加权基因共表达网络分析（weighted gene co-expression network analysis，WGCNA）构建共表达网络，鉴定与疾病表型最相关模块；基因本体（gene ontology，GO）和京都基因与基因组百科全书（Kyoto encyclopedia of genes and genomes，KEGG）富集分析关键基因功能；构建7种机器学习模型，SHapley可加性解释（SHapley additive exPlanations，SHAP）特征重要性；基于人类蛋白质相互作用（protein-protein interaction，PPI）网络计算中药靶点模块与胃癌关键基因的网络距离，筛选拓扑接近中药并统计四气、五味、归经与功效。结果共获455个DEGs，WGCNA划分31个模块，浅黄色模块（r＝0.56，q＜0.01）含194个枢纽基因（hub genes），与DEGs交集得177个关键基因。富集分析显示GO-生物过程（biological processes，BP）集中于细胞外基质组织与黏附，GO-细胞成分（cell component，CC）富集含胶原细胞外基质（extracellular matrix，ECM）与黏附斑，GO-分子功能（molecular function，MF）突出整合素/生长因子结合；KEGG涵盖actin骨架调控、磷脂酰肌醇-3-羟激酶（phosphatidylinositol-3-hydroxykinase，PI3K）-蛋白激酶B（protein kinase B，Akt）及白细胞介素-17（interleukin-17，IL-17）信号。随机森林（random forest，RF）模型准确率0.991，SHAP一致识别SULF1、THY1、DNER、SPINK7为首位贡献基因。网络距离筛选出牛蒡子、夏枯草、苍术、川贝、女贞子、地耳草、桃仁等前15味中药，四气以凉为主，五味以苦为先，归经肝、胃、肺，功效以清热为主、补虚次之。结论系统揭示了胃癌的分子机制，预测清热解毒、滋阴活血类中药具多靶点抗胃癌潜力，为胃癌精准诊疗与中医药现代化提供新策略。

关键词

胃癌 / 差异基因 / SULF1 / THY1 / DNER / SPINK7 / 清热解毒 / 滋阴活血

Abstract

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Objective To integrate differentially expressed genes (DEGs) in gastric cancer (GC) from the Gene Expression Omnibus (GEO) database, systematically identify core targets associated with tumor progression, and predict therapeutic Chinese medicines via network distance, providing molecular evidence for integrated traditional Chinese and Western medicine precision intervention in GC. Methods A total of 21 GC datasets (2 125 GC, 367 normal samples) were downloaded from GEO to construct an expression matrix. DEGs were screened using the limma package (|log₂(FC)| > 1, FDR < 0.05), weighted gene co-expression network analysis (WGCNA) was performed to identify modules most correlated with disease phenotype, gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) enrichment analyses were conducted on key genes, seven machine learning models were built with SHapley additive exPlanations (SHAP) for feature importance interpretation; network distance between Chinese medicine target modules and GC key genes was calculated based on the human PPI network to screen topologically proximal medicines, with statistics on four properties, five flavors, meridian tropism, and efficacy. Results A total of 455 DEGs were obtained. WGCNA yielded 31 modules, with the light-yellow module (r = 0.56, q < 0.01) containing 194 hub genes; intersection with DEGs produced 177 key genes. Enrichment analysis showed GO-biological processes (BP) focused on extracellular matrix organization and adhesion, GO-cell component (CC) on collagen-containing ECM and focal adhesion, GO-molecular function (MF) on integrin/growth factor binding, and KEGG on actin cytoskeleton regulation, phosphatidylinositol-3-hydroxykinase (PI3K)-protein kinase B (Akt), and interleukin-17 (IL-17) signaling. The random forest (RF) model achieved 0.991 accuracy, with SHAP consistently ranking SULF1, THY1, DNER, and SPINK7 as top contributors. Network distance screening identified Arctii Fructus, Prunellae Spica, Atractylodis Rhizoma, Fritillariae Cirrhosae Bulbus, Ligustri Lucidi Fructus, Hypocreaceae, and Persicae Semen among the top 15 medicines, characterized by cool/cold properties, bitter flavor, liver/stomach/lung tropism, and primarily heat-clearing with deficiency-tonifying efficacy. Conclusion This study systematically elucidates GC molecular mechanisms, predicting multi-target anti-GC potential of heat-clearing, yin-nourishing, and blood-activating Chinese medicines, and provides novel strategies for GC precision diagnosis/treatment and modernization of traditional Chinese medicine.

Key words

gastric cancer / differentially expressed genes / SULF1 / THY1 / DNER / SPINK7 / heat-clearing and detoxifying / yin-nourishing and blood-activating

引用本文

沈翊康, 庞华鑫, 刘明睿, 刘海煜, 马鹏珍, 李雅宁, 王麒皓, 谢晓霞, 张小平, 赵玉凤. 基于GEO数据库的胃癌差异表达基因系统分析及潜在中药靶向筛选. 中草药, 2026 , 57 (8) : 3099 -3109 . DOI: 10.7501/j.issn.0253-2670.2026.08.022

SHEN Yikang, PANG Huaxin, LIU Mingrui, LIU Haiyu, Ma Pengzhen, LI Yaning, WANG Qihao, XIE Xiaoxia, ZHANG Xiaoping, ZHAO Yufeng. Systematic analysis of differentially expressed genes in gastric cancer based on GEO database and targeted screening of potential traditional Chinese medicines[J]. Chinese Traditional and Herbal Drugs, 2026 , 57 (8) : 3099 -3109 . DOI: 10.7501/j.issn.0253-2670.2026.08.022

基金

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国家自然科学基金面上项目 (82374621); 中国中医科学院创新工程项目 (CI2021A05042); 国家自然科学基金面上项目 (82575263)

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Sung H, Ferlay J, Siegel R L, et al. Global cancer statistics 2020:GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries[J]. CA Cancer J Clin, 2021, 71(3):209-249.
World Cancer Research Fund. Stomach cancer statistics[EB/OL]. (2024-04-10)[2025-11-09]. https://www.wcrf.org/preventing-cancer/cancer-statistics/stomach-cancer-statistics/.
Morgan E, Arnold M, Camargo M C, et al. The current and future incidence and mortality of gastric cancer in 185 countries, 2020-2040[J]. EClin Med, 2022, 47:101406.
国家癌症中心. 中国恶性肿瘤流行病学报告(2023)[R]. 北京:国家癌症中心, 2023.
国家卫生健康委员会. 中国胃癌诊疗规范(2023版)[M]. 北京:人民卫生出版社, 2023.
Zeng H, Wang C, Song L Y, et al. Economic evaluation of FLOT and ECF/ECX perioperative chemotherapy in patients with resectable gastric or gastro-oesophageal junction adenocarcinoma[J]. BMJ Open, 2022, 12(11):e060983.
Bang Y J, Van Cutsem E, Feyereislova A, et al. Trastuzumab in combination with chemotherapy versus chemotherapy alone for treatment of HER2-positive advanced gastric or gastro-oesophageal junction cancer (ToGA):A phase 3, open-label, randomised controlled trial[J]. Lancet, 2010, 376(9742):687-697.
Janjigian Y Y, Ajani J A, Moehler M, et al. First-line nivolumab plus chemotherapy for advanced gastric, gastroesophageal junction, and esophageal adenocarcinoma:3-year follow-up of the phase III CheckMate 649 trial[J]. J Clin Oncol, 2024, 42(17):2012-2020..
Bagheri M, Akrami H. Studying the non-coding RNA expression and its role in drug resistance mechanisms of gastric cancer[J]. Pathol Res Practice, 2025, 265:155742.
张声生. 中华脾胃病学[M]. 北京:人民卫生出版社, 2016:1895.
Xie W J, Zhang Y S, Tang J Y, et al. Efficacy and safety of traditional Chinese medicines as a complementary therapy combined with chemotherapy in the treatment of gastric cancer:An overview of systematic reviews and meta-analyses[J]. Integr Cancer Ther, 2024, 23:15347354231225961.
Dai Z L, Tan C F, Wang J, et al. Traditional Chinese medicine for gastric cancer:An evidence mapping[J]. Phytother Res, 2024, 38(6):2707-2723.
Cristescu R, Lee J, Nebozhyn M, et al. Molecular analysis of gastric cancer identifies subtypes associated with distinct clinical outcomes[J]. Nat Med, 2015, 21(5):449-456.
Cancer Genome Atlas Research Network. Comprehensive molecular characterization of gastric adenocarcinoma[J]. Nature, 2014, 513(7517):202-209.
Barrett T, Wilhite S E, Ledoux P, et al. NCBI GEO:Archive for functional genomics data sets:Update[J]. Nucleic Acids Res, 2013, 41:D991-D995.
Ritchie M E, Phipson B, Wu D, et al. Limma powers differential expression analyses for RNA-sequencing and microarray studies[J]. Nucleic Acids Res, 2015, 43(7):e47.
Zhang Y Q, Parmigiani G, Johnson W E. ComBat-seq:Batch effect adjustment for RNA-seq count data[J]. NAR Genom Bioinform, 2020, 2(3):lqaa078.
Subramanian A, Tamayo P, Mootha V K, et al. Gene set enrichment analysis:A knowledge-based approach for interpreting genome-wide expression profiles[J]. Proc Natl Acad Sci USA, 2005, 102(43):15545-15550.
LaValley M P. Logistic regression[J]. Circulation, 2008, 117(18):2395-2399.
Chen X, Ishwaran H. Random forests for genomic data analysis[J]. Genomics, 2012, 99(6):323-329.
Byvatov E, Schneider G. Support vector machine applications in bioinformatics[J]. Appl Bioinformatics, 2003, 2(2):67-77.
Zhang Z H. Introduction to machine learning:K-nearest neighbors[J]. Ann Transl Med, 2016, 4(11):218.
Kamel H, Abdulah D, Al-Tuwaijari J M. Cancer classification using Gaussian naive Bayes algorithm[A]//2019 International Engineering Conference (IEC)[C]. Erbil:IEEE, 2020:165-170.
de Ville B. Decision trees[J]. Wires Comput Stat, 2013, 5(6):448-455.
Natekin A, Knoll A. Gradient boosting machines, a tutorial[J]. Front Neurorobot, 2013, 7:21.
Lundberg S M, Lee S I. A unified approach to interpreting model predictions[A]//Neural Information Processing Systems[C]. Long Beach:NIPS, 2017.
Morselli Gysi D, do Valle Í, Zitnik M, et al. Network medicine framework for identifying drug-repurposing opportunities for COVID-19[J]. Proc Natl Acad Sci USA, 2021, 118(19):e2025581118.
Yan D Y, Zheng G H, Wang C C, et al. HIT 2.0:An enhanced platform for Herbal Ingredients' Targets[J]. Nucleic Acids Res, 2022, 50(D1):D1238-D1243.
Liu Z H, Cai C P, Du J W, et al. TCMIO:A comprehensive database of traditional Chinese medicine on immuno-oncology[J]. Front Pharmacol, 2020, 11:439.
Ru J L, Li P, Wang J N, et al. TCMSP:A database of systems pharmacology for drug discovery from herbal medicines[J]. J Cheminform, 2014, 6:13.
Huang L, Xie D L, Yu Y R, et al. TCMID 2.0:A comprehensive resource for TCM[J]. Nucleic Acids Res, 2018, 46(D1):D1117-D1120.
Chen X, Zhou H, Liu Y B, et al. Database of traditional Chinese medicine and its application to studies of mechanism and to prescription validation[J]. Br J Pharmacol, 2006, 149(8):1092-1103.
Gao H M, Wang Z M, Li Y J, et al. Overview of the quality standard research of traditional Chinese medicine[J]. Front Med, 2011, 5(2):195-202.
Wang M X, Carver J J, Phelan V V, et al. Sharing and community curation of mass spectrometry data with Global Natural Products Social Molecular Networking[J]. Nat Biotechnol, 2016, 34(8):828-837.
Szklarczyk D, Santos A, Von Mering C, et al. STITCH 5:augmenting protein-chemical interaction networks with tissue and affinity data[J]. Nucleic Acids Res, 2016, 44(D1):D380-D384.
Menche J, Sharma A, Kitsak M, et al. Disease networks. Uncovering disease-disease relationships through the incomplete interactome[J]. Science, 2015, 347(6224):1257601.
Cheng F X, Kovács I A, Barabási A L. Network-based prediction of drug combinations[J]. Nat Commun, 2019, 10(1):1197.
Winkler J, Abisoye-Ogunniyan A, Metcalf K J, et al. Concepts of extracellular matrix remodelling in tumour progression and metastasis[J]. Nat Commun, 2020, 11(1):5120.
Leggett S E, Hruska A M, Guo M, et al. The epithelial-mesenchymal transition and the cytoskeleton in bioengineered systems[J]. Cell Commun Signaling, 2021, 19(1):32.
Gordon-Weeks A, Yuzhalin A E. Cancer extracellular matrix proteins regulate tumour immunity[J]. Cancers, 2020, 12(11):3331.
Li S Y, Sampson C, Liu C H, et al. Integrin signaling in cancer:Bidirectional mechanisms and therapeutic opportunities[J]. Cell Commun Signal, 2023, 21(1):266.
Mao D L, Xu R, Chen H X, et al. Cross-talk of focal adhesion-related gene defines prognosis and the immune microenvironment in gastric cancer[J]. Front Cell Dev Biol, 2021, 9:716461.
Chu Y Q, Ye Z Y, Tao H Q, et al. Relationship between cell adhesion molecules expression and the biological behavior of gastric carcinoma[J]. World J Gastroenterol, 2008, 14(13):1990-1996.
Hałas-Wiśniewska M, Zawadka P, Arendt W, et al. From adhesion to invasion:Integrins, focal adhesion signaling, and actin binding proteins in cervical cancer progression-a scoping review[J]. Cells, 2025, 14(20):1640.
Wang J F, Wang Y, Zhang S W, et al. Expression and prognostic analysis of integrins in gastric cancer[J]. J Oncol, 2020, 2020:8862228.
Fang X C, Chen D M, Yang X Y, et al. Cancer associated fibroblasts-derived SULF1 promotes gastric cancer metastasis and CDDP resistance through the TGFBR3-mediated TGF-β signaling pathway[J]. Cell Death Discov, 2024, 10(1):111.
Rohan P, Dos Santos E C, Abdelhay E, et al. High expression of THY1 in intestinal gastric cancer as a key factor in tumor biology:A poor prognosis-independent marker related to the epithelial-mesenchymal transition profile[J]. Genes, 2023, 15(1):28.
Wang L J, Wu Q, Zhu S, et al. Delta/Notch-like epidermal growth factor-related receptor (DNER) orchestrates stemness and cancer progression in prostate cancer[J]. Am J Transl Res, 2017, 9(11):5031-5039.
Wali Z, Neha, Shamsi A, et al. The SPINK protein family in cancer:Emerging roles in tumor progression, therapeutic resistance, and precision oncology[J]. Pharmaceuticals, 2025, 18(8):1194.
朱虹. 《鼠疫汇编温热逢源》临证精解[M]. 北京:中国医药科技出版社, 2024:55.
杨振方, 王保卫, 刘大勇, 等. 牛蒡子苷元通过调控SETDB1表达对胃癌细胞增殖、迁移和侵袭的影响及其机制研究[J]. 中国药学杂志, 2020, 55(19):1596-1602.
李云川, 王刚, 李强, 等. 夏枯草抑制人胃癌细胞上皮间质化的机制[J]. 西北药学杂志, 2024, 39(2):93-98.
陈晓霞, 路晓庆, 刘向荣, 等. 女贞子-黄芪抗胃癌研究及作用机制的探讨[J]. 中国药物与临床, 2021, 21(23):3801-3804.
韦佳宋. 林才志教授运用桂枝茯苓丸加减治疗寒凝血瘀型消化道恶性肿瘤临床探讨[D]. 南宁:广西中医药大学, 2025.

2026年第57卷第8期

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doi: 10.7501/j.issn.0253-2670.2026.08.022

接收时间：2025-11-13
首发时间：2026-09-09

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2种不同金属材料的力学参数

科 Family	属数 Number of genus	种数 Number of species	占总种数比例 Percentage of total species (%)	属 Genus	种数 Number of species	占总种数比例 Percentage of total species (%)
鹅膏菌科Amanitaceae	2	11	5.26	鹅膏菌属 Amanita	10	4.78
小菇科 Mycenaceae	2	12	5.74	丝盖伞属 Inocybe	5	2.39
多孔菌科 Polyporaceae	8	14	6.70	蜡蘑属 Laccaria	5	2.39
红菇科 Russulaceae	3	23	11.00	小皮伞属 Marasmius	6	2.87
				小菇属 Mycena	11	5.26
				光柄菇属 Pluteus	5	2.39
				红菇属 Russula	17	8.13
				栓菌属 Trametes	5	2.39

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