Article(id=1309634759366631901, tenantId=1146029695717560320, journalId=1227999776411738115, issueId=1309634747681304819, articleNumber=null, orderNo=null, doi=10.12048/j.issn.1674-229X.2026.01.006, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1740585600000, receivedDateStr=2025-02-27, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1790170858470, onlineDateStr=2026-09-23, pubDate=1769270400000, pubDateStr=2026-01-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1790170858470, onlineIssueDateStr=2026-09-23, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1790170858470, creator=13701087609, updateTime=1790170858470, updator=13701087609, issue=Issue{id=1309634747681304819, tenantId=1146029695717560320, journalId=1227999776411738115, year='2026', volume='36', issue='1', pageStart='1', pageEnd='80', issueExtLink='null', onlineDate='null', pubDate='1769270400000', pubDateStr='2026-01-25', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1790170855683, creator='13701087609', updateTime=1790219109143, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1309837137445482891, tenantId=1146029695717560320, journalId=1227999776411738115, issueId=1309634747681304819, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1309837137445482892, tenantId=1146029695717560320, journalId=1227999776411738115, issueId=1309634747681304819, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=24, endPage=33, ext={EN=ArticleExt(id=1309634761094685150, articleId=1309634759366631901, tenantId=1146029695717560320, journalId=1227999776411738115, language=EN, title=Bioinformatics-based Screening of Natural Active Ingredients for Chronic Atrophic Gastritis from An Ferroptosis Perspective, columnId=null, journalTitle=Pharmacy Today, columnName=null, runingTitle=null, highlight=null, articleAbstract=
OBJECTIVE The aim of this study is to explore the bioinformatics-based screening of natural active ingredients for chronic atrophic gastritis (CAG) from the perspective of ferroptosis using machine learning and network pharmacology, thereby opening a new horizon for the treatment of CAG.
METHODS Datasets related to CAG were retrieved from the GEO database, and ferroptosis-related targets were collected from the FerrDb database. The R package GEO-query was used to obtain the expression profiles and clinical information of the CAG-related dataset GSE27411, followed by screening of differential targets and CAG differentially expressed targets were used to construct a protein-protein interaction (PPI) network, and the phenotypes of the key targets were analyzed using the VarElect tool. Support vector machine (SVM), a machine learning algorithm, was employed to further screen disease target genes. These natural active ingredients take the overlapping targets of ferroptosis and CAG as their potential action targets. Gene Ontology (GO) functional enrichment analysis and WikiPathwayss enrichment analysis were performed on the potential targets using the R package clusterProfiler. LASSO regression was utilized to screen natural active ingredients related to ferroptosis and CAG. The Coremine database was used to identify small-molecule drugs, and the TCMSP database was employed to find the natural drugs from which these small-molecule natural products are derived as well as their corresponding targets, followed by molecular docking analysis.
RESULTS The GSE27411 dataset was selected for analysis. After differential expression analysis, 993 CAG-related differential targets were identified, along with 455 ferroptosis-related targets. A total of 29 overlapping targets were obtained through intersection analysis. Functional enrichment analysis revealed that these overlapping targets were associated with lipids and therosclerosis, reactive oxygen species (ROS), the RIG-I-like receptor signaling pathway, the FoxO signaling pathway, among others. Seven diagnostic genes were identified via LASSO-COX regression analysis: JUN, EGFR, BRD4, ATG5, BACH1, QSOX1, and NR1D2. Coremine identified several small-molecule drugs, including Apabetalone, cobaltiprotoporphyrin, sulmazole, 4-(4-fluorophenyl)-2-(4-hydroxyphenyl)-5-(4-pyridyl) imidazole, tin protoporphyrin IX, Chloroquine and Gefitinib, all of which exhibited favorable molecular docking result. Among these, cobaltiprotoporphyrin showed the optimal docking performance, suggesting its potential significance for CAG treatment.
CONCLUSION Through bioinformatics approaches and drug regularity analysis, this study provides basic theoretical and data support for the treatment of CAG from the perspective of ferroptosis, and offers a new entry point for the search of therapeutic drugs targeting CAG.
, authors=Yihui ZHENG, Jinye HU, Shaojie HUANG, Yang ZHENG, Jiahui WANG, Lei WANG, Shiquan YANG
*, authorsList=Yihui ZHENG, Jinye HU, Shaojie HUANG, Yang ZHENG, Jiahui WANG, Lei WANG, Shiquan YANG, authorCompany=null, correspAuthors=Shiquan YANG, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1309634761463783903, articleId=1309634759366631901, tenantId=1146029695717560320, journalId=1227999776411738115, language=CN, title=基于生物信息学从铁死亡角度筛选慢性萎缩性胃炎的天然活性成分, columnId=1228268589086606089, journalTitle=今日药学, columnName=论著, runingTitle=null, highlight=null, articleAbstract=
目的 本研究旨在利用机器学习和网络药理学探讨基于生物信息学从铁死亡角度筛选慢性萎缩性胃炎的天然活性成分，为治疗慢性萎缩性胃炎（CAG）开辟新视野。
方法 在GEO数据库中检索与慢性萎缩性胃炎相关的数据集，在FerrDb数据库收集与铁死亡相关靶点。利用R软件包GEO-query获取慢性萎缩性胃炎（CAG）相关数据集GSE27411的表达谱和临床信息，并筛选差异靶点，将铁死亡与CAG差异表达的交互靶点构建蛋白互作网络，并对关键靶点借助VarElect工具进行表型分析。通过机器学习算法支持向量机（support vector machine, SVM）进一步筛选疾病靶点基因。铁死亡与CAG的天然活性成分为潜在作用靶点。利用clusterProfiler包对潜在靶点进行基因本体（Gene Ontology, GO）功能富集分析和WikiPathways通路富集分析。利用Lasso回归筛选铁死亡与CAG的天然活性成分。利用Coremine定位药物小分子，并通过TCMSP找到天然药物小分子所来源的天然药物及其所对应靶点后进行分子对接分析。
结果 筛选到GSE27411数据集，差异分析后获取993个靶点，同时得到455个铁死亡靶点。进行交互处理后得到29个共同靶点，功能富集分析发现共同靶点与脂质与动脉样硬化、活性氧、RIG-I-样受体信号通路、FoxO信号通路等有关。通过LASSO-COX获取7个诊断性基因：JUN、EGFR、BRD4、ATG5、BACH1、QSOX1、NR1D2。Coremine显示Apabetalone、cobaltiprotoporphyrin、sulmazole、4-（4-fluorophenyl）-2-（4-hydroxyphenyl）-5-（4-pyridyl）imidazole、tin protoporphyrin IX、Chloroquine、Gefitinib等多个药物成分，形成了较好的分子对接。其中cobaltiprotoporphyrin对接结果最佳，可能对治疗CAG具有重要意义。
结论 本研究通过生物信息学手段和药物规律分析，从铁死亡角度治疗CAG方向上提供基础理论与数据支持，为寻找CAG治疗靶点药物的研究提供一个新的切入点。
, authors=郑伊惠, 胡金叶, 黄少杰, 郑洋, 王佳慧, 汪磊, 杨仕权
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