Article(id=1308842066650686289, tenantId=1146029695717560320, journalId=1189982191388893191, issueId=1308841897058198067, articleNumber=null, orderNo=null, doi=10.16438/j.0513-4870.2026-0179, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1770652800000, receivedDateStr=2026-02-10, revisedDate=1775664000000, revisedDateStr=2026-04-09, acceptedDate=null, acceptedDateStr=null, onlineDate=1789981865799, onlineDateStr=2026-09-21, pubDate=1783785600000, pubDateStr=2026-07-12, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1789981865799, onlineIssueDateStr=2026-09-21, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1789981865799, creator=13701087609, updateTime=1789981865799, updator=13701087609, issue=Issue{id=1308841897058198067, tenantId=1146029695717560320, journalId=1189982191388893191, year='2026', volume='61', issue='7', pageStart='2377', pageEnd='2820', issueExtLink='null', onlineDate='null', pubDate='1783785600000', pubDateStr='2026-07-12', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1789981825365, creator='13701087609', updateTime=1789984154811, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1308851667525792021, tenantId=1146029695717560320, journalId=1189982191388893191, issueId=1308841897058198067, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1308851667525792022, tenantId=1146029695717560320, journalId=1189982191388893191, issueId=1308841897058198067, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=2668, endPage=2674, ext={EN=ArticleExt(id=1308842066877178706, articleId=1308842066650686289, tenantId=1146029695717560320, journalId=1189982191388893191, language=EN, title=GBCDB: Gallbladder cancer transcriptome/single-cell sequencing data analysis and drug resistance prediction platform, columnId=1190335348761793317, journalTitle=Acta Pharmaceutica Sinica, columnName=Original Articles, runingTitle=null, highlight=null, articleAbstract=

Gallbladder cancer (GBC) is a highly invasive malignant tumor of the digestive system. Chemotherapy plays a crucial role in its advanced treatment, yet significant interpatient variability in drug response and prominent drug resistance issues persist. Currently, there is a lack of systematic transcriptomic databases and drug response prediction tools specifically tailored for GBC. To address this gap, we developed the Gallbladder Cancer Database (GBCDB, http://www.gbcdb.com/). This platform integrates five bulk-transcriptome datasets (126 samples) and one single-cell dataset (24 samples, encompassing 140 870 cells across eight major cell lineages), comprehensively covering neoplastic tissues, adjacent normal tissues, and cholecystitis tissues. GBCDB offers a user-friendly interface supporting flexible query modes for single-gene, dual-gene, and multi-gene analyses. The platform enables comprehensive comparative analysis through multiple visualization tools: Box plots display expression distributions across sample types, correlation scatter plots reveal gene associations, heatmaps visualize gene expression patterns, and Unified Manifold Approximation and Projection (UMAP) is applied to single-cell transcriptomic data. Additionally, the RefDNN model predicts resistance to five commonly used gallbladder cancer chemotherapeutics — gemcitabine, cisplatin, 5-fluorouracil, mitomycin C, and paclitaxel. GBCDB provides systematic data resources and analytical tools for molecular mechanism research and chemotherapy resistance assessment in gallbladder cancer, offering scientific foundations for developing personalized treatment strategies.

, authors=Zheng LU^{1, 2}, Fang-ming SONG³, Jing FU⁴, Chun-liang LIU^{4, *}, Xiao-fei CHEN^{2, *}, authorsList=Zheng LU, Fang-ming SONG, Jing FU, Chun-liang LIU, Xiao-fei CHEN, authorCompany=null, correspAuthors=Chun-liang LIU, Xiao-fei CHEN, authorNote=null, correspAuthorsNote=null, copyrightStatement=Copyright ©2026 Acta Pharmaceutica Sinica. All rights reserved., 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=1308842067980280669, articleId=1308842066650686289, tenantId=1146029695717560320, journalId=1189982191388893191, language=CN, title=GBCDB: 胆囊癌转录组/单细胞测序数据分析及耐药性预测平台, columnId=1190335348896011050, journalTitle=药学学报, columnName=研究论文, runingTitle=null, highlight=null, articleAbstract=

胆囊癌(gallbladder cancer, GBC) 是一类高度侵袭性的消化系统恶性肿瘤, 化疗在其中晚期治疗中具有重要地位, 但患者间的药物反应差异明显, 耐药问题突出。目前尚缺乏专门面向GBC的系统性转录组数据库及药物反应预测工具。为此, 作者开发了胆囊癌数据库(GBCDB, http://www.gbcdb.com/)。该平台整合了5个批量转录组数据集(126份样本) 及1个单细胞数据集(24份样本, 涵盖8大细胞谱系共140 870个细胞), 全面覆盖癌变组织、邻近正常组织及胆囊炎组织。GBCDB提供用户友好界面, 支持单基因、双基因及多基因等灵活查询模式。该平台通过多种可视化工具实现全面比较分析: 箱线图展示样本类型间的表达分布, 相关性散点图呈现基因间关联, 热图将基因表达可视化, 统一流形逼近与投影图(UMAP) 则用于单细胞转录组数据。同时引入RefDNN模型, 成功对吉西他滨、顺铂、5-氟尿嘧啶、丝裂霉素C与紫杉醇共5种胆囊癌常用化疗药物进行耐药性预测。GBCDB为胆囊癌分子机制研究与化疗耐药性评估提供了系统化的数据资源与分析工具, 有望为个体化治疗策略的制定提供科学依据。

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Datasets	Type	Sample size	Source
GBCDB1	Bulk transcriptomics	6	GSE74048
GBCDB2	Bulk transcriptomics	8	GSE100363
GBCDB3	Bulk transcriptomics	6	GSE132223
GBCDB4	Bulk transcriptomics	10	GSE166915
GBCDB5	Bulk transcriptomics	96	EGAS00001003004
GBCDBsc	Single-cell transcriptomics	24	Cell Discovery[13]

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Data Sources

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Datasets	Type	Sample size	Source
GBCDB1	Bulk transcriptomics	6	GSE74048
GBCDB2	Bulk transcriptomics	8	GSE100363
GBCDB3	Bulk transcriptomics	6	GSE132223
GBCDB4	Bulk transcriptomics	10	GSE166915
GBCDB5	Bulk transcriptomics	96	EGAS00001003004
GBCDBsc	Single-cell transcriptomics	24	Cell Discovery[13]

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