PDF(Pubmed)
Abstract：
UNASSIGNED: Due to a lack of early diagnosis methods and effective drugs, pancreatic ductal adenocarcinoma (PDAC) has an extremely poor prognosis. DNA methylation, transcriptome expression and gene copy number variation (CNV) have critical relationships with development and progression of various diseases. The purpose of the study was to screen reliable early diagnostic biomarkers and potential drugs based on integrative multiomics analysis.
UNASSIGNED: We used methylation, transcriptome and CNV profiles to build a diagnostic model for PDAC. The protein expression of three model-related genes were externally validated using PDAC samples. Then, potential therapeutic drugs for PDAC were identified by interaction information related to existing drugs and genes.
UNASSIGNED: Four significant differentially methylated regions (DMRs) were selected from 589 common DMRs to build a high-performance diagnostic model for PDAC. Then, four hub genes, PHF12, FXYD3, PRKCB and ZNF582, were obtained. The external validation results showed that PHF12, FXYD3 and PRKCB protein expression levels were all upregulated in tumor tissues compared with adjacent normal tissues (P<0.05). Promising candidate drugs with activity against PDAC were screened and repurposed through gene expression analysis of online datasets. The five drugs, including topotecan, PD-0325901, panobinostat, paclitaxel and 17-AAG, with the highest activity among 27 PDAC cell lines were filtered.
UNASSIGNED: Overall, the diagnostic model built based on four significant DMRs could accurately distinguish tumor and normal tissues. The five drug candidates might be repurposed as promising therapeutics for particular PDAC patients.
UNASSIGNED: We used methylation, transcriptome and CNV profiles to build a diagnostic model for PDAC. The protein expression of three model-related genes were externally validated using PDAC samples. Then, potential therapeutic drugs for PDAC were identified by interaction information related to existing drugs and genes.
UNASSIGNED: Four significant differentially methylated regions (DMRs) were selected from 589 common DMRs to build a high-performance diagnostic model for PDAC. Then, four hub genes, PHF12, FXYD3, PRKCB and ZNF582, were obtained. The external validation results showed that PHF12, FXYD3 and PRKCB protein expression levels were all upregulated in tumor tissues compared with adjacent normal tissues (P<0.05). Promising candidate drugs with activity against PDAC were screened and repurposed through gene expression analysis of online datasets. The five drugs, including topotecan, PD-0325901, panobinostat, paclitaxel and 17-AAG, with the highest activity among 27 PDAC cell lines were filtered.
UNASSIGNED: Overall, the diagnostic model built based on four significant DMRs could accurately distinguish tumor and normal tissues. The five drug candidates might be repurposed as promising therapeutics for particular PDAC patients.
摘要：
■由于缺乏早期诊断方法和有效药物，胰腺导管腺癌(PDAC)预后极差.DNA甲基化，转录组表达和基因拷贝数变异（CNV）与各种疾病的发生和发展有着重要的关系。这项研究的目的是筛选可靠的早期诊断生物标志物和潜在的药物的基础上整合的多组分析。
■我们使用甲基化，转录组和CNV谱建立PDAC诊断模型。使用PDAC样品外部验证三个模型相关基因的蛋白质表达。然后，PDAC的潜在治疗药物通过与现有药物和基因相关的相互作用信息进行鉴定.
■从589个常见DMRs中选择了四个显着的差异甲基化区域（DMRs），以建立PDAC的高性能诊断模型。然后，四个枢纽基因，获得PHF12、FXYD3、PRKCB和ZNF582。外部验证结果显示,与癌旁正常组织相比,肿瘤组织中PHF12、FXYD3和PRKCB蛋白表达水平均上调(P<0.05)。通过在线数据集的基因表达分析，筛选并重新利用具有抗PDAC活性的有希望的候选药物。五种药物，包括托普替康,PD-0325901,帕比司他,紫杉醇和17-AAG，在27个PDAC细胞系中活性最高的被过滤。
■总的来说，基于4个显著DMRs建立的诊断模型能准确区分肿瘤组织和正常组织。五种候选药物可能被重新用作特定PDAC患者的有希望的治疗剂。
■我们使用甲基化，转录组和CNV谱建立PDAC诊断模型。使用PDAC样品外部验证三个模型相关基因的蛋白质表达。然后，PDAC的潜在治疗药物通过与现有药物和基因相关的相互作用信息进行鉴定.
■从589个常见DMRs中选择了四个显着的差异甲基化区域（DMRs），以建立PDAC的高性能诊断模型。然后，四个枢纽基因，获得PHF12、FXYD3、PRKCB和ZNF582。外部验证结果显示,与癌旁正常组织相比,肿瘤组织中PHF12、FXYD3和PRKCB蛋白表达水平均上调(P<0.05)。通过在线数据集的基因表达分析，筛选并重新利用具有抗PDAC活性的有希望的候选药物。五种药物，包括托普替康,PD-0325901,帕比司他,紫杉醇和17-AAG，在27个PDAC细胞系中活性最高的被过滤。
■总的来说，基于4个显著DMRs建立的诊断模型能准确区分肿瘤组织和正常组织。五种候选药物可能被重新用作特定PDAC患者的有希望的治疗剂。
