Identification of candidate diagnostic and prognostic biomarkers for pancreatic carcinoma
Identification of candidate diagnostic and prognostic biomarkers for pancreatic carcinoma
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胰腺癌候选诊断和预后生物标志物的鉴定
DOI：
10.1016/j.ebiom.2019.01.003
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发表时间：
2019-02-01
期刊：
影响因子：
11.1
通讯作者：
Zhu, Yun
中科院分区：
文献类型：
--
作者：
Cheng, Yang;Wang, Kunyuan;Zhu, Yun
背景：胰腺癌（Pancreatic carcinoma，PC）是影响人类健康最严重的恶性肿瘤之一。因此，寻找合适的生物标志物对前列腺癌的诊断和预后判断至关重要。本研究旨在探讨PC的诊断和预后生物标志物。方法：从GSE 62452、GSE 28735和GSE 16515的mRNA表达谱中筛选差异表达基因（DEG）。通过功能分析和蛋白质-蛋白质相互作用网络分析，探讨了DEG的生物学功能。使用ROC曲线分析确定PC的诊断标志物。通过TCGA数据的生存分析确定预后标志物。在临床组织样品中验证了所鉴定基因的蛋白质表达模式。采用回顾性临床研究方法，评价候选蛋白表达与患者生存时间的相关性。此外，使用TCGA数据和临床数据对用于PC预后预测的多个基因/蛋白质的组合进行综合分析。在体外研究进行阐述这些生物标志物在克隆性和PC cells.Findings的侵袭的潜在作用：在总数中，389 DEG被确定。这些基因主要与胰腺分泌、蛋白质消化吸收、细胞色素P450药物代谢和能量代谢途径有关。根据Fisher精确检验过滤出前10个基因。ROC曲线分析显示TMPRSS 4、SERPINB 5、SLC 6A 14、SCEL和TNS 4可作为PC诊断的生物标志物。TCGA数据和临床数据的生存分析表明，TMC 7，TMPRSS 4，SCEL，SLC 2A 1，CENPF，SERPINB 5和SLC 6A 14可以作为PC预后的潜在生物标志物。综合分析表明，所鉴定的基因/蛋白质的组合可以预测PC的预后。结论：综合多组公开数据，初步阐明了PC的作用途径和功能。候选分子标志物被确定为PC的诊断和预后预测，包括一个新的基因，TMC 7。此外，我们发现TMC 7、TMPRSS 4、SCEL、SLC 2A 1、CENPF、SERPINB 5和SLC 6A 14的组合可以作为PC患者预后的有希望的指标。这些候选蛋白可能与PC细胞的克隆性和侵袭性有关。这项研究为PC的分子机制以及诊断和预后标志物提供了新的见解。(C)2019由Elsevier B. V.出版
Background: Pancreatic carcinoma (PC) is one of the most aggressive cancers affecting human health. It is essential to identify candidate biomarkers for the diagnosis and prognosis of PC. The present study aimed to investigate the diagnosis and prognosis biomarkers of PC.Methods: Differentially expressed genes (DEGs) were identified from the mRNA expression profiles of GSE62452, GSE28735 and GSE16515. Functional analysis and the protein-protein interaction network analysis was performed to explore the biological function of the identified DEGs. Diagnosis markers for PC were identified using ROC curve analysis. Prognosis markers were identified via survival analysis of TCGA data. The protein expression pattern of the identified genes was verified in clinical tissue samples. A retrospective clinical study was performed to evaluate the correlation between the expression of candidate proteins and survival time of patients. Moreover, comprehensive analysis of the combination of multiple genes/proteins for the prognosis prediction of PC was performed using both TCGA data and clinical data. In vitro studies were undertaken to elaborate the potential roles of these biomarkers in clonability and invasion of PC cells.Findings: In total, 389 DEGs were identified. These genes were mainly associated with pancreatic secretion, protein digestion and absorption, cytochrome P450 drug metabolism, and energy metabolism pathway. The top 10 genes were filtered out following Fisher's exact test. ROC curve analysis demonstrated that TMPRSS4, SERPINB5, SLC6A14, SCEL, and TNS4 could be used as biomarkers for the diagnosis of PC. Survival analysis of TCGA data and clinical data suggested that TMC7, TMPRSS4, SCEL, SLC2A1, CENPF, SERPINB5 and SLC6A14 can be potential biomarkers for the prognosis of PC. Comprehensive analysis show that a combination of identified genes/proteins can predict the prognosis of PC. Mechanistically, the identified genes attributes to clonability and invasiveness of PC cells.Interpretation: We synthesized several sets of public data and preliminarily clarified pathways and functions of PC. Candidate molecular markers were identified for diagnosis and prognosis prediction of PC including a novel gene, TMC7. Moreover, we found that the combination of TMC7, TMPRSS4, SCEL, SLC2A1, CENPF, SERPINB5 and SLC6A14 can serve as a promising indicator of the prognosis of PC patients. The candidate proteins may attribute to clonability and invasiveness of PC cells. This research provides a novel insight into molecular mechanisms as well as diagnostic and prognostic markers of PC. (C) 2019 Published by Elsevier B.V.