Identification and validation of the microRNAs and hub genes for pancreatic ductal adenocarcinoma by an integrated bioinformatic analysis.
Identification and validation of the microRNAs and hub genes for pancreatic ductal adenocarcinoma by an integrated bioinformatic analysis.
复制标题
DOI：
10.21037/jgo-23-192
复制
发表时间：
2023-04-29
影响因子：
2.1
通讯作者：
Fang, Luo
中科院分区：
文献类型：
--
作者：
Xin, Wenxiu;Fu, Yuxuan;Chen, Liangsheng;Ding, Haiying;Feng, Tingting;Sun, Jiao;Qi, Yajun;Hu, Yan;Fang, Qilu;Fang, Luo
在胰腺导管腺癌（PDAC）的发展过程中，异常的微小RNA（miRNAs）表达起着至关重要的作用。本研究旨在鉴定和验证参与PDAC的关键miRNAs和潜在靶基因。进行生物信息学分析以确定它们作为生物标志物和治疗靶点的潜在用途。从Gene Expression Omnibus数据库检索基因谱数据集（GSE 41372和GSE 32688）。差异表达的miRNA（DEM），P值<0.05，以及|倍数变化|>2被发现使用在线服务器Kaplan-Meier法评估DEM的预后价值。此外，使用大卫6.7进行基因本体术语和京都基因和基因组途径百科全书分析。蛋白质相互作用分析采用STRING软件，miRNA-hub基因网络构建采用Cytoscape软件。PDAC细胞用miRNA抑制剂或模拟物转染。细胞计数试剂盒-8测定和末端脱氧核苷酸转移酶dUTP缺口末端标记（TUNEL）染色分别检测细胞增殖和凋亡。进行伤口愈合测定以评价细胞迁移。鉴定了三种DEM（hsa-miR-21- 5 p、hsa-miR-135 b-5 p和hsa-miR-222- 3 p）。hsa-miR-21- 5 p、hsa-miR-135 b-5 p或hsa-miR-222- 3 p的高表达水平预测PDAC患者的总体生存率较差。通路分析显示，DEMs的预测靶基因与多条信号通路（包括“癌症通路”、“癌症中的miRNAs”、“铂类药物耐药”、“脂质与动脉粥样硬化”和“MAPK信号通路”）密切相关。MYC原癌基因（MYC）、磷酸盐和张力蛋白同源基因（PTEN）、聚（ADP-核糖）聚合酶1（PARP 1）、von Hippel-Lindau（VHL）和叉头盒p3（FOXP 3）被确定为潜在的靶基因。抑制hsa-miR-21- 5 p、hsa-miR-135 b-5 p或hsa-miR-222- 3 p表达降低了细胞增殖。hsa-miR-21- 5 p、hsa-miR-135 b-5 p或hsa-miR-222- 3 p的过表达促进PDAC细胞迁移。本研究构建了miRNA-hub基因网络，为PDAC进展提供了新的见解。虽然还需要进一步的研究，但我们的研究结果为PDAC的新的潜在预后标志物和治疗靶点提供了线索。
In the progression of pancreatic ductal adenocarcinoma (PDAC), aberrant micro RNAs (miRNAs) expression plays a crucial role. This study sought to identify and validate the key miRNAs and potential target genes involved in PDAC. A bioinformatic analysis was conducted to determine their potential use as biomarkers and therapeutic targets. Gene profiling data sets (GSE41372 and GSE32688) were retrieved from the Gene Expression Omnibus database. Differentially expressed miRNAs (DEMs) with a P value <0.05, and |fold change| >2 was identified. The prognostic value of the DEMs was accessed using the online server Kaplan-Meier plotter. Further, gene ontology terms and Kyoto Encyclopedia of Genes and Genomes pathway analyses were performed using DAVID 6.7. The protein-protein interaction analyses were conducted with STRING, and miRNA-hub gene networks were constructed using Cytoscape software. The PDAC cells were transfected with miRNA inhibitors or mimics. Cell Counting Kit-8 assays and terminal deoxynucleotidyl transferase dUTP nick end labeling (TUNEL) staining were used to examine cell proliferation and apoptosis, respectively. Wound-healing assays were performed to evaluate cell migration. Three DEMs (hsa-miR-21-5p, hsa-miR-135b-5p, and hsa-miR-222-3p) were identified. High expression levels of hsa-miR-21-5p, hsa-miR-135b-5p, or hsa-miR-222-3p predicted poor overall survival in PDAC patients. The pathway analysis revealed that the predicted target genes of the DEMs were closely related to several signaling pathways (including ‘pathways in cancer’, ‘miRNAs in cancer’, ‘platinum drug resistance’, ‘lipid and atherosclerosis’, and ‘MAPK signaling pathway’). The MYC proto-oncogene (MYC), phosphate and tensin homolog gene (PTEN), poly(ADP-ribose) polymerase 1 (PARP1), von Hippel-Lindau (VHL), and fork head box p3 (FOXP3) were identified as potential target genes. The inhibition of hsa-miR-21-5p, hsa-miR-135b-5p, or hsa-miR-222-3p expression decreased cell proliferation. The overexpression of hsa-miR-21-5p, hsa-miR-135b-5p, or hsa-miR-222-3p facilitated PDAC cell migration. This study constructed the miRNA-hub gene network, which provides novel insights into the PDAC progression. Although further research is required, our results offer clues for new potential prognostic markers and therapeutic targets of PDAC.
登录
查看更多内容
循环单核细胞是胰腺导管腺癌患者的新型预后生物标志物。
影响因子：
5.2
作者：
Hansen, Frederik J.;David, Paul;Akram, Marina;Knoedler, Samuel;Mittelstaedt, Anke;Merkel, Susanne;Podolska, Malgorzata J.;Swierzy, Izabela;Rossdeutsch, Lotta;Kloesch, Bettina;Kouhestani, Dina;Anthuber, Anna;Benard, Alan;Brunner, Maximilian;Krautz, Christian;Gruetzmann, Robert;Weber, Georg F.
通讯作者：
Weber, Georg F.
影响因子：
254.7
作者：
Miller, Kimberly D.;Nogueira, Leticia;Siegel, Rebecca L.
通讯作者：
Siegel, Rebecca L.
影响因子：
14.9
作者：
Huang DW;Sherman BT;Tan Q;Kir J;Liu D;Bryant D;Guo Y;Stephens R;Baseler MW;Lane HC;Lempicki RA
通讯作者：
Lempicki RA
影响因子：
6.1
作者：
通讯作者：
--
影响因子：
29.4
作者：
Sah, Raghuwansh P.;Sharma, Ayush;Chari, Suresh T.
通讯作者：
Chari, Suresh T.