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整合分析鉴定胰腺癌进展中与通路相关的竞争性内源 RNA 网络。

Integrated analysis identifies a pathway-related competing endogenous RNA network in the progression of pancreatic cancer.

机构信息

Shengjing Hospital of China Medical University, Shenyang, 110004, Liaoning, China.

Department of General Surgery, Shengjing Hospital of China Medical University, Shenyang, 110004, Liaoning, China.

出版信息

BMC Cancer. 2020 Oct 2;20(1):958. doi: 10.1186/s12885-020-07470-4.

Abstract

BACKGROUND

It is well acknowledged that cancer-related pathways play pivotal roles in the progression of pancreatic cancer (PC). Employing Integrated analysis, we aim to identify the pathway-related ceRNA network associated with PC progression.

METHODS

We divided eight GEO datasets into three groups according to their platform, and combined TCGA and GTEx databases as a group. Additionally, we screened out the differentially expressed genes (DEGs) and performed functional enrichment analysis in each group, and recognized the top hub genes in the most enriched pathway. Furthermore, the upstream of miRNAs and lncRNAs were predicted and validated according to their expression and prognostic roles. Finally, the co-expression analysis was applied to identify a pathway-related ceRNA network in the progression of PC.

RESULTS

A total of 51 significant pathways that common enriched in all groups were spotted. Enrichment analysis indicated that pathway in cancer was greatly linked with tumor formation and progression. Next, the top 20 hug genes in this pathway were recognized, and stepwise prediction and validation from mRNA to lncRNA, including 11 hub genes, 4 key miRNAs, and 2 key lncRNAs, were applied to identify a meaningful ceRNA network according to ceRNA rules. Ultimately, we identified the PVT1/miR-20b/CCND1 axis as a promising pathway-related ceRNA axis in the progression of PC.

CONCLUSION

Overall, we elucidate the pathway-related ceRNA regulatory network of PVT1/miR-20b/CCND1 in the progression of PC, which can be considered as therapeutic targets and encouraging prognostic biomarkers for PC.

摘要

背景

众所周知,癌症相关通路在胰腺癌(PC)的进展中起着关键作用。采用综合分析,我们旨在确定与 PC 进展相关的通路相关 ceRNA 网络。

方法

我们根据平台将八个 GEO 数据集分为三组,并将 TCGA 和 GTEx 数据库组合成一组。此外,我们在每组中筛选出差异表达基因(DEGs)并进行功能富集分析,识别最丰富通路中的顶级枢纽基因。进一步根据表达和预后作用预测和验证 miRNA 和 lncRNA 的上游。最后,进行共表达分析以确定 PC 进展中与通路相关的 ceRNA 网络。

结果

共发现 51 个在所有组中均显著富集的通路。富集分析表明,癌症通路与肿瘤的形成和进展密切相关。接下来,识别出该通路中排名前 20 的基因,根据 ceRNA 规则,从 mRNA 到 lncRNA 逐步进行预测和验证,包括 11 个枢纽基因、4 个关键 miRNA 和 2 个关键 lncRNA,以识别有意义的 ceRNA 网络。最终,我们确定了 PVT1/miR-20b/CCND1 轴作为 PC 进展中具有前景的通路相关 ceRNA 轴。

结论

总的来说,我们阐明了 PVT1/miR-20b/CCND1 在 PC 进展中的通路相关 ceRNA 调控网络,可作为 PC 的治疗靶点和有希望的预后生物标志物。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c451/7532576/e7c62b0127e3/12885_2020_7470_Fig1_HTML.jpg

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