计算机分析揭示了非小细胞肺癌（NSCLC）的潜在环状 RNA-miRNA-mRNA 调控网络。
In silico analysis revealed the potential circRNA-miRNA-mRNA regulative network of non-small cell lung cancer (NSCLC).
机构信息
Laboratory of Integrative Genomics, Department of Integrative Biology, School of BioSciences and Technology, Vellore Institute of Technology (VIT), Vellore, 632014, Tamil Nadu, India.
Laboratory of Integrative Genomics, Department of Integrative Biology, School of BioSciences and Technology, Vellore Institute of Technology (VIT), Vellore, 632014, Tamil Nadu, India.
出版信息
Comput Biol Med. 2023 Jan;152:106315. doi: 10.1016/j.compbiomed.2022.106315. Epub 2022 Nov 18.
BACKGROUND
The primary source of death in the world is non-small cell lung cancer (NSCLC). However, NSCLCs pathophysiology is still not completely understood. The current work sought to study the differential expression of mRNAs involved in NSCLC and their interactions with miRNAs and circRNAs.
METHODS
We utilized three microarray datasets (GSE21933, GSE27262, and GSE33532) from the GEO NCBI database to identify the differentially expressed genes (DEGs) in NSCLC. We employed DAVID Functional annotation tool to investigate the underlying GO biological process, molecular functions, and KEGG pathways involved in NSCLC. We performed the Protein-protein interaction (PPI) network, MCODE, and CytoHubba analysis from Cytoscape software to identify the significant DEGs in NSCLC. We utilized miRnet to anticipate and build interaction between miRNAs and mRNAs in NSCLC and ENCORI to predict the miRNA-circRNA relationships and build the ceRNA regulatory network. Finally, we executed the gene expression and Kaplan-Meier survival analysis to validate the significant DEGs in the ceRNA network utilizing TCGA NSCLC and GEPIA data.
RESULTS
We revealed a total of 156 overlapped DEGs (47 upregulated and 109 downregulated genes) in NSCLC. The PPI network, MCODE, and CytoHubba analysis revealed 12 hub genes (cdkn3, rrm2, ccnb1, aurka, nuf2, tyms, kif11, hmmr, ccnb2, nek2, anln, and birc5) that are associated with NSCLC. We identified that these 12 genes encode 12 mRNAs that are strongly linked with 8 miRNAs, and further, we revealed that 1 circRNA was associated with this 5 miRNA. We constructed the ceRNAs network that contained 1circRNA-5miRNAs-7mRNAs. The expression of these seven significant genes in LUAD & LUSC (NSCLC) was considerably higher in the TCGA database than in normal tissues. Kaplan-Meier survival plot reveals that increased expression of these hub genes was related to a poor survival rate in LUAD.
CONCLUSION
Overall, we developed a circRNA-miRNA-mRNA regulation network to study the probable mechanism of NSCLC.
背景
全球主要的死亡原因是非小细胞肺癌（NSCLC）。然而，NSCLC 的病理生理学仍未完全被理解。本研究旨在研究 NSCLC 中涉及的差异表达的 mRNA 及其与 miRNA 和 circRNA 的相互作用。
方法
我们利用 GEO NCBI 数据库中的三个微阵列数据集（GSE21933、GSE27262 和 GSE33532）来鉴定 NSCLC 中的差异表达基因（DEGs）。我们使用 DAVID 功能注释工具来研究 NSCLC 中涉及的 GO 生物学过程、分子功能和 KEGG 途径。我们从 Cytoscape 软件中进行蛋白质-蛋白质相互作用（PPI）网络、MCODE 和 CytoHubba 分析，以鉴定 NSCLC 中的重要 DEGs。我们利用 miRnet 来预测和构建 NSCLC 中 miRNA 和 mRNAs 之间的相互作用，利用 ENCORI 来预测 miRNA-circRNA 关系并构建 ceRNA 调控网络。最后，我们利用 TCGA NSCLC 和 GEPIA 数据进行基因表达和 Kaplan-Meier 生存分析，以验证 ceRNA 网络中的重要 DEGs。
结果
我们在 NSCLC 中共鉴定出 156 个重叠的 DEGs（47 个上调和 109 个下调基因）。PPI 网络、MCODE 和 CytoHubba 分析揭示了 12 个与 NSCLC 相关的核心基因（cdkn3、rrm2、ccnb1、aurka、nuf2、tyms、kif11、hmmr、ccnb2、nek2、anln 和 birc5）。我们发现这 12 个基因编码的 12 个 mRNAs 与 8 个 miRNA 强烈相关，进一步发现 1 个 circRNA 与这 5 个 miRNA 相关。我们构建了包含 1 个 circRNA-5 个 miRNA-7 个 mRNAs 的 ceRNAs 网络。在 TCGA 数据库中，这些 7 个重要基因在 LUAD 和 LUSC（NSCLC）中的表达明显高于正常组织。Kaplan-Meier 生存图显示，这些核心基因的表达增加与 LUAD 患者的生存率降低有关。
结论
总体而言，我们构建了一个 circRNA-miRNA-mRNA 调控网络，以研究 NSCLC 的可能机制。
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