Bioinformatics analysis of lncRNA‑associated ceRNA network in melanoma

黑色素瘤中lncRNA相关ceRNA网络的生物信息学分析

阅读:1

Abstract

Melanoma is an extremely malignant tumor with early metastasis and high mortality. Little is known about the process of by which melanoma occurs, as its mechanism is very complex and only limited data are available on its long non-coding RNA (lncRNA)-associated competing endogenous RNAs (ceRNAs). The purpose of this study was to screen out potential prognostic molecules and identify a ceRNA network related to the occurrence of melanoma. We screened 169 differentially expressed mRNAs (DEmRNAs) from E-MTAB-1862 and GSE3189; gene ontology (GO) enrichment analysis showed that these genes were closely related to the development of skin. In the protein-protein interaction network, we screened out a total of 19 hub genes. Furthermore, we predicted the microRNAs (miRNAs) that regulate hub genes using the miRWalk database and then intersected these with GSE35579, resulting in nine DEmiRNAs. We also predicted the lncRNAs that regulate the miRNAs using the LncBasev.2 database. According to the ceRNA hypothesis, and based on the intersection of the DElncRNAs with merged GTEx and TCGA data, we obtained 20 DElncRNAs. A total of four DEmRNAs, nine DEmiRNAs, and 20 DElncRNAs were included in the ceRNA network. Based on Cox stepwise regression and survival analysis, we identified five biomarkers, ZSCAN16-AS1, LINC00520, XIST, DTL, and let-7a-5p, and obtained risk scores. The results showed that most of the differentially expressed genes were related to epithelial-mesenchymal transition (EMT) in melanoma. Finally, we obtained a LINC00520/let-7a-5p/DTL molecular regulatory network. These results suggest that ceRNA networks have an important role in evaluating the prognosis of patients with melanoma and provide a new experimental basis for exploring the EMT process in the development of melanoma.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。