Expression and Gene Regulation Network of Metabolic Enzyme Phosphoglycerate Mutase Enzyme 1 in Breast Cancer Based on Data Mining

Wang, Yongxuan; Xiong, Xuming; Hua, Xin; Liu, Wei

doi:10.1155/2021/6670384

Cited by 2 publications

(2 citation statements)

References 26 publications

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“…Regarding the two online servers, miRTissue_ce [29] and Encori (i.e., starBase v2) [30] are two web servers that integrate ceRNA data sources, ceRNA prediction algorithms, and even some data analyses and visualizations that can be easily accessed by the users. Fiannaca et al [29] have compared miRTissue_ce and Encori in terms of many features.…”

Section: Plos Computational Biologymentioning

confidence: 99%

ceRNAR: An R package for identification and analysis of ceRNA-miRNA triplets

Hsiao

Wang

2022

PLoS Comput Biol

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Competitive endogenous RNA (ceRNA) represents a novel mechanism of gene regulation that controls several biological and pathological processes. Recently, an increasing number of in silico methods have been developed to accelerate the identification of such regulatory events. However, there is still a need for a tool supporting the hypothesis that ceRNA regulatory events only occur at specific miRNA expression levels. To this end, we present an R package, ceRNAR, which allows identification and analysis of ceRNA-miRNA triplets via integration of miRNA and RNA expression data. The ceRNAR package integrates three main steps: (i) identification of ceRNA pairs based on a rank-based correlation between pairs that considers the impact of miRNA and a running sum correlation statistic, (ii) sample clustering based on gene-gene correlation by circular binary segmentation, and (iii) peak merging to identify the most relevant sample patterns. In addition, ceRNAR also provides downstream analyses of identified ceRNA-miRNA triplets, including network analysis, functional annotation, survival analysis, external validation, and integration of different tools. The performance of our proposed approach was validated through simulation studies of different scenarios. Compared with several published tools, ceRNAR was able to identify true ceRNA triplets with high sensitivity, low false-positive rates, and acceptable running time. In real data applications, the ceRNAs common to two lung cancer datasets were identified in both datasets. The bridging miRNA for one of these, the ceRNA for MAP4K3, was identified by ceRNAR as hsa-let-7c-5p. Since similar cancer subtypes do share some biological patterns, these results demonstrated that our proposed algorithm was able to identify potential ceRNA targets in real patients. In summary, ceRNAR offers a novel algorithm and a comprehensive pipeline to identify and analyze ceRNA regulation. The package is implemented in R and is available on GitHub (https://github.com/ywhsiao/ceRNAR).

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Section: Plos Computational Biologymentioning

confidence: 99%

ceRNAR: An R package for identification and analysis of ceRNA-miRNA triplets

Hsiao

Wang

2022

PLoS Comput Biol

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“…In the article titled “Expression and Gene Regulation Network of Metabolic Enzyme Phosphoglycerate Mutase Enzyme 1 in Breast Cancer Based on Data Mining” [ 1 ], Figure 5 included additional parts (b–y) which should have been omitted. The corrected figure is shown as follows.…”

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confidence: 99%