Re: Cross-Species Regulatory Network Analysis Identifies a Synergistic Interaction between FOXM1 and CENPF that Drives Prostate Cancer Malignancy

2014 ◽  
Vol 192 (6) ◽  
pp. 1884-1885
Author(s):  
Anthony Atala
Cancer Cell ◽  
2014 ◽  
Vol 25 (5) ◽  
pp. 638-651 ◽  
Author(s):  
Alvaro Aytes ◽  
Antonina Mitrofanova ◽  
Celine Lefebvre ◽  
Mariano J. Alvarez ◽  
Mireia Castillo-Martin ◽  
...  

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Qinghong Shi ◽  
Hanxin Yao

Abstract Background Our study aimed to investigate signature RNAs and their potential roles in type 1 diabetes mellitus (T1DM) using a competing endogenous RNA regulatory network analysis. Methods Expression profiles of GSE55100, deposited from peripheral blood mononuclear cells of 12 T1DM patients and 10 normal controls, were downloaded from the Gene Expression Omnibus to uncover differentially expressed long non-coding RNAs (lncRNAs), mRNAs, and microRNAs (miRNAs). The ceRNA regulatory network was constructed, then functional and pathway enrichment analysis was conducted. AT1DM-related ceRNA regulatory network was established based on the Human microRNA Disease Database to carry out pathway enrichment analysis. Meanwhile, the T1DM-related pathways were retrieved from the Comparative Toxicogenomics Database (CTD). Results In total, 847 mRNAs, 41 lncRNAs, and 38 miRNAs were significantly differentially expressed. The ceRNA regulatory network consisted of 12 lncRNAs, 10 miRNAs, and 24 mRNAs. Two miRNAs (hsa-miR-181a and hsa-miR-1275) were screened as T1DM-related miRNAs to build the T1DM-related ceRNA regulatory network, in which genes were considerably enriched in seven pathways. Moreover, three overlapping pathways, including the phosphatidylinositol signaling system (involving PIP4K2A, INPP4A, PIP4K2C, and CALM1); dopaminergic synapse (involving CALM1 and PPP2R5C); and the insulin signaling pathway (involving CBLB and CALM1) were revealed by comparing with T1DM-related pathways in the CTD, which involved four lncRNAs (LINC01278, TRG-AS1, MIAT, and GAS5-AS1). Conclusion The identified signature RNAs may serve as important regulators in the pathogenesis of T1DM.


2018 ◽  
Vol 25 (2) ◽  
pp. 146-157 ◽  
Author(s):  
Jin-Hua He ◽  
Ze-Ping Han ◽  
Mao-Xian Zou ◽  
Li Wang ◽  
Yu Bing Lv ◽  
...  

2020 ◽  
pp. 1052-1075 ◽  
Author(s):  
Dina Elsayad ◽  
A. Ali ◽  
Howida A. Shedeed ◽  
Mohamed F. Tolba

The gene expression analysis is an important research area of Bioinformatics. The gene expression data analysis aims to understand the genes interacting phenomena, gene functionality and the genes mutations effect. The Gene regulatory network analysis is one of the gene expression data analysis tasks. Gene regulatory network aims to study the genes interactions topological organization. The regulatory network is critical for understanding the pathological phenotypes and the normal cell physiology. There are many researches that focus on gene regulatory network analysis but unfortunately some algorithms are affected by data size. Where, the algorithm runtime is proportional to the data size, therefore, some parallel algorithms are presented to enhance the algorithms runtime and efficiency. This work presents a background, mathematical models and comparisons about gene regulatory networks analysis different techniques. In addition, this work proposes Parallel Architecture for Gene Regulatory Network (PAGeneRN).


Medicine ◽  
2020 ◽  
Vol 99 (14) ◽  
pp. e19628
Author(s):  
Xuan Chen ◽  
Jingyao Wang ◽  
Xiqi Peng ◽  
Kaihao Liu ◽  
Chunduo Zhang ◽  
...  

2018 ◽  
Vol 17 (1) ◽  
Author(s):  
Alex Root ◽  
Azadeh Beizaei ◽  
H. Alexander Ebhardt

Author(s):  
Jian‐Fei Kuang ◽  
Chao‐Jie Wu ◽  
Yu‐Fan Guo ◽  
Dirk Walther ◽  
Wei Shan ◽  
...  

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