scholarly journals Bioinformatics analysis of BUB1 expression and gene regulation network in lung adenocarcinoma

2020 ◽  
Vol 9 (8) ◽  
pp. 4820-4833
Author(s):  
Luyao Wang ◽  
Xue Yang ◽  
Ning An ◽  
Jia Liu
2019 ◽  
Author(s):  
Luyao Wang ◽  
Xue Yang ◽  
Ning An ◽  
Jia Liu

Abstract Lung adenocarcinoma is the most common type of lung cancer with high morbidity and mortality. Potential mechanisms and therapeutic targets of lung adenocarcinoma need further study. BUB1 (BUB1 mitotic checkpoint serine/threonine kinase) encodes a serine/threonine protein kinase which is critical in the mitosis. It is associated with poor prognosis in multiple cancer types. Oncomine database was used to determine the differential expression of BUB1 in normal and lung adenocarcinoma tissues, while UALCAN was used to perform analysis of the relative expression and survival of BUB1 between tumor and normal tissues in different tumor subgroups. We used the cBioPortal for Cancer Genomics to perform GO analysis and KEGG analysis of the top 50 altered neighbor genes of BUB1. The LinkedOmics database was used to determine differential gene expression with BUB1 and to perform functional analysis. The kinase, miRNA and transcription factor target networks correlated with BUB1 were also analysed by LinkedOmics database. The results revealed that BUB1 was highly expressed in lung adenocarcinoma patients. BUB1 involved multiple tumor-related pathways, such as cell cycle, oocyte meiosis and p53 signaling pathway. BUB1 is associated with tumor-associated kinases, microRNAs and transcription factors. Our study reveal BUB1 expression and potential gene regulation networks in lung adenocarcinoma based on bioinformatics analysis, guiding further study on the role and regulation of BUB1 in lung adenocarcinoma.


2021 ◽  
Author(s):  
Yongli SITU ◽  
Li-na LONG ◽  
Hai-jian LI ◽  
Zhi-xin FANG ◽  
Hong NIE

Abstract Background NUDT21 belongs to NUDT families, which is thought to play an essential role in cancer growth and progression in recent years. Abnormal NUDT21 expression is closely related to lung adenocarcinoma (LUAD). However, the expression level, gene regulation network, and prognostic value of NUDT21 in LUAD remain unclear. Besides, the active compounds of Pinellia ternata against LUAD are still not clear yet. Therefore, an in-depth study of the expression and gene regulation network of NUDT21 is of great theoretical significance and clinical demand for discovering new targets and strategies for the treatment of LUAD and the further improvement of the therapeutic effect of LUAD. Also, the targeted NUDT21 active ingredients of Pinellia ternata were sought to provide a theoretical basis for its clinical application in the treatment of LUAD. Methods A variety of online analysis tools were used in this study, including cBioPortal, ONCOMINE, GeneMANIA, GEPIA, Metascape, UALCAN, LinkedOmics, Metascape, TIMER, TRRUST, The Human Protein Atlas, TCMSP, and AutoDock Vina. Results The levels of transcription and expression of NUDT21 were significantly increased in patients with LUAD. Gene altered of NUDT21 was up to 12% in LUAD patients. However, the promoter methylation level of NUDT21 in LUAD was lower compared to normal human. LUAD patients' survival with the low expression level of NUDT21 was better prognostic value than LUAD patients with high expression level. Forty-eight nodes and 572 edges were found in the PPI network constructed with NUDT21 and its neighboring genes. Regulatory region DNA binding, transcription regulatory region DNA binding, and regulatory region nucleic acid binding were the primary function of NUDT21 and its neighboring genes. The KEGG pathway of NUDT21 and its neighboring genes were mainly involved in the apelin signaling pathway, PI3K-Akt signaling pathway, and axon guidance. Our results showed that DNMT1, HDAC1, and MYC were the critical transcription factor targets involved in the network of NUDT21 and its neighboring genes. We also found that CDK1, ATM and PLK1 were main kinase targets in the NUDT21 kinase-target network. The NUDT21 miRNA-target network was associated with MIR-302C, MIR-9, and MIR-330. Moreover, the expression of NUDT21 was positively related to the infiltration of CD8 + T cells, macrophages, neutrophils and dendritic cell. 13 active compounds of Pinellia ternata were retrieved from the TCMSP. Among them, baicalein was the best combination with NUDT21. Conclusions Our results revealed the expression and potential regulatory network of NUDT21 in LUAD, laying a foundation for further research on the role of NUDT21 in cancer. Furthermore, we offer new therapeutic targets and prognostic biomarkers for the reference. Finally, we provide potential therapeutic drugs from traditional Chinese medicine in the treatment of LUAD.


2018 ◽  
Author(s):  
Jingxiang Shen ◽  
Mariela D. Petkova ◽  
Yuhai Tu ◽  
Feng Liu ◽  
Chao Tang

AbstractComplex biological functions are carried out by the interaction of genes and proteins. Uncovering the gene regulation network behind a function is one of the central themes in biology. Typically, it involves extensive experiments of genetics, biochemistry and molecular biology. In this paper, we show that much of the inference task can be accomplished by a deep neural network (DNN), a form of machine learning or artificial intelligence. Specifically, the DNN learns from the dynamics of the gene expression. The learnt DNN behaves like an accurate simulator of the system, on which one can perform in-silico experiments to reveal the underlying gene network. We demonstrate the method with two examples: biochemical adaptation and the gap-gene patterning in fruit fly embryogenesis. In the first example, the DNN can successfully find the two basic network motifs for adaptation – the negative feedback and the incoherent feed-forward. In the second and much more complex example, the DNN can accurately predict behaviors of essentially all the mutants. Furthermore, the regulation network it uncovers is strikingly similar to the one inferred from experiments. In doing so, we develop methods for deciphering the gene regulation network hidden in the DNN “black box”. Our interpretable DNN approach should have broad applications in genotype-phenotype mapping.SignificanceComplex biological functions are carried out by gene regulation networks. The mapping between gene network and function is a central theme in biology. The task usually involves extensive experiments with perturbations to the system (e.g. gene deletion). Here, we demonstrate that machine learning, or deep neural network (DNN), can help reveal the underlying gene regulation for a given function or phenotype with minimal perturbation data. Specifically, after training with wild-type gene expression dynamics data and a few mutant snapshots, the DNN learns to behave like an accurate simulator for the genetic system, which can be used to predict other mutants’ behaviors. Furthermore, our DNN approach is biochemically interpretable, which helps uncover possible gene regulatory mechanisms underlying the observed phenotypic behaviors.


Life Sciences ◽  
2020 ◽  
Vol 253 ◽  
pp. 117600 ◽  
Author(s):  
Wancong Zhang ◽  
Hanxing Zhao ◽  
Jiasheng Chen ◽  
Xiaoping Zhong ◽  
Weiping Zeng ◽  
...  

Sign in / Sign up

Export Citation Format

Share Document