Expression and Gene Regulation Network of NUDT21 in Lung Adenocarcinoma and Prediction of Anticancer Components of Pinellia Ternata Based on Data Mining

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.

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

Translational Cancer Research ◽

10.21037/tcr-20-1045 ◽

2020 ◽

Vol 9 (8) ◽

pp. 4820-4833

Author(s):

Luyao Wang ◽

Xue Yang ◽

Ning An ◽

Jia Liu

Keyword(s):

Gene Regulation ◽

Lung Adenocarcinoma ◽

Bioinformatics Analysis ◽

Gene Regulation Network ◽

Regulation Network

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Gene Regulation Network Based Analysis Associated with TGF-beta Stimulation in Lung Adenocarcinoma Cells

Iranian Journal of Biotechnology ◽

10.15171/ijb.1308 ◽

2017 ◽

Vol 15 (1) ◽

pp. 1-9

Author(s):

Lin Hua ◽

Hong Xia ◽

Wei-ying Zheng ◽

Li An

Keyword(s):

Gene Regulation ◽

Lung Adenocarcinoma ◽

Tgf Beta ◽

Gene Regulation Network ◽

Adenocarcinoma Cells ◽

Regulation Network ◽

Lung Adenocarcinoma Cells

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System analysis of VEGFA in renal cell carcinoma: The expression, prognosis, gene regulation network and regulation targets

The International Journal of Biological Markers ◽

10.1177/17246008211063501 ◽

2021 ◽

pp. 172460082110635

Author(s):

Yongli Situ ◽

Qinying Xu ◽

Li Deng ◽

Yan Zhu ◽

Ruxiu Gao ◽

...

Keyword(s):

Renal Cell Carcinoma ◽

T Cells ◽

Gene Regulation ◽

Cell Carcinoma ◽

Renal Cell ◽

Prognostic Value ◽

System Analysis ◽

Target Prediction ◽

Gene Regulation Network ◽

Regulation Network

Background VEGFA is one of the most important regulators of angiogenesis and plays a crucial role in cancer angiogenesis and progression. Recent studies have highlighted a relationship between VEGFA expression and renal cell carcinoma occurrence. However, the expression level, gene regulation network, prognostic value, and target prediction of VEGFA in renal cell carcinoma remain unclear. Therefore, system analysis of the expression, gene regulation network, prognostic value, and target prediction of VEGFA in patients with renal cell carcinoma is of great theoretical significance as there is a clinical demand for the discovery of new renal cell carcinoma treatment targets and strategies to further improve renal cell carcinoma treatment efficacy. Methods This study used multiple free online databases, including cBioPortal, TRRUST, GeneMANIA, GEPIA, Metascape, UALCAN, LinkedOmics, Metascape, and TIMER for the abovementioned analysis. Results VEGFA was upregulated in patients with kidney renal clear cell carcinoma (KIRC) and kidney chromophobe (KICH), and downregulated in patients with kidney renal papillary cell carcinoma (KIRP). Moreover, genetic alterations of VEGFA were found in patients with renal cell carcinoma as follows: 4% (KIRC), 8% (KICH), and 4% (KIRP). The promoter methylation of VEGFA was lower and higher in patients with clinical stages of KIRC and stage 1 KIRP, respectively. VEGFA expression significantly correlated with KIRC and KIRP pathological stages. Furthermore, patients with KICH and KIRP having low VEGFA expression levels had a longer survival than those having high VEGFA expression levels. VEGFA and its neighboring genes functioned in the regulation of protein methylation and glycosylation, as well as muscle fiber growth and differentiation in patients with renal cell carcinoma. Gene Ontology enrichment analysis revealed that the functions of VEGFA and its neighboring genes in patients with renal cell carcinoma are mainly related to cell adhesion molecule binding, catalytic activity, acting on RNA, ATPase activity, actin filament binding, protease binding, transcription coactivator activity, cysteine-type peptidase activity, and calmodulin binding. Transcription factor targets of VEGFA and its neighboring genes in patients with renal cell carcinoma were found: HIF1A, TFAP2A, and ESR1 in KIRC; STAT3, NFKB1, and HIPK2 in KICH; and FOXO3, TFAP2A, and ETS1 in KIRP. We further explored the VEGFA-associated kinase (ATM in KICH as well as CDK1 and AURKB in KIRP) and VEGFA-associated microRNA (miRNA) targets (MIR-21 in KICH as well as MIR-213, MIR-383, and MIR-492 in KIRP). Furthermore, the following genes had the strongest correlation with VEGFA expression in patients with renal cell carcinoma: NOTCH4, GPR4, and TRIB2 in KIRC; CKMT2, RRAGD, and PPARGC1A in KICH; and FLT1, C6orf223, and ESM1 in KIRP. VEGFA expression in patients with renal cell carcinoma was positively associated with immune cell infiltration, including CD8+T cells, CD4+T cells, macrophages, neutrophils, and dendritic cells. Conclusions This study revealed VEGFA expression and potential gene regulatory network in patients with renal cell carcinoma, thereby laying a foundation for further research on the role of VEGFA in renal cell carcinoma occurrence. Moreover, the study provides new renal cell carcinoma therapeutic targets and prognostic biomarkers as a reference for fundamental and clinical research.

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Dissecting gene regulation network in human early embryos at single-cell and single-base resolution

Reproduction Abstracts ◽

10.1530/repabs.1.s024 ◽

2014 ◽

Author(s):

Fu-Chou Tang ◽

Jie Qiao

Keyword(s):

Gene Regulation ◽

Single Cell ◽

Single Base ◽

Gene Regulation Network ◽

Early Embryos ◽

Regulation Network ◽

Single Base Resolution

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

BioMed Research International ◽

10.1155/2021/9764265 ◽

2021 ◽

Vol 2021 ◽

pp. 1-2

Author(s):

Yongxuan Wang ◽

Xifeng Xiong ◽

Xing Hua ◽

Wei Liu

Keyword(s):

Breast Cancer ◽

Data Mining ◽

Gene Regulation ◽

Phosphoglycerate Mutase ◽

Metabolic Enzyme ◽

Gene Regulation Network ◽

Regulation Network

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Deciphering gene regulation from gene expression dynamics using deep neural network

10.1101/374439 ◽

2018 ◽

Cited By ~ 1

Author(s):

Jingxiang Shen ◽

Mariela D. Petkova ◽

Yuhai Tu ◽

Feng Liu ◽

Chao Tang

Keyword(s):

Neural Network ◽

Gene Expression ◽

Machine Learning ◽

Gene Regulation ◽

Gene Network ◽

Deep Neural Network ◽

Biological Functions ◽

Gene Regulation Network ◽

Regulation Network ◽

Gene Expression Dynamics

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.

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