Centromere protein U is a potential target for gene therapy of human bladder cancer

Wang, Sheng; Liu, Beibei; Zhang, Jiajun; Sun, Wei; Dai, Changyuan; Sun, Wenyan; Qing-wen, LI

doi:10.3892/or.2017.5769

Cited by 21 publications

(26 citation statements)

References 29 publications

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“…Studies have revealed that CENPU upregulation is correlated with progression and prognosis in luminal breast cancer, 56 familial colorectal cancer, 57 and bladder cancer. 58 Additionally, CENPU promotes prostate cancer cell proliferation and colony formation and significantly inhibits apoptosis; however, no clear change in CENPU expression has been identified. 59 Moreover, no data are available regarding the oncogenic role and clinical significance of CENPU in lung cancer.…”

Section: Discussionmentioning

confidence: 99%

Integrated analysis reveals key genes with prognostic value in lung adenocarcinoma

Song

Tan

Xin-huai

et al. 2018

CMAR

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BackgroundLung cancer is one of the most common malignant tumors. Despite advances in lung cancer therapies, prognosis of non-small-cell lung cancer is still unfavorable. The aim of this study was to identify the prognostic value of key genes in lung tumorigenesis.MethodsDifferentially expressed genes (DEGs) were screened out by GEO2R from three Gene Expression Omnibus cohorts. Common DEGs were selected for Kyoto Encyclopedia of Genes and Genomes pathway analysis and Gene Ontology enrichment analysis. Protein– protein interaction networks were constructed by the STRING database and visualized by Cytoscape software. Hub genes, filtered from the CytoHubba, were validated using the Gene Expression Profiling Interactive Analysis database, and their genomic alterations were identified by performing the cBioportal. Finally, overall survival analysis of hub genes was performed using Kaplan–Meier Plotter.ResultsFrom three datasets, 169 DEGs (70 upregulated and 99 downregulated) were identified. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses showed that upregulated DEGs were significantly enriched in cell cycle, p53 pathway, and extracellular matrix–receptor interactions; the downregulated DEGs were significantly enriched in PPAR pathway and tyrosine metabolism. The protein–protein interaction network consisted of 71 nodes and 305 edges, including 49 upregulated and 22 downregulated genes. The hub genes, including AURKB, BUB1B, KIF2C, HMMR, CENPF, and CENPU, were overexpressed compared with the normal group by Gene Expression Profiling Interactive Analysis analysis, and associated with reduced overall survival in lung cancer patients. In the genomic alterations analysis, two hotspot mutations (S2021C/F and E314K/V) were identified in Pfam protein domains.ConclusionDEGs, including AURKB, BUB1B, KIF2C, HMMR, CENPF, and CENPU, might be potential biomarkers for the prognosis and treatment of lung adenocarcinoma.

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Section: Discussionmentioning

confidence: 99%

Integrated analysis reveals key genes with prognostic value in lung adenocarcinoma

Song

Tan

Xin-huai

et al. 2018

CMAR

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“…The clinical relevance analysis suggested that MLF1IP (also known as CENPU) and ITGB4 were significantly correlated with shorter overall survival. Role of MLF1IP had been preliminarily elucidated in some cancers including bladder cancer, ovarian cancer and prostate cancer, while little is known in PDAC progression [33–35]. The precise function of MLF1IP in PDAC need to be further investigated.…”

Section: Discussionmentioning

confidence: 99%

Analysis of dynamic molecular networks for pancreatic ductal adenocarcinoma progression

Pan

Fang

et al. 2018

Cancer Cell Int

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BackgroundPancreatic ductal adenocarcinoma (PDAC) is one of the deadliest solid tumors. The rapid progression of PDAC results in an advanced stage of patients when diagnosed. However, the dynamic molecular mechanism underlying PDAC progression remains far from clear.MethodsThe microarray GSE62165 containing PDAC staging samples was obtained from Gene Expression Omnibus and the differentially expressed genes (DEGs) between normal tissue and PDAC of different stages were profiled using R software, respectively. The software program Short Time-series Expression Miner was applied to cluster, compare, and visualize gene expression differences between PDAC stages. Then, function annotation and pathway enrichment of DEGs were conducted by Database for Annotation Visualization and Integrated Discovery. Further, the Cytoscape plugin DyNetViewer was applied to construct the dynamic protein–protein interaction networks and to analyze different topological variation of nodes and clusters over time. The phosphosite markers of stage-specific protein kinases were predicted by PhosphoSitePlus database. Moreover, survival analysis of candidate genes and pathways was performed by Kaplan–Meier plotter. Finally, candidate genes were validated by immunohistochemistry in PDAC tissues.ResultsCompared with normal tissues, the total DEGs number for each PDAC stage were 994 (stage I), 967 (stage IIa), 965 (stage IIb), 1027 (stage III), 925 (stage IV), respectively. The stage-course gene expression analysis showed that 30 distinct expressional models were clustered. Kyoto Encyclopedia of Genes and Genomes analysis indicated that the up-regulated DEGs were commonly enriched in five fundamental pathways throughout five stages, including pathways in cancer, small cell lung cancer, ECM-receptor interaction, amoebiasis, focal adhesion. Except for amoebiasis, these pathways were associated with poor PDAC overall survival. Meanwhile, LAMA3, LAMB3, LAMC2, COL4A1 and FN1 were commonly shared by these five pathways and were unfavorable factors for prognosis. Furthermore, by constructing the stage-course dynamic protein interaction network, 45 functional molecular modules and 19 nodes were identified as featured regulators for all PDAC stages, among which the collagen family and integrins were considered as two main regulators for facilitating aggressive progression. Additionally, the clinical relevance analysis suggested that the stage IV featured nodes MLF1IP and ITGB4 were significantly correlated with shorter overall survival. Moreover, 15 stage-specific protein kinases were identified from the dynamic network and CHEK1 was particularly activated at stage IV. Experimental validation showed that MLF1IP, LAMA3 and LAMB3 were progressively increased from tumor initiation to progression.ConclusionsOur study provided a view for a better understanding of the dynamic landscape of molecular interaction networks during PDAC progression and offered potential targets for therapeutic intervention.Electronic supplementary materialThe online version of this art...

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“…Hub genes and their corresponded target miRNAs are presented in Table . Finally, 31 miRNAs, whose functions were correlated with tumor progression (Chang et al, ; Chang et al, ; Chen et al, ; Gao et al, ; Gu et al, ; Han et al, ; He et al, ; Kucuksayan et al, ; Li, Ma, Zhang, Ji, & Jin, ; Li, Ran, et al, ; Li & He, ; Li, Fu, et al, ; Li, Cui, Li, Zhang, & Li, ; Liu et al, ; Liu, Liu, Deng, et al, ; Liu, Liu, & Lu, ; Liu, Liu, Zhang, Tong, & Gan, ; Liu, Miao, et al, ; Luo et al, ; Tang et al, ; Wang et al, ; Wang, Cao, Su, Li, & Yan, ; Wu et al, ; Wu, Mo, Wan, Dan, & Hu, ; Xia, Jing, Li, & Lv, ; Xu et al, ; Zeng et al, ; Zhang, Hao, et al, ; Zhang et al, , ; Zhu et al, ), drug resistance (Bian et al, ; Gao et al, ; Lin, Xie, Lu, Hu, & Chang, ; Pan, Chen, Shen, & Tantai, ; Su et al, ; Xu et al, ; Zhou, Wang, & Feng, ), radiosensitivity (Liu, Li, & Gao, ), recurrence (Sim et al, ) or autophagy (Pan et al, ) in lung cancer, were identified (Table ).…”

Section: Resultsmentioning

confidence: 99%

Common and distinct features of potentially predictive biomarkers in small cell lung carcinoma and large cell neuroendocrine carcinoma of the lung by systematic and integrated analysis

Dong

Liang

Zhai

et al. 2020

Molec Gen & Gen Med

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Background Large‐cell neuroendocrine carcinoma of the lung (LCNEC) and small‐cell lung carcinoma (SCLC) are neuroendocrine neoplasms. However, the underlying mechanisms of common and distinct genetic characteristics between LCNEC and SCLC are currently unclear. Herein, protein expression profiles and possible interactions with miRNAs were provided by integrated bioinformatics analysis, in order to explore core genes associated with tumorigenesis and prognosis in SCLC and LCNEC. Methods http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE1037 gene expression profiles were obtained from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) in LCNEC and SCLC, as compared with normal lung tissues, were selected using the GEO2R online analyzer and Venn diagram software. Gene ontology (GO) analysis was performed using Database for Annotation, Visualization and Integrated Discovery. The biological pathway analysis was performed using the FunRich database. Subsequently, a protein–protein interaction (PPI) network of DEGs was generated using Search Tool for the Retrieval of Interacting Genes and displayed via Cytoscape software. The PPI network was analyzed by the Molecular Complex Detection app from Cytoscape, and 16 upregulated hub genes were selected. The Oncomine database was used to detect expression patterns of hub genes for validation. Furthermore, the biological pathways of these 16 hub genes were re‐analyzed, and potential interactions between these genes and miRNAs were explored via FunRich. Results A total of 384 DEGs were identified. A Venn diagram determined 88 common DEGs. The PPI network was constructed with 48 nodes and 221 protein pairs. Among them, 16 hub genes were extracted, 14 of which were upregulated in SCLC samples, as compared with normal lung specimens, and 10 were correlated with the cell cycle pathway. Furthermore, 57 target miRNAs for 8 hub genes were identified, among which 31 miRNAs were correlated with the progression of carcinoma, drug‐resistance, radio‐sensitivity, or autophagy in lung cancer. Conclusion This study provided effective biomarkers and novel therapeutic targets for diagnosis and prognosis of SCLC and LCNEC.

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Centromere protein U is a potential target for gene therapy of human bladder cancer

Cited by 21 publications

References 29 publications

Integrated analysis reveals key genes with prognostic value in lung adenocarcinoma

Integrated analysis reveals key genes with prognostic value in lung adenocarcinoma

Analysis of dynamic molecular networks for pancreatic ductal adenocarcinoma progression

Common and distinct features of potentially predictive biomarkers in small cell lung carcinoma and large cell neuroendocrine carcinoma of the lung by systematic and integrated analysis

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