TSGene: a web resource for tumor suppressor genes

Zhao, Min; Sun, Jingjing; Zhao, Zhongming

doi:10.1093/nar/gks937

Cited by 303 publications

(265 citation statements)

References 51 publications

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“…The TSG database (TSGene) provides detailed annotations for each TSG, including cancer mutations, gene expressions, methylation sites, TF regulations and PPIs (23). The TAG database was designed to utilize information from well-characterized oncogenes and TSGs to facilitate cancer research (24).…”

Section: Methodsmentioning

confidence: 99%

Analysis of the molecular mechanism of osteosarcoma using a bioinformatics approach

Yang

Wang

2016

Oncology Letters

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Abstract. The aim of this study was to explore the underlying molecular mechanism related to the process and progression of osteosarcoma (OS). The differentially expressed genes (DEGs) were downloaded from the Gene Expression Omnibus database. The pathway and gene ontology (GO) enrichment analysis, as well as transcription factor, tumor-associated gene and tumor suppressor gene analyses were performed to investigate the functions of DEGs. Next, the protein-protein interaction (PPI) network was constructed and module analysis was further assessed by cluster analysis with the overlapping neighborhood expansion (Cluster ONE) cytoscape plug-in. A total of 359 upregulated and 614 downregulated DEGs were identified to be differentially expressed between OS samples and normal controls. Pathways significantly enriched by DEGs included the focal adhesion and chromosome maintenance pathways. Significant GO terms were cell adhesion, cell cycle and nucleic acid metabolic processes. The upregulated PPI network was constructed with 170 nodes and the downregulated PPI network was constructed with 332 nodes. Breast-ovarian cancer gene 1 (BRCA1), melanocyte-stimulating hormone 2 (MSH2), cyclin D1 (CCND1) and integrin α5 (ITGA5) were identified to be hub proteins in PPI. In conclusion, the dysregulated genes played key roles in the progression of OS. Cell adhesion is a significant biological process in OS development, and the genes BRCA1, MSH2, CCND1 and ITGA5 may be potential targets in the therapy of OS.

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Section: Methodsmentioning

confidence: 99%

Analysis of the molecular mechanism of osteosarcoma using a bioinformatics approach

Yang

Wang

2016

Oncology Letters

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“…In order to obtain more solid results, target genes that occurred in not less than three databases were regarded as the target genes of breast cancer-related differentially expressed miRNAs. In addition, the published oncogenes and suppressors of breast cancer were selected from TSGene (http://bioinfo.mc.vanderbilt.edu/TSGene/) (17) and Tumor Associated Gene (TAG; http://www.binfo.ncku.edu.tw/TAG/) databases (18).…”

Section: Methodsmentioning

confidence: 99%

Predicting associations between microRNAs and target genes in breast cancer by bioinformatics analyses

et al. 2016

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Abstract. Breast cancer is the leading type of cancer among females. However, the association between microRNAs (miRNAs) and target genes in breast tumorigenesis is poorly studied. The original data set GSE26659 was downloaded from the Gene Expression Omnibus, and then the differentially expressed miRNAs among 77 breast cancer patients and 17 controls were identified using the Limma package in R software. Furthermore, breast cancer-related differentially expressed miRNAs were selected from a human miRNA disease database and their target genes were selected from five miRNA databases. Then, functional analysis was performed for the target genes followed by construction of a miRNA-target gene network. A total of 34 differentially expressed miRNAs were identified, including 13 breast cancer-related miRNAs. Moreover, the target genes of the 13 miRNAs were significantly enriched in regulation of transcription (P=7.43E-09) and pathways related to cancer (P=3.33E-11). Finally, eight upregulated miRNAs (including hsa-miR-425) and five downregulated miRNAs (including hsa-miR-143, hsa-miR-145 and hsa-miR-125b) were identified in the miRNA-target gene network. In conclusion, using bioinformatics approaches, we demonstrate that the changes in regulation of transcription and cancer pathways may play significant roles in the process of breast cancerogenesis. Differentially expressed miRNAs and their target genes may be new targets for breast cancer therapy.

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“…Additionally, Tumor Suppressor Gene (TSGene) database (http://bioinfo.mc.vanderbilt.edu/TSGene/) provides not only a comprehensive resource of TSGs for studies on cancer and to further experimental design, but also a comprehensive TSG catalog biology-based analyses of advanced systems (24). In order to identify the functions of the TGs that may function during genesis of LS, the TSGene and TAG databases were used to perform the identification of TSGs and TAGs among the TGs, respectively.…”

Section: D E N T If I C a T I O N O F T Ra N S C R I P T I O N F A mentioning

confidence: 99%

Hsa-miR-137, hsa-miR-520e and hsa-miR-590-3p perform crucial roles in Lynch syndrome

Zhou

et al. 2016

Oncology Letters

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Abstract. The aim of the present study was to identify the differentially expressed microRNAs (DEMs) between Lynch syndrome (LS) and the normal colonic (N-C) control samples, predict the target genes (TGs) and analyze the potential functions of the DEMs and TGs. The miRNA expression dataset GSE30454, which included data of 13 LS and 20 N-C tissue samples, was downloaded from the Gene Expression Omnibus. The classical t-test in Linear Models for Microarray Data package was used for DEM identification. TG prediction was performed using 5 databases. The regulatory network of the DEMs and their TGs was constructed using Cytoscape. Functional and pathway enrichment analysis was performed. The transcription factors (TFs), tumor-associated genes (TAG) and tumor suppressor genes (TSGs) were then identified. Three key DEMs hsa-miR-137, hsa-miR-520e, and hsa-miR-590-3p were identified. Hsa-miR-520e and hsa-miR-137 had 4 common TGs, including SNF related kinase, metal-regulatory transcription factor 1 (MTF1), round spermatid basic protein 1 and YTH N6-methyladenosine RNA binding protein 3; hsa-miR-590-3p and hsa-miR-137 had 14 common TGs, including NCK adaptor protein 1 (NCK1), EPH receptor A7, and stress-associated endoplasmic reticulum protein 1; hsa-miR-590-3p and hsa-miR-520e had 12 common TGs, including Krüppel-like factor (KLF) 13, twinfilin actin binding protein 1, and nuclear factor I B. Through the functional and pathway enrichments analysis, MTF1 was involved in regulation of gene expression and metabolic processes, and sequence-specific DNA binding TF activity. KLF13 was involved in regulation of gene expression and regulation of cellular metabolic processes. NCK1 was enriched in the axon guidance pathway. In addition, the functional and pathway enrichment analysis showed certain TGs, such as hypoxia-inducible factor 1α, AKT serine/threonine kinase 2, and rapamycin-insensitive companion of mammalian target of rapamycin, participated in the mTOR signaling pathway. The 3 key DEMs hsa-miR-137, hsa-miR-520e, and hsa-miR-590-3p may have important roles in the process of LS.

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TSGene: a web resource for tumor suppressor genes

Cited by 303 publications

References 51 publications

Analysis of the molecular mechanism of osteosarcoma using a bioinformatics approach

Analysis of the molecular mechanism of osteosarcoma using a bioinformatics approach

Predicting associations between microRNAs and target genes in breast cancer by bioinformatics analyses

Hsa-miR-137, hsa-miR-520e and hsa-miR-590-3p perform crucial roles in Lynch syndrome

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