CCNB1 is a prognostic biomarker for ER+ breast cancer

Ding, Kun; Li, Wenqing; Zou, Zhiqiang; Zou, Xianzhi; Cheng-ru, Wang

doi:10.1016/j.mehy.2014.06.013

Cited by 110 publications

(80 citation statements)

References 40 publications

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“…MUC1 is a known marker for breast cancer (23), and it associates with TP53 (24,25) and plays an important role in breast cancer (26,27). CCNB1 is an important known marker for breast cancer, and it was reported to be related with TP53 (28,29) and have important implications for cancer prognosis (30,31). …”

Section: Resultsmentioning

confidence: 99%

Personalized characterization of diseases using sample-specific networks

et al. 2016

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A complex disease generally results not from malfunction of individual molecules but from dysfunction of the relevant system or network, which dynamically changes with time and conditions. Thus, estimating a condition-specific network from a single sample is crucial to elucidating the molecular mechanisms of complex diseases at the system level. However, there is currently no effective way to construct such an individual-specific network by expression profiling of a single sample because of the requirement of multiple samples for computing correlations. We developed here with a statistical method, i.e. a sample-specific network (SSN) method, which allows us to construct individual-specific networks based on molecular expressions of a single sample. Using this method, we can characterize various human diseases at a network level. In particular, such SSNs can lead to the identification of individual-specific disease modules as well as driver genes, even without gene sequencing information. Extensive analysis by using the Cancer Genome Atlas data not only demonstrated the effectiveness of the method, but also found new individual-specific driver genes and network patterns for various types of cancer. Biological experiments on drug resistance further validated one important advantage of our method over the traditional methods, i.e. we can even identify such drug resistance genes that actually have no clear differential expression between samples with and without the resistance, due to the additional network information.

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Section: Resultsmentioning

confidence: 99%

Personalized characterization of diseases using sample-specific networks

et al. 2016

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“…Cyclin overexpression is linked to gastrointestinal cancers and is often associated with less favorable prognoses and outcomes [21]. High level of CCNB1 expression has been detected in a variety of cancers [22–26], including GC [27], resulting in uncontrolled cell growth. A recent study reported that cyclin B1 and cyclin B2 were the most significant candidate biomarkers in GC [28].…”

Section: Discussionmentioning

confidence: 99%

ISL1, a novel regulator ofCCNB1,CCNB2andc-MYCgenes, promotes gastric cancer cell proliferation and tumor growth

et al. 2016

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Islet-1 (ISL1) belongs to the LIM homeodomain transcription factor family, which is specifically expressed in certain tissue types only. Previously, we reported that ISL1 is aberrantly overexpressed in gastric cancer (GC). However, its role in GC is not clear. Here, we report that ISL1 is aberrantly upregulated not only in human gastric carcinoma tissues but also in some GC cell lines. Upregulated ISL1 expression enhanced xenografted gastric carcinoma development, while ISL1 knockdown inhibited GC growth in nude mice. ISL1 overexpression promoted GC cell proliferation, colony formation, and cell growth in soft agar, and facilitated cell cycle transition in GC cells, demonstrated an increase in the proportion of cells in the G2/M and S phases and a decrease in the proportion of cells in the G1 phase. Furthermore, we provide evidence that ISL1 is a novel regulator of the cyclin B1 (CCNB1), cyclin B2 (CCNB2) and c-myc (c-MYC) genes. ISL1 activated the expression of these genes in GC cells by binding to the conserved binding sites on their promoters or enhancers. The expression levels of the genes were decreased in response to ISL1 knockdown. Therefore, ISL1 may serve as a potential therapeutic target in GC.

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“…The increased expression of CCNB1 has also been observed in multiple cancer types, including non-small cell lung cancer (29) and gastrointestinal stromal tumors (30). Moreover, CCNB1 serves as a biomarker for the prognosis of estrogen receptor-positive breast cancer (31). CCNB1 plays important roles in the cell proliferation at the G2 phase.…”

Section: Discussionmentioning

confidence: 99%

Identification of potential therapeutic targets for colorectal cancer by bioinformatics analysis

et al. 2016

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The aim of the present study was to identify potential therapeutic targets for colorectal cancer (CRC). The gene expression profile GSE32323, containing 34 samples, including 17 specimens of CRC tissues and 17 of paired normal tissues from CRC patients, was downloaded from the Gene Expression Omnibus database. Following data preprocessing using the Affy and preprocessCore packages, the differentially-expressed genes (DEGs) between the two types of samples were identified with the Linear Models for Microarray Analysis package. Next, functional and pathway enrichment analysis of the DEGs was performed using the Database for Annotation Visualization and Integrated Discovery. The protein-protein interaction (PPI) network was established using the Search Tool for the Retrieval of Interacting Genes database. Utilizing WebGestalt, the potential microRNAs (miRNAs/miRs) of the DEGs were screened and the integrated miRNA-target network was built. A cohort of 1,347 DEGs was identified, the majority of which were mainly enriched in cell cycle-related biological processes and pathways. Cyclin-dependent kinase 1 (CDK1), cyclin B1 (CCNB1), MAD2 mitotic arrest deficient-like 1 (MAD2L1) and BUB1 mitotic checkpoint serine/threonine kinase B (BUB1B) were prominent in the PPI network, while the over-represented genes in the integrated miRNA-target network were SRY (sex determining region Y)-box 4 (SOX4; targeted by hsa-mir-129), v-myc avian myelocytomatosis viral oncogene homolog (MYC; targeted by hsa-let-7c and hsa-mir-145) and cyclin D1 (CCND1; targeted by hsa-let-7b). CDK1, CCNB1 and CCND1 were also associated with the p53 signaling pathway. Overall, several genes associated with the cell cycle and p53 pathway were identified as biomarkers for CRC. CDK1, CCNB1, MAD2L1, BUB1B, SOX4, collagen type I α2 chain and MYC may play significant roles in CRC progression by affecting the cell cycle-related pathways, while CDK1, CCNB1 and CCND1 may serve as crucial regulators in the p53 signaling pathway. Furthermore, SOX4, MYC and CCND1 may be targets of miR-129, hsa-mir-145 and hsa-let-7c, respectively. However, further validation of these data is required.

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CCNB1 is a prognostic biomarker for ER+ breast cancer

Cited by 110 publications

References 40 publications

Personalized characterization of diseases using sample-specific networks

Personalized characterization of diseases using sample-specific networks

ISL1, a novel regulator ofCCNB1,CCNB2andc-MYCgenes, promotes gastric cancer cell proliferation and tumor growth

Identification of potential therapeutic targets for colorectal cancer by bioinformatics analysis

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