Dataset Information

Identification of yeast cell cycle regulated genes based on genomic features.

ABSTRACT:

Background

Time-course microarray experiments have been widely used to identify cell cycle regulated genes. However, the method is not effective for lowly expressed genes and is sensitive to experimental conditions. To complement microarray experiments, we propose a computational method to predict cell cycle regulated genes based on their genomic features - transcription factor binding and motif profiles.

Results

Through integrating gene-expression data with ChIP-chip binding and putative binding sites of transcription factors, our method shows high accuracy in discriminating yeast cell cycle regulated genes from non-cell cycle regulated ones. We predict 211 novel cell cycle regulated genes. Our model rediscovers the main cell cycle transcription factors and provides new insights into the regulatory mechanisms. The model also reveals a regulatory circuit mediated by a number of key cell cycle regulators.

Conclusions

Our model suggests that the periodical pattern of cell cycle genes is largely coded in their promoter regions, which can be captured by motif and transcription factor binding data. Cell cycle is controlled by a relatively small number of master transcription factors. The concept of genomic feature based method can be readily extended to human cell cycle process and other transcriptionally regulated processes, such as tissue-specific expression.

SUBMITTER: Cheng C

PROVIDER: S-EPMC3734186 | biostudies-literature | 2013 Jul

REPOSITORIES: biostudies-literature

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Publications

Identification of yeast cell cycle regulated genes based on genomic features.

Cheng Chao C Fu Yao Y Shen Linsheng L Gerstein Mark M

BMC systems biology 20130729

<h4>Background</h4>Time-course microarray experiments have been widely used to identify cell cycle regulated genes. However, the method is not effective for lowly expressed genes and is sensitive to experimental conditions. To complement microarray experiments, we propose a computational method to predict cell cycle regulated genes based on their genomic features - transcription factor binding and motif profiles.<h4>Results</h4>Through integrating gene-expression data with ChIP-chip binding and ...[more]

PMID: 23895232

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Project description:Prostate cancer (PCa) is clinically and biologically heterogeneous, making it difficult to predict at detection whether it will take an indolent or aggressive disease course. Cell cycle-regulated genes may be more highly expressed in actively dividing cells, with transcript levels reflecting tumor growth rate. Here, we evaluated expression of cell cycle genes in relation to PCa outcomes in a population-based cohort.Gene expression data were generated from tumor tissues obtained at radical prostatectomy for 383 population-based patients (12.3-years average follow-up). The overall mean and individual transcript levels of 30 selected cell cycle genes was compared between patients with no evidence of recurrence (73%) and those who recurred (27%) or died (7%) from PCa.The multivariate adjusted hazard ratio (HR) for a change from the 25th to 75th percentile of mean gene expression level (range 8.02-10.05) was 1.25 (95%CI 0.96-1.63; P?=?0.10) for PCa recurrence risk, and did not vary substantially by Gleason score, TMPRSS2-ERG fusion status, or family history of PCa. For lethal PCa, the HR for a change (25th to 75th percentile) in mean gene expression level was 2.04 (95%CI 1.26-3.31; P?=?0.004), adjusted for clinicopathological variables. The ROC curve for mean gene expression level alone (AUC?=?0.740) did not perform as well as clinicopathological variables alone (AUC?=?0.803) for predicting lethal PCa, and the addition of mean gene expression to clinicopathological variables did not substantially improve prediction (AUC?=?0.827; P?=?0.18). Higher TK1 expression was strongly associated with both recurrent (P?=?6.7?×?10(-5)) and lethal (P?=?6.4?×?10(-6)) PCa.Mean expression level for 30 selected cell cycle-regulated genes was unrelated to recurrence risk, but was associated with a twofold increase in risk of lethal PCa. However, gene expression had less discriminatory accuracy than clinical variables alone for predicting lethal events. Transcript levels for several genes in the panel were significantly overexpressed in lethal versus non-recurrent PCa.

Dataset Information

Identification of yeast cell cycle regulated genes based on genomic features.

Background

Results

Conclusions

Publications

Identification of yeast cell cycle regulated genes based on genomic features.

Similar Datasets

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