Dataset Information

A microRNA expression signature for cervical cancer prognosis.

ABSTRACT: Invasive cervical cancer is a leading cause of cancer death in women worldwide, resulting in about 300,000 deaths each year. The clinical outcomes of cervical cancer vary significantly and are difficult to predict. Thus, a method to reliably predict disease outcome would be important for individualized therapy by identifying patients with high risk of treatment failures before therapy. In this study, we have identified a microRNA (miRNA)-based signature for the prediction of cervical cancer survival. miRNAs are a newly identified family of small noncoding RNAs that are extensively involved in human cancers. Using an established PCR-based miRNA assay to analyze 102 cervical cancer samples, we identified miR-200a and miR-9 as two miRNAs that could predict patient survival. A logistic regression model was developed based on these two miRNAs and the prognostic value of the model was subsequently validated with independent cervical cancers. Furthermore, functional studies were done to characterize the effect of miRNAs in cervical cancer cells. Our results suggest that both miR-200a and miR-9 could play important regulatory roles in cervical cancer control. In particular, miR-200a is likely to affect the metastatic potential of cervical cancer cells by coordinate suppression of multiple genes controlling cell motility.

SUBMITTER: Hu X

PROVIDER: S-EPMC2844247 | biostudies-literature | 2010 Feb

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

A microRNA expression signature for cervical cancer prognosis.

Hu Xiaoxia X Schwarz Julie K JK Lewis James S JS Huettner Phyllis C PC Rader Janet S JS Deasy Joseph O JO Grigsby Perry W PW Wang Xiaowei X

Cancer research 20100202 4

Invasive cervical cancer is a leading cause of cancer death in women worldwide, resulting in about 300,000 deaths each year. The clinical outcomes of cervical cancer vary significantly and are difficult to predict. Thus, a method to reliably predict disease outcome would be important for individualized therapy by identifying patients with high risk of treatment failures before therapy. In this study, we have identified a microRNA (miRNA)-based signature for the prediction of cervical cancer surv ...[more]

PMID: 20124485

Similar Datasets

Project description:BackgroundGlobally, the incidence of cervical cancer (CC) is highest among all tumors of the female reproductive system. Numerous studies have shown that the expression level of microRNA (miRNA) is highly correlated with cancer. This study aimed to establish a molecular prognostic model of CC based on miRNAs in order to provide more individualized treatment to CC patients.MethodsHuman tissues were selected from the Cancer Hospital (Chinese Academy of Medical Sciences and Peking Union Medical College) for miRNA gene sequencing. CC transcriptome expression and clinical data were downloaded from The Cancer Genome Atlas (TCGA). We distinguished between common differentially expressed miRNAs of CC miRNA-seq and TCGA-CC. To obtain a molecular prognostic model, R package was used to perform univariate Cox proportional hazard regression and least absolute shrinkage and selection operator (LASSO) Cox regression for common differentially-expressed miRNAs. Next, the model performance was evaluated by survival analysis, receiver operating characteristic (ROC) curve analysis, as well as univariate and multivariate analyses in the TCGA-CC dataset. Quantitative Real-time polymerase chain reaction (qPCR) detection was to verify the expression changes of miRNA. Transwell was used to verify the role of molecules in CC cell migration and invasion.ResultsThirty-nine miRNAs were distinguished in TCGA-CC and CC miRNA-seq, LASSO regression analysis to obtain the risk model (risk score =-0.310× expression of hsa-miR-142-3p +0.439× expression of hsa-miR-100-3p). The survival analysis, ROC curve analysis, patient risk assessment, and univariate and multivariate analyses showed that the risk score model had good predictive ability in assessing patient survival (P<0.01), risk of death, and independent prognosis (P<0.01). qPCR detection of clinical samples and cells showed that the expression of hsa-miR-142-3p and hsa-miR-100-3p was consistent with the results of the database analysis. The Transwell results indicated that miR-142-3p is an inhibiting factor and miR-100-3p serves as a promoting factor in CC cell migration and invasion.ConclusionsTwelve miRNA-seq and TCGA-CC tissues were used to build a prognostic model for CC. We have obtained a two-miRNA risk score model. Our results provide a new strategy for the individualized diagnosis and treatment of CC.

Dataset Information

A microRNA expression signature for cervical cancer prognosis.

Publications

A microRNA expression signature for cervical cancer prognosis.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets