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An integrated bioinformatical analysis to evaluate the role of KIF4A as a prognostic biomarker for breast cancer.

ABSTRACT: Purpose:The aim of this study was to investigate the diagnostic and prognostic value of human kinesin family member 4A (KIF4A) as an effective biomarker for breast cancer. Materials and methods:Cancer Genome Atlas data and 12 independent public breast cancer microarray data sets were downloaded and analyzed using individual and pooled approaches. Results:The results of our study revealed a strong and positive correlation between KIF4A expression and malignant features of breast cancer. KIF4A had a strong prognostic value in both ER-positive and ER-negative breast cancers comparable to or even better than tumor size, lymph node invasion, and Elston grade. We also found that KIF4A might be the target gene of microRNA-335, which can suppress KIF4A expression by targeting the 3'-untranslated region of its mRNA. Conclusion:KIF4A might serve as a robust prognostic predictor for breast cancer. Targeting KIF4A activity could be a promising therapeutic option in breast cancer treatment.

SUBMITTER: Xue D

PROVIDER: S-EPMC6091482 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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An integrated bioinformatical analysis to evaluate the role of KIF4A as a prognostic biomarker for breast cancer.

Xue Dan D Cheng Pu P Han Mengjiao M Liu Xiyong X Xue Lijun L Ye Chenyi C Wang Ke K Huang Jian J

OncoTargets and therapy 20180810

<h4>Purpose</h4>The aim of this study was to investigate the diagnostic and prognostic value of human kinesin family member 4A (KIF4A) as an effective biomarker for breast cancer.<h4>Materials and methods</h4>Cancer Genome Atlas data and 12 independent public breast cancer microarray data sets were downloaded and analyzed using individual and pooled approaches.<h4>Results</h4>The results of our study revealed a strong and positive correlation between KIF4A expression and malignant features of br ...[more]

PMID: 30127624

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Project description:PurposesCervical cancer (CC) is one of the highest frequently occurred malignant gynecological tumors with high rates of morbidity and mortality. Here, we aimed to identify significant genes associated with poor outcome. Materials and methods. Differentially expressed genes (DEGs) between CC tissues and normal cervical tissues were picked out by GEO2R tool and Venn diagram software. Database for Annotation, Visualization and Integrated Discovery (DAVID) was performed to analyze gene ontology (GO) and Kyoto Encyclopedia of Gene and Genome (KEGG) pathway. The protein-protein interactions (PPIs) of these DEGs were visualized by Cytoscape with Search Tool for the Retrieval of Interacting Genes (STRING). Afterwards, Kaplan-Meier analysis was applied to analyze the overall survival among these genes. The Gene Expression Profiling Interactive Analysis (GEPIA) was applied for further validation of the expression level of these genes.ResultsThe mRNA expression profile datasets of GSE63514, GSE27678, and GSE6791 were downloaded from the Gene Expression Omnibus database (GEO). In total, 76 CC tissues and 35 normal tissues were collected in the three profile datasets. There were totally 73 consistently expressed genes in the three datasets, including 65 up-regulated genes and 8 down-regulated genes. Of PPI network analyzed by Molecular Complex Detection (MCODE) plug-in, all 65 up-regulated genes and 4 down-regulated genes were selected. The results of the Kaplan-Meier survival analysis showed that 3 of the 65 up-regulated genes had a significantly worse prognosis, while 3 of the 4 down-regulated genes had a significantly better outcome. For validation in GEPIA, 4 of 6 genes (PLOD2, ANLN, AURKA, and AR) were confirmed to be significantly deregulated in CC tissues compared to normal tissues.ConclusionWe have identified three up-regulated (PLOD2, ANLN, and AURKA) and a down-regulated DEGs (AR) with poor prognosis in CC on the basis of integrated bioinformatical methods, which could be regarded as potential therapeutic targets for CC patients.

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An integrated bioinformatical analysis to evaluate the role of KIF4A as a prognostic biomarker for breast cancer.

Publications

An integrated bioinformatical analysis to evaluate the role of KIF4A as a prognostic biomarker for breast cancer.

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