An integrated bioinformatical analysis to evaluate the role of KIF4A as a prognostic biomarker for breast cancer
Received 6 February 2018
Accepted for publication 28 April 2018
Published 10 August 2018 Volume 2018:11 Pages 4755—4768
Checked for plagiarism Yes
Review by Single-blind
Peer reviewer comments 2
Editor who approved publication: Dr Cho
Dan Xue,1,* Pu Cheng,2,* Mengjiao Han,3 Xiyong Liu,4,5 Lijun Xue,6 Chenyi Ye,7 Ke Wang,8 Jian Huang8,9
1Department of Plastic Surgery, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China; 2Department of Gynaecology, Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China; 3Department of Medical Oncology, Key Laboratory of Biotherapy in Zhejiang, Sir Run Run Shaw Hospital, Medical School of Zhejiang University, Hangzhou, China; 4Biomarker Development, California Cancer Institute, Temple City, CA, USA; 5School of Medicine, Taipei Medical University, Taipei, Taiwan, Republic of China; 6Department of Pathology, Loma Linda University Medical Center, Loma Linda, CA, USA; 7Department of Orthopaedics, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China; 8Department of Surgical Oncology, Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China; 9Gastroenterology Institute, Zhejiang University School of Medicine, Hangzhou, China
*These authors contributed equally to this work
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.
Keywords: breast cancer, KIF4A, microarray, microRNA, prognosis
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