Research Perspectives:

Integrative microRNA and gene profiling data analysis reveals novel biomarkers and mechanisms for lung cancer

Ling Hu, Junmei Ai, Hui Long, Weijun Liu, Xiaomei Wang, Yi Zuo, Yan Li, Qingming Wu and Youping Deng _

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Oncotarget. 2016; 7:8441-8454. https://doi.org/10.18632/oncotarget.7264

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Ling Hu1,2,*, Junmei Ai2,*, Hui Long3,*, Weijun Liu4, Xiaomei Wang5, Yi Zuo6, Yan Li2, Qingming Wu7 and Youping Deng7,2

1 Department of Anesthesiology, Tianyou Hospital, Wuhan University of Science and Technology, Wuhan, China

2 Department of Internal Medicine and Biochemistry, Rush University Medical Center, Chicago, IL, USA

3 Department of Gastroenterology, Tianyou Hospital, Wuhan University of Science and Technology, Wuhan, China

4 Department of Orthopedics, Pu Ai Hospital, Affiliated to Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China

5 Department of Biological Science and Technology, Wuhan Bioengineering Institute, Wuhan, China

6 Department of Orthopedic, Tianyou Hospital, Wuhan University of Science and Technology, Wuhan, China

7 Medical College, Wuhan University of Science and Technology, Wuhan, China

* These authors have contributed equally to this work

Correspondence to:

Youping Deng, email:

Qingming Wu, email:

Keywords: microRNAs, lung cancer, meta-analysis, target gene, biomarker

Received: July 14, 2015 Accepted: January 13, 2016 Published: February 08, 2016


Background: Studies on the accuracy of microRNAs (miRNAs) in diagnosing non-small cell lung cancer (NSCLC) have still controversial. Therefore, we conduct to systematically identify miRNAs related to NSCLC, and their target genes expression changes using microarray data sets.

Methods: We screened out five miRNAs and six genes microarray data sets that contained miRNAs and genes expression in NSCLC from Gene Expression Omnibus.

Results: Our analysis results indicated that fourteen miRNAs were significantly dysregulated in NSCLC. Five of them were up-regulated (miR-9, miR-708, miR-296-3p, miR-892b, miR-140-5P) while nine were down-regulated (miR-584, miR-218, miR-30b, miR-522, miR486-5P, miR-34c-3p, miR-34b, miR-516b, miR-592). The integrating diagnosis sensitivity (SE) and specificity (SP) were 82.6% and 89.9%, respectively. We also found that 4 target genes (p < 0.05, fold change > 2.0) were significant correlation with the 14 discovered miRNAs, and the classifiers we built from one training set predicted the validation set with higher accuracy (SE = 0.987, SP = 0.824).

Conclusions: Our results demonstrate that integrating miRNAs and target genes are valuable for identifying promising biomarkers, and provided a new insight on underlying mechanism of NSCLC. Further, our well-designed validation studies surely warrant the investigation of the role of target genes related to these 14 miRNAs in the prediction and development of NSCLC.

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