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Comparative Study
, 7 (8), 8441-54

Integrative microRNA and Gene Profiling Data Analysis Reveals Novel Biomarkers and Mechanisms for Lung Cancer

Affiliations
Comparative Study

Integrative microRNA and Gene Profiling Data Analysis Reveals Novel Biomarkers and Mechanisms for Lung Cancer

Ling Hu et al. Oncotarget.

Abstract

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.

Keywords: biomarker; lung cancer; meta-analysis; microRNAs; target gene.

Conflict of interest statement

CONFLICTS OF INTERESTS

The authors declare that they have no competing interests.

Figures

Figure 1
Figure 1. Flowchart of miRNAs studies in this meta-analysis
Figure 2
Figure 2. 3D plot of principal components analytic scores (6 = GSE15008, 9 = GSE36681, 10 = GSE29248)
A. raw data without normalization, B. with normalization and batch effect removal.
Figure 3
Figure 3. Most significant canonical pathways of putative target genes of lung cancer regulated 14 promising miRNAs
(The threshold lines indicate 5% P-value. The bigger the -log (p-value) of pathway is, the more significantly the pathway is adjusted).
Figure 4
Figure 4. Gene network using target genes from 14 promising miRNAs
The network was produced by IPA. Nodes colored in red or green indicate up-regulated and down-regulated gene, respectively.
Figure 5
Figure 5. Hierarchical clustering analysis of two sets based on 4 core genes was performed using samples from (A) training set and (B) testing set
The relative level of gene expression is indicted by the color scale at the bottom word “c” on the each clustering plot represent cancer sample. Word “n” on each clustering plot represent control sample.
Figure 6
Figure 6. Flowchart of studies (including miRNA and target gene) in this research
Figure 7
Figure 7. A hypothetical model to explain the molecular mechanisms of NSCLC based on enrolled data sets
Figure 8
Figure 8. Verification of miRNA and gene expression of integrative microarray results using real time QRT-PCR
A. Verification of 4 miRNA results. B. Verification of 4 gene results. The positive value indicates up-regulated fold change of lung cancer cell line A549 compared to normal lung epithelial cells NL20. The negative value indicates the down-regulated fold change of lung cancer cell line A549 compared to normal lung epithelial cells NL20. Values refer to the mean ± SD of three independent samples, each run in triplicate.

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