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. 2016 Feb 8:6:20616.
doi: 10.1038/srep20616.

Colorectal cancer characterization and therapeutic target prediction based on microRNA expression profile

Peng Xu  1 , Yanliang Zhu  1 , Bo Sun  1 , Zhongdang Xiao  1
Affiliations

Colorectal cancer characterization and therapeutic target prediction based on microRNA expression profile

Peng Xu et al. Sci Rep. .

Abstract

Colorectal cancer (CRC) is one of the most commonly diagnosed cancers and a major cause of cancer death. However, the molecular mechanisms underlying CRC initiation, growth and metastasis are poorly understood. In this study, based on our previous work for comprehensively analyzing miRNA sequencing data, we examined a series of colorectal cancer microRNAs expression profiles data. Results show that all these CRC samples share the same four pathways including TGF-beta signaling pathway, which is important in colorectal carcinogenesis. Twenty-one microRNAs that evolved in the four overlapped pathways were then discovered. Further analysis selected miR-21 as an important regulator for CRC through TGF-beta pathways. This study develops methods for discovering tumor specific miRNA cluster as biomarker and for screening new cancer therapy targets based on miRNA sequencing.

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Figures

Figure 1
Figure 1. Significantly enriched KEGG pathways of submitted genes that significantly regulated by miRNAs in four CRC samples (S1, S2, S3 and S4).
Gray filled block indicates genes are significantly enriched in this pathway in corresponding sample while white filled block indicates genes are not enriched in this pathway.
Figure 2
Figure 2. Venn Diagram of miRNAs correlated to the pathways respectively in four samples.
All miRNAs that will target to the significantly repressed mRNAs occurring in enriched pathways respectively in four samples (a); miRNAs were filtered out after doing differential analysis on the repressed effect for miRNA-mRNA interactions (b).
Figure 3
Figure 3. Venn Diagram of miRNAs (a) and mRNAs (b) correlated to the overlapped four pathways respectively in four samples.
Figure 4
Figure 4. A panel of 11 miRNAs is sufficient to distinguish CRC patients from the healthy subjects.
Identifiers for data samples were on the right side of the figure, thereinto, which end with ‘11’ are for healthy subjects and ‘01’ are for CRC samples.
Figure 5
Figure 5. The expression profiles of 11 miRNAs respectively in normal colorectal and CRC samples.
Figure 6
Figure 6. The number of target genes regulated by the 21 miRNAs respectively in the four overlapped pathways (Blue:hsa04350TGF-beta signaling pathway; Red:hsa04940Type I diabetes mellitus; Green: hsa05330Allograft rejection; Purple:hsa05332Graft-versus-host disease).
Figure 7
Figure 7. Venn Diagram significantly differential repressed mRNAs in hsa04350TGF-beta signaling pathway respectively in four CRC samples.
Figure 8
Figure 8. TGF-beta signaling pathway: the display of nine genes on the TGF-beta signaling pathway.
Figure 9
Figure 9. qPCR analysis of miR-21 expression level in three groups of CRC samples versus their corresponding control samples.
Figure 10
Figure 10. PCR analysis of expression level of Smad6 treated with and without inhibition of miR-21.
PCR analysis were performed on Smad6, in which Actin was taken as internal reference (a). The same PCR analysis experiment were repeated 4 times, by using of software of imageJ, mean grey level of the PCR results were got and represented as mean ± SD of four independent experiments (p_value = 0.003613) (b) .
Figure 11
Figure 11. Effects of inhibition of miR-21 on cell cycle distribution in human CRC cell lines.
See this image and copyright information in PMC

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