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An In Vitro Protocol for Evaluating MicroRNA Levels, Functions, and Associated Target Genes in Tumor Cells
Published on: May 21, 2019
Identifying cancer-related microRNAs based on gene expression data
Xing-Ming Zhao1, Ke-Qin Liu2, Guanghui Zhu1
1School of Electronics and Information Engineering, Tongji University, Shanghai 201804, China, Center for Bioinformatics and Systems Biology, Department of Radiology, Wake Forest School of Medicine, Winston-Salem, NC 27157, USA, LERIA, University of Angers, 49045 Angers Cedex 01, France, Department of Mathematics, Shanghai University, Shanghai 200444, China and Key Laboratory of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China.
Motivation:
MicroRNAs (miRNAs) are short non-coding RNAs that play important roles in post-transcriptional regulations as well as other important biological processes. Recently, accumulating evidences indicate that miRNAs are extensively involved in cancer. However, it is a big challenge to identify which miRNAs are related to which cancer considering the complex processes involved in tumors, where one miRNA may target hundreds or even thousands of genes and one gene may regulate multiple miRNAs. Despite integrative analysis of matched gene and miRNA expression data can help identify cancer-associated miRNAs, such kind of data is not commonly available. On the other hand, there are huge amount of gene expression data that are publicly accessible. It will significantly improve the efficiency of characterizing miRNA's function in cancer if we can identify cancer miRNAs directly from gene expression data.
Results:
We present a novel computational framework to identify the cancer-related miRNAs based solely on gene expression profiles without requiring either miRNA expression data or the matched gene and miRNA expression data. The results on multiple cancer datasets show that our proposed method can effectively identify cancer-related miRNAs with higher precision compared with other popular approaches. Furthermore, some of our novel predictions are validated by both differentially expressed miRNAs and evidences from literature, implying the predictive power of our proposed method. In addition, we construct a cancer-miRNA-pathway network, which can help explain how miRNAs are involved in cancer.
Availability And Implementation:
The R code and data files for the proposed method are available at http://comp-sysbio.org/miR_Path/
Contact:
liukeq@gmail.com
Supplementary Information:
supplementary data are available at Bioinformatics online.
Insights
This study introduces a new computational method to identify cancer-related microRNAs (miRNAs) using only gene expression data. The approach effectively predicts cancer-associated miRNAs and reveals their roles in cancer pathways.
Area of Science:
- Bioinformatics
- Computational Biology
- Genomics
Background:
- MicroRNAs (miRNAs) are crucial regulators in biological processes and are increasingly implicated in cancer development.
- Identifying specific cancer-associated miRNAs is challenging due to complex gene regulatory networks and limited availability of matched gene and miRNA expression data.
- Leveraging abundant public gene expression data offers a promising avenue for cancer-related miRNA discovery.
Purpose of the Study:
- To develop a novel computational framework for identifying cancer-related miRNAs using solely gene expression profiles.
- To overcome the limitations of data scarcity for miRNA expression and matched gene-miRNA expression data.
- To enhance the efficiency of characterizing miRNA functions in cancer.
Main Methods:
- Developed a computational framework that analyzes gene expression profiles to predict cancer-associated miRNAs.
- The method does not require miRNA expression data or matched gene-miRNA expression data.
- Validation was performed on multiple cancer datasets.
Main Results:
- The proposed method effectively identifies cancer-related miRNAs with high precision.
- Novel predictions were validated through differential expression analysis and existing literature.
- A cancer-miRNA-pathway network was constructed to elucidate miRNA involvement in cancer.
Conclusions:
- The developed computational framework accurately identifies cancer-associated miRNAs from gene expression data.
- The findings highlight the predictive power of the method and provide insights into miRNA functions in cancer.
- The constructed network aids in understanding the mechanistic roles of miRNAs in tumorigenesis.
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