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Detection of a Circulating MicroRNA Custom Panel in Patients with Metastatic Colorectal Cancer
Published on: March 14, 2019
Clear cell renal cell carcinoma associated microRNA expression signatures identified by an integrated bioinformatics
Jiajia Chen1, Daqing Zhang, Wenyu Zhang
1Center for Systems Biology, Soochow University, Suzhou 215006, China.
Background:
Clear cell renal cell carcinoma (ccRCC) represents the most invasive and common adult kidney neoplasm. Mounting evidence suggests that microRNAs (miRNAs) are important regulators of gene expression. But their function in tumourigenesis in this tumour type remains elusive. With the development of high throughput technologies such as microarrays and NGS, aberrant miRNA expression has been widely observed in ccRCC. Systematic and integrative analysis of multiple microRNA expression datasets may reveal potential mechanisms by which microRNAs contribute to ccRCC pathogenesis.
Methods:
We collected 5 public microRNA expression datasets in ccRCC versus non-matching normal renal tissues from GEO database and published literatures. We analyzed these data sets with an integrated bioinformatics framework to identify expression signatures. The framework incorporates a novel statistic method for abnormal gene expression detection and an in-house developed predictor to assess the regulatory activity of microRNAs. We then mapped target genes of DE-miRNAs to different databases, such as GO, KEGG, GeneGo etc, for functional enrichment analysis.
Results:
Using this framework we identified a consistent panel of eleven deregulated miRNAs shared by five independent datasets that can distinguish normal kidney tissues from ccRCC. After comparison with 3 RNA-seq based microRNA profiling studies, we found that our data correlated well with the results of next generation sequencing. We also discovered 14 novel molecular pathways that are likely to play a role in the tumourigenesis of ccRCC.
Conclusions:
The integrative framework described in this paper greatly improves the inter-dataset consistency of microRNA expression signatures. Consensus expression profile should be identified at pathway or network level to address the heterogeneity of cancer. The DE-miRNA signature and novel pathways identified herein could provide potential biomarkers for ccRCC that await further validation.
Insights
This study identified eleven deregulated microRNAs (miRNAs) consistently found in clear cell renal cell carcinoma (ccRCC) tissues, distinguishing them from normal kidney tissue. These findings, along with novel molecular pathways, may offer potential biomarkers for ccRCC diagnosis and treatment.
Area of Science:
- Oncology
- Genomics
- Bioinformatics
Background:
- Clear cell renal cell carcinoma (ccRCC) is the most common and aggressive adult kidney cancer.
- MicroRNAs (miRNAs) are implicated in gene regulation, but their role in ccRCC tumorigenesis is not fully understood.
- Aberrant miRNA expression is frequently observed in ccRCC, necessitating integrated analysis of expression datasets.
Purpose of the Study:
- To systematically analyze multiple microRNA expression datasets in ccRCC.
- To identify consistent miRNA expression signatures and potential regulatory mechanisms in ccRCC pathogenesis.
- To discover novel molecular pathways involved in ccRCC development.
Main Methods:
- Collected and integrated 5 public microRNA expression datasets for ccRCC and normal kidney tissues.
- Employed an integrated bioinformatics framework with novel statistical methods for detecting abnormal gene expression.
- Assessed microRNA regulatory activity and performed functional enrichment analysis of target genes using GO, KEGG, and GeneGo databases.
Main Results:
- Identified a consistent panel of eleven deregulated miRNAs across five independent datasets, capable of differentiating ccRCC from normal tissues.
- Validated findings against 3 RNA-sequencing based microRNA profiling studies, showing good correlation.
- Discovered 14 novel molecular pathways potentially involved in ccRCC tumorigenesis.
Conclusions:
- The developed integrative framework enhances the consistency of microRNA expression signatures across datasets.
- Consensus expression profiling at pathway or network level is crucial for addressing cancer heterogeneity.
- The identified miRNA signature and novel pathways present potential biomarkers for ccRCC, requiring further validation.
