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Published on: March 30, 2019
Differential Expression Analysis Based on Ensemble Strategy on miRNA Profiles of Kidney Clear Cell Carcinoma
Enyang Zhao1,2, Ziqi Xi3, Qiong Wu1
1School of Life Science and Technology, Harbin Institute of Technology, 150006 Harbin, Heilongjiang, China.
Background:
Kidney clear cell carcinoma (KIRC) is the most common type of kidney cancer, accounting for approximately 60-85% of all the kidney cancers. However, there are few options available for early treatment. Therefore, it is extremely important to identify biomarkers and study therapeutic targets for KIRC.
Methods:
Since there are few studies on KIRC, we used a data-driven approach to identify differential genes. Here, we used miRNA gene expression profile data from the TCGA database species of KIRC and proposed a machine learning-based approach to quantify the importance score of each gene. Then, an ensemble method was utilized to find the optimal subset of genes used to predict KIRC by clustering. The most genetic subset was then used to classify and predict KIRC.
Results:
Differential genes were screened by several traditional differential analysis methods, and the selected gene subset showed a better performance. Independent testing sets from the GEO database were used to verify the effectiveness of the optimal subset of genes. Besides, cross-validation was made to verify the effectiveness of the approach.
Conclusions:
Finally, important genes, such as miR-140 and miR-210, were found to be involved in the biochemical processes of KIRC, which also proved the effectiveness of our approach.
Insights
Identifying key genes for kidney clear cell carcinoma (KIRC) is crucial due to limited early treatment options. This study uses a data-driven machine learning approach to find important biomarkers for KIRC prediction.
Area of Science:
- Oncology
- Bioinformatics
- Genomics
Background:
- Kidney clear cell carcinoma (KIRC) is the predominant kidney cancer subtype, representing 60-85% of cases.
- Limited therapeutic options for early-stage KIRC necessitate the identification of novel biomarkers and therapeutic targets.
Purpose of the Study:
- To identify differential genes and potential biomarkers for kidney clear cell carcinoma (KIRC) using a data-driven approach.
- To develop a machine learning model for predicting KIRC based on gene expression profiles.
Main Methods:
- Utilized miRNA gene expression profile data from The Cancer Genome Atlas (TCGA) for KIRC.
- Employed a machine learning approach to quantify gene importance and an ensemble method for optimal gene subset selection.
- Validated the identified gene subset using independent testing sets from the Gene Expression Omnibus (GEO) database and cross-validation.
Main Results:
- Screened differential genes using traditional methods, with the selected subset demonstrating superior performance.
- The optimal gene subset effectively classified and predicted KIRC.
- Independent datasets confirmed the robustness and effectiveness of the identified gene subset.
Conclusions:
- Identified key genes, including miR-140 and miR-210, implicated in KIRC's biochemical processes.
- The study successfully demonstrated the effectiveness of the data-driven, machine learning-based approach for KIRC biomarker discovery.
