IMR Press / FBL / Volume 28 / Issue 11 / DOI: 10.31083/j.fbl2811283
Open Access Original Research
Differential Expression Analysis Based on Ensemble Strategy on miRNA Profiles of Kidney Clear Cell Carcinoma
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1 School of Life Science and Technology, Harbin Institute of Technology, 150006 Harbin, Heilongjiang, China
2 The Second Affiliated Hospital of Harbin Medical University, Harbin Medical University, 150076 Harbin, Heilongjiang, China
3 College of Aulin, Northeast Forestry University, 150006 Harbin, Heilongjiang, China
*Correspondence: (Qiong Wu)
Front. Biosci. (Landmark Ed) 2023, 28(11), 283;
Submitted: 9 February 2023 | Revised: 11 March 2023 | Accepted: 21 March 2023 | Published: 8 November 2023
Copyright: © 2023 The Author(s). Published by IMR Press.
This is an open access article under the CC BY 4.0 license.

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.

kidney cancer
differential gene
machine learning
2020-067/Heilongjiang Health Commission Project
Fig. 1.
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