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On integrative analysis of multi-level gene expression data in Kidney cancer subgrouping
Pratheeba Jeyananthan1, Maduranga W P N1, Rodrigo S M1
1Faculty of Engineering, University of Jaffna, Kilinochchi, Sri Lanka.
Abstract:
Kidney cancer is one of the most dangerous cancer mainly targeting men. In 2020, around 430, 000 people were diagnosed with this disease worldwide. It can be divided into three prime subgroups such as kidney renal cell carcinoma (KIRC), kidney renal papilliary cell carcinoma (KIRP) and kidney chromophobe (KICH). Correct identification of these subgroups on time is crucial for the initiation and determination of proper treatment. On-time identification of this disease and its subgroup can help both the clinicians and patients to improve the situation. Hence, this study checks the possibility of using multi-omics data in the kidney cancer subgrouping, whether integrating multiple omics data will increase the subgrouping accuracy or not. Four different molecular data such as genomics, proteomics, epigenomics and miRNA from The Cancer Genome Atlas (TCGA) are used in this study. As the data is in a very high dimension world, this study starts with selecting the relevant features of the study using Pearson's correlation coefficient. Those selected features are used with three different classification algorithms such as k-nearest neighbor (KNN), supporting vector machines (SVMs) and random forest. Performances are compared to see whether the integration of multi-omics data can improve the accuracy of kidney cancer subgrouping. This study shows that integration of multi-omics data can improve the performance of the kidney cancer subgrouping. The highest performance (accuracy value of 0.98±0.03) is gained by top 400 features selected from integrated multi-omics data, with support vector machines.
Insights
Integrating multi-omics data improves kidney cancer subgrouping accuracy. Combining genomics, proteomics, epigenomics, and miRNA data with machine learning enhances diagnostic precision for kidney renal cell carcinoma, kidney renal papilliary cell carcinoma, and kidney chromophobe.
Area of Science:
- Oncology
- Bioinformatics
- Genomics
Background:
- Kidney cancer, a significant global health concern, comprises distinct subgroups (KIRC, KIRP, KICH) requiring precise identification for effective treatment.
- Accurate and timely diagnosis of kidney cancer subtypes is critical for guiding clinical decisions and improving patient outcomes.
- The Cancer Genome Atlas (TCGA) provides a rich resource of multi-omics data for investigating complex diseases like kidney cancer.
Purpose of the Study:
- To evaluate the efficacy of integrating multi-omics data for improving the accuracy of kidney cancer subgroup classification.
- To determine if combining genomics, proteomics, epigenomics, and miRNA data enhances subgrouping performance compared to single-omic approaches.
- To identify optimal features and machine learning models for accurate kidney cancer subtyping.
Main Methods:
- Utilized four types of molecular data (genomics, proteomics, epigenomics, miRNA) from The Cancer Genome Atlas (TCGA).
- Applied feature selection using Pearson's correlation coefficient to manage high-dimensional omics data.
- Employed and compared three classification algorithms: k-nearest neighbor (KNN), support vector machines (SVMs), and random forest.
Main Results:
- Integration of multi-omics data demonstrated improved performance in kidney cancer subgrouping.
- The highest accuracy (0.98±0.03) was achieved using the top 400 features from integrated multi-omics data with support vector machines.
- Feature selection and data integration proved effective in enhancing classification accuracy.
Conclusions:
- Multi-omics data integration significantly enhances the accuracy of kidney cancer subgroup classification.
- Support vector machines, combined with selected features from integrated omics data, offer a powerful approach for precise kidney cancer subtyping.
- This study highlights the potential of multi-omics data analysis in advancing precision oncology for kidney cancer.
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