Multi-Omics Data Integration for Improved Cancer Subtyping via Denoising Autoencoder-Based Multi-Kernel Learning

Objectives: Cancer, characterized by its profound complexity and heterogeneity, arises from a multitude of molecular disruptions. The pursuit of identifying distinct cancer subtypes is driven by the need to stratify patients into clinically coherent subgroups, each exhibiting unique prognostic outco...

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Vydáno v:	Genes Ročník 16; číslo 11; s. 1246
Hlavní autoři:	Yao, Xiukun, Wang, Tong, Yang, Qi, Wang, Jiawen, Qi, Yao, Xu, Tong, Wei, Zhiwen, Cui, Yuehua, Cao, Hongyan, Yun, Keming
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Switzerland MDPI AG 22.10.2025
Témata:	Autoencoder Biological analysis Biomarkers, Tumor - genetics Cancer Clustering Computational Biology - methods Datasets Deep Learning DNA methylation Genomes Genomics - methods Glioma Glioma - classification Glioma - genetics Gliomas Humans Kidney Neoplasms - classification Kidney Neoplasms - genetics Methods Multiomics Neoplasms - classification Neoplasms - genetics Noise control Oncology, Experimental Performance evaluation Precision medicine Prognosis Renal cell carcinoma Simulation methods China deep learning hierarchical multi-kernel learning denoising autoencoder multi-omics data integration subtypes identification
ISSN:	2073-4425, 2073-4425
On-line přístup:	Získat plný text
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Popis
Shrnutí:	Objectives: Cancer, characterized by its profound complexity and heterogeneity, arises from a multitude of molecular disruptions. The pursuit of identifying distinct cancer subtypes is driven by the need to stratify patients into clinically coherent subgroups, each exhibiting unique prognostic outcomes. The integration of multi-omics datasets enhances the precision of subtyping and advances precision medicine. Methods: Considering the high-dimensional nature inherent to various multi-omics data types, we introduce an innovative deep learning framework, DAE-MKL, which integrates denoising autoencoders with multi-kernel learning for identifying cancer subtypes. Leveraging the capabilities of DAE, we extract non-linearly transformed features that retain pertinent information while mitigating noise and redundancy. These refined data representations are then funneled into the MKL framework, thereby enhancing the accuracy of subtype identification. We applied the DAE-MKL framework to both simulated studies and empirical datasets derived from two distinct cancer types, low-grade glioma (LGG, n = 86) and kidney renal clear cell carcinoma (KIRC, n = 285), thereby validating its utility and feasibility. Results: In simulations, DAE-MKL achieved superior performance with NMI gains up to 0.78 compared to other state-of-the-art methods. For real datasets, DAE-MKL identified three LGG subtypes and three KIRC subtypes, showing significant survival differences (KIRC log-rank p = 3.33 × 10−8, LGG log-rank p = 3.99 × 10−8). Additionally, we explored potential cancer-related biomarkers. Conclusions: The DAE-MKL effectively identifies molecular subtypes, reduces data dimensionality, and improves prognostic stratification in multi-omics cancer datasets, providing an effective tool for precision oncology.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ISSN:	2073-4425 2073-4425
DOI:	10.3390/genes16111246