Supervised graph contrastive learning for cancer subtype identification through multi-omics data integration.

Chen, Fangxu; Peng, Wei; Dai, Wei; Wei, Shoulin; Fu, Xiaodong; Liu, Li; Liu, Lijun

Chen, Fangxu; Peng, Wei; Dai, Wei; Wei, Shoulin; Fu, Xiaodong; Liu, Li; Liu, Lijun.

Afiliación

Chen F; Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, 650500 Yunnan China.
Peng W; Computer Technology Application Key Lab of Yunnan Province, Kunming University of Science and Technology, Kunming, 650050 China.
Dai W; Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, 650500 Yunnan China.
Wei S; Computer Technology Application Key Lab of Yunnan Province, Kunming University of Science and Technology, Kunming, 650050 China.
Fu X; Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, 650500 Yunnan China.
Liu L; Computer Technology Application Key Lab of Yunnan Province, Kunming University of Science and Technology, Kunming, 650050 China.
Liu L; Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, 650500 Yunnan China.

Health Inf Sci Syst ; 12(1): 12, 2024 Dec.

Article en En | MEDLINE | ID: mdl-38404715

ABSTRACT

ABSTRACT

Cancer is one of the most deadly diseases in the world. Accurate cancer subtype classification is critical for patient diagnosis, treatment, and prognosis. Ever-increasing multi-omics data describes the characteristics of the patients from different views and serves as complementary information to promote cancer subtype identification. However, omics data generally have different distributions and high dimensions. How to effectively integrate multiple omics data to classify cancer subtypes accurately is a challenge for researchers. This work proposes a method integrating multi-omics data based on supervised graph contrast learning (MCRGCN) to classify cancer subtypes. The method considers the unique feature distribution of each omics data and the interaction of different omics data features to improve the accuracy of cancer subtype classification. To achieve this, MCRGCN first constructs different sample networks based on the multi-omics data of the samples. Then, it puts the omics data and adjacency matrix of the sample into different residual graph convolution models to get multi-omics features of the samples, which are trained with a supervised comparison loss to maintain that the sample features of each omics should be as consistent as possible. Finally, we input the sample features combining multi-omics features into a classifier to obtain the cancer subtypes. We applied MCRGCN to the invasive breast carcinoma (BRCA) and glioblastoma multiforme (GBM) datasets, integrating gene expression, miRNA expression, and DNA methylation data. The results demonstrate that our model is superior to other methods in integrating multi-omics data. Moreover, the results of survival analysis experiments demonstrate that the cancer subtypes identified by our model have significant clinical features. Furthermore, our model can help to identify potential biomarkers and pathways associated with cancer subtypes.

Palabras clave

Cancer-subtype classification; Graph contrastive learning; Multi-omics integration

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Health Inf Sci Syst Año: 2024 Tipo del documento: Article Pais de publicación: Reino Unido

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