Liu Jian, Xue Xinzheng, Wen Pengbo, Song Qian, Yao Jun, Ge Shuguang
School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, China.
School of Medical Information and Engineering, Xuzhou Medical University, Xuzhou, China.
Front Genet. 2024 Nov 14;15:1466825. doi: 10.3389/fgene.2024.1466825. eCollection 2024.
The combination of next-generation sequencing technology and Cancer Genome Atlas (TCGA) data provides unprecedented opportunities for the discovery of cancer subtypes. Through comprehensive analysis and in-depth analysis of the genomic data of a large number of cancer patients, researchers can more accurately identify different cancer subtypes and reveal their molecular heterogeneity.
In this paper, we propose the SMMSN (Self-supervised Multi-fusion Strategy Network) model for the discovery of cancer subtypes. SMMSN can not only fuse multi-level data representations of single omics data by Graph Convolutional Network (GCN) and Stacked Autoencoder Network (SAE), but also achieve the organic fusion of multi- -omics data through multiple fusion strategies. In response to the problem of lack label information in multi-omics data, SMMSN propose to use dual self-supervise method to cluster cancer subtypes from the integrated data.
We conducted experiments on three labeled and five unlabeled multi-omics datasets to distinguish potential cancer subtypes. Kaplan Meier survival curves and other results showed that SMMSN can obtain cancer subtypes with significant differences.
In the case analysis of Glioblastoma Multiforme (GBM) and Breast Invasive Carcinoma (BIC), we conducted survival time and age distribution analysis, drug response analysis, differential expression analysis, functional enrichment analysis on the predicted cancer subtypes. The research results showed that SMMSN can discover clinically meaningful cancer subtypes.
下一代测序技术与癌症基因组图谱（TCGA）数据的结合为发现癌症亚型提供了前所未有的机遇。通过对大量癌症患者的基因组数据进行全面分析和深入剖析，研究人员能够更准确地识别不同的癌症亚型，并揭示其分子异质性。
在本文中，我们提出了用于发现癌症亚型的SMMSN（自监督多融合策略网络）模型。SMMSN不仅可以通过图卷积网络（GCN）和堆叠自编码器网络（SAE）融合单组学数据的多层次数据表示，还能通过多种融合策略实现多组学数据的有机融合。针对多组学数据中标签信息缺失的问题，SMMSN提出使用双重自监督方法从整合数据中对癌症亚型进行聚类。
我们在三个有标签和五个无标签的多组学数据集上进行实验，以区分潜在的癌症亚型。卡普兰-迈耶生存曲线等结果表明，SMMSN能够获得具有显著差异的癌症亚型。
在多形性胶质母细胞瘤（GBM）和乳腺浸润性癌（BIC）的病例分析中，我们对预测的癌症亚型进行了生存时间和年龄分布分析、药物反应分析、差异表达分析、功能富集分析。研究结果表明，SMMSN能够发现具有临床意义的癌症亚型。
