A Novel Attention-Mechanism Based Cox Survival Model by Exploiting Pan-Cancer Empirical Genomic Information

• Published in: Cells (Basel, Switzerland) Switzerland), 2022-04, Vol.11 (9), p.1421
• Main Authors: Meng, Xiangyu, Wang, Xun, Zhang, Xudong, Zhang, Chaogang, Zhang, Zhiyuan, Zhang, Kuijie, Wang, Shudong
• Format: Article
• Language: English
• Subjects: Breast cancer; Cancer; cancer prognosis; Cox regression; Datasets; Deep learning; Gene expression; Genome; Genomics - methods; Humans; Medical prognosis; Neoplasms - genetics; Neural networks; Prognosis; Proportional Hazards Models; Prostate; Support vector machines; Survival; Survival Analysis; Transcriptomes; Transfer learning
• ISSN: 2073-4409; 2073-4409
• DOI: 10.3390/cells11091421

Cancer prognosis is an essential goal for early diagnosis, biomarker selection, and medical therapy. In the past decade, deep learning has successfully solved a variety of biomedical problems. However, due to the high dimensional limitation of human cancer transcriptome data and the small number of training samples, there is still no mature deep learning-based survival analysis model that can completely solve problems in the training process like overfitting and accurate prognosis. Given these problems, we introduced a novel framework called SAVAE-Cox for survival analysis of high-dimensional transcriptome data. This model adopts a novel attention mechanism and takes full advantage of the adversarial transfer learning strategy. We trained the model on 16 types of TCGA cancer RNA-seq data sets. Experiments show that our module outperformed state-of-the-art survival analysis models such as the Cox proportional hazard model (Cox-ph), Cox-lasso, Cox-ridge, Cox-nnet, and VAECox on the concordance index. In addition, we carry out some feature analysis experiments. Based on the experimental results, we concluded that our model is helpful for revealing cancer-related genes and biological functions.