Towards key genes identification for breast cancer survival risk with neural network models

Breast cancer, one common malignant tumor all over the world, has a considerably high rate of recurrence, which endangers the health and life of patients. While more and more data have been available, how to leverage the gene expression data to predict the survival risk of cancer patients and identify key genes has become a hot topic for cancer research. Therefore, in this work, we investigate the gene expression and clinical data of breast cancer patients, specifically a novel framework is proposed focusing on the survival risk classification and key gene identification task. We firstly combine the differential expression and univariate Cox regression analysis to achieve dimensional reduction of gene expression data. The median survival time is subsequently proposed as the risk classification threshold and a learning model based on neural network is trained to classify the survival risk of patients. Innovatively, in this work, the activation region visualization technology is selected as the identification tool, which identify 20 key genes related to the survival risk of breast cancer patients. We further analyze the gene function of these 20 key genes based on STRING database. It is critical to learn that, the genetic biomarkers identified in this paper may possess value for the following clinical treatment of breast cancer according to the literature findings. Importantly, the genetic biomarkers identified in this paper may possess value for the following clinical treatment of breast cancer according to the literature findings. Our work accomplishes the objective of proposing a targeted approach to enhancing the survival analysis and therapeutic strategies in breast cancer through advanced computational techniques and gene analysis.