An Autoencoder Based Bioinformatics Framework for Predicting Prognosis of Breast Cancer Patients

It is crucial to find prognostic biomarkers that can predict the cancer prognosis and estimate risk, as they can be used in clinical settings to treat patients. Probing the biomarkers themselves will reveal important insights into the cancer dynamics and molecular pathways underlying pathological behavior. To achieve that goal, this work proposes a bioinformatics framework, taking advantage of the deep learning-based feature selection method Concrete Autoencoder (CAE) to identify key genes and to build a prognostic score model that can assess the risk of cancer patients. 48 gene-pairs were identified to form a prognostic signature model that can significantly differentiate between high-risk and low-risk patients with breast cancer. This prognostic signature was comprised of 42 genes enriched in cancer-related pathways and molecular functions. The proposed framework and the prognostic model can be used as clinical tools to assess the risk levels of breast cancer patients. The identified genes can be studied further for potential targets for cancer therapy.