Protein-protein interaction (PPI) networks are essential resources in cancer research, providing a system-level perspective on the molecular mechanisms underlying tumorigenesis. In this chapter, a novel deep learning methodology is proposed that leverages PPI network topology for the prediction of cancer hub genes. The ability to predict cancer hub genes quickly and accurately provides potential targets for the development of cancer therapies. In this chapter, Node2Vec is utilized to generate high-dimensional feature vectors for each gene in the PPI network, and a convolutional autoencoder is employed to reduce dimensionality while preserving essential features. The cancer genes are predicted using a Long Short-Term Memory network model. The chapter highlights the potential of deep learning techniques and PPI networks for identifying important cancer genes, which could lead to more effective cancer treatments. The proposed model achieved an accuracy of 99.7%, surpassing other state-of-the-art models.