A method for mining core modules of cancer based on multi-omics biological network

Abstract

Cancer is closely linked to factors such as human living environment, individual genetic factors, etc. Because of the serious threat that cancer brings to human health, numerous scientific institutions around the world are engaged in cancer research to understand its pathogenesis. With the advent of next-generation sequencing technology, it will be more convenient to find important information about cancer and the relationships in genome. This paper considers multi-omics data from a data integration perspective. It takes lncRNA omic data into consideration and constructs a biological network model to mine cancer-associated core gene modules through a cluster method. We present a systematic approach to the identification core gene modules that can lead to the occurrence of cancer. We apply this approach to lung squamous cell carcinoma and find core gene modules containing 15 genes that have strong relationship with cancer by analyzing their functions and pathways. We also distinguish high-risk and low-risk groups by survival analysis. The results show that our approach can identify core gene modules and their dysregulated genes by integrating multi-omics biological data, which is useful in cancer research.