Identifying RNAseq-based coding-noncoding co-expression interactions in breast cancer

Long non-coding RNAs (lncRNAs) are suspected to have a wide range of roles in cellular functions. The precise transcriptional mechanisms and the interactions with coding RNAs (genes) are yet to be elucidated. In this paper we present a novel methodology that explores interactions between coding genes and lncRNAs and constructs gene-lncRNA co-expression networks, taking into account their unique expression characteristics. We evaluated several similarity measures to associate a gene and a lncRNA from RNA sequencing data of breast cancer patients and determined correlation to be the metric appropriately suited to this kind of data. Based on an empirically determined threshold, we selected a number of pairs to construct co-expression networks and identified sub-networks that capture previously-unknown lncRNA partners of key players in breast cancer like estrogen receptor. In essence, we have developed a data-driven approach to identify important, functional, coding-lncRNA interactions that sets the stage for more in-depth analyses capturing how non-coding interactions influence expression of protein coding genes and modulate pathways contributing to cancer.