CADTAD: CAncer Driver Topologically Associated Domains identify oncogenic and tumor suppressive lncRNAs

Abstract

AbstractCancer lncRNAs have been identified by both experimental and in silico methods. However, the current approaches for mining cancer lncRNAs are not sufficient and accurate. To deeply discover them, we focus on the core cancer driver lncRNAs (CDLs) which directly interact with cancer driver protein-coding genes. We investigated various aspects of cancer-related lncRNAs (CRLs), including their genomic locations, expression patterns, and their direct interactions with cancer driver protein-coding genes. We found that most CRLs located in cancer driver topologically associated domains (CDTs). Moreover, some CRLs showed a high tendency for co-expression and binding sites with cancer driver protein-coding genes. Utilizing these distinctive characteristics and integrating >4000 multi-omics data, we developed a pipeline CADTAD to unearth conserved candidate CDLs in pan-cancer, including 256 oncogenic lncRNAs, 177 tumor suppressive lncRNAs, and 75 dual-function lncRNAs, as well as some specific candidate CDLs in three individual cancer types and validated their cancer-related characteristics. Importantly, the function of 10 putative CDLs in prostate cancer was subsequently validated through cell studies. In light of these findings, our study offers a new perspective from the 3D genome to study the roles of lncRNAs in cancer. Furthermore, we provide a valuable set of potential lncRNAs that could deepen our understanding of the oncogenic mechanism of CDLs.