Abstract 5547: A computational approach to predict tissue level cell cycle regulatory network for normal proliferating and cancer cells

Abstract

Abstract Cell cycle control and regulation has been widely studied in terms of cyclin-dependent kinases (CDKs) activities, while only limited information are available on tissue contexts and signaling. In this study, we adopted a systems biology approach aiming to comprehensively capture how tissue level signaling events orchestrate cell cycle phases in a CDKs activities independent manner in both of normal and tumor tissues. We have applied our in house gene co-expression and bi-clustering analysis on a collection of 8 single cell transcriptomic sequencing datasets of normal proliferating human embryonic cells, 17 RNA-seq transcriptomic datasets of inflammatory tissues and 30 microarray data sets of chronic inflammatory diseases as normal proliferating reference and TCGA RNA-seq data for 20 cancer types aiming to identify cell cycle associated gene co-expression modules. Our results suggest that the tissue level signaling events are generally show distinct regulatory structures between tumor and normal tissues: 1) the Rho GTPase signaling pathways that control cell polarity, activate in G2/M transition phase in normal tissue, while mainly in G1 and S phases in tumor. 2) Focal adhesion kinases (FAK) drives G1/S transition in normal tissues, while activated in multiple phases in tumor. 3) Hippo signaling for cell size control activates at G2/M transition phase in normal tissue while in multiple phases in tumor. In addition, we have also generated tissue level regulatory networks for cell cycle controls by predicting possible collective effects of extra- and intra-cellular signaling for both normal proliferating and cancer cells. Citation Format: Tao Sheng, Sha Cao, Chi Zhang, Ying Xu. A computational approach to predict tissue level cell cycle regulatory network for normal proliferating and cancer cells [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 5547. doi:10.1158/1538-7445.AM2017-5547