A complex disease, especially cancer, always has pre-deterioration stage during its progression, which is difficult to identify but crucial to drug research and clinical intervention. However, using a few samples to find mechanisms that propel cancer crossing the pre-deterioration stage is still a complex problem. In this study, we successfully developed a novel single-sample model based on node entropy with a priori established protein interaction network. Using this model, critical stages were successfully detected in simulation data and four TCGA datasets, indicating its sensitivity and robustness. Besides, compared with the results of the differential analysis, our results showed that most of dynamic network biomarkers identified by node entropy, such as NKD2 or DAAM1, located in upstream in many important cancer-related signaling pathways regulated intergenic signaling within pathways. We also identified some novel prognostic biomarkers such as PER2, TNFSF4, MMP13 and ENO4 using node entropy rather than expression level. More importantly, we found the switch of non-specific pathways related to DNA damage repairing was the main driven force for cancer progression. In conclusion, we have successfully developed a dynamic node entropy model based on single case data to find out tipping point and possible mechanism for cancer progression. These findings may provide new target genes in therapeutic intervention tactics.
Keywords: cancer molecular network; critical transition stage; drug targets; dynamic network biomarker; single-sample node entropy.
Copyright © 2020 Han, Zhong, Hu, Liu, Liu and Ling.