Defining disease-related modules based on weighted miRNA synergistic network. (January 2023) - Li, Chao; Dou, Peng; Wang, Tianxiang; Lu, Xin; Xu, Guowang; Lin, Xiaohui
Abstract: MicroRNAs (miRNAs) play an important role in the biological process. Their expression and functional changes have been observed in most cancers. Meanwhile, there exists cooperative regulation among miRNAs which is important for studying the mechanisms of complex post-transcriptional regulations. Hence, studying miRNA synergy and identifying miRNA synergistic modules can help understand the development and progression of complex diseases, such as cancers. This work studies miRNA synergy and proposes a new method for defining disease-related modules (DDRM) by combining the knowledge databases and miRNA data. DDRM measures the miRNA synergy not only by the co-regulating target subset but also by the non-common target set to construct the weighted miRNA synergistic network (WMSN). The experiments on twelve the cancer genome atlas (TCGA) datasets showed that the important modules identified by DDRM can well distinguish the cancer samples from the normal samples, and DDRM performed better than the previous method in most cases. An external dataset of prostate cancer was applied to validate the module biomarkers determined by DDRM on the prostate cancer data of TCGA. The area under the receiver operating characteristic curve (AUC) value is 0.92 and the performance is superior. Hence, combining the miRNA synergy networks from the knowledge databases and the miRNA data can determine the important functional modules related to diseases, which is of great significance to the study of disease mechanism. Highlights: A network analysis method DDRM for defining disease-related modules by combining the knowledge and miRNA data is proposed. The miRNA synergy is measured by both the co-regulating target subset and the non-common regulating target mRNAs. The defined modules by DDRM contain the unmeasured miRNAs which may have meaningful information for the disease study. Experiments on 12 TCGA cancer datasets show that DDRM can identify important disease-related modules.