Subpathway-LNCE: Identify dysfunctional subpathways competitively regulated by lncRNAs through integrating lncRNA-mRNA expression profile and pathway topologies

Abstract

Recently, studies have reported that long noncoding RNAs (lncRNAs) can act as modulators of mRNAs through competitively binding to microRNAs (miRNAs) and have relevance to tumorigenesis as well as other diseases. Identify lncRNA competitively regulated subpathway not only can gain insight into the initiation and progression of disease, but also help for understanding the functional roles of lncRNAs in the disease context. Here, we present an effective method, Subpathway-LNCE, which was specifically designed to identify lncRNAs competitively regulated functions and the functional roles of these competitive regulation lncRNAs have not be well characterized in diseases. Moreover, the method integrated lncRNA-mRNA expression profile and pathway topologies. Using prostate cancer datasets and LUAD data sets, we confirmed the effectiveness of our method in identifying disease associated dysfunctional subpathway that regulated by lncRNAs. By analyzing kidney renal clear cell carcinoma related lncRNA competitively regulated subpathway network, we show that Subpathway-LNCE can help uncover disease key lncRNAs. Furthermore, we demonstrated that our method is reproducible and robust. Subpathway-LNCE provide a flexible tool to identify lncRNA competitively regulated signal subpathways underlying certain condition, and help to expound the functional roles of lncRNAs in various status. Subpathway-LNCE has been developed as an R package freely available at https://cran.rstudio.com/web/packages/SubpathwayLNCE/.

Topologically inferring active miRNA‐mediated subpathways toward precise cancer classification by directed random walk

“…In addition, there are some probability‐based approaches to estimate the pathway activity (Efroni et al ., ; Su et al ., ). Moreover, a wide variety of high‐throughput omics data were integrated together to detect disease‐specific pathways (Feng et al ., ; Li et al ., ; Shi et al ., ; Vrahatis et al ., 2016a). For example, Li et al .…”

“…In addition, there are some probability-based approaches to estimate the pathway activity (Efroni et al, 2007;Su et al, 2009). Moreover, a wide variety of high-throughput omics data were integrated together to detect disease-specific pathways Li et al, 2017;Shi et al, 2016;Vrahatis et al, 2016a). For example, Li et al (2017) performed integrative pathway analysis of gene and metabolite to reveal metabolism abnormal regions in the ESCC.…”

“…For example, Li et al (2017) performed integrative pathway analysis of gene and metabolite to reveal metabolism abnormal regions in the ESCC. Shi et al (2016) presented a new method, which identified dysfunctional pathway by integrating lncRNA-mRNA expression profile and pathway topologies. These methods successfully incorporated different high-throughput omics data into the disease classification procedures and achieved better classification performance.…”

Inference of patient‐specific subpathway activities reveals a functional signature associated with the prognosis of patients with breast cancer

“…We overcame this problem by incorporating gene expression data into pathway structure and using a greedy algorithm to search the subpathways associated with the prognosis of patients. Although a number of methods for identifying subpathways associated with cancer have been proposed, the advantage of our method is that it considered patient‐specific clinical and prognosis information in the identification of subpathways, and constructed subpathway activity profiles for each patient in the data set.…”

“…However, entire pathways are often too large to enable accurate interpretation of relevant biological phenomena. In recent years, key subpathway regions representative of the entire corresponding pathway are believed to be more useful in terms of interpreting relevant biological phenomena, and abnormalities of these subpathway regions may contribute to the aetiology of diseases . Subpathway‐GM was proposed to identify disease‐relevant subpathways by integrating information from genes and metabolites and pathway structure information within the given pathway.…”