LMSM: a modular approach for identifying lncRNA related miRNA sponge modules in breast cancer

Abstract

<jats:title>Abstract</jats:title><jats:p>Until now, existing methods for identifying lncRNA related miRNA sponge modules mainly rely on lncRNA related miRNA sponge interaction networks, which may not provide a full picture of miRNA sponging activities in biological conditions. Hence there is a strong need of new computational methods to identify lncRNA related miRNA sponge modules. In this work, we propose a framework, LMSM, to identify <jats:underline>L</jats:underline>ncRNA related <jats:underline>M</jats:underline>iRNA <jats:underline>S</jats:underline>ponge <jats:underline>M</jats:underline>odules from heterogeneous data. To understand the miRNA sponging activities in biological conditions, LMSM uses gene expression data to evaluate the influence of the shared miRNAs on the clustered sponge lncRNAs and mRNAs. We have applied LMSM to the human breast cancer (BRCA) dataset from The Cancer Genome Atlas (TCGA). As a result, we have found that the majority of LMSM modules are significantly implicated in BRCA and most of them are BRCA subtype-specific. Most of the mediating miRNAs act as crosslinks across different LMSM modules, and all of LMSM modules are statistically significant. Multi-label classification analysis shows that the performance of LMSM modules is significantly higher than baseline’s performance, indicating the biological meanings of LMSM modules in classifying BRCA subtypes. The consistent results suggest that LMSM is robust in identifying lncRNA related miRNA sponge modules. Moreover, LMSM can be used to predict miRNA targets. Finally, LMSM outperforms a graph clustering-based strategy in identifying BRCA-related modules. Altogether, our study shows that LMSM is a promising method to investigate modular regulatory mechanism of sponge lncRNAs from heterogeneous data.</jats:p><jats:sec><jats:title>Author summary</jats:title><jats:p>Previous studies have revealed that long non-coding RNAs (lncRNAs), as microRNA (miRNA) sponges or competing endogenous RNAs (ceRNAs), can regulate the expression levels of messenger RNAs (mRNAs) by decreasing the amount of miRNAs interacting with mRNAs. In this work, we hypothesize that the “tug-of-war” between RNA transcripts for attracting miRNAs is across groups or modules. Based on the hypothesis, we propose a framework called LMSM, to identify <jats:underline>L</jats:underline>ncRNA related <jats:underline>M</jats:underline>iRNA <jats:underline>S</jats:underline>ponge <jats:underline>M</jats:underline>odules. Based on the two miRNA sponge modular competition principles, significant sharing of miRNAs and high canonical correlation between the sponge lncRNAs and mRNAs, LMSM is also capable of predicting miRNA targets. LMSM not only extends the ceRNA hypothesis, but also provides a novel way to investigate the biological functions and modular mechanism of lncRNAs in breast cancer.</jats:p></jats:sec>

Distal gene regulation mediated by non-coding RNAs contributes to germline risk for breast and prostate cancer

“…Again, in tumor-specific gene-gene associations, many ncRNAs had associations with multiple pcGenes, leading to vertical bands in the location plots in Figure 2B. For example, in breast tumors, LINC000906 , a predicted miRNA sponge in breast tumors (70), was associated with 115 pcGenes. The largest association was with MS4A5 , whose hypomethylation is shown to be prognostic for multiple cancer types (71, 72).…”

Time to infer miRNA sponge modules

“…Recently, to investigate the module‐level miRNA sponging activities in biological conditions, Zhang et al (2020) present LMSM to assess the influence of common miRNAs on miRNA sponge modules. In the first step, a (bi)clustering method (e.g., WGCNA [Langfelder & Horvath, 2008]) is employed to infer gene co‐expression modules (candidate miRNA sponge modules) from the matched sponge RNA and target RNA expression data.…”

“…With regard to the category of step‐wise evaluation, a well‐cited gene co‐expression analysis method named WGCNA (Langfelder & Horvath, 2008) in LMSM (Zhang et al, 2020) is firstly used to generate gene co‐expression modules. Then, the three criteria (significant sharing of miRNAs, high canonical correlation, and adequate sensitivity canonical correlation conditioning on shared miRNAs) in LMSM (Zhang et al, 2020) are adopted to assess whether the identified gene co‐expression modules can act as miRNA sponge modules.…”

“…To tackle this issue, the analysis of heterogeneous data is considered a promising way to uncover the underlying miRNA sponging activities (Calonder et al, 2006). For instance, recent studies (Deng et al, 2018; Wen et al, 2020; Xiao et al, 2019; Zhang et al, 2020) have shown that module identification by using heterogeneous data could help to understand miRNA sponging activities, and provide a new way to discern the competitive effects of miRNA sponges on human complex diseases. Different from the methods using ceRNA network data for module identification, these recent methods map multiple data types (including multiple gene expression data and various putative gene interactions) to the competitive modules, and directly infer miRNA sponge modules from heterogeneous data.…”

Long Noncoding RNA Mediated Regulation in Human Embryogenesis, Pluripotency, and Reproduction

“…(4) lncRNAs could serve as scaffold molecules to assemble various effector molecules into macromolecules to achieve precise and specific control of biological events [19]. Finally, (5) lncRNAs could function as competing endogenous RNAs (ceRNAs) to sequester miRNAs, leading to the active transcription of their mRNA targets [20]. Several studies have shown that when TEs were embedded in lncRNAs, they may function in the processing, stability, and localization of lncRNAs.…”