Abstract Background The heterogeneity of breast cancer poses a fundamental challenge to clinical management, manifesting both in molecular subtype diversity and functionally distinct gene modules. Long non-coding RNAs (lncRNAs) acting as competing endogenous RNAs (ceRNAs) or microRNA (miRNA) sponges are emerging biomarkers of breast cancer. Accurate identification of subtype-specific ceRNA modules could contribute to precision medicine in breast cancer. Results In this work, we propose a novel framework Scene (Subtype-specific ceRNA modules) to infer lncRNA-related breast cancer subtype-specific ceRNA modules from heterogeneous data, including gene expression data and priori information of miRNA targets. For five breast cancer subtypes, most of ceRNA modules tend to be unique. Across 22 breast cancer-specific ceRNA modules, 20 ceRNA modules are significantly enriched in various biological processes or pathways, indicating that these modules play distinct biological functions in different subtypes. Survival analysis further indicates that all identified ceRNA modules serve as potential prognostic biomarkers capable of discriminating between high- and low-risk breast cancer groups. Moreover, classification analysis shows that all inferred ceRNA modules function as potential diagnostic biomarkers for distinguishing breast cancer subtypes. Finally, immune infiltration analysis reveals that all identified ceRNA modules show significant correlation with one or more immune cell types, suggesting their potential involvement in immune regulation within the tumor microenvironment. Conclusions This study provides a new perspective for investigating the molecular mechanism of breast cancer subtypes, and lays a theoretical foundation for the development of breast cancer subtype-specific biomarkers.
Yang et al. (Wed,) studied this question.
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