Stratification of lncRNA modulation networks in breast cancer

Background Recently, non-coding RNAs are of growing interest, and more scientists attach importance to research on their functions. Long non-coding RNAs (lncRNAs) are defined as non-protein coding transcripts longer than 200 nucleotides. We already knew that lncRNAs are related to cancers and will be dysregulated in them. But most of their functions are still left to further study. A mechanism of RNA regulation, known as competing endogenous RNAs (ceRNAs), has been proposed to explain the complex relationships among mRNAs and lncRNAs by competing for binding with shared microRNAs (miRNAs). Methods We proposed an analysis framework to construct the association networks among lncRNA, mRNA, and miRNAs based on their expression patterns and decipher their network modules. Results We collected a large-scale gene expression dataset of 1,061 samples from breast invasive carcinoma (BRCA) patients, each consisted of the expression profiles of 4,359 lncRNAs, 16,517 mRNAs, and 534 miRNAs, and applied the proposed analysis approach to interrogate them. We have uncovered the underlying ceRNA modules and the key modulatory lncRNAs for different subtypes of breast cancer. Conclusions We proposed a modulatory analysis to infer the ceRNA effects among mRNAs and lncRNAs and performed functional analysis to reveal the plausible mechanisms of lncRNA modulation in the four breast cancer subtypes. Our results might provide new directions for breast cancer therapeutics and the proposed method could be readily applied to other diseases.

Automated summary

This study proposes an analysis framework to investigate the relationship between lncRNAs and different subtypes of breast cancer through gene expression data analysis. The results reveal potential regulatory mechanisms and therapeutic directions for breast cancer treatment.