Inferring Cell Subtypes and LncRNA Function by a Cell-Specific CeRNA Network in Breast Cancer

Area of Science:

• Oncology
• Genomics
• Bioinformatics

Background:

• Single-cell RNA sequencing (scRNA-seq) is crucial for understanding breast cancer heterogeneity.
• Identifying estrogen-responsive cell subtypes is vital for treatment strategies.
• The complex transcriptional regulation of estrogen, involving non-coding RNAs, remains poorly understood at the single-cell level.

Purpose of the Study:

• To propose a novel computational strategy for specifying breast cancer cell subtypes using a cell-specific competing endogenous RNA network (CCN).
• To explore the functional roles of hub long non-coding RNAs (lncRNAs) within these identified subtypes.
• To identify potential biomarkers and therapeutic targets for RNA-based interventions.

Main Methods:

• Constructed a cell-specific CCN by integrating a cell-specific RNA-RNA co-expression network (RCN) with a known ceRNA network.
• Built the cell-specific RCN using scRNA-seq data and reference cells.
• Inferred cell subtypes by enriching RNAs in the CCN against the estrogen response hallmark.

Main Results:

• Identified edge biomarkers within the early estrogen response subtype.
• NEAT1 was identified as a hub lncRNA in the early response subtype, with its ceRNAs predicting patient survival.
• DLEU2, another hub lncRNA, was implicated in G protein-coupled receptor (GPCR) signaling via the CCN.

Conclusions:

• The proposed CCN method enables network-based inference of cell subtypes from scRNA-seq data.
• This approach facilitates the functional exploration of hub lncRNAs in breast cancer.
• Hub lncRNAs like NEAT1 and DLEU2 represent promising targets for novel RNA-based therapeutics in breast cancer treatment.