Network based multifactorial modelling of miRNA-target interactions

Area of Science:

• Genomics
• Bioinformatics
• Molecular Biology

Background:

• Competing endogenous RNA (ceRNA) networks involve complex crosstalk between non-coding RNAs.
• MicroRNA (miRNA):target interactions are crucial but can be indirectly affected by network dynamics.
• Understanding ceRNA networks is vital for deciphering gene regulation.

Purpose of the Study:

• To develop a network-based model for analyzing miRNA:ceRNA interactions and their impact on gene expression.
• To investigate the effects of perturbations within miRNA:target networks, considering factors like binding energy.
• To identify key regulatory elements in breast cancer using this novel approach.

Main Methods:

• Developed a network model integrating miRNA:ceRNA interactions with gene expression data.
• Calculated network-wide effects of expression perturbations, incorporating miRNA binding characteristics.
• Analyzed large-scale miRNA:target networks derived from breast cancer patient data.

Main Results:

• Identified highly perturbing genes and miRNAs that are significantly associated with breast cancer.
• The network-based approach effectively accounts for the 'sponge effect' in miRNA regulation.
• Unveiled intricate crosstalk between network nodes, highlighting previously unrecognized regulatory mechanisms.

Conclusions:

• The developed network model provides a powerful tool for understanding complex ceRNA regulations.
• It has the potential to uncover novel biological insights by considering network context.
• The R package 'ceRNAnetsim' is scalable and adaptable for emerging RNA effectors like circRNAs and lncRNAs.