Global Prioritizing Disease Candidate lncRNAs via a Multi-level Composite Network.

Area of Science:

• Genomics
• Bioinformatics
• Computational Biology

Background:

• Long non-coding RNAs (lncRNAs) are crucial in biological processes, but their roles in human diseases remain largely unexplored.
• Prioritizing disease-associated lncRNAs is essential for advancing disease research and understanding molecular mechanisms.

Purpose of the Study:

• To develop a novel algorithm, LncPriCNet, for prioritizing candidate long non-coding RNAs (lncRNAs) associated with human diseases.
• To evaluate the performance of LncPriCNet against existing methods and assess its utility in data-scarce scenarios.

Main Methods:

• Developed LncPriCNet, a multi-level composite network algorithm integrating genes, lncRNAs, phenotypes, and their associations.
• Applied LncPriCNet to identify disease-related lncRNAs, including novel candidates for breast cancer.
• Constructed a human disease-lncRNA landscape to analyze network modularity and identify lncRNA hotspots.

Main Results:

• LncPriCNet demonstrated superior performance compared to previous methods, achieving high Area Under the Curve (AUC) values up to 0.93.
• The algorithm effectively identified known and novel disease-related lncRNAs, even with limited prior information.
• Pathway analysis of identified lncRNAs provided insights into their potential functions in breast cancer.

Conclusions:

• LncPriCNet is an effective tool for prioritizing disease-associated lncRNAs, facilitating the discovery of novel disease mechanisms.
• The study contributes to understanding the complex landscape of human diseases at the lncRNA level.