Robust Selection Algorithm (RSA) for Multi-Omic Biomarker Discovery; Integration with Functional Network Analysis to...

Area of Science:

• Genomics
• Cancer Biology
• Bioinformatics

Background:

• MicroRNAs (miRNAs) regulate gene expression and are implicated in cancer.
• Identifying reliable multi-omic biomarkers is crucial for clinical cancer research.
• Existing bioinformatics tools lack robust classification for oncogenes versus tumor suppressors.

Purpose of the Study:

• To develop a novel methodology, the Robust Selection Algorithm (RSA), for analyzing big omics data.
• To enable robust classification of oncogenes and tumor suppressors using miRNA expression data.
• To identify functional pathways and potential therapeutic targets in pan-cancer analysis.

Main Methods:

• Developed the Robust Selection Algorithm (RSA) for omics data analysis.
• Ensured survival analysis robustness via optimal cutoff identification and p-value computation through random resampling.
• Integrated omics data with functional network analysis for pan-cancer miRNA data.
• Accounted for non-normality in data and differential expression.

Main Results:

• Successfully applied RSA to pan-cancer miRNA patient data.
• Identified functional pathways associated with cancer progression.
• Highlighted selected miRNAs as potential biomarkers and therapeutic candidates.
• Demonstrated the integration of survival analysis with functional networks.

Conclusions:

• RSA provides a robust method for identifying cancer biomarkers from omics data.
• The approach facilitates the classification of oncogenes and tumor suppressors.
• This methodology can accelerate the discovery of novel therapeutic candidates across various cancers.