Integrative Analysis of Multi-Omics Data for Biomarker Discovery

Area of Science:

• Biomarker discovery
• Systems biology
• Personalized medicine

Background:

• Individual omics studies have limitations for comprehensive biomarker discovery.
• Biological systems complexity necessitates a holistic, multi-omics integration approach.
• Integrative analysis enhances accuracy in predicting disease onset, progression, and treatment response.

Purpose of the Study:

• To explore statistical and deep learning methods for multi-omics data integration.
• To identify a panel of multi-omics features distinguishing hepatocellular carcinoma (HCC) from liver cirrhosis (CIRR).
• To uncover biological pathways enriched in HCC using integrated multi-omics data.

Main Methods:

• Liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis of serum samples.
• Integration of metabolomics, lipidomics, peptidomics, proteomics, and glycoproteomics data.
• Application of statistical and deep learning techniques for data analysis and feature selection.

Main Results:

• Identification of a multi-omics feature panel differentiating HCC from cirrhotic controls.
• Pathway analysis revealed significant enrichment of LXR/RXR Activation and Acute Response signaling.
• Demonstrated the utility of multi-omics integration for biomarker discovery in HCC.

Conclusions:

• Multi-omics integration provides a powerful approach for identifying robust biomarkers.
• The identified multi-omics signatures and pathways offer potential for improved HCC diagnosis and targeted therapies.
• This study supports the advancement of personalized medicine through comprehensive molecular profiling.