A weighted relative difference accumulation algorithm for dynamic metabolomics data: long-term elevated bile acids...

Area of Science:

• Biochemistry
• Systems Biology
• Oncology

Background:

• Dynamic metabolomics offers insights into metabolic changes during disease progression.
• Analyzing time-series metabolomics data is crucial for understanding biological processes.
• Existing methods often analyze isolated time points, missing temporal dynamics.

Purpose of the Study:

• To develop and apply a novel weighted method for analyzing dynamic metabolomics data.
• To identify metabolic risk factors associated with hepatocellular carcinoma (HCC) development.
• To systematically analyze temporal metabolic trajectories in disease progression.

Main Methods:

• A weighted method based on means and variations across time points was developed.
• The method was applied to retrospective rat model data and prospective HCC data.
• Permutation testing for noise filtering and false discovery rate (FDR) for feature selection were employed.

Main Results:

• Long-term elevated serum bile acids were identified as significant risk factors for HCC.
• The proposed weighted method effectively extracts temporal information from metabolomics data.
• Key metabolic features associated with HCC development were systematically identified.

Conclusions:

• Dynamic metabolomics, analyzed with a time-series weighted method, can reveal critical disease risk factors.
• Elevated serum bile acids represent a potential early warning biomarker for hepatocellular carcinoma.
• This approach enhances the understanding of metabolic contributions to cancer development.