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A Fecal-Microbial-Extracellular-Vesicles-Based Metabolomics Machine Learning Framework and Biomarker Discovery for

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Metabolites
|May 26, 2023
PubMed
Summary

High-throughput metabolomics identified five fecal metabolites, including aminoisobutyric acid, as potential biomarkers for colorectal cancer (CRC) screening. These metabolites show promise for early diagnosis and targeted treatment strategies in CRC patients.

Keywords:
biomarker discoverycolorectal cancermachine learningmetabolomics profiling

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Area of Science:

  • Metabolomics
  • Oncology
  • Biomarker Discovery

Background:

  • Colorectal cancer (CRC) is a leading cause of cancer-related mortality.
  • Metabolites are increasingly recognized for their role in CRC development and progression.
  • Identifying reliable biomarkers is crucial for early CRC detection and management.

Purpose of the Study:

  • To identify potential fecal metabolite biomarkers for colorectal cancer (CRC) diagnosis and treatment using high-throughput metabolomics.
  • To evaluate the discriminatory potential of identified metabolites and develop a predictive model for CRC screening.

Main Methods:

  • Fecal samples from CRC patients and healthy controls were analyzed using high-throughput metabolomics.
  • Statistical analyses included univariate ROC analysis, t-tests, fold change analysis, and multivariate methods (SVM, PLS-DA, RF).
  • Metabolites with FDR-corrected p-value < 0.05 and AUC > 0.70 were selected for further analysis.

Main Results:

  • Five significantly differentially expressed fecal metabolites were identified: succinic acid, aminoisobutyric acid, butyric acid, isoleucine, and leucine.
  • Aminoisobutyric acid demonstrated high discriminatory potential (AUC = 0.806) and was down-regulated in CRC patients.
  • A multivariate model using these five metabolites achieved a high AUC of 0.985 for CRC screening.

Conclusions:

  • Fecal metabolomics can effectively identify potential biomarkers for colorectal cancer.
  • Aminoisobutyric acid and other identified metabolites show promise for non-invasive CRC detection.
  • The developed metabolomic model offers a highly accurate approach for CRC screening.