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Prediction of Metabolic Profiles from Transcriptomics Data in Human Cancer Cell Lines.

Maria Vittoria Cavicchioli1,2, Mariangela Santorsola1,3, Nicola Balboni1

  • 1Department of Pharmacy and Biotechnology, University of Bologna, 40126 Bologna, Italy.

International Journal of Molecular Sciences
|April 12, 2022
PubMed
Summary

This study reveals that gene expression and metabolite levels in cancer cells are interconnected. We built a network to predict metabolite levels using gene expression data, offering new insights for cancer research and biomarker discovery.

Keywords:
cancercorrelation networksmachine learningmetabolomicstranscriptomics

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Area of Science:

  • Cancer Biology
  • Systems Biology
  • Bioinformatics

Background:

  • The interplay between the metabolome and transcriptome within cancer cells is crucial for understanding cancer progression.
  • Quantifiable correlations exist between gene expression and metabolite abundance, offering potential for novel biomarker and therapeutic target discovery.

Purpose of the Study:

  • To investigate the correlation between gene expression and metabolite levels in cancer cell lines.
  • To build a direct correlation network connecting these two molecular layers.
  • To assess the utility of this network for predicting metabolite levels.

Main Methods:

  • Utilized the Cancer Cell Line Encyclopedia dataset to construct a metabolite/transcript correlation network.
  • Applied the developed network to predict metabolite levels in independent datasets (NCI-60, TCGA).
  • Evaluated prediction accuracy on both sample-by-sample and differential contrast bases.

Main Results:

  • Successfully built a direct correlation network between metabolite abundance and gene expression.
  • Demonstrated the network's capability to predict metabolite levels across different cancer cell line datasets.
  • Confirmed the potential for predicting metabolite levels using transcriptomics data in various cancer contexts.

Conclusions:

  • Metabolite and transcript levels exhibit predictable correlations in cancer.
  • A metabolite/transcript correlation network is a viable tool for predicting metabolite abundance.
  • This approach facilitates leveraging widespread transcriptomics data for metabolite prediction, aiding cancer research and biomarker discovery.