A joint analysis of transcriptomic and metabolomic data uncovers enhanced enzyme-metabolite coupling in breast cancer
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Disrupted regulation of cellular processes is considered one of the hallmarks of cancer. We analyze metabolomic and transcriptomic profiles jointly collected from breast cancer and hepatocellular carcinoma patients to explore the associations between the expression of metabolic enzymes and the levels of the metabolites participating in the reactions they catalyze. Surprisingly, both breast cancer and hepatocellular tumors exhibit an increase in their gene-metabolites associations compared to noncancerous adjacent tissues. Following, we build predictors of metabolite levels from the expression of the enzyme genes catalyzing them. Applying these predictors to a large cohort of breast cancer samples we find that depleted levels of key cancer-related metabolites including glucose, glycine, serine and acetate are significantly associated with improved patient survival. Thus, we show that the levels of a wide range of metabolites in breast cancer can be successfully predicted from the transcriptome, going beyond the limited set of those measured.
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In the literature, concatenated multi-omics data in a joint matrix is used as input to classical machine learning methods such as decision trees, naive Bayes, artificial neural networks, support vector machines, k-nearest neighbours, random forests and K-Star.
“The concatenated multi-omics data (in the form of a joint matrix) is provided as input to different classical ML methods such as DT (decision tree) (Quinlan, 1993), NB (naive Bayes) (Domingos and Pazzani, 1997), ANN (artificial neural networks) (Bishop, 1995), SVM (support vector machine) (Vapnik, 1995), KNN (k-nearest neighbors) (Altman, 1992), RF (random forest) (Breiman, 2001) and K-Star (Cle”
From Using machine learning approaches for multi-omics data analysis: A review (2021) · cites this paper as Auslander et al., 2016
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Incorporating Machine Learning Into Bioinformatics Frameworks
Auslander, N., Gussow, A. B., & Koonin, E. V. (2021)
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- Authors
- Auslander, N., Yizhak, K., Weinstock, A., Budhu, A., Tang, W., Wang, X. W., Ambs, S., & Ruppin, E.
- Affiliations
- University of Maryland, College Park (US)
- Tel Aviv University (IL)
- National Cancer Institute (US)
- National Institutes of Health (US)
- Corresponding author
- Auslander, Noam
- Yizhak, Keren
- Ruppin, Eytan
- Venue
- Scientific Reports
- Published
- 2016-07-13
- Volume
- 6
- Issue
- 1
- Pages
- 29662-29662
- ISSN
- 2045-2322 (ISSN-L)
- PMC
- PMC4942812
- OpenAlex
- W2466672339
- Cited by
- 50
- References
- 44
- Citations by year
- 2025: 3 · 2024: 8 · 2023: 3 · 2022: 4 · 2021: 9 · 2020: 8 · 2019: 6 · 2018: 6 · 2017: 3
- FWCI
- 2.08
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Scientific Reports (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Cancer, Hypoxia, and Metabolism
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Metabolomics and Mass Spectrometry Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Keywords
- breast cancer, hepatocellular carcinoma, metabolic enzymes, prognostic factors, patient survival, glucose, glycine, serine, acetate
- MeSH terms
- Breast Neoplasms, Female, Carcinoma, Hepatocellular, Humans, Liver Neoplasms, Gene Expression Regulation, Neoplastic, Gene Expression Profiling, Metabolomics, Transcriptome
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