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

ABSTRACT: The Metabolome and Transcriptome are mutually communicating within cancer cells, and this interplay is translated into the existence of quantifiable correlation structures between gene expression and metabolite abundance levels. Studying these correlations could provide a novel venue of understanding cancer and the discovery of novel biomarkers and pharmacological strategies, as well as laying the foundation for the prediction of metabolite quantities by leveraging information from the more widespread transcriptomics data. In the current paper, we investigate the correlation between gene expression and metabolite levels in the Cancer Cell Line Encyclopedia dataset, building a direct correlation network between the two molecular ensembles. We show that a metabolite/transcript correlation network can be used to predict metabolite levels in different samples and datasets, such as the NCI-60 cancer cell line dataset, both on a sample-by-sample basis and in differential contrasts. We also show that metabolite levels can be predicted in principle on any sample and dataset for which transcriptomics data are available, such as the Cancer Genome Atlas (TCGA).

SUBMITTER: Cavicchioli MV

PROVIDER: S-EPMC8998886 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature

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

Cavicchioli Maria Vittoria MV Santorsola Mariangela M Balboni Nicola N Mercatelli Daniele D Giorgi Federico Manuel FM

International journal of molecular sciences 20220331 7

The Metabolome and Transcriptome are mutually communicating within cancer cells, and this interplay is translated into the existence of quantifiable correlation structures between gene expression and metabolite abundance levels. Studying these correlations could provide a novel venue of understanding cancer and the discovery of novel biomarkers and pharmacological strategies, as well as laying the foundation for the prediction of metabolite quantities by leveraging information from the more wide ...[more]

PMID: 35409231

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Project description:Cell surface proteins have been shown to be effective therapeutic targets. In addition, shed forms of these proteins and secreted proteins can serve as biomarkers for diseases, including cancer. Thus, identification of cell surface and secreted proteins has been a prime area of interest in the proteomics field. Most cell surface and secreted proteins are known to be glycosylated, and therefore, a proteomics strategy targeting these proteins was applied to obtain proteomic profiles from various thyroid cancer cell lines that represent the range of thyroid cancers of follicular cell origin. In this study, we oxidized the carbohydrates of secreted proteins and those on the cell surface with periodate and isolated them via covalent coupling to hydrazide resin. The glycoproteins obtained were identified from tryptic peptides and N-linked glycopeptides released from the hydrazide resin using two-dimensional liquid chromatography-tandem mass spectrometry in combination with the gas phase fractionation. Thyroid cancer cell lines derived from papillary thyroid cancer (TPC-1), follicular thyroid cancer (FTC-133), Hurthle cell carcinoma (XTC-1), and anaplastic thyroid cancer (ARO and DRO-1) were evaluated. An average of 150 glycoproteins were identified per cell line, of which more than 57% are known cell surface or secreted glycoproteins. The usefulness of the approach for identifying thyroid cancer associated biomarkers was validated by the identification of glycoproteins (e.g., CD44, galectin 3 and metalloproteinase inhibitor 1) that have been found to be useful markers for thyroid cancer. In addition to glycoproteins that are commonly expressed by all of the cell lines, we identified others that are only expressed in the more well-differentiated thyroid cancer cell lines (follicular, Hurthle cell and papillary), or by cell lines derived from undifferentiated tumors that are uniformly fatal forms of thyroid cancer (i.e., anaplastic). On the basis of the results obtained, a set of glycoprotein biomarker candidates for thyroid cancer is proposed.

Dataset Information

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

Publications

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

Similar Datasets

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