Metabolic Adaptation Processes in Cancer: Transcriptomic, Proteomic and Metabolomic Studies

A special issue of Cells (ISSN 2073-4409). This special issue belongs to the section "Cellular Metabolism".

Deadline for manuscript submissions: closed (1 June 2024) | Viewed by 2896

Special Issue Editors


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Guest Editor
Brazilian Biosciences National Laboratory (LNBio), National Center for Research in Energy and Materials (CNPEM), Campinas, SP, Brazil
Interests: tumor metabolism; breast cancer; cancer biology; glutaminase

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Guest Editor
Department of Ecological and Biological Sciences (DEB), University of Tuscia, 01100 Viterbo, Italy
Interests: proteomics/metabolomics/lipidomics; systems biology; post-translational modifications; plant responses to biotic and abiotic stresses; omics for health and disease
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Brazilian Biosciences National Laboratory (LNBio), National Center for Research in Energy and Materials (CNPEM), Campinas, SP, Brazil
Interests: transcriptomics; metabolomics; cancer biology

Special Issue Information

Dear Colleagues,

Deregulating cellular energetics is a crucial cancer hallmark, defined as the changes in cell metabolism to support the elevated proliferation rates in tumors. However, newer insights about the effects of metabolism on immune system evasion and cancer cell invasion, metastasis, angiogenesis, and chemotherapy resistance show that cancer reliance on metabolic changes is not limited to energy and building block production. This Special Issue in Cells aims to group new findings and views on how metabolic adaptation processes can both participate in tumorigenesis and be affected by it.

Original full-length papers and reviews that further describe how metabolism is reshaped during cancer emergence and metastasis are welcome. Relevant topics include, but are not limited to, carbon cycling in cancer, tumor microenvironment and metabolism, immune system metabolism and cancer, and submissions that involve omics studies, such as transcriptomics, proteomics and metabolomics.

Dr. Sandra Martha Gomes Dias
Prof. Dr. Sara Rinalducci
Dr. Douglas Adamoski Meira
Guest Editors

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Keywords

  • cancer cell metabolism
  • inflammation
  • anticancer drugs
  • tumor-specific metabolic biomarkers
  • oxidative stress
  • omics sciences applied to cancer research
  • tumor immune microenvironment

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Published Papers (1 paper)

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Research

39 pages, 7889 KiB  
Article
Combined High—Throughput Proteomics and Random Forest Machine-Learning Approach Differentiates and Classifies Metabolic, Immune, Signaling and ECM Intra-Tumor Heterogeneity of Colorectal Cancer
by Cristina Contini, Barbara Manconi, Alessandra Olianas, Giulia Guadalupi, Alessandra Schirru, Luigi Zorcolo, Massimo Castagnola, Irene Messana, Gavino Faa, Giacomo Diaz and Tiziana Cabras
Cells 2024, 13(16), 1311; https://doi.org/10.3390/cells13161311 - 6 Aug 2024
Cited by 2 | Viewed by 2325
Abstract
Colorectal cancer (CRC) is a frequent, worldwide tumor described for its huge complexity, including inter-/intra-heterogeneity and tumor microenvironment (TME) variability. Intra-tumor heterogeneity and its connections with metabolic reprogramming and epithelial–mesenchymal transition (EMT) were investigated with explorative shotgun proteomics complemented by a Random Forest [...] Read more.
Colorectal cancer (CRC) is a frequent, worldwide tumor described for its huge complexity, including inter-/intra-heterogeneity and tumor microenvironment (TME) variability. Intra-tumor heterogeneity and its connections with metabolic reprogramming and epithelial–mesenchymal transition (EMT) were investigated with explorative shotgun proteomics complemented by a Random Forest (RF) machine-learning approach. Deep and superficial tumor regions and distant-site non-tumor samples from the same patients (n = 16) were analyzed. Among the 2009 proteins analyzed, 91 proteins, including 23 novel potential CRC hallmarks, showed significant quantitative changes. In addition, a 98.4% accurate classification of the three analyzed tissues was obtained by RF using a set of 21 proteins. Subunit E1 of 2-oxoglutarate dehydrogenase (OGDH-E1) was the best classifying factor for the superficial tumor region, while sorting nexin-18 and coatomer-beta protein (beta-COP), implicated in protein trafficking, classified the deep region. Down- and up-regulations of metabolic checkpoints involved different proteins in superficial and deep tumors. Analogously to immune checkpoints affecting the TME, cytoskeleton and extracellular matrix (ECM) dynamics were crucial for EMT. Galectin-3, basigin, S100A9, and fibronectin involved in TME–CRC–ECM crosstalk were found to be differently variated in both tumor regions. Different metabolic strategies appeared to be adopted by the two CRC regions to uncouple the Krebs cycle and cytosolic glucose metabolism, promote lipogenesis, promote amino acid synthesis, down-regulate bioenergetics in mitochondria, and up-regulate oxidative stress. Finally, correlations with the Dukes stage and budding supported the finding of novel potential CRC hallmarks and therapeutic targets. Full article
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