Metabolic and Lipidomic Reprogramming in Cancer: Mechanisms and Therapeutic Potential

A special issue of Metabolites (ISSN 2218-1989). This special issue belongs to the section "Cell Metabolism".

Deadline for manuscript submissions: 31 July 2026 | Viewed by 1856

Special Issue Editors


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Guest Editor
Department of Pathology and Laboratory Medicine, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH 45229, USA
Interests: stable isotope metabolomics; lipidomics; nuclear magnetic resonance; mass spectrometry; metabolism; cancer biology; cancer hallmarks; metabolic reprogramming

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Guest Editor
Laboratory of Translational Research, Azienda USL-IRCCS di Reggio Emilia, 42122 Reggio Emilia, Italy
Interests: lipid metabolism; regulation of gene expression; mitochondrial dynamics; cancer development and progression

Special Issue Information

Dear Colleagues,

Cancer cells undergo profound metabolic and lipidomic reprogramming to sustain rapid proliferation, evade apoptosis, and adapt to dynamic microenvironments. These alterations reflect a fundamental rewiring of cellular energetics and biosynthetic pathways that directly contribute to malignancy. Recent advances in metabolomic and lipidomic technologies have revealed that shifts in energy metabolism, lipid synthesis and degradation, and signaling pathways are pivotal drivers of oncogenesis and disease progression.

This Special Issue centers on the mechanistic understanding of how cancer cells reconfigure their metabolic and lipid profile in response to genetic, environmental, and therapeutic factors. We welcome original research and review articles that explore the application of metabolomics and lipidomics, including the use of stable isotope tracers, to elucidate key pathways involved in cancer hallmarks. Emphasis is placed on integrative studies that investigate metabolic perturbations in response to drug treatments, nutrient availability, and microenvironmental stressors. Our objective is to highlight the critical roles of altered metabolic and lipidomic pathways in cancer initiation, promotion, progression, therapy resistance, and recurrence. By showcasing these molecular adaptations, this issue aims to advance precision oncology through identification of new diagnostic markers and targetable metabolic vulnerabilities, ultimately leading to improved therapeutic strategies.

Dr. Sara Vicente-Muñoz
Dr. Mariaelena Pistoni
Guest Editors

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Keywords

  • cancer hallmarks
  • metabolomics
  • lipidomics
  • stable isotopes
  • biomarkers
  • therapeutic targets
  • metabolic pathways

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Published Papers (1 paper)

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Research

15 pages, 1016 KB  
Article
Identification of a Novel Lipidomic Biomarker for Hepatocyte Carcinoma Diagnosis: Advanced Boosting Machine Learning Techniques Integrated with Explainable Artificial Intelligence
by Fatma Hilal Yagin, Cemil Colak, Fahaid Al-Hashem, Sarah A. Alzakari, Amel Ali Alhussan and Mohammadreza Aghaei
Metabolites 2025, 15(11), 716; https://doi.org/10.3390/metabo15110716 - 1 Nov 2025
Cited by 1 | Viewed by 1377
Abstract
Background: Hepatocellular carcinoma (HCC) is a leading cause of cancer-related mortality worldwide, often diagnosed at late stages due to the limited sensitivity of current screening tools. This study explores whether blood-based lipidomic profiling, combined with explainable artificial intelligence (XAI), can improve early and [...] Read more.
Background: Hepatocellular carcinoma (HCC) is a leading cause of cancer-related mortality worldwide, often diagnosed at late stages due to the limited sensitivity of current screening tools. This study explores whether blood-based lipidomic profiling, combined with explainable artificial intelligence (XAI), can improve early and interpretable detection of HCC. Methods: We analyzed lipidomic data from 219 HCC patients and 219 matched healthy controls using liquid chromatography-mass spectrometry. An Explainable Boosting Machine (EBM) was employed to identify discriminatory lipid biomarkers and was compared against several standard machine learning algorithms. Results: The EBM model achieved superior performance with 87.0% accuracy, 87.7% sensitivity, 86.3% specificity, and an AUC of 91.8%, outperforming other models. Key lipid biomarkers identified included specific phosphatidylcholines (PC 38:2, PC 40:4), sphingomyelins (SM d40:2 B), and lysophosphatidylcholines (LPC 18:2), which exhibited significant alterations in HCC patients and highlighted disruptions in sphingolipid metabolism. Conclusions: Integration of lipidomics with explainable machine learning offers a powerful, transparent approach for HCC biomarker discovery, achieving high diagnostic accuracy while providing biological insights. This strategy holds promise for developing non-invasive, clinically interpretable screening tools to improve early detection of liver cancer. Full article
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