Search Results (1,090)

The composition of the gut and/or tumor microbiome has been intricately involved in the onset of carcinogenesis, tumor progression, therapy response, and patient outcomes in diverse solid cancers. The microbiome type, composition, and their metabolome have been functionally implicated in the multifarious cellular processes, transformation, proliferation, tumor immune evasion, cellular migration, etc. Despite such compelling evidence on the role of microbiome interactions in cancer, the realization of their role in neuroblastoma (NB), the deadly extracranial tumor in infants is few and fragmentary. This review comprehends the composition, diversity, and significance of microbiota in human health. Further, this review discusses the microbiota composition, their mode of action, and their signaling flow through and cellular processes in diverse cancers including NB. Precisely, this study for the first time has realized the functional relevance and clinical significance of the gut and tumor microbiome for NB. Interestingly, large cohort clinical and preclinical in vivo models of NB realized the following: gut microbiota predicts the risk for NB; postnatal (and or not maternal transmission) microbiome rearrangements; gut microbial effect on NB pathogenesis; tumor-altering gut microbial composition; microbial composition predicts treatment outcomes in NB; prebiotic remedies for stabilizing NB-associated microbial rearrangements; microbial composition in tumor-infiltrating microbiota predicts NB outcomes. Full article

Background/Objectives: The breast cancer susceptibility gene 1 (BRCA1) is a tumor suppressor gene whose mutations are associated with increased susceptibility to develop breast or ovarian cancer. BRCA1 mainly exerts its protective effects through DNA double-strand break repair. Although not itself a transcriptional factor, BRCA1, through its multiple protein interaction domains, exerts transcriptional coregulation. In addition, BRCA1 expression alters cellular metabolism including inhibition of de novo fatty acid synthesis, changes in cellular bioenergetics, and activation of antioxidant defenses. Some of these actions may contribute to its global oncosuppressive effects. However, the breadth of metabolic pathways reprogrammed by BRCA1 is not fully elucidated. Methods: Breast cancer cells expressing BRCA1 were investigated by multiplatform metabolomics, metabolism-related transcriptomics, and joint metabolomics/transcriptomics data processing techniques, namely two-way orthogonal partial least squares and pathway analysis. Results: Joint analyses revealed the most important metabolites, genes, and pathways of metabolic reprogramming in BRCA1-expressing breast cancer cells. The breadth of metabolic reprogramming included fatty acid synthesis, bioenergetics, HIF-1 signaling pathway, antioxidation, nucleic acid synthesis, and other pathways. Among them, rewiring of glycerophospholipid (including phosphatidylcholine, -serine and -inositol) metabolism and increased arginine metabolism have not been reported yet. Conclusions: Rewired glycerophospholipid and arginine metabolism were identified as components of BRCA1-induced metabolic reprogramming in breast cancer cells. The study helps to identify metabolites that are candidate biomarkers of the BRCA1 genotype and metabolic pathways that can be exploited in targeted therapies. Full article

Cancer is a systemic disease rather than a localized pathology and is characterized by widespread effects, including whole-body exhaustion and chronic inflammation. A thorough understanding of cancer pathophysiology requires a systemic approach that accounts for the complex interactions between cancer cells and host tissues. To explore these dynamics, we employed a comprehensive metabolomic analysis of plasma samples from patients with either esophageal or head and neck squamous cell carcinoma (SCC). Plasma samples from 149 patients were metabolically profiled and correlated with clinical data. Among the metabolites identified, lysophosphatidylcholine (LPC) emerged as the sole biomarker strongly correlated with prognosis. A significant reduction in plasma LPC levels was linked to poorer overall survival. Plasma LPC levels demonstrated minimal correlation with patient-specific factors, such as tumor size and general condition, but showed significant association with the response to immune checkpoint inhibitor therapy. Proteomic and cytokine analyses revealed that low plasma LPC levels reflected systemic chronic inflammation, characterized by high levels of inflammatory proteins, the cytokines interleukin-6 and tumor necrosis factor-α, and coagulation-related proteins. These findings indicate that plasma LPC levels may be used as reliable biomarkers for predicting prognosis and evaluating the efficacy of immunotherapy in patients with SCC. Full article

Cancer metabolic reprogramming plays a critical role in tumor progression and therapeutic resistance, underscoring the need for advanced analytical strategies. Metabolomics, leveraging mass spectrometry and nuclear magnetic resonance (NMR) spectroscopy, offers a comprehensive and functional readout of tumor biochemistry. By enabling both targeted metabolite quantification and untargeted profiling, metabolomics captures the dynamic metabolic alterations associated with cancer. The integration of metabolomics with machine learning (ML) approaches further enhances the interpretation of these complex, high-dimensional datasets, providing powerful insights into cancer biology from biomarker discovery to therapeutic targeting. This review systematically examines the transformative role of ML in cancer metabolomics. We discuss how various ML methodologies—including supervised algorithms (e.g., Support Vector Machine, Random Forest), unsupervised techniques (e.g., Principal Component Analysis, t-SNE), and deep learning frameworks—are advancing cancer research. Specifically, we highlight three major applications of ML–metabolomics integration: (1) cancer subtyping, exemplified by the use of Similarity Network Fusion (SNF) and LASSO regression to classify triple-negative breast cancer into subtypes with distinct survival outcomes; (2) biomarker discovery, where Random Forest and Partial Least Squares Discriminant Analysis (PLS-DA) models have achieved >90% accuracy in detecting breast and colorectal cancers through biofluid metabolomics; and (3) prognostic modeling, demonstrated by the identification of race-specific metabolic signatures in breast cancer and the prediction of clinical outcomes in lung and ovarian cancers. Beyond these areas, we explore applications across prostate, thyroid, and pancreatic cancers, where ML-driven metabolomics is contributing to earlier detection, improved risk stratification, and personalized treatment planning. We also address critical challenges, including issues of data quality (e.g., batch effects, missing values), model interpretability, and barriers to clinical translation. Emerging solutions, such as explainable artificial intelligence (XAI) approaches and standardized multi-omics integration pipelines, are discussed as pathways to overcome these hurdles. By synthesizing recent advances, this review illustrates how ML-enhanced metabolomics bridges the gap between fundamental cancer metabolism research and clinical application, offering new avenues for precision oncology through improved diagnosis, prognosis, and tailored therapeutic strategies. Full article

Mitochondria, pivotal organelles in cellular metabolism and energy production, have emerged as critical players in the pathogenesis of cancer. This review outlines the progress in mitochondrial profiling through mass spectrometry-based metabolomics and its applications in cancer research. We provide unprecedented insights into the mitochondrial metabolic rewiring that fuels tumorigenesis, metastasis, and therapeutic resistance. The purpose of this review is to provide a comprehensive guide for the implementation of mitochondrial metabolomics, integrating advanced methodologies—including isolation, detection, and data integration—with insights into cancer-specific metabolic rewiring. We first summarize current methodologies for mitochondrial sample collection and pretreatment. Furthermore, we then discuss the recent advancements in mass spectrometry-based methodologies that facilitate the detailed profiling of mitochondrial metabolites, unveiling significant metabolic reprogramming associated with tumorigenesis. We emphasize how recent technological advancements have addressed longstanding challenges in the field and explore the role of mitochondrial metabolism-driven cancer development and progression for novel drug discovery and translational research applications in cancer. Collectively, this review delineates emerging opportunities for therapeutic discovery and aims to establish a foundation for future investigations into the therapeutic modulation of mitochondrial pathways in cancer, thereby paving the way for innovative diagnostic and therapeutic approaches targeting mitochondrial pathways. Full article

The human gut microbiota significantly influences host health through its metabolic products and interaction with immune, neural, and metabolic systems. Among these, short-chain fatty acids (SCFAs), especially butyrate, play key roles in maintaining gut barrier integrity, modulating inflammation, and supporting metabolic regulation. Dysbiosis is increasingly linked to diverse conditions such as gastrointestinal, metabolic, and neuropsychiatric disorders, cardiovascular diseases, and colorectal cancer (CRC). Probiotics offer therapeutic potential by restoring microbial balance, enhancing epithelial defenses, and modulating immune responses. This review highlights the physiological functions of gut microbiota and SCFAs, with a particular focus on butyrate’s anti-inflammatory and anti-cancer effects in CRC. It also examines emerging microbial therapies like probiotics, synbiotics, postbiotics, and engineered microbes. Emphasis is placed on the need for precision microbiome medicine, tailored to individual host–microbiome interactions and metabolomic profiles. These insights underscore the promising role of gut microbiota modulation in advancing preventive and personalized healthcare. Full article

Breast cancer (BC) is a neoplasm characterized by high heterogeneity and is influenced by intrinsic molecular subtypes and clinical stage, aspects that remain underexplored in the Colombian population. This study aimed to characterize metabolic alterations associated with subtypes and disease progression in a group of newly diagnosed, treatment-naive Colombian women using an untargeted metabolomics approach. To improve metabolite coverage, samples were analyzed using LC-QTOF-MS and GC-QTOF-MS, along with amino acid profiling. The Luminal B subtype exhibited elevated levels of long-chain acylcarnitines and higher free fatty acid concentrations than the other subtypes. It also presented elevated levels of carbohydrates and essential glycolytic intermediates, suggesting that this subtype may adopt a hybrid metabolic phenotype characterized by increased glycolytic flux as well as enhanced fatty acid catabolism. Tumor, Node, and Metastasis (TNM) staging analysis revealed progressive metabolic reprogramming of BC. In advanced stages, a sustained increase in phosphatidylcholines and a decrease in lysophosphatidylcholines were observed, reflecting lipid alterations associated with key roles in tumor progression. In early stages (I-II), plasma metabolites with high discriminatory power were identified, such as glutamic acid, ribose, and glycerol, which are associated with dysfunctions in energy and carbohydrate metabolism. These results highlight metabolomics as a promising tool for the early diagnosis, clinical follow-up, and molecular characterization of BC. Full article

Cholesterol stress profoundly modulates cellular processes, but its underlying mechanisms remain incompletely understood. To investigate cholesterol-responsive networks, we performed integrated transcriptome (RNA-seq) and metabolome (LC-MS) analyses on HeLa cells treated with cholesterol for 6 and 24 h. Through transcriptomic analysis of cholesterol-stressed HeLa cells, we identified stage-specific responses characterized by early-phase stress responses and late-phase immune-metabolic coordination. This revealed 1340 upregulated and 976 downregulated genes after a 6 h cholesterol treatment, including induction and suppression of genes involved in cholesterol efflux and sterol biosynthesis, respectively, transitioning to Nuclear Factor kappa-B (NF-κB) activation and Peroxisome Proliferator-Activated Receptor (PPAR) pathway modulation by 24 h. Co-expression network analysis prioritized functional modules intersecting with differentially expressed genes. We also performed untargeted metabolomics using cells treated with cholesterol for 6 h, which demonstrated extensive remodeling of lipid species. Interestingly, integrated transcriptomic and metabolic analysis uncovered GFPT1-driven Uridine Diphosphate-N-Acetylglucosamine (UDP-GlcNAc) accumulation and increased taurine levels. Validation experiments confirmed GFPT1 upregulation and ANGPTL4 downregulation through RT-qPCR and increased O-GlcNAcylation via Western blot. Importantly, clinical datasets further supported the correlations between GFPT1/ANGPTL4 expression and cholesterol levels in Non-Alcoholic Steatohepatitis (NASH) liver cancer patients. This work establishes a chronological paradigm of cholesterol sensing and identifies GFPT1 and ANGPTL4 as key regulators bridging glycosylation and lipid pathways, providing mechanistic insights into cholesterol-associated metabolic disorders. Full article

Hair, as a non-invasive biospecimen, retains metabolic deposits from sebaceous glands and capillaries, reflecting substances from the peripheral circulation, and provides valuable biochemical information linked to phenotypes, yet its application in animal disease research remains limited. This work applied ultra-high-performance liquid chromatography–tandem mass spectrometry (UHPLC-MS/MS) to compare the hair metabolomic characteristics of healthy forest musk deer (FMD, Moschus berezovskii) and those diagnosed with hemorrhagic pneumonia (HP), phytobezoar disease (PD), and abscess disease (AD). A total of 2119 metabolites were identified in the FMD hair samples, comprising 1084 metabolites in positive ion mode and 1035 metabolites in negative ion mode. Differential compounds analysis was conducted utilizing the orthogonal partial least squares–discriminant analysis (OPLS-DA) model. In comparison to the healthy control group, the HP group displayed 85 upregulated and 92 downregulated metabolites, the PD group presented 124 upregulated and 106 downregulated metabolites, and the AD group exhibited 63 upregulated and 62 downregulated metabolites. Functional annotation using the Kyoto Encyclopedia of Genes and Genomes (KEGG) indicated that the differential metabolites exhibited significant enrichment in pathways associated with cancer, parasitism, energy metabolism, and stress. Receiver operating characteristic (ROC) analysis revealed that both the individual and combined panels of differential metabolites exhibited area under the curve (AUC) values exceeding 0.7, demonstrating good sample discrimination capability. This research indicates that hair metabolomics can yield diverse biochemical insights and facilitate the development of non-invasive early diagnostic techniques for diseases in captive FMD. Full article

The distinctive nature of ferroptosis is that it is induced chemically and signifies a regulated cell death dependent on iron-dependent lipid peroxidation. The mechanism of ferroptosis involves oxidative damage to the membrane lipids. It differs from apoptosis and necroptosis, triggering metabolic changes in the iron-lipid homeostasis and antioxidant defense, such as glutathione (GSH) and glutathione peroxidase 4 (GPX4). Herein, the molecular mechanisms of ferroptosis and its role in the tumorigenesis process and infection-related diseases are presented. It also discusses metabolic reprogramming as a factor that modifies the levels of cell-sensitizing polyunsaturated fatty acids (PUFAs), iron dysregulation, and oxidative stress in aggressive cancers and inflammatory diseases such as sepsis, tuberculosis, and COVID-19. Particular attention is given to chemical modulators of ferroptosis, including synthetic inducers and inhibitors, as well as bioactive natural compounds. Our focus is on the significance of analytical tools, such as lipidomics and metabolomics, in understanding the phenomenon of ferroptosis. Finally, we explore novel therapeutic approaches targeting ferroptosis in cancer and infectious diseases, while navigating both the opportunities and challenges in drug development. The review then draws on chemical biology and disease pathology to propose promising areas of study for ferroptosis-related therapies. Full article

Glucose metabolism reprogramming as a defining hallmark of cancer has become a pivotal frontier in oncology research. Recent technological advances in single-cell sequencing, spatial omics, and metabolic imaging have transformed the field from static bulk analyses to dynamic investigations of spatiotemporal heterogeneity at a single-cell resolution. This review systematically summarizes the current knowledge on tumor glucose metabolism dynamics, discussing spatial heterogeneity and temporal evolution patterns, metabolic subpopulation interactions revealed by single-cell metabolomics, the glucose metabolism–epigenetics–immunology regulatory axis, and therapeutic strategies targeting metabolic vulnerabilities. Recent technological advances in single-cell sequencing and spatial omics have transformed our understanding of tumor glucose metabolism by providing high-resolution insights into metabolic heterogeneity and regulatory mechanisms, contrasting with classical bulk analyses. Spatiotemporal heterogeneity critically influences therapeutic outcomes by enabling tumor cells to adapt metabolically under selective pressures (e.g., hypoxia, nutrient deprivation), fostering treatment resistance and relapse. Deciphering these dynamics is essential for developing spatiotemporally targeted strategies that address intratumoral diversity and microenvironmental fluctuations. By integrating cutting-edge advances, this review deepens our understanding of tumor metabolic complexity and provides a conceptual framework for developing spatiotemporally precise interventions. Full article

Background and Objectives: Regional and systemic analgesic techniques, such as erector spinae plane (ESP) block and opioid administration, implemented during cancer surgery, have been shown to influence immune responses and potentially affect cancer outcomes. Surgical stress and analgesic techniques used in cancer surgery—such as regional nerve blocks or systemic opioids—not only affect pain control but also influence immune and inflammatory pathways that may impact cancer progression. To understand the biological consequences of these interventions, metabolomic profiling has emerged as a powerful approach for capturing systemic metabolic and immunological changes, which are particularly relevant in the oncologic perioperative setting. In this study, we examined the impact of the ESP on the metabolomic profile, as well as levels of VEGF, cortisol, and CRP, in addition to its analgesic effects in breast cancer surgery. Materials and Methods: Ninety patients were placed into three different analgesia groups (morphine, ESP, and control groups). Demographic data, ASA classification, comorbidities, surgery types, and pain scores were documented. Blood samples were taken at preoperative hour 0, postoperative hour 1, and postoperative hour 24 (T0, T1, and T24). VEGF, cortisol, and CRP levels were measured, and metabolomic analysis was performed. Results: Study groups were comparable regarding demographic findings, comorbidities, and surgery types (p > 0.05). NRS scores of group ESP were lowest in the first 12 h period (p < 0.01) and ESP block reduced opioid consumption (p < 0.01). VEGF and cortisol levels of group morphine were similar to ESP at T24 (p > 0.05). Group ESP had lower VEGF and cortisol levels than the control at T24 (p = 0.025, p = 0.041, respectively.). The CRP level of group morphine was higher than both ESP and control at T24 (p = 0.022). Metabolites involved in primary bile acid, steroid hormone biosynthesis, amino acid, and glutathione metabolism were changed in group ESP. Conclusions: Metabolites in bile acid biosynthesis and steroid hormone pathways, which play a key role in immune responses, were notably lower in the ESP group. Accordingly, VEGF and cortisol peaks were more moderate in group ESP. In conclusion, we think that ESP block, which provides adequate analgesia, is an acceptable approach in terms of modulating immune responses in breast cancer surgery. Full article

Background: Huang Qin Decoction (HQD) is a well-established Traditional Chinese Medicine (TCM) formulation recognized for its application in the treatment of colorectal cancer (CRC). However, the precise therapeutic mechanisms remain inadequately defined. Methods: This study integrates metabolomics from a mouse model and network pharmacology to screen potential targets and bio-active ingredients of HQD. The pharmacological activity of HQD for CRC was evidenced via single-cell RNA sequencing (scRNA-seq), molecular docking, and molecular dynamics simulations. Atomic force microscopy (AFM) assays and cellular experimental validation were used to confirm the relative mechanisms. Results: The metabolite profile undergoes significant alterations, with metabolic reprogramming evident during the malignant progression of CRC liver metastasis. Network pharmacology analysis identified that HQD regulates several metabolic pathways, including arginine biosynthesis, alanine, aspartate, and glutamate metabolism, nitrogen metabolism, phenylalanine metabolism, and linoleic acid metabolism, by targeting key proteins such as aspartate aminotransferase (GOT1), cytochrome P450 1A2 (CYP1A2), and carbonic anhydrase 2 (CA2). ScRNA-seq analysis indicated that HQD may enhance the functionality of cytotoxic T cells, thereby reversing the immunosuppressive microenvironment. Virtual verification revealed a strong binding affinity between the identified hub targets and active constituents of HQD, a finding subsequently corroborated by AFM assays. Cellular experiments confirmed that naringenin treatment inhibits the proliferation, migration, and invasion of CRC cells by downregulating GOT1 expression and disrupting glutamine metabolism. Conclusions: Computational prediction and in vitro validation reveal the active ingredients, potential targets, and molecular mechanisms of HQD against CRC liver metastasis, thereby providing a scientific foundation for the application of TCM in CRC treatment. Full article

Brachial plexus root avulsion [BPRA] and concomitant spinal cord injury [SCI] represent devastating injuries that come with limited hope for recovery owing to the adult spinal cord’s loss of intrinsic ability to spontaneously regenerate. BPRA/SCI is an enormous public health issue the world over, and its catastrophic impact goes beyond the patient, the family, businesses, and national health budgets, draining billions of dollars annually. The rising population and economic growth have seen the incidence of SCI surging. Genomic, transcriptomic, and proteomic studies have yielded loads of information on the various molecular events that precede, regulate, and support both regenerative and degenerative pathways post-SCI. Metabolomics, on the other hand, comes in as the search for a cure and the objective monitoring of SCI severity and prognosis remains on the horizon. Despite the large number of review articles on metabolomics and its application fields such as in cancer and diabetes research, there is no comprehensive review on metabolite profiling to study disease mechanisms, biomarkers, or neuroprotection in SCI. First, we present a short review on BPRA/SCI. Second, we discuss potential benefits of metabolomics as applied in BPRA/SCI cases. Next, a look at the analytical techniques that are used in metabolomics. Next, we present an overview of the studies that have used metabolomics to reveal SCI metabolic fingerprints and point out areas of further investigation. Finally, we discuss future research directions. Full article

As tumor research has deepened, the deregulation of cellular metabolism has emerged as yet another recognized hallmark of cancer. Tumor cells adapt different biochemical pathways to support their rapid growth, proliferation, and invasion, resulting in distinct anabolic and catabolic activities compared with healthy tissues. Certain metabolic shifts, such as altered glucose and glutamine utilization and increased de novo fatty acid synthesis, are critical early on, while others may become essential only during metastasis. These metabolic adaptations are closely shaped by, and in turn remodel, the tumor microenvironment, creating favorable conditions for their spread. Anticancer metabolic strategies should integrate pharmacological approaches aimed at inhibiting specific biochemical pathways with well-defined dietary interventions as adjunctive therapies, considering also the role of gut microbiota in modulating diet and treatment responses. Given the established link between the consumption of foods rich in saturated fatty acids and sugars and an increased cancer risk, the effects of diet cannot be ignored. However, current evidence from controlled and multicenter clinical trials remains insufficient to provide definitive clinical recommendations. Further research using modern omics methods, such as metabolomics, proteomics, and lipidomics, is necessary to understand the changes in the metabolic profiles of various cancers at different stages of their development and to determine the potential for modifying these profiles through pharmacological agents and dietary modifications. Therefore, clinical trials should combine standard treatments with novel approaches targeting metabolic reprogramming, such as inhibition of specific enzymes and transporters or binding proteins, alongside the implementation of dietary restrictions that limit nutrient availability for tumor growth. However, to optimize therapeutic efficacy, a precision medicine approach should be adopted that balances the destruction of cancer cells with the protection of healthy ones. This approach, among others, should be based on cell type-specific metabolic profiling, which is crucial for personalizing oncology treatment. Full article