Comprehensive metabolomics expands precision medicine for triple-negative breast cancer

Exploring lipidomic profiles and their correlation with hormone receptor and HER2 status in breast cancer

Dysregulated lipid metabolism promotes the progression of various cancer types, including breast cancer. The present study aimed to explore the lipidomic profiles of patients with breast cancer, providing insights into the correlation between lipid compositions and tumor subtypes characterized by hormone receptor (HR) and human epidermal growth factor receptor 2 (HER2) status. Briefly, 30 patients with breast cancer were categorized into four groups based on their HR and HER2 status: HR+ no HER2 expression (HER2-0), HR+ HER2-low; HR+ HER2-positive (pos) and HR- HER2-pos. The lipidomic profiles of these patients were analyzed using high-throughput liquid chromatography-mass spectrometry. The data were processed through principal component analysis (PCA), partial least squares-discriminant analysis (PLS-DA) and random forest (RF) classification to assess the lipidomic variations and significant lipid features among these groups. The profiles of the lipids, particularly triglycerides (TG) such as TG(16:0-18:1-18:1)+NH4, were significantly different across the groups. PCA and PLS-DA identified unique lipid profiles in the HR+ HER2-pos and HR+ HER2-0 groups, while RF highlighted phosphatidylinositol-3,4,5-trisphosphate(21:2)+NH4 as a crucial lipid feature for accurate patient grouping. Advanced statistical analysis showed significant correlations between lipid carbon chain length and the number of double bonds within the classifications, providing insights into the role of structural lipid properties in tumor biology. Additionally, a clustering heatmap and network analysis indicated significant lipid-lipid interactions. Pathway enrichment analysis showed critical biological pathways, such as the 'Assembly of active LPL and LIPC lipase complexes', which has high enrichment ratio and statistical significance. In conclusion, the present study underscored that lipidomic profiling is crucial in understanding the metabolic alterations associated with different breast cancer subtypes. These findings highlighted specific lipid features and interactions that may serve as potential biomarkers for breast cancer classification and target pathways for therapeutic intervention. Furthermore, advanced lipidomic analyses can be integrated to decipher complex biological data, offering a foundation for further research into the role of lipid metabolism in cancer progression.

Epoxy metabolites of linoleic acid promote the development of breast cancer via orchestrating PLEC/NFκB1/CXCL9-mediated tumor growth and metastasis

Breast cancer (BC) is a common malignant tumor in women and requires a comprehensive understanding of its pathogenesis for the development of new therapeutic strategies. Polyunsaturated fatty acids (PUFAs) metabolism-driven inflammation is a causative factor in cancer development. However, the function of PUFAs' metabolism in BC remains largely unknown. Here we report the role and underlying mechanism of epoxyoctadecenoic acids (EpOMEs), the metabolites of linoleic acid mediated by cytochrome P450 (CYP) monooxygenases, in promoting the development of BC, particularly triple-negative BC (TNBC). A metabolomics study identified that EpOMEs were significantly increased in the plasma of BC patients and MMTV-PyMT mice, which accounted for the upregulation of CYP2J2 in BC tumor tissues and tumor cells. Decreased EpOMEs by treatment of CYP monooxygenase inhibitors significantly alleviated tumor development in MMTV-PyMT mice. Treatment with EpOMEs and overexpression of CYP2J2 to increase EpOMEs in TNBC cells significantly promoted cellular proliferation, migration, tumor growth, and metastasis. Whereas knockdown of CYP2J2 to decrease EpOMEs inhibited tumorigenesis and lung metastasis of TNBC, which was reversed by EpOME administration. Transcriptomics and proteomics analyses revealed CXCL9 and PLEC were critical for EpOME-mediated promotion of TNBC. Knockdown of CXCL9 and PLEC inhibited TNBC progression and EpOME-mediated promotion of TNBC. Both overexpression of CYP2J2 and EpOME treatment upregulate PLEC, while PLEC upregulates NFκB1, which is a transcription regulator of CXCL9. This study extends the understanding of the function of PUFAs metabolism in BC development, providing potential therapeutic targets and dietary guidelines for patients with TNBC and other BCs. The illustration of the hypothetical mechanism CYP2J2/EpOMEs promotes the tumorigenesis and metastasis of TNBC via PLEC/NFKB1/CXCL9 signaling pathway.

Design, synthesis and biological evaluation of new SIRT3 activators for the treatment of triple-negative breast cancer

Triple-negative breast cancer (TNBC) represents a highly malignant subtype of breast cancer with limited therapeutic options. In this study, we designed and synthesized a series of 1,4-DHP derivatives by structure-based strategy, 43 was documented to be a potent SIRT3 activator and exhibited profound anti-proliferative activity in BT-549 and MDA-MB-231 cells with low toxicity over normal cells. Additionally, 43 displayed the ability of direct binding to SIRT3 with a Kd value of 51.51 μM in BLI assay, and the potential bonding mode was elucidated through molecular docking. 43 could inhibit the proliferation, migration, and glycolysis, induced mitochondrial membrane potential decreased and apoptosis in BT-549 and MDA-MB-231 cells. Collectively, these results demonstrate that 43 is a potent SIRT3 activator with the potential to anti-TNBC through signaling pathways regulated by SIRT3.

Metabolomics-Based Machine Learning Models Accurately Predict Breast Cancer Estrogen Receptor Status

Breast cancer is a global concern as a leading cause of death for women. Early and precise diagnosis can be vital in handling the disease efficiently. Breast cancer subtyping based on estrogen receptor (ER) status is crucial for determining prognosis and treatment. This study uses metabolomics data from plasma samples to detect metabolite biomarkers that could distinguish ER-positive from ER-negative breast cancers in a non-invasive manner. The dataset includes demographic information, ER status, and metabolite levels from 188 breast cancer patients and 73 healthy controls. Recursive Feature Elimination (RFE) with a Random Forest (RF) classifier identified an optimal subset of 30 features-29 biomarkers and age-that achieved the highest area under the curve (AUC). To address the class imbalance, Gaussian noise-based augmentation and Adaptive Synthetic Oversampling (ADASYN) were applied, ensuring balanced representation during training. Four machine learning (ML) algorithms-Random Forest, Support Vector Classifier (SVC), XGBoost, and Logistic Regression (LR)-were evaluated using grid search. The Random Forest classifier emerged as the top performer, achieving an AUC of 0.95 and an accuracy of 93%. These results suggest that ML has great promise for identifying specific metabolites linked to ER expression, paving the development of a novel analytical tool that can minimize current challenges in identifying ER status, and improve the precision of breast cancer subtyping.

Advances in novel biosensors in biomedical applications

Biosensors, devices capable of detecting biomolecules or bioactive substances, have recently become one of the important tools in the fields of bioanalysis and medical diagnostics. A biosensor is an analytical system composed of biosensitive elements and signal-processing elements used to detect various biological and chemical substances. Biomimetic elements are key to biosensor technology and are the components in a sensor that are responsible for identifying the target analyte. The construction methods and working principles of biosensors based on synthetic biomimetic elements, such as DNAzyme, molecular imprinted polymers and aptamers, and their updated applications in biomedical analysis are summarised. Finally, the technical bottlenecks and future development prospects for biomedical analysis are summarised and discussed.

Harnessing the synergistic potential of NK1R antagonists and selective COX-2 inhibitors for simultaneous targeting of TNBC cells and cancer stem cells

Triple-negative breast cancer (TNBC) lacks the expression of oestrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2), rendering it unresponsive to endocrine therapy and HER2 targeted treatments. Though certain chemotherapeutics targeting the cell cycle have shown efficacy to a certain extent, the presence of chemotherapy-resistant cancer stem cells (CSCs) presents a significant challenge in tackling TNBC. Multiple lines of evidence suggest the upregulation of neuropeptide Substance P (SP), its NK-1 receptor (NK1R) and the Cyclooxygenase-2 (COX-2) enzyme in TNBC patients. Upregulation of the SP/NK1R system and COX-2 influences major signalling pathways involved in cell proliferation, growth, survival, angiogenesis, inflammation, metastasis and stem cell activity. The simultaneous activation and crosstalk between the pathways activated by SP/NK1R and COX-2 consequently increase the levels of key regulators of self-renewal pathways in CSCs, promoting stemness. The combination therapy with NK1R antagonists and COX-2 inhibitors can simultaneously target TNBC cells and CSCs, thereby enhancing treatment efficacy and reducing the risk of recurrence and relapse. This review discusses the rationale for combining NK1R antagonists and COX-2 inhibitors for the better management of TNBC and a novel strategy to deliver drug cargo precisely to the tumour site to address the challenges associated with off-target binding.

Identification of a Novel Biomarker Panel for Breast Cancer Screening

Breast cancer remains a major public health concern, and early detection is crucial for improving survival rates. Metabolomics offers the potential to develop non-invasive screening and diagnostic tools based on metabolic biomarkers. However, the inherent complexity of metabolomic datasets and the high dimensionality of biomarkers complicates the identification of diagnostically relevant features, with multiple studies demonstrating limited consensus on the specific metabolites involved. Unlike previous studies that rely on singular feature selection techniques such as Partial Least Square (PLS) or LASSO regression, this research combines supervised and unsupervised machine learning methods with random sampling strategies, offering a more robust and interpretable approach to feature selection. This study aimed to identify a parsimonious and robust set of biomarkers for breast cancer diagnosis using metabolomics data. Plasma samples from 185 breast cancer patients and 53 controls (from the Cooperative Human Tissue Network, USA) were analyzed. This study also overcomes the common issue of dataset imbalance by using propensity score matching (PSM), which ensures reliable comparisons between cancer and control groups. We employed Univariate Naïve Bayes, L2-regularized Support Vector Classifier (SVC), Principal Component Analysis (PCA), and feature engineering techniques to refine and select the most informative features. Our best-performing feature set comprised 11 biomarkers, including 9 metabolites (SM(OH) C22:2, SM C18:0, C0, C3OH, C14:2OH, C16:2OH, LysoPC a C18:1, PC aa C36:0 and Asparagine), a metabolite ratio (Kynurenine-to-Tryptophan), and 1 demographic variable (Age), achieving an area under the ROC curve (AUC) of 98%. These results demonstrate the potential for a robust, cost-effective, and non-invasive breast cancer screening and diagnostic tool, offering significant clinical value for early detection and personalized patient management.

DHDH-mediated D-xylose metabolism induces immune evasion in triple-negative breast cancer

BACKGROUND

Although the prognosis of triple-negative breast cancer (TNBC) has significantly improved in the era of immunotherapy, many TNBC patients are resistant to therapies, and their disease progresses rapidly. Deciphering the metabolic mechanisms regulating anticancer immunity will provide new insights into therapeutic strategies for TNBC.

METHODS

In this study, we performed bioinformatics analysis in our multi-omics TNBC database and identified that a metabolic enzyme, dihydrodiol dehydrogenase (DHDH), might promote the phenotype of "cold tumor" in TNBC. The biological function of DHDH was verified by in vitro and in vivo functional experiments, and the potential molecular mechanism of DHDH promoting TNBC immune escape was further explored.

FINDINGS

Mechanistically, DHDH mediated the synthesis and depletion of the substrate D-xylose and inhibited the activation of the proteasome subunit beta type 9 (PSMB9) and further induction of the immune response. We demonstrated that D-xylose supplementation could enhance the proliferation of CD8+ T cells and the expression of cytotoxic markers against cocultured DHDH-wild type (WT) cells. Consistently, D-xylose supplementation in vivo promoted CD8+ T cell infiltration and the expression of cytotoxic markers and increased the sensitivity of DHDH-overexpressing tumors to immune checkpoint blockade (ICB).

CONCLUSIONS

Our findings reveal that a D-xylose-regulated PSMB9-dependent pathway governs tumor-intrinsic immunogenicity and, hence, the sensitivity to ICB, which may provide approaches to promote the "cold-to-hot" transition in TNBC.

FUNDING

This study was funded by the National Key Research and Development Plan of China, Shanghai Science and Technology Commission, National Natural Science Foundation of China, and China Postdoctoral Science Foundation.

Evaluation of Bayesian Linear Regression models for gene set prioritization in complex diseases

Genome-wide association studies (GWAS) provide valuable insights into the genetic architecture of complex traits, yet interpreting their results remains challenging due to the polygenic nature of most traits. Gene set analysis offers a solution by aggregating genetic variants into biologically relevant pathways, enhancing the detection of coordinated effects across multiple genes. In this study, we present and evaluate a gene set prioritization approach utilizing Bayesian Linear Regression (BLR) models to uncover shared genetic components among different phenotypes and facilitate biological interpretation. Through extensive simulations and analyses of real traits, we demonstrate the efficacy of the BLR model in prioritizing pathways for complex traits. Simulation studies reveal insights into the model's performance under various scenarios, highlighting the impact of factors such as the number of causal genes, proportions of causal variants, heritability, and disease prevalence. Comparative analyses with MAGMA (Multi-marker Analysis of GenoMic Annotation) demonstrate BLR's superior performance, especially in highly overlapped gene sets. Application of both single-trait and multi-trait BLR models to real data, specifically GWAS summary data for type 2 diabetes (T2D) and related phenotypes, identifies significant associations with T2D-related pathways. Furthermore, comparison between single- and multi-trait BLR analyses highlights the superior performance of the multi-trait approach in identifying associated pathways, showcasing increased statistical power when analyzing multiple traits jointly. Additionally, enrichment analysis with integrated data from various public resources supports our results, confirming significant enrichment of diabetes-related genes within the top T2D pathways resulting from the multi-trait analysis. The BLR model's ability to handle diverse genomic features, perform regularization, conduct variable selection, and integrate information from multiple traits, genders, and ancestries demonstrates its utility in understanding the genetic architecture of complex traits. Our study provides insights into the potential of the BLR model to prioritize gene sets, offering a flexible framework applicable to various datasets. This model presents opportunities for advancing personalized medicine by exploring the genetic underpinnings of multifactorial traits.

Evolving therapeutics and ensuing cardiotoxicities in triple-negative breast cancer

Defined as scarce expression of hormone receptors and human epidermal growth factor receptor 2, triple-negative breast cancer (TNBC) is labeled as the most heterogeneous subtype of breast cancer with poorest prognosis. Despite rapid advancements in precise subtyping and tailored therapeutics, the ensuing cancer therapy-related cardiovascular toxicity (CTR-CVT) could exert detrimental impacts to TNBC survivors. Nowadays, this interdisciplinary issue is incrementally concerned by cardiologists, oncologists and other pertinent experts, propelling cardio-oncology as a booming field focusing on the whole-course management of cancer patients with potential cardiovascular threats. Here in this review, we initially profile the evolving molecular subtyping and therapeutic landscape of TNBC. Further, we introduce various monitoring approaches of CTR-CVT. In the main body, we elaborate on typical cardiotoxicities ensuing anti-TNBC treatments in detail, ranging from chemotherapy (especially anthracyclines), surgery, anesthetics, radiotherapy to immunotherapy, with future perspectives on promising directions in the era of artificial intelligence and traditional Chinese medicine.

FABP4-mediated lipid metabolism promotes TNBC progression and breast cancer stem cell activity

Metabolic remodeling is a pivotal feature of cancer, with cancer stem cells frequently showcasing distinctive metabolic behaviors. Nonetheless, understanding the metabolic intricacies of triple-negative breast cancer (TNBC) and breast cancer stem cells (BCSCs) has remained elusive. In this study, we meticulously characterized the metabolic profiles of TNBC and BCSCs and delved into their potential implications for TNBC treatment. Our findings illuminated the robust lipid metabolism activity within TNBC tumors, especially in BCSCs. Furthermore, we discovered that Fabp4, through its mediation of fatty acid uptake, plays a crucial role in regulating TNBC lipid metabolism. Knocking down Fabp4 or inhibiting its activity significantly suppressed TNBC tumor progression in both the MMTV-Wnt1 spontaneous TNBC model and the TNBC patient-derived xenograft model. Mechanistically, Fabp4's influence on TNBC tumor progression was linked to its regulation of mitochondrial stability, the CPT1-mediated fatty acid oxidation process, and ROS production. Notably, in a high-fat diet model, Fabp4 deficiency proved to be a substantial inhibitor of obesity-accelerated TNBC progression. Collectively, these findings shed light on the unique metabolic patterns of TNBC and BCSCs, underscore the biological significance of Fabp4-mediated fatty acid metabolism in governing TNBC progression, and offer a solid theoretical foundation for considering metabolic interventions in breast cancer treatment. SIGNIFICANCE: Triple-negative breast cancer progression and breast cancer stem cell activity can be restricted by targeting a critical regulator of lipid responses, FABP4.

Metabolomics-driven approaches for identifying therapeutic targets in drug discovery

Identification of therapeutic targets can directly elucidate the mechanism and effect of drug therapy, which is a central step in drug development. The disconnect between protein targets and phenotypes under complex mechanisms hampers comprehensive target understanding. Metabolomics, as a systems biology tool that captures phenotypic changes induced by exogenous compounds, has emerged as a valuable approach for target identification. A comprehensive overview was provided in this review to illustrate the principles and advantages of metabolomics, delving into the application of metabolomics in target identification. This review outlines various metabolomics-based methods, such as dose-response metabolomics, stable isotope-resolved metabolomics, and multiomics, which identify key enzymes and metabolic pathways affected by exogenous substances through dose-dependent metabolite-drug interactions. Emerging techniques, including single-cell metabolomics, artificial intelligence, and mass spectrometry imaging, are also explored for their potential to enhance target discovery. The review emphasizes metabolomics' critical role in advancing our understanding of disease mechanisms and accelerating targeted drug development, while acknowledging current challenges in the field.

Serum and Fecal Metabolite Profiles Linking With Gut Microbiome in Triple-Negative Breast Cancer Patients

Background

Triple-negative breast cancer (TNBC) is a subtype of breast cancer characterized by poor prognosis due to the absence of effective targeted therapies. Emerging evidence indicates that the gut microbiota and its metabolites play a key role in the occurrence and development of TNBC. This study aimed to explore the metabolic changes and potential mechanisms associated with TNBC.

Objectives

This study aimed to explore the potential relationship between targeted metabolites and the gut microbiota in TNBC.

Design

We recruited 8 participants, including 4 with TNBC and 4 with benign fibroadenomas as controls.

Methods

The gut microbiota was analyzed using metagenomics on fecal samples. Liquid chromatography-mass spectrometry (LC-MS) was employed to identify differential metabolites in serum and fecal samples. The correlation between the gut microbiota and metabolites was analyzed using Spearman's correlation analysis.

Results

Analysis of altered serum metabolites in the TNBC group revealed changes, particularly in carboxylic acids and derivatives, benzene, and substituted derivatives. Kyoto Encyclopedia of Genes and Genomes (KEGG) metabolic pathway analysis revealed significant enrichment in 18 pathways. Regarding fecal metabolites, differences between the 2 groups also included carboxylic acids and derivatives, benzene, and substituted derivatives, with 28 metabolic pathways enriched based on KEGG pathway analysis. Metagenomics analysis showed differences in the relative abundance of Anaerococcus, Fischerella, and Schizosaccharomyces at the genus level, which have been previously associated with breast cancer. Furthermore, 4 serum metabolites-L-glutamine, citrate, creatinine, and creatine-along with 9 fecal metabolites, were associated with the aforementioned microbiota.

Conclusion

Our findings highlight distinct metabolite profiles in the serum and feces of patients with TNBC. The identification of gut microbiota and their associated metabolites provides new insights into the pathophysiological mechanisms underlying TNBC.

Biomarkers in the Immuno-oncology Interface of Triple Negative Breast Cancer: A Scoping Review with Perioperative Considerations

PURPOSE OF REVIEW

Identification of biomarkers for immunotherapy treatment in triple negative breast cancer remains crucial for improving outcomes and optimising regimes, particularly in the perioperative setting. There is a need to conduct a scoping review to provide an overview of current research, explore the wider context, and highlight future research considerations in this field.

RECENT FINDINGS

The most commonly assessed biomarkers are PD-L1, TILs and CD8 + cells with correlation to outcomes mainly focused on survival. There is a growing interest in evaluating genetic markers. Conclusions are currently limited by knowledge gaps around contextual factors. Important areas of focus for future research include a greater understanding of complex cellular, genetic and metabolic interactions in the perioperative tumour microenvironment, including patient-specific immune profiles. An important challenge remains elucidating the clinical significance of the immunological effects of interventions at each stage of the perioperative period, including the use of anaesthetic agents.

The Use of Patient-Derived Organoids in the Study of Molecular Metabolic Adaptation in Breast Cancer

Around 13% of women will likely develop breast cancer during their lifetime. Advances in cancer metabolism research have identified a range of metabolic reprogramming events, such as altered glucose and amino acid uptake, increased reliance on glycolysis, and interactions with the tumor microenvironment (TME), all of which present new opportunities for targeted therapies. However, studying these metabolic networks is challenging in traditional 2D cell cultures, which often fail to replicate the three-dimensional architecture and dynamic interactions of real tumors. To address this, organoid models have emerged as powerful tools. Tumor organoids are 3D cultures, often derived from patient tissue, that more accurately mimic the structural and functional properties of actual tumor tissues in vivo, offering a more realistic model for investigating cancer metabolism. This review explores the unique metabolic adaptations of breast cancer and discusses how organoid models can provide deeper insights into these processes. We evaluate the most advanced tools for studying cancer metabolism in three-dimensional culture models, including optical metabolic imaging (OMI), matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI), and recent advances in conventional techniques applied to 3D cultures. Finally, we explore the progress made in identifying and targeting potential therapeutic targets in breast cancer metabolism.

Cancer cell stiffening via CoQ10 and UBIAD1 regulates ECM signaling and ferroptosis in breast cancer

CoQ10 (Coenzyme Q10) is an essential fat-soluble metabolite that plays a key role in cellular metabolism. A less-known function of CoQ10 is whether it may act as a plasma membrane-stabilizing agent and whether this property can affect cancer development and progression. Here, we show that CoQ10 and its biosynthetic enzyme UBIAD1 play a critical role in plasmamembrane mechanical properties that are of interest for breast cancer (BC) progression and treatment. CoQ10 and UBIAD1 increase membrane fluidity leading to increased cell stiffness in BC. Furthermore, CoQ10 and UBIAD1 states impair ECM (extracellular matrix)-mediated oncogenic signaling and reduce ferroptosis resistance in BC settings. Analyses on human patients and mouse models reveal that UBIAD1 loss is associated with BC development and progression and UBIAD1 expression in BC limits CTCs (circulating tumor cells) survival and lung metastasis formation. Overall, this study reveals that CoQ10 and UBIAD1 can be further investigated to develop therapeutic interventions to treat BC patients with poor prognosis.

Targeting SNRNP200-induced splicing dysregulation offers an immunotherapy opportunity for glycolytic triple-negative breast cancer

Metabolic dysregulation is prominent in triple-negative breast cancer (TNBC), yet therapeutic strategies targeting cancer metabolism are limited. Here, utilizing multiomics data from our TNBC cohort (n = 465), we demonstrated widespread splicing deregulation and increased spliceosome abundance in the glycolytic TNBC subtype. We identified SNRNP200 as a crucial mediator of glucose-driven metabolic reprogramming. Mechanistically, glucose induces acetylation at SNRNP200 K1610, preventing its proteasomal degradation. Augmented SNRNP200 then facilitates splicing key metabolic enzyme-encoding genes (GAPDH, ALDOA, and GSS), leading to increased lactic acid and glutathione production. Targeting SNRNP200 with antisense oligonucleotide therapy impedes tumor metabolism and enhances the efficacy of anti-PD-1 therapy by activating intratumoral CD8+ T cells while suppressing regulatory T cells. Clinically, higher SNRNP200 levels indicate an inferior response to immunotherapy in glycolytic TNBCs. Overall, our study revealed the intricate interplay between RNA splicing and metabolic dysregulation, suggesting an innovative combination strategy for immunotherapy in glycolytic TNBCs.

A Dual Bispecific Hydrolysis Peptide-Drug Conjugate Responsive to Micro-Acidic and Reduction Circumstance Promotes Antitumor Efficacy in Triple-Negative Breast Cancer

Paclitaxel and its derivates are the first-line chemotherapeutic agents of breast cancer, which also showed tremendous clinical value in many other diseases including ovarian cancer, lung cancer etc. However, there are many drawbacks for almost all paclitaxel or its derivates, including extremely short half-life, poor solubility and adverse events, which significantly limits their clinical applications. In this work, we designed and constructed a bispecific hydrolysis PAP-SS-PTX (term as PDC), consisting with pro-apoptosis peptide (PAP) and paclitaxel (PTX) that were conjugated together via disulfide and ester bonds. On the one hand, PAP could improve the solubility of PTX and promote cellular uptake for drugs. On the other hand, it was able to prolong the PTX half-life. We performed series of chemo-dynamical assays and showed that PDC would release active drug molecules under micro-acidic and reduction circumstance. The further assays elucidated that PDC could interrupt DNA synthesis and arrest cell division through downregulating CDK4/6 and Histone methylation that inhibit tumor growth in vitro. What's more, it could not only inhibit 4T1 breast tumor growth, but also prolong the survival time of mice and exert antitumor efficacy in vivo. It may provide a new research idea for cancer therapies via controlled release strategy in tumor microenvironment.

Integrated landscape of plasma metabolism and proteome of patients with post-traumatic deep vein thrombosis

Deep vein thrombosis (DVT) is a leading cause of morbidity and mortality after trauma. Here, we integrate plasma metabolomics and proteomics to evaluate the metabolic alterations and their function in up to 680 individuals with and without DVT after trauma (pt-DVT). We identify 28 metabolites and 2 clinical parameter clusters associated with pt-DVT. Then, we develop a panel of 9 metabolites (hexadecanedioic acid, pyruvic acid, L-Carnitine, serotonin, PE(P-18:1(11Z)/18:2(9Z,12Z)), 3-Hydroxycapric acid, 5,6-DHET, 3-Methoxybenzenepropanoic acid and pentanenitrile) that can predict pt-DVT with high performance, which can be verified in an independent cohort. Furthermore, the integration analysis of metabolomics and proteomics data indicates that the upregulation of glycolysis/gluconeogenesis-TCA cycle may promote thrombosis by regulating ROS levels in red blood cells, suggesting that interfering with this process might be potential therapeutic strategies for pt-DVT. Together, our study comprehensively delineates the metabolic and hematological dysregulations for pt-DVT, and provides potential biomarkers for early detection.

Bioactive sphingolipids as emerging targets for signal transduction in cancer development

Sphingolipids, crucial components of cellular membranes, play a vital role in maintaining cellular structure and signaling integrity. Disruptions in sphingolipid metabolism are increasingly implicated in cancer development. Key bioactive sphingolipids, such as ceramides, sphingosine-1-phosphate (S1P), ceramide-1-phosphate (C1P), and glycosphingolipids, profoundly impact tumor biology. They influence the behavior of tumor cells, stromal cells, and immune cells, affecting tumor aggressiveness, angiogenesis, immune modulation, and extracellular matrix remodeling. Furthermore, abnormal expression of sphingolipids and their metabolizing enzymes modulates the secretion of tumor-derived extracellular vesicles (TDEs), which are key players in creating an immunosuppressive tumor microenvironment, remodeling the extracellular matrix, and facilitating oncogenic signaling within in situ tumors and distant pre-metastatic niches (PMNs). Understanding the role of sphingolipids in the biogenesis of tumor-derived extracellular vesicles (TDEs) and their bioactive contents can pave the way for new biomarkers in cancer diagnosis and prognosis, ultimately enhancing comprehensive tumor treatment strategies.

New Metabolomic Insights Into Cancer

ABSTRACT

Cancer has been marked by metabolic irregularities that fuel various aggressive activities such as rapid cell proliferation, evasion of the immune system, and spread to distant organs. Therefore, exploiting cancer metabolism for diagnosis, monitoring, or treatment has been extensively studied in the past couple of decades with various molecular and cellular techniques. More recently, investigating cancer diagnostics and treatments through advanced metabolomics has emerged, and these comprehensive approaches provide a holistic understanding of cancer metabolism, which supported the discovery of metabolic targets relevant across multiple cancer types and the development of more effective treatments. This study offers highlights of new knowledge on cancer metabolism enabled by recent metabolomics studies and their potential applications in aiding cancer research and predicting cancer treatment outcomes. Specifically, we discussed the use of advanced metabolomics in cancer metabolism, tumor microenvironment, and cancer immunotherapy studies to provide valuable insights that can shape future research efforts in the dynamic field of cancer metabolism research.

Docetaxel-Encapsulated Catalytic Pt/Au Nanotubes for Synergistic Chemo-Photothermal Therapy of Triple-Negative Breast Cancer

The combination of photothermal therapy with chemotherapy has emerged as a promising therapeutic modality for addressing triple-negative breast cancer (TNBC). This manuscript describes a novel hybrid nanoplatform comprising ultrathin catalytic platinum/gold (Pt/Au) nanotubes encapsulated with docetaxel and phase-change materials (PCMs) for the photoacoustic imaging-guided synergistic chemo-photothermal therapy of TNBC. Upon irradiation of near-infrared laser, the photothermal heating of nanotubes converts solid-state PCM into liquid, triggering the controlled release of the encapsulated docetaxel. The thin Pt layer within nanotubes enhances the nanotube's thermal stability, thus prolonging the photothermal ablation of tumors. Furthermore, platinum effectively mitigates tumor hypoxia by catalyzing the decomposition of hydrogen peroxides to generate oxygen in the tumor microenvironment, thus improving the efficiency of chemotherapy. It is demonstrated that the drug-loaded nanotubes achieve significant tumor inhibition rates of 75.4% in vivo on 4T1 tumor-bearing mice, significantly surpassing control groups. These nanotubes also notably extend survival, attributable to the synergistic effects of prolonged photothermal therapy facilitated by platinum and oxygenation-enhanced chemotherapy. This combination leverages the unique properties of the Pt/Au NTs-DTX/PCM nanoplatform, optimizing therapeutic outcomes. It is envisioned that this nanoplatform may find important applications in managing superficial malignant solid tumors in general.

Application and prospect of organoid technology in breast cancer

Breast cancer is the most common malignant tumor in women. Due to the high heterogeneity of breast cancer cells, traditional in vitro research models still have major limitations. Therefore, it is urgent to establish an experimental model that can accurately simulate the characteristics of human breast cancer. Breast cancer organoid technology emerged as the times required, that is, to construct tissue analogs with organ characteristics by using a patient's tumor tissue through 3D culture in vitro. Since the breast cancer organoid can fully preserve the histology and genetic characteristics of the original tumor, it provides a reliable model for preclinical drug screening, establishment of breast cancer organoid biobanks, research into the mechanisms of tumor development, and determination of cancer targets. It has promoted personalized treatment for clinical breast cancer patients. This article mainly focuses on recent research progress and applications of organoid technology in breast cancer, discussing the current limitations and prospects of breast cancer organoid technology.

Multi-omics and chemical profiling approaches to understand the material foundation and pharmacological mechanism of sophorae tonkinensis radix et rhizome-induced liver injury in mice

ETHNOPHARMACOLOGICAL RELEVANCE

Sophorae tonkinensis Radix et Rhizoma (STR) is an extensively applied traditional Chinese medicine (TCM) in southwest China. However, its clinical application is relatively limited due to its hepatotoxicity effects.

AIM OF THE STUDY

To understand the material foundation and liver injury mechanism of STR.

MATERIALS AND METHODS

Chemical compositions in STR and its prototypes in mice were profiled by ultra-performance liquid chromatography coupled quadrupole-time of flight mass spectrometry (UPLC-Q/TOF MS). STR-induced liver injury (SILI) was comprehensively evaluated by STR-treated mice mode. The histopathologic and biochemical analyses were performed to evaluate liver injury levels. Subsequently, network pharmacology and multi-omics were used to analyze the potential mechanism of SILI in vivo. And the target genes were further verified by Western blot.

RESULTS

A total of 152 compounds were identified or tentatively characterized in STR, including 29 alkaloids, 21 organic acids, 75 flavonoids, 1 quinone, and 26 other types. Among them, 19 components were presented in STR-medicated serum. The histopathologic and biochemical analysis revealed that hepatic injury occurred after 4 weeks of intragastric administration of STR. Network pharmacology analysis revealed that IL6, TNF, STAT3, etc. were the main core targets, and the bile secretion might play a key role in SILI. The metabolic pathways such as taurine and hypotaurine metabolism, purine metabolism, and vitamin B6 metabolism were identified in the STR exposed groups. Among them, taurine, hypotaurine, hypoxanthine, pyridoxal, and 4-pyridoxate were selected based on their high impact value and potential biological function in the process of liver injury post STR treatment.

CONCLUSIONS

The mechanism and material foundation of SILI were revealed and profiled by a multi-omics strategy combined with network pharmacology and chemical profiling. Meanwhile, new insights were taken into understand the pathological mechanism of SILI.

Causal role of blood metabolites in HER-positive and HER-negative breast cancer: a Mendelian randomization (MR) study

BACKGROUND

Previous studies provide evidence that in vivo metabolites are associated with breast cancer (BC). However, the causal relationship between blood metabolites and BC remains unclear.

METHOD

Comprehensive two-sample Mendelian randomization analysis was conducted to determine the causal association between 1400 publicly available genetic data on metabolic factors and human epidermal growth factor receptor positive (HER+) BC or HER- BC in this study.

RESULT

Epiandrosterone sulfate levels (OR = 1.07, 95% CI = 1.02 ~ 1.10, p = 0.0013), 5alpha-androstan-3beta,17beta-diol monosulfate (2) levels (OR = 1.07, 95% CI = 1.03 ~ 1.12, p = 0.0012), glycohyocholate levels (OR = 0.85, 95% CI = 0.77 ~ 0.93, p = 0.0007) and etiocholanolone glucuronide levels (OR = 1.12, 95% CI = 1.05 ~ 1.20, p = 0.0013) were causally correlated with HER+ BC. 5 metabolites were causally correlated with HER- BC: Vanillic acid glycine levels (OR = 1.14, 95% CI = 1.06 ~ 1.22, p = 0.0003), Thyroxine levels (OR = 1.26, 95% CI = 1.11 ~ 1.44, p = 0.0004), 1-palmitoyl-2-linoleoyl-GPI (16:0/18:2) levels (OR = 0.86, 95% CI = 0.79 ~ 0.94, p = 0.0010), N-acetylphenylalanine levels (OR = 1.12, 95% CI = 1.05 ~ 1.19, p = 0.0007) and Glucose-to-mannose ratio (OR = 1.15, 95% CI = 1.06 ~ 1.24, p = 0.0008). Two common causally related metabolites were identified: Gamma-glutamyl glutamate and X-12849 levels.

CONCLUSIONS

Our study has respectively demonstrated the connection between blood metabolites and HER+ or HER- BC by genetic means, thereby offering opportunities for therapeutic targets.

From Lipid Signatures to Cellular Responses: Unraveling the Complexity of Melanoma and Furthering Its Diagnosis and Treatment

Recent advancements in mass spectrometry have significantly enhanced our understanding of complex lipid profiles, opening new avenues for oncological diagnostics. This review highlights the importance of lipidomics in the comprehension of certain metabolic pathways and its potential for the detection and characterization of various cancers, in particular melanoma. Through detailed case studies, we demonstrate how lipidomic analysis has led to significant breakthroughs in the identification and understanding of cancer types and its potential for detecting unique biomarkers that are instrumental in its diagnosis. Additionally, this review addresses the technical challenges and future perspectives of these methodologies, including their potential expansion and refinement for clinical applications. The discussion underscores the critical role of lipidomic profiling in advancing cancer diagnostics, proposing a new paradigm in how we approach this devastating disease, with particular emphasis on its application in comparative oncology.

A Multiomics, Molecular Atlas of Breast Cancer Survivors

Breast cancer imposes a significant burden globally. While the survival rate is steadily improving, much remains to be elucidated. This observational, single time point, multiomic study utilizing genomics, proteomics, targeted and untargeted metabolomics, and metagenomics in a breast cancer survivor (BCS) and age-matched healthy control cohort (N = 100) provides deep molecular phenotyping of breast cancer survivors. In this study, the BCS cohort had significantly higher polygenic risk scores for breast cancer than the control group. Carnitine and hexanoyl carnitine were significantly different. Several bile acid and fatty acid metabolites were significantly dissimilar, most notably the Omega-3 Index (O3I) (significantly lower in BCS). Proteomic and metagenomic analyses identified group and pathway differences, which warrant further investigation. The database built from this study contributes a wealth of data on breast cancer survivorship where there has been a paucity, affording the ability to identify patterns and novel insights that can drive new hypotheses and inform future research. Expansion of this database in the treatment-naïve, newly diagnosed, controlling for treatment confounders, and through the disease progression, can be leveraged to profile and contextualize breast cancer and breast cancer survivorship, potentially leading to the development of new strategies to combat this disease and improve the quality of life for its victims.

Metabolomics in Radiotherapy-Induced Early Adverse Skin Reactions of Breast Cancer Patients

Background

Early adverse skin reactions (EASRs) are common side effects of radiotherapy (RT) that impact the quality of life of breast cancer patients. This study used global metabolomics profiles of breast cancer populations to identify metabolic pathways and biomarkers significantly associated with RT-induced EASRs to identify potential targets for precision interventions.

Methods

We used a frequency-matched study design to identify pre-RT urine samples from 60 female breast cancer patients (30 with high and 30 with low EASRs) for metabolomic analysis by Metabolon Inc. using UPLC-MS/MS and GC-MS. Using MetaboAnalyst, we performed metabolomic data analysis and visualization on 84 candidate metabolites from 478 total compounds. We used the Oncology Nursing Society (ONS) Skin Toxicity Criteria (0-6) for EASRs assessment.

Results

Seven metabolic pathways were significantly associated with RT-induced EASRs, including alanine, aspartate, and glutamate metabolism (p = 0.0028), caffeine metabolism (p = 0.0360), pentose and glucuronate interconversions (p = 0.0028), glycine, serine, and threonine metabolism (p = 0.0360), beta-alanine metabolism (p = 0.0210), pantothenate and CoA biosynthesis (p = 0.0028), and glutathione metabolism (p = 0.0490). The alanine, aspartate, and glutamate metabolic pathway had the lowest false discovery rate (FDR)-adjusted p-value and the highest impact value of 0.60. Thirteen metabolite biomarkers were significantly associated with RT-induced EASRs.

Conclusion

Our data show that the alanine, aspartate, and glutamate metabolism pathways had the highest impact value on RT-induced EASRs. Future larger studies are warranted to validate our findings and facilitate targeted interventions for preventing or mitigating RT-induced EASRs, offering a promising direction for further research and clinical applications.

Plasma Metabolome Signatures to Predict Responsiveness to Neoadjuvant Chemotherapy in Breast Cancer

BACKGROUND

Neoadjuvant chemotherapy (NACT) has arisen as a treatment option for breast cancer (BC). However, the response to NACT is still unpredictable and dependent on cancer subtype. Metabolomics is a tool for predicting biomarkers and chemotherapy response. We used plasma to verify metabolomic alterations in BC before NACT, relating to clinical data.

METHODS

Liquid chromatography coupled to mass spectrometry (LC-MS) was performed on pre-NACT plasma from patients with BC (n = 75). After data filtering, an SVM model for classification was built and validated with 75%/25% of the data, respectively.

RESULTS

The model composed of 19 identified metabolites effectively predicted NACT response for training/validation sets with high sensitivity (95.4%/93.3%), specificity (91.6%/100.0%), and accuracy (94.6%/94.7%). In both sets, the panel correctly classified 95% of resistant and 94% of sensitive females. Most compounds identified by the model were lipids and amino acids and revealed pathway alterations related to chemoresistance.

CONCLUSION

We developed a model for predicting patient response to NACT. These metabolite panels allow clinical gain by building precision medicine strategies based on tumor stratification.

Metabolite correlation permutation after mice acute exposure to PM2.5: Holistic exploration of toxicometabolomics by network analysis

Many environmental toxicants can cause systemic effects, such as fine particulate matter (PM2.5), which can penetrate the respiratory barrier and induce effects in multiple tissues. Although metabolomics has been used to identify biomarkers for PM2.5, its multi-tissue toxicology has not yet been explored holistically. Our objective is to explore PM2.5 induced metabolic alterations and unveil the intra-tissue responses along with inter-tissue communicational effects. In this study, following a single intratracheal instillation of multiple doses (0, 25, and 150 μg as the control, low, and high dose), non-targeted metabolomics was employed to evaluate the metabolic impact of PM2.5 across multiple tissues. PM2.5 induced tissue-specific and dose-dependent disturbances of metabolites and their pathways. The remarkable increase of both intra- and inter-tissue correlations was observed, with emphasis on the metabolism connectivity among lung, spleen, and heart; the tissues' functional specificity has marked their toxic modes. Beyond the inter-status comparison of the metabolite fold-changes, the current correlation network built on intra-status can offer additional insights into how the multiple tissues and their metabolites coordinately change in response to external stimuli such as PM2.5 exposure.

Deciphering metabolic crosstalk in context: lessons from inflammatory diseases

Metabolism plays a crucial role in regulating the function of immune cells in both health and disease, with altered metabolism contributing to the pathogenesis of cancer and many inflammatory diseases. The local microenvironment has a profound impact on the metabolism of immune cells. Therefore, immunological and metabolic heterogeneity as well as the spatial organization of cells in tissues should be taken into account when studying immunometabolism. Here, we highlight challenges of investigating metabolic communication. Additionally, we review the capabilities and limitations of current technologies for studying metabolism in inflamed microenvironments, including single-cell omics techniques, flow cytometry-based methods (Met-Flow, single-cell energetic metabolism by profiling translation inhibition (SCENITH)), cytometry by time of flight (CyTOF), cellular indexing of transcriptomes and epitopes by sequencing (CITE-Seq), and mass spectrometry imaging. Considering the importance of metabolism in regulating immune cells in diseased states, we also discuss the applications of metabolomics in clinical research, as well as some hurdles to overcome to implement these techniques in standard clinical practice. Finally, we provide a flowchart to assist scientists in designing effective strategies to unravel immunometabolism in disease-relevant contexts.

Comprehensive profiling of lipid metabolic reprogramming expands precision medicine for HCC

BACKGROUND AND AIMS

Liver HCC is the second leading cause of cancer-related deaths worldwide. The heterogeneity of this malignancy is driven by a wide range of genetic alterations, leading to a lack of effective therapeutic options. In this study, we conducted a systematic multi-omics characterization of HCC to uncover its metabolic reprogramming signature.

APPROACH AND RESULTS

Through a comprehensive analysis incorporating transcriptomic, metabolomic, and lipidomic investigations, we identified significant changes in metabolic pathways related to glucose flux, lipid oxidation and degradation, and de novo lipogenesis in HCC. The lipidomic analysis revealed abnormal alterations in glycerol-lipids, phosphatidylcholine, and sphingolipid derivatives. Machine-learning techniques identified a panel of genes associated with lipid metabolism as common biomarkers for HCC across different etiologies. Our findings suggest that targeting phosphatidylcholine with saturated fatty acids and long-chain sphingolipid biosynthesis pathways, particularly by inhibiting lysophosphatidylcholine acyltransferase 1 ( LPCAT1 ) and ceramide synthase 5 ( CERS5 ) as potential therapeutic strategies for HCC in vivo and in vitro. Notably, our data revealed an oncogenic role of CERS5 in promoting tumor progression through lipophagy.

CONCLUSIONS

In conclusion, our study elucidates the metabolic reprogramming nature of lipid metabolism in HCC, identifies prognostic markers and therapeutic targets, and highlights potential metabolism-related targets for therapeutic intervention in HCC.

Metabolome-wide association identifies altered metabolites and metabolic pathways in the serum of patients with cholangiocarcinoma

Background & Aims

Metabolomic and lipidomic analyses provide an opportunity for novel biological insights. Cholangiocarcinoma (CCA) remains a highly lethal cancer with limited response to systemic, targeted, and immunotherapeutic approaches. Using a global metabolomics and lipidomics platform, this study aimed to discover and characterize metabolomic variations and associated pathway derangements in patients with CCA.

Methods

Leveraging a biospecimen collection, including samples from patients with digestive diseases and normal controls, global serum metabolomic and lipidomic profiling was performed on 213 patients with CCA and 98 healthy controls. The CCA cohort of patients included representation of intrahepatic, perihilar, and distal CCA tumours. Metabolome-wide association studies utilizing multivariable linear regression were used to perform case-control comparisons, followed by pathway enrichment analysis, CCA subtype analysis, and disease stage analysis. The impact of biliary obstruction was evaluated by repeating analyses in subsets of patients only with normal bilirubin levels.

Results

Of the 420 metabolites that discriminated patients with CCA from controls, decreased abundance of cysteine-glutathione disulfide was most closely associated with CCA. Additional conjugated bile acid species were found in increased abundance even in the absence of clinically relevant biliary obstruction denoted by elevated serum bilirubin levels. Pathway enrichment analysis also revealed alterations in caffeine metabolism and mitochondrial redox-associated pathways in the serum of patients with CCA.

Conclusions

The presented metabolomic and lipidomic profiling demonstrated multiple alterations in the serum of patients with CCA. These exploratory data highlight novel metabolic pathways in CCA and support future work in therapeutic targeting of these pathways and the development of a precision biomarker panel for diagnosis.

Impact and implications

Cholangiocarcinoma (CCA) is a highly lethal hepatobiliary cancer with limited treatment response, highlighting the need for a better understanding of the disease biology. Using a global metabolomics and lipidomics platform, we characterized distinct changes in the serum of 213 patients with CCA compared with healthy controls. The results of this study elucidate novel metabolic pathways in CCA. These findings benefit stakeholders in both the clinical and research realms by providing a foundation for improved disease diagnostics and identifying novel targets for therapeutic design.

Metabolic landscape of head and neck squamous cell carcinoma informs a novel kynurenine/Siglec-15 axis in immune escape

BACKGROUND

Metabolic reprograming and immune escape are two hallmarks of cancer. However, how metabolic disorders drive immune escape in head and neck squamous cell carcinoma (HNSCC) remains unclear. Therefore, the aim of the present study was to investigate the metabolic landscape of HNSCC and its mechanism of driving immune escape.

METHODS

Analysis of paired tumor tissues and adjacent normal tissues from 69 HNSCC patients was performed using liquid/gas chromatography-mass spectrometry and RNA-sequencing. The tumor-promoting function of kynurenine (Kyn) was explored in vitro and in vivo. The downstream target of Kyn was investigated in CD8+ T cells. The regulation of CD8+ T cells was investigated after Siglec-15 overexpression in vivo. An engineering nanoparticle was established to deliver Siglec-15 small interfering RNA (siS15), and its association with immunotherapy response were investigated. The association between Siglec-15 and CD8+ programmed cell death 1 (PD-1)+ T cells was analyzed in a HNSCC patient cohort.

RESULTS

A total of 178 metabolites showed significant dysregulation in HNSCC, including carbohydrates, lipids and lipid-like molecules, and amino acids. Among these, amino acid metabolism was the most significantly altered, especially Kyn, which promoted tumor proliferation and metastasis. In addition, most immune checkpoint molecules were upregulated in Kyn-high patients based on RNA-sequencing. Furthermore, tumor-derived Kyn was transferred into CD8+ T cells and induced T cell functional exhaustion, and blocking Kyn transporters restored its killing activity. Accroding to the results, mechanistically, Kyn transcriptionally regulated the expression of Siglec-15 via aryl hydrocarbon receptor (AhR), and overexpression of Siglec-15 promoted immune escape by suppressing T cell infiltration and activation. Targeting AhR in vivo reduced Kyn-mediated Siglec-15 expression and promoted intratumoral CD8+ T cell infiltration and killing capacity. Finally, a NH2-modified mesoporous silica nanoparticle was designed to deliver siS15, which restored CD8+ T cell function status and enhanced anti-PD-1 efficacy in tumor-bearing immunocompetent mice. Clinically, Siglec-15 was positively correlated with AhR expression and CD8+PD-1+ T cell infiltration in HNSCC tissues.

CONCLUSIONS

The findings describe the metabolic landscape of HNSCC comprehensively and reveal that the Kyn/Siglec-15 axis may be a novel potential immunometabolism mechanism, providing a promising therapeutic strategy for cancers.

FBXO22 is a potential therapeutic target for recurrent chondrosarcoma

Chondrosarcoma (CHS) is a malignant bone tumor with insensitivity to both radiotherapy and chemotherapy, and a high recurrence rate. However, the latent mechanism of recurrent CHS (Re-CHS) remains elusive. Here, we discovered that FBXO22 was highly expressed in clinical samples of Re-CHS. FBXO22 played a significant role in various cancers. However, the role of FBXO22 in Re-CHS remained unclear. Our research demonstrated that suppressing FBXO22 abated the proliferation and migration of CHS cells and facilitated their apoptosis. In addition, suppressing FBXO22 raised the expression of PD-L1 in Re-CHS. All these findings provide new evidence for using FBXO22 and PD-L1 as combined targets to prevent and treat Re-CHS, which may prove to be a novel strategy for immunotherapy of CHS, especially Re-CHS.

A review of big data technology and its application in cancer care

The development of modern medical devices and information technology has led to a rapid growth in the amount of data available for health protection information, with the concept of medical big data emerging globally, along with significant advances in cancer care relying on data-driven approaches. However, outstanding issues such as fragmented data governance, low-quality data specification, and data lock-in still make sharing challenging. Big data technology provides solutions for managing massive heterogeneous data while combining artificial intelligence (AI) techniques such as machine learning (ML) and deep learning (DL) to better mine the intrinsic connections between data. This paper surveys and organizes recent articles on big data technology and its applications in cancer, dividing them into three different types to outline their primary content and summarize their critical role in assisting cancer care. It then examines the latest research directions in big data technology in cancer and evaluates the current state of development of each type of application. Finally, current challenges and opportunities are discussed, and recommendations are made for the further integration of big data technology into the medical industry in the future.

Epigenetic modulations in triple-negative breast cancer: Therapeutic implications for tumor microenvironment

Triple-negative breast cancer (TNBC) is an aggressive subtype lacking estrogen receptors, progesterone receptors and lacks HER2 overexpression. This absence of critical molecular targets poses significant challenges for conventional therapies. Immunotherapy, remarkably immune checkpoint blockade, offers promise for TNBC treatment, but its efficacy remains limited. Epigenetic dysregulation, including altered DNA methylation, histone modifications, and imbalances in regulators such as BET proteins, plays a crucial role in TNBC development and resistance to treatment. Hypermethylation of tumor suppressor gene promoters and the imbalance of histone methyltransferases such as EZH2 and histone deacetylases (HDACs) profoundly influence tumor cell proliferation, survival, and metastasis. In addition, epigenetic alterations critically shape the tumor microenvironment (TME), including immune cell composition, cytokine signaling, and immune checkpoint expression, ultimately contributing to immune evasion. Targeting these epigenetic mechanisms with specific inhibitors such as EZH2 and HDAC inhibitors in combination with immunotherapy represents a compelling strategy to remodel the TME, potentially overcoming immune evasion and enhancing therapeutic outcomes in TNBC. This review aims to comprehensively elucidate the current understanding of epigenetic modulation in TNBC, its influence on the TME, and the potential of combining epigenetic therapies with immunotherapy to overcome the challenges posed by this aggressive breast cancer subtype.

Building consensus on the application of organoid-based drug sensitivity testing in cancer precision medicine and drug development

Patient-derived organoids (PDOs) have emerged as a promising platform for clinical and translational studies. A strong correlation exists between clinical outcomes and the use of PDOs to predict the efficacy of chemotherapy and/or radiotherapy. To standardize interpretation and enhance scientific communication in the field of cancer precision medicine, we revisit the concept of PDO-based drug sensitivity testing (DST). We present an expert consensus-driven approach for medication selection aimed at predicting patient responses. To further standardize PDO-based DST, we propose guidelines for clarification and characterization. Additionally, we identify several major challenges in clinical prediction when utilizing PDOs.

The colonization of Penicillium oxalicum SL2 on rice root surface increased Pb interception capacity of iron plaque and decreased Pb uptake by roots

The exploration of microbial resources to reduce Pb accumulation in rice attracted great attention. In this study, we found Penicillium oxalicum SL2, a Pb-tolerant strain with good capability of dissolving phosphorus and stabilizing Pb in soil, was able to colonize on the root surface of rice seedlings without additional carbon sources, and promoted the secretion of metabolites related to amino acid metabolism, organic acid metabolism, signal transduction and other pathways in rhizosphere exudates, in which the secretion of oxalate increased by 47.7 %. However, P. oxalicum SL2 increased Fe(II) proportion and Fe availability on the root surface, resulting in iron plaque content decrease. Moreover, by converting root surface Pb from Pb-Fe state to PbC2O4 and Pb-P compounds, P. oxalicum SL2 increased Pb intercept capacity of iron plaque by 118.0 %. Furthermore, P. oxalicum SL2 regulated element distribution on the root surface, and reduced the relative content of Pb on the maturation zone of root tip, which was conducive to reducing Pb uptake by apoplastic pathway and the risk of Pb accumulation in root system. Our findings further revealed the interaction between P. oxalicum SL2 and rice root, providing a theoretical basis for the development and application of microbial agents in Pb-contaminated farmland.

Metabolomics at the tumor microenvironment interface: Decoding cellular conversations

Cancer heterogeneity remains a significant challenge for effective cancer treatments. Altered energetics is one of the hallmarks of cancer and influences tumor growth and drug resistance. Studies have shown that heterogeneity exists within the metabolic profile of tumors, and personalized-combination therapy with relevant metabolic interventions could improve patient response. Metabolomic studies are identifying novel biomarkers and therapeutic targets that have improved treatment response. The spatial location of elements in the tumor microenvironment are becoming increasingly important for understanding disease progression. The evolution of spatial metabolomics analysis now allows scientists to deeply understand how metabolite distribution contributes to cancer biology. Recently, these techniques have spatially resolved metabolite distribution to a subcellular level. It has been proposed that metabolite mapping could improve patient outcomes by improving precision medicine, enabling earlier diagnosis and intraoperatively identifying tumor margins. This review will discuss how altered metabolic pathways contribute to cancer progression and drug resistance and will explore the current capabilities of spatial metabolomics technologies and how these could be integrated into clinical practice to improve patient outcomes.

Multi-omics staging of locally advanced rectal cancer predicts treatment response: a pilot study

Treatment response assessment of rectal cancer patients is a critical component of personalized cancer care and it allows to identify suitable candidates for organ-preserving strategies. This pilot study employed a novel multi-omics approach combining MRI-based radiomic features and untargeted metabolomics to infer treatment response at staging. The metabolic signature highlighted how tumor cell viability is predictively down-regulated, while the response to oxidative stress was up-regulated in responder patients, showing significantly reduced oxoproline values at baseline compared to non-responder patients (p-value < 10-4). Tumors with a high degree of texture homogeneity, as assessed by radiomics, were more likely to achieve a major pathological response (p-value < 10-3). A machine learning classifier was implemented to summarize the multi-omics information and discriminate responders and non-responders. Combining all available radiomic and metabolomic features, the classifier delivered an AUC of 0.864 (± 0.083, p-value < 10-3) with a best-point sensitivity of 90.9% and a specificity of 81.8%. Our results suggest that a multi-omics approach, integrating radiomics and metabolomic data, can enhance the predictive value of standard MRI and could help to avoid unnecessary surgical treatments and their associated long-term complications.

Integrating Machine Learning in Metabolomics: A Path to Enhanced Diagnostics and Data Interpretation

Metabolomics, leveraging techniques like NMR and MS, is crucial for understanding biochemical processes in pathophysiological states. This field, however, faces challenges in metabolite sensitivity, data complexity, and omics data integration. Recent machine learning advancements have enhanced data analysis and disease classification in metabolomics. This study explores machine learning integration with metabolomics to improve metabolite identification, data efficiency, and diagnostic methods. Using deep learning and traditional machine learning, it presents advancements in metabolic data analysis, including novel algorithms for accurate peak identification, robust disease classification from metabolic profiles, and improved metabolite annotation. It also highlights multiomics integration, demonstrating machine learning's potential in elucidating biological phenomena and advancing disease diagnostics. This work contributes significantly to metabolomics by merging it with machine learning, offering innovative solutions to analytical challenges and setting new standards for omics data analysis.

NOX4 promotes tumor progression through the MAPK-MEK1/2-ERK1/2 axis in colorectal cancer

BACKGROUND

Metabolic reprogramming plays a key role in cancer progression and clinical outcomes; however, the patterns and primary regulators of metabolic reprogramming in colorectal cancer (CRC) are not well understood.

AIM

To explore the role of nicotinamide adenine dinucleotide phosphate oxidase 4 (NOX4) in promoting progression of CRC.

METHODS

We evaluated the expression and function of dysregulated and survival-related metabolic genes using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes. Consensus clustering was used to cluster CRC based on dysregulated metabolic genes. A prediction model was constructed based on survival-related metabolic genes. Sphere formation, migration, invasion, proliferation, apoptosis and clone formation was used to evaluate the biological function of NOX4 in CRC. mRNA sequencing was utilized to explore the alterations of gene expression NOX4 over-expression tumor cells. In vivo subcutaneous and lung metastasis mouse tumor model was used to explore the effect of NOX4 on tumor growth.

RESULTS

We comprehensively analyzed 3341 metabolic genes in CRC and identified three clusters based on dysregulated metabolic genes. Among these genes, NOX4 was highly expressed in tumor tissues and correlated with worse survival. In vitro, NOX4 overexpression induced clone formation, migration, invasion, and stemness in CRC cells. Furthermore, RNA-sequencing analysis revealed that NOX4 overexpression activated the mitogen-activated protein kinase-MEK1/2-ERK1/2 signaling pathway. Trametinib, a MEK1/2 inhibitor, abolished the NOX4-mediated tumor progression. In vivo, NOX4 overexpression promoted subcutaneous tumor growth and lung metastasis, whereas trametinib treatment can reversed the metastasis.

CONCLUSION

Our study comprehensively analyzed metabolic gene expression and highlighted the importance of NOX4 in promoting CRC metastasis, suggesting that trametinib could be a potential therapeutic drugs of CRC clinical therapy targeting NOX4.

Calcification-associated molecular traits and therapeutic strategies in hormone receptor-positive HER2-negative breast cancer

OBJECTIVE

Mammographic calcifications are a common feature of breast cancer, but their molecular characteristics and treatment implications in hormone receptor-positive (HR+)/human epidermal growth factor receptor 2-negative (HER2-) breast cancer remain unclear.

METHODS

We retrospectively collected mammography records of an HR+/HER2- breast cancer cohort (n = 316) with matched clinicopathological, genomic, transcriptomic, and metabolomic data. On the basis of mammographic images, we grouped tumors by calcification status into calcification-negative tumors, tumors with probably benign calcifications, tumors with calcification of low-moderate suspicion for maligancy and tumors with calcification of high suspicion for maligancy. We then explored the molecular characteristics associated with each calcification status across multiple dimensions.

RESULTS

Among the different statuses, tumors with probably benign calcifications exhibited elevated hormone receptor immunohistochemical staining scores, estrogen receptor (ER) pathway activation, lipid metabolism, and sensitivity to endocrine therapy. Tumors with calcifications of high suspicion for malignancy had relatively larger tumor sizes, elevated lymph node metastasis incidence, Ki-67 staining scores, genomic instability, cell cycle pathway activation, and may benefit from cyclin-dependent kinase 4 and 6 (CDK4/6) inhibitors.

CONCLUSIONS

Our research established links between tumor calcifications and molecular features, thus proposing potential precision treatment strategies for HR+/HER2- breast cancer.

Genetic interactions reveal distinct biological and therapeutic implications in breast cancer

Co-occurrence and mutual exclusivity of genomic alterations may reflect the existence of genetic interactions, potentially shaping distinct biological phenotypes and impacting therapeutic response in breast cancer. However, our understanding of them remains limited. Herein, we investigate a large-scale multi-omics cohort (n = 873) and a real-world clinical sequencing cohort (n = 4,405) including several clinical trials with detailed treatment outcomes and perform functional validation in patient-derived organoids, tumor fragments, and in vivo models. Through this comprehensive approach, we construct a network comprising co-alterations and mutually exclusive events and characterize their therapeutic potential and underlying biological basis. Notably, we identify associations between TP53mut-AURKAamp and endocrine therapy resistance, germline BRCA1mut-MYCamp and improved sensitivity to PARP inhibitors, and TP53mut-MYBamp and immunotherapy resistance. Furthermore, we reveal that precision treatment strategies informed by co-alterations hold promise to improve patient outcomes. Our study highlights the significance of genetic interactions in guiding genome-informed treatment decisions beyond single driver alterations.

Metabolic Heterogeneity and Potential Immunotherapeutic Responses Revealed by Single-Cell Transcriptomics of Breast Cancer

BACKGROUND

Breast cancer (BC) exhibits remarkable heterogeneity. However, the transcriptomic heterogeneity of BC at the single-cell level has not been fully elucidated.

METHODS

We acquired BC samples from 14 patients. Single-cell RNA sequencing (scRNA-seq), bioinformatic analyses, along with immunohistochemistry (IHC) and immunofluorescence (IF) assays were carried out.

RESULTS

According to the scRNA-seq results, 10 different cell types were identified. We found that Cancer-Associated Fibroblasts (CAFs) exhibited distinct biological functions and may promote resistance to therapy. Metabolic analysis of tumor cells revealed heterogeneity in glycolysis, gluconeogenesis, and fatty acid synthetase reprogramming, which led to chemotherapy resistance. Furthermore, patients with multiple metastases and progression were predicted to benefit from immunotherapy based on a heterogeneity analysis of T cells and tumor cells.

CONCLUSIONS

Our findings provide a comprehensive understanding of the heterogeneity of BC, provide comprehensive insight into the correlation between cancer metabolism and chemotherapy resistance, and enable the prediction of immunotherapy responses based on T-cell heterogeneity.

Identification and validation of serum metabolite biomarkers for endometrial cancer diagnosis

Endometrial cancer (EC) stands as the most prevalent gynecological tumor in women worldwide. Notably, differentiation diagnosis of abnormity detected by ultrasound findings (e.g., thickened endometrium or mass in the uterine cavity) is essential and remains challenging in clinical practice. Herein, we identified a metabolic biomarker panel for differentiation diagnosis of EC using machine learning of high-performance serum metabolic fingerprints (SMFs) and validated the biological function. We first recorded the high-performance SMFs of 191 EC and 204 Non-EC subjects via particle-enhanced laser desorption/ionization mass spectrometry (PELDI-MS). Then, we achieved an area-under-the-curve (AUC) of 0.957-0.968 for EC diagnosis through machine learning of high-performance SMFs, outperforming the clinical biomarker of cancer antigen 125 (CA-125, AUC of 0.610-0.684, p < 0.05). Finally, we identified a metabolic biomarker panel of glutamine, glucose, and cholesterol linoleate with an AUC of 0.901-0.902 and validated the biological function in vitro. Therefore, our work would facilitate the development of novel diagnostic biomarkers for EC in clinics.

Integrated multiomic profiling of breast cancer in the Chinese population reveals patient stratification and therapeutic vulnerabilities

Molecular profiling guides precision treatment of breast cancer; however, Asian patients are underrepresented in publicly available large-scale studies. We established a comprehensive multiomics cohort of 773 Chinese patients with breast cancer and systematically analyzed their genomic, transcriptomic, proteomic, metabolomic, radiomic and digital pathology characteristics. Here we show that compared to breast cancers in white individuals, Asian individuals had more targetable AKT1 mutations. Integrated analysis revealed a higher proportion of HER2-enriched subtype and correspondingly more frequent ERBB2 amplification and higher HER2 protein abundance in the Chinese HR+HER2+ cohort, stressing anti-HER2 therapy for these individuals. Furthermore, comprehensive metabolomic and proteomic analyses revealed ferroptosis as a potential therapeutic target for basal-like tumors. The integration of clinical, transcriptomic, metabolomic, radiomic and pathological features allowed for efficient stratification of patients into groups with varying recurrence risks. Our study provides a public resource and new insights into the biology and ancestry specificity of breast cancer in the Asian population, offering potential for further precision treatment approaches.

High Intensity Focused Ultrasound-Driven Nanomotor for Effective Ferroptosis-Immunotherapy of TNBC

The heterogeneity of triple-negative breast cancers (TNBC) remains challenging for various treatments. Ferroptosis, a recently identified form of cell death resulting from the unrestrained peroxidation of phospholipids, represents a potential vulnerability in TNBC. In this study, a high intensity focused ultrasound (HIFU)-driven nanomotor is developed for effective therapy of TNBC through induction of ferroptosis. Through bioinformatics analysis of typical ferroptosis-associated genes in the FUSCCTNBC dataset, gambogic acid is identified as a promising ferroptosis drug and loaded it into the nanomotor. It is found that the rapid motion of nanomotors propelled by HIFU significantly enhanced tumor accumulation and penetration. More importantly, HIFU not only actuated nanomotors to trigger effective ferroptosis of TNBC cells, but also drove nanomotors to activate ferroptosis-mediated antitumor immunity in primary and metastatic TNBC models, resulting in effective tumor regression and prevention of metastases. Overall, HIFU-driven nanomotors show great potential for ferroptosis-immunotherapy of TNBC.