Metabolomics, metabolic flux analysis and cancer pharmacology

A novel in-depth "static- dynamic" lipidomics workflow to reveal lipids reprogramming in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is a leading cause of cancer-related deaths globally, and with current treatments proving less effective, there is an urgent need for specific biomarkers and therapeutic targets. Lipid metabolism reprogramming is a crucial cancer hallmark, yet comprehensive studies on lipid metabolic fluxes remain limited. In this study, combined with non-targeted lipidomics, a comprehensive workflow for stable isotope tracing lipidomics was established to analyze changes in lipid levels of HepG2 cells and LO2 cells from both static and dynamic perspectives. Through the screening of differential metabolites and the enrichment analysis of lipid metabolic pathways, the most significant differential metabolic pathways were found. Finally, the TCGA and CPTAC databases were utilized to analyze the gene expression levels and protein expression levels of pivotal enzymes in the differential metabolic pathways, and these findings were verified by Western Blotting experiments. The results demonstrated that the lipid metabolism of HCC was disordered, and the metabolic pathways that caused lipid changes in HCC were mainly glycerophospholipid metabolism and sphingolipid signaling pathway. LPCAT1 and SMPD1 played a crucial role in the reprogramming of lipid metabolism in HCC. The established "static-dynamic" lipidomics workflow improves the coverage and accuracy of dynamic lipid monitoring, elucidating the roles of lipids in physiological and pathological processes, providing tools for studying lipid function, and offering new perspectives on the pathogenesis of HCC as well as the identification of drug targets.

Potential mechanism of dietary palm kernel meal effect on muscle tenderness in Tibetan sheep revealed by proteomics and phosphorylated proteomics

The labe free proteomics technology and 4D labe free phosphorylation proteomics technology was used to systematically analyse the protein expression regulatory mechanisms of muscle tenderness. 59 differentially expressed proteins were screened by proteomic data analysis. Phosphorylated proteomic analysis showed 681 modified peptide levels were changed, of which 235 modified peptide levels corresponded to 132 proteins up-regulated and 446 modified peptide levels corresponded to 253 proteins down-regulated. Then, the two-omics analysis further predicted that the regulatory mechanism of tenderness was mainly based on glycolysis, regulating mitochondrial autophagy, apoptosis, AMPK and HIF-1 signaling pathway to regulate muscle tenderness, which was specifically manifested in the modulation of Ca2+ release to promote the degradation of myofibrillar fibrillar proteins by the relevant proteins, shortening of post-slaughter muscle glycolysis and reducing the degree of muscle glycolysis. Which was verified by WB, P53, ENO5, ALDOA, ENDOG and PINK1 were identified as potential factors for tenderness regulation.

A relative metabolic flux analysis model of glucose anaplerosis

Glucose provides substrate for the predominant anaplerotic pathway which involves the activity of pyruvate carboxylase (PC). PC-mediated anaplerosis has been extensively studied as a metabolic regulator in glycolytic cells during tumorigenesis and metastasis. Herein, inaccuracies in established methods to measure relative intracellular flux through PC are highlighted and a compartmentalized condensed metabolic network (CCMN) is used to resolve the total malate pool into relative contributions from PC and other sources by metabolic flux analysis (MFA) with [U-13C6]glucose tracing. Performance of the CCMN method is evaluated in breast cancer cell lines that are exposed to small molecules targeting metabolism. Across conditions and cell lines, the CCMN approach yields results nearest to an accepted gold-standard methodology, using [3-13C]glucose, or even exposes the gold standard's limitations. The CCMN method does not require a separate experiment with a much more costly and generally less informative metabolic tracer, such as [3-13C]glucose, and in some cases, may outperform its application.

Metabolite biomarkers of screening neonatal congenital hypothyroidism based on dried blood spot metabolomics

Congenital hypothyroidism (CH) can be detected early during thyroid-stimulating hormone (TSH) screening based on dried blood spot (DBS) samples. However, it falls short in differentiating between neonates with elevated TSH levels and those with CH, which leads to many neonates undergoing secondary diagnosis through venous blood sampling. Metabolomics was used to analyze metabolic alterations in neonates with CH, and identify DBS-based metabolite biomarkers for CH screening to reduce secondary diagnosis. Based on non-targeted metabolomics, the metabolic alterations in neonates with CH were analyzed in a discovery set and novel biomarkers were identified. The results of the discovery set revealed that the metabolic alterations in neonates with CH were primarily in amino acid and lipid metabolism, and identified novel metabolite biomarkers were thyroxine, 2-piperidinone, and PC (14:0/20:4). Then, these biomarkers were validated in a validation set, and the screening performance was still satisfactory. A rapid 3.5-min targeted method for three potential biomarkers was further developed and used to analyze a confirmation set. Analysis of the confirmation set re-validated the reliability of the biomarkers, and a biomarker combinational model equation and an appropriate cutoff value were defined. Each set of DBS samples included neonates with health, hyperthyrotropinemia, and CH. The novel metabolite biomarkers from DBS samples demonstrate significant potential for CH screening in neonates, effectively reducing the requirement associated with secondary diagnosis of mis-screened neonates.

Exploring cellular metabolic kinetics through spectroscopic analysis of extracellular environments

Studying the kinetics of metabolic pathways, such as glycolysis and glutaminolysis, is valuable due to their fundamental links to various diseases, including cancer. This study explores the potential of Attenuated Total Reflectance-Fourier Transform Infrared (ATR-FTIR) spectroscopy for analysing low concentrations of metabolites in extracellular media. It also evaluates the use of the Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) method to data mine the kinetic evolution of the spectroscopic signatures of the glycolysis metabolic pathway and to explore the impact of the presence of glutamine on it. By extracting samples at specific time intervals and drying them on the ATR crystal, ATR-FTIR could effectively measure individual metabolites of glucose, glutamine and lactate at low concentrations, providing clear spectra with strong correlations between peak absorbance and metabolite concentrations. In data mining, MCR-ALS successfully resolved two components, glucose and lactate, from time-series data of cellular glucose metabolism (glycolysis), showing approximately 28 % glucose consumption and 1 mM lactate production at a constant rate of 0.0016 min-1. However, when glutamine was introduced as a third component, the overlap of the peaks of glutamine and lactate limited the method's ability to deconvolute the data, highlighting constraints of MCR-ALS in complex mixtures.

Tanshinone IIA alleviates pulmonary fibrosis by modulating glutamine metabolic reprogramming based on [U-13C5]-glutamine metabolic flux analysis

INTRODUCTION

Glutamine metabolic reprogramming, mediated by glutaminase (GLS), is an important signal during pulmonary fibrosis (PF) progression. Tanshinone IIA (Tan IIA) is a naturally lipophilic diterpene with antioxidant and antifibrotic properties. However, the potential mechanisms of Tan IIA for regulating glutamine metabolic reprogramming are not yet clear.

OBJECTIVES

This study aimed was to evaluate the role of Tan IIA in intervening in glutamine metabolic reprogramming to exert anti-PF and to explore the potential new mechanisms of metabolic regulation.

METHODS

Fibrotic characteristics was detected via immunofluorescence and western blotting analysis. Cell proliferation was examined with EdU Assay. Cell metabolites were labeled by using stable isotope [U-13C5]-glutamine. By utilizing 100% 13C glutamine tracers and employing network analysis to investigate the activation of metabolic pathways in fibroblasts, as well as evaluating the impact of Tan IIA on these pathways, we accurately quantified the absolute flux of glutaminolysis, proline synthesis, and the TCA cycle pathway using isotopomer network compartmental analysis (INCA), a user-friendly software tool for 13C metabolic flux analysis (13C-MFA). Molecular docking was used for identifying the binding of Tan IIA with target protein.

RESULTS

Tan IIA ameliorate TGF-β1-induced myofibroblast proliferation, reduce collagen I and III and α-SMA protein expression in MRC-5 and NIH-3T3 cells. Furthermore, Tan IIA regulate mitochondrial energy metabolism by modulating TGF-β1-stimulated glutamine metabolic reprogramming in NIH-3T3 cells and inhibiting GLS1 expression, which reduced the metabolic flux of glutamine into mitochondria in myofibroblasts, and also targeted inhibited the expression of Δ1-pyrroline-5-carboxylate synthase (P5CS), P5C reductase 1 (PYCR1), and phosphoserine aminotransferase 1 (PSAT1), and reduced proline hydroxylation and blocked the collagen synthesis pathway.

CONCLUSION

Tan IIA reverses glutamine metabolic reprogramming, reduces mitochondrial energy expenditure, and inhibits collagen matrix synthesis by modulating potential targets in glutamine metabolism. This novel perspective sheds light on the essential role of glutamine metabolic reprogramming in PF.

One-Pot Construction of NHS-Activated Magnetic Particles for Chemoselective Capture of Carboxyl Metabolites

Chemoselective probes immobilize on magnetic materials show great promise in simplifying sample handling and enhancing detection sensitivity. However, their complicated preparation and associated expense limit broader application. In this study, novel magnetic particles with abundant N-hydroxysuccinimide (NHS) esters on the surface are conveniently synthesized using a one-pot method without carbodiimide activation carboxylate molecules. Subsequently, multifunctional probes are synthesized by immobilizing high-density chemical probes on the surface of the magnetic materials through a postsynthetic modification strategy. This versatile probe facilitates the rapid capture of carboxylated compounds from complex matrices, with the labeled metabolites release from the magnetic materials subsequently analyzed using ultra-high performance liquid chromatography-mass spectrometry (UHPLC-MS). The advantages of this innovative chemical biological tool include the simplicity and low cost of the synthesis, as well as the capability to analyze polar and volatile carboxylated metabolites via LC-MS. This new strategy is successfully applied to analyze short-chain fatty acids (SCFAs) in rat cecal contents, demonstrating the reliability of the analytical method. This study presents a cost-effective and easy-to-implement approach for preparing NHS-activated magnetic particles and offers a versatile probe with chemoselective extraction and labeling capabilities, providing a practical tool for analyzing SCFAs in the gut.

A new paradigm for drug discovery in the treatment of complex diseases: drug discovery and optimization

In the past, the drug research and development has predominantly followed a "single target, single disease" model. However, clinical data show that single-target drugs are difficult to interfere with the complete disease network, are prone to develop drug resistance and low safety in clinical use. The proposal of multi-target drug therapy (also known as "cocktail therapy") provides a new approach for drug discovery, which can affect the disease and reduce adverse reactions by regulating multiple targets. Natural products are an important source for multi-target innovative drug development, and more than half of approved small molecule drugs are related to natural products. However, there are many challenges in the development process of natural products, such as active drug screening, target identification and preclinical dosage optimization. Therefore, how to develop multi-target drugs with good drug resistance from natural products has always been a challenge. This article summarizes the applications and shortcomings of related technologies such as natural product bioactivity screening, clarify the mode of action of the drug (direct/indirect target), and preclinical dose optimization. Moreover, in response to the challenges faced by natural products in the development process and the trend of interdisciplinary and multi-technology integration, and a multi-target drug development strategy of "active substances - drug action mode - drug optimization" is proposed to solve the key challenges in the development of natural products from multiple dimensions and levels.

MTHFD2 Enhances cMYC O-GlcNAcylation to Promote Sunitinib Resistance in Renal Cell Carcinoma

Sunitinib is a first-line targeted therapy for patients with renal cell carcinoma (RCC), but resistance represents a significant obstacle to the treatment of advanced and metastatic RCC. Metabolic reprogramming is a characteristic of RCC, and changes in metabolic processes might contribute to resistance to sunitinib. In this study, we identified methylenetetrahydrofolate dehydrogenase 2 (MTHFD2), a mitochondrial enzyme involved in one-carbon metabolism, as a critical mediator of sunitinib resistance in RCC. MTHFD2 was elevated in sunitinib-resistant RCC cells, and loss of MTHDF2 conferred sensitivity to sunitinib. In patients, MTHFD2 was highly expressed in RCC and was associated with poor outcomes. Mechanistically, MTHFD2 stimulated UDP-N-acetylglucosamine (UDP-GlcNAc) biosynthesis and promoted cMYC O-GlcNAcylation by driving the folate cycle. O-GlcNAcylation enhanced cMYC stability and promoted MTHFD2 and cyclin D1 transcription. Targeting MTHFD2 or cyclin D1 sensitized tumor cells to sunitinib in vitro and in vivo. Consistently, development of a peptide drug capable of efficiently degrading MTHFD2 enabled reversal of sunitinib resistance in RCC. These findings identify a noncanonical metabolic function of MTHFD2 in cell signaling and response to therapy and reveal the interplay between one-carbon metabolism and sunitinib resistance in RCC. Targeting MTHFD2 could be an effective approach to overcome sunitinib resistance. Significance: MTHFD2 regulates cMYC O-GlcNAcylation to promote sunitinib resistance in renal cell carcinoma, highlighting the important role of one-carbon metabolism in sunitinib resistance and proposing therapeutic strategies to improve patient outcomes.

Untargeted Metabolomics and Liquid Biopsy Investigation of Circulating Biomarkers in Soft Tissue Sarcoma

BACKGROUND

Soft tissue sarcomas (STSs) are rare, highly malignant mesenchymal tumours, comprising approximately 1% of all adult cancers and about 15% of paediatric solid tumours. STSs exhibit considerable genomic complexity with diverse subtypes, posing significant clinical challenges.

OBJECTIVES

This study aims to characterise the molecular signature of primary STS through liquid biopsies and the untargeted metabolomic profiling of 75 patients, providing deep insights into cellular processes and potential therapeutic targets.

METHODS

This study analysed serum samples using nuclear magnetic resonance (NMR) spectroscopy for metabolomic profiling. Multivariate data analysis and machine learning classifiers were employed to identify biomarkers.

RESULTS

A panel of eleven significant deregulated metabolites were discovered in serum samples of patients with STS, with potential implications for cancer diagnosis and treatment.

CONCLUSIONS

Choline decrease emerged as a marker for cancer progression, highlighting the potential of targeting its metabolism for therapeutic approaches in STS. The NMR analysis protocol proved effective for determining circulating biomarkers from liquid biopsies, making it suitable for rare disease research.

Exploring changes in metabolites and fecal microbiota of advanced gastric cancer based on plasma metabolomics and 16S rDNA sequencing

Metabolomics and 16S rDNA sequencing have shown great potential in elucidating complex mechanisms associated with diseases. Currently, there is little research on the omics of gastric cancer and it lacks effective biomarkers.

Objective

Based on plasma metabolomics and 16S rDNA sequencing to evaluate the changes in metabolites and fecal microbiota of advanced gastric cancer.

Method

Firstly, plasma metabolomics was used to screen for differential metabolites and metabolic pathways in gastric cancer. Then, 16S rDNA sequencing was performed on fecal samples to study the differential intestinal microbiota in gastric cancer patients. Finally, conduct a correlation analysis between them.

Result

A total of 152 differential metabolites were identified, and we screened 10 of them. All metabolites were enriched into 42 differential metabolic pathways, of which 13 have P values less than 0.05. 16S rDNA sequencing showed significant differences in 4 microbial communities at the phylum level. There are significant differences in 23 communities at the genus level. We focus on Lactobacillales, Lactobacillus, Streptococcus, Veillonella, Bacilli and Megasphaera. Correlation analysis shows that the intestinal microbiota and plasma metabolites jointly affect the occurrence and development of gastric cancer.

Conclusion

For the first time, we comprehensively used plasma metabolomics and 16S rDNA sequencing to reveal the changes and correlations between metabolites and intestinal microbiota in advanced gastric cancer. We have discovered new potential biomarkers for gastric cancer. This deepens our understanding of the physiological and pathological mechanisms of advanced gastric cancer and helps to improve the diagnosis and treatment of advanced gastric cancer.

Optimized MALDI2-Mass Spectrometry Imaging for Stable Isotope Tracing of Tissue-Specific Metabolic Pathways in Mice

Spatial stable isotope tracing metabolic imaging is a cutting-edge technique designed to investigate tissue-specific metabolic functions and heterogeneity. Traditional matrix-assisted laser desorption ionization-mass spectrometry imaging (MALDI-MSI) techniques often struggle with low coverage of low-molecular-weight (LMW) metabolites, which are often crucial for spatial metabolic studies. To address this, we developed a high-coverage spatial isotope tracing metabolic method that incorporates optimized matrix selection, sample preparation protocols, and enhanced post-ionization (MALDI2) techniques. We employed this approach to mouse kidney, brain, and breast tumors to visualize the spatial dynamics of metabolic flow. Our results revealed diverse regional distributions of nine labeled intermediates derived from 13C6-glucose across glycolysis, glycogen metabolism, and the tricarboxylic acid (TCA) cycle in kidney tissues. In brain sections, we successfully mapped six intermediates from the TCA cycle and glutamate-glutamine (Glu-Gln) cycle simultaneously in distinct neurological regions. Furthermore, in breast cancer tumor tissues, our approach facilitated the mapping of nine metabolic intermediates in multiple pathways, including glycolysis, the pentose phosphate pathway (PPP), and the TCA cycle, illustrating metabolic heterogeneity within the tumor microenvironment. This methodology enhances metabolite coverage, enabling more comprehensive imaging of isotope-labeled metabolites and opening new avenues for exploring the metabolic landscape in various biological contexts.

Impact of Metabolites from Foodborne Pathogens on Cancer

Foodborne pathogens are microorganisms that cause illness through contamination, presenting significant risks to public health and food safety. This review explores the metabolites produced by these pathogens, including toxins and secondary metabolites, and their implications for human health, particularly concerning cancer risk. We examine various pathogens such as Salmonella sp., Campylobacter sp., Escherichia coli, and Listeria monocytogenes, detailing the specific metabolites of concern and their carcinogenic mechanisms. This study discusses analytical techniques for detecting these metabolites, such as chromatography, spectrometry, and immunoassays, along with the challenges associated with their detection. This study covers effective control strategies, including food processing techniques, sanitation practices, regulatory measures, and emerging technologies in pathogen control. This manuscript considers the broader public health implications of pathogen metabolites, highlighting the importance of robust health policies, public awareness, and education. This review identifies research gaps and innovative approaches, recommending advancements in detection methods, preventive strategies, and policy improvements to better manage the risks associated with foodborne pathogens and their metabolites.

The role of 1400 plasma metabolites in gastric cancer: A bidirectional Mendelian randomization study and metabolic pathway analysis

While observational studies have illustrated correlations between plasma metabolites and gastric cancer (GC), the causal association between the 2 is still unclear. Our study aims to delineate the bidirectional relationship between plasma metabolites and GC and find potential metabolic pathways. We undertook a bidirectional 2-sample Mendelian randomization (MR) analysis to investigate the causal relationship, specificity, and direction of association between 1400 plasma metabolites and GC. The GWAS data for metabolites was obtained from a cohort of 8299 European individuals. And the GC's GWAS data was from FinnGen Consortium with 2384 European individuals, and the GWAS catalog with 1029 European ancestry cases for validation. Causal estimates were primarily calculated by the inverse-variance weighted (IVW) method. To ensure robustness, we performed comprehensive sensitivity analyses to assess heterogeneity and address concerns regarding horizontal pleiotropy. We validated the forward relationship between metabolites and GC from another database and implemented meta-analysis. Furthermore, we conducted metabolic enrichment and pathway analysis of these causal metabolites using MetaboAnalyst5.0/6.0 with the database of Kyoto Encyclopedia of Genes and Genomes. All statistical analysis was carried out using R software. Metabolites like 2s, 3R-dihydroxybutyrate, 4-acetamidobutanoate, ferulic acid 4-sulfate and methyl indole-3-acetate was proven positively linked with the development of GC. Asparagine, glucose to maltose ratio, glycohyocholate, Gulonate levels, linoleoyl ethanolamide and Spermidine to (N(1) + N(8))-acetylspermidine ratio was proven to be negatively associated with GC. Moreover, linoleic acid, histidine, glutamine, bilirubin, Succinate to proline ratio were found to be potentially linked to the development of GC. Furthermore, our analysis identified 18 significant metabolic pathways, including Arginine and proline metabolism (P < .009) and Valine, leucine, and isoleucine biosynthesis (P < .031). Our findings offer evidence supporting potential casual relations between multiple plasma metabolites and GC. These findings may offer great potential for future application of these biomarkers in GC screening and clinical prevention strategies.

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.

Tumor Metabolism: A New Field for the Treatment of Glioma

The clinical treatment of glioma remains relatively immature. Commonly used clinical treatments for gliomas are surgery combined with chemotherapy and radiotherapy, but there is a problem of drug resistance. In addition, immunotherapy and targeted therapies also suffer from the problem of immune evasion. The advent of metabolic therapy holds immense potential for advancing more efficacious and tolerable therapies against this aggressive disease. Metabolic therapy alters the metabolic processes of tumor cells at the molecular level to inhibit tumor growth and spread, and lead to better outcomes for patients with glioma that are insensitive to conventional treatments. Moreover, compared with conventional therapy, it has less impact on normal cells, less toxicity and side effects, and higher safety. The objective of this review is to examine the changes in metabolic characteristics throughout the development of glioma, enumerate the current methodologies employed for studying tumor metabolism, and highlight the metabolic reprogramming pathways of glioma along with their potential molecular mechanisms. Importantly, it seeks to elucidate potential metabolic targets for glioblastoma (GBM) therapy and summarize effective combination treatment strategies based on various studies.

Exploration of the intracellular chiral metabolome in pediatric BCP-ALL: a pilot study investigating the metabolic phenotype of IgH locus aberrations

Background and aims

Aberrations in the immunoglobulin heavy chain (IgH) locus are associated with poor prognosis in pediatric precursor B-cell acute lymphoblastic leukemia (BCP-ALL) patients. The primary objective of this pilot study is to enhance our understanding of the IgH phenotype by exploring the intracellular chiral metabolome.

Materials and methods

Leukemia cells were isolated from the bone marrow of BCP-ALL pediatric patients at diagnosis. The samples' metabolome and transcriptome were characterized using untargeted chiral metabolomic and next-generation sequencing transcriptomic analyses.

Results

For the first time D- amino acids were identified in the leukemic cells' intracellular metabolome from the bone marrow niche. Chiral metabolic signatures at diagnosis was indicative of a resistant phenotype. Through integrated network analysis and Pearson correlation, confirmation was obtained regarding the association of the IgH phenotype with several genes linked to poor prognosis.

Conclusion

The findings of this study have contributed to the understanding that the chiral metabolome plays a role in the poor prognosis observed in an exceptionally rare patient cohort. The findings include elevated D-amino acid incorporation in the IgH group, the emergence of several unknown, potentially enantiomeric, metabolites, and insights into metabolic pathways that all warrant further exploration.

Feasible mechanisms and therapeutic potential of food probiotics to mitigate diabetes‐associated cancers: A comprehensive review and in silico validation

People with diabetes mellitus (DM) and hyperglycemia are linked with cancer risk. Diabetes and cancer have been corroborated by high morbidity and mortality rates. Studies revealed that elevated levels of insulin secretions trigger insulin‐like growth factor 1 (IGF‐1) production. Moreover, IGF‐1 is a key regulator involved in promoting cancer cell progression and is linked with DM. Cancer drug resistance and ototoxic effects can adversely affect the health and lifespan of an individual. However, naturally derived bioactive compounds are gaining attention for their nontoxic properties and specific behavior. Likewise, probiotics have also been regarded as safe and successful alternatives to treat DM‐linked cancers. The present review aims to highlight the therapeutic potential and feasible functions of probiotics to mitigate or inhibit DM‐associated cancers. Meanwhile, the intracellular signaling cascades involved in promoting DM‐linked cancer are enumerated for future prospective research. However, metabolomics interactions and protein–protein interactions are to be discussed for deeper insights into affirmative principles in diabetic‐linked cancers. Drug discovery and innovative preclinical evaluation need further adjuvant and immune‐enhancement therapies. Furthermore, the results of the in silico assessment could provide scientific excellence of IGF‐1 in diabetes and cancer. Overall, this review summarizes the mechanistic insights and therapeutic targets for diabetes‐associated cancer.

Cutaneous Melanoma and 486 Human Blood Metabolites: A Mendelian Randomization Study

BACKGROUND

Cutaneous melanoma (CM) has long been recognized as a lethal form of cancer. Despite persistent research endeavors, the precise underlying pathological mechanisms remain largely unclear, and the optimal treatment for this patient population remains undetermined.

OBJECTIVES

This study aims to examine the causal associations between CM and 486 metabolites.

METHODS

A two-sample Mendelian randomization (MR) analysis was conducted to ascertain the causal relationship between blood metabolites and CM. The causality analysis involved the inverse variance weighted (IVW) method, followed by the MR-Egger and weighted median (WM) methods. To increase the robustness of our findings, several sensitivity analyses, including the MR-Egger intercept, Cochran's Q test, and MR-pleiotropy residual sum and outlier (MR-PRESSO), were performed. The robustness of our results was further validated in independent outcome samples followed by a meta-analysis. Additionally, a metabolic pathway analysis was carried out.

RESULTS

The two-sample MR analysis yielded a total of 27 metabolites as potential causal metabolites. After incorporating the outcomes of the sensitivity analyses, seven causal metabolites remained. Palmitoylcarnitine (OR 0.9903 95% CI 0.9848-0.9958, p = 0.0005) emerged as the sole metabolite with a significant causality after Bonferroni correction. Furthermore, the reverse MR analysis provided no evidence of reverse causality from CM to the identified metabolites.

CONCLUSIONS

This study suggested a causal relationship between seven human blood metabolites and the development of CM, thereby offering novel insights into the underlying mechanisms involved.

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Hallmarks of cancer resistance

This review explores the hallmarks of cancer resistance, including drug efflux mediated by ATP-binding cassette (ABC) transporters, metabolic reprogramming characterized by the Warburg effect, and the dynamic interplay between cancer cells and mitochondria. The role of cancer stem cells (CSCs) in treatment resistance and the regulatory influence of non-coding RNAs, such as long non-coding RNAs (lncRNAs), microRNAs (miRNAs), and circular RNAs (circRNAs), are studied. The chapter emphasizes future directions, encompassing advancements in immunotherapy, strategies to counter adaptive resistance, integration of artificial intelligence for predictive modeling, and the identification of biomarkers for personalized treatment. The comprehensive exploration of these hallmarks provides a foundation for innovative therapeutic approaches, aiming to navigate the complex landscape of cancer resistance and enhance patient outcomes.

Homogentisic acid metabolism inhibits papillary thyroid carcinoma proliferation through ROS and p21-induced cell cycle arrest

Thyroid cancer is one of the most common primary endocrine malignancies worldwide, and papillary thyroid carcinoma (PTC) is the predominant histological type observed therein. Although PTC has been studied extensively, our understanding of the altered metabolism and metabolic profile of PTC tumors is limited. We identified that the content of metabolite homogentisic acid (HGA) in PTC tissues was lower than that in adjacent non-cancerous tissues. We evaluated the potential of HGA as a novel molecular marker in the diagnosis of PTC tumors, as well as its ability to indicate the degree of malignancy. Studies have further shown that HGA contributes to reactive oxygen species (ROS) associated oxidative stress, leading to toxicity and inhibition of proliferation. In addition, HGA caused an increase in p21 expression levels in PTC cells and induced G1 arrest. Moreover, we found that the low HGA content in PTC tumors was due to the low expression levels of tyrosine aminotransferase (TAT) and p-hydroxyphenylpyruvate hydroxylase (HPD), which catalyze the conversion of tyrosine to HGA. The low expression levels of TAT and HPD are strongly associated with a higher probability of PTC tumor invasion and metastasis. Our study demonstrates that HGA could be used to diagnose PTC and provides mechanisms linking altered HGA levels to the biological behavior of PTC tumors.

Plasma-based lipidomics reveals potential diagnostic biomarkers for esophageal squamous cell carcinoma: a retrospective study

Background

Esophageal squamous cell carcinoma (ESCC) is highly prevalent and has a high mortality rate. Traditional diagnostic methods, such as imaging examinations and blood tumor marker tests, are not effective in accurately diagnosing ESCC due to their low sensitivity and specificity. Esophageal endoscopic biopsy, which is considered as the gold standard, is not suitable for screening due to its invasiveness and high cost. Therefore, this study aimed to develop a convenient and low-cost diagnostic method for ESCC using plasma-based lipidomics analysis combined with machine learning (ML) algorithms.

Methods

Plasma samples from a total of 40 ESCC patients and 31 healthy controls were used for lipidomics study. Untargeted lipidomics analysis was conducted through liquid chromatography-mass spectrometry (LC-MS) analysis. Differentially expressed lipid features were filtered based on multivariate and univariate analysis, and lipid annotation was performed using MS-DIAL software.

Results

A total of 99 differential lipids were identified, with 15 up-regulated lipids and 84 down-regulated lipids, suggesting their potential as diagnostic targets for ESCC. In the single-lipid plasma-based diagnostic model, nine specific lipids (FA 15:4, FA 27:1, FA 28:7, FA 28:0, FA 36:0, FA 39:0, FA 42:0, FA 44:0, and DG 37:7) exhibited excellent diagnostic performance, with an area under the curve (AUC) exceeding 0.99. Furthermore, multiple lipid-based ML models also demonstrated comparable diagnostic ability for ESCC. These findings indicate plasma lipids as a promising diagnostic approach for ESCC.

NMR spectroscopy for metabolomics in the living system: recent progress and future challenges

Metabolism is a fundamental process that underlies human health and diseases. Nuclear magnetic resonance (NMR) techniques offer a powerful approach to identify metabolic processes and track the flux of metabolites at the molecular level in living systems. An in vitro study through in-cell NMR tracks metabolites in real time and investigates protein structures and dynamics in a state close to their most natural environment. This technique characterizes metabolites and proteins involved in metabolic pathways in prokaryotic and eukaryotic cells. In vivo magnetic resonance spectroscopy (MRS) enables whole-organism metabolic monitoring by visualizing the spatial distribution of metabolites and targeted proteins. One limitation of these NMR techniques is the sensitivity, for which a possible improved approach is through isotopic enrichment or hyperpolarization methods, including dynamic nuclear polarization (DNP) and parahydrogen-induced polarization (PHIP). DNP involves the transfer of high polarization from electronic spins of radicals to surrounding nuclear spins for signal enhancements, allowing the detection of low-abundance metabolites and real-time monitoring of metabolic activities. PHIP enables the transfer of nuclear spin polarization from parahydrogen to other nuclei for signal enhancements, particularly in proton NMR, and has been applied in studies of enzymatic reactions and cell signaling. This review provides an overview of in-cell NMR, in vivo MRS, and hyperpolarization techniques, highlighting their applications in metabolic studies and discussing challenges and future perspectives.

Deciphering the antifungal mechanism and functional components of cinnamomum cassia essential oil against Candida albicans through integration of network-based metabolomics and pharmacology, the greedy algorithm, and molecular docking

ETHNOPHARMACOLOGICAL RELEVANCE

Fungal pathogens can cause deadly invasive infections and have become a major global public health challenge. There is an urgent need to find new treatment options beyond established antifungal agents, as well as new drug targets that can be used to develop novel antifungal agents. Cinnamomum cassia is a tropical aromatic plant that has a wide range of applications in traditional Chinese medicine, especially in the treatment of bacterial and fungal infections.

AIM OF THE STUDY

The present study aimed to explore the mechanism of action and functional components of Cinnamomum cassia essential oil (CEO) against Candida albicans using an integrated strategy combining network-based metabolomics and pharmacology, the greedy algorithm and molecular docking.

MATERIALS AND METHODS

CEO was extracted using hydrodistillation and its chemical composition was identified by GC-MS. Cluster analysis was performed on the compositions of 19 other CEOs from the published literature, as well as the sample obtained in this study. The damages of C. albicans cells upon treatment with CEO was observed using a scanning electron microscope. The mechanisms of its antifungal effect at a subinhibitory concentration of 0.1 × MIC were determined using microbial metabolomics and network analysis. The functional components were studied using the greedy algorithm and molecular docking.

RESULTS

A total of 69 compounds were identified in the chemical analysis of CEO, which accounted for 90% of the sample. The major compounds were terpenoids (34.04%), aromatic compounds (4.52%), aliphatic compounds (0.9%), and others. Hierarchical cluster analysis of the compositions of 20 essential oils extracted from Cinnamomum cassia grown in different geographical locations showed a wide diversity of chemical composition with four major chemotypes. CEO showed strong antifungal activity and caused destruction of cell membranes in a concentration-dependent way. Metabolic fingerprint analysis identified 29 metabolites associated with lipid metabolism, which were mapped to 23 core targets mainly involved in fatty acid biosynthesis and metabolism. Six antifungal functional components of CEO were identified through network construction, greedy algorithm and molecular docking, including trans-cinnamaldehyde, δ-cadinol, ethylcinnamate, safrole, trans-anethole, and trans-cinnamyl acetate, which showed excellent binding with specific targets of AKR1B1, PPARG, BCHE, CYP19A1, CYP2C19, QPCT, and CYP51A1.

CONCLUSIONS

This study provides a systematic understanding of the antifungal activity of CEO and offers an integrated strategy for deciphering the potential metabolism and material foundation of complex component drugs.

Circular-SWAT for deep learning based diagnostic classification of Alzheimer's disease: application to metabolome data

BACKGROUND

Deep learning has shown potential in various scientific domains but faces challenges when applied to complex, high-dimensional multi-omics data. Alzheimer's Disease (AD) is a neurodegenerative disorder that lacks targeted therapeutic options. This study introduces the Circular-Sliding Window Association Test (c-SWAT) to improve the classification accuracy in predicting AD using serum-based metabolomics data, specifically lipidomics.

METHODS

The c-SWAT methodology builds upon the existing Sliding Window Association Test (SWAT) and utilizes a three-step approach: feature correlation analysis, feature selection, and classification. Data from 997 participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI) served as the basis for model training and validation. Feature correlations were analyzed using Weighted Gene Co-expression Network Analysis (WGCNA), and Convolutional Neural Networks (CNN) were employed for feature selection. Random Forest was used for the final classification.

FINDINGS

The application of c-SWAT resulted in a classification accuracy of up to 80.8% and an AUC of 0.808 for distinguishing AD from cognitively normal older adults. This marks a 9.4% improvement in accuracy and a 0.169 increase in AUC compared to methods without c-SWAT. These results were statistically significant, with a p-value of 1.04 × 10ˆ-4. The approach also identified key lipids associated with AD, such as Cer(d16:1/22:0) and PI(37:6).

INTERPRETATION

Our results indicate that c-SWAT is effective in improving classification accuracy and in identifying potential lipid biomarkers for AD. These identified lipids offer new avenues for understanding AD and warrant further investigation.

FUNDING

The specific funding of this article is provided in the acknowledgements section.

Analysis of the Inhibitory Effect of hsa-miR-145-5p and hsa-miR-203a-5p on Imatinib-Resistant K562 Cells by GC/MS Metabolomics Method

Imatinib (IM) resistance is considered to be a significant challenge in the management of chronic myeloid leukemia (CML). Previous studies have reported that hsa-miR-145-5p and hsa-miR-203a-5p can overcome IM resistance and hsa-miR-203a-5p can alter glutathione metabolism in IM-resistant cells. The purpose of this study was to examine whether hsa-miR-145-5p or hsa-miR-203a-5p counters IM resistance by targeting the overall metabolic profile of IM-resistant K562 cells. The metablic profiling of cell lysates obtained from IM-sensitive, IM-resistant, and miR-transfected IM-resistant K562 cells was carried out using the GC-MS technique. Overall, 75 major metabolites were detected, of which 32 were present in all samples. The pathway analysis of MetaboAnalyst 5.0 revealed that the majorly enriched pathways included glucose metabolism, fatty acid biosynthesis, lipogenesis, and nucleotide metabolism. Eleven of identified metabolites, l-glutamine, l-glutamic acid, l-lactic acid, phosphoric acid, 9,12-octadecadienoic acid, 9-octadecenoic acid, myristic acid, palmitic acid, cholesterol, and β-alanine, appeared in enriched pathways. IM-resistant cells had comparatively higher concentrations of all of these metabolites. Notably, the introduction of hsa-miR-145-5p or hsa-miR-203a-5p into resistant cells resulted in a decrease in levels of these metabolites. The efficacy of miR-203a-5p was particularly remarkable in comparison with miR-145-5p, as evidenced by partial least-squares-discriminant analysis (PLS-DA), which showed a high level of similarity in metabolic profile between IM-sensitive K562 cells and IM-resistant cells transfected with hsa-miR-203a-5p. The results indicate that GC-MS-based metabolic profiling has the potential to distinguish between drug-resistant and -sensitive cells. This approach can also be used to routinely monitor therapeutic response in drug-resistant patients, thus, enabling personalized therapy.

Revealing the Preventable Effects of Fu-Zheng-Qu-Xie Decoction against Recurrence and Metastasis of Postoperative Early-Stage Lung Adenocarcinoma Based on Network Pharmacology Coupled with Metabolomics Analysis

Fu-Zheng-Qu-Xie (FZQX) decoction is a traditional Chinese herbal prescription for the treatment of lung cancer and exerts proapoptotic and immunomodulatory effects. It has been clinically suggested to be effective in improving the survival of postoperative early-stage lung adenocarcinoma (LUAD), but the mechanism remains unclear. In this study, we used network pharmacology coupled with metabolomics approaches to explore the pharmacological action and effective mechanism of FZQX against the recurrence and metastasis of postoperative early-stage LUAD. Network pharmacology analysis showed that FZQX could prevent the recurrence and metastasis of postoperative early-stage LUAD by regulating a series of targets involving vascular endothelial growth factor receptor 2, estrogen receptor 1, sarcoma gene, epidermal growth factor receptor, and protein kinase B and by influencing the Ras, PI3K-Akt, and mitogen-activated protein kinase signaling pathways. In liquid chromatography-mass spectrometry analysis, 11 differentially expressed metabolites, including PA(12:0/18:4(6Z,9Z,12Z,15Z)), PC(16:0/0:0)[U], LysoPC(18:1(11Z)), and LysoPC(18:0), were discovered in the FZQX-treated group compared to those in the model group before treatment or normal group. They were enriched in cancer metabolism-related signaling pathways such as central carbon metabolism in cancer, choline metabolism, and glycerol phospholipid metabolism. Collectively, our results suggest that the multicomponent and multitarget interaction network of FZQX inhibits the recurrence and metastasis of postoperative early-stage LUAD by activating the receptor signal transduction pathway to inhibit proliferation, induce cell apoptosis, inhibit aerobic glycolysis, and reprogram tumor lipid metabolism.

Surviving the hunger games: Metabolic reprogramming in medulloblastoma

Medulloblastoma is a highly malignant pediatric brain tumor characterized by its aggressive nature and limited treatment options. Metabolic changes have recently emerged as key factors in the development, progression, and response to therapy in various types of cancer. Cancer cells exhibit remarkable adaptability by modulating glucose, lipids, amino acids, and nucleotide metabolism to survive in nutrient- and oxygen-deprived environments. Although medulloblastoma has been extensively studied from a genomic perspective, leading to the identification of four subgroups and their respective subcategories, the investigation of its metabolic phenotype has remained relatively understudied. This review focus on the available literature, aiming to summarize the current knowledge about the main metabolic pathways that are deregulated in medulloblastoma tumors, while emphasizing the controversial aspects and the progress that is yet to be made. Furthermore, we underscored the insights gained so far regarding the impact of metabolism on the development of drug resistance in medulloblastoma and the therapeutic strategies employed to target specific metabolic pathways.

Prognostic Implication of Plasma Metabolites in Gastric Cancer

Gastric cancer (GC) typically carries a poor prognosis as it is often diagnosed at a late stage. Altered metabolism has been found to impact cancer outcomes and affect patients' quality of life, and the role of metabolites in gastric cancer prognosis has not been sufficiently understood. We aimed to establish a prognostic prediction model for GC patients based on a metabolism-associated signature and identify the unique role of metabolites in the prognosis of GC. Thus, we conducted untargeted metabolomics to detect the plasma metabolites of 218 patients with gastric adenocarcinoma and explored the metabolites related to the survival of patients with gastric cancer. Firstly, we divided patients into two groups based on the cutoff value of the abundance of each of the 60 metabolites and compared the differences using Kaplan-Meier (K-M) survival analysis. As a result, 23 metabolites associated with gastric cancer survival were identified. To establish a risk score model, we performed LASSO regression and Cox regression analysis on the 60 metabolites and identified 8 metabolites as an independent prognostic factor. Furthermore, a nomogram incorporating clinical parameters and the metabolic signature was constructed to help individualize outcome predictions. The results of the ROC curve and nomogram plot showed good predictive performance of metabolic risk features. Finally, we performed pathway analysis on the 24 metabolites identified in the two parts, and the results indicated that purine metabolism and arachidonic acid metabolism play important roles in gastric cancer prognosis. Our study highlights the important role of metabolites in the progression of gastric cancer and newly identified metabolites could be potential biomarkers or therapeutic targets for gastric cancer patients.

BUN level is associated with cancer prevalence

Blood urea nitrogen (BUN) was an important biomarker for the development and prognosis of many diseases. Numerous studies had demonstrated that BUN had a strong relationship with long-term mortality, survival and the prevalence of some diseases. The diagnosis and treatment, prognosis and long-term survival rate of cancer were the focus of clinical research at present. However, the relationship between BUN level and cancer prevalence was not clear. To investigate the relationship between BUN level and cancer prevalence, we performed a statistical analysis of population data from the National Health and Nutrition Examination Survey (NHANES) database. The results of the study showed that BUN level were positively correlated with cancer prevalence, and the correlation was more pronounced in breast cancer.

Discovering metabolic vulnerability using spatially resolved metabolomics for antitumor small molecule-drug conjugates development as a precise cancer therapy strategy

Against tumor-dependent metabolic vulnerability is an attractive strategy for tumor-targeted therapy. However, metabolic inhibitors are limited by the drug resistance of cancerous cells due to their metabolic plasticity and heterogeneity. Herein, choline metabolism was discovered by spatially resolved metabolomics analysis as metabolic vulnerability which is highly active in different cancer types, and a choline-modified strategy for small molecule-drug conjugates (SMDCs) design was developed to fool tumor cells into indiscriminately taking in choline-modified chemotherapy drugs for targeted cancer therapy, instead of directly inhibiting choline metabolism. As a proof-of-concept, choline-modified SMDCs were designed, screened, and investigated for their druggability in vitro and in vivo. This strategy improved tumor targeting, preserved tumor inhibition and reduced toxicity of paclitaxel, through targeted drug delivery to tumor by highly expressed choline transporters, and site-specific release by carboxylesterase. This study expands the strategy of targeting metabolic vulnerability and provides new ideas of developing SMDCs for precise cancer therapy.

Cancer metabolites: promising biomarkers for cancer liquid biopsy

Cancer exerts a multitude of effects on metabolism, including the reprogramming of cellular metabolic pathways and alterations in metabolites that facilitate inappropriate proliferation of cancer cells and adaptation to the tumor microenvironment. There is a growing body of evidence suggesting that aberrant metabolites play pivotal roles in tumorigenesis and metastasis, and have the potential to serve as biomarkers for personalized cancer therapy. Importantly, high-throughput metabolomics detection techniques and machine learning approaches offer tremendous potential for clinical oncology by enabling the identification of cancer-specific metabolites. Emerging research indicates that circulating metabolites have great promise as noninvasive biomarkers for cancer detection. Therefore, this review summarizes reported abnormal cancer-related metabolites in the last decade and highlights the application of metabolomics in liquid biopsy, including detection specimens, technologies, methods, and challenges. The review provides insights into cancer metabolites as a promising tool for clinical applications.

Systematic comparison of local approaches for isotopically nonstationary metabolic flux analysis

Quantification of reaction fluxes of metabolic networks can help us understand how the integration of different metabolic pathways determine cellular functions. Yet, intracellular fluxes cannot be measured directly but are estimated with metabolic flux analysis (MFA) that relies on the patterns of isotope labeling of metabolites in the network. For metabolic systems, typical for plants, where all potentially labeled atoms effectively have only one source atom pool, only isotopically nonstationary MFA can provide information about intracellular fluxes. There are several global approaches that implement MFA for an entire metabolic network and estimate, at once, a steady-state flux distribution for all reactions with identifiable fluxes in the network. In contrast, local approaches deal with estimation of fluxes for a subset of reactions, with smaller data demand for flux estimation. Here we present a systematic comparative review and benchmarking of the existing local approaches for isotopically nonstationary MFA. The comparison is conducted with respect to the required data and underlying computational problems solved on a synthetic network example. Furthermore, we benchmark the performance of these approaches in estimating fluxes for a subset of reactions using data obtained from the simulation of nitrogen fluxes in the Arabidopsis thaliana core metabolism. The findings pinpoint practical aspects that need to be considered when applying local approaches for flux estimation in large-scale plant metabolic networks.

Advances of stable isotope technology in food safety analysis and nutrient metabolism research

Food quality, safety, and the regulatory metabolism of food nutrients in cells are primary factors in determining human health. However, residues of undesirable or hazardous compounds in food products and dysregulation in the nutrient metabolism inevitably occur occasionally. For years, chromatography-mass spectrometry technology has been recognized as an essential research tool in food analysis and nutrient metabolism research, and it is more accurate and robust when coupled with stable isotopes. In this study, we summarize the applications of stable isotope technology in the quantification of contaminant residues (pesticides, veterinary drugs, mycotoxins, polycyclic aromatic hydrocarbons, and other hazardous compounds) in foods and in the nutrients (glucose, lipids, amino acids and proteins) metabolism research. The aim of this review was to serve as a reference for providing effective analysis techniques for protecting food quality and human health, and to pave the way for the broader application of stable isotope technology.

Metabolomics and network analysis uncovered profound inflammation-associated alterations in hepatitis B virus-related cirrhosis patients with early hepatocellular carcinoma

Patients with hepatitis B virus (HBV)-related liver cirrhosis (LC) are at high risk for hepatocellular carcinoma (HCC). Limitations in the early detection of HCC give rise to poor survival in this high-risk population. Here, we performed comprehensive metabolomics on health individuals and HBV-related LC patients with and without early HCC. Compared to non-HCC patients (N = 108) and health controls (N = 80), we found that patients with early HCC (N = 224) exhibited a specific plasma metabolome map dominated by lipid alterations, including lysophosphatidylcholines, lysophosphatidic acids and bile acids. Pathway and function network analyses indicated that these metabolite alterations were closely associated with inflammation responses. Using multivariate regression and machine learning approaches, we identified a five-metabolite combination that showed significant performances in differentiating early-HCC from non-HCC than α-fetoprotein (area under the curve values, 0.981 versus 0.613). At metabolomic levels, this work provides additional insights of metabolic dysfunction related to HCC progressions and demonstrates the plasma metabolites might be measured to identify early HCC in patients with HBV-related LC.

Elevated serum β-hydroxybutyrate, a circulating ketone metabolite, accelerates colorectal cancer proliferation and metastasis via ACAT1

Colorectal cancer (CRC) ranks third in incidence and second in mortality worldwide. Metabolic disorders are known to be closely associated with CRC. Functional metabolomics aims to translate metabolomics-derived biomarkers to disease mechanisms. Previous work based on untargeted liquid chromatography identified 30 differential metabolites of CRC. Among them, only β-hydroxybutyrate (BHB) was elevated in CRC. Here, we first confirm the increased level of β-hydroxybutyrate by targeted metabolomic analysis using an independent cohort of 400 serum samples by UPLC-QQQ-MS/MS analysis. Using appropriate cell and animal models, we find that treatment with pathological levels of β-hydroxybutyrate expedites CRC proliferation and metastasis. Out of four major rate-limiting enzymes of ketolysis, only acetyl-coenzyme A acetyltransferase1 (ACAT1) expression is increased in paired human CRC tissues. These findings suggest probable clinical relevance for the functional implications of β-hydroxybutyrate in CRC. We demonstrate that β-hydroxybutyrate may exert its tumorigenic effects via regulation of ACAT1, due to induction of downstream isocitrate dehydrogenase1 (IDH1) acetylation. Genetic silencing of ACAT1 significantly suppresses the progression of CRC and abrogates the effects of β-hydroxybutyrate both in vitro and in vivo. Overall, this study suggests that targeting β-hydroxybutyrate and its major rate-limiting enzyme ACAT1 may provide a new avenue for therapeutic intervention in CRC.

Advancing CAR T cell therapy through the use of multidimensional omics data

Despite the notable success of chimeric antigen receptor (CAR) T cell therapies in the treatment of certain haematological malignancies, challenges remain in optimizing CAR designs and cell products, improving response rates, extending the durability of remissions, reducing toxicity and broadening the utility of this therapeutic modality to other cancer types. Data from multidimensional omics analyses, including genomics, epigenomics, transcriptomics, T cell receptor-repertoire profiling, proteomics, metabolomics and/or microbiomics, provide unique opportunities to dissect the complex and dynamic multifactorial phenotypes, processes and responses of CAR T cells as well as to discover novel tumour targets and pathways of resistance. In this Review, we summarize the multidimensional cellular and molecular profiling technologies that have been used to advance our mechanistic understanding of CAR T cell therapies. In addition, we discuss current applications and potential strategies leveraging multi-omics data to identify optimal target antigens and other molecular features that could be exploited to enhance the antitumour activity and minimize the toxicity of CAR T cell therapy. Indeed, fully utilizing multi-omics data will provide new insights into the biology of CAR T cell therapy, further accelerate the development of products with improved efficacy and safety profiles, and enable clinicians to better predict and monitor patient responses.

An omics approach to delineating the molecular mechanisms that underlie the biological effects of physical plasma

The use of physical plasma to treat cancer is an emerging field, and interest in its applications in oncology is increasing rapidly. Physical plasma can be used directly by aiming the plasma jet onto cells or tissue, or indirectly, where a plasma-treated solution is applied. A key scientific question is the mechanism by which physical plasma achieves selective killing of cancer over normal cells. Many studies have focused on specific pathways and mechanisms, such as apoptosis and oxidative stress, and the role of redox biology. However, over the past two decades, there has been a rise in omics, the systematic analysis of entire collections of molecules in a biological entity, enabling the discovery of the so-called "unknown unknowns." For example, transcriptomics, epigenomics, proteomics, and metabolomics have helped to uncover molecular mechanisms behind the action of physical plasma, revealing critical pathways beyond those traditionally associated with cancer treatments. This review showcases a selection of omics and then summarizes the insights gained from these studies toward understanding the biological pathways and molecular mechanisms implicated in physical plasma treatment. Omics studies have revealed how reactive species generated by plasma treatment preferentially affect several critical cellular pathways in cancer cells, resulting in epigenetic, transcriptional, and post-translational changes that promote cell death. Finally, this review considers the outlook for omics in uncovering both synergies and antagonisms with other common cancer therapies, as well as in overcoming challenges in the clinical translation of physical plasma.

Graphene quantum dots disturbed the energy homeostasis by influencing lipid metabolism of macrophages

To investigate the potential factors of graphene quantum dots (GQDs), the assessment impact on the innate immune system is one of the most important. As the innate immune cell, macrophages possess phagocytosis activity and affect immunomodulation. Higher oxygen consumption rates (OCR) are used to gain insight into GQDs' effects on macrophages. Metabolomics profiling also revealed that GQDs exposure provoked an increase in phosphoglycerides, sphingolipids, and oxidized lipids in macrophages. The molecular pathways disrupted by GQDs were associated with lipid and energy metabolisms. Metabolite flux analysis was used to evaluate changes in the lipid metabolism of macrophages exposed to 100 µg mL^-1 GQDs for 24 and 48 h. A combination of ¹³C-flux analysis and metabolomics revealed the regulation of lipid biosynthesis influenced the balance of energy metabolism. Integrated proteomics and metabolomics analyses showed that nicotinic acid adenine dinucleotide and coenzyme Q10 were significantly increased under GQDs treatment, alongside upregulated protein activity (e.g., Cox5b and Cd36). The experimental evidences were expected to be provided in this study to reveal the potential harmful effect from exposure to GQDs.

Metabolomic profiling identifies hair as a robust biological sample for identifying women with cervical cancer

Metabolomics serves as a useful tool for identifying biomarkers of disease and uncovering pathogenic mechanisms. However, most metabolomic studies use biological fluids such as blood and urine as biospecimens, which could be dramatically influenced by daily activities and dietary variation, resulting in measurement fluctuations. In contrast, hair may serve as a robust source of stable longitudinal metabolite information. Here, we conducted a pilot study to investigate the possibility of using hair as a biospecimen for the metabolomic analysis of cervical cancer. Hair, plasma, urine, and cervical tissue samples from cervical cancer and benign tumor patients were collected. Biospecimens were then tested using a gas chromatography-mass spectrometry-based metabolomic platform. The expressions of enzymatic genes related to metabolic changes were validated using qPCR. Statistical analyses were calculated via the R-console platform. Metabolite profiles in both hair and cervical tissue samples were significantly different between cancer and control groups, while no difference was observed in plasma and urine samples. Further analysis showed that most of the altered metabolites in hair were upregulated, and they had a negative correlation with those in the cervical tissue. Eight common metabolites showed an area under the Receiver Operating Characteristic curve greater than 0.95. These metabolites primarily participated in amino acid metabolism, cofactor synthesis, ferroptosis, and glycolysis. The gene expressions (IDH1, OGDH, GLUD1, ENO1, GSS, and GPX4) associated with the shortlisted metabolic pathways were also upregulated. Our study is the first to reveal metabolomic changes of hair in cervical cancer patients and demonstrates the potential for the hair metabolome to be used for biomarker identification in cervical cancer.

Metabolomics and integrated network pharmacology analysis reveal that ginkgolides act as potential active anticancer components by regulating one-carbon metabolism

ETHNOPHARMACOLOGICAL RELEVANCE

Ginkgo biloba L. is a rare tree species unique to China. Ginkgo biloba is a traditional Chinese medicinal with a long history, acting on the heart and lung meridians, and has been reported to have a significant effect on non-small cell lung cancer. However, the mechanism underlying this metabolic effect is poorly understood.

AIM OF THE STUDY

To identify the active components of Ginkgo biloba extract that may have effects on non-small cell lung cancer and their mechanisms of metabolic regulation.

MATERIALS AND METHODS

In this study, LC-MS/MS was used to investigate the chemical constituents of Ginkgo biloba extract. Network pharmacology was used to identify the active components potentially valuable in the treatment of non-small cell lung cancer. Antitumor activity was evaluated using CCK-8 and apoptosis assays. The mechanisms of metabolic regulation by the active components were further explored using untargeted metabolomics, targeted metabolomics, and western blot experiments.

RESULTS

Network pharmacology and component analysis of Ginkgo biloba extract identified four ginkgolides that significantly affect non-small cell lung cancer. Their antiproliferative activity in A549 cells was evaluated using CCK-8 and apoptosis assays. The metabolomics results indicated that the ginkgolides had a significant regulatory effect on metabolic pathways related to one-carbon metabolisms, such as purine metabolism, glutathione metabolism, and the methionine cycle. Further targeted metabolomics analysis on one-carbon metabolism found that the ginkgolides may significantly affect the content of multiple metabolites in A549 cells, including purine, S-adenyl methionine, S-adenylyl homocysteine, and glutathione upregulated, and adenosine, tetrahydrofolate, and 10-Formyl-tetrahydrofolate significantly decreased. Notably, dihydrofolate reductase (DHFR) and methylenetetrahydrofolate dehydrogenases (MTHFR) were found to be altered after the treatment of ginkgolides.

CONCLUSION

This in vitro study indicated that ginkgolides might inhibit the growth of A549 cells by targeting one-carbon metabolism. This study also demonstrated that metabolomics combined with network pharmacology is a powerful tool for identifying traditional Chinese medicines' active components and metabolic mechanisms.

Drug Resistance in Cancers: A Free Pass for Bullying

The cancer burden continues to grow globally, and drug resistance remains a substantial challenge in cancer therapy. It is well established that cancerous cells with clonal dysplasia generate the same carcinogenic lesions. Tumor cells pass on genetic templates to subsequent generations in evolutionary terms and exhibit drug resistance simply by accumulating genetic alterations. However, recent evidence has implied that tumor cells accumulate genetic alterations by progressively adapting. As a result, intratumor heterogeneity (ITH) is generated due to genetically distinct subclonal populations of cells coexisting. The genetic adaptive mechanisms of action of ITH include activating "cellular plasticity", through which tumor cells create a tumor-supportive microenvironment in which they can proliferate and cause increased damage. These highly plastic cells are located in the tumor microenvironment (TME) and undergo extreme changes to resist therapeutic drugs. Accordingly, the underlying mechanisms involved in drug resistance have been re-evaluated. Herein, we will reveal new themes emerging from initial studies of drug resistance and outline the findings regarding drug resistance from the perspective of the TME; the themes include exosomes, metabolic reprogramming, protein glycosylation and autophagy, and the relates studies aim to provide new targets and strategies for reversing drug resistance in cancers.

A Photoactivated Sorafenib-Ruthenium(II) Prodrug for Resistant Hepatocellular Carcinoma Therapy through Ferroptosis and Purine Metabolism Disruption

The curative effect of sorafenib in hepatocellular carcinoma (HCC) is limited and sorafenib resistance remains a major obstacle for HCC. To overcome this obstacle, a new photoactive sorafenib-Ru(II) complex Ru-Sora has been designed. Upon irradiation (λ = 465 nm), Ru-Sora rapidly releases sorafenib and generates reactive oxygen species, which can oxidize intracellular substances such as GSH. Cellular experiments show that irradiated Ru-Sora is highly cytotoxic toward Hep-G2 cells, including sorafenib-resistant Hep-G2-SR cells. Compared to sorafenib, Ru-Sora has a significant photoactivated chemotherapeutic effect against Hep-G2-SR cancer cells and 3D Hep-G2 multicellular tumor spheroids. Furthermore, Ru-Sora inducing apoptosis and ferroptosis is proved by GSH depletion, GPX4 downregulation, and lipid peroxide accumulation. Metabolomics results suggest that Ru-Sora exerts photocytotoxicity by disrupting the purine metabolism, which is expected to inhibit tumor development. This study provides a promising strategy for enhancing chemotherapy and combating drug-resistant HCC disease.

13C metabolic flux analysis: Classification and characterization from the perspective of mathematical modeling and application in physiological research of neural cell

¹³C metabolic flux analysis (¹³C-MFA) has emerged as a forceful tool for quantifying in vivo metabolic pathway activity of different biological systems. This technology plays an important role in understanding intracellular metabolism and revealing patho-physiology mechanism. Recently, it has evolved into a method family with great diversity in experiments, analytics, and mathematics. In this review, we classify and characterize the various branch of ¹³C-MFA from a unified perspective of mathematical modeling. By linking different parts in the model to each step of its workflow, the specific technologies of ¹³C-MFA are put into discussion, including the isotope labeling model (ILM), isotope pattern measuring technique, optimization algorithm and statistical method. Its application in physiological research in neural cell has also been reviewed.

Metabolic Reprogramming in Hematologic Malignancies: Advances and Clinical Perspectives

Metabolic reprogramming is a hallmark of cancer progression. Metabolic activity supports tumorigenesis and tumor progression, allowing cells to uptake essential nutrients from the environment and use the nutrients to maintain viability and support proliferation. The metabolic pathways of malignant cells are altered to accommodate increased demand for energy, reducing equivalents, and biosynthetic precursors. Activated oncogenes coordinate with altered metabolism to control cell-autonomous pathways, which can lead to tumorigenesis when abnormalities accumulate. Clinical and preclinical studies have shown that targeting metabolic features of hematologic malignancies is an appealing therapeutic approach. This review provides a comprehensive overview of the mechanisms of metabolic reprogramming in hematologic malignancies and potential therapeutic strategies to target cancer metabolism.

An improved NMR approach for metabolomics of intact serum samples

NMR metabolomics has inherent capabilities for studying biofluids, such as reproducibility, minimal sample preparation, non-destructiveness, and molecular structure elucidation; however, reliable quantitation of metabolites is still a challenge because of the complex matrix of the samples. The serum is one of the most common samples in clinical studies but possibly the most difficult for NMR analysis because of the high content of proteins, which hampers the detection and quantification of metabolites. Different processes for protein removal, such as ultrafiltration and precipitation, have been proposed, but require sample manipulation, increase time and cost, and possibly lead to loss of information in the metabolic profile. Alternative methods that rely on filtering protein signals by NMR pulse sequencing are commonly used, but standardisation of acquisition parameters and spectra calibration is far from being reached. The present technical note is a critical assessment of the sparsely suggested calibrants, pulse sequences and acquisition parameters toward an optimised combination of the three for accurate and reproducible quantification of metabolites in intact serum.

Coral-like Magnetic Particles for Chemoselective Extraction of Anionic Metabolites

To date, advanced chemical biology tools for chemoselective extraction of metabolites are limited. In this study, unique coral-like polymer particles were synthesized via high concentrations of 1-ethyl-3-(3-(dimethylamino)propyl) carbodiimide hydrochloride (EDC)/N-hydroxysuccinimide (NHS), which are usually used as condensation agents. The polymers can wrap or adhere Fe3O4 nanoparticles (Fe3O4-NPs) to form polymer magnetic microparticles (PMMPs). With abundant NHS-activated moieties on their surface, the coral-like PMMPs could be modified by cystamine for the chemoselective extraction of phosphate/carboxylate anion metabolites from complex biological samples. Finally, 97 metabolites including nucleotides, phosphates, phosphate sugars, carboxylate sugars, and organic acids were extracted and identified from serum, tissues, and cells. These metabolites are involved in four major metabolic pathways including glycolysis, the tricarboxylic acid cycle, the pentose phosphate pathway, and nucleotide metabolism. This study has provided a cost-effective and easy-to-implement preparation of PMMPs with a robust chemoselective extraction ability and versatile applications.

Identification of characteristic metabolic panels for different stages of prostate cancer by 1H NMR-based metabolomics analysis

Background

Prostate cancer (PCa) is the second most prevalent cancer in males worldwide, yet detecting PCa and its metastases remains a major challenging task in clinical research setups. The present study aimed to characterize the metabolic changes underlying the PCa progression and investigate the efficacy of related metabolic panels for an accurate PCa assessment.

Methods

In the present study, 75 PCa subjects, 62 PCa patients with bone metastasis (PCaB), and 50 benign prostatic hyperplasia (BPH) patients were enrolled, and we performed a cross-sectional metabolomics analysis of serum samples collected from these subjects using a ¹H nuclear magnetic resonance (NMR)-based metabolomics approach.

Results

Multivariate analysis revealed that BPH, PCa, and PCaB groups showed distinct metabolic divisions, while univariate statistics integrated with variable importance in the projection (VIP) scores identified a differential metabolite series, which included energy, amino acid, and ketone body metabolism. Herein, we identified a series of characteristic serum metabolic changes, including decreased trends of 3-HB and acetone as well as elevated trends of alanine in PCa patients compared with BPH subjects, while increased levels of 3-HB and acetone as well as decreased levels of alanine in PCaB patients compared with PCa. Additionally, our results also revealed the metabolic panels of discriminant metabolites coupled with the clinical parameters (age and body mass index) for discrimination between PCa and BPH, PCaB and BPH, PCaB and PCa achieved the AUC values of 0.828, 0.917, and 0.872, respectively.

Conclusions

Overall, our study gave successful discrimination of BPH, PCa and PCaB, and we characterized the potential metabolic alterations involved in the PCa progression and its metastases, including 3-HB, acetone and alanine. The defined biomarker panels could be employed to aid in the diagnosis and classification of PCa in clinical practice.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12967-022-03478-5.