Metabolomics, metabolic flux analysis and cancer pharmacology

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.

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.

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.

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.

Metabolic characterization of hypertrophic cardiomyopathy in human heart

Hypertrophic cardiomyopathy (HCM) is a common inherited cardiovascular disease with heterogeneous clinical presentations, governed by multiple molecular mechanisms. Metabolic perturbations underlie most cardiovascular diseases; however, the metabolic alterations and their function in HCM are unknown. Here, we describe the metabolome and lipidome of heart and plasma samples from individuals with and without HCM. Correlation analyses showed strong association between metabolic alterations and cardiac function and prognosis of patients with HCM. Using machine learning we identified metabolite panels as potential HCM diagnostic markers or predictors of survival. Clustering based on metabolome and lipidome of heart enabled stratification of patients with HCM into three subgroups with distinct cardiac function and survival. Integration of metabolomics and proteomics data identified metabolic pathways significantly altered in patients with HCM, with the pentose phosphate pathway and oxidative stress being particularly upregulated. Thus, targeting the pentose phosphate pathway and oxidative stress may serve as potential therapeutic strategies for HCM.

3,3′-Diindolylmethane Enhances Fluorouracil Sensitivity via Inhibition of Pyrimidine Metabolism in Colorectal Cancer

Chemoresistance limits treatment outcomes in colorectal cancer (CRC) patients. A dimeric metabolite of indole-3-carbinol, 3,3′-diindolylmethane (DIM) is abundant in cruciferous vegetables and has shown anticancer efficacy. The role of DIM in regulating chemosensitivity in CRC remains unknown. In this study, we demonstrated that DIM treatment inhibits the malignant progression of CRC. RNA sequencing indicated that pyrimidine synthesis genes are attenuated by DIM treatment. Stable 1³C-labeled glucose tracing revealed that DIM inhibits de novo pyrimidine biosynthesis in CRC. DIM increases 5-FU cytotoxicity in CRC via regulation of the expression of pyrimidine metabolism-related genes. DIM synergizes with 5-FU to enhance its inhibitory effects on CRC both in vivo and in vitro. Our results suggest that DIM improves the therapeutic outcomes of FU-based chemotherapy in CRCs by inhibiting pyrimidine metabolism, identifying a new strategy for clinical therapy.

Understanding Inborn Errors of Metabolism through Metabolomics

Inborn errors of metabolism (IEMs) are rare diseases caused by a defect in a single enzyme, co-factor, or transport protein. For most IEMs, no effective treatment is available and the exact disease mechanism is unknown. The application of metabolomics and, more specifically, tracer metabolomics in IEM research can help to elucidate these disease mechanisms and hence direct novel therapeutic interventions. In this review, we will describe the different approaches to metabolomics in IEM research. We will discuss the strengths and weaknesses of the different sample types that can be used (biofluids, tissues or cells from model organisms; modified cell lines; and patient fibroblasts) and when each of them is appropriate to use.

Recent advances in metabolomics analysis for early drug development

The pharmaceutical industry adapted proteomics and other 'omics technologies for drug research early following their initial introduction. Although metabolomics lacked behind in this development, it has now become an accepted and widely applied approach in early drug development. Over the past few decades, metabolomics has evolved from a pure exploratory tool to a more mature and quantitative biochemical technology. Several metabolomics-based platforms are now applied during the early phases of drug discovery. Metabolomics analysis assists in the definition of the physiological response and target engagement (TE) markers as well as elucidation of the mode of action (MoA) of drug candidates under investigation. In this review, we highlight recent examples and novel developments of metabolomics analyses applied during early drug development.

Functional metabolome profiling may improve individual outcomes in colorectal cancer management implementing concepts of predictive, preventive, and personalized medical approach

Objectives

Colorectal cancer (CRC) is one of the most common solid tumors worldwide, but its diagnosis and treatment are limited. The objectives of our study were to compare the metabolic differences between CRC patients and healthy controls (HC), and to identify potential biomarkers in the serum that can be used for early diagnosis and as effective therapeutic targets. The aim was to provide a new direction for CRC predictive, preventive, and personalized medicine (PPPM).

Methods

In this study, CRC patients (n = 30) and HC (n = 30) were recruited. Serum metabolites were assayed using an ultra-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF/MS) technology. Subsequently, CRC cell lines (HCT116 and HCT8) were treated with metabolites to verify their function. Key targets were identified by molecular docking, thermal shift assay, and protein overexpression/inhibition experiments. The inhibitory effect of celastrol on tumor growth was also assessed, which included IC50 analysis, nude mice xenografting, molecular docking, protein overexpression/inhibition experiments, and network pharmacology technology.

Results

In the CRC group, 15 serum metabolites were significantly different in comparison with the HC group. The level of glycodeoxycholic acid (GDCA) was positively correlated with CRC and showed high sensitivity and specificity for the clinical diagnostic reference (AUC = 0.825). In vitro findings showed that GDCA promoted the proliferation and migration of CRC cell lines (HCT116 and HCT8), and Poly(ADP-ribose) polymerase-1 (PARP-1) was identified as one of the key targets of GDCA. The IC50 of celastrol in HCT116 cells was 121.1 nM, and the anticancer effect of celastrol was supported by in vivo experiments. Based on the potential of GDCA in PPPM, PARP-1 was found to be significantly correlated with the anticancer functions of celastrol.

Conclusion

These findings suggest that GDCA is an abnormally produced metabolite of CRC, which may provide an innovative molecular biomarker for the predictive identification and targeted prevention of CRC. In addition, PARP-1 was found to be an important target of GDCA that promotes CRC; therefore, celastrol may be a potential targeted therapy for CRC via its effects on PARP-1. Taken together, the pathophysiology and progress of tumor molecules mediated by changes in metabolite content provide a new perspective for predictive, preventive, and personalized medical of clinical cancer patients based on the target of metabolites in vivo.Clinical trials registration number: ChiCTR2000039410.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13167-021-00269-8.

Metabolomic Fingerprinting for the Detection of Early-Stage Lung Cancer: From the Genome to the Metabolome

The five-year survival rate of lung cancer patients is very low, mainly because most newly diagnosed patients present with locally advanced or metastatic disease. Therefore, early diagnosis is key to the successful treatment and management of lung cancer. Unfortunately, early detection methods of lung cancer are not ideal. In this brief review, we described early detection methods such as chest X-rays followed by bronchoscopy, sputum analysis followed by cytological analysis, and low-dose computed tomography (LDCT). In addition, we discussed the potential of metabolomic fingerprinting, compared to that of other biomarkers, including molecular targets, as a low-cost, high-throughput blood-based test that is both feasible and affordable for early-stage lung cancer screening of at-risk populations. Accordingly, we proposed a paradigm shift to metabolomics as an alternative to molecular and proteomic-based markers in lung cancer screening, which will enable blood-based routine testing and be accessible to those patients at the highest risk for lung cancer.

Elevated serum phosphatidylcholine (16:1/22:6) levels promoted by fish oil and vitamin D3 are highly correlated with biomarkers of non-alcoholic fatty liver disease in Chinese subjects

Phosphatidylcholine (16:1/22:6) was associated with improving inflammation and lipid metabolism.

FV-429 enhances the efficacy of paclitaxel in NSCLC by reprogramming HIF-1α-modulated FattyAcid metabolism

Solid tumors often exhibit hypoxia in their centers, which has been associated with a marked reduction in the sensitivity of the tumor cells to anti-tumor and chemotherapeutic interventions. Here, we found that the occurrence and progress of hypoxic insensitivity to paclitaxel in non-small cell lung cancer (NSCLC) are closely associated with the HIF-1α pathway. The HIF-1α protein upregulated the expression of adipose differentiation-related protein (ADRP), fatty acid synthase (FASN), and sterol regulatory element binding protein 1(SREBP1), while simultaneously downregulating carnitine palmitoyltransferase 1 (CPT1), thereby leading to a more pronounced uptake of lipids and reduced oxidation of fatty acids. Diminished levels of fatty acids led to reduced Wnt pathway activation and β-catenin nuclear translocation, leading to G2/M cell cycle arrest. In this study, FV-429, a derivative of the natural flavonoid wogonin, reprogrammed metabolism of cancer cells and decreased fatty acid levels. Moreover, paclitaxel-induced G2/M phase arrest in hypoxia-resistant NSCLC was hampered but FV-429 improved the sensitivity of these cancer cells to paclitaxel. FV-429 activated and modulated fatty acid metabolism in NSCLC cells, significantly reduced levels of fatty acids within cells and increased the oxidation of these fatty acids. The results of our study demonstrated that FV-429 could reshape fatty acid metabolism in hypoxia-induced paclitaxel-resistant NSCLC and enhance the sensitivity of NSCLC cells to paclitaxel through G2/M phase arrest deterioration, by inactivating the Wnt pathway, and suggested the possibility of using FV-429 as a promising candidate therapeutic agent for advanced NSCLC.