Serum metabolite signatures of epithelial ovarian cancer based on targeted metabolomics

Serum metabolic fingerprints encode functional biomarkers for ovarian cancer diagnosis: a large-scale cohort study

BACKGROUND

Ovarian cancer (OC) ranks as the most lethal gynaecological malignancy worldwide, with early diagnosis being crucial yet challenging. Current diagnostic methods like transvaginal ultrasound and blood biomarkers show limited sensitivity/specificity. This study aimed to identify and validate serum metabolic biomarkers for OC diagnosis using the largest cohort reported to date.

METHODS

We constructed a large-scale OC-associated cohort of 1432 subjects, including 662 OC, 563 benign ovarian disease, and 207 healthy control subjects, across retrospective (n = 1073) and set-aside validation (n = 359) cohorts. Serum metabolic fingerprints (SMFs) were recorded using nanoparticle-enhanced laser desorption/ionization mass spectrometry (NELDI-MS). A diagnostic panel was developed through machine learning of SMFs in the discovery cohort and validated in independent verification and set-aside validation cohorts. The identified metabolic biomarkers were further validated using liquid chromatography MS and their biological functions were assessed in OC cell lines.

FINDINGS

We identified a metabolic biomarker panel including glucose, histidine, pyrrole-2-carboxylic acid, and dihydrothymine. This panel achieved consistent areas under the curve (AUCs) of 0.87-0.89 for distinguishing between malignant and benign ovarian masses across all cohorts, and improved to AUCs of 0.95-0.99 when combined with risk of ovarian malignancy algorithm (ROMA). In vitro validation provided initial biological context for the metabolic alterations observed in our diagnostic panel.

INTERPRETATION

Our study established a reliable serum metabolic biomarker panel for OC diagnosis with potential clinical translations. The NELDI-MS based approach offers advantages of fast analytical speed (∼30 s/sample) and low cost (∼2-3 dollars/sample), making it suitable for large-scale clinical applications.

FUNDING

MOST (2021YFA0910100), NSFC (82421001, 823B2050, 824B2059, and 82173077), Medical-Engineering Joint Funds of Shanghai Jiao Tong University (YG2021GD02, YG2024ZD07, and YG2023ZD08), Shanghai Science and Technology Committee Project (23JC1403000), Shanghai Institutions of Higher Learning (2021-01-07-00-02-E00083), Shanghai Jiao Tong University Inner Mongolia Research Institute (2022XYJG0001-01-16), Sichuan Provincial Department of Science and Technology (2024YFHZ0176), Innovation Research Plan by the Shanghai Municipal Education Commission (ZXWF082101), Innovative Research Team of High-Level Local Universities in Shanghai (SHSMU-ZDCX20210700), Basic-Clinical Collaborative Innovation Project from Shanghai Immune Therapy Institute, Guangdong Basic and Applied Basic Research Foundation (2024A1515013255).

The tumor-promoting role of methionyl-tRNA synthetase 1 in ovarian cancer and its potential mechanisms

Methionyl-tRNA synthetase 1 (MARS) is an enzyme that belongs to the family of aminoacyl-tRNA synthetases. High levels of MARS have been shown to correlate with a poorer prognosis in a variety of tumor types. However, its specific role and the underlying mechanism in cancer, especially in ovarian cancer, are not well understood. This study aims to investigate the roles and potential mechanisms of MARS in ovarian cancer. Our findings reveal that MARS protein levels are elevated in ovarian cancer tissues, and that high MARS expression is associated with reduced overall survival and progression-free survival. Silencing of MARS significantly inhibited the proliferation, colony formation, migration, and invasion of ovarian cancer cells in vitro and mildly suppressed ovarian tumor growth in vivo. MARS silencing contributes to the upregulation of p53 protein. Moreover, RNA sequencing and subsequent in vitro and in vivo validation showed that the TP53-regulated cell cycle genes and immune-related cell surface receptor and cytokine-encoding genes were downregulated following MARS knockdown, suggesting a potential mechanism for the observed attenuation of tumor progression. Our results suggest MARS as a potential biomarker and therapeutic target in ovarian cancer, highlighting the need for further investigation into its multifaceted role in tumor biology and immune cell function.

WTAP contributes to platinum resistance in high-grade serous ovarian cancer by up-regulating malic acid: insights from liquid chromatography and mass spectrometry analysis

High-grade serous cancer (HGSC) is the most prevalent and aggressive subtype of ovarian cancer. In this study, we utilized liquid chromatography and mass spectrometry analysis to investigate metabolic alterations in HGSC. Among the 1353 metabolites identified, 140 were significantly differed between HGSC and normal ovarian tissue. KEGG pathway enrichment analysis revealed 23 distinct metabolic pathways, including the alanine/aspartate/glutamate metabolism, pyruvate metabolism, biosynthesis of amino acids, and citrate cycle, etc. Of the significantly differentiated metabolites, malic acid, fumarate, and phosphoenolpyruvate were found in the citrate cycle and glycolysis. In further analysis, 22 differentially expressed genes (DEGs) of glucose metabolism were found between HGSC and normal controls. Multivariate Cox analysis of the 22 DEGs showed that ME1, ALDOC, and RANBP2 were associated with overall survival in the TCGA cohort.Bioinformatic analysis indicated WTAP is strongly correlated to the expression of ME1, which is a rate-limiting enzyme that regulates the shuttle of malic acid in mitochondria and cytoplasm. After the knockdown of WTAP in A2780 and OVCAR-3 cells, the activity of the malic enzyme decreased which led to the accumulation of malic acid and citric acid, and the reduction of pyruvate and lactic acid. In A2780 and OVCAR-3 cells, the IC50 to platinum was increased after the knockdown of WTAP. After the knockdown of WTAP, the expression of ME1 was down-regulated and the m6A modification was down-regulated in ovarian cell lines. On the SRAMP website, there were two binding sites with high m6A scores at the 5 '-UTR 177 and 970 of ME1 mRNA. WTAP contributes to the platinum resistance through regulating the conversion from aerobic glycolysis to OXPHOS by upregulating the expression of ME1.

Large-scale analysis to identify risk factors for ovarian cancer

OBJECTIVE

Ovarian cancer is characterized by late-stage diagnoses and poor prognosis. We aimed to identify factors that can inform prevention and early detection of ovarian cancer.

METHODS

We used a data-driven machine learning approach to identify predictors of epithelial ovarian cancer from 2920 input features measured 12.6 years (IQR 11.9 to 13.3 years) before diagnoses. Analyses included 221 732 female participants in the UK Biobank without a history of cancer. During the follow-up 1441 women developed ovarian cancer. For factors that contributed to model prediction, we used multivariate logistic regression to evaluate the association with ovarian cancer, with evidence for causality tested by Mendelian randomization (MR) analyses in the Ovarian Cancer Genetics Consortium (25 509 cases).

RESULTS

Greater parity and ever-use of oral contraception were associated with lower ovarian cancer risk (ever vs never OR 0.74, 95% CI 0.66 to 0.84). After adjustment for established risk factors, greater height, weight, and greater red blood cell distribution width were associated with increased ovarian cancer risk, while higher aspartate aminotransferase levels and mean corpuscular volume were associated with lower risk. MR analyses confirmed observational associations with anthropometric/adiposity traits (eg, body fat percentage per standard deviation (SD); OR inverse-variance weighted (ORIVW) 1.28, 95% CI 1.13 to 1.46) and aspartate aminotransferase (ORIVW 0.87, 95% CI 0.78 to 0.98). MR also provided genetic evidence for a protective association of higher total serum protein on ovarian cancer, higher lymphocyte count on serous and endometrioid ovarian cancer, and greater forced expiratory volume in 1 s on serous ovarian cancer among other findings.

CONCLUSIONS

This study shows that certain risk factors for ovarian cancer are modifiable, suggesting that weight reduction and interventions to reduce the number of ovulations may provide potential for future prevention. We also identified blood biomarkers associated with ovarian cancer years before diagnoses, warranting further investigation.

A review on trends in development and translation of omics signatures in cancer

The field of cancer genomics and transcriptomics has evolved from targeted profiling to swift sequencing of individual tumor genome and transcriptome. The steady growth in genome, epigenome, and transcriptome datasets on a genome-wide scale has significantly increased our capability in capturing signatures that represent both the intrinsic and extrinsic biological features of tumors. These biological differences can help in precise molecular subtyping of cancer, predicting tumor progression, metastatic potential, and resistance to therapeutic agents. In this review, we summarized the current development of genomic, methylomic, transcriptomic, proteomic and metabolic signatures in the field of cancer research and highlighted their potentials in clinical applications to improve diagnosis, prognosis, and treatment decision in cancer patients.

Using liquid chromatography and mass spectrometry to predict potential biomarkers for missed miscarriage and its metabolic pathways in a tertiary center: A cross-sectional analytic study

OBJECTIVE

To determine and compare the serum metabolites in missed abortion versus normal early pregnancy using ultra-high-performance liquid chromatography and tandem time-of-flight mass spectrometry, and to determine how these metabolites can be used to predict the potential biomarkers and possible metabolic pathways of a missed abortion.

METHODS

The serum of patients with a missed abortion was used as the experimental group and the serum of patients with an induced abortion during normal early pregnancy was used as the control group. Principal component analysis and orthogonal partial least square discriminant analysis were additionally used to observe the difference in metabolite distribution between the two groups. A variable weight value (variable importance in the projection; VIP) obtained from the orthogonal partial least squares discriminant analysis model more than 1 and P less than 0.05 were taken to indicate significant differences in metabolite screening. After this, enrichment analysis of the metabolic pathways of these metabolites was conducted using Fisher precise test in order to find the metabolic pathway with the highest correlation with the differential metabolites.

RESULTS

In total, 30 patients were included in the experimental group, with 30 patients in the control group. Fifty-five metabolites (VIP > 1, P < 0.05) with significant differences related to missed abortion were selected, among which 35 metabolites increased and 20 decreased in patients with a missed abortion. KEGG pathway enrichment analysis showed that the four metabolic pathways with the highest correlation were cholesterol metabolism, arginine biosynthesis, cell apoptosis, and the FoxO signaling pathway.

CONCLUSION

The missed abortion serum metabolites and changes in related metabolic pathways reported in this study provide a basis for the early prediction and diagnosis of a missed abortion.

Metabolomics for searching validated biomarkers in cancer studies: a decade in review

INTRODUCTION

In the dynamic landscape of modern healthcare, the ability to anticipate and diagnose diseases, particularly in cases where early treatment significantly impacts outcomes, is paramount. Cancer, a complex and heterogeneous disease, underscores the critical importance of early diagnosis for patient survival. The integration of metabolomics information has emerged as a crucial tool, complementing the genotype-phenotype landscape and providing insights into active metabolic mechanisms and disease-induced dysregulated pathways.

AREAS COVERED

This review explores a decade of developments in the search for biomarkers validated within the realm of cancer studies. By critically assessing a diverse array of research articles, clinical trials, and studies, this review aims to present an overview of the methodologies employed and the progress achieved in identifying and validating biomarkers in metabolomics results for various cancer types.

EXPERT OPINION

Through an exploration of more than 800 studies, this review has allowed to establish a general idea about state-of-art in the search of biomarkers in metabolomics studies involving cancer which include certain level of results validation. The potential for metabolites as diagnostic markers to reach the clinic and make a real difference in patient health is substantial, but challenges remain to be explored.

Metabolomics for Clinical Biomarker Discovery and Therapeutic Target Identification

As links between genotype and phenotype, small-molecule metabolites are attractive biomarkers for disease diagnosis, prognosis, classification, drug screening and treatment, insight into understanding disease pathology and identifying potential targets. Metabolomics technology is crucial for discovering targets of small-molecule metabolites involved in disease phenotype. Mass spectrometry-based metabolomics has implemented in applications in various fields including target discovery, explanation of disease mechanisms and compound screening. It is used to analyze the physiological or pathological states of the organism by investigating the changes in endogenous small-molecule metabolites and associated metabolism from complex metabolic pathways in biological samples. The present review provides a critical update of high-throughput functional metabolomics techniques and diverse applications, and recommends the use of mass spectrometry-based metabolomics for discovering small-molecule metabolite signatures that provide valuable insights into metabolic targets. We also recommend using mass spectrometry-based metabolomics as a powerful tool for identifying and understanding metabolic patterns, metabolic targets and for efficacy evaluation of herbal medicine.

Loss of mitochondrial pyruvate carrier 1 supports proline-dependent proliferation and collagen biosynthesis in ovarian cancer

The pyruvate transporter MPC1 (mitochondrial pyruvate carrier 1) acts as a tumour-suppressor, loss of which correlates with a pro-tumorigenic phenotype and poor survival in several tumour types. In high-grade serous ovarian cancers (HGSOC), patients display copy number loss of MPC1 in around 78% of cases and reduced MPC1 mRNA expression. To explore the metabolic effect of reduced expression, we demonstrate that depleting MPC1 in HGSOC cell lines drives expression of key proline biosynthetic genes; PYCR1, PYCR2 and PYCR3, and biosynthesis of proline. We show that altered proline metabolism underpins cancer cell proliferation, reactive oxygen species (ROS) production, and type I and type VI collagen formation in ovarian cancer cells. Furthermore, exploring The Cancer Genome Atlas, we discovered the PYCR3 isozyme to be highly expressed in a third of HGSOC patients, which was associated with more aggressive disease and diagnosis at a younger age. Taken together, our study highlights that targeting proline metabolism is a potential therapeutic avenue for the treatment of HGSOC.

Metabolomic biomarkers in liquid biopsy: accurate cancer diagnosis and prognosis monitoring

Liquid biopsy, a novel detection method, has recently become an active research area in clinical cancer owing to its unique advantages. Studies on circulating free DNA, circulating tumor cells, and exosomes obtained by liquid biopsy have shown great advances and they have entered clinical practice as new cancer biomarkers. The metabolism of the body is dynamic as cancer originates and progresses. Metabolic abnormalities caused by cancer can be detected in the blood, sputum, urine, and other biological fluids via systemic or local circulation. A considerable number of recent studies have focused on the roles of metabolic molecules in cancer. The purpose of this review is to provide an overview of metabolic markers from various biological fluids in the latest clinical studies, which may contribute to cancer screening and diagnosis, differentiation of cancer typing, grading and staging, and prediction of therapeutic response and prognosis.

Exploring the causal role of multiple metabolites on ovarian cancer: a two sample Mendelian randomization study

BACKGROUND

The mechanisms and risk factors underlying ovarian cancer (OC) remain under investigation, making the identification of new prognostic biomarkers and improved predictive factors critically important. Recently, circulating metabolites have shown potential in predicting survival outcomes and may be associated with the pathogenesis of OC. However, research into their genetic determinants is limited, and there are some inadequacies in understanding the distinct subtypes of OC. In this context, we conducted a Mendelian randomization study aiming to provide evidence for the relationship between genetically determined metabolites (GDMs) and the risk of OC and its subtypes.

METHODS

In this study, we consolidated genetic statistical data of GDMs with OC and its subtypes through a genome-wide association study (GWAS) and conducted a two-sample Mendelian randomization (MR) analysis. The inverse variance weighted (IVW) method served as the primary approach, with MR-Egger and weighted median methods employed for cross-validation to determine whether a causal relationship exists between the metabolites and OC risk. Moreover, a range of sensitivity analyses were conducted to validate the robustness of the results. MR-Egger intercept, and Cochran's Q statistical analysis were used to evaluate possible heterogeneity and pleiotropy. False discovery rate (FDR) correction was applied to validate the findings. We also conducted a reverse MR analysis to validate whether the observed blood metabolite levels were influenced by OC risk. Additionally, metabolic pathway analysis was carried out using the MetaboAnalyst 5.0 software.

RESULTS

In MR analysis, we discovered 18 suggestive causal associations involving 14 known metabolites, 8 metabolites as potential risk factors, and 6 as potential cancer risk reducers. In addition, three significant pathways, "caffeine metabolism," "arginine biosynthesis," and "citrate cycle (TCA cycle)" were associated with the development of mucinous ovarian cancer (MOC). The pathways "caffeine metabolism" and "alpha-linolenic acid metabolism" were associated with the onset of endometrioid ovarian cancer (OCED).

CONCLUSIONS

Our MR analysis revealed both protective and risk-associated metabolites, providing insights into the potential causal relationships between GDMs and the metabolic pathways related to OC and its subtypes. The metabolites that drive OC could be potential candidates for biomarkers.

Serum Metabolomics of Retinoblastoma: Assessing the Differential Serum Metabolic Signatures of Unilateral and Bilateral Patients

Retinoblastoma (Rb) is the most common pediatric eye cancer. To identify the biomarkers for early diagnosis and monitoring the progression of Rb in patients, mapping of the alterations in their metabolic profiles is essential. The present study aims at exploring the metabolic disparity in serum from Rb patients and controls using NMR-based metabolomics. A total of 72 metabolites, including carbohydrates, amino acids, and organic acids, were quantified in serum samples from 24 Rb patients and 26 controls. Distinct clusters of Rb patients and controls were obtained using the partial least-squares discriminant analysis (PLS-DA) model. Further, univariate and multivariate analyses of unilateral and bilateral Rb patients with respect to their age-matched controls depicted their distinct metabolic fingerprints. Metabolites including 2-phosphoglycerate, 4-aminobutyrate, proline, O-phosphocholine, O-phosphoethanolamine, and Sn-glycero-3-phosphocholine (Sn-GPC) showed significant perturbation in both unilateral and bilateral Rb patients. However, metabolic differences among the bilateral Rb cases were more pronounced than those in unilateral Rb cases with respect to controls. In addition to major discriminatory metabolites for Rb, unilateral and bilateral Rb cases showed specific metabolic changes, which might be the result of their differential genetic/somatic mutational backgrounds. This further suggests that the aberrant metabolic perturbation in bilateral patients signifies the severity of the disease in Rb patients. The present study demonstrated that identified serum metabolites have potential to serve as a noninvasive method for detection of Rb, discriminate bilateral from unilateral Rb patients, and aid in better understanding of the RB tumor biology.

A multi-omics approach to understand the influence of polyphenols in ovarian cancer for precision nutrition: a mini-review

The impact of polyphenols in ovarian cancer is widely studied observing gene expression, epigenetic alterations, and molecular mechanisms based on new 'omics' technologies. Therefore, the combination of omics technologies with the use of phenolic compounds may represent a promising approach to precision nutrition in cancer. This article provides an updated review involving the current applications of high-throughput technologies in ovarian cancer, the role of dietary polyphenols and their mechanistic effects in ovarian cancer, and the current status and challenges of precision nutrition and their relationship with big data. High-throughput technologies in different omics science can provide relevant information from different facets for identifying biomarkers for diagnosis, prognosis, and selection of specific therapies for personalized treatment. Furthermore, the field of omics sciences can provide a better understanding of the role of polyphenols and their function as signaling molecules in the prevention and treatment of ovarian cancer. Although we observed an increase in the number of investigations, there are several approaches to data acquisition, analysis, and integration that still need to be improved, and the standardization of these practices still needs to be implemented in clinical trials.

Machine Learning Reveals Lipidome Remodeling Dynamics in a Mouse Model of Ovarian Cancer

Ovarian cancer (OC) is one of the deadliest cancers affecting the female reproductive system. It may present little or no symptoms at the early stages and typically unspecific symptoms at later stages. High-grade serous ovarian cancer (HGSC) is the subtype responsible for most ovarian cancer deaths. However, very little is known about the metabolic course of this disease, particularly in its early stages. In this longitudinal study, we examined the temporal course of serum lipidome changes using a robust HGSC mouse model and machine learning data analysis. Early progression of HGSC was marked by increased levels of phosphatidylcholines and phosphatidylethanolamines. In contrast, later stages featured more diverse lipid alterations, including fatty acids and their derivatives, triglycerides, ceramides, hexosylceramides, sphingomyelins, lysophosphatidylcholines, and phosphatidylinositols. These alterations underscored unique perturbations in cell membrane stability, proliferation, and survival during cancer development and progression, offering potential targets for early detection and prognosis of human ovarian cancer.

GC-MS-based untargeted metabolic profiling of malignant mesothelioma plasma

Background

Malignant mesothelioma (MM) is a cancer caused mainly by asbestos exposure, and is aggressive and incurable. This study aimed to identify differential metabolites and metabolic pathways involved in the pathogenesis and diagnosis of malignant mesothelioma.

Methods

By using gas chromatography-mass spectrometry (GC-MS), this study examined the plasma metabolic profile of human malignant mesothelioma. We performed univariate and multivariate analyses and pathway analyses to identify differential metabolites, enriched metabolism pathways, and potential metabolic targets. The area under the receiver-operating curve (AUC) criterion was used to identify possible plasma biomarkers.

Results

Using samples from MM (n = 19) and healthy control (n = 22) participants, 20 metabolites were annotated. Seven metabolic pathways were disrupted, involving alanine, aspartate, and glutamate metabolism; glyoxylate and dicarboxylate metabolism; arginine and proline metabolism; butanoate and histidine metabolism; beta-alanine metabolism; and pentose phosphate metabolic pathway. The AUC was used to identify potential plasma biomarkers. Using a threshold of AUC = 0.9, five metabolites were identified, including xanthurenic acid, (s)-3,4-hydroxybutyric acid, D-arabinose, gluconic acid, and beta-d-glucopyranuronic acid.

Conclusions

To the best of our knowledge, this is the first report of a plasma metabolomics analysis using GC-MS analyses of Asian MM patients. Our identification of these metabolic abnormalities is critical for identifying plasma biomarkers in patients with MM. However, additional research using a larger population is needed to validate our findings.

Designed Concave Octahedron Heterostructures Decode Distinct Metabolic Patterns of Epithelial Ovarian Tumors

Epithelial ovarian cancer (EOC) is a polyfactorial process associated with alterations in metabolic pathways. A high-performance screening tool for EOC is in high demand to improve prognostic outcome but is still missing. Here, a concave octahedron Mn₂ O₃ /(Co,Mn)(Co,Mn)₂ O₄ (MO/CMO) composite with a heterojunction, rough surface, hollow interior, and sharp corners is developed to record metabolic patterns of ovarian tumors by laser desorption/ionization mass spectrometry (LDI-MS). The MO/CMO composites with multiple physical effects induce enhanced light absorption, preferred charge transfer, increased photothermal conversion, and selective trapping of small molecules. The MO/CMO shows ≈2-5-fold signal enhancement compared to mono- or dual-enhancement counterparts, and ≈10-48-fold compared to the commercialized products. Subsequently, serum metabolic fingerprints of ovarian tumors are revealed by MO/CMO-assisted LDI-MS, achieving high reproducibility of direct serum detection without treatment. Furthermore, machine learning of the metabolic fingerprints distinguishes malignant ovarian tumors from benign controls with the area under the curve value of 0.987. Finally, seven metabolites associated with the progression of ovarian tumors are screened as potential biomarkers. The approach guides the future depiction of the state-of-the-art matrix for intensive MS detection and accelerates the growth of nanomaterials-based platforms toward precision diagnosis scenarios.

Exploring the key factors of schizophrenia relapse by integrating LC-MS/1H NMR metabolomics and weighted correlation network analysis

BACKGROUND

Lack of comprehending key factors of schizophrenia relapse has impeded its effective treatment, indicating that the mechanism clarification and available intervention of schizophrenia relapse required further amelioration.

METHOD

Based on the integration of LC-MS and ¹H NMR metabolomics, a weighted correlation network was established to screen pivotal factors of accelerating schizophrenia relapse. Then, the cluster most correlated with schizophrenia relapse was explored, and the biological function of cluster was investigated. Next, the key biomarker related to schizophrenia relapse was obtained through multiple algorithms. Moreover, the Lilikoi algorithm and correlation analysis were implemented to reveal the association between key biomarker and schizophrenia relapse.

RESULT

Results showed that 458 different forms of metabolites were identified for structuring the weighted correlation network. The module-trait correlation indicated that the turquoise module was the most highly correlated with schizophrenia relapse. Further, network analysis revealed that, in turquoise module, cluster 1 composed of 139 metabolites (involved in lipid metabolism and energy metabolism) was the most important subnetwork relevant to schizophrenia relapse. Finally, phenylalanylphenylalanine was recommended as the key biomarker related to schizophrenia relapse. Moreover, the correlation analysis indicated that phenylalanylphenylalanine might affect the progression of schizophrenia by intervening in energy metabolism.

CONCLUSION

In summary, critical factors of schizophrenia relapse have been revealed in our research, expounding the schizophrenia progression more systemically, which could shed some light on improving the intervention of schizophrenia relapse.

Serum metabolic profiling of rats infected with Clonorchis sinensis using LC-MS/MS method

Background

Clonorchiasis is an important foodborne parasitic disease. The omics-based-techniques could illuminate parasite biology and further make innovations in the research for parasitic diseases. However, knowledge about the serum metabolic profiles and related metabolic pathways in clonorchiasis is very limited.

Methods

A untargeted ultra-high performance liquid tandem chromatography quadrupole time of flight mass spectrometry (UHPLC-QTOF-MS) was used to profile the serum metabolites of rats at both 4 and 8 weeks post infection (wpi) with Clonorchis sinensis (C. sinensis). Additionally, multivariate statistical analysis methods were employed to identify differential metabolites. Next, serum amino acids and phosphatidylcholines (PCs) levels were determined by targeted metabolomics analysis.

Result

A total of 10530 and 6560 ions were identified in ESI+ and ESI- modes. The levels of phosphatidylcholines, glycerophosphocholine and choline were significantly changed, with the shift in lipid metabolism. Significant changes were also observed in amino acids (isoleucine, valine, leucine, threonine, glutamate and glutamine). Targeted analysis showed that BCAAs (isoleucine, valine, leucine) levels significantly increased at 4 wpi and decreased at 8 wpi; threonine was increased at 8 wpi, whereas glutamate and glutamine showed a decreasing trend at 8 wpi. Additionally, the level of 17 PCs were significantly changed in infected rats. Marked metabolic pathways were involved in clonorchiasis, including glycerophospholipid metabolism, alanine, aspartate and glutamate metabolism, histidine metabolism and pyrimidine metabolism.

Conclusion

These results show that C. sinensis infection can cause significant changes in the rat serum metabolism, especially in amino acids and lipids. The metabolic signature together with perturbations in metabolic pathways could provide more in depth understanding of clonorchiasis and further make potential therapeutic interventions.

Machine Learning Reveals Lipidome Remodeling Dynamics in a Mouse Model of Ovarian Cancer

Ovarian cancer (OC) is one of the deadliest cancers affecting the female reproductive system. It may present little or no symptoms at the early stages, and typically unspecific symptoms at later stages. High-grade serous ovarian cancer (HGSC) is the subtype responsible for most ovarian cancer deaths. However, very little is known about the metabolic course of this disease, particularly in its early stages. In this longitudinal study, we examined the temporal course of serum lipidome changes using a robust HGSC mouse model and machine learning data analysis. Early progression of HGSC was marked by increased levels of phosphatidylcholines and phosphatidylethanolamines. In contrast, later stages featured more diverse lipids alterations, including fatty acids and their derivatives, triglycerides, ceramides, hexosylceramides, sphingomyelins, lysophosphatidylcholines, and phosphatidylinositols. These alterations underscored unique perturbations in cell membrane stability, proliferation, and survival during cancer development and progression, offering potential targets for early detection and prognosis of human ovarian cancer.

Teaser

Time-resolved lipidome remodeling in an ovarian cancer model is studied through lipidomics and machine learning.

Metabolic reprogramming of the tumor immune microenvironment in ovarian cancer: A novel orientation for immunotherapy

Ovarian cancer is the most lethal gynecologic tumor, with the highest mortality rate. Numerous studies have been conducted on the treatment of ovarian cancer in the hopes of improving therapeutic outcomes. Immune cells have been revealed to play a dual function in the development of ovarian cancer, acting as both tumor promoters and tumor suppressors. Increasingly, the tumor immune microenvironment (TIME) has been proposed and confirmed to play a unique role in tumor development and treatment by altering immunosuppressive and cytotoxic responses in the vicinity of tumor cells through metabolic reprogramming. Furthermore, studies of immunometabolism have provided new insights into the understanding of the TIME. Targeting or activating metabolic processes of the TIME has the potential to be an antitumor therapy modality. In this review, we summarize the composition of the TIME of ovarian cancer and its metabolic reprogramming, its relationship with drug resistance in ovarian cancer, and recent research advances in immunotherapy.

Cerebrospinal Fluid Amino Acid Metabolite Signatures of Diabetic Cognitive Dysfunction Based on Targeted Mass Spectrometry

BACKGROUND

Diabetic cognitive dysfunction (DCD) is one of severe diabetic complications and might develop to irreversible dementia. Early diagnosis and detection of DCD is significant for prevention and treatment.

OBJECTIVE

The main objective of this study was to investigate the amino acid profiles of rat with DCD in the cerebrospinal fluid (CSF) to distinguish the early specific biomarkers.

METHODS

In total, rats were assigned into control and model groups. Model was induced by intraperitoneal injection of streptozotocin. The Morris water maze (MWM) method was used to evaluate learning and memory in rats on the 13th week after the model established. CSF samples were collected via cisterna magna puncture at the 0th, 5th, 9th, and 13th week, and amino acids profiling of CSF samples were performed via ultra performance liquid chromatography multiple reaction monitoring mass spectrometry (UPLC-MRM-MS). The amino acid profile was processed through multivariate analysis to identify potential biomarkers, and the related metabolic pathways were analyzed by MetaboAnalyst 5.0.

RESULTS

Compared to the control group, the escape latency of the MWM was significantly prolonged in model group rats (p <  0.05). Different amino acid profiles were obtained between two groups. L-Alanine, L-Glutamine, L-Lysine, L-Serine, and L-Threonine were identified as potential biomarkers for DCD. These biomarkers are principally involved in glycine, serine, and threonine metabolism, aminoacyl-tRNA biosynthesis, alanine, aspartate, and glutamate metabolism, and glyoxylate and dicarboxylate metabolism.

CONCLUSION

There are amino acid biomarkers in the CSF of rat with DCD. The mechanism of DCD is related to those pathways, which provide help for the early diagnosis and treatment and mechanism research.

Free Amino Acid Alterations in Patients with Gynecological and Breast Cancer: A Review

Gynecological and breast cancers still remain a significant health problem worldwide. Diagnostic methods are not sensitive and specific enough to detect the disease at an early stage. During carcinogenesis and tumor progression, the cellular need for DNA and protein synthesis increases leading to changes in the levels of amino acids. An important role of amino acids in many biological pathways, including biosynthesis of proteins, nucleic acids, enzymes, etc., which serve as an energy source and maintain redox balance, has been highlighted in many research articles. The aim of this review is a detailed analysis of the literature on metabolomic studies of gynecology and breast cancers with particular emphasis on alterations in free amino acid profiles. The work includes a brief overview of the metabolomic methodology and types of biological samples used in the studies. Special attention was paid to the possible role of selected amino acids in the carcinogenesis, especially proline and amino acids related to its metabolism. There is a clear need for further research and multiple external validation studies to establish the role of amino acid profiling in diagnosing gynecological and breast cancers.