Differential levels of L-homocysteic acid and lysophosphatidylcholine (16:0) in sera of patients with ovarian cancer

Plasma-based untargeted metabolomics reveals potential biomarkers for screening and distinguishing of ovarian tumors

Ovarian cancer (OC), a leading cause of gynecological cancer mortality, is frequently detected at advanced stages due to asymptomatic early progression. This study investigates plasma-based untargeted metabolomics for identifying biomarkers to screen and differentiate ovarian tumors (OT). Plasma samples from OC, benign ovarian tumors (BOT), and healthy controls (HC) were analyzed. Samples were randomized into train and test sets, with differential metabolites screened via two-tailed Student's t-test and partial least squares discriminant analysis. ROC models evaluated discriminatory capacity. Key metabolites demonstrated high predictive value: TMAO and hippuric acid distinguished OT from HC (AUC > 0.95), while linoleic acid, alpha-linolenic acid, and arachidonic acid (AUC > 0.9) further supported OT screening. Kynurenine differentiated OC from BOT (AUC = 0.808). Reduced levels of specific lysophosphatidylcholines (LPC (17:0/0:0), LPC (15:0/0:0)) also distinguished OT from HC (AUC = 0.771-0.89). These findings suggest plasma metabolomics holds promise for noninvasive biomarker discovery in OT screening and distinguishing between malignant and benign cases, though further validation of metabolite quantification is warranted prior to clinical application.

Diagnostic Accuracy of Ambient Mass Spectrometry with Blood Plasma in a Murine Glioma Model Using Machine Learning

OBJECTIVE

Malignant glioma progresses rapidly and shows poor prognosis, but clinically applicable blood plasma-based biochemical tumor markers remain lacking. This study aimed to develop a diagnostic system using probe electrospray ionization mass spectrometry (PESI-MS) and a machine-learning logistic regression model to detect plasma changes at various time points in a murine glioma model.

METHODS

We used a syngeneic intracranial orthotopic murine model with GL261 glioma cells. Blood plasmas were collected before and 3, 7, and 14 days after intracranial transplantation of glioma cells (tumor group, n = 7) or injection of phosphate-buffered saline (control group, n = 8). Mass spectra from those samples were obtained using PESI-MS and compared between control and tumor groups. We explored changes in mass spectra at the 3 time points (3, 7, and 14 days) after transplantation. The performance of machine-learning logistic regression-based diagnosis algorithm was evaluated to clarify the potential utility for early diagnosis.

RESULTS

Sixteen significant mass spectrum peaks were identified between the tumor and control groups. Multiple logistic regression analysis revealed 5 key mass spectra, achieving sensitivity of 0.875 and specificity of 0.943 for tumor discrimination. The area under the receiver operating characteristic curve was 0.981, outperforming analyses of individual spectra.

CONCLUSIONS

These results indicate that PESI-MS combined with machine learning-based diagnostics in blood plasma could be a promising approach to accurate detection of malignant glioma.

Identification of urinary metabolites correlated with tacrolimus levels through high-precision liquid chromatography-mass spectrometry and machine learning algorithms in kidney transplant patients

Background and aim

Tacrolimus, a widely used immunosuppressive drug in kidney transplant recipients, exhibits a narrow therapeutic window necessitating careful monitoring of its concentration to balance efficacy and minimize dose-related toxic effects. Although essential, this approach is not optimal, and tacrolinemia, even in the therapeutic interval, might be associated with toxicity and rejection within range. This study aimed to identify specific urinary metabolites associated with tacrolimus levels in kidney transplant patients using a combination of serum high-precision liquid chromatography-mass spectrometry (HPLC-MS) and machine learning algorithms.

Methods

A cohort of 42 kidney transplant patients, comprising 19 individuals with high tacrolimus levels (>8 ng/mL) and 23 individuals with low tacrolimus levels (<5 ng/mL), were included in the analysis. Urinary samples were subjected to HPLC-MS analysis, enabling comprehensive metabolite profiling across the study cohort. Additionally, tacrolimus concentrations were quantified using established clinical assays.

Results

Through an extensive analysis of the HPLC-MS data, a panel of five metabolites were identified that exhibited a significant correlation with tacrolimus levels (Valeryl carnitine, Glycyl-tyrosine, Adrenosterone, LPC 18:3 and 6-methylprednisolone). Machine learning algorithms were then employed to develop a predictive model utilizing the identified metabolites as features. The logistic regression model achieved an area under the curve of 0.810, indicating good discriminatory power and classification accuracy of 0.690.

Conclusions

This study demonstrates the potential of integrating HPLC-MS metabolomics with machine learning algorithms to identify urinary metabolites associated with tacrolimus levels. The identified metabolites are promising biomarkers for monitoring tacrolimus therapy, aiding in dose optimization and personalized treatment approaches.

Lysophosphatidylcholine induces oxidative stress and calcium-mediated cell death in human blood platelets

Platelets are essential component of circulation that plays a major role in hemostasis and thrombosis. During activation and its demise, platelets release platelet-derived microvesicles, with lysophosphatidylcholine (LPC) being a prominent component in their lipid composition. LPC, an oxidized low-density lipoprotein, is involved in cellular metabolism, but its higher level is implicated in pathologies like atherosclerosis, diabetes, and inflammatory disorders. Despite this, its impact on platelet function remains relatively unexplored. To address this, we studied LPC's effects on washed human platelets. A multimode plate reader was employed to measure reactive oxygen species and intracellular calcium using H2DCF-DA and Fluo-4-AM, respectively. Flow cytometry was utilized to measure phosphatidylserine expression, mitochondrial membrane potential (ΔΨm), and mitochondrial permeability transition pore (mPTP) formation using FITC-Annexin V, JC-1, and CoCl2/calcein-AM, respectively. Additionally, platelet morphology and its ultrastructure were observed via phase contrast and electron microscopy. Sonoclot and light transmission aggregometry were employed to examine fibrin formation and platelet aggregation, respectively. The findings demonstrate that LPC induced oxidative stress and increased intracellular calcium in platelets, resulting in increased phosphatidylserine expression and reduced ΔΨm. LPC triggered caspase-independent platelet death and mPTP opening via cytosolic and mitochondrial calcium, along with microvesiculation and reduced platelet counts. LPC increased the platelet's size, adopting a balloon-shaped morphology, causing membrane fragmentation and releasing its cellular contents, while inducing a pro-coagulant phenotype with increased fibrin formation and reduced integrin αIIbβ3 activation. Conclusively, this study reveals LPC-induced oxidative stress and calcium-mediated platelet death, necrotic in nature with pro-coagulant properties, potentially impacting inflammation and repair mechanisms during vascular injury.

Metabolomic landscape of renal cell carcinoma in von Hippel-Lindau syndrome in a Chinese cohort

Von Hippel-Lindau (VHL) syndrome is a rare autosomal dominant disorder, where renal cell carcinoma (RCC) serves as a significant cause of mortality. We collected peripheral blood from 61 VHL-RCC patients and 31 healthy individuals, along with 19 paired RCC tumor and adjacent non-malignant samples. Using liquid chromatography-mass spectrometry, we identified 238 plasma and 241 tissue differentially abundant metabolites (DAMs), highlighting key pathways such as arginine and proline metabolism. The top 10 of the 23 DAMs, common to both plasma and tissue, were instrumental in constructing a high-performance diagnostic model. These DAMs demonstrated significant correlations with VHL gene mutation types. Cox regression analysis revealed that plasma levels of N2,N2-dimethylguanosine were associated with the timing of RCC onset in VHL patients, acting as an independent predictive factor. This study enhances diagnostic accuracy for this rare condition and opens new avenues for exploring metabolic mechanisms of the disease and potential therapeutic directions.

"Find Me" and "Eat Me" signals: tools to drive phagocytic processes for modulating antitumor immunity

Phagocytosis, a vital defense mechanism, involves the recognition and elimination of foreign substances by cells. Phagocytes, such as neutrophils and macrophages, rapidly respond to invaders; macrophages are especially important in later stages of the immune response. They detect "find me" signals to locate apoptotic cells and migrate toward them. Apoptotic cells then send "eat me" signals that are recognized by phagocytes via specific receptors. "Find me" and "eat me" signals can be strategically harnessed to modulate antitumor immunity in support of cancer therapy. These signals, such as calreticulin and phosphatidylserine, mediate potent pro-phagocytic effects, thereby promoting the engulfment of dying cells or their remnants by macrophages, neutrophils, and dendritic cells and inducing tumor cell death. This review summarizes the phagocytic "find me" and "eat me" signals, including their concepts, signaling mechanisms, involved ligands, and functions. Furthermore, we delineate the relationships between "find me" and "eat me" signaling molecules and tumors, especially the roles of these molecules in tumor initiation, progression, diagnosis, and patient prognosis. The interplay of these signals with tumor biology is elucidated, and specific approaches to modulate "find me" and "eat me" signals and enhance antitumor immunity are explored. Additionally, novel therapeutic strategies that combine "find me" and "eat me" signals to better bridge innate and adaptive immunity in the treatment of cancer patients are discussed.

Characterization of Lipid Alterations by Oncogenic PIK3CA Mutations Using Untargeted Lipidomics in Breast Cancer

Lipids play crucial biological roles in health and disease, including in cancers. The phosphatidylinositol 3-kinase (PI3K) signaling pathway is a pivotal promoter of cell growth and proliferation in various types of cancer. The somatic mutations in PIK3CA, the gene coding for the catalytic subunit p110α of PI3K, are frequently present in cancer cells, including breast cancer. Although the most prominent mutants, represented by single amino acid substitutions in the helical domain in exon 9 (E545K) and the kinase domain in exon 20 (H1047R) are known to cause a gain of PI3K function, activate AKT signaling and induce oncogenic transformation, the effect of these mutations on cellular lipid profiles has not been studied. We carried out untargeted lipidomics using liquid chromatography-tandem mass spectrometry to detect the lipid alterations in mammary gland epithelial MCF10A cells with isogenic knockin of these mutations. A total of 536 species of lipids were analyzed. We found that the levels of monosialogangliosides, signaling molecules known to enhance cell motility through PI3K/AKT pathway, were significantly higher in both mutants. In addition, triglycerides and ceramides, lipid molecules known to be involved in promoting lipid droplet production, cancer cell migration and invasion, were increased, whereas lysophosphatidylcholines and phosphatidylcholines that are known to inhibit cancer cell motility were decreased in both mutants. Our results provide novel insights into a potential link between altered lipid profile and carcinogenesis caused by the PIK3CA hotspot mutations. In addition, we suggest untargeted lipidomics offers prospects for precision/personalized medicine by unpacking new molecular substrates of cancer biology.

Lysophosphatidylcholine inhibits lung cancer cell proliferation by regulating fatty acid metabolism enzyme long-chain acyl-coenzyme A synthase 5

Lung cancer is a widespread malignancy with a high death rate and disorder of lipid metabolism. Lysophosphatidylcholine (lysoPC) has anti-tumour effects, although the underlying mechanism is not entirely known. The purpose of this study aims at defining changes in lysoPC in lung cancer patients, the effects of lysoPC on lung cancer cells and molecular mechanisms. Lung cancer cell sensitivity to lysoPC was evaluated and decisive roles of long-chain acyl-coenzyme A synthase 5 (ACSL5) in lysoPC regulation were defined by comprehensively evaluating transcriptomic changes of ACSL5-downregulated epithelia. ACSL5 over-expressed in ciliated, club and Goblet cells in lung cancer patients, different from other lung diseases. LysoPC inhibited lung cancer cell proliferation, by inducing mitochondrial dysfunction, altering lipid metabolisms, increasing fatty acid oxidation and reprograming ACSL5/phosphoinositide 3-kinase/extracellular signal-regulated kinase-regulated triacylglycerol-lysoPC balance. Thus, this study provides a general new basis for the discovery of reprogramming metabolisms and metabolites as a new strategy of lung cancer precision medicine.

Non-Targeted Serum Lipidomics Analysis and Potential Biomarkers of Laryngeal Cancer Based on UHPLC-QTOF-MS

Laryngeal cancer is a common head and neck malignant cancer type. However, effective biomarkers for diagnosis are lacking and pathogenesis is unclear. Lipidomics is a powerful tool for identifying biomarkers and explaining disease mechanisms. Hence, in this study, non-targeted lipidomics based on ultra-performance liquid chromatography-quadrupole time of flight-mass spectrometry (UHPLC-QTOF-MS) were applied to screen the differential lipid metabolites in serum and allowed for exploration of the remodeled lipid metabolism of laryngeal cancer, laryngeal benign tumor patients, and healthy crowds. Multivariate analysis and univariate analysis were combined to screen for differential lipid metabolites among the three groups. The results showed that, across a total of 57 lipid metabolic markers that were screened, the regulation of the lipid metabolism network occurred mainly in phosphatidylcholine (PC), lysophosphatidylcholine (LPC), and sphingomyelin (SM) metabolism. Of note, the concentration levels of sphingolipids 42:2 (SM 42:2) and sphingolipids 42:3 (SM 42:3) correlated with laryngeal cancer progression and were both significantly different among the three groups. Both of them could be considered as potential biomarkers for diagnosis and indicators for monitoring the progression of laryngeal cancer. From the perspective of lipidomics, this study not only revealed the regulatory changes in the lipid metabolism network, but also provided a new possibility for screening biomarkers in laryngeal cancer.

Multiplatform Metabolomics Studies of Human Cancers With NMR and Mass Spectrometry Imaging

The status of metabolomics as a scientific branch has evolved from proof-of-concept to applications in science, particularly in medical research. To comprehensively evaluate disease metabolomics, multiplatform approaches of NMR combining with mass spectrometry (MS) have been investigated and reported. This mixed-methods approach allows for the exploitation of each individual technique's unique advantages to maximize results. In this article, we present our findings from combined NMR and MS imaging (MSI) analysis of human lung and prostate cancers. We further provide critical discussions of the current status of NMR and MS combined human prostate and lung cancer metabolomics studies to emphasize the enhanced metabolomics ability of the multiplatform approach.

Lipid and protein tumor markers for head and neck squamous cell carcinoma identified by imaging mass spectrometry

Head and neck squamous cell carcinoma (HNSCC) is the sixth most common cancer worldwide. To improve pre- and post-operative diagnosis and prognosis novel molecular markers are desirable. Here we used MALDI imaging mass spectrometry (IMS) and immunohistochemistry (IHC) to seek tumor specific expression of proteins and lipids in HNSCC samples. Among low molecular weight proteins visualized, S100A8 and S100A9 were found to be expressed in the regions of tumor tissue but not in the surrounding healthy stroma of a post-operative microdissected tissue. Marker potential of S100A8 and S100A9 was confirmed by immunohistochemistry of paraffin-embedded pathological samples. Imaging lipids showed a remarkable depletion of lysophosphatidylcholine species LPC[16:0], LPC[18:2] and, in parallel, accumulation of major glycerophospholipid species PE-P[36:4], PC[32:1], PC[34:1] in neoplastic areas. This was confirmed by shotgun lipidomics of dissected healthy and tumor tissue sections. A combination of the negative (LPC[16:0]) and positive (PC[32:1], PC[34:1]) markers was also applicable to uncover tumorous character of a pre-operative biopsy. Furthermore, marker potential of lysophospholipids was supported by elevated expression levels of the lysophospholipid degrading enzyme lysophospholipase A1 (LYPLA1) in the tumor regions of paraffin-embedded HNSCC samples. Finally, experimental evidence of 3D cell spheroid tests showed that LPC[16:0] facilitates HNSCC invasion, implying that HNSCC progression in vivo may be dependent on lysophospholipid supply. Altogether, a series of novel proteins and lipid species were identified by IMS and IHC screening, which may serve as potential molecular markers for tumor diagnosis, prognosis, and may pave the way to better understand HNSCC pathophyisiology.

Simultaneous Analysis of Multiple Cancer Biomarkers Using MALDI-TOF Mass Spectrometry Based on a Parylene-Matrix Chip

Recently, the parylene-matrix chip was developed for quantitative analysis of small molecules less than 1 kDa. In this study, MALDI-TOF MS based on the parylene-matrix chip was performed to clinically diagnose intrahepatic cholangiocarcinoma (IHCC) and colorectal cancer (CRC). The parylene-matrix chip was applied for the detection of small cancer biomarkers, including N-methyl-2-pyridone-5-carboxamide (2PY), glutamine, lysophosphatidylcholine (LPC) 16:0, and LPC 18:0. The feasibility of MALDI-TOF MS based on the parylene-matrix chip was confirmed via analysis of spot-to-spot and shot-to-shot reproducibility. Serum metabolite markers of IHCC, N-methyl-2-pyridone-5-carboxamide (2PY), and glutamine were quantified using MALDI-TOF MS based on the parylene-matrix chip. For clinical diagnosis of CRC, two water-insoluble (barely soluble) biomarkers, lysophosphatidylcholine (LPC) 16:0 and LPC 18:0, were quantified. Finally, glutamine and LPC 16:0 were simultaneously detected at a range of concentrations in sera from colon cancer patients using the parylene-matrix chip. Thus, this method yielded high-throughput detection of cancer biomarkers for the mixture samples of water-soluble analytes (2PY and glutamine) and water-insoluble analytes (LPC 16:0 and LPC 18:0).

A validated LC-MS/MS method for the determination of homocysteic acid in biological samples

The role of homocysteic acid (HCA) in severe diseases like Alzheimer's disease is under discussion and some recent studies correlate elevated HCA concentrations with the diagnosis of Alzheimer's. However, non-selective and insufficiently sensitive methods have been used to quantitate HCA and results of different studies show large differences in the determined HCA concentration in samples from patients and controls, and therefore non-comparable results. An accurate and precise quantitation method for the determination of HCA in human serum, urine and CSF has been developed by using a combination of protein precipitation and solid phase extraction for sample preparation followed by an LC-MS/MS analysis using a combination of a HILIC separation and tandem mass spectrometry. The developed method has been fully validated in accordance with the guidelines provided by the US Food and Drug administration FDA and the European Medicines Agency EMA. Furthermore, the method has demonstrated its ability to determine the endogenous HCA concentration in serum and urine samples from healthy volunteers.

Lysophosphatidylcholine induces expression of genes involved in cholesterol biosynthesis in THP-1 derived macrophages

Lysophosphatidylcholine (LPC), a major component of oxidized low-density lipoprotein, is associated with atherosclerosis, obesity, stroke, and cancer. However, the direction and mechanism of this relationship remains unclear. In this study, we conducted RNA profiling in THP-1 derived macrophages treated with LPC and uncovered a relationship between LPC and the cholesterol biosynthesis pathway. Principal component analysis (PCA) of RNA profiling showed that untreated THP-1 cells and those treated with 10, 20, or 40 µM LPC were distinctly distributed. Functional annotation revealed that LPC affected the expression of genes involved in cytokine-cytokine receptor interaction, TNF signaling, and MAPK signaling. Interestingly, LPC also altered the expression of 11 genes involved in cholesterol synthesis such as those in terpenoid backbone biosynthesis and steroid biosynthesis pathways. This increased gene expression occurred in a dose-dependent manner in response to LPC treatment. Especially, LPC with saturated acyl groups enhanced the expression of these genes compared to LPC with unsaturated acyl groups, and similar results were shown in response to saturated and unsaturated free fatty acids. Our findings demonstrate that LPCs with saturated acyl groups induce the expression of genes involved in cholesterol biosynthesis and may have implications for cholesterol related diseases.

Individualized metabolic profiling stratifies pancreatic and biliary tract cancer: a useful tool for innovative screening programs and predictive strategies in healthcare

BACKGROUND

Pancreatic cancer (PC) and biliary tract cancer (BTC) are highly aggressive cancers, characterized by their rarity, difficulty in diagnosis, and overall poor prognosis. Diagnosis of PC and BTC is complex and is made using a combination of appropriate clinical suspicion, imaging and endoscopic techniques, and cytopathological examination. However, the late-stage detection and poor prognosis of this tumor have led to an urgent need for biomarkers for early and/or predictive diagnosis and improved personalized treatments.

WORKING HYPOTHESIS

There are two hypotheses for focusing on low-mass metabolites in the blood. First, valuable information can be obtained from the masses and relative amounts of such metabolites, which present as low-mass ions (LMIs) in mass spectra. Second, metabolic profiling of individuals may provide important information regarding biological changes in disease states that is useful for the early diagnosis of PC and BTC.

MATERIALS AND METHODS

To assess whether profiling metabolites in serum can serve as a non-invasive screening tool for PC and BTC, 320 serum samples were obtained from patients with PC (n = 51), BTC (n = 39), colorectal cancer (CRC) (n = 100), and ovarian cancer (OVC) (n = 30), and from healthy control subjects (control) (n = 100). We obtained information on the relative amounts of metabolites, as LMIs, via triple time-of-flight mass spectrometry. All data were analyzed according to the peak area ratios of discriminative LMIs.

RESULTS AND CONCLUSIONS

The levels of the 14 discriminative LMIs were higher in the PC and BTC groups than in the control, CRC and OVC groups, but only two LMIs discriminated between PC and BTC: lysophosphatidylcholine (LysoPC) (16:0) and LysoPC(20:4). The levels of these two LysoPCs were also slightly lower in the PC/BTC/CRC/OVC groups compared with the control group. Taken together, the data showed that metabolic profiling can precisely denote the status of cancer, and, thus, could be useful for screening. This study not only details efficient methods to identify discriminative LMIs for cancer screening but also provides an example of metabolic profiling for distinguishing PC from BTC. Furthermore, the two metabolites [LysoPC(16:0), LysoPC(20:4)] shown to discriminate these diseases are potentially useful when combined with other, previously identified protein or metabolic biomarkers for predictive, preventive and personalized medical approach.

Reduced levels of N'-methyl-2-pyridone-5-carboxamide and lysophosphatidylcholine 16:0 in the serum of patients with intrahepatic cholangiocarcinoma, and the correlation with recurrence-free survival

We searched for metabolic biomarkers that may predict the prognosis of patients with intrahepatic cholangiocarcinoma (IHCC). To this end, a total of 237 serum samples were obtained from IHCC patients (n = 87) and healthy controls (n = 150), and serum metabolites were analyzed by liquid chromatography-tandem mass spectrometry (LC-MS/MS). Two stratified algorithms were used to select the metabolites, the levels of which predicted the prognosis of IHCC patients. We performed MS/MS and multiple-reaction-monitoring MS analyses to identify and quantify the selected metabolites. Continuous biomarker levels were dichotomized based on cutoffs that maximized between-group differences in recurrence-free survival (RFS) in terms of the log-rank test statistic. These RFS differences were analyzed using the log-rank test, and survival curves were drawn with the aid of the Kaplan–Meier method. Six metabolites (l-glutamine, lysophosphatidylcholine [LPC] 16:0, LPC 18:0, N’-methyl-2-pyridone-5-carboxamide [2PY], fibrinopeptide A [FPA] and uric acid) were identified as candidate metabolic biomarkers for predicting the prognosis of IHCC patients. Of these metabolites, levels of l-glutamine, uric acid, LPC 16:0, and LPC 18:0 were significantly lower in the serum from IHCC patients, whereas levels of 2PY and FPA were significantly higher (p < 0.01). 2PY and LPC 16:0 showed significantly better RFS at low level than high level (2PY, median RFS: 15.16 months vs. 5.90 months, p = 0.037; LPC 16:0, median RFS: 15.62 months vs. 9.83 months, p = 0.035). The findings of this study suggest that 2PY and LPC 16:0 identified by metabolome-based approaches may be useful biomarkers for IHCC patients.

Identification of Metabolite Biomarkers for Gout Using Capillary Ion Chromatography with Mass Spectrometry

Gout is a common form of inflammatory arthritis, and the detailed pathogenic mechanisms for this metabolic disorder remain largely unknown. In this study, we first profiled the salivary metabolites in 8 patients with gout, 15 patients with hyperuricaemia (HUA), and 15 healthy individuals using capillary ion chromatography (CIC) with tandem mass spectrometry (MS/MS). Forty-nine salivary metabolites were found to be significantly changed between gout patient and healthy control groups, and 26 salivary metabolites were significantly different between gout and HUA patient groups. Three metabolite biomarkers, uric acid, oxalic acid, and l-homocysteic acid (HCA), were selected for validation in the saliva samples of 30 patients with gout, 30 patients with HUA, and 30 healthy control subjects. By using commercial assay kits for the measurements, salivary uric acid and oxalic acid levels were found to be significantly higher in gout patients than healthy controls, whereas salivary HCA level was significantly higher in gout patients than both HUA patients and healthy controls. These assay measurements were in line with those obtained by CIC-MS/MS. In conclusion, we have demonstrated a new application of CIC-MS/MS for the discovery of novel metabolite biomarkers of gout. Validated biomarkers may be used for noninvasive, diagnostic and prognostic applications in gout. Future studies are warranted to investigate the clinical utility of these identified biomarkers for monitoring gout flare and/or treatment efficacy.

Anti-melanoma activity of Forsythiae Fructus aqueous extract in mice involves regulation of glycerophospholipid metabolisms by UPLC/Q-TOF MS-based metabolomics study

Metabolomics is a comprehensive assessment of endogenous metabolites of a biological system in a holistic context. In this study, we evaluated the in vivo anti-melanoma activity of aqueous extract of Forsythiae Fructus (FAE) and globally explored the serum metabolome characteristics of B16-F10 melanoma-bearing mice. UPLC/Q-TOF MS combined with pattern recognition approaches were employed to examine the comprehensive metabolic signatures and differentiating metabolites. The results demonstrated that FAE exhibited remarkable antitumor activity against B16-F10 melanoma in C57BL/6 mice and restored the disturbed metabolic profile by tumor insult. We identified 17 metabolites which were correlated with the antitumor effect of FAE. Most of these metabolites are involved in glycerophospholipid metabolisms. Notably, several lysophosphatidylcholines (LysoPCs) significantly decreased in tumor model group, while FAE treatment restored the changes of these phospholipids to about normal condition. Moreover, we found that lysophosphatidylcholine acyltransferase 1 (LPCAT1) and autotaxin (ATX) were highly expressed in melanoma, and FAE markedly down-regulated their expression. These findings indicated that modulation of glycerophospholipid metabolisms may play a pivotal role in the growth of melanoma and the antitumor activity of FAE. Besides, our results suggested that serum LysoPCs could be potential biomarkers for the diagnosis and prognosis of melanoma and other malignant tumors.

Low-mass-ion discriminant equation (LOME) for ovarian cancer screening

Background

A low-mass-ion discriminant equation (LOME) was constructed to investigate whether systematic low-mass-ion (LMI) profiling could be applied to ovarian cancer (OVC) screening.

Results

Matrix-assisted laser desorption/ionization-time of flight (MALDI-TOF) mass spectrometry was performed to obtain mass spectral data on metabolites detected as LMIs up to a mass-to-charge ratio (m/z) of 2500 for 1184 serum samples collected from healthy individuals and patients with OVC, other types of cancer, or several types of benign tumor. Principal component analysis-based discriminant analysis and two search algorithms were employed to identify discriminative low-mass ions for distinguishing OVC from non-OVC cases. OVC LOME with 13 discriminative LMIs produced excellent classification results in a validation set (sensitivity, 93.10 %; specificity, 100.0 %). Among 13 LMIs showing differential mass intensities in OVC, 3 metabolic compounds were identified and semi-quantitated. The relative amount of LPC 16:0 was somewhat decreased in OVC, but not significantly so. In contrast, _D,L-glutamine and fibrinogen alpha chain fragment were significantly increased in OVC compared to the control group (p = 0.001 and 0.002, respectively).

Conclusion

The present study suggested that OVC LOME might be a useful non-invasive tool with high sensitivity and specificity for OVC screening. The LOME approach could enable screening for multiple diseases, including various types of cancer, based on a single blood sample. Furthermore, the serum levels of three metabolic compounds—_D,L-glutamine, LPC 16:0 and fibrinogen alpha chain fragment—might facilitate screening for OVC.

Electronic supplementary material

The online version of this article (doi:10.1186/s13040-016-0111-7) contains supplementary material, which is available to authorized users.