Increased phosphatidylethanolamine N-methyltransferase gene expression in non-small-cell lung cancer tissue predicts shorter patient survival

Biochemistry and Diseases Related to the Interconversion of Phosphatidylcholine, Phosphatidylethanolamine, and Phosphatidylserine

Phospholipids are crucial structural components of cells. Phosphatidylcholine and phosphatidylethanolamine (both synthesized via the Kennedy pathway) and phosphatidylserine undergo interconversion. The dysregulation of this process is implicated in various diseases. This paper discusses the role of enzymes involved in the interconversion of phosphatidylcholine, phosphatidylethanolamine, and phosphatidylserine, specifically phosphatidylethanolamine N-methyltransferase (PEMT), phosphatidylserine synthases (PTDSS1 and PTDSS2), and phosphatidylserine decarboxylase (PISD), with a focus on their biochemical properties. Additionally, we describe the effects of the deregulation of these enzymes and their roles in both oncological and non-oncological diseases, including nonalcoholic fatty liver disease (NAFLD), Alzheimer's disease, obesity, insulin resistance, and type II diabetes. Current knowledge on inhibitors of these enzymes as potential therapeutic agents is also reviewed, although in most cases, inhibitors are yet to be developed. The final section of this article presents a bioinformatic analysis using the GEPIA portal to explore the significance of these enzymes in cancer processes.

A Systematic Role of Metabolomics, Metabolic Pathways, and Chemical Metabolism in Lung Cancer

Lung cancer (LC) is considered as one of the leading causes of cancer-associated mortalities. Cancer cells' reprogrammed metabolism results in changes in metabolite concentrations, which can be utilized to identify a distinct metabolic pattern or fingerprint for cancer detection or diagnosis. By detecting different metabolic variations in the expression levels of LC patients, this will help and enhance early diagnosis methods as well as new treatment strategies. The majority of patients are identified at advanced stages after undergoing a number of surgical procedures or diagnostic testing, including the invasive procedures. This could be overcome by understanding the mechanism and function of differently regulated metabolites. Significant variations in the metabolites present in the different samples can be analyzed and used as early biomarkers. They could also be used to analyze the specific progression and type as well as stages of cancer type making it easier for the treatment process. The main aim of this review article is to focus on rewired metabolic pathways and the associated metabolite alterations that can be used as diagnostic and therapeutic targets in lung cancer diagnosis as well as treatment strategies.

Metabolomics of Esophageal Squamous Cell Carcinoma Tissues: Potential Biomarkers for Diagnosis and Promising Targets for Therapy

Background

The incidence of esophageal squamous cell carcinoma in China ranks first in the world. The early diagnosis technology is underdeveloped, and the prognosis is poor, which seriously threatens the quality of life of the Chinese people. Epidemiological findings are related to factors such as diet, living habits, and age. The specific mechanism is not clear yet. Metabolomics is a kind of omics that simultaneously and quantitatively analyzes the comprehensive profile of metabolites in living systems. It has unique advantages in the study of the diagnosis and pathogenesis of tumor-related diseases, especially in the search for biomarkers. Therefore, it is desirable to perform metabolic profiling analysis of cancer tissues through metabolomics to find potential biomarkers for the diagnosis and treatment of esophageal squamous cell carcinoma.

Methods

HPLC-TOF-MS/MS technology and Illumina Hiseq Xten Sequencing was used for the analysis of 210 pairs of matched esophageal squamous cell carcinoma tissues and normal tissues in Zhenjiang City, Jiangsu Province, a high-incidence area of esophageal cancer in China. Bioinformatics analysis was also performed.

Results

Through metabolomic and transcriptomic analysis, this study found that a total of 269 differential metabolites were obtained in esophageal squamous cell carcinoma and normal tissues, and 48 differential metabolic pathways were obtained through KEGG enrichment analysis. After further screening and identification, 12 metabolites with potential biomarkers to differentiate esophageal squamous cell carcinoma from normal tissues were obtained.

Conclusions

From the metabolomic data, 4 unknown compounds were found to be abnormally expressed in esophageal squamous cell carcinoma for the first time, such as 9,10-epoxy-12,15-octadecadienoate; 3 metabolites were found in multiple abnormal expression in another tumor, but upregulation or downregulation was found for the first time in esophageal cancer, such as oleoyl glycine; at the same time, it was further confirmed that five metabolites were abnormally expressed in esophageal squamous cell carcinoma, which was similar to the results of other studies, such as PE.

Lipidomic Profiling Identifies a Novel Lipid Signature Associated with Ethnicity-Specific Disparity of Bladder Cancer

Bladder Cancer (BLCA) is the ninth most frequently diagnosed cancer globally and the sixth most common cancer in the US. African Americans (AA) exhibit half the BLCA incidence compared to European Americans (EA), but they have a 70% higher risk of cancer-related death; unfortunately, this disparity in BLCA mortality remains poorly understood. In this study, we have used an ethnicity-balanced cohort for unbiased lipidomics profiling to study the changes in the lipid fingerprint for AA and EA BLCA tissues collected from similar geographical regions to determine a signature of ethnic-specific alterations. We identified 86 lipids significantly altered between self-reported AA and EA BLCA patients from Augusta University (AU) cohort. The majority of altered lipids belong to phosphatidylcholines (PCs), phosphatidylethanolamines (PEs), ly sophosphatidylcholines (lysoPCs), phosphatidylserines (PSs), and diglycerides (DGs). Interestingly, levels of four lysoPCs (lyso PCs 20:3, lyso PCs 22:1, lyso PCs 22:2, and lyso PCs 26:1) were elevated while, in contrast, the majority of the PCs were reduced in AA BLCA. Significant alterations in long-chain monounsaturated (MonoUN) and polyunsaturated (PolyUN) lipids were also observed between AA and EA BLCA tumor tissues. These first-in-field results implicate ethnic-specific lipid alterations in BLCA.

Bilayer Forming Phospholipids as Targets for Cancer Therapy

Phospholipids represent a crucial component for the structure of cell membranes. Phosphatidylcholine and phosphatidylethanolamine are two phospholipids that comprise the majority of cell membranes. De novo biosynthesis of phosphatidylcholine and phosphatidylethanolamine occurs via the Kennedy pathway, and perturbations in the regulation of this pathway are linked to a variety of human diseases, including cancer. Altered phosphatidylcholine and phosphatidylethanolamine membrane content, phospholipid metabolite levels, and fatty acid profiles are frequently identified as hallmarks of cancer development and progression. This review summarizes the research on how phospholipid metabolism changes over oncogenic transformation, and how phospholipid profiling can differentiate between human cancer and healthy tissues, with a focus on colorectal cancer, breast cancer, and non-small cell lung cancer. The potential for phospholipids to serve as biomarkers for diagnostics, or as anticancer therapy targets, is also discussed.

Lipidomic and metabolomic analysis reveals changes in biochemical pathways for non-small cell lung cancer tissues

Lung cancer represents one of the leading worldwide causes of cancer death, but the pathobiochemistry of this disease is still not fully understood. Here we characterize the lipidomic and metabolomic profiles of the tumor and surrounding normal tissues for 23 patients with non-small cell lung cancer. In total, 500 molecular species were identified and quantified by a combination of the lipidomic shotgun tandem mass spectrometry (MS/MS) analysis and the targeted metabolomic approach using liquid chromatography (LC) - MS/MS. The statistical evaluation includes multivariate and univariate methods with the emphasis on paired statistical approaches. Our research revealed significant changes in several biochemical pathways related to the central carbon metabolism, acylcarnitines, dipeptides as well as the disruption in the lipid metabolism observed mainly for glycerophospholipids, sphingolipids, and cholesteryl esters.

Comparable Plasma Lipid Changes in Patients with High-Grade Cervical Intraepithelial Neoplasia and Patients with Cervical Cancer

Cervical cancer is the fourth most prevalent cancer among women worldwide and usually develops from cervical intraepithelial neoplasia (CIN). In the present study, we compared alterations in lipids associated with high-grade CIN and cervical cancer with those associated with a normal status and low-grade CIN by performing global lipid profiling on plasma (66 healthy controls and 55 patients with CIN1, 44 with CIN2/3, and 60 with cervical cancer) using ultraperformance liquid chromatography/quadrupole time-of-flight mass spectrometry. We identified 246 lipids and found 31 lipids with similar alterations in both high-grade CIN and cervical cancer. Among these 31 lipids, four lipid classes (phosphatidylcholine, phosphatidylethanolamine, diglyceride, and free fatty acids) were identified as the major lipid classes with significant differences in the patients with CIN2/3 and cervical cancer compared to the healthy controls and the patients with CIN1. Lipid metabolites belonging to the same classes were positively correlated with each other. High-grade CIN and cervical cancer induce comparable changes in lipid levels, which are closely related to the development of cervical tumors. These results suggest that lipid profiling is a useful method for monitoring progression to cervical cancer.

In situ metabolite and lipid analysis of GluN2D-/- and wild-type mice after ischemic stroke using MALDI MSI

The N-methyl-D-aspartate (NMDA) receptor is a crucial mediator of pathological glutamate-driven excitotoxicity and subsequent neuronal death in acute ischemic stroke. Although the roles of the NMDAR's composite GluN2A-C subunits have been investigated in this phenomenon, the relative importance of the GluN2D subunit has yet to be evaluated. Herein, GluN2D^-/- mice were studied in a model of ischemic stroke using MALDI FT-ICR mass spectrometry imaging to investigate the role of the GluN2D subunit of the NMDA receptor in brain ischemia. GluN2D^-/- mice underwent middle cerebral artery occlusion (MCAO) and brain tissue was subsequently harvested, frozen, and cryosectioned. Tissue sections were analyzed via MALDI FT-ICR mass spectrometry imaging. MALDI analyses revealed increases in several calcium-related species, namely vitamin D metabolites, LysoPC, and several PS species, in wild-type mouse brain tissue when compared to wild type. In addition, GluN2D^-/- mice also displayed an increase in PC, as well as a decrease in DG, suggesting reduced free fatty acid release from brain ischemia. These trends indicate that GluN2D^-/- mice show enhanced rates of neurorecovery and neuroprotection from ischemic strokes compared to wild-type mice. The cause of neuroprotection may be the result of an increase in PGP in knockout mice, contributing to greater cardiolipin synthesis and decreased sensitivity to apoptotic signals. Graphical abstract.

3-O-Acetyl-11-keto- -boswellic acid ameliorated aberrant metabolic landscape and inhibited autophagy in glioblastoma

Glioblastoma is the most common and aggressive primary tumor in the central nervous system, accounting for 12%-15% of all brain tumors. 3-O-Acetyl-11-keto-β-boswellic acid (AKBA), one of the most active ingredients of gum resin from Boswellia carteri Birdw., was reported to inhibit the growth of glioblastoma cells and subcutaneous glioblastoma. However, whether AKBA has antitumor effects on orthotopic glioblastoma and the underlying mechanisms are still unclear. An orthotopic mouse model was used to evaluate the anti-glioblastoma effects of AKBA. The effects of AKBA on tumor growth were evaluated using MRI. The effects on the alteration of metabolic landscape were detected by MALDI-MSI. The underlying mechanisms of autophagy reducing by AKBA treatment were determined by immunoblotting and immunofluorescence, respectively. Transmission electron microscope was used to check morphology of cells treated by AKBA. Our results showed that AKBA (100 mg/kg) significantly inhibited the growth of orthotopic U87-MG gliomas. Results from MALDI-MSI showed that AKBA improved the metabolic profile of mice with glioblastoma, while immunoblot assays revealed that AKBA suppressed the expression of ATG5, p62, LC3B, p-ERK/ERK, and P53, and increased the ratio of p-mTOR/mTOR. Taken together, these results suggested that the antitumor effects of AKBA were related to the normalization of aberrant metabolism in the glioblastoma and the inhibition of autophagy. AKBA could be a promising chemotherapy drug for glioblastoma.

Metabolic profiles of serum samples from ground glass opacity represent potential diagnostic biomarkers for lung cancer

Background

Lung cancer is a leading cause of cancer deaths worldwide. Low-dose computed tomography (LDCT) screening trials indicated that LDCT is effective for the early detection of lung cancer, but the findings were accompanied by high false positive rates. Therefore, the detection of lung cancer needs complementary blood biomarker tests to reduce false positive rates.

Methods

In order to evaluate the potential of metabolite biomarkers for diagnosing lung cancer and increasing the effectiveness of clinical interventions, serum samples from subjects participating in a low-dose CT-scan screening were analyzed by using untargeted liquid chromatography-hybrid quadrupole time-of-flight mass spectrometry (LC-Q-TOF-MS). Samples were acquired from 34 lung patients with ground glass opacity diagnosed lung cancer and 39 healthy controls.

Results

In total, we identified 9 metabolites in electron spray ionization (ESI)(+) mode and 7 metabolites in ESI(-) mode. L-(+)-gulose, phosphatidylethanolamine (PE)(22:2(13Z,16Z)/15:0), cysteinyl-glutamine, S-japonin, threoninyl-glutamine, chlorate, 3-oxoadipic acid, dukunolide A, and malonic semialdehyde levels were observed to be elevated in serum samples of lung cancer cases when compared to those of healthy controls. By contrast, 1-(2-furanylmethyl)-1H-pyrrole, 2,4-dihydroxybenzoic acid, monoethyl carbonate, guanidinosuccinic acid, pseudouridine, DIMBOA-Glc, and 4-feruloyl-1,5-quinolactone levels were lower in serum samples of lung cancer cases compared with those of healthy controls.

Conclusions

This study demonstrates evidence of early metabolic alterations that can possibly distinguish malignant ground glass opacity from benign ground glass opacity. Further studies in larger pools of samples are warranted.

In-vitro metabolomics to evaluate toxicity of particulate matter under environmentally realistic conditions

In this pilot study three fractions of particulate matter (PM_0.25, PM_2.5-0.25, and PM_10-2.5) were collected in three environments (classroom, home, and outdoors) in a village located nearby an industrial complex. Time-activity pattern of 20 students attending the classroom was obtained, and the dose of particles reaching the children's lungs under actual environmental conditions (i.e. real dose) was calculated via dosimetry model. The highest PM concentrations were reached in the classroom. Simulations showed that heavy intensity outdoor activities played a major role in PM deposition, especially in the upper part of the respiratory tract. The mass of PM_10-2.5 reaching the alveoli was minor, while PM_2.5-0.25 and PM_0.25 apportion for most of the PM mass retained in the lungs. Consequently, PM_2.5-0.25 and PM_0.25 were the only fractions used in two subsequent toxicity assays onto alveolar cells (A549). First, a cytotoxicity dose-response assay was performed, and doses corresponding to 5% mortality (LC₅) were estimated. Afterwards, two LC-MS metabolomic assays were conducted: one applying LC₅, and another applying real dose. A lower estimated LC₅ value was obtained for PM_0.25 than PM_2.5-0.25 (8.08 and 73.7 ng/mL respectively). The number of altered features after LC₅ exposure was similar for both fractions (39 and 38 for PM_0.25 and PM_2.5-0.25 respectively), while after real dose exposure these numbers differed (10 and 5 for PM_0.25 and PM_2.5-0.25 respectively). The most metabolic changes were related to membrane and lung surfactant lipids. This study highlights the capacity of PM to alter metabolic profile of lung cells at conventional environmental levels.

Metabolite profiling for biomarkers in Schistosoma haematobium infection and associated bladder pathologies

BACKGROUND

Metabolic fingerprinting analysis can offer insights into underlying reactions in a biological system; hence it is crucial to the understanding of disease pathogenesis and could provide useful tools for discovering biomarkers. We sought to examine the urine and plasma metabolome in individuals affected by urogenital schistosomiasis and its associated-bladder pathologies.

METHODOLOGY

Blood and midstream urine were obtained from volunteers who matched our inclusion criteria among residents from Eggua, southwestern Nigeria. Samples were screened by urinalysis, microscopy, PCR and ultrasonography, and categorised as advanced (urogenital schistosomiasis associated-bladder pathologies), infection-only (urogenital schistosomiasis alone) and controls (no infection and no pathology). Metabolites were extracted and data acquired with ultra high-performance liquid chromatography coupled with Thermo Q-Exactive orbitrap HRMS. Data was analysed with MetaboAnalyst, Workflow4Metabolomics, HMDB, LipidMaps and other bioinformatics tools, with univariate and multivariate statistics for metabolite selection.

PRINCIPAL FINDINGS

There were low levels of host sex steroids, and high levels of several benzenoids, catechols and lipids (including ganglioside, phosphatidylcholine and phosphatidylethanolamine), in infection-only and advanced cases (FDR<0.05, VIP>2, delta>2.0). Metabolites involved in biochemical pathways related to chorismate production were abundant in controls, while those related to choline and sphingolipid metabolism were upregulated in advanced cases (FDR<0.05). Some of these human host and Schistosoma haematobium molecules, including catechol estrogens, were good markers to distinguish infection-only and advanced cases.

CONCLUSIONS

Altered glycerophospholipid and sphingolipid metabolism could be key factors promoting the development of bladder pathologies and tumours during urogenital schistosomiasis.

Plasma lipid profiling and diagnostic biomarkers for oral squamous cell carcinoma

Biological requirements for tumor cell proliferation include the sustained increase of structural, energetic, signal transduction and biosynthetic precursors. Because lipids participate in membrane construction, energy storage, and cell signaling. We hypothesized that the differences in lipids between malignant carcinoma and normal controls could be reflected in the bio-fluids. A total of 100 pre-operative plasma samples were collected from 50 oral squamous cell carcinoma (OSCC), 50 normal patients and characterize by lipid profiling using ultra performance liquid chromatography/electro spray ionization mass spectrometry (UPLC-MS). The lipid profiles of the OSCC and control samples as well as the different stages were compared. Differentially expressed lipids were categorized as glycerophospholipids and sphingolipids. All glycerophospholipids were decreased, especially phosphatidylcholine and phosphoethanolamine plasmalogens, whereas sphingolipids were increased in the OSCC patients compared to the controls. We further identified 12 staging related lipids, which could be utilized to discriminate early stage patients from advanced stage patients. In the future, the differential lipids may provide biologists with additional information regarding lipid metabolism and guide clinicians in making individualized therapeutic decisions if these results are confirmed in a larger study.

Toxicogenomic and bioinformatics platforms to identify key molecular mechanisms of a curcumin-analogue DM-1 toxicity in melanoma cells

Melanoma is a highly invasive and metastatic cancer with high mortality rates and chemoresistance. Around 50% of melanomas are driven by activating mutations in BRAF that has led to the development of potent anti-BRAF inhibitors. However resistance to anti-BRAF therapy usually develops within a few months and consequently there is a need to identify alternative therapies that will bypass BRAF inhibitor resistance. The curcumin analogue DM-1 (sodium 4-[5-(4-hydroxy-3-methoxy-phenyl)-3-oxo-penta-1,4-dienyl]-2-methoxy-phenolate) has substantial anti-tumor activity in melanoma, but its mechanism of action remains unclear. Here we use a synthetic lethal genetic screen in Saccharomyces cerevisiae to identify 211 genes implicated in sensitivity to DM-1 toxicity. From these 211 genes, 74 had close human orthologues implicated in oxidative phosphorylation, insulin signaling and iron and RNA metabolism. Further analysis identified 7 target genes (ADK, ATP6V0B, PEMT, TOP1, ZFP36, ZFP36L1, ZFP36L2) with differential expression during melanoma progression implicated in regulation of tumor progression, cell differentiation, and epithelial-mesenchymal transition. Of these TOP1 and ADK were regulated by DM-1 in treatment-naïve and vemurafenib-resistant melanoma cells respectively. These data reveal that the anticancer effect of curcumin analogues is likely to be mediated via multiple targets and identify several genes that represent candidates for combinatorial targeting in melanoma.

Serum phosphatidylethanolamine levels distinguish benign from malignant solitary pulmonary nodules and represent a potential diagnostic biomarker for lung cancer

BACKGROUND

Recent computed tomography (CT) screening trials showed that it is effective for early detection of lung cancer, but were plagued by high false positive rates. Additional blood biomarker tests designed to complement CT screening and reduce false positive rates are highly desirable.

OBJECTIVE

Identify blood-based metabolite biomarkers for diagnosing lung cancer.

MEHTODS

Serum samples from subjects participating in a CT screening trial were analyzed using untargeted GC-TOFMS and HILIC-qTOFMS-based metabolomics. Samples were acquired prior to diagnosis (pre-diagnostic, n= 17), at-diagnosis (n= 25) and post-diagnosis (n= 19) of lung cancer and from subjects with benign nodules (n= 29).

RESULTS

Univariate analysis identified 40, 102 and 30 features which were significantly different between subjects with malignant (pre-, at- and post-diagnosis) solitary pulmonary nodules (SPNs) and benign SPNs, respectively. Ten metabolites were consistently different between subjects presenting malignant (pre- and at-diagnosis) or benign SPNs. Three of these 10 metabolites were phosphatidylethanolamines (PE) suggesting alterations in lipid metabolism. Accuracies of 77%, 83% and 78% in the pre-diagnosis group and 69%, 71% and 67% in the at-diagnosis group were determined for PE(34:2), PE(36:2) and PE(38:4), respectively.

CONCLUSIONS

This study demonstrates evidence of early metabolic alterations that can possibly distinguish malignant from benign SPNs. Further studies in larger pools of samples are warranted.

Targeting Phospholipid Metabolism in Cancer

All cancers tested so far display abnormal choline and ethanolamine phospholipid metabolism, which has been detected with numerous magnetic resonance spectroscopy (MRS) approaches in cells, animal models of cancer, as well as the tumors of cancer patients. Since the discovery of this metabolic hallmark of cancer, many studies have been performed to elucidate the molecular origins of deregulated choline metabolism, to identify targets for cancer treatment, and to develop MRS approaches that detect choline and ethanolamine compounds for clinical use in diagnosis and treatment monitoring. Several enzymes in choline, and recently also ethanolamine, phospholipid metabolism have been identified, and their evaluation has shown that they are involved in carcinogenesis and tumor progression. Several already established enzymes as well as a number of emerging enzymes in phospholipid metabolism can be used as treatment targets for anticancer therapy, either alone or in combination with other chemotherapeutic approaches. This review summarizes the current knowledge of established and relatively novel targets in phospholipid metabolism of cancer, covering choline kinase α, phosphatidylcholine-specific phospholipase D1, phosphatidylcholine-specific phospholipase C, sphingomyelinases, choline transporters, glycerophosphodiesterases, phosphatidylethanolamine N-methyltransferase, and ethanolamine kinase. These enzymes are discussed in terms of their roles in oncogenic transformation, tumor progression, and crucial cancer cell properties such as fast proliferation, migration, and invasion. Their potential as treatment targets are evaluated based on the current literature.

Multivariate models from RNA-Seq SNVs yield candidate molecular targets for biomarker discovery: SNV-DA

BACKGROUND

It has recently been shown that significant and accurate single nucleotide variants (SNVs) can be reliably called from RNA-Seq data. These may provide another source of features for multivariate predictive modeling of disease phenotype for the prioritization of candidate biomarkers. The continuous nature of SNV allele fraction features allows the concurrent investigation of several genomic phenomena, including allele specific expression, clonal expansion and/or deletion, and copy number variation.

RESULTS

The proposed software pipeline and package, SNV Discriminant Analysis (SNV-DA), was applied on two RNA-Seq datasets with varying sample sizes sequenced at different depths: a dataset containing primary tumors from twenty patients with different disease outcomes in lung adenocarcinoma and a larger dataset of primary tumors representing two major breast cancer subtypes, estrogen receptor positive and triple negative. Predictive models were generated using the machine learning algorithm, sparse projections to latent structures discriminant analysis. Training sets composed of RNA-Seq SNV features limited to genomic regions of origin (e.g. exonic or intronic) and/or RNA-editing sites were shown to produce models with accurate predictive performances, were discriminant towards true label groupings, and were able to produce SNV rankings significantly different from than univariate tests. Furthermore, the utility of the proposed methodology is supported by its comparable performance to traditional models as well as the enrichment of selected SNVs located in genes previously associated with cancer and genes showing allele-specific expression. As proof of concept, we highlight the discovery of a previously unannotated intergenic locus that is associated with epigenetic regulatory marks in cancer and whose significant allele-specific expression is correlated with ER+ status; hereafter named ER+ associated hotspot (ERPAHS).

CONCLUSION

The use of models from RNA-Seq SNVs to identify and prioritize candidate molecular targets for biomarker discovery is supported by the ability of the proposed method to produce significantly accurate predictive models that are discriminant towards true label groupings. Importantly, the proposed methodology allows investigation of mutations outside of exonic regions and identification of interesting expressed loci not included in traditional gene annotations. An implementation of the proposed methodology is provided that allows the user to specify SNV filtering criteria and cross-validation design during model creation and evaluation.