Metabolic profiling and novel plasma biomarkers for predicting survival in epithelial ovarian cancer

Serum Lipidome Profiling Reveals a Distinct Signature of Ovarian Cancer in Korean Women

BACKGROUND

Distinguishing ovarian cancer from other gynecological malignancies is crucial for patient survival yet hindered by non-specific symptoms and limited understanding of ovarian cancer pathogenesis. Accumulating evidence suggests a link between ovarian cancer and deregulated lipid metabolism. Most studies have small sample sizes, especially for early-stage cases, and lack racial/ethnic diversity, necessitating more inclusive research for improved ovarian cancer diagnosis and prevention.

METHODS

Here, we profiled the serum lipidome of 208 ovarian cancer, including 93 early-stage patients with ovarian cancer and 117 nonovarian cancer (other gynecological malignancies) patients of Korean descent. Serum samples were analyzed with a high-coverage liquid chromatography high-resolution mass spectrometry platform, and lipidome alterations were investigated via statistical and machine learning (ML) approaches.

RESULTS

We found that lipidome alterations unique to ovarian cancer were present in Korean women as early as when the cancer is localized, and those changes increase in magnitude as the diseases progresses. Analysis of relative lipid abundances revealed specific patterns for various lipid classes, with most classes showing decreased abundance in ovarian cancer in comparison with other gynecological diseases. ML methods selected a panel of 17 lipids that discriminated ovarian cancer from nonovarian cancer cases with an AUC value of 0.85 for an independent test set.

CONCLUSIONS

This study provides a systemic analysis of lipidome alterations in human ovarian cancer, specifically in Korean women.

IMPACT

Here, we show the potential of circulating lipids in distinguishing ovarian cancer from nonovarian cancer conditions.

Racial Differences in Vaginal Fluid Metabolites and Association with Systemic Inflammation Markers among Ovarian Cancer Patients: A Pilot Study

The vaginal microbiome differs by race and contributes to inflammation by directly producing or consuming metabolites or by indirectly inducing host immune response, but its potential contributions to ovarian cancer (OC) disparities remain unclear. In this exploratory cross-sectional study, we examine whether vaginal fluid metabolites differ by race among patients with OC, if they are associated with systemic inflammation, and if such associations differ by race. Study participants were recruited from the Ovarian Cancer Epidemiology, Healthcare Access, and Disparities Study between March 2021 and September 2022. Our study included 36 study participants with ovarian cancer who provided biospecimens; 20 randomly selected White patients and all 16 eligible Black patients, aged 50-70 years. Acylcarnitines (n = 45 species), sphingomyelins (n = 34), and ceramides (n = 21) were assayed on cervicovaginal fluid, while four cytokines (IL-1β, IL-10, TNF-α, and IL-6) were assayed on saliva. Seven metabolites showed >2-fold differences, two showed significant differences using the Wilcoxon rank-sum test (p < 0.05; False Discovery Rate > 0.05), and 30 metabolites had coefficients > ±0.1 in a Penalized Discriminant Analysis that achieved two distinct clusters by race. Arachidonoylcarnitine, the carnitine adduct of arachidonic acid, appeared to be consistently different by race. Thirty-eight vaginal fluid metabolites were significantly correlated with systemic inflammation biomarkers, irrespective of race. These findings suggest that vaginal fluid metabolites may differ by race, are linked with systemic inflammation, and hint at a potential role for mitochondrial dysfunction and sphingolipid metabolism in OC disparities. Larger studies are needed to verify these findings and further establish specific biological mechanisms that may link the vaginal microbiome with OC racial disparities.

The Contribution of Lipidomics in Ovarian Cancer Management: A Systematic Review

Lipidomics is a comprehensive study of all lipid components in living cells, serum, plasma, or tissues, with the aim of discovering diagnostic, prognostic, and predictive biomarkers for diseases such as malignant tumors. This systematic review evaluates studies, applying lipidomics to the diagnosis, prognosis, prediction, and differentiation of malignant and benign ovarian tumors. A literature search was performed in PubMed, Science Direct, and SciFinder. Only publications written in English after 2012 were included. Relevant citations were identified from the reference lists of primary included studies and were also included in our list. All studies included referred to the application of lipidomics in serum/plasma samples from human cases of OC, some of which also included tumor tissue samples. In some of the included studies, metabolome analysis was also performed, in which other metabolites were identified in addition to lipids. Qualitative data were assessed, and the risk of bias was determined using the ROBINS-I tool. A total of twenty-nine studies were included, fifteen of which applied non-targeted lipidomics, seven applied targeted lipidomics, and seven were reviews relevant to our objectives. Most studies focused on the potential application of lipidomics in the diagnosis of OC and showed that phospholipids and sphingolipids change most significantly during disease development. In conclusion, this systematic review highlights the potential contribution of lipids as biomarkers in OC management.

Tumor metabolism rewiring in epithelial ovarian cancer

The mortality rate of epithelial ovarian cancer (EOC) remains the first in malignant tumors of the female reproductive system. The characteristics of rapid proliferation, extensive implanted metastasis, and treatment resistance of cancer cells require an extensive metabolism rewiring during the progression of cancer development. EOC cells satisfy their rapid proliferation through the rewiring of perception, uptake, utilization, and regulation of glucose, lipids, and amino acids. Further, complete implanted metastasis by acquiring a superior advantage in microenvironment nutrients competing. Lastly, success evolves under the treatment stress of chemotherapy and targets therapy. Understanding the above metabolic characteristics of EOCs helps to find new methods of its treatment.

Circulating metabolites associated with tumor hypoxia and early response to treatment in bevacizumab-refractory glioblastoma after combined bevacizumab and evofosfamide

Glioblastomas (GBM) are the most common and aggressive form of primary malignant brain tumor in the adult population, and, despite modern therapies, patients often develop recurrent disease, and the disease remains incurable with median survival below 2 years. Resistance to bevacizumab is driven by hypoxia in the tumor and evofosfamide is a hypoxia-activated prodrug, which we tested in a phase 2, dual center (University of Texas Health Science Center in San Antonio and Dana Farber Cancer Institute) clinical trial after bevacizumab failure. Tumor hypoxic volume was quantified by 18F-misonidazole PET. To identify circulating metabolic biomarkers of tumor hypoxia in patients, we used a high-resolution liquid chromatography-mass spectrometry-based approach to profile blood metabolites and their specific enantiomeric forms using untargeted approaches. Moreover, to evaluate early response to treatment, we characterized changes in circulating metabolite levels during treatment with combined bevacizumab and evofosfamide in recurrent GBM after bevacizumab failure. Gamma aminobutyric acid, and glutamic acid as well as its enantiomeric form D-glutamic acid all inversely correlated with tumor hypoxia. Intermediates of the serine synthesis pathway, which is known to be modulated by hypoxia, also correlated with tumor hypoxia (phosphoserine and serine). Moreover, following treatment, lactic acid was modulated by treatment, likely in response to a hypoxia mediated modulation of oxidative vs glycolytic metabolism. In summary, although our results require further validation in larger patients’ cohorts, we have identified candidate metabolic biomarkers that could evaluate the extent of tumor hypoxia and predict the benefit of combined bevacizumab and evofosfamide treatment in GBM following bevacizumab failure.

Identification and validation of a gene-based signature reveals SLC25A10 as a novel prognostic indicator for patients with ovarian cancer

Background

Ovarian cancer is a common gynecological cancer with poor prognosis and poses a serious threat to woman life and health. In this study, we aimed to establish a prognostic signature for the risk assessment of ovarian cancer.

Methods

The Cancer Genome Atlas (TCGA) dataset was used as the training set and the International Cancer Genome Consortium (ICGC) dataset was set as an independent external validation. A multi-stage screening strategy was used to determine the prognostic features of ovarian cancer with R software. The relationship between the prognosis of ovarian cancer and the expression level of SLC25A10 was selected for further analysis.

Results

A total of 16 prognosis-associated genes were screened to construct the risk score signature. Survival analysis showed that patients in the high-risk score group had a poor prognosis compared to the low-risk group. Accuracy of this prognostic signature was confirmed by the receiver operating characteristic (ROC) curve and decision curve analysis (DCA), and validated with ICGC cohort. This signature was identified as an independent factor for predicting overall survival (OS). Nomogram constructed by multiple clinical parameters showed excellent performance for OS prediction. Finally, it’s found that patients with low expression of SLC25A10 generally had poor survival and higher resistance to most chemotherapeutic drugs.

Conclusions

In sum, we developed a 16-gene prognostic signature, which could serve as a promising tool for the prognostic prediction of ovarian cancer, and the expression level of SLC25A10 was tightly associated with OS of the patients.

Lung cancer metabolomic data from tumor core biopsies enables risk-score calculation for progression-free and overall survival

INTRODUCTION

Metabolomics has emerged as a powerful method to provide insight into cancer progression, including separating patients into low- and high-risk groups for overall (OS) and progression-free survival (PFS). However, survival prediction based mainly on metabolites obtained from biofluids remains elusive.

OBJECTIVES

This proof-of-concept study evaluates metabolites as biomarkers obtained directly from tumor core biopsies along with covariates age, sex, pathological stage at diagnosis (I/II vs. III/VI), histological subtype, and treatment vs. no treatment to risk stratify lung cancer patients in terms of OS and PFS.

METHODS

Tumor core biopsy samples obtained during routine lung cancer patient care at the University of Louisville Hospital and Norton Hospital were evaluated with high-resolution 2DLC-MS/MS, and the data were analyzed by Kaplan-Meier survival analysis and Cox proportional hazards regression. A linear equation was developed to stratify patients into low and high risk groups based on log-transformed intensities of key metabolites. Sparse partial least squares discriminant analysis (SPLS-DA) was performed to predict OS and PFS events.

RESULTS

Univariable Cox proportional hazards regression model coefficients divided by the standard errors were used as weight coefficients multiplied by log-transformed metabolite intensity, then summed to generate a risk score for each patient. Risk scores based on 10 metabolites for OS and 5 metabolites for PFS were significant predictors of survival. Risk scores were validated with SPLS-DA classification model (AUROC 0.868 for OS and AUROC 0.755 for PFS, when combined with covariates).

CONCLUSION

Metabolomic analysis of lung tumor core biopsies has the potential to differentiate patients into low- and high-risk groups based on OS and PFS events and probability.

Study on the levels of N-nitrosamine compounds and untargeted metabolomics in patients with colorectal cancer

Plasma samples were collected from 34 patients with advanced CRC and 92 healthy persons (control group), and the levels of 9 VNAs were measured using GC-MS. Untargeted metabolomics analysis was performed using LC-MS/MS. Partial least squares discriminant analysis (PLS-DA) and hierarchical cluster analysis were used to determine differential metabolites between the 2 groups. Receiver operating characteristic (ROC) curve analysis and Kyoto Encyclopedia of Genes and Genomes pathway analysis were performed on the differential metabolites. It turned out that the detection rates of N-nitrosodimethylamine (NDMA) and N-nitrosopyrrolidine (NPYR) in patients with CRC were higher than in the control group (P < 0.05). N-nitrosomethylethylamine (NMEA) and N-nitrosodiphenylamine (NDPhA) were not detected in CRC patients. NDMA, N-nitrosodibutylamine (NDBA), N-nitrosopiperidine (NPIP), and NPYR were detected in male and female patients with CRC. There was no difference in VNAs exposure between the sexes of CRC patients. In the positive and negative ion mode, a total of 132 differential metabolites and 6 differential metabolic pathways were detected. Adenosine 5'-monophosphate, hypoxanthine, 11,12-epoxy-(5Z,8Z,11Z)-icosatrienoic acid, 16(R)-HETE, acetylcarnitine, and lysophosphatidic acid (LPA 20:5, LPA 20:4) were candidate biomarkers with higher predictive value. Hypoxanthine and xanthine metabolic pathways were associated with changes in VNAs in CRC patients. In summary, the effects of changes of VNAs in the plasma of CRC patients (especially NDMA and NPYR) on the progression of CRC should attract attention. Abnormalities of adenine and guanine and downstream hypoxanthine-xanthine metabolic pathways were closely related to changes of VNAs and metabolomics in CRC patients.

Gut Microbiota and Gynecological Cancers: A Summary of Pathogenetic Mechanisms and Future Directions

Over the past 20 years, important relationships between the microbiota and human health have emerged. A link between alterations of microbiota composition (dysbiosis) and cancer development has been recently demonstrated. In particular, the composition and the oncogenic role of intestinal bacterial flora has been extensively investigated in preclinical and clinical studies focusing on gastrointestinal tumors. Overall, the development of gastrointestinal tumors is favored by dysbiosis as it leads to depletion of antitumor substances (e.g., short-chain fatty acids) produced by healthy microbiota. Moreover, dysbiosis leads to alterations of the gut barrier, promotes a chronic inflammatory status through activation of toll-like receptors, and causes metabolic and hormonal dysregulations. However, the effects of these imbalances are not limited to the gastrointestinal tract and they can influence gynecological tumor carcinogenesis as well. The purpose of this Review is to provide a synthetic update about the mechanisms of interaction between gut microbiota and the female reproductive tract favoring the development of neoplasms. Furthermore, novel therapeutic approaches based on the modulation of microbiota and their role in gynecological oncology are discussed.

Serum metabolite signatures of epithelial ovarian cancer based on targeted metabolomics

BACKGROUND

Epithelial ovarian cancer (EOC) is a common gynecological cancer with high mortality rates. The main objective of this study was to investigate the serum amino acid and organic acid profiles to distinguish key metabolites for screening EOC patients.

METHODS

In total, 39 patients with EOC and 31 healthy controls were selected as the training set. Serum amino acid and organic acid profiles were determined using the targeted metabolomics approach. Metabolite profiles were processed via multivariate analysis to identify potential metabolites and construct a metabolic network. Finally, a test dataset derived from 29 patients and 28 healthy controls was constructed to validate the potential metabolites.

RESULTS

Distinct amino acid and organic acid profiles were obtained between EOC and healthy control groups. Methionine, glutamine, asparagine, glutamic acid and glycolic acid were identified as potential metabolites to distinguish EOC from control samples. The areas under the curve for methionine, glutamine, asparagine, glutamic acid and glycolic acid were 0.775, 0 778, 0.955, 0.874 and 0.897, respectively, in the validation study. Metabolic network analysis of the training set indicated key roles of alanine, aspartate and glutamate metabolism as well as D-glutamine and D-glutamate metabolism in the pathogenesis of EOC.

CONCLUSIONS

Amino acid and organic acid profiles may serve as potential screening tools for EOC. Data from this study provide useful information to bridge gaps in the understanding of the amino acid and organic acid alterations associated with epithelial ovarian cancer.

Construction autophagy-related prognostic risk signature to facilitate survival prediction, individual treatment and biomarker excavation of epithelial ovarian cancer patients

Background

Existing clinical methods for prognosis evaluating for Epithelial Ovarian Cancer (EOC) patients had defects of invasive, unsystematic and subjective and little data are available for individualizing treatment, therefore, to identify potential prognostic markers and new therapeutic targets for EOC is urgently required.

Results

Expression of 232 autophagy-related genes (ARGs) in 354 EOC and 56 human ovarian surface epithelial specimens from 7 independent laboratories were analyzed, 31 mRNAs were identified as DEARGs. We did functional and pathway enrichment analysis and constructed protein–protein interaction network for all DEARGs. To screen out candidate DEARGs related to EOC patients’ survival and construct an autophagy-related prognostic risk signature, univariate and multivariate Cox proportional hazards models were established separately. Finally, 5 optimal independent prognostic DEARGs (PEX3, DNAJB9, RB1, HSP90AB1 and CXCR4) were confirmed and the autophagy-related risk model was established by the 5 prognostic DEARGs. The accuracy and robustness of the prognostic risk model for survival prediction were evaluated and verified by analyzing the correlation between EOC patients’ survival status, clinicopathological features and risk scores.

Conclusions

The autophagy-related prognostic risk model can be independently used to predict overall survival in EOC patients, it can also potentially assist in individualizing treatment and biomarker development.

The application of metabolomics in ovarian cancer management: a systematic review

Metabolomics, the global analysis of metabolites in a biological specimen, could potentially provide a fast method of biomarker identification for ovarian cancer. This systematic review aims to examine findings from studies that apply metabolomics to the diagnosis, prognosis, treatment, and recurrence of ovarian cancer. A systematic search of English language publications was conducted on PubMed, Science Direct, and SciFinder. It was augmented by a snowball strategy, whereby further relevant studies are identified from reference lists of included studies. Studies in humans with ovarian cancer which focus on metabolomics of biofluids and tumor tissue were included. No restriction was placed on the time of publication. A separate review of targeted metabolomic studies was conducted for completion. Qualitative data were summarized in a comprehensive table. The studies were assessed for quality and risk of bias using the ROBINS-I tool. 32 global studies were included in the main systematic review. Most studies applied metabolomics to diagnosing ovarian cancer, within which the most frequently reported metabolite changes were a down-regulation of phospholipids and amino acids: histidine, citrulline, alanine, and methionine. Dysregulated phospholipid metabolism was also reported in the separately reviewed 18 targeted studies. Generally, combinations of more than one significant metabolite as a panel, in different studies, achieved a higher sensitivity and specificity for diagnosis than a single metabolite; for example, combinations of different phospholipids. Widespread metabolite differences were observed in studies examining prognosis, treatment, and recurrence, and limited conclusions could be drawn. Cellular processes of proliferation and invasion may be reflected in metabolic changes present in poor prognosis and recurrence. For example, lower levels of lysine, with increased cell invasion as an underlying mechanism, or glutamine dependency of rapidly proliferating cancer cells. In conclusion, this review highlights potential metabolites and biochemical pathways which may aid the clinical care of ovarian cancer if further validated.

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.

Metabolomic Biomarkers in Gynecology: A Treasure Path or a False Path?

Omic-technologies (genomics, transcriptomics, proteomics and metabolomics) have become more important in current medical science. Among them, it is metabolomics that most accurately reflects the minor changes in body functioning, as it focuses on metabolome - the group of the metabolism products, both intermediate and end. Therefore, metabolomics is actively engaged in fundamental and clinical studies and search for potential biomarkers. The biomarker could be used in diagnostics, management and stratification of the patients, as well as in prognosing the outcomes. The good example is gynecology, since many gynecological diseases lack effective biomarkers. In the current review, we aimed to summarize the results of the studies, devoted to the search of potential metabolomic biomarkers for the most common gynecological diseases.

Metabolomics in endometrial cancer diagnosis: A systematic review

INTRODUCTION

Endometrial cancer (EC) is the most common gynecological malignancy in the developed world. The prognosis of EC strongly depends on tumor stage, hence the importance of improving diagnosis. Metabolomics has recently appeared as a promising test for a non-invasive diagnosis of several diseases. Nevertheless, no metabolic marker has been approved for use in the routine practice. We aimed to provide an overview of metabolomics findings in the diagnosis of EC.

MATERIAL AND METHODS

A systematic review was performed by searching eight electronic databases from their inception to October 2019 for studies assessing metabolomics in EC diagnosis. Extracted data included characteristics of patients and EC, serum concentration of metabolites in women with and without EC and its association with EC diagnosis, tumor behavior and pathological characteristics.

RESULTS

Six studies with 732 women (356 cases and 376 controls) were included. Several metabolites were found able to predict the presence of EC, tumor behavior (progression and recurrence) and pathological characteristics (histotype, myometrial invasion and lymph vascular space invasion).

CONCLUSIONS

Metabolomics might be suitable for a non-invasive diagnosis and screening of EC, offering the possibility to predict tumor behavior and pathological characteristics. Further studies are necessary to validate these results.

The presence of chronic diseases contributes to the occurrence risk factors for gynecological cancers in Japan

The aim of the present study was to determine whether chronic diseases (CD), such as hypertension, diabetes mellitus, dyslipidemia, heart diseases and cerebrovascular diseases, are occurrence risk factors and affect the survival of patients with gynecological cancers (GC). The correlations between CD and the characteristics and survival of 1,590 GC patients [685 with cervical cancer (CC), 613 with endometrial cancer (EM) and 292 with ovarian cancer (OV)] were investigated in the present study. Of the CD patients, 189 had CC (27.6%), 265 had EM (43.2%) and 72 had OV (24.7%). The incidence of CD increased with age in GC patients. The number of CD patients aged ≥70 years, was 8.6-fold higher in the CC group, 3.0-fold higher in the EM group, and 9.6-fold higher in the OV group compared with those aged <50 years. CD and excess body weight were associated with GC regardless of patient age. However, there was no correlation between CD and survival at any age in GC patients. These findings indicate that CD contribute to >24% of the occurrence risk factors in GC patients in Japan.

Mass spectrometry based proteomics and metabolomics in personalized oncology

Precision medicine (PM) means the customization of healthcare with decisions and practices adjusted to the individual patient. It includes personalized diagnostics, patients' sub-classification, individual treatment selection and the monitoring of its effectiveness. Currently, in oncology, PM is based on the molecular and cellular features of a tumor, its microenvironment and the patient's genetics and lifestyle. Surprisingly, the available targeted therapies were found effective only in a subset of patients. An in-depth understanding of tumor biology is crucial to improve their effectiveness and develop new therapeutic targets. Completion of genetic information with proteomics and metabolomics can give broader knowledge about tumor biology which consequently provides novel biomarkers and indicates new therapeutic targets. Recently, metabolomics and proteomics have extensively been applied in the field of oncology. In the context of PM, human studies, with the use of mass spectrometry (MS) which allows the detection of thousands of molecules in a large number of samples, are the most valuable. Such studies, focused on cancer biomarkers discovery or patients' stratification, are presented in this review. Moreover, the technical aspects of MS-based clinical proteomics and metabolomics are described.

Blood Metabolites Associate with Prognosis in Endometrial Cancer

Endometrial cancer has a high prevalence among post-menopausal women in developed countries. We aimed to explore whether certain metabolic patterns could be related to the characteristics of aggressive disease and poorer survival among endometrial cancer patients in Western Norway. Patients with endometrial cancer with short survival (n = 20) were matched according to FIGO (International Federation of Gynecology and Obstetrics, 2009 criteria) stage, histology, and grade, with patients with long survival (n = 20). Plasma metabolites were measured on a multiplex system including 183 metabolites, which were subsequently determined using liquid chromatography-mass spectrometry. Partial least square discriminant analysis, together with hierarchical clustering, was used to identify patterns which distinguished short from long survival. A proposed signature of metabolites related to survival was suggested, and a multivariate receiver operating characteristic (ROC) analysis yielded an area under the curve (AUC) of 0.820–0.965 (p ≤ 0.001). Methionine sulfoxide seems to be particularly strongly associated with poor survival rates in these patients. In a subgroup with preoperative contrast-enhanced computed tomography data, selected metabolites correlated with the estimated abdominal fat distribution parameters. Metabolic signatures may predict prognosis and be promising supplements when evaluating phenotypes and exploring metabolic pathways related to the progression of endometrial cancer. In the future, this may serve as a useful tool in cancer management.

Kynurenine Pathway Metabolites as Biomarkers for Amyotrophic Lateral Sclerosis

Amyotrophic Lateral Sclerosis (ALS) currently lacks a robust and well-defined biomarker that can 1) assess the progression of the disease, 2) predict and/or delineate the various clinical subtypes, and 3) evaluate or predict a patient's response to treatments. The kynurenine Pathway (KP) of tryptophan degradation represent a promising candidate as it is involved with several neuropathological features present in ALS including neuroinflammation, excitotoxicity, oxidative stress, immune system activation and dysregulation of energy metabolism. Some of the KP metabolites (KPMs) can cross the blood brain barrier, and many studies have shown their levels are dysregulated in major neurodegenerative diseases including ALS. The KPMs can be easily analyzed in body fluids and tissue and as they are small molecules, and are stable. KPMs have a Janus face action, they can be either or both neurotoxic and/or neuroprotective depending of their levels. This mini review examines and presents evidence supporting the use of KPMs as a relevant set of biomarkers for ALS, and highlights the criteria required to achieve a valid biomarker set for ALS.

Wide spectrum targeted metabolomics identifies potential ovarian cancer biomarkers

AIMS

Despite of almost a hundred years of research on cancer metabolism, the biological background of cancerogenesis and cancer-related reprogramming of metabolism remains not fully understood. In order to comprehensively and effectively diagnose and treat the deadliest diseases, the mechanisms underlying these diseases have to be discovered urgently. Among the gynecological malignancies, ovarian cancer is the most common cause of death. The aim of the study was to search for potential cancer-related differences in concentrations of metabolites and interactions between them in serum of women with ovarian cancer and benign ovarian tumor in comparison with healthy controls using targeted metabolomics. These metabolites might serve as biomarkers in the future.

MAIN METHODS

We used wide spectrum targeted metabolomics to evaluate serum concentrations of metabolites related to ovarian cancer and compared them against benign ovarian tumors and healthy controls. The measurements were performed using high performance liquid chromatography coupled with triple quadrupole tandem mass spectrometry technique in highly-selective multiple reaction monitoring mode.

KEY FINDINGS

In this study we confirmed our previous findings about the role of histidine and citrulline in ovarian cancer as well as we indicated new lipid compounds (lysoPC a C16:1, PC aa C32:2, PC aa C34:4 and PC aa C 36:6) potentially involved in cancer metabolism.

SIGNIFICANCES

We indicated interesting interactions between metabolites for further in-depth research which could potentially serve as clinically useful biomarkers in future. Moreover, the presented work attempts to visualize a possible 3D-network of relationships between the molecules found to be related to ovarian malignancy.

Next-generation sequencing-based genomic profiling analysis reveals novel mutations for clinical diagnosis in Chinese primary epithelial ovarian cancer patients

BACKGROUND

Ovarian cancer (OC) is one of the most malignant gynecological tumors, associated with excess death rate (50-60%) in ovarian cancer patients. Particularly, among newly occurred ovarian cancer patients, 70% of clinical cases are diagnosed at the advanced stage, which definitely delay the timely treatment and lead to high mortality rate within 5 years post diagnosis. Therefore, identification of sensitive gene markers, as well as development of reliable genetic diagnosis, are important for the early detection and precise therapy for OC patients. This study aims to identify novel genetic mutations and develop a feasible clinical approach for early OC diagnosis.

METHODS

The OC tissue-derived DNA sample was acquired from 31 OC patients, and the somatic gene mutations will be identified after comparison with normal samples, using Genome-wide analysis and next-generation sequencing.

RESULTS

A total of 463 somatic mutations, which were considered as potential pathogenic sites, were assigned to 473 genes. Among them, 15 genes (TP53, TTN, MUC16, OR4N2, BRCA1, CAD, CCDC129, INSR, NAV3, NELL2, NRAS, OBSCN, PGLYRP4, RBM15B and TRPC7) were mutated on at least two sites. These genes were mapped to RNA sequencing (RNAseq) data, and a total of 117 genes had an absolute fold- change ≥ 2 and p ≤ 0.01. Five genes were mutated in at least two OC patients. Gene ontology (GO) classification indicated that a majority of genes participated in biological processes. Kyoto Enrichment of Genes and Genomes (KEGG) enrichment pathway analysis revealed that the genes were mainly involved in the regulation of metabolic signaling pathways.

CONCLUSIONS

Taken together, this study identified several novel genetic alterations pathway for early clinical diagnosis and provided abundant information for understanding molecular mechanisms of the OC occurrence and development.

Elevated kynurenine levels in diffuse cutaneous and anti-RNA polymerase III positive systemic sclerosis

Systemic sclerosis (SSc) is a systemic disease characterized by vasculopathy, progressive fibrosis and autoimmune activation. Tryptophan (Trp) metabolism has been linked to altered immune cell function and to malignancy. We have investigated the role of Trp metabolic pathway in SSc measuring serum Trp, Kynurenine (Kyn) and Trp/Kyn ratio in a cohort of 97 SSc patients and 10 healthy controls. Association with disease characteristics was evaluated. We found that Trp levels in SSc patients were significantly lower compared to HCs. We also found that patients with diffuse cutaneous (dcSSc) had lower levels of Trp compared to limited cutaneous (lcSSc). These results were paralleled by higher levels of Kyn found in SSc patients compared to HCs and significantly lower levels in dcSSc compared to lcSSc. The autoantibody profile was also found to be significantly associated with Kyn and Trp levels as anti-RNA-polymerase III (ARA) positive patients were shown to have lower Trp levels and higher Kyn levels compared with anti-centromere and anti-topoisomerase I positive patients. Moreover, the highest Trp/Kyn was found in ARA+ patients with dcSSc, suggesting that an activation of the Kyn pathway, is more specifically associated with this subset of SSc patients. Stability over time makes these markers of Trp metabolism feasible for SSc stratification.

PML-Regulated Mitochondrial Metabolism Enhances Chemosensitivity in Human Ovarian Cancers

High-grade serous ovarian cancer (HGSOC) remains an unmet medical challenge. Here, we unravel an unanticipated metabolic heterogeneity in HGSOC. By combining proteomic, metabolomic, and bioergenetic analyses, we identify two molecular subgroups, low- and high-OXPHOS. While low-OXPHOS exhibit a glycolytic metabolism, high-OXPHOS HGSOCs rely on oxidative phosphorylation, supported by glutamine and fatty acid oxidation, and show chronic oxidative stress. We identify an important role for the PML-PGC-1α axis in the metabolic features of high-OXPHOS HGSOC. In high-OXPHOS tumors, chronic oxidative stress promotes aggregation of PML-nuclear bodies, resulting in activation of the transcriptional co-activator PGC-1α. Active PGC-1α increases synthesis of electron transport chain complexes, thereby promoting mitochondrial respiration. Importantly, high-OXPHOS HGSOCs exhibit increased response to conventional chemotherapies, in which increased oxidative stress, PML, and potentially ferroptosis play key functions. Collectively, our data establish a stress-mediated PML-PGC-1α-dependent mechanism that promotes OXPHOS metabolism and chemosensitivity in ovarian cancer.

Compound Fuling Granule Suppresses Ovarian Cancer Development and Progression by disrupting mitochondrial function, galactose and fatty acid metabolism

Our previous studies have demonstrated that the compound fuling granule (CFG), a traditional Chinese medicine, suppresses ovarian cancer cell growth, migration and metastasis. However, the underlying mechanisms remain to be fully elucidated. In this study, we found that CFG could induce mitochondrial fragmentation, mitochondrial membrane potential reduction and cytochrome c release in ovarian SKOV3 cancer cells. In addition, both metabolomics and transcriptomics approaches were applied to illustrate the systemic mechanism of CFG on ovarian cancer formation and progression. To this end, we established two tumor-bearing mice models with subcutaneous injection or tail intravenous injection. Functionally, administration of CFG suppresses in situ tumor growth and distant lung metastasis. Subsequently, gas chromatography-mass spectrometry (GC-MS) was applied to determine the metabolic alterations among the plasma samples from these in vivo models. In the subcutaneous injection model, 26 distinguishable metabolites were identified and 12 metabolic pathways were reprogrammed. Meanwhile, 19 metabolites involved in 7 metabolic pathways showed significant differences in the tail intravenous injection model. Importantly, integrative metabolomics and transcriptomics analysis showed these metabolites were highly associated with galactose metabolism and fatty acid metabolism. This study suggests that CFG may suppress ovarian cancer cell proliferation and metastasis by regulating mitochondrion-related energy metabolisms.

One-Carbon Metabolism: Biological Players in Epithelial Ovarian Cancer

Metabolism is deeply involved in cell behavior and homeostasis maintenance, with metabolites acting as molecular intermediates to modulate cellular functions. In particular, one-carbon metabolism is a key biochemical pathway necessary to provide carbon units required for critical processes, including nucleotide biosynthesis, epigenetic methylation, and cell redox-status regulation. It is, therefore, not surprising that alterations in this pathway may acquire fundamental importance in cancer onset and progression. Two of the major actors in one-carbon metabolism, folate and choline, play a key role in the pathobiology of epithelial ovarian cancer (EOC), the deadliest gynecological malignancy. EOC is characterized by a cholinic phenotype sustained via increased activity of choline kinase alpha, and via membrane overexpression of the alpha isoform of the folate receptor (FRα), both of which are known to contribute to generating regulatory signals that support EOC cell aggressiveness and proliferation. Here, we describe in detail the main biological processes associated with one-carbon metabolism, and the current knowledge about its role in EOC. Moreover, since the cholinic phenotype and FRα overexpression are unique properties of tumor cells, but not of normal cells, they can be considered attractive targets for the development of therapeutic approaches.

Association of serum lipids and severity of epithelial ovarian cancer: an observational cohort study of 349 Chinese patients

While obesity and fat intake have been associated with the risk and prognosis of epithelial ovarian cancer, the association between the lipid levels and epithelial ovarian cancer phenotype remains controversial. We conducted a retrospective study of 349 epithelial ovarian cancer patients who received treatment at Jiangsu Cancer Hospital, China between 2011 and 2017. We analyzed age at diagnosis, blood pressure, plasma glucose content, body mass index (BMI), lipid levels and clinical parameters. Severity of epithelial ovarian cancer was classified according to the International Federation of Gynecology and Obstetrics (FIGO) grading system. Univariate analysis of the clinical factors according to the severity of epithelial ovarian cancer was followed by logistic regression analysis to identify clinical factors significantly associated with epithelial ovarian cancer severity. Univariate analysis indicated that age, BMI, triglyceride (TG), and high density lipoproteins (HDL) differed significantly among different stages of epithelial ovarian cancer (P<0.05). In the logistic regression model, elevated TG (OR: 1.883; 95% CI= 1.207-2.937), and low HDL (OR: 0.497; 95% CI= 0.298-0.829) levels were significantly associated with the high severity epithelial ovarian cancer. Our data indicate that high TG and low HDL levels correlate with a high severity of epithelial ovarian cancer. These data provide important insight into the potential relationship between the lipid pathway and epithelial ovarian cancer phenotype and development.

Identification of Metabolomic Biomarkers for Endometrial Cancer and Its Recurrence after Surgery in Postmenopausal Women

Endometrial cancer (EC) is the most frequent gynecological cancer in developed countries. Most EC occurs after menopause and is diagnosed as endometrioid (type I) carcinomas, which exhibit a favorable prognosis. In contrast, non-endometrioid (type II) carcinomas such as serous tumors have a poor prognosis. Our goal was to identify novel blood-based markers associated with EC subtypes and recurrence after surgery in postmenopausal women. Using mass spectrometry-based untargeted metabolomics, we examined preoperative serum metabolites among control women (n = 18) and those with non-recurrent (NR) and recurrent (R) cases of type I endometrioid (n = 24) and type II serous (n = 12) carcinomas. R and NR cases were similar with respect to pathological characteristics, body mass index, and age. A total of 1,592 compounds were analyzed including 14 different lipid classes. When we compared EC cases with controls, 137 metabolites were significantly different. A combination of spermine and isovalerate resulted in an age-adjusted area under the receiver-operating characteristic curve (AUC_adj) of 0.914 (P < 0.001) for EC detection. The combination of 2-oleoylglycerol and TAG42:2-FA12:0 allowed the distinction of R cases from NR cases with an AUC_adj of 0.901 (P < 0.001). Type I R cases were also characterized by much lower levels of bile acids and elevated concentrations of phosphorylated fibrinogen cleavage peptide, whereas type II R cases displayed higher levels of ceramides. The findings from our pilot study provide a detailed metabolomics study of EC and identify putative serum biomarkers for defining clinically relevant risk groups.

LC-MS-based metabolomics revealed SLC25A22 as an essential regulator of aspartate-derived amino acids and polyamines in KRAS-mutant colorectal cancer

SLC25A22, which encodes the mitochondrial glutamate transporter, is overexpressed in colorectal cancer (CRC) and is essential for the proliferation of CRC cells harboring KRAS mutations. However, the role of SLC25A22 on metabolic regulation in KRAS-mutant CRC cells has not been comprehensively characterized. We performed non-targeted metabolomics, targeted metabolomics and isotope kinetic analysis of KRAS-mutant DLD1 cells with or without SLC25A22 knockdown using ultra-high-performance liquid chromatography (UHPLC) coupled to Orbitrap mass spectrometry (MS) or tandem MS (MS/MS). Global metabolomics analysis identified 35 altered metabolites, which were attributed to alanine, aspartate and glutamate metabolism, urea cycle and polyamine metabolism. Targeted metabolomics including 24 metabolites revealed that most tricarboxylic acid (TCA) cycle intermediates, aspartate-derived asparagine, alanine and ornithine-derived polyamines were strongly down-regulated in SLC25A22 knockdown cells. Moreover, targeted kinetic isotope analysis showed that most of the ¹³C-labeled ornithine-derived polyamines were significantly decreased in SLC25A22 knockdown cells and culture medium. Exogenous addition of polyamines could significantly promote cell proliferation in DLD1 cells, highlighting their potential role as oncogenic metabolites that function downstream of SLC25A22-mediated glutamine metabolism. Collectively, SLC25A22 acts as an essential metabolic regulator during CRC progression as it promotes the synthesis of aspartate-derived amino acids and polyamines in KRAS mutant CRC cells.