Saturated fatty acids as possible important metabolites for epithelial ovarian cancer based on the free and esterified fatty acid profiles determined by GC-MS analysis

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

INTRODUCTION

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

AREAS COVERED

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

EXPERT OPINION

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

Cancer metabolites: promising biomarkers for cancer liquid biopsy

Cancer exerts a multitude of effects on metabolism, including the reprogramming of cellular metabolic pathways and alterations in metabolites that facilitate inappropriate proliferation of cancer cells and adaptation to the tumor microenvironment. There is a growing body of evidence suggesting that aberrant metabolites play pivotal roles in tumorigenesis and metastasis, and have the potential to serve as biomarkers for personalized cancer therapy. Importantly, high-throughput metabolomics detection techniques and machine learning approaches offer tremendous potential for clinical oncology by enabling the identification of cancer-specific metabolites. Emerging research indicates that circulating metabolites have great promise as noninvasive biomarkers for cancer detection. Therefore, this review summarizes reported abnormal cancer-related metabolites in the last decade and highlights the application of metabolomics in liquid biopsy, including detection specimens, technologies, methods, and challenges. The review provides insights into cancer metabolites as a promising tool for clinical applications.

Applications of Multi-omics Approaches for Exploring the Molecular Mechanism of Ovarian Carcinogenesis

Ovarian cancer ranks as the fifth most common cause of cancer-related death in females. The molecular mechanisms of ovarian carcinogenesis need to be explored in order to identify effective clinical therapies for ovarian cancer. Recently, multi-omics approaches have been applied to determine the mechanisms of ovarian oncogenesis at genomics (DNA), transcriptomics (RNA), proteomics (proteins), and metabolomics (metabolites) levels. Multi-omics approaches can identify some diagnostic and prognostic biomarkers and therapeutic targets for ovarian cancer, and these molecular signatures are beneficial for clarifying the development and progression of ovarian cancer. Moreover, the discovery of molecular signatures and targeted therapy strategies could noticeably improve the prognosis of ovarian cancer patients.

Untargeted Metabolomic Characterization of Ovarian Tumors

Simple Summary

This study utilized untargeted metabolomic techniques to detect urine and plasma metabolites. Using support vector machine algorithm, three models for ovarian tumors diagnosis, benign-malignant distinguishing, early diagnosis and borderline-malignant distinguishing were developed. These models have good classification performance and provided a novel insight for non-invasive diagnosis of ovarian cancer.

Abstract

Diagnosis of ovarian cancer is difficult due to the lack of clinical symptoms and effective screening algorithms. In this study, we aim to develop models for ovarian cancer diagnosis by detecting metabolites in urine and plasma samples. Ultra-high-performance liquid chromatography and quadrupole time-of-flight mass spectrometry (UHPLC-QTOF-MS) in positive ion mode was used for metabolome quantification in 235 urine samples and 331 plasma samples. Then, Urine and plasma metabolomic profiles were analyzed by univariate and multivariate statistics. Four groups of samples: normal control, benign, borderline and malignant ovarian tumors were enrolled in this study. A total of 1330 features and 1302 features were detected from urine and plasma samples respectively. Based on two urine putative metabolites, five plasma putative metabolites and five urine putative metabolites, three models for distinguishing normal-ovarian tumors, benign-malignant (borderline + malignant) and borderline-malignant ovarian tumors were developed respectively. The AUC (Area Under Curve) values were 0.987, 0876 and 0.943 in discovery set and 0.984, 0.896 and 0.836 in validation set for three models. Specially, the diagnostic model based on 5 plasma putative metabolites had better early-stage diagnosis performance than CA125 alone. The AUC values of the model were 0.847 and 0.988 in discovery and validation set respectively. Our results showed that normal and ovarian tumors have unique metabolic signature in urine and plasma samples, which shed light on the ovarian cancer diagnosis and classification.

Emerging Role of Metabolomics in Ovarian Cancer Diagnosis

Ovarian cancer is considered a silent killer due to the lack of clear symptoms and efficient diagnostic tools that often lead to late diagnoses. Over recent years, the impelling need for proficient biomarkers has led researchers to consider metabolomics, an emerging omics science that deals with analyses of the entire set of small-molecules (≤1.5 kDa) present in biological systems. Metabolomics profiles, as a mirror of tumor-host interactions, have been found to be useful for the analysis and identification of specific cancer phenotypes. Cancer may cause significant metabolic alterations to sustain its growth, and metabolomics may highlight this, making it possible to detect cancer in an early phase of development. In the last decade, metabolomics has been widely applied to identify different metabolic signatures to improve ovarian cancer diagnosis. The aim of this review is to update the current status of the metabolomics research for the discovery of new diagnostic metabolomic biomarkers for ovarian cancer. The most promising metabolic alterations are discussed in view of their potential biological implications, underlying the issues that limit their effective clinical translation into ovarian cancer diagnostic tools.

Palmitic acid negatively regulates tumor suppressor PTEN through T366 phosphorylation and protein degradation

Chronic elevated free fatty (FFA) levels are linked to metabolic disorders and tumorigenesis. However, the molecular mechanism by which FFAs induce cancer remains poorly understood. Here, we show that the tumor suppressor PTEN protein levels were decreased in high fat diet (HFD) fed mice. As palmitic acid (PA, C16:0) showed a significant increase in the HFD fed mice, we further investigated its role in PTEN down regulation. Our studies revealed that exposure of cells to high doses of PA induced mTOR/S6K-mediated phosphorylation of PTEN at T366. The phosphorylation subsequently enhanced the interaction of PTEN with the E3 ubiquitin ligase WW domain-containing protein 2 (WWP2), which promoted polyubiquitination of PTEN and protein degradation. Consistent with PTEN degradation, exposure of cells to increased concentrations of PA also promoted PTEN-mediated AKT activation and cell proliferation. Significantly, a higher level of S6K activation, PTEN T366 phosphorylation, and AKT activation were also observed in the livers of the HFD fed mice. These results provide a molecular mechanism by which a HFD and elevated PA regulate cell proliferation through inactivation of tumor suppressor PTEN.

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.

Egg oil from Portunus trituberculatus alleviates insulin resistance through activation of insulin signaling in mice

Marine bioactive lipids have been utilized to overcome insulin resistance. However, oil from swimming crab has never been studied. Here, we analyzed the constituents of egg oil from Portunus trituberculatus (Pt-egg oil) and investigated its protective effects against insulin resistance in mice on a high-fat diet. The results showed that Pt-egg oil contained 52.05% phospholipids, 8.61% free fatty acids (especially eicosapentaenoic acid and docosahexaenoic acid), 32.38% triglyceride, 4.79% total cholesterol, and ditissimus astaxanthin. Animal experiments showed that Pt-egg oil significantly mitigated insulin resistance and was associated with reductions in blood glucose, insulin, glucose tolerance, insulin tolerance, serum lipids, and hepatic glycogen. Pt-egg oil activated the phosphatidylinositol 3-hydroxy kinase (PI3K)/protein kinase B (Akt)/glucose transporter 4 pathway in skeletal muscle both at the transcriptional level and at the translational level. Pt-egg oil also promoted hepatic glycogen synthesis through activation of the PI3K/Akt/glycogen synthase kinase-3 beta pathway. These indicate that Pt-egg oil can be used as an alternative to marine bioactive lipids to improve insulin resistance.

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.

A review of validated biomarkers obtained through metabolomics

INTRODUCTION

Studying changes in the whole set of small molecules, final products of biochemical reactions in living systems or metabolites, is extremely appealing because they represent the best approach to identifying what occurs in an organism when samples are collected. However, their usefulness as potential biomarkers is limited by discoveries obtained in small groups without proper validation or even confirmation of the chemical structure. Areas covered: During the past 5 years, more than 900 papers have been published on metabolomics for biomarker discovery, but the numbers are much lower when some criteria of validation are applied. In total, 102 papers have been included in this review. The most frequent disease areas in which these markers have been discovered include the following: cancer, diabetes, and related diseases and neurodegenerative, cardiovascular, autoimmune, liver, and kidney diseases. Expert commentary: Metabolomics has been demonstrated as rapidly growing due to the improvements in instrumentation, mainly mass spectrometry, and data mining software. For application in the clinic, the results should be validated in different stages, from analytical validation to validation in independent sets of samples, using thousands of samples from different sources.

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.