A serum lipidomic strategy revealed potential lipid biomarkers for early-stage cervical cancer

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.

Lipidomic analysis of serum exosomes identifies a novel diagnostic marker for type 2 diabetes mellitus

BACKGROUND

Type 2 diabetes mellitus (T2DM) intricately involves disrupted lipid metabolism. Exosomes emerge as carriers of biomarkers for early diagnosis and monitoring. This study aims to identify lipid metabolites in serum exosomes for T2DM diagnosis.

METHODS

Serum samples were collected from newly diagnosed T2DM patients and age and body mass index-matched healthy controls. Exosomes were isolated using exosome isolation reagent, and untargeted/targeted liquid chromatography-tandem mass spectrometry (LC-MS/MS) was used to identify and validate altered lipid metabolites. Receiver operating characteristic curve analysis was used to evaluate the diagnostic value of candidate lipid metabolites.

RESULTS

Serum exosomes were successfully isolated from both groups, with untargeted LC-MS/MS revealing distinct lipid metabolite alterations. Notably, phosphatidylethanolamine (PE) (22:2(13Z,16Z)/14:0) showed stable elevation in T2DM-serum exosomes. Targeted LC-MS/MS confirmed significant increase of PE (22:2(13Z,16Z)/14:0) in T2DM exosomes but not in serum. PE (22:2(13Z,16Z)/14:0) levels not only positively correlated with hemoglobin A1C levels and blood glucose levels, but also effectively distinguished T2DM patients from healthy individuals (area under the curve = 0.9141).

CONCLUSION

Our research sheds light on the importance of serum exosome lipid metabolites in diagnosing T2DM, providing valuable insights into the complex lipid metabolism of diabetes.

Lipid Identification of Biomarkers in Esophageal Squamous Cell Carcinoma by Lipidomic Analysis

Lipids participate in many important biological functions through energy storage, membrane structure stabilization, signal transduction, and molecular recognition. Previous studies have shown that patients with esophageal squamous cell carcinoma (ESCC) have abnormal lipid metabolism. However, studies characterizing lipid metabolism in ESCC patients through lipidomics are limited. Plasma lipid profiles of 65 ESCC patients and 42 healthy controls (HC) were characterized by lipidomics-based ultraperformance liquid chromatography quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF-MS). Single-factor and multi-factor statistical analysis were used to screen the differences in blood lipids between groups, and combined with component ratio analysis and receiver operating characteristic (ROC) curve diagnostic efficiency assessment, to reveal the potential mechanisms and biomarkers of ESCC. There were significant differences in lipid profiles between the ESCC and HC groups. Thirty-six differential lipids (11 up-regulated and 25 down-regulated) were selected based on the criteria of p < .05 and fold change > 1.3 or < 0.77. Glycerophospholipids were the major differential lipids, suggesting that these lipid metabolic pathways exhibit a significant imbalance that may contribute to the development of esophageal squamous cell carcinoma. Among them, the seven candidate biomarkers for esophageal squamous cell carcinoma with the highest diagnostic value are three phosphatidylserine (PS), three fatty acids (FA) and one phosphatidylcholine (PC).

Advances in mass spectrometry imaging for spatial cancer metabolomics

Mass spectrometry (MS) has become a central technique in cancer research. The ability to analyze various types of biomolecules in complex biological matrices makes it well suited for understanding biochemical alterations associated with disease progression. Different biological samples, including serum, urine, saliva, and tissues have been successfully analyzed using mass spectrometry. In particular, spatial metabolomics using MS imaging (MSI) allows the direct visualization of metabolite distributions in tissues, thus enabling in-depth understanding of cancer-associated biochemical changes within specific structures. In recent years, MSI studies have been increasingly used to uncover metabolic reprogramming associated with cancer development, enabling the discovery of key biomarkers with potential for cancer diagnostics. In this review, we aim to cover the basic principles of MSI experiments for the nonspecialists, including fundamentals, the sample preparation process, the evolution of the mass spectrometry techniques used, and data analysis strategies. We also review MSI advances associated with cancer research in the last 5 years, including spatial lipidomics and glycomics, the adoption of three-dimensional and multimodal imaging MSI approaches, and the implementation of artificial intelligence/machine learning in MSI-based cancer studies. The adoption of MSI in clinical research and for single-cell metabolomics is also discussed. Spatially resolved studies on other small molecule metabolites such as amino acids, polyamines, and nucleotides/nucleosides will not be discussed in the context.

Lipidomics Investigation Reveals the Reversibility of Hepatic Injury by Silica Nanoparticles in Rats After a 6-Week Recovery Duration

Given the inevitable human exposure owing to its increasing production and utilization, the comprehensive safety evaluation of silica nanoparticles (SiNPs) has sparked concerns. Substantial evidence indicated liver damage by inhaled SiNPs. Notwithstanding, few reports focused on the persistence or reversibility of hepatic injuries, and the intricate molecular mechanisms involved remain limited. Here, rats are intratracheally instilled with SiNPs in two regimens (a 3-month exposure and a subsequent 6-week recovery after terminating SiNPs administration) to assess the hepatic effects. Nontargeted lipidomics revealed alterations in lipid metabolites as a contributor to the hepatic response and recovery effects of SiNPs. In line with the functional analysis of differential lipid metabolites, SiNPs activated oxidative stress, and induced lipid peroxidation and lipid deposition in the liver, as evidenced by the elevated hepatic levels of ROS, MDA, TC, and TG. Of note, these indicators showed great improvements after a 6-week recovery, even returning to the control levels. According to the correlation, ROC curve, and SEM analysis, 11 lipids identified as potential regulatory molecules for ameliorating liver injury by SiNPs. Collectively, the work first revealed the reversibility of SiNP-elicited hepatotoxicity from the perspective of lipidomics and offered valuable laboratory evidence and therapeutic strategy to facilitate nanosafety.

Disturbed Plasma Lipidomic Profiles in Females with Diffuse Large B-Cell Lymphoma: A Pilot Study

Lipidome dysregulation is a hallmark of cancer and inflammation. The global plasma lipidome and sub-lipidome of inflammatory pathways have not been reported in diffuse large B-cell lymphoma (DLBCL). In a pilot study of plasma lipid variation in female DLBCL patients and BMI-matched disease-free controls, we performed targeted lipidomics using LC-MRM to quantify lipid mediators of inflammation and immunity, and those known or hypothesised to be involved in cancer progression: sphingolipids, resolvin D1, arachidonic acid (AA)-derived oxylipins, such as hydroxyeicosatetraenoic acids (HETEs) and dihydroxyeicosatrienoic acids, along with their membrane structural precursors. We report on the role of the eicosanoids in the separation of DLBCL from controls, along with lysophosphatidylinositol LPI 20:4, implying notable changes in lipid metabolic and/or signalling pathways, particularly pertaining to AA lipoxygenase pathway and glycerophospholipid remodelling in the cell membrane. We suggest here the set of S1P, SM 36:1, SM 34:1 and PI 34:1 as DLBCL lipid signatures which could serve as a basis for the prospective validation in larger DLBCL cohorts. Additionally, untargeted lipidomics indicates a substantial change in the overall lipid metabolism in DLBCL. The plasma lipid profiling of DLBCL patients helps to better understand the specific lipid dysregulations and pathways in this cancer.

Untargeted lipidomic analysis and network pharmacology for parthenolide treated papillary thyroid carcinoma cells

BACKGROUND

With fast rising incidence, papillary thyroid carcinoma (PTC) is the most common head and neck cancer. Parthenolide, isolated from traditional Chinese medicine, inhibits various cancer cells, including PTC cells. The aim was to investigate the lipid profile and lipid changes of PTC cells when treated with parthenolide.

METHODS

Comprehensive lipidomic analysis of parthenolide treated PTC cells was conducted using a UHPLC/Q-TOF-MS platform, and the changed lipid profile and specific altered lipid species were explored. Network pharmacology and molecular docking were performed to show the associations among parthenolide, changed lipid species, and potential target genes.

RESULTS

With high stability and reproducibility, a total of 34 lipid classes and 1736 lipid species were identified. Lipid class analysis indicated that parthenolide treated PTC cells contained higher levels of fatty acid (FA), cholesterol ester (ChE), simple glc series 3 (CerG3) and lysophosphatidylglycerol (LPG), lower levels of zymosterol (ZyE) and Monogalactosyldiacylglycerol (MGDG) than controlled ones, but with no significant differences. Several specific lipid species were changed significantly in PTC cells treated by parthenolide, including the increasing of phosphatidylcholine (PC) (12:0e/16:0), PC (18:0/20:4), CerG3 (d18:1/24:1), lysophosphatidylethanolamine (LPE) (18:0), phosphatidylinositol (PI) (19:0/20:4), lysophosphatidylcholine (LPC) (28:0), ChE (22:6), and the decreasing of phosphatidylethanolamine (PE) (16:1/17:0), PC (34:1) and PC (16:0p/18:0). Four key targets (PLA2G4A, LCAT, LRAT, and PLA2G2A) were discovered when combining network pharmacology and lipidomics. Among them, PLA2G2A and PLA2G4A were able to bind with parthenolide confirmed by molecular docking.

CONCLUSIONS

The changed lipid profile and several significantly altered lipid species of parthenolide treated PTC cells were observed. These altered lipid species, such as PC (34:1), and PC (16:0p/18:0), may be involved in the antitumor mechanisms of parthenolide. PLA2G2A and PLA2G4A may play key roles when parthenolide treated PTC cells.

Serum lipidomic profiling by UHPLC-MS/MS may be able to detect early-stage endometrial cancer

Nowadays, screening for endometrial cancer (EC) primarily relies on clinical symptoms and imaging, which makes it difficult to detect early-stage disease. Here, we conducted a widely targeted lipidomic analysis of 38 human serum samples in a discovery set and 40 human serum samples in a validation set to profile the dysregulated lipid species and establish lipid biomarkers for early-stage EC. This comprehensive lipidomic determination of 616 serum lipids indicated significant differences between early-stage EC patients and healthy controls. Three phases of lipid biomarker investigation (discovery, validation, and determination of the lipid biomarker panel) were performed, which revealed the upregulation of some sphingolipid, glycerophospholipid, and glycerolipids and downregulation of some carnitine. Consistently, the perturbation of sphingolipid and glycerophospholipid metabolism was also observed from pathway enrichment analysis. Moreover, a lipid biomarker panel, including ursodeoxycholic acid, PC(O-14:0_20:4), and Cer(d18:1/18:0), was established. This panel was assessed as an effective diagnostic model to distinguish early-stage EC patients from healthy controls and atypical endometrial hyperplasia patients within the area under the receiver operating characteristic curve (AUC) reaching 0.903 and 0.928, respectively. In particular, the comparison results of the diagnostic efficacy indicated that the lipid biomarker panel was superior to clinically established indicators for EC diagnosis, including HE4, CA125, CA153, and CA199, suggesting that it could be used as an excellent supplementary method for the diagnosis of early-stage EC. In conclusion, we established a novel and non-invasive lipid biomarker for early-stage EC detection and these findings may provide new insight into the pathological mechanisms of EC.

Differential Plasma Metabolites between High- and Low-Grade Meningioma Cases

Meningiomas (MGMs) are currently classified into grades I, II, and III. High-grade tumors are correlated with decreased survival rates and increased recurrence rates. The current grading classification is based on histological criteria and determined only after surgical tumor sampling. This study aimed to identify plasma metabolic alterations in meningiomas of different grades, which would aid surgeons in predefining the ideal surgical strategy. Plasma samples were collected from 51 patients with meningioma and classified into low-grade (LG) (grade I; n = 43), and high-grade (HG) samples (grade II, n = 5; grade III, n = 3). An untargeted metabolomic approach was used to analyze plasma metabolites. Statistical analyses were performed to select differential biomarkers among HG and LG groups. Metabolites were identified using tandem mass spectrometry along with database verification. Five and four differential biomarkers were identified for HG and LG meningiomas, respectively. To evaluate the potential of HG MGM metabolites to differentiate between HG and LG tumors, a receiving operating characteristic curve was constructed, which revealed an area under the curve of 95.7%. This indicates that the five HG MGM metabolites represent metabolic alterations that can differentiate between LG and HG meningiomas. These metabolites may indicate tumor grade even before the appearance of histological features.

Non-Invasive Differential Diagnosis of Cervical Neoplastic Lesions by the Lipid Profile Analysis of Cervical Scrapings

Cervical cancer is one of the most common cancers in women with pronounced stages of precancerous lesions. Accurate differential diagnosis of such lesions is one of the primary challenges of medical specialists, which is vital to improving patient survival. The aim of this study was to develop and test an algorithm for the differential diagnosis of cervical lesions based on lipid levels in scrapings from the cervical epithelium and cervicovaginal canal. The lipid composition of the samples was analyzed by high-performance chromato-mass spectrometry. Lipid markers were selected using the Mann–Whitney test with a cutoff value of 0.05 and by projections to latent structures discriminant analysis, where a projection threshold of one was chosen. The final selection of variables for binomial logistic regressions was carried out using the Akaike information criterion. As a result, a final neoplasia classification method, based on 20 logistic regression sub-models, has an accuracy of 79% for discrimination NILM/cervicitis/LSIL/HSIL/cancer. The model has a sensitivity of 83% and a specificity of 88% for discrimination of several lesions (HSIL and cancer). This allows us to discuss the prospective viability of further validation of the developed non-invasive method of differential diagnosis.

UHPLC-HRMS–based serum lipisdomics reveals novel biomarkers to assist in the discrimination between colorectal adenoma and cancer

The development of a colorectal adenoma (CA) into carcinoma (CRC) is a long and stealthy process. There remains a lack of reliable biomarkers to distinguish CA from CRC. To effectively explore underlying molecular mechanisms and identify novel lipid biomarkers promising for early diagnosis of CRC, an ultrahigh-performance liquid chromatography tandem high-resolution mass spectrometry (UHPLC-HRMS) method was employed to comprehensively measure lipid species in human serum samples of patients with CA and CRC. Results showed significant differences in serum lipid profiles between CA and CRC groups, and 85 differential lipid species (P &lt; 0.05 and fold change &gt; 1.50 or &lt; 0.67) were discovered. These significantly altered lipid species were mainly involved in fatty acid (FA), phosphatidylcholine (PC), and triacylglycerol (TAG) metabolism with the constituent ratio &gt; 63.50%. After performance evaluation by the receiver operating characteristic (ROC) curve analysis, seven lipid species were ultimately proposed as potential biomarkers with the area under the curve (AUC) &gt; 0.800. Of particular value, a lipid panel containing docosanamide, SM d36:0, PC 36:1e, and triheptanoin was selected as a composite candidate biomarker with excellent performance (AUC = 0.971), and the highest selected frequency to distinguish patients with CA from patients with CRC based on the support vector machine (SVM) classification model. To our knowledge, this study was the first to undertake a lipidomics profile using serum intended to identify screening lipid biomarkers to discriminate between CA and CRC. The lipid panel could potentially serve as a composite biomarker aiding the early diagnosis of CRC. Metabolic dysregulation of FAs, PCs, and TAGs seems likely involved in malignant transformation of CA, which hopefully will provide new clues to understand its underlying mechanism.

Direct analysis of metabolites in the liver tissue of zebrafish exposed to fiproles by internal extractive electrospray ionization mass spectrometry

Exploring the metabolic disturbance of fipronil and its derivatives in aquatic organisms may provide a more comprehensive understanding of the impact of fipronil on the ecological environment. In this work, internal extractive electrospray ionization mass spectrometry (iEESI-MS) was used to directly analyze metabolites in the liver tissue of zebrafish exposed to fipronil and its three derivatives. Partial least squares-discriminant analysis (PLS-DA) revealed that 32 signals were considered as differential signals in zebrafish after the exposure treatment of fipronil and its derivatives, including phosphatidylcholines (PCs), lysophosphatidylcholines (LPCs), phosphatidylethanolamines (PEs), fatty acids and so on. The pathway analysis result showed that both fipronil and its derivatives have a significant impact on the glycerophospholipid metabolism of zebrafish. Besides, the intensities of PC signals in the liver samples of each group showed such a trend: mixed fiprole exposed group > fipronil sulfone exposed group ≈ fipronil sulfide exposed group > fipronil exposed group > fipronil desulfinyl exposed group > control group, indicating that mixed exposure of fipronil and its derivatives exhibited more significant metabolic disturbance in zebrafish. Taken together, iEESI-MS is applied to environmental toxicology and investigating the metabolic disturbance induced by fipronil and its derivatives in aquatic organisms, providing a new analytical method for this field.

Proteomic and lipidomic analyses reveal saturated fatty acids, phosphatidylinositol, phosphatidylserine, and associated proteins contributing to intramuscular fat deposition

Intramuscular fat (IMF) content is an important factor in porcine meat quality. Previous studies have screened multiple candidate genes related to IMF deposition, but the lipids that affect IMF deposition and their lipid-protein network remain unknown. In this study, we performed proteomic and lipidomic analyses of the longissimus dorsi (LD) muscle from high-IMF (IMFH) and low-IMF (IMF-L) groups of Xidu black pigs. Eighty-eight proteins and 143 lipids were differentially abundant between the groups. The differentially abundant proteins were found to be involved in cholesterol metabolism, the PPAR signaling pathway, and ferroptosis. The triacylglycerols (TAGs) upregulated in the IMF-H group were mainly shown to be synthesized by saturated fatty acids (SFAs), while the downregulated TAGs were mainly synthesized by polyunsaturated fatty acids (PUFAs). All differentially abundant phosphatidylinositols (PIs) and phosphatidylserines (PSs) were found to be upregulated in the IMF-H group. A correlation analysis of the proteomic and lipidomic revealed candidate proteins (APOA4, VDAC3, PRNP, CTSB, GSPT1) related to TAG, PI, and PS lipids. These results revealed differences in proteins and lipids between the IMF-H and IMF-L groups, which represent new candidate proteins and lipids that should be investigated to determine the molecular mechanisms controlling IMF deposition in pigs. SIGNIFICANCE: Intramuscular fat (IMF) is a key factor affecting meat quality, and meat with a higher IMF content can have a better flavor. In this study, proteomic results show that the ferroptosis pathway, including the PRNP, VDAC3 and CP proteins, affects IMF deposition. Lipid composition is the key factor affecting IMF deposition, but there are few reports on this. In this study, through lipidomic analysis, we suggest that saturated fatty acid (SFA), phosphatidylinositol (PI), and phosphatidylserine (PS) may contribute to IMF deposition. A correlation analysis reveals the potential regulatory network between lipids and proteins. This study clarifies the difference in protein and lipid compositions in longissimus dorsi (LD) muscle with high and low IMF contents. This information suggests that it would be beneficial to increase the intramuscular fat content of pork not only from a genetic perspective but also from a nutritional perspective.

Roles of Lipid Profiles in Human Non-Small Cell Lung Cancer

Aims: This review aims to identify lipid biomarkers of non-small cell lung cancer (NSCLC) in human tissue samples and discuss the roles of lipids in tissue molecular identification, the discovery of potential biomarkers, and surgical margin assessment. Methods: A review of the literature focused on lipid-related research using mass spectrometry (MS) techniques in human NSCLC tissues from January 1, 2015, to November 20, 2020, was conducted. The quality of included studies was assessed using the QUADAS-2 tool. Results: Twelve studies met the inclusion criteria and were included in the review. The risk of bias was unclear in the majority of the studies. The contents of lipids including fatty acids, phosphatidyl choline, phosphatidyl ethanolamine, phosphatidyl inositol, cardiolipin, phosphatidyl serine, phosphatidyl glycerol, ceramide, lysophosphatidylethanolamine, lysophosphatidylcholine, and lysophosphatidylglycerol differed significantly between cancer and healthy tissues. The sensitivity or specificity of the discrimination model was reported in 8 studies, and the sensitivity and specificity varied among the reported methods. The lipid profiles differed between adenocarcinoma and squamous cell carcinoma NSCLC subtypes. Conclusion: In preclinical studies, MS analysis and multiple discrimination models can be combined to distinguish NSCLC tissues from healthy tissues based on lipid profiles, which provides a new opportunity to evaluate the surgical margin and cancer subtype intraoperatively. Future studies should provide guidance for selecting patients and discrimination models to develop an improved method for clinical application.