A Novel Strategy for Targeted Lipidomics Based on LC-Tandem-MS Parameters Prediction, Quantification, and Multiple Statistical Data Mining: Evaluation of Lysophosphatidylcholines as Potential Cancer Biomarkers

The Utility of Lipidomic Analysis in Colorectal Cancer Diagnosis and Prognosis-A Systematic Review of Recent Literature

Colorectal cancer (CRC) is among the most prevalent and lethal malignancies. Lipidomic investigations have revealed numerous disruptions in lipid profiles across various cancers. Studies on CRC exhibit potential for identifying novel diagnostic or prognostic indicators through lipidomic signatures. This review examines recent literature regarding lipidomic markers for CRC. PubMed database was searched for eligible articles concerning lipidomic biomarkers of CRC. After selection, 36 articles were included in the review. Several studies endeavor to establish sets of lipid biomarkers that demonstrate promising potential to diagnose CRC based on blood samples. Phosphatidylcholine, phosphatidylethanolamine, ceramides, and triacylglycerols (TAGs) appear to offer the highest diagnostic accuracy. In tissues, lysophospholipids, ceramides, and TAGs were among the most altered lipids, while unsaturated fatty acids also emerged as potential biomarkers. In-depth analysis requires both cell culture and animal studies. CRC involves multiple lipid metabolism alterations. Although numerous lipid species have been suggested as potential diagnostic markers, the establishment of standardized methods and the conduct of large-scale studies are necessary to facilitate their clinical application.

Comprehensive analysis of phospholipid in milk and their biological roles as nutrients and biomarkers

Phospholipids (PL) have garnered significant attention due to their physiological activities. Milk and other dairy products are important dietary sources for humans and have been extensively used to analyze the presence of PL by various analytical techniques. In this paper, the analysis techniques of PL were reviewed with the eight trigrams of phospholipidomics and a comprehensive fingerprint of 1295 PLs covering 8 subclasses in milk and other dairy products, especially. Technology is the primary productive force. Based on phospholipidomics technology, we further review the relationship between the composition of PL and factors that may be involved in processing and experimental operation, and emphasized the significance of the biological role played by PL in dietary supplements and biomarkers (production, processing and clinical research), and providing the future research directions.

Recent Analytical Methodologies in Lipid Analysis

Lipids represent a large group of biomolecules that are responsible for various functions in organisms. Diseases such as diabetes, chronic inflammation, neurological disorders, or neurodegenerative and cardiovascular diseases can be caused by lipid imbalance. Due to the different stereochemical properties and composition of fatty acyl groups of molecules in most lipid classes, quantification of lipids and development of lipidomic analytical techniques are problematic. Identification of different lipid species from complex matrices is difficult, and therefore individual analytical steps, which include extraction, separation, and detection of lipids, must be chosen properly. This review critically documents recent strategies for lipid analysis from sample pretreatment to instrumental analysis and data interpretation published in the last five years (2019 to 2023). The advantages and disadvantages of various extraction methods are covered. The instrumental analysis step comprises methods for lipid identification and quantification. Mass spectrometry (MS) is the most used technique in lipid analysis, which can be performed by direct infusion MS approach or in combination with suitable separation techniques such as liquid chromatography or gas chromatography. Special attention is also given to the correct evaluation and interpretation of the data obtained from the lipid analyses. Only accurate, precise, robust and reliable analytical strategies are able to bring complex and useful lipidomic information, which may contribute to clarification of some diseases at the molecular level, and may be used as putative biomarkers and/or therapeutic targets.

Salivary Lipids of Patients with Non-Small Cell Lung Cancer Show Perturbation with Respect to Plasma

A comprehensive lipid profile was analyzed in patients with non-small cell lung cancer (NSCLC) using nanoflow ultrahigh-performance liquid chromatography-electrospray ionization-tandem mass spectrometry. This study investigated 297 and 202 lipids in saliva and plasma samples, respectively, comparing NSCLC patients to healthy controls. Lipids with significant changes (>2-fold, p < 0.05) were further analyzed in each sample type. Both saliva and plasma exhibited similar lipid alteration patterns in NSCLC, but saliva showed more pronounced changes. Total triglycerides (TGs) increased (>2-3-fold) in plasma and saliva samples. Three specific TGs (50:2, 52:5, and 54:6) were significantly increased in NSCLC for both sample types. A common ceramide species (d18:1/24:0) and phosphatidylinositol 38:4 decreased in both plasma and saliva by approximately two-fold. Phosphatidylserine 36:1 was selectively detected in saliva and showed a subsequent decrease, making it a potential biomarker for predicting lung cancer. We identified 27 salivary and 10 plasma lipids as candidate markers for NSCLC through statistical evaluations. Moreover, this study highlights the potential of saliva in understanding changes in lipid metabolism associated with NSCLC.

Determination of lysophosphatidylcholine using peroxidase-mimic PVP/PtRu nanozyme

Lysophosphatidylcholine (LPC) can be used as a biomarker for diseases such as cancer, diabetes, atherosclerosis, and sepsis. In this study, we demonstrated the ability of nanozymes to displace the natural derived enzyme in enzyme-based assays for the measurement of LPC. Synthesized polyvinylpyrrolidone-stabilized platinum-ruthenium nanozymes (PVP/PtRu NZs) had a uniform size of 2.48 ± 0.24 nm and superb peroxidase-mimicking activity. We demonstrated that the nanozymes had high activity over a wide pH and temperature range and high stability after long-term storage. The LPC concentration could be accurately analyzed through the absorbance and fluorescence signals generated by the peroxidation reaction using the synthesized nanozyme with substrates such as 3,3',5,5'-tetramethylbenzidine (TMB) and 10-acetyl-3,7-dihydroxyphenoxazine (Ampliflu™ Red). LPC at a concentration of 0-400 µM was used for the analysis, and the coefficient of determination (R²) was 0.977, and the limit of detection (LOD) was 23.1 µM by colorimetric assay. In the fluorometric assay, the R² was 0.999, and the LOD was 8.97 µM. The spiked recovery values for the determination of LPC concentration in human serum samples were 102-115%. Based on these results, we declared that PVP/PtRu NZs had an ability comparable to that of the native enzyme horseradish peroxidase (HRP) in the enzyme-based LPC detection method.

Progress with Metabolomic Blood Tests for Gastrointestinal Cancer Diagnosis-An Assessment of Biomarker Translation

There is an urgent need for cost-effective, non-invasive tools to detect early stages of gastrointestinal cancer (colorectal, gastric, and esophageal cancers). Despite many publications suggesting circulating metabolites acting as accurate cancer biomarkers, few have reached the clinic. In upper gastrointestinal cancer this is critically important, as there is no test to complement gold-standard endoscopic evaluation in patients with mild symptoms that do not meet referral criteria. Therefore, this study aimed to describe and solve this translational gap. Studies reporting diagnostic accuracy of metabolomic blood-based gastrointestinal cancer biomarkers from 2007 to 2020 were systematically reviewed and progress of each biomarker along the discovery-validation-adoption pathway was mapped. Successful biomarker translation was defined as a composite endpoint, including patent protection/FDA approval/recommendation in national guidelines. The review found 77 biomarker panels of gastrointestinal cancer, including 25 with an AUROC >0.9. All but one was stalled at the discovery phase, 9.09% were patented and none were clinically approved, confirming the extent of biomarker translational gap. In addition, there were numerous "re-discoveries," including histidine, discovered in 7 colorectal studies. Finally, this study quantitatively supports the presence of a translational gap between discovery and clinical adoption, despite clear evidence of highly performing biomarkers with significant potential clinical value.

Evaluation of characteristic metabolites of aromatic amino acids in patients with HIV infection at different stages of disease

BACKGROUND

Acquired immune deficiency syndrome (AIDS), human immunodeficiency virus (HIV) infection, and antiretroviral therapy are usually associated with metabolic disorders. Screening for biomarkers to evaluate the progression of metabolic disorders is important for the diagnosis and treatment of HIV infection. This study aimed to establish and validate a method to quantify serum aromatic amino acid (AAA) metabolites as biomarkers of metabolic disorders in patients with HIV.

METHODS

The AAAs and metabolites were analyzed using high-performance liquid chromatography-tandem mass spectrometry. Pearson's correlation, heatmap, and receiver operating characteristic curve analyses were used for statistical analysis.

RESULTS

Under optimal detection conditions, the lower limits of phenylalanine (Phe), tryptophan (Trp), kynurenine (Kyn), tyrosine, phenylacetylglutamine (PAGln), and 5-hydroxytryptamine quantification reached 0.02, 0.02, 0.01, 0.02, 0.01, and 0.002 μg/ml, respectively, and the precision of intra- and inter-day was stay below 10.30%. Serum samples were stable for at least 6 months when stored at -80°C. The inter-group differences and associations between the biomarkers exhibited a particular volatility trend in PAGln, Trp, and Kyn metabolism in HIV-infected patients with metabolic syndrome.

CONCLUSIONS

The developed method can be used for rapid and sensitive quantification of the AAA metabolism profile in vivo to further appraise the process of HIV infection, evaluate intervening measures, conduct mechanistic investigations, and further study the utility of PAGln, a characteristic metabolite of AAA, as a biomarker of HIV infection coupled with metabolic syndrome.

Study on endocrine disruption effect of paclobutrazol and uniconazole on the thyroid of male and female rats based on lipidomics

The present study investigated the effects of paclobutrazol and uniconazole on thyroid endocrine system in rats. Lipidomic analysis was performed to obtain the biomarkers of thyroid endocrine disruption induced by paclobutrazol and uniconazole. Network pharmacology was further used to discover potential targets of biomarkers related to drugs and diseases. After paclobutrazol and uniconazole administration, seven and four common biomarkers related to thyroid endocrine disruption for female and male rats were obtained, respectively. Paclobutrazol and uniconazole significantly increased the biomarker levels of PG (12:0/15:0), PS (14:0/16:0), PA (20:1/15:0) and PG (13:0/17:0) in both sexes of rats. Exposure to paclobutrazol additionally caused a significant decrease of PG (22:6/20:2), PE (24:1/18:1) and PE (24:0/18:0) in female rats, while an increase in male rats. Changes of the common biomarkers for paclobutrazol and uniconazole revealed similar endocrine disruption effect, which was higher in the females. Network pharmacology and KEGG pathway analysis indicated that the thyroid endocrine disrupting effects of paclobutrazol and uniconazole may be related to V-akt murine thymoma viral oncogene homolog (Akts), mitogen-activated protein kinase (MAPKs), epidermal growth factor receptor (EGFR), Insulin-like growth factor (IGF-1), IGF-IR and V-Raf murine sarcoma viral oncogene homolog B1 (BRAF). The results demonstrated that paclobutrazol and uniconazole could cause thyroid endocrine disorders in male and female rats, which were sex-specific, thus highlighting the importance of safe and effective application of these plant growth regulators.

Serum untargeted lipidomics by UHPLC-ESI-HRMS aids the biomarker discovery of colorectal adenoma

BACKGROUND

Colorectal adenoma (CA) is an important precancerous lesion and early screening target of colorectal cancer (CRC). Lipids with numerous physiological functions are proved to be involved in the development of CRC. However, there is no lipidomic study with large-scale serum samples on diagnostic biomarkers for CA.

METHODS

The serum lipidomics of CA patients (n = 50) and normal control (NR) (n = 50) was performed by ultra high performance liquid chromatography-high resolution mass spectrometry with electrospray ionization (UHPLC-ESI-HRMS). Univariate and multivariate statistical analyses were utilized to screen the differential lipids between groups, and combining the constituent ratio analysis and diagnostic efficiency evaluation by receiver operating characteristic (ROC) curve disclosed the potential mechanism and biomarkers for CA.

RESULTS

There were obvious differences in serum lipid profiles between CA and NR groups. Totally, 79 differential lipids were selected by criterion of P < 0.05 and fold change > 1.5 or < 0.67. Triacylglycerols (TAGs) and phosphatidylcholines (PCs) were the major differential lipids with ratio > 60%, indicating these two lipid metabolic pathways showed evident disequilibrium, which could contribute to CA formation. Of them, 12 differential lipids had good diagnostic ability as candidate biomarkers for CA (AUC ≥ 0.900) by ROC analysis.

CONCLUSIONS

To our knowledge, this is the first attempt to profile serum lipidomics and explore lipid biomarkers of CA to help early screening of CRC. 12 differential lipids are obtained to act as potential diagnostic markers of CA. PCs and fatty acids were the main dysregulated biomarkers for CA in serum.

Metabolic profiling of lysophosphatidylcholines in chlorpromazine hydrochloride- and N-acetyl-p-amino-phenoltriptolide-induced liver injured rats based on ultra-performance liquid chromatography quadrupole time-of-flight mass spectrometry

Chlorpromazine hydrochloride (CH) and N-acetyl-p-amino-phenoltriptolide (APAP) are typical acentral dopamine receptor antagonists and antipyretic analgesics in clinical applications, respectively. However, it has been reported that these 2 drugs could cause liver damage. Lysophosphatidylcholines (LPCs) have multiple physiological functions and are metabolized primarily in the liver, where it undergoes significant changes when the liver is damaged. In the study, 15 LPCs in the rat serum with CH- and APAP-induced liver injury were quantified based on ultra-performance liquid chromatography quadrupole time-of-flight mass spectrometry, and multivariate statistical analyses including principal component analysis (PCA) and orthogonal partial least squares discriminate analysis (OPLS-DA) were combined to understand CH- and APAP-induced liver injury from the perspective of LPC metabolic profiling. The quantitative results showed that there were significant changes in 10 LPCs and 5 LPCs after CH- and APAP-administration, separately. The results of PCA and OPLS-DA indicated that CH- and APAP-induced liver injury could be well distinguished by the LPC metabolic profiling, and 7 LPCs and 1 LPC biomarkers that could characterize CH- and APAP-induced liver damage in turn had been screened. This study will not only provide a new perspective for the clinical diagnosis of CH- and APAP-induced liver injury, but also offer a reference for further study of their hepatotoxicity mechanisms.

Evaluation of lipid metabolism imbalance in HIV-infected patients with metabolic disorders using high-performance liquid chromatography-tandem mass spectrometry

Human immunodeficiency virus (HIV) infection and highly active antiretroviral therapy use are associated with the disruption of lipid and glucose metabolism. Herein, a sensitive and robust high-performance liquid chromatography-tandem mass spectrometry method for the quantitation of lysophosphatidylcholines (LPCs) and acylcarnitines (ACs) in human blood serum was developed and validated to investigate them as markers of metabolic disorders in HIV-infected patients. Under optimal extraction and detection conditions, the lower limits of quantification reached 5 ng/mL (LPCs) and 0.1 ng/mL (ACs), and precision and accuracy for both intra- and inter-day analyses were generally below 15%. Serum samples were stable for at least six months when stored at - 80 °C and for at least 12 h when stored at 4 °C or 25 °C. We investigated inter-group differences and associations between the biomarkers and observed a particular volatilitytrend of LPCs and ACs for HIV-infected patients with metabolic disorders. Thus, the developed method can be used for the rapid and sensitive quantitation of LPCs and ACs in vivo to further appraise the process of HIV infection, evaluate interveningmeasures, conduct mechanistic investigations, and further study the utility of LPCs and ACs as biomarkers of HIV infection coupled with metabolic disorders.

Development of computational models using omics data for the identification of effective cancer metabolic biomarkers

Identification of novel biomarkers has been an active area of study for the effective diagnosis, prognosis and treatment of cancers. Among various types of cancer biomarkers, metabolic biomarkers, including enzymes, metabolites and metabolic genes, deserve attention as they can serve as a reliable source for diagnosis, prognosis and treatment of cancers. In particular, efforts to identify novel biomarkers have been greatly facilitated by a rapid increase in the volume of multiple omics data generated for a range of cancer cells. These omics data in turn serve as ingredients for developing computational models that can help derive deeper insights into the biology of cancer cells, and identify metabolic biomarkers. In this review, we provide an overview of omics data generated for cancer cells, and discuss recent studies on computational models that were developed using omics data in order to identify effective cancer metabolic biomarkers.

Revealing potential lipid biomarkers in clear cell renal cell carcinoma using targeted quantitative lipidomics

BACKGROUND

The high drug resistance and metabolic reprogramming of clear cell renal cell carcinoma (ccRCC) are considered responsible for poor prognosis. In-depth research at multiple levels is urgently warranted to illustrate the lipid composition, distribution, and metabolic pathways of clinical ccRCC specimens.

METHODS

In this project, a leading-edge targeted quantitative lipidomic study was conducted using 10 pairs of cancerous and adjacent normal tissues obtained from ccRCC patients. Accurate lipid quantification was performed according to a linear equation calculated using internal standards. Qualitative and quantitative analyses of lipids were performed with multiple reaction monitoring analysis based on ultra-performance liquid chromatography (UPLC) and mass spectrometry (MS). Additionally, a multivariate statistical analysis was performed using data obtained on lipids.

RESULTS

A total of 28 lipid classes were identified. Among them, the most abundant were triacylglycerol (TG), diacylglycerol (DG), phosphatidylcholine (PC), and phosphatidylethanolamine (PE). Cholesteryl ester (CE) was the lipid exhibiting the most considerable difference between normal samples and tumor samples. Lipid content, chain length, and chain unsaturation of acylcarnitine (CAR), CE, and DG were found to be significantly increased. Based on screening for variable importance in projection scores ≥1, as well as fold change limits between 0.5 and 2, 160 differentially expressed lipids were identified. CE was found to be the most significantly upregulated lipid, while TG was observed to be the most significantly downregulated lipid.

CONCLUSION

Based on the absolute quantitative analysis of lipids in ccRCC specimens, it was observed that the content and change trends varied in different lipid classes. Upregulation of CAR, CE, and DG was observed, and analysis of changes in the distribution helped clarify the causes of lipid accumulation in ccRCC and possible carcinogenic molecular mechanisms. The results and methods described herein provide a comprehensive analysis of ccRCC lipid metabolism and lay a theoretical foundation for cancer treatment.

High-resolution metabolomic biomarkers for lung cancer diagnosis and prognosis

Lung cancer is the leading cause of human cancer mortality due to the lack of early diagnosis technology. The low-dose computed tomography scan (LDCT) is one of the main techniques to screen cancers. However, LDCT still has a risk of radiation exposure and it is not suitable for the general public. In this study, plasma metabolic profiles of lung cancer were performed using a comprehensive metabolomic method with different liquid chromatography methods coupled with a Q-Exactive high-resolution mass spectrometer. Metabolites with different polarities (amino acids, fatty acids, and acylcarnitines) can be detected and identified as differential metabolites of lung cancer in small volumes of plasma. Logistic regression models were further developed to identify cancer stages and types using those significant biomarkers. Using the Variable Importance in Projection (VIP) and the area under the curve (AUC) scores, we have successfully identified the top 5, 10, and 20 metabolites that can be used to differentiate lung cancer stages and types. The discrimination accuracy and AUC score can be as high as 0.829 and 0.869 using the five most significant metabolites. This study demonstrated that using 5 + metabolites (Palmitic acid, Heptadecanoic acid, 4-Oxoproline, Tridecanoic acid, Ornithine, and etc.) has the potential for early lung cancer screening. This finding is useful for transferring the diagnostic technology onto a point-of-care device for lung cancer diagnosis and prognosis.

Serum phospholipids are potential biomarkers for the early diagnosis of gastric cancer

BACKGROUND AND AIMS

Early diagnosis is key to improving the prognosis of gastric cancer. Altered phospholipid metabolism has been observed in different types of cancer. This study assessed serum phospholipid levels of patients with gastric cancer to explore biomarkers for its early diagnosis.

MATERIALS AND METHODS

A total of 199 participants were enrolled, including patients with early gastric cancer or precancerous gastric lesions and healthy controls. Serum phospholipids were extracted and identified using mass spectrometry. The relative abundances of these phospholipids were compared among patients at different disease stages. Twenty-four patients with early gastric cancer were followed up, and their serum phospholipid levels were compared beween before and after resection.

RESULTS

Fifty-four phospholipids were identified. Phosphatidylethanolamine (36:3), phosphatidylethanolamine (36:2), phosphatidylcholine (32:0), and sphingomyelin (d18:0/18:1(9Z)) were more abundant in patients with early gastric cancer than in healthy controls. The area under the receiver operating curve of sphingomyelin (d18:0/18:1(9Z)) reached 0.883 in the training set (sensitivity 81.08%, specificity 78.82%) and 0.874 in the validation set. The levels of phosphatidylethanolamine (36:2), phosphatidylcholine (32:0), and sphingomyelin (d18:0/18:1(9Z)) significantly declined after the cancerous lesions were resected.

CONCLUSION

Serum phospholipids can serve as potential biomarkers for the early diagnosis of gastric cancer.

Optimisation of high-speed lipidome analysis by nanoflow ultrahigh-performance liquid chromatography-tandem mass spectrometry: Application to identify candidate biomarkers for four different cancers

Lipid analysis is a powerful tool that can elucidate the pathogenic roles of lipids in metabolic diseases, and facilitate the development of potential biomarkers. Lipid analysis by large-scale lipidomics requires a high-speed and high-throughput analytical platform. In the present study, a high-speed analytical method for lipid analysis using nanoflow ultrahigh-performance liquid chromatography-electrospray ionisation-tandem mass spectrometry (nUHPLC-ESI-MS/MS) was optimised by investigating the effects of column flow rate, pump flow rate, dwell time, initial binary mobile phase composition, and gradient duration on the separation efficiency of standard lipid mixtures. The minimum gradient time for high-speed lipid separation was determined by examining the time-based separation efficiency and spectral overlap of isobaric lipid species during selected reaction monitoring-based quantification of sphingomyelin and a second isotope of phosphatidylcholine, which differ in molecular weight by only 1 Da. Finally, the optimised nUHPLC-ESI-MS/MS method was applied to analyse 200 plasma samples from patients with liver, gastric, lung, and colorectal cancer to evaluate its performance by measuring previously identified candidate lipid biomarkers. About 73% of the reported marker candidates (6 out of 7 in liver, 5/9 in gastric, 4/6 in lung, and 6/7 in colorectal cancer) could be assigned using the optimised method, supporting its use for high-throughput lipid analysis.

Multi-Omics Analysis of Brain Metastasis Outcomes Following Craniotomy

Background

The incidence of brain metastasis continues to increase as therapeutic strategies have improved for a number of solid tumors. The presence of brain metastasis is associated with worse prognosis but it is unclear if distinctive biomarkers can separate patients at risk for CNS related death.

Methods

We executed a single institution retrospective collection of brain metastasis from patients who were diagnosed with lung, breast, and other primary tumors. The brain metastatic samples were sent for RNA sequencing, proteomic and metabolomic analysis of brain metastasis. The primary outcome was distant brain failure after definitive therapies that included craniotomy resection and radiation to surgical bed. Novel prognostic subtypes were discovered using transcriptomic data and sparse non-negative matrix factorization.

Results

We discovered two molecular subtypes showing statistically significant differential prognosis irrespective of tumor subtype. The median survival time of the good and the poor prognostic subtypes were 7.89 and 42.27 months, respectively. Further integrated characterization and analysis of these two distinctive prognostic subtypes using transcriptomic, proteomic, and metabolomic molecular profiles of patients identified key pathways and metabolites. The analysis suggested that immune microenvironment landscape as well as proliferation and migration signaling pathways may be responsible to the observed survival difference.

Conclusion

A multi-omics approach to characterization of brain metastasis provides an opportunity to identify clinically impactful biomarkers and associated prognostic subtypes and generate provocative integrative understanding of disease.

Lipidomic Signatures for Colorectal Cancer Diagnosis and Progression Using UPLC-QTOF-ESI+MS

Metabolomics coupled with bioinformatics may identify relevant biomolecules such as putative biomarkers of specific metabolic pathways related to colorectal diagnosis, classification and prognosis. This study performed an integrated metabolomic profiling of blood serum from 25 colorectal cancer (CRC) cases previously classified (Stage I to IV) compared with 16 controls (disease-free, non-CRC patients), using high-performance liquid chromatography and mass spectrometry (UPLC-QTOF-ESI⁺ MS). More than 400 metabolites were separated and identified, then all data were processed by the advanced Metaboanalyst 5.0 online software, using multi- and univariate analysis, including specificity/sensitivity relationships (area under the curve (AUC) values), enrichment and pathway analysis, identifying the specific pathways affected by cancer progression in the different stages. Several sub-classes of lipids including phosphatidylglycerols (phosphatidylcholines (PCs), phosphatidylethanolamines (PEs) and PAs), fatty acids and sterol esters as well as ceramides confirmed the “lipogenic phenotype” specific to CRC development, namely the upregulated lipogenesis associated with tumor progression. Both multivariate and univariate bioinformatics confirmed the relevance of some putative lipid biomarkers to be responsible for the altered metabolic pathways in colorectal cancer.

"Modeling-Prediction" Strategy for Deep Profiling of Lysophosphatidic Acids by Liquid Chromatography-Mass Spectrometry: Exploration Biomarkers of Breast Cancer

Lysophosphatidic acids (LPAs) are important bioactive phospholipids consisting of various species involved in a wide array of physiological and pathological processes. However, LPAs were rarely identified in untargeted lipidomics studies because of the incompatibility with analytical methods. Moreover, in targeted studies, the coverages of LPAs remained unsatisfactorily low due to the limitation of reference standards. Herein, a "modeling-prediction" workflow for deep profiling of LPAs by liquid chromatography-mass spectrometry was developed. Multiple linear regression models of qualitative and quantitative parameters were established according to features of fatty acyl tails of the commercial standards to predict the corresponding parameters for unknown LPAs. Then 72 multiple reaction monitoring (MRM) transitions were monitored simultaneously and species of LPA 14:0, LPA 16:1, LPA 18:3, LPA 20:3 and LPA 20:5 were firstly characterized and quantified in plasma. Finally, the workflow was applied to explore the changes of LPAs in plasma of breast cancer patients compared with healthy volunteers. Multi-LPAs indexes with strong diagnostic ability for breast cancer were identified successfully using Student's t- test, orthogona partial least-squares discrimination analysis (OPLS-DA) and logistic regression- receiver operating characteristic (ROC) curve analysis. The proposed workflow with high sensitivity, high accuracy, high coverage and reliable identification would be a powerful complement to untargeted lipidomics and shed a light on the analysis of other lipids.

Systems biology approaches to study lipidomes in health and disease

Lipids have many important biological roles, such as energy storage sources, structural components of plasma membranes and as intermediates in metabolic and signaling pathways. Lipid metabolism is under tight homeostatic control, exhibiting spatial and dynamic complexity at multiple levels. Consequently, lipid-related disturbances play important roles in the pathogenesis of most of the common diseases. Lipidomics, defined as the study of lipidomes in biological systems, has emerged as a rapidly-growing field. Due to the chemical and functional diversity of lipids, the application of a systems biology approach is essential if one is to address lipid functionality at different physiological levels. In parallel with analytical advances to measure lipids in biological matrices, the field of computational lipidomics has been rapidly advancing, enabling modeling of lipidomes in their pathway, spatial and dynamic contexts. This review focuses on recent progress in systems biology approaches to study lipids in health and disease, with specific emphasis on methodological advances and biomedical applications.

Systematic Analysis of the Whole-Body Tissue Distribution and Fatty Acid Compositions of Membrane Lipids in CD1 and NMRI Mice and Wistar Rats

Understanding the tissue distribution of phospholipids and glycerolipids in animal models enables promoting the pharmacokinetic study of drugs and related PK predictions. The measurement of lipid compositions in animal models, usually mice and rats, without a standardized approach hindered the accuracy of PBPK investigation. In this work, high resolution mass spectrometry was applied to profile the tissue distribution of phospholipids and glycerolipids in 12 organs/tissues of mice and rats. Using this method, not only the amounts of phospholipids and glycerolipids in each organ/tissue but also the fatty acid compositions were acquired. In order to explore the interspecies specificity of lipid distribution in different organs/tissues, three animal species including CD1 mice, NMRI mice, and Wister rats were used in this systematic study. Globally, more organ specificity was observed. It was found that the brain is the organ containing the most abundant phosphatidylserine lipids (PSs) in all three animal models, leading to brain tissues having the most concentrated acidic phospholipids. Diverse fatty acid compositions in each lipid class were clearly revealed. Certain tissues/organs also had a specific selection of unique fatty acid compositions, for example, unreferenced FA(18 : 2) in the brain. It turned out that the access of free fatty acids affects the incorporation of acyl chain in phospholipids and glycerolipids. In the analysis, ether lipids were also profiled with the observation of dominant ePEs in brain tissues. However, little interspecies difference was found for fatty acid constituents and tissues distribution of phospholipids and glycerolipids.

Association between Metabolites and the Risk of Lung Cancer: A Systematic Literature Review and Meta-Analysis of Observational Studies

Globally, lung cancer is the most prevalent cancer type. However, screening and early detection is challenging. Previous studies have identified metabolites as promising lung cancer biomarkers. This systematic literature review and meta-analysis aimed to identify metabolites associated with lung cancer risk in observational studies. The literature search was performed in PubMed and EMBASE databases, up to 31 December 2019, for observational studies on the association between metabolites and lung cancer risk. Heterogeneity was assessed using the I2 statistic and Cochran's Q test. Meta-analyses were performed using either a fixed-effects or random-effects model, depending on study heterogeneity. Fifty-three studies with 297 metabolites were included. Most identified metabolites (252 metabolites) were reported in individual studies. Meta-analyses were conducted on 45 metabolites. Five metabolites (cotinine, creatinine riboside, N-acetylneuraminic acid, proline and r-1,t-2,3,c-4-tetrahydroxy-1,2,3,4-tetrahydrophenanthrene) and five metabolite groups (total 3-hydroxycotinine, total cotinine, total nicotine, total 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanol (sum of concentrations of the metabolite and its glucuronides), and total nicotine equivalent (sum of total 3-hydroxycotinine, total cotinine and total nicotine)) were associated with higher lung cancer risk, while three others (folate, methionine and tryptophan) were associated with lower lung cancer risk. Significant heterogeneity was detected across most studies. These significant metabolites should be further evaluated as potential biomarkers for lung cancer.

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.

Multiplatform Investigation of Plasma and Tissue Lipid Signatures of Breast Cancer Using Mass Spectrometry Tools

Plasma and tissue from breast cancer patients are valuable for diagnostic/prognostic purposes and are accessible by multiple mass spectrometry (MS) tools. Liquid chromatography-mass spectrometry (LC-MS) and ambient mass spectrometry imaging (MSI) were shown to be robust and reproducible technologies for breast cancer diagnosis. Here, we investigated whether there is a correspondence between lipid cancer features observed by desorption electrospray ionization (DESI)-MSI in tissue and those detected by LC-MS in plasma samples. The study included 28 tissues and 20 plasma samples from 24 women with ductal breast carcinomas of both special and no special type (NST) along with 22 plasma samples from healthy women. The comparison of plasma and tissue lipid signatures revealed that each one of the studied matrices (i.e., blood or tumor) has its own specific molecular signature and the full interposition of their discriminant ions is not possible. This comparison also revealed that the molecular indicators of tissue injury, characteristic of the breast cancer tissue profile obtained by DESI-MSI, do not persist as cancer discriminators in peripheral blood even though some of them could be found in plasma samples.

A conjunctive lipidomic approach reveals plasma ethanolamine plasmalogens and fatty acids as early diagnostic biomarkers for colorectal cancer patients

Background: Colorectal cancer (CRC) represents a third leading cause of cancer-related death worldwide. The reliable diagnostic biomarkers for detecting CRC at early stage is critical for decreasing the mortality.Method: A conjunctive lipidomic approach was employed to investigate the differences in plasma lipid profiles of CRC patients (n = 101) and healthy volunteers (n = 52). Based on UHPLC-Q-TOF MS and UHPLC-QQQ MS platforms, a total of 236 lipids were structurally detected. Multivariate data analysis was conducted for biomarkers discovery.Results: A total of 11 lipid species, including 1 Glycerophosphoethanolamine (PE), 3 ethanolamine plasmalogens (PlsEtn), 1 plasmanyl glycerophosphatidylethanolamine (PE-O), 3 fatty acids (FFA), 1 Fatty acid ester of hydroxyl fatty acid (FAHFA) and 2 Diacylglycerophosphates (PA) were identified to distinguish the CRC patients at early stage from healthy controls. In addition, these potential lipid biomarkers achieved an estimated AUC=0.981 in a validation set for univariate ROC analysis.Conclusion: By combining Q-TOF MS and QQQ MS analysis, the 11 lipids exhibited good performance in differentiating early-stage CRC and healthy control. This study also demonstrated that lipidomics is a powerful tool in discovering new potential biomarkers for cancer diagnosis.

Multivariate Analysis for Molecular Species of Cholesteryl Ester in the Human Serum

Cholesteryl ester (CE) is an ester of cholesterol and fatty acid (FA). Plasma CE reflects complicated metabolisms of cholesterol, phospholipids, lipoproteins, and dietary FAs. An informatics approach could be useful for analysis of the CE species. In this study, two basic dimension reduction methods, principal component analysis (PCA) and factor analysis, were applied to serum CE species determined by LC-MS/MS in a Japanese population (n = 545). PCA and factor analysis both reflected the size (concentration), food source, fat solubility, and biological aspect of the CE species. In a comparison between PCA (PC4) and factor analysis (factor 4), the latter was found to be more suggestive from a biological aspect of n-6 FAs. Cholesteryl docosahexaenoate (DHA) was found to be unique by a factor analysis, possibly relevant to the unique accumulation of DHA in the brain. An informatics approach, especially factor analysis, might be useful for the analysis of complicated metabolism of CE species in the serum.

A High-Performing Plasma Metabolite Panel for Early-Stage Lung Cancer Detection

The objective of this research is to use metabolomic techniques to discover and validate plasma metabolite biomarkers for the diagnosis of early-stage non-small cell lung cancer (NSCLC). The study included plasma samples from 156 patients with biopsy-confirmed NSCLC along with age and gender-matched plasma samples from 60 healthy controls. A fully quantitative targeted mass spectrometry (MS) analysis (targeting 138 metabolites) was performed on all samples. The sample set was split into a discovery set and validation set. Metabolite concentration data, clinical data, and smoking history were used to determine optimal sets of biomarkers and optimal regression models for identifying different stages of NSCLC using the discovery sets. The same biomarkers and regression models were used and assessed on the validation models. Univariate and multivariate statistical analysis identified β-hydroxybutyric acid, LysoPC 20:3, PC ae C40:6, citric acid, and fumaric acid as being significantly different between healthy controls and stage I/II NSCLC. Robust predictive models with areas under the curve (AUC) > 0.9 were developed and validated using these metabolites and other, easily measured clinical data for detecting different stages of NSCLC. This study successfully identified and validated a simple, high-performing, metabolite-based test for detecting early stage (I/II) NSCLC patients in plasma. While promising, further validation on larger and more diverse cohorts is still required.

Clinical lipidomics in understanding of lung cancer: Opportunity and challenge

Disordered lipid metabolisms have been evidenced in lung cancer as well as its subtypes. Lipidomics with in-depth mining is considered as a critical member of the multiple omics family and a lipid-specific tool to understand disease-associated lipid metabolism and disease-specific dysfunctions of lipid species, discover biomarkers and targets for monitoring therapeutic strategies, and provide insights into lipid profiling and pathophysiological mechanisms in lung cancer. The present review describes the characters and patterns of lipidomic profiles in patients with different lung cancer subtypes, important values of comprehensive lipidomic profiles in understanding of lung cancer heterogeneity, urgent needs of standardized methodologies, potential mechanisms by lipid-associated enzymes and proteins, and the importance of integration between clinical phenomes and lipidomic profiles. The characteristics of lipidomic profiles in different lung cancer subtypes are extremely varied among study designs, objects, methods, and analyses. Preliminary data from recent studies demonstrate the specificity of lipidomic profiles specific for lung cancer stage, severity, subtype, and response to drugs. The heterogeneity of lipidomic profiles and lipid metabolism may be part of systems heterogeneity in lung cancer and be responsible for the development of drug resistance, although there are needs for direct evidence to show the existence of intra- or inter-lung cancer heterogeneity of lipidomic profiles. With an increasing understanding of expression profiles of genes and proteins, lipidomic profiles should be associated with activities of enzymes and proteins involved in the processes of lipid metabolism, which can be profiled with genomics and proteomics, and to provide the opportunity for the integration of lipidomic profiles with gene and protein expression profiles. The concept of clinical trans-omics should be emphasized to integrate data of lipidomics with clinical phenomics to identify disease-specific and phenome-specific biomarkers and targets, although there are still a large number of challenges to be overcome in the integration between clinical phenomes and lipidomic profiles.