Lipid profiling in malignant mesothelioma reveals promising signatures for diagnosis and prognosis: A plasma-based LC-MS lipidomics study

Prognostic potential of lipid profiling in cancer patients: a systematic review of mass spectrometry-based studies

Cancer prognosis remains a critical clinical challenge. Lipidomic analysis via mass spectrometry (MS) offers the potential for objective prognostic prediction, leveraging the distinct lipid profiles of cancer patient-derived specimens. This review aims to systematically summarize the application of MS-based lipidomic analysis in prognostic prediction for cancer patients. Our systematic review summarized 38 studies from the past decade that attempted prognostic prediction of cancer patients through lipidomics. Commonly analyzed cancers included colorectal, prostate, and breast cancers. Liquid (serum and urine) and tissue samples were equally used, with liquid chromatography-tandem MS being the most common analytical platform. The most frequently evaluated prognostic outcomes were overall survival, stage, and recurrence. Thirty-eight lipid markers (including phosphatidylcholine, ceramide, triglyceride, lysophosphatidylcholine, sphingomyelin, phosphatidylethanolamine, diacylglycerol, phosphatidic acid, phosphatidylserine, lysophosphatidylethanolamine, lysophosphatidic acid, dihydroceramide, prostaglandin, sphingosine-1-phosphate, phosphatidylinosito, fatty acid, glucosylceramide and lactosylceramide) were identified as prognostic factors, demonstrating potential for clinical application. In conclusion, the potential for developing lipidomics in cancer prognostic prediction was demonstrated. However, the field is still nascent, necessitating future studies for validating and establishing lipid markers as reliable prognostic tools in clinical practice.

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.

Widely targeted quantitative lipidomics and prognostic model reveal plasma lipid predictors for nasopharyngeal carcinoma

BACKGROUND

Dysregulation of lipid metabolism is closely associated with cancer progression. The study aimed to establish a prognostic model to predict distant metastasis-free survival (DMFS) in patients with nasopharyngeal carcinoma (NPC), based on lipidomics.

METHODS

The plasma lipid profiles of 179 patients with locoregionally advanced NPC (LANPC) were measured and quantified using widely targeted quantitative lipidomics. Then, patients were randomly split into the training (125 patients, 69.8%) and validation (54 patients, 30.2%) sets. To identify distant metastasis-associated lipids, univariate Cox regression was applied to the training set (P < 0.05). A deep survival method called DeepSurv was employed to develop a proposed model based on significant lipid species (P < 0.01) and clinical biomarkers to predict DMFS. Concordance index and receiver operating curve analyses were performed to assess model effectiveness. The study also explored the potential role of lipid alterations in the prognosis of NPC.

RESULTS

Forty lipids were recognized as distant metastasis-associated (P < 0.05) by univariate Cox regression. The concordance indices of the proposed model were 0.764 (95% confidence interval (CI), 0.682-0.846) and 0.760 (95% CI, 0.649-0.871) in the training and validation sets, respectively. High-risk patients had poorer 5-year DMFS compared with low-risk patients (Hazard ratio, 26.18; 95% CI, 3.52-194.80; P < 0.0001). Moreover, the six lipids were significantly correlated with immunity- and inflammation-associated biomarkers and were mainly enriched in metabolic pathways.

CONCLUSIONS

Widely targeted quantitative lipidomics reveals plasma lipid predictors for LANPC, the prognostic model based on that demonstrated superior performance in predicting metastasis in LANPC patients.

Integration of transcriptomics, metabolomics, and lipidomics reveals the mechanisms of doxorubicin-induced inflammatory responses and myocardial dysfunction in mice

Doxorubicin (DOX) is an anthracycline antineoplastic agent that has limited clinical utility due to its dose-dependent cardiotoxicity. Although the exact mechanism remains unknown, inflammatory responses have been implicated in DOX-induced cardiotoxicity (DIC). In this study, we analyzed the transcriptomic, metabolomic as well as lipidomic changes in the DOX-treated mice to explore the underlying mechanisms of DIC. We found that continuous intraperitoneal DOX injections (3 mg/kg/d) for a period of five days significantly induced cardiac dysfunction and cardiac injury in male C57BL/6 J mice (8 weeks old). This corresponded to a significant increase in the myocardial levels of IL-4, IL-6, IL-10, IL-17 and IL-12p70. Furthermore, inflammation-related genes such as Ptgs2, Il1b, Cxcl5, Cxcl1, Cxcl2, Mmp3, Ccl2, Ccl12, Nfkbia, Fos, Mapk11 and Tnf were differentially expressed in the DOX-treated group, and enriched in the IL-17 and TNF signaling pathways. Besides, amino acids, peptides, imidazoles, toluenes, hybrid peptides, fatty acids and lipids such as Hex1Cer, Cer, SM, PG and ACCa were significantly associated with the expression pattern of inflammation-related genes. In conclusion, the integration of transcriptomic, metabolomic and lipidomic data identified potential new targets and biomarkers of DIC.

Plasma Lipid-based Machine Learning Models Provides a Potential Diagnostic Tool for Colorectal Cancer Patients

Colorectal cancer is the second leading cause of cancer-related death across the world. So far, screening methods for colorectal cancer are limited to blood test, imaging test, and digital rectal examination, that are either invasive or ineffective. So, this study aims to explore novel, more convenient and effective diagnostic methods for colorectal cancer. First, the experiment cohort was randomly split to train set and test set, and LC-MS-based plasma lipidomics was applied to identify lipid features in colorectal cancer. Second, univariate and multivariate analyses were performed to screen for significantly differentially expressed lipids. Third, single-lipid-based ROC analysis and multiple-lipid-based machine learning modelling were conducted to assess differential lipids' diagnostic performance. Lastly, survival analyses were used to evaluate lipids' prognostic values. In total, 41 differential lipids were screened out, 10 were upregulated and 31 were downregulated in CRC. Only CerP(d15:0_22:0+O) showed fine predictive accuracy in single-lipid-base ROC analysis. Among the four machine learning models, SVM showed best predictive performance with accuracy (in predicting test set) of 1.0000 (95%CI: 0.8806, 1.0000), that can be reached by modelling with only 14 lipids. Four lipids had significant prognostic values, that were TG(11:0_18:0_18:0) (HR: 0.34), TG(18:0_18:0_18:1) (HR: 0.34), PC(22:1_12:3) (HR: 2.22), LPC(17:0) (HR: 3.16). In conclusion, this study discovered novel lipid features that has potential diagnostic and prognostic values, and showed combination of plasma lipidomics and machine learning modelling could have outstanding diagnostic performance and may serve as a convenient and more accessible way to aid clinical diagnosis of colorectal cancer.

The Lipid Asset Is Unbalanced in Peripheral Nerve Sheath Tumors

Peripheral nerve sheath tumors (PNSTs) include schwannomas, neurofibromas (NFs), and plexiform neurofibromas (PNFs), among others. While they are benign tumors, according to their biological behavior, some have the potential for malignant degeneration, mainly PNFs. The specific factors contributing to the more aggressive behavior of some PNSTs compared to others are not precisely known. Considering that lipid homeostasis plays a crucial role in fibrotic/inflammatory processes and in several cancers, we hypothesized that the lipid asset was also unbalanced in this group of nerve tumors. Through untargeted lipidomics, NFs presented a significant increase in ceramide, phosphatidylcholine, and Vitamin A ester. PNFs displayed a marked decrease in 34 out of 50 lipid class analyzed. An increased level of ether- and oxidized-triacylglycerols was observed; phosphatidylcholines were reduced. After sphingolipidomic analysis, we observed six sphingolipid classes. Ceramide and dihydroceramides were statistically increased in NFs. All the glycosylated species appeared reduced in NFs, but increased in PNFs. Our findings suggested that different subtypes of PNSTs presented a specific modulation in the lipidic profile. The untargeted and targeted lipidomic approaches, which were not applied until now, contribute to better clarifying bioactive lipid roles in PNS natural history to highlight disease molecular features and pathogenesis.