Lipids have a number of key biological functions in cancer. There are characteristic changes in lipid metabolism and metabolites in different physiological and pathological processes. Lipidomics is an emerging discipline of metabolomics that is used for the systematic analysis of lipids and the molecules that interact with them in organisms, tissues or cells. With the development of new analytical techniques, especially the application and development of mass spectrometry, the determination of lipids can be carried out quickly, accurately and with high throughput. A large number of studies have shown that abnormal lipid metabolism is closely related to the occurrence and development of tumor, the application of lipid omics technology can reveal abnormal lipid changes associated with cancer and relative metabolic pathways, and identification of lipid biomarkers in tumor, early diagnosis of tumor, anticancer aspects such as the discovery of drug targets shows a broad application prospect. This chapter mainly introduces the application and development of lipidomics in the diagnosis and treatment of different tumors.
Lipids are an important class of biomolecules that are involved in many key cellular processes. Due to their hydrophobicity, lipids are a major component of biofilms (Figure 2.1). As such, they are the physical basis of all living organisms because they provide the ability to separate an organism from its natural environment. Lipids not only provide energy for cells, but also participate in extracellular and intracellular signaling processes, in which lipids conduct signals and amplify and regulate cascades.
Clinical lipomics is a new extension of lipomics that studies lipid profiles, pathways, and networks by characterizing and quantifying complete lipid molecules in patient cells, biopsy tissue, or body fluids. It is expected to be more stable during treatment, more sensitive to change, more disease-specific, and enable more effective data analysis and more standardized measurements to meet clinical needs. Lipidomics is expected to become a more critical approach in clinical applications as an important tool for early diagnosis of cancer and for assessing disease progression.
Due to the role of lipid molecules in cellular structure, energy, and signal transductions, characterization of changes in cellular and extracellular lipid composition is critical to understanding cancer biology. In addition, some mass spectroscopy-based analysis and imaging studies suggest that lipid molecules may help enhance existing biochemical and histomathological methods for cancer diagnosis, staging, and prognosis. Therefore, the analysis of lipid metabolic changes associated with cancer cells and tumor tissues is useful for both basic and translational research. In the field of tumor lipidomics, scientists focus on applications in the diagnosis and treatment of tumors.
The tumor diagnosis
Lipids undergo subtle metabolic changes in the early stages of tumor development. Thus, capturing the signal of these changes in the molecular spectrum will greatly facilitate the early diagnosis of cancer. Most of the clinical serum biomarkers used for cancer detection were established in the early 1980s, when the Nobel Prize in Physiology or Medicine was awarded for “the discovery of how monoclonal antibodies are produced”. Using this “Nobel” technique, various monoclonal antibodies have been developed and ligands on the surface of cancer cells have been characterized. Abnormal glycan structure and abnormal glycan associated glycoproteins have been identified as standard features of cancer cell surfaces through specific interactions with monoclonal antibodies. Subsequently, sugar-related biomarkers were detected in the serum of cancer patients and developed into clinically prevalent serum biomarkers such as CA125, CA153, CA195, CA199, CA242, and CA724.
Lipid metabolic reprogramming is an important indicator of tumor genesis and development. Changes in tumor metabolism, including the accumulation of metabolites, lead to local immunosuppression of the tumor microenvironment. Hao et al. conducted a systematic analysis of The multiomics data from The Cancer Genome Atlas (TCGA) and found that The most widely altered lipid metabolism pathways in generalized Cancer were fatty acid metabolism, arachiidonic acid metabolism, cholesterol metabolism, and PPAR signaling.
Recent reports on the role of lipidomics in the diagnosis of tumors have covered most organs in the body and will be discussed below.
Stomach Cancer
The growth of malignant tumors is characterized by marked changes in metabolites. Sun et al. found that palmitic acid (PA) was significantly down-regulated in gastric cancer. The proliferation of gastric cancer (GC) cell lines such as AGS, SGC-7901 and MGC-803 was inhibited by high concentration of PA in vitro, which impaired the invasion and migration of cells. In addition, sterol-regulated element-binding protein 1 (SREBP-1C) is activated in human GC, promoting the expression of various genes related to fatty acid synthesis, such as SCD1 and FASN. Down-regulation of SREBP-1C saved the migration and invasion defects of AGS and SGC-7901 gastric cancer cells. Based on breakthroughs in genomics, TCGA recently proposed an integrated genomic analysis method in which gas chromatography is divided into four subtypes based on chromosomal instability (CIN) states. Hung et al. collected GC and non-cancer tissue samples from cancer patients and analyzed them according to the TCGA classification. They identified 409 tumor gene and tumor suppressor gene sequences and divided the samples into CIN and non-CIN types. Using LC-MS, the authors determined the lipid profiles of GC samples and adjacent non-cancer tissue samples. Compared with adjacent non-cancerous tissues, gas chromatographic samples showed distinct characteristics of lysophospholipids, phosphatidylcholine, phosphatidylethanolamine, phosphatidylinositol, phosphatidylserine, sphingomyelin, ceramides and triglycerides. GPs (phosphocholine, phosphatidyl ethanolamine, and phosphatidyl inositol) levels increased 1.4 to 2.3 times in the CIN group compared to the non-CIN group (P < 0.05). These changes in the glycerol and glycerophospholipid pathways indicate progress from GC to CIN.
Prostate Cancer
EVs from non-tumorigenic prostate cancer (PCa) patients is rich in fatty acids, glucolipids, and precursor oils. In contrast, EVs in tumorigenic or metastatic cells are rich in glucolipids, sphingolipids, and glycerophospholipids. Zhou et al. compared PCa with benign prostatic tissue (BPT). The results showed that the contents of total fatty acids, monounsaturated fatty acids, polyunsaturated fatty acids and total n6 fatty acids in PCa group were significantly higher than those in BPT group. The concentrations of N-6FFA and N-3FFA in PCa were significantly higher in most fatty acid parameters, which were correlated with Gleason grade and clinical stage. However, the fatty acids associated with the occurrence, development, and racial differences between African Americans and Caucasian Americans, as well as the differentially expressed fatty acids, remain unclear. Kregel et al. observed that bromine-containing and external (BET) degradators inhibited the growth of PCa cells in vivo and in vitro. These drugs preferentially affect AR positive PCa cells (22 Rv1, LNCaP, VCaP) over AR negative cells (PC3 and DU145). The increase of polyunsaturated fatty acids and thioredoxin interacting protein (TXNIP) suggests their potential as pharmacological biomarkers targeting BET proteins.
Endometrial Cancer
In endometrial cancer, preoperative biomarkers used to identify patients at low risk for disease progression can help determine the appropriate level of surgery needed and avoid the complications that can arise from radical surgery. Knific et al. used electrospray ionization tandem mass spectrometry to quantify 163 metabolites in 126 plasma samples from 61 patients with endometrial cancer and 65 controls. The levels of three single phosphatidylcholines were significantly reduced in patients with endometrial cancer. Cummings et al. discussed changes in the expression of eicosane metabolism genes in the epithelium during endometrial carcinoma. These were combined with eicosane-like characteristics in matched clinical specimens. Expression of candidate eicosane-metabolic enzymes, low HPGD combined with high ALOX5 expression, was associated with poor overall survival and progression-free survival, highlighting HPGD and ALOX5 as potential therapeutic targets for invasive EC subtypes.
Bladder Cancer
Bladder cancer is an elusive disease because of its rapid recurrence and drug resistance. Patients with recurrent BC and high fever have a poor prognosis. Lee et al. performed a comparative lipidomic analysis of two homogenous human T24 bladder cancer cell lines. Ultra-performance liquid chromatography-mass spectrometry (UPLC-MS) analysis of 1864 lipids identified differentially expressed lipid levels suspected to be associated with cisplatin resistance. Vantaku et al. used the NISTP-MS metabolomics outline and the lipid BLAST MS/MS library to identify 519 metabolites and 19 lipids that were differentially expressed between low-grade and high-grade bladder cancers, respectively. They identified the metabolic characteristics of high-grade bladder cancer by integrating unbiased metabolomics, lipidomics, and transcriptomics to predict patient survival and identify new therapeutic targets.
Glial Tumors
Isocitrate dehydrogenase (IDH)1 mutation is a very common event in low-grade glioblastoma and secondary glioblastoma. Zhou et al. found that the glycolysis and lipid metabolism of IDH1 mutant glioma tissues had significant changes compared with IDH1 wild-type glioma through comprehensive metabolic studies on clinical specimens of IDH1 mutant glioma. More pyruvate was found to enter the TCA cycle in IDH1 mutated gliomas, showing a decrease in triglycerides and sphinolipids.
Conclusion
Conventional research on cancer cell metabolism has mostly focused on glutamine breakdown and glycolysis. However, in the past decade, as lipidomic techniques have evolved, new knowledge and theories have deepened the understanding of the relationship between lipid metabolism and the biology of cancer. Recent studies have shown that reprogramming of cell lipid metabolism is directly involved in the malignant transformation and progression of cells. For example, de novo synthesis of lipids can provide a phospholipid component for proliferation, forming the plasma and organelle membranes of newly dividing cells. In addition, the up-regulated expression of mitochondrial microglobulin contributes to the maintenance of energy metabolism and REDOX homeostasis in tumor cells. Lipid-derived messenger molecules can regulate related signaling pathways and coordinate immunosuppression. Thus, lipid metabolism is involved in a variety of carcinogenic processes, including proliferation, differentiation, migration, invasion, and drug resistance. However, the underlying mechanism of whether we can safely and effectively regulate cancer therapy through lipid metabolism remains unclear.
In addition to peripheral blood as a common sample for early tumor diagnosis, other easily available body fluids are also receiving more and more research attention. As a biological fluid, human saliva is increasingly used to diagnose diseases, monitor systemic disease status and predict disease progression. The discovery of biomarkers in saliva provides a unique opportunity to assess patient health through the use of oral fluids, avoiding invasive blood collection. Saliva is clinically significant because its components can be found in blood plasma. Salivary lipids are one of the most important cellular components in human saliva, so they have great potential as biomarkers. The lipid composition in cells and tissues varies with physiological changes, and the lipid composition in normal tissues differs from that in diseased tissues. Lipid imbalances are strongly associated with many lifeway-related diseases, such as atherosclerosis, diabetes, metabolic syndrome, systemic cancer, neurodegenerative diseases, and infectious diseases. Therefore, lipid biomarkers can be used to diagnose disease and assess disease status and response to treatment. However, further research is needed into whether saliva can be used as a substitute for lipid profiles, as the development of reliable diagnostic and salivary disease surveillance tests requires the use of highly sensitive, low-limit methods to identify salivary biomarkers. In recent years, the continuous development of mass spectrometry and the introduction of high precision and high resolution mass spectrometry detectors have also greatly improved the methods of lipidomics.