Metabolic features of tumor-derived extracellular vesicles: challenges and opportunities
0 
Abstract
Tumor-derived extracellular vesicles (TDEVs) play crucial roles in intercellular communication both in the local tumor microenvironment and systemically, facilitating tumor progression and metastatic spread. They carry a variety of molecules with bioactive properties, such as nucleic acids, proteins and metabolites, that trigger different signaling processes in receptor cells and induce, among other downstream effects, metabolic reprogramming. Interestingly, the cargo of TDEVs also reflects the metabolic status of the producing cells in a time- and context-dependent manner, providing information on the functionality and state of those cells. For these reasons, together with their ability to be detected in diverse biofluids, there is increasing interest in the study of TDEVs, particularly their metabolic cargo, as diagnostic and prognostic tools in cancer management. This review presents a compilation of metabolism-related molecules (enzymes and metabolites) described in cancer extracellular vesicles (EVs) with potential use as cancer biomarkers, and discusses the challenges arising in this rapidly evolving field.
Keywords
INTRODUCTION: FUNCTION, CLASSIFICATION AND MARKERS
The term extracellular vesicles (EVs) refers to a heterogeneous population of membrane-derived vesicles secreted by both eukaryotic and prokaryotic cells. In the early days, EVs were considered only a cellular mechanism to dispose of unwanted material, but owing to advances in purification techniques and subsequent molecular analyses, we currently know that EVs are involved in many different crucial physiological functions such as stem cell maintenance, tissue repair, immune surveillance, and blood coagulation[1-3]. In this sense, EVs play a major role in cellular and organismal communication and signaling, since they transmit information to distant cells by activating surface receptors or transferring intracellular contents, including biomolecules such as proteins, lipids, nucleic acids and sugars, via internalization[4]. Considering that the cargo (content) of EVs mirrors the phenotype of the secreting cells and their relative abundance in biofluids, the analysis of EVs from liquid biopsies has great diagnostic potential for different diseases, such as cancer[5]. In fact, they have been detected in most body fluids including blood, urine, saliva, breast milk, cerebrospinal fluid, semen, amniotic fluid, breath condensate, and ascites[6-11].
EVs were initially classified into three subtypes on the basis of their mechanism of biogenesis: exosomes, microvesicles, and apoptotic bodies. While exosomes (40-150 nm) are exclusively formed through the endolysosomal pathway, microvesicles (150-1,000 nm) arise from budding at the plasma membrane. On the other hand, apoptotic bodies (1-5 μm) are formed only during programmed cell death[12]. In 2018 and 2023, the International Society for Extracellular Vesicles (ISEV) redefined EVs as “particles released from the cells that are delimited by a lipid bilayer and cannot replicate”[13,14]. Considering the difficulties in reaching a global consensus in the classification of EVs and the absence of specific markers for each subtype, the ISEV still recommends the use of operational terms for EV classification, but with caution. For example, EVs with a diameter smaller than 200 nm are called S-EVs (small), and those with a larger size should be considered L-EVs (large)[15]. However, these cut-offs are not strict and separation methods such as ultracentrifugation yield populations of mixed sizes. In this sense, we have adapted the nomenclature used in papers cited in this review to adhere as much as possible to the current classification.
EVs express several common surface molecules that have been consistently used in isolation and characterization techniques, such as CD9, CD81, CD63, CD82, flotillin, TSG101, Alix, and heat shock proteins (HSP60, HSP70, HSPA5, and CCT2)[16]. Among these markers, tetraspanins such as CD9, CD63, or CD81 are the most commonly used since they are ubiquitously expressed across cell types and their expression is especially high[17]. The family of tetraspanins shares a common structure consisting of four transmembrane domains enriched in polar residues followed by an extracellular loop and conserved cysteines on the extracellular side. This configuration enables tetraspanins to interact with other family members through homophilic interactions, as well as with receptors and signaling molecules at the membrane through heterophilic interactions, thus creating a complex pleiotropic signaling network that regulates EV biogenesis, cargo and uptake[18].
TUMOR-DERIVED EXTRACELLULAR VESICLES
The study of tumor-derived extracellular vesicles (TDEVs) has attracted great attention in the past few years, considering their exciting potential for diagnostic purposes in liquid biopsies, especially in cases where direct regular biopsy is not always possible[19]. Interestingly, while the release of EVs is relatively universal across cell populations, cancer cells tend to produce more vesicles overall, facilitating their detection in biofluids where EVs originating from other normal cells and tissues are present[20]. Although TDEVs express the general EV markers mentioned in the previous section (several examples are included in Supplementary Table 1), the expression of cancer-specific markers has also been described (a selection of markers is shown in Table 1), especially for diagnostic and therapeutic purposes. Indeed, immunoaffinity-based isolation techniques using cancer-specific markers represent the only way to enrich or even purify TDEVs in complex biological samples, as described previously[32].
A selection of cancer-specific protein markers found in TDEVs
| Biomarker | Source of EVs | Cancer type | Application |
| CD147[21] | Serum | Colorectal cancer | Diagnosis and prognosis |
| EGFRvIII[22] | Plasma | Glioblastoma | Prognosis |
| EpCAM[23] | Plasma | Colorectal cancer | Early diagnosis |
| Glypican-1[24] | Serum | Pancreatic cancer | Diagnosis |
| HSP70[25] | Plasma | Melanoma | Prognosis |
| LGALS3BP[26] | Serum | Endometrial cancer | Early diagnosis |
| LRG1[27] | Plasma | Non-small cell lung cancer | Diagnosis |
| MDA-9, GRP78[28] | Serum | Metastatic melanoma | Early diagnosis |
| PDCD6IP, FASN, XPO1, ENO1[29] | Plasma | Prostate cancer | Diagnosis |
| PD-L1[30,31] | Serum | Metastatic melanoma | Prognosis |
| TYRP2[21] | Plasma | Glioblastoma | Diagnosis |
| VLA-4[25] | Plasma | Melanoma | Diagnosis |
Interestingly, in addition to conventional targets such as cytosolic proteins or tetraspanins, different groups have highlighted the potential of detecting specific nucleic acids encapsulated within TDEVs for diagnosis[33], including microRNAs (miRNAs), circular RNAs (circRNAs), or long noncoding RNAs (lncRNAs). Although beyond the scope of this literature review, we provide some examples in Supplementary Table 2.
The increased release of EVs by cancer cells underscores their pivotal role in tumor homeostasis and progression, mediating the active communication between the tumor microenvironment and the primary tumor. Thus, TDEVs contribute to multiple cancer hallmarks, including tumor initiation, angiogenesis, metabolic reprogramming, pluripotency, metastasis, and immunosuppression[20]. While the implications of TDEVs in these phenomena have been comprehensively reviewed elsewhere[34,35], we provide some representative examples below.
During cancer initiation, the dynamic interplay between proliferating tumor cells and surrounding stromal cells is crucial for maintaining a proper microenvironment, which explains why cancer-associated fibroblast (CAF) activation is increased by TDEVs[36,37]. For example, S-EVs from bladder cancer cells carry cytokines such as transforming growth factor Beta (TGF-β), which triggers CAF differentiation via activation of the SMAD pathway[38]. On the other hand, miR-630 or miR-210 contained in ovarian and lung cancer TDEVs may prompt fibroblasts differentiation into CAFs via NF-kB and Janus kinase 2 signals, respectively[39,40].
Interestingly, TDEVs allow horizontal information transfer between different tumor cell populations or even with nontransformed cells during the initiation and progression stages. In 2008, Al-Nedawi et al. first demonstrated that a heterogeneous population of vesicles derived from brain tumors enhanced the aggressiveness and proliferation of their less aggressive counterparts[41,42]. Subsequent studies revealed how the activated isoform of the epithelial growth factor receptor (EGFR), EGFRvIII - a major cargo of TDEVs - was horizontally transferred to other cancer cells within the TME[41]. Notably, pro-invasive proteins such as CD44 or CD155 are often associated with TDEVs expressing EGFRvIII in glioma cells[43,44], suggesting that the coordinated transfer of several molecules is associated with aggressiveness. On the other hand, the driver oncogene K-Ras was enriched in TDEVs derived from mutant pancreatic cancer cells, thereby increasing the invasiveness and proliferation of nontransformed wild-type cells[45,46].
Moreover, TDEVs seem to be crucial for the maintenance of cancer stem cells (CSCs), the main tumoral subpopulation contributing to chemoresistance and relapse[47]. The most frequently described mechanism for stemness enhancement mediated by TDEVs is the activation of the Wnt/Notch/β-catenin pathway in CSCs by biological components present in the cargo of vesicles released by either differentiated cancer cells[48] or stromal cells such as CAFs[49]. On the other hand, CSCs have been proposed to reprogram
As briefly mentioned above, communication via TDEVs facilitates metastasis by two different mechanisms. On the one hand, S-EVs produced by tumor cells or CAFs induce EMT in recipient cells by transferring miRNAs, which promote the upregulation of EMT markers such as vimentin, N-cadherin, ZEB1, SNAIL, SLUG, and TWIST1 and the activation of the AKT or ERK pathways in breast or prostate tumors[51,52]. On the other hand, TDEVs mediate the formation of the metastatic niche in specific target organs, such as prostate cancer spread to the bone[53]. Indeed, the identity and location of the recipient cells in target organs or tissues are finely regulated by TDEVs: Hoshino et al. reported that metastatic cancer cells that form
Recent studies have indicated that TDEVs also play a role in immunosuppression via different mechanisms. First, checkpoint proteins such as PD-L1 have been identified in EVs from melanoma and brain cancer cells[55,56]. Indeed, Ricklefs et al. reported that patients suffering from an aggressive form of melanoma, but not healthy individuals, harbored TDEVs carrying PD-L1[55]. Consequently, when these TDEVs were injected in vivo, the number of CD8+ T cells was significantly reduced[57]. Moreover, the polarization of macrophages toward M2 pro-tumoral behavior could also be explained by S-EVs activating diverse signaling pathways such as the PI3K/AKT, STAT3, p38, MAPK, ERK or NF-κB pathways, which then induce the synthesis and secretion of immunosuppressive molecules and interleukins, such as IL-6, IL-8,
Finally, TDEVs contribute to cancer chemoresistance through diverse mechanisms, which involve mainly direct regulation of drug efflux and promotion of prosurvival signaling. Indeed, oral squamous cell carcinoma cells with both de novo and acquired resistance to cisplatin eliminate the drug by accumulating it inside S-EVs[60]. TDEVs can also transfer the ATP-binding cassette (ABC) transporter P-gp to chemosensitive cells[35]. On the other hand, miR-21 or miR-24 contained in TDEVs induce chemoresistance by targeting the tumor suppressor genes PDCD4 and PTEN, thus promoting cell survival in leukemia, breast cancer, and squamous cell carcinoma[34,62]. Moreover, S-EVs produced by breast cancer cells after treatment with paclitaxel are enriched in survivin, which promotes cell survival and resistance to apoptosis[62].
METABOLITE CARGO OF TDEVs
In a normal physiological state, the most efficient form of energy production for cells is based on mitochondrial oxidative phosphorylation, in which carbohydrates, especially glucose, as well as proteins and lipids, are catabolized into intermediates that enter the tricarboxylic acid (TCA) cycle[63]. In the context of cancer, cells undergo a series of profound changes in their bioenergetics, which are necessary to support rapid cell growth and proliferation in environments characterized by oxygen and nutrient scarcity. As a result, tumor cells undergo a process of metabolic reprogramming, where they promote nutrient uptake and activate anabolic biosynthetic pathways. The most common changes associated with metabolic reprogramming in cancer are increased glucose uptake and metabolism through glycolysis, elevated glutamine consumption, lipid and amino acid biosynthesis, and redox homeostasis[64].
Although far less studied than nucleic acids and proteins, EVs carry numerous metabolites in their cargoes, which can reflect the actual metabolic state of the producing cells in a time- and context-specific manner. Considering the profound metabolic reprogramming suffered by cancer cells, it is not surprising that an increasing number of metabolites specifically up- or downregulated in TDEVs compared with noncancer tissues are being discovered in a variety of biofluids such as urine, blood or saliva[65]. In this sense, the analysis of the TDEVs metabolite cargo can be useful for cancer diagnosis or prognosis evaluation in the clinical setting, since the detection of cancer-specific metabolites in a complex biological sample containing EVs from different sources would be indicative of the presence of cancer cells.
In the following subsections, we summarize a selection of changes in the metabolic cargo of TDEVs reported in the literature in recent years, classified by metabolic pathway. When available, we also discuss the clinical implications of the findings.
Glycolysis
Metabolic reprogramming in cancer has been classically associated with increased glucose consumption in tumor cells as a source of energy and building blocks necessary to meet their increased proliferative needs. Unlike normal cells, tumor cells exhibit high levels of glycolysis and reduced mitochondrial respiration, even in the presence of oxygen, leading to a state termed aerobic glycolysis or the Warburg effect[66]. In general, the glycolytic state is characterized by increased expression of glycolytic enzymes [e.g., hexokinase II (HK2), phosphofructokinase I (PFK1), lactate dehydrogenase (LDH) and pyruvate kinase II (PKM2), among others] and glucose and lactate transporters [glucose transporter 1 (GLUT1) and monocarboxylate transporters 1 and 4 (MCT1, MCT4)], as well as the accumulation of oncometabolites (lactate, glutamate, fumarate and succinate). In that sense, lactate can be found at high concentrations in S-EVs, especially under hypoxic conditions when glycolysis is further increased[67]. Interestingly, Joshi et al. reported higher levels of L-lactic acid in S-EVs from breast cancer patients with residual disease than in those with a complete response to neoadjuvant chemotherapy[68]. Moreover, glycolic acid, a byproduct of glycolysis, was also found to be one of the most important increased metabolites in cell-derived S-EVs from colorectal cancer patients[69]. These examples illustrate the advantage of metabolite detection in detecting TDEVs in complex clinical samples.
Lipid metabolism
Cancer cells reprogram their lipid metabolism to sustain important cellular functions such as the synthesis of the cell membranes needed for increased proliferation, the biosynthesis of lipid-derived signaling molecules, and energy production[70]. Indeed, the plasma membrane of cancer cells shows important differences in lipid composition compared with that of normal cells, promoting changes in membrane fluidity and favoring cellular signaling through the formation of cholesterol-rich lipid rafts. In this sense, lipids are especially important in TDEVs not only because of their crucial function in EV biogenesis and membrane formation but also because of their ability to reflect the lipid composition of cancer cell membranes[71]. Indeed, the majority of studies characterizing lipid metabolism in TDEVs have identified differences in membrane structural lipids (glycerophospholipids, glycolipids, and cholesterol), some of which suggest their use as disease biomarkers, as summarized in Table 2.
Metabolic markers for lipid metabolism found in TDEVs, arranged by tumor type
| Cancer type | Sample type | Metabolites | Application | Ref. |
| Breast cancer | Plasma | ↑ Lyso-phosphatidylcholine | Diagnosis, prognosis | [72] |
| Plasma | ↑ Diglycerides ↓ Cholesteryl esters | Diagnosis | [73] | |
| Colorectal cancer | Supernatant cells | ↑ Glycerolipids, cholesterol and sphingolipids | Diagnosis | [74] |
| Supernatant cells and serum | ↑ Glycerophospholipid, arachidonic acid and propionate | Diagnosis | [69] | |
| Feces | ↑ Fatty acids | Diagnosis | [75] | |
| Plasma | ↓ Phosphatidylcholine | Diagnosis | [76,77] | |
| Hepatocellular carcinoma | Plasma | ↑ Sphingosines, dilysocardiolipins, lyso- phosphatidylserine, 1-hydroxy fatty acids ↓ SM4, acylGlcSitosterol | Diagnosis | [78] |
| Lung cancer | Serum | ↑ Cholesteryl esters ↓ Phosphatidylcholine | Diagnosis | [79] |
| Melanoma | Plasma positive CD81 EVs | ↑ Fatty acids | Prognosis | [80] |
| Oesophageal squamous cell carcinoma | Plasma recurrent and nonrecurrent | ↑ Palmitoleic acid, palmitaldehyde, isobutyl decanoate | Prognosis | [81] |
| Ovarian cancer | Supernatant cells | ↑ GM3, zymosterol, cholesteryl esters, lysophosphatidic acids ↓ Ceramides, digalactosyl diglycerides, phosphatidic acids | Diagnosis | [82] |
| Pancreatic cancer | Serum | ↑ Lyso- phosphatidylcholine, plasmenyl- phosphatidylcholine, phosphatidylethanolamine | Diagnosis | [83] |
| Supernatant cells | ↑ Diglycerides | Prognosis | [84] | |
| Prostate cancer | Supernatant cells | ↑ Phosphatidylserine, glycosphingolipids, sphingomyelin, cholesterol | Diagnosis | [85] |
| Urine | ↑ Steroids, steroid hormone dehydroepiandrosterone sulfate | Diagnosis | [86] | |
| Supernatant cells | ↑ Cholesteryl esters | Diagnosis | [87] | |
| Renal cell carcinoma | Urine | ↑ Lysophospholipids, phosphatidylcholine, phosphatidylethanolamine and glycerolipids | Diagnosis | [88] |
For example, Buentzel et al. reported elevated levels of lyso-phosphatidylcholine in plasma L-EVs from breast cancer patients compared with those from controls and demonstrated their association with worse overall survival, suggesting that it is a potential diagnostic and prognostic biomarker[72]. Similarly, the levels of lyso-phosphatidylcholines and other phosphoglycerides such as plasmenyl- and phosphatidylethanolamine were found to be increased in S-EVs isolated from the serum of patients with pancreatic cancer. These lipids are correlated with the tumor markers CA19-9, CA242, and CEA and other clinical parameters such as tumor stage or overall survival, suggesting their potential value as pancreatic cancer biomarkers[83]. Indeed, detection of these lipids in EVs may improve the diagnostic and prognostic value of CA19-9, the most widely used clinical biomarker for PDAC diagnosis and patient follow-up.
Although a correlation with clinical parameters has not been reported, several studies have demonstrated an increased content of certain membrane lipids (among others) in TDEVs, reinforcing their potential as disease biomarkers. For example, structural phosphatidylserine and cholesterol, together with glycosphingolipids and sphingomyelin, have been shown to be abundant in EVs isolated by ultracentrifugation from prostate cancer cells[85]. A similar pattern has been described in colorectal cancer cells, where S-EVs from the LIM125 cell line have been reported to contain higher levels of glycerolipids, cholesterol, and sphingolipids than nontumoral controls[74]. Similarly, Eylem et al. reported that colorectal cancer S-EVs upregulate lipids via pathways related to the metabolism of glycerophospholipids, arachidonic acid, and propionate both in cell culture and in clinical samples[69]. In addition, other authors have demonstrated an alteration in fatty acids in fecal-derived EVs and a decrease in phosphatidylcholine in plasma-derived EVs from colorectal cancer patients[75-77], all of which were isolated by ultracentrifugation and, thus, likely represent a heterogeneous population of EVs.
In addition, cholesteryl esters have been proposed as diagnostic and prognostic biomarkers in patient samples from several cancer types. For example, Smolarz et al. reported elevated levels of cholesteryl esters and lower levels of phosphatidylcholine in serum-derived S-EVs from lung cancer patients than in those from healthy controls[79]. Moreover, Brzozowski et al. detected a very high abundance of cholesteryl esters in S-EVs derived from metastatic prostate cancer cells, indicating increased accumulation in samples representing late disease/metastatic stages[87]. Similarly, Nishida-Aoki et al. reported that, compared with those from low-metastatic cells, plasma S-EVs from highly-metastatic breast cancer cells presented lower levels of cholesteryl esters and were more enriched in unsaturated diglycerides, which was strongly associated with the stimulation of angiogenesis[73].
In contrast to the other structural lipids described above, some lipid species are important bioactive molecules that mediate cell signaling and communication in cancer and are upregulated in TDEVs. Indeed, Altadill et al. reported that EVs isolated by ultracentrifugation from PANC-1 cells treated with transforming growth factor beta have higher levels of diglycerides than those from control cells, suggesting a role in cell-to-cell communication and signaling[84]. Interestingly, TDEVs isolated from the sucrose gradient of supernatants from breast cancer cells contained PGE2, which is important for the induction of myeloid suppressor cells, thus promoting immune evasion[89]. Furthermore, some studies using prostate cancer urinary EVs (both S- and L-EVs) have demonstrated abnormal levels of steroids such as androgens and the steroid hormone dehydroepiandrosterone sulfate, suggesting a potential role of EVs in androgen signaling to neighboring cells during disease progression and perineural invasion[86].
Amino acids
Extensive changes in the levels of different amino acids and derivatives resulting from metabolic reprogramming have been reported in cancer. Interestingly, these changes can be detected not only in plasma from cancer patients but also in EVs from different body fluids[65,90] [Table 3].
Amino acids found in TDEVs
| Cancer type | Sample type | Metabolites/Enzymes | Application | Ref. |
| Colorectal Cancer | Supernatant cells, serum, stool | ↑ Lysine | Diagnosis | [69] |
| Supernatant cells, serum | ↑ Glycine, valine, tryptophan ↓ Proline | Diagnosis | [91] | |
| Supernatant cells | ↑ Proline | Diagnosis | [92] | |
| Lung cancer | Pleural fluid | ↑ Leucine and phenylalanine | Diagnosis | [93] |
| Pancreatic cancer | Plasma patients treated with chemotherapy | ↑ Alanyl-histidine, 6-dimethylaminopurine, leucyl-proline, methionine sulfoxide | Recurrence | [94] |
Changes in certain specific amino acids have been specifically attributed to one cancer type, facilitating its use as a diagnostic tool. For example, increased lysine has been detected in S-EVs from in vitro cells and serum from patients with colorectal cancer[69], as well as in a heterogeneous population of EVs in stool[75]. Although changes in amino acid composition in stool EVs may be caused by microbiota dysregulation, the detection of increased lysine in S-EVs from serum and culture supernatants strongly supports the specificity of this marker for colorectal cancer cells. On the other hand, leucine and phenylalanine were upregulated in the L-EVs of pleural fluid samples from patients with cancer-related malignancies compared with those from patients with tuberculosis. Increased levels of these two amino acids were validated in two independent cohorts of patients as biomarkers to distinguish both malignancies[93].
Other amino acids have been reported to be commonly modulated in EVs from cancers with no histological or mechanistic relationship. Indeed, glycine, valine and tryptophan are enriched in EVs isolated by ultracentrifugation from samples from colorectal cancer patients compared with those from healthy individuals[75]. On the other hand, the concentrations of glycine and tryptophan are increased in S-EVs released by glioblastoma cells with respect to their cytoplasmic abundance[91], although not compared with those in noncancerous EVs. Moreover, Palviainen et al. demonstrated that proline was elevated in a heterogeneous population of EVs from prostate cancer, cutaneous T-cell lymphoma, and colon cancer as compared with noncancerous cell lines[92]. In contrast, the proline concentration was diminished in EVs isolated by ultracentrifugation from colorectal cancer patients in the study mentioned above[75].
Other metabolites
Since nucleotide synthesis is a crucial pathway sustaining metabolic reprogramming in cancer cells, detecting many nucleotides in TDEVs is common [Table 4]. Zhu et al. reported that 3’-uridine monophosphate (3’-UMP) is the most important biomarker in plasma S-EVs for distinguishing between the recurrent and nonrecurrent groups in esophageal squamous cell carcinoma[81]. Similarly, Hayasaka et al. reported that inosine, uridine, and cytidine are among the top 20 metabolites in S-EVs from pancreatic cancer cells[95]. In addition, Liu et al. reported low levels of dihydrothymidine in urine S-EVs derived from patients with prostate cancer[99]. Additionally, a panel of metabolites reflecting the upregulation of purine and pyrimidine synthesis in cancer cells found in urinary S-EVs (Kanzonol Z, Xanthosine, Nervonyl carnitine and 3,4-Dihydroxybenzaldehyde) differentiated lung cancer patients from healthy controls for early detection[100].
Other metabolites detected in TDEVs
| Class | Cancer type | Sample type | Metabolites | Application | Ref. |
| Nucleotides | Oesophageal squamous cell carcinoma | Plasma recurrent and nonrecurrent | ↑ 3’-UMP | Diagnosis | [81] |
| Pancreatic cancer | Supernatant cells | ↑ Inosine, uridine and cytidine | Diagnosis | [95] | |
| Prostate cancer | Urine | ↓ Dihydrothymidine | Diagnosis | [96] | |
| Organic acids | Colorectal cancer | Feces | ↑ Furoic, succinic, oxalic | Diagnosis | [75] |
| Head and neck cancer | Plasma | ↑ Succinic acid ↓ Citric acid | Diagnosis | [97] | |
| Oesophageal squamous cell carcinoma | Plasma recurrent and nonrecurrent | ↑ Organic acids and derivatives | Diagnosis | [81] | |
| Other | Colorectal cancer | Feces | ↑ Ethanolamine, phenol | Diagnosis | [75] |
| Serum | ↑ 1,4-dithiothreitol | Prognosis | [98] | ||
| Endometrioid adenocarcinoma | Plasma | ↑ Coenzyme Q10, ubiquinone 9, vitamin D3 derivatives, 10-formyldihydrofolate, acetylglucosamine bisphosphate, malonyl-CoA, picolinic acid | Diagnosis | [84] |
The category of organic acids encompasses intermediate metabolites of critical metabolic pathways such as glycolysis and the tricarboxylic acid cycle (TCA), which are also present in EVs[101]. Zhu et al. reported that organic acids and their derivatives are abundant in plasma S-EVs from esophageal squamous cell carcinoma patients[81]. Moreover, succinic acid has been found to be increased, whereas citric acid is decreased in plasma S-EVs from patients with head and neck cancer[97]. Finally, furoic, succinic and oxalic acids are increased in EVs isolated by ultracentrifugation derived from colorectal cancer feces compared with healthy controls[75].
Additional compounds have been detected in TDEVs. Kim et al. reported that alcohol-derived metabolites (ethanolamine and phenol) in fecal EVs were higher in patients with colorectal cancer than in healthy controls[75]. On the other hand, Strybel et al. revealed that 1,4-dithiothreitol in serum S-EVs is good at discriminating colorectal cancer patients with different responses to neoradiotherapy[98]. Finally, Altadill et al. identified several interesting metabolites in plasma EVs (containing both S and L-EVs) of endometrioid adenocarcinoma, such as coenzyme Q10, ubiquinone 9, 25-hydroxyhexadehydrovitamin D3,
In contrast, high cellular demand for glucuronate, D-ribose 5-phosphate and isobutyryl-L-carnitine leads to a reduction in these metabolites in urinary and platelet S-EVs from prostate cancer patients[90]. In this case, however, it should be noted that the changes were detected only from EVs by normalization to EV-derived factors or with metabolite ratios rather than from the original urine samples.
METABOLITE CARGO IN THE CLINICAL SETTING: CHALLENGES
The detection of metabolites in TDEVs poses several challenges that must be carefully considered. First, and contrary to expectations, EVs are metabolically active units, indicating that, in some instances, the metabolite cargo does not necessarily reflect the content present in the producing cells[102]. Indeed, Iraci
Importantly, the metabolic state is naturally plastic and dynamic; thus, cellular and external signals such as inflammation or different stress sources can alter the cargo of TDEVs and even regulate their release process. Possible examples are the change in the lipid composition of the S-EV membrane caused by low pH or the increase in S-EV secretion under hypoxia induced by pyruvate kinase M2 (PKM2), an enzyme crucial for glucose metabolism in cancer cells[105,106]. Relatedly, the analysis of hydrophilic metabolites in the S-EVs of pancreatic cells under hypoxia revealed an increase in the levels of 2-deoxyribose 1-phosphate, a metabolite that is usually related to the adaptation of cell proliferation under hypoxia and resistance to hypoxic stress-induced apoptosis, as well as the promotion of angiogenesis[95]. Furthermore, the content of EVs isolated by ultracentrifugation can vary in pancreatic cancer after chemotherapeutic treatment, with an increase in the amino acid derivatives alanyl-histidine, 6-dimethylaminopurine, leucyl-proline and methionine sulfoxide[94].
Moreover, it is crucial to consider different technical challenges. Indeed, several groups have reported that the metabolite cargo depends on the techniques used for EV production and isolation[107,108]. For example, the contents of both polar and nonpolar metabolites in S-EVs obtained from bioreactor-cultured cells differ significantly from those obtained from conventional cell culture[109].
Finally, considering that the cargo can be considered a fingerprint from the producing cell, differences in metabolite content can reflect interpatient heterogeneity: S-EVs from the LIM125 cell line have higher levels of cholesterol, sphingolipids, glycerol and glycerolipids than those from other colorectal cancer cell lines[74].
METABOLISM-RELATED FUNCTIONS OF TDEVs
Cancer cells can influence the metabolism of noncancer cells present in the tumor microenvironment or even at distant sites via different mechanisms, to counteract nutrient scarcity and promote several of the cancer hallmarks mentioned in section 2 of this review. Considering that EVs transport genetic material, proteins, enzymes and metabolites within the tumor microenvironment, it is not surprising that they play a central role in promoting metabolic reprogramming in recipient cells[110].
First, TDEVs can mediate metabolic cooperation between cancer cells and stromal cells, regulating nutrient availability. Indeed, cancer cells induce aerobic glycolysis in stromal cells and use their end products, such as pyruvate and lactate, to fuel mitochondrial oxidative phosphorylation through the reverse Warburg effect[111]. Since both the Warburg effect and the reverse Warburg effect share the same key enzymes, the latter process can be mediated by TDEVs through the transport of glycolytic enzymes, which are among the 100 proteins most identified in TDEVs[112]. For example, epithelial cancer cells promote glycolysis in CAFs via the transfer of PKM2 and LDH[113]. In addition to direct enzyme transfer, an alternative mechanism for reverse Warburg induction is mediated by nucleic acids. For example, Yan et al. reported that breast cancer cells release a mixed population of EVs containing miR-105 that induce metabolic reprogramming in CAFs in a process mediated by the oncoprotein c-MYC[114]. In this report, the authors describe a model in which CAFs sense metabolic needs in the tumor microenvironment, modulating their metabolism in order to maintain cancer cell proliferation. Similarly, S-EVs derived from CAFs in prostate and pancreatic cancer have been found to be enriched in essential amino acids such as glutamine and arginine, which are used as nutrients by cancer cells. In addition, they can increase glutamine-dependent reductive carboxylation when they enter cancer cells[115,116].
In addition to CAFs, TDEVs have also been shown to mediate metabolic reprogramming in other stromal cells present in the tumor microenvironment or even in distant tissues, thus promoting systemic changes. For example, TDEVs from Lewis lung cancer cells exhibit increased levels of phosphorylated hormone-sensitive lipase (p-HSL), an enzyme that mediates lipolysis. Indeed, incubation of 3T3-L1 adipocytes with EVs isolated by ultracentrifugation induced lipolysis, which was detected as a lower content of lipid droplets and increased glycerol release. Interestingly, these findings suggest that TDEVs may contribute to systemic cachexia by inducing adipocyte browning[117]. On the other hand, breast tumor cells secrete a mixed population of EVs with high levels of miR-122 to inhibit glucose uptake by nontumor cells in distant premetastatic areas, thus facilitating metastasis by increasing glucose bioavailability[118].
In addition to metabolic cooperation, TDVEs facilitate tumor progression via metabolic reprogramming of stromal cells, resulting in angiogenesis or immunoevasion. For example, Wang et al. demonstrated that
CONCLUSION
Over the past few years, the crucial role of TDEVs in cancer initiation and progression has become increasingly clear. Indeed, they contribute to the bidirectional communication between cancer cells and stromal cells in both local and distant microenvironments, which promotes phenotypic changes involving metabolic reprogramming, angiogenesis, drug resistance, metastasis and immunosuppression, among other processes. In this sense, the increased secretion of EVs by cancer cells and the detection of TDEVs in a variety of biofluids have attracted the attention of the scientific community to the study of these vesicles for diagnostic and prognostic purposes, as well as potential targets for cancer treatment. TDEVs carry specific cargoes composed of nucleic acids, proteins, and metabolites, all of which have potential bioactivity (examples in four specific cancer types are summarized in Figure 1). For example, metabolites contained in TDEVs can reflect the metabolic state of the producing cells, although we need to consider that cellular metabolism is plastic and dynamic depending on the cellular context, microenvironmental conditions, and other factors. This metabolite cargo may have various effects on receptor cells, for example, providing key nutrients to support the proliferation of cancer cells or inducing metabolic reprogramming to promote a system of metabolic cooperation between cancer cells and stromal cells. However, in addition to nutrient interchange, very little is known about the mechanisms mediating the effects of this metabolic cargo on receptor cells and the duration of these effects.
Figure 1. Biomarkers of TDEVs described in four representative cancer types. Specific biomarkers (proteins, nucleic acids and metabolites) as well as general EV-related biomarkers have been included for breast, colorectal, pancreatic, and prostate cancers. Lipids: CE: cholesteryl esters; DAG: diacylglycerol; FA: fatty acids; GL: glycolipids; GP: glycerophospholipids. Glyco-PS: glycophosphosphingolipids; Lyso-PC: lysophospatidylcholine; P: propionate; PC: phosphatidylcholine; PE: phosphatidyletanolamine; PS: phosphatidylserine; SM: sphingomyelin; SP: sphingolipids; ST: sterols; TDEVs: tumor-derived extracellular vesicles.
The field of EV metabolism is actively evolving, but important challenges need to be addressed. First, the variety of biological samples used (supernatants from cell lines cultured under different conditions, and patient samples from different origins) is the main reason for the lack of consistency among studies found in the literature. Undeniably, studies performed in patient samples are especially relevant, but most of these studies include few samples that were collected at a specific center. In this sense, it would be essential to perform studies in large multicentric cohorts with standardized protocols for sampling and subsequent processing. Indeed, technical problems derived from isolation and purification methodologies can largely affect the EV population analyzed (S or L-EVs or a mixture of both), their content, and cross-contamination from normal vs. TDEVs. Additionally, the techniques used to reliably detect metabolites in the low concentration ranges found in TDEVs are still evolving.
In summary, although it holds great potential, the field of EV metabolism in cancer is still in its infancy, and a joint effort by the scientific community on methodology standardization is essential to advance.
DECLARATIONS
Authors’ contributions
Made substantial contributions to the conception and design of the study: Espiau-Romera P, Sancho P
Wrote the manuscript draft and designed the figure: Espiau-Romera P, Gordo-Ortiz A, Ortiz-de-Solórzano I, Sancho P
Edited and wrote the final version of the manuscript: Sancho P
Availability of data and materials
Not applicable.
Financial support and sponsorship
The research was supported by the Instituto de Salud Carlos III through the Miguel Servet Program (CPII21/00005 to Sancho P), a PFIS predoctoral contract (FI21/00031 to Espiau-Romera P), and Fondo de Investigaciones Sanitarias (PI20/00921, to Sancho P) (all co-financed by European funds (FSE: “El FSE invierte en tu futuro” and FEDER: “Una manera de hacer Europa”, respectively).
Conflicts of interest
All authors declared that there are no conflicts of interest.
Ethical approval and consent to participate
Not applicable.
Consent for publication
Not applicable.
Copyright
© The Author(s) 2024.
Supplementary Materials
REFERENCES
1. Raposo G, Nijman HW, Stoorvogel W, et al. B lymphocytes secrete antigen-presenting vesicles. J Exp Med 1996;183:1161-72.
2. Gatti S, Bruno S, Deregibus MC, et al. Microvesicles derived from human adult mesenchymal stem cells protect against ischaemia-reperfusion-induced acute and chronic kidney injury. Nephrol Dial Transplant 2011;26:1474-83.
3. Conde I, Shrimpton CN, Thiagarajan P, López JA. Tissue-factor-bearing microvesicles arise from lipid rafts and fuse with activated platelets to initiate coagulation. Blood 2005;106:1604-11.
4. Yáñez-Mó M, Siljander PRM, Andreu Z, et al. Biological properties of extracellular vesicles and their physiological functions. J Extracell Vesicles 2015;4:27066.
5. Zhang Y, Liu Y, Liu H, Tang WH. Exosomes: biogenesis, biologic function and clinical potential. Cell Biosci 2019;9:19.
6. Street JM, Koritzinsky EH, Glispie DM, Star RA, Yuen PS. Urine exosomes: an emerging trove of biomarkers. Adv Clin Chem 2017;78:103-22.
7. Yagi Y, Ohkubo T, Kawaji H, et al. Next-generation sequencing-based small RNA profiling of cerebrospinal fluid exosomes. Neurosci Lett 2017;636:48-57.
8. Madison MN, Jones PH, Okeoma CM. Exosomes in human semen restrict HIV-1 transmission by vaginal cells and block intravaginal replication of LP-BM5 murine AIDS virus complex. Virology 2015;482:189-201.
9. Machida T, Tomofuji T, Ekuni D, et al. MicroRNAs in salivary exosome as potential biomarkers of aging. Int J Mol Sci 2015;16:21294-309.
10. Peng P, Yan Y, Keng S. Exosomes in the ascites of ovarian cancer patients: origin and effects on anti-tumor immunity. Oncol Rep 2011;25:749-62.
11. Dobhal G, Datta A, Ayupova D, Teesdale-Spittle P, Goreham RV. Isolation, characterisation and detection of breath-derived extracellular vesicles. Sci Rep 2020;10:17381.
12. Sheta M, Taha EA, Lu Y, Eguchi T. Extracellular vesicles: new classification and tumor immunosuppression. Biology 2023;12:110.
13. Théry C, Witwer KW, Aikawa E, et al. Minimal information for studies of extracellular vesicles 2018 (MISEV2018): a position statement of the International Society for Extracellular Vesicles and update of the MISEV2014 guidelines. J Extracell Vesicles 2018;7:1535750.
14. Welsh JA, Goberdhan DCI, O’Driscoll L, et al; MISEV Consortium. Minimal information for studies of extracellular vesicles (MISEV2023): From basic to advanced approaches. J Extracell Vesicles 2024;13:e12404.
15. Pezzicoli G, Tucci M, Lovero D, Silvestris F, Porta C, Mannavola F. Large extracellular vesicles-a new frontier of liquid biopsy in oncology. Int J Mol Sci 2020;21:6543.
16. Konoshenko MY, Lekchnov EA, Vlassov AV, Laktionov PP. Isolation of extracellular vesicles: general methodologies and latest trends. Biomed Res Int 2018;2018:8545347.
17. Yoshioka Y, Konishi Y, Kosaka N, Katsuda T, Kato T, Ochiya T. Comparative marker analysis of extracellular vesicles in different human cancer types. J Extracell Vesicles 2013;2:20424.
18. Jankovičová J, Sečová P, Michalková K, Antalíková J. Tetraspanins, more than markers of extracellular vesicles in reproduction. Int J Mol Sci 2020;21:7568.
19. Eguchi A, Kostallari E, Feldstein AE, Shah VH. Extracellular vesicles, the liquid biopsy of the future. J Hepatol 2019;70:1292-4.
20. Antonyak MA, Li B, Boroughs LK, et al. Cancer cell-derived microvesicles induce transformation by transferring tissue transglutaminase and fibronectin to recipient cells. Proc Natl Acad Sci U S A 2011;108:4852-7.
21. Gu C, Shang A, Liu G, et al. Identification of CD147-positive extracellular vesicles as novel non-invasive biomarkers for the diagnosis and prognosis of colorectal cancer. Clin Chim Acta 2023;548:117510.
22. Shao H, Chung J, Balaj L, et al. Protein typing of circulating microvesicles allows real-time monitoring of glioblastoma therapy. Nat Med 2012;18:1835-40.
23. Silva J, Garcia V, Rodriguez M, et al. Analysis of exosome release and its prognostic value in human colorectal cancer. Genes Chromosomes Cancer 2012;51:409-18.
24. Melo SA, Luecke LB, Kahlert C, et al. Glypican-1 identifies cancer exosomes and detects early pancreatic cancer. Nature 2015;523:177-82.
25. Peinado H, Alečković M, Lavotshkin S, et al. Melanoma exosomes educate bone marrow progenitor cells toward a pro-metastatic phenotype through MET. Nat Med 2012;18:883-91.
26. Song Y, Wang M, Tong H, et al. Plasma exosomes from endometrial cancer patients contain LGALS3BP to promote endometrial cancer progression. Oncogene 2021;40:633-46.
27. Li Y, Zhang Y, Qiu F, Qiu Z. Proteomic identification of exosomal LRG1: a potential urinary biomarker for detecting NSCLC. Electrophoresis 2011;32:1976-83.
28. Guan M, Chen X, Ma Y, et al. MDA-9 and GRP78 as potential diagnostic biomarkers for early detection of melanoma metastasis. Tumour Biol 2015;36:2973-82.
29. Duijvesz D, Burnum-Johnson KE, Gritsenko MA, et al. Proteomic profiling of exosomes leads to the identification of novel biomarkers for prostate cancer. PLoS One 2013;8:e82589.
30. Chen G, Huang AC, Zhang W, et al. Exosomal PD-L1 contributes to immunosuppression and is associated with anti-PD-1 response. Nature 2018;560:382-6.
31. Theodoraki MN, Yerneni SS, Hoffmann TK, Gooding WE, Whiteside TL. Clinical significance of PD-L1+ exosomes in plasma of head and neck cancer patients. Clin Cancer Res 2018;24:896-905.
32. Whiteside TL. Immunosuppressive functions of melanoma cell-derived exosomes in plasma of melanoma patients. Front Cell Dev Biol 2022;10:1080925.
33. Jeppesen DK, Fenix AM, Franklin JL, et al. Reassessment of exosome composition. Cell 2019;177:428-45.e18.
34. Law ZJ, Khoo XH, Lim PT, et al. Extracellular vesicle-mediated chemoresistance in oral squamous cell carcinoma. Front Mol Biosci 2021;8:629888.
35. Console L, Scalise M. Extracellular vesicles and cell pathways involved in cancer chemoresistance. Life 2022;12:618.
36. Donnarumma E, Fiore D, Nappa M, et al. Cancer-associated fibroblasts release exosomal microRNAs that dictate an aggressive phenotype in breast cancer. Oncotarget 2017;8:19592-608.
38. Goulet C, Bernard G, Tremblay S, Chabaud S, Bolduc S, Pouliot F. Exosomes induce fibroblast differentiation into cancer-associated fibroblasts through TGFβ signaling. Mol Cancer Res 2018;16:1196-204.
39. Fan J, Xu G, Chang Z, Zhu L, Yao J. miR-210 transferred by lung cancer cell-derived exosomes may act as proangiogenic factor in cancer-associated fibroblasts by modulating JAK2/STAT3 pathway. Clin Sci 2020;134:807-25.
40. Asleh K, Dery V, Taylor C, Davey M, Djeungoue-Petga MA, Ouellette RJ. Extracellular vesicle-based liquid biopsy biomarkers and their application in precision immuno-oncology. Biomark Res 2023;11:99.
41. Al-Nedawi K, Meehan B, Micallef J, et al. Intercellular transfer of the oncogenic receptor EGFRvIII by microvesicles derived from tumour cells. Nat Cell Biol 2008;10:619-24.
42. Robles-Flores M. Cancer cell signaling: methods and protocols. New York, NY, USA: Springer; 2021.
43. Lucotti S, Kenific CM, Zhang H, Lyden D. Extracellular vesicles and particles impact the systemic landscape of cancer. EMBO J 2022;41:e109288.
44. Lobb RJ, Hastie ML, Norris EL, van Amerongen R, Gorman JJ, Möller A. Oncogenic transformation of lung cells results in distinct exosome protein profile similar to the cell of origin. Proteomics 2017;17:1600432.
45. Clark DJ, Fondrie WE, Yang A, Mao L. Triple SILAC quantitative proteomic analysis reveals differential abundance of cell signaling proteins between normal and lung cancer-derived exosomes. J Proteomics 2016;133:161-9.
46. Demory Beckler M, Higginbotham JN, Franklin JL, et al. Proteomic analysis of exosomes from mutant KRAS colon cancer cells identifies intercellular transfer of mutant KRAS. Mol Cell Proteomics 2013;12:343-55.
47. Sancho P, Barneda D, Heeschen C. Hallmarks of cancer stem cell metabolism. Br J Cancer 2016;114:1305-12.
48. Sun Z, Wang L, Zhou Y, et al. Glioblastoma stem cell-derived exosomes enhance stemness and tumorigenicity of glioma cells by transferring notch1 protein. Cell Mol Neurobiol 2020;40:767-84.
49. Ren J, Ding L, Zhang D, et al. Carcinoma-associated fibroblasts promote the stemness and chemoresistance of colorectal cancer by transferring exosomal lncRNA H19. Theranostics 2018;8:3932-48.
50. Wang Z, Sun H, Provaznik J, Hackert T, Zöller M. Pancreatic cancer-initiating cell exosome message transfer into noncancer-initiating cells: the importance of CD44v6 in reprogramming. J Exp Clin Cancer Res 2019;38:132.
51. Wang B, Mao JH, Wang BY, et al. Exosomal miR-1910-3p promotes proliferation, metastasis, and autophagy of breast cancer cells by targeting MTMR3 and activating the NF-κB signaling pathway. Cancer Lett 2020;489:87-99.
52. Zhang Y, Zhao J, Ding M, et al. Loss of exosomal miR-146a-5p from cancer-associated fibroblasts after androgen deprivation therapy contributes to prostate cancer metastasis. J Exp Clin Cancer Res 2020;39:282.
53. Obenauf AC, Massagué J. Surviving at a distance: organ-specific metastasis. Trends Cancer 2015;1:76-91.
54. Hoshino A, Costa-Silva B, Shen TL, et al. Tumour exosome integrins determine organotropic metastasis. Nature 2015;527:329-35.
55. Ricklefs FL, Alayo Q, Krenzlin H, et al. Immune evasion mediated by PD-L1 on glioblastoma-derived extracellular vesicles. Sci Adv 2018;4:eaar2766.
56. Najaflou M, Shahgolzari M, Khosroushahi AY, Fiering S. Tumor-derived extracellular vesicles in cancer immunoediting and their potential as oncoimmunotherapeutics. Cancers 2022;15:82.
57. Poggio M, Hu T, Pai CC, et al. Suppression of exosomal PD-L1 induces systemic anti-tumor immunity and memory. Cell 2019;177:414-27.e13.
58. Turcotte M, Spring K, Pommey S, et al. CD73 is associated with poor prognosis in high-grade serous ovarian cancer. Cancer Res 2015;75:4494-503.
59. Vijayan D, Young A, Teng MWL, Smyth MJ. Targeting immunosuppressive adenosine in cancer. Nat Rev Cancer 2017;17:709-24.
60. Khoo XH, Paterson IC, Goh BH, Lee WL. Cisplatin-resistance in oral squamous cell carcinoma: regulation by tumor cell-derived extracellular vesicles. Cancers 2019;11:1166.
61. Shi S, Yu ZL, Jia J. The roles of exosomes in the diagnose, development and therapeutic resistance of oral squamous cell carcinoma. Int J Mol Sci 2023;24:1968.
62. Kreger BT, Johansen ER, Cerione RA, Antonyak MA. The enrichment of survivin in exosomes from breast cancer cells treated with paclitaxel promotes cell survival and chemoresistance. Cancers 2016;8:111.
63. Fernandez-de-Cossio-Diaz J, Vazquez A. A physical model of cell metabolism. Sci Rep 2018;8:8349.
65. Bai L, Bu F, Li X, Zhang S, Min L. Mass spectrometry-based extracellular vesicle micromolecule detection in cancer biomarker discovery: an overview of metabolomics and lipidomics. VIEW 2023;4:20220086.[DOI:10.1002/VIW.20220086].
66. Warburg O, Wind F, Negelein E. The metabolism of tumors in the body. J Gen Physiol 1927;8:519-30.
67. Panigrahi GK, Praharaj PP, Peak TC, et al. Hypoxia-induced exosome secretion promotes survival of African-American and Caucasian prostate cancer cells. Sci Rep 2018;8:3853.
68. Joshi S, Garlapati C, Bhattarai S, et al. Exosomal metabolic signatures are associated with differential response to neoadjuvant chemotherapy in patients with breast cancer. Int J Mol Sci 2022;23:5324.
69. Eylem CC, Yilmaz M, Derkus B, et al. Untargeted multi-omic analysis of colorectal cancer-specific exosomes reveals joint pathways of colorectal cancer in both clinical samples and cell culture. Cancer Lett 2020;469:186-94.
70. Royo-García A, Courtois S, Parejo-Alonso B, Espiau-Romera P, Sancho P. Lipid droplets as metabolic determinants for stemness and chemoresistance in cancer. World J Stem Cells 2021;13:1307-17.
72. Buentzel J, Klemp HG, Kraetzner R, et al. Metabolomic profiling of blood-derived microvesicles in breast cancer patients. Int J Mol Sci 2021;22:13540.
73. Nishida-Aoki N, Izumi Y, Takeda H, Takahashi M, Ochiya T, Bamba T. Lipidomic analysis of cells and extracellular vesicles from high- and low-metastatic triple-negative breast cancer. Metabolites 2020;10:67.
74. Lydic TA, Townsend S, Adda CG, Collins C, Mathivanan S, Reid GE. Rapid and comprehensive “shotgun” lipidome profiling of colorectal cancer cell derived exosomes. Methods 2015;87:83-95.
75. Kim DJ, Yang J, Seo H, et al. Colorectal cancer diagnostic model utilizing metagenomic and metabolomic data of stool microbial extracellular vesicles. Sci Rep 2020;10:2860.
76. Elmallah MIY, Ortega-Deballon P, Hermite L, Pais-De-Barros JP, Gobbo J, Garrido C. Lipidomic profiling of exosomes from colorectal cancer cells and patients reveals potential biomarkers. Mol Oncol 2022;16:2710-8.
77. Bestard-Escalas J, Reigada R, Reyes J, de la Torre P, Liebisch G, Barceló-Coblijn G. Fatty acid unsaturation degree of plasma exosomes in colorectal cancer patients: a promising biomarker. Int J Mol Sci 2021;22:5060.
78. Sanchez JI, Jiao J, Kwan SY, et al. Lipidomic profiles of plasma exosomes identify candidate biomarkers for early detection of hepatocellular carcinoma in patients with cirrhosis. Cancer Prev Res 2021;14:955-62.
79. Smolarz M, Kurczyk A, Jelonek K, et al. The lipid composition of serum-derived small extracellular vesicles in participants of a lung cancer screening study. Cancers 2021;13:3414.
80. Paolino G, Huber V, Camerini S, et al. The fatty acid and protein profiles of circulating CD81-positive small extracellular vesicles are associated with disease stage in melanoma patients. Cancers 2021;13:4157.
81. Zhu Q, Huang L, Yang Q, et al. Metabolomic analysis of exosomal-markers in esophageal squamous cell carcinoma. Nanoscale 2021;13:16457-64.
82. Cheng J, Fujita A, Ohsaki Y, Suzuki M, Shinohara Y, Fujimoto T. Quantitative electron microscopy shows uniform incorporation of triglycerides into existing lipid droplets. Histochem Cell Biol 2009;132:281-91.
83. Tao L, Zhou J, Yuan C, et al. Metabolomics identifies serum and exosomes metabolite markers of pancreatic cancer. Metabolomics 2019;15:86.
84. Altadill T, Campoy I, Lanau L, et al. Enabling metabolomics based biomarker discovery studies using molecular phenotyping of exosome-like vesicles. PLoS One 2016;11:e0151339.
85. Llorente A, Skotland T, Sylvänne T, et al. Molecular lipidomics of exosomes released by PC-3 prostate cancer cells. Biochim Biophys Acta 2013;1831:1302-9.
86. Clos-Garcia M, Loizaga-Iriarte A, Zuñiga-Garcia P, et al. Metabolic alterations in urine extracellular vesicles are associated to prostate cancer pathogenesis and progression. J Extracell Vesicles 2018;7:1470442.
87. Brzozowski JS, Jankowski H, Bond DR, et al. Lipidomic profiling of extracellular vesicles derived from prostate and prostate cancer cell lines. Lipids Health Dis 2018;17:211.
88. Del Boccio P, Raimondo F, Pieragostino D, et al. A hyphenated microLC-Q-TOF-MS platform for exosomal lipidomics investigations: application to RCC urinary exosomes. Electrophoresis 2012;33:689-96.
89. Xiang X, Poliakov A, Liu C, et al. Induction of myeloid-derived suppressor cells by tumor exosomes. Int J Cancer 2009;124:2621-33.
90. Puhka M, Takatalo M, Nordberg ME, et al. Metabolomic profiling of extracellular vesicles and alternative normalization methods reveal enriched metabolites and strategies to study prostate cancer-related changes. Theranostics 2017;7:3824-41.
91. Čuperlović-Culf M, Khieu NH, Surendra A, Hewitt M, Charlebois C, Sandhu JK. Analysis and simulation of glioblastoma cell lines-derived extracellular vesicles metabolome. Metabolites 2020;10:88.
92. Palviainen M, Laukkanen K, Tavukcuoglu Z, et al. Cancer alters the metabolic fingerprint of extracellular vesicles. Cancers 2020;12:3292.
93. Luo P, Mao K, Xu J, et al. Metabolic characteristics of large and small extracellular vesicles from pleural effusion reveal biomarker candidates for the diagnosis of tuberculosis and malignancy. J Extracell Vesicles 2020;9:1790158.
94. Luo X, An M, Cuneo KC, Lubman DM, Li L. High-performance chemical isotope labeling liquid chromatography mass spectrometry for exosome metabolomics. Anal Chem 2018;90:8314-9.
95. Hayasaka R, Tabata S, Hasebe M, et al. Metabolomic analysis of small extracellular vesicles derived from pancreatic cancer cells cultured under normoxia and hypoxia. Metabolites 2021;11:215.
96. Liu Z, Liu X, Liu S, Cao Q. Cholesterol promotes the migration and invasion of renal carcinoma cells by regulating the KLF5/miR-27a/FBXW7 pathway. Biochem Biophys Res Commun 2018;502:69-75.
97. Wojakowska A, Zebrowska A, Skowronek A, et al. Metabolic profiles of whole serum and serum-derived exosomes are different in head and neck cancer patients treated by radiotherapy. J Pers Med 2020;10:229.
98. Strybel U, Marczak L, Zeman M, et al. Molecular composition of serum exosomes could discriminate rectal cancer patients with different responses to neoadjuvant radiotherapy. Cancers 2022;14:993.
99. Liu P, Wang W, Wang F, et al. Alterations of plasma exosomal proteins and motabolies are associated with the progression of castration-resistant prostate cancer. J Transl Med 2023;21:40.
100. Yang Q, Luo J, Xu H, et al. Metabolomic investigation of urinary extracellular vesicles for early detection and screening of lung cancer. J Nanobiotechnology 2023;21:153.
101. Tsoukalas D, Alegakis A, Fragkiadaki P, et al. Application of metabolomics: Focus on the quantification of organic acids in healthy adults. Int J Mol Med 2017;40:112-20.
102. Royo F, Moreno L, Mleczko J, et al. Hepatocyte-secreted extracellular vesicles modify blood metabolome and endothelial function by an arginase-dependent mechanism. Sci Rep 2017;7:42798.
103. Iraci N, Gaude E, Leonardi T, et al. Extracellular vesicles are independent metabolic units with asparaginase activity. Nat Chem Biol 2017;13:951-5.
104. Ronquist KG, Ek B, Morrell J, et al. Prostasomes from four different species are able to produce extracellular adenosine triphosphate (ATP). Biochim Biophys Acta 2013;1830:4604-10.
105. Gong C, Zhang X, Shi M, et al. Tumor exosomes reprogrammed by low pH are efficient targeting vehicles for smart drug delivery and personalized therapy against their homologous tumor. Adv Sci 2021;8:2002787.
106. Wei Y, Wang D, Jin F, et al. Pyruvate kinase type M2 promotes tumour cell exosome release via phosphorylating synaptosome-associated protein 23. Nat Commun 2017;8:14041.
107. Williams C, Palviainen M, Reichardt NC, Siljander PR, Falcón-Pérez JM. Metabolomics applied to the study of extracellular vesicles. Metabolites 2019;9:276.
108. Freitas D, Balmaña M, Poças J, et al. Different isolation approaches lead to diverse glycosylated extracellular vesicle populations. J Extracell Vesicles 2019;8:1621131.
109. Palviainen M, Saari H, Kärkkäinen O, et al. Metabolic signature of extracellular vesicles depends on the cell culture conditions. J Extracell Vesicles 2019;8:1596669.
110. Fridman ES, Ginini L, Gil Z. The role of extracellular vesicles in metabolic reprogramming of the tumor microenvironment. Cells 2022;11:1433.
111. Wilde L, Roche M, Domingo-Vidal M, et al. Metabolic coupling and the reverse Warburg Effect in cancer: implications for novel biomarker and anticancer agent development. Semin Oncol 2017;44:198-203.
112. Keerthikumar S, Chisanga D, Ariyaratne D, et al. ExoCarta: a web-based compendium of exosomal cargo. J Mol Biol 2016;428:688-92.
113. Pavlides S, Whitaker-Menezes D, Castello-Cros R, et al. The reverse Warburg effect: aerobic glycolysis in cancer associated fibroblasts and the tumor stroma. Cell Cycle 2009;8:3984-4001.
114. Yan W, Wu X, Zhou W, et al. Cancer-cell-secreted exosomal miR-105 promotes tumour growth through the MYC-dependent metabolic reprogramming of stromal cells. Nat Cell Biol 2018;20:597-609.
115. Zhao H, Yang L, Baddour J, et al. Tumor microenvironment derived exosomes pleiotropically modulate cancer cell metabolism. Elife 2016;5:e10250.
116. Netea-Maier RT, Smit JWA, Netea MG. Metabolic changes in tumor cells and tumor-associated macrophages: a mutual relationship. Cancer Lett 2018;413:102-9.
117. Hu W, Ru Z, Xiao W, et al. Adipose tissue browning in cancer-associated cachexia can be attenuated by inhibition of exosome generation. Biochem Biophys Res Commun 2018;506:122-9.
118. Fong MY, Zhou W, Liu L, et al. Breast-cancer-secreted miR-122 reprograms glucose metabolism in premetastatic niche to promote metastasis. Nat Cell Biol 2015;17:183-94.
119. Wang B, Wang X, Hou D, et al. Exosomes derived from acute myeloid leukemia cells promote chemoresistance by enhancing glycolysis-mediated vascular remodeling. J Cell Physiol 2019;234:10602-14.
120. Park JE, Dutta B, Tse SW, et al. Hypoxia-induced tumor exosomes promote M2-like macrophage polarization of infiltrating myeloid cells and microRNA-mediated metabolic shift. Oncogene 2019;38:5158-73.
Cite This Article
Export citation file: BibTeX | EndNote | RIS
OAE Style
Espiau-Romera P, Gordo-Ortiz A, Ortiz-de-Solórzano I, Sancho P. Metabolic features of tumor-derived extracellular vesicles: challenges and opportunities. Extracell Vesicles Circ Nucleic Acids 2024;5:555-70. http://dx.doi.org/10.20517/evcna.2024.12
AMA Style
Espiau-Romera P, Gordo-Ortiz A, Ortiz-de-Solórzano I, Sancho P. Metabolic features of tumor-derived extracellular vesicles: challenges and opportunities. Extracellular Vesicles and Circulating Nucleic Acids. 2024; 5(3):555-70. http://dx.doi.org/10.20517/evcna.2024.12
Chicago/Turabian Style
Espiau-Romera, Pilar, Inés Ortiz-de-Solórzano, and Patricia Sancho. 2024. "Metabolic features of tumor-derived extracellular vesicles: challenges and opportunities" Extracellular Vesicles and Circulating Nucleic Acids. 5, no.3: 555-70. http://dx.doi.org/10.20517/evcna.2024.12
ACS Style
Espiau-Romera, P.; Gordo-Ortiz A.; Ortiz-de-Solórzano I.; Sancho P. Metabolic features of tumor-derived extracellular vesicles: challenges and opportunities. Extracell. Vesicles. Circ. Nucleic. Acids. 2024, 5, 555-70. http://dx.doi.org/10.20517/evcna.2024.12
About This Article
Special Issue
Copyright
Data & Comments
Data
0
Comments
Comments must be written in English. Spam, offensive content, impersonation, and private information will not be permitted. If any comment is reported and identified as inappropriate content by OAE staff, the comment will be removed without notice. If you have any queries or need any help, please contact us at support@oaepublish.com.