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. 2019 Dec 9;17(4):1559325819891004. doi: 10.1177/1559325819891004

Isolation and Detection Technologies of Extracellular Vesicles and Application on Cancer Diagnostic

Chunyan Ma 1, Fan Jiang 2, Yifan Ma 3, Jinqiao Wang 2, Hongjuan Li 2,, Jingjing Zhang 3,
PMCID: PMC6902397  PMID: 31839757

Abstract

The vast majority of cancers are treatable when diagnosed early. However, due to the elusive trace and the limitation of traditional biopsies, most cancers have already spread widely and are at advanced stages when they are first diagnosed, causing ever-increasing mortality in the past decades. Hence, developing reliable methods for early detection and diagnosis of cancer is indispensable. Recently, extracellular vesicles (EVs), as circulating phospholipid vesicles secreted by cells, are found to play significant roles in the intercellular communication as well as the setup of tumor microenvironments and have been identified as one of the key factors in the next-generation technique for cancer diagnosis. However, EVs present in complex biofluids that contain various contaminations such as nonvesicle proteins and nonspecific EVs, resulting in the interference of screening for desired biomarkers. Therefore, applicable isolation and enrichment methods that guarantee scale-up of sample volume, purity, speed, yield, and tumor specificity are necessary. In this review, we introduce current technologies for EV separation and summarize biomarkers toward EV-based cancer liquid biopsy. In conclusion, a novel systematic isolation method that guarantees high purity, recovery rate, and tumor specificity is still missing. Besides that, a dual-model EV-based clinical trial system includes isolation and detection is a hot trend in the future due to efficient point-of-care needs. In addition, cancer-related biomarkers discovery and biomarker database establishment are essential objectives in the research field for diagnostic settings.

Keywords: extracellular vesicles, isolation, cancer diagnostic

Introduction

Extracellular vesicles (EVs), as phospholipid vesicles secreted by cells,1 can be classified into 3 categories: apoptotic bodies, microvesicles (MVs), and exosomes.15 Among them, MVs originate from the buddings of the plasma membranes of tumors, neutrophils, and platelets1 with the diameter larger than 200 nm,6 while exosomes are derived from the inward buddings of the plasma membranes7 with the size between 30 and 200 nm.8 Firstly, the inward buddings of the membrane inside the endosome produce the multivesicular bodies (MVBs),9 then exosomes are released because MVBs fuse with the plasma membranes of various cells.1012 In virtue of appropriate isolation technologies, EVs can be easily extracted from blood,13 urine,14 breast milk,15,16 saliva,17 and cerebral spinal fluid body fluids, and contain proteins,1820 messenger RNAs (mRNAs), microRNAs (miRNAs),2123 and DNA24 with similar genetic characteristics of their parenting cells.25,26 Hence, EVs can be used for diagnostic settings, especially for cancers.2730 In addition to EVs, circulating tumor cells (CTCs) and circulating mRNA can be also used for liquid biopsy.31,32 However, their undesirable maneuverability and high minimum amount for detection limit further application. In this review, we will highlight the EV-based liquid biopsy.

Comparing with traditional tissue biopsies that require complex sampling procedures and are invasive,31 EV-based liquid biopsy shows advantages of monitoring tumors with time evolution,33-36 long-term treatment response of the tumors, repeated sampling,37,38 and easy sampling managements.39,40 Moreover, traditional biopsies based on symptoms may fail to detect early-stage cancer because some symptoms only show at late-stage cancers; on the other hand, EV-based liquid biopsy, on genetic level, can waive the symptoms limitations and detect early-stage cancer. Extracellular vesicle–based liquid biopsy is also important for liver diseases,41,42 immune diseases,43,44 neuro diseases,45 and Parkinson disease.46 We only focus on cancer diagnostic in this review paper.

Extracellular vesicles show potential cancer diagnostic functions; however, EV-based liquid biopsy is limited in identifying EVs since they have size heterogeneities (30 nm to 1 mm)25 and present in various human biofluids that contain outside vesicular small molecules (eg, RNAs and proteins).47 Hence, efficient EV isolation methods are necessary before clinical analysis. In addition, normal EVs secreted by host cells influence the specificity and sensitivity of tumor EVs,48 which can be possibly resolved through tumor biomarker immunoaffinity, so defining reliable cancer biomarkers is essential.

This review article presents recent EV isolation and characterization techniques and highlights the diagnosis progresses among various cancers. We compare the isolation methods by purity, recovery rate, processing time, and tumor specificity and evaluate the characterization techniques. Even though some reviews have published, we believe this review is helpful because of compiling latest diagnostic progresses for various cancers.

Extracellular Vesicle Isolation and Enrichment

Various isolation methods have been developed in the past decades and have been evaluated by recovery rate, purity, integrity, tumor specificity, and processing time.49 In this review, we present bulk isolations that rely on size, density, coprecipitation, and affinity binding,49 and subpopulation isolation methods that isolate and enrich subpopulation by targeting antibodies against EV surface biomarkers include common biomarkers (eg, CD9, CD63, and CD81)50,51 and tumor-specific biomarkers (eg, HER2, EpCAM, EGFR, EGFRvIII, and GPC1).22,5254

Size-Based Isolation

Ultracentrifugation

Ultracentrifugation (UC) is the gold standard protocol for isolating EVs.55 Ultracentrifugation can scale-up sample volume and isolate EVs with high centrifugal force (100 000g),56 while expensive equipment, skill dependent, time consuming, low purity, and yield are the drawbacks. Tangential flow filtration (TFF): Comparing with traditional dead-end filtration, TFF is a cross-flow filtration that avoids the filter cake and fractionates EVs through a module contains hollow fiber filters.57 When the sample flows through the module, small molecules (permeate) pass through the hollow fiber filters, while EVs (retentate) are remained inside the module. Tangential flow filtration has been applied to isolate EVs from cell culture medium with 500 kDa hollow fiber filters, and enriched EVs from scalable sample volume at the meantime purified EVs with high recovery rate (5 times higher than UC) in a rapid, sterilized manner.57 Size-exclusion chromatography: Size-exclusion chromatography (SEC) isolates EVs based on gel column with specific size pores.5860 When the sample goes through the gel, smaller molecules are trapped into the pores, while EVs flow through directly that come faster than smaller molecules. Commercial columns (qEV; iZON, Boston, MA) have been applied to separate EVs from various biofluids such as plasma, serum, and cell culture media. Takov et al recently compared UC and qEV and showed qEV contained high EV number, EV protein, and stronger marker signal than UC; nevertheless, the qEV failed to remove all non-EV proteins.61 Based on SEC principle, there is a balance between purity and recovery rate. Moreover, several factors such as pore size, structure, length of column, and loading volume can contribute to the yield and purity to some extent.

Density-Based Isolation

Sucrose density gradient UC separates EVs in a continuous size-based UC mode; serial ultracentrifugations are applied to remove living cells and cell debris, then the EV and proteins are separated based on different flotation densities under UC.62 Despite the high purity, sucrose has lengthy problem due to multiple centrifugations, and the recovery rate is low due to multiple centrifugations.

Coprecipitation Isolation

Coprecipitation reduces the solubility of EVs by adding polymer or reagent (eg, ExoQuick) and causes precipitation, and EVs are isolated from precipitation with lower centrifugal forces later.63,64 Comparing with UC, coprecipitation saves processing time but lacks scale-up and EV specificity due to additives.

Affinity Binding–Based Isolation

Membrane affinity binding

Recently, commercial kits (eg, exoEasy) use membrane-based affinity binding to isolate EVs,65 which are selected based on generic, biochemical feature of vesicles and fixed on the affinity spin column that can be washed and eluted with buffers. The advantages of kits are easy handling, high purity, and extremely fast (25 minutes); however, low throughput and recovery rate are the shortcomings. Macías et al tested the kit and demonstrated it had low EV number and weak CD63 and CD9 Western blotting signal.66 TiO2 and lipid bilayer of EV binding: Gao et al utilized the advantage of the specific interaction between titanium oxide and EV phosphate lipid bilayer to separate EVs from serum.67 Micron-sized TiO2 particles enriched EVs through the bidentate binding with high recovery rate (93.4%) in a simple manner. They also tested the platform with patients with pancreatic cancer (PC) and found 29 novel proteins, which showed high potential diagnostic function. However, this method is limited by low throughput.

Immunoaffinity

One of the advantages of immunoaffinity compared to bulk isolation is the EV subpopulation isolation with high recovery rate and specificity. The immunoaffinity is the specific affinity between an antibody and antigen, which normally means the bindings between a capture antibody, a EV surface antigen, and a detection antibody.68,69 Shao et al applied immunomagnetic beads to extract glioblastoma multiforme (GBM) EVs with 93% specificity, and they detected EPHA2, EGFR, and PDPN mRNA from patients with GBM successfully.70 Recently, Sharma et al used the monoclonal antibody (mAb) with magnetic beads to capture tumor EVs from patients with melanoma that expresses CSPG4 epitope.71 Masud et al applied gold-loaded ferric oxide nanocubes functionalized with antibodies that work as “dispersible nanocarriers” on separating population of EVs.72 Bai et al have captured lung cancer EVs through queued beads functionalized with antibodies and combined with quantum dots in a microarray, which showed distinctive lung cancer marker detection level.73 Immunoaffinity can isolate EV subpopulation, especially for tumor EVs, while limited sample processing volume is a big challenge for the immunoaffinity.

Comparison

Different isolation techniques are compared in Table 1 based on processing time, purity, recovery rate, sample scale, and tumor specificity. High recovery rate and purity are important because of clinical sensitivity and accuracy.47 Flexible sample scale is another key point for clinical settings since various biofluids require different pretreatment, for example, urine must be in large scale to obtain enough EV number, while serum and plasma must be in small scale due to collecting limitation. It is worth to note that immunoaffinity isolation methods can be directly used for point-of-care diagnostics since tumor subpopulation capture.74 However, immunoaffinity is highly dependent on the quality of antibodies. Until then, a platform that can guarantee high yield, purity, rapid time, and tumor specificity is still missing.

Table 1.

Comparison of EV Enrichment, Separation, and Purification Methods.

Platform Principle Advantages Limitations
Ultracentrifuge Size Large scale, gold standard Lengthy, low yield, low purity, expensive equipment, lack tumor specificity
Tangential flow filtration Size Large scale, high purity, yield, rapid, integrity Lack tumor specificity
Size-exclusion chromatography Size User-friendly, relative high yield Low purity, small scale, lack tumor specificity
Sucrose density gradient centrifuge Density User-friendly, gold standard, purity Lengthy, expensive equipment, low yield, no tumor specificity
Coprecipitation Charge User-friendly Lack tumor specificity, small scale
Membrane affinity binding Surface High yield, integrity Lack tumor specificity, small scale
TiO2 and lipid bilayer binding Surface High yield, integrity Lack tumor specificity, small scale
Immunoaffinity Surface Tumor specificity Small scale, low recovery rate, low purity

Extracellular Vesicle Detection and Characterization

Microscopy Quality Characterization

Scanning electron microscopy

Scanning electron microscopy (SEM) is a high-resolution technique in the EV field.22,75,76 Extracellular vesicles are fixed by chemicals such as glutaraldehyde and dehydrated by critical point dry in ethanol; osmium tetroxide can be used to increase contrasts. Scanning electron microscopy scans EV surface with a focused beam of electrons, normally a thin sputter gold coating is required for focusing, and generates EV topography image due to the interaction between electrons and atoms in the EV sample. Majorities of EVs present spherical-shaped or cup-shaped morphologies under the SEM.77 Transmission electron microscopy (TEM): TEM has higher resolution compared with SEM77 and has similar procedures for fixation and contrast enhancement. The focused beam of electrons are transmitted through samples to generate images, and molecular contents on the EV surface can be characterized through TEM with immunolabeling.78 Atomic force microscopy (AFM): AFM is another high-resolution microscopy in EV studies.79,80 Extracellular vesicles are fixed on a mica substrate coated with 3-aminopropyltriethoxysilane and are air dried or nitrogen dried after extensive Deionized (DI) water washing steps. A metal probe is used for scanning EV surface to provide surface topography information under the amplitude modulation and local stiffness and adhesion information under phase modulation.80 All 3 microscopies show outstanding images of EVs. Among them, SEM and TEM show higher resolution than AFM but require complex sample preparations. Moreover, TEM is able to characterize molecular content level, while AFM is professional in acquiring stiffness information.

Quantitative Characterization

Dynamic light scattering

Dynamic light scattering (DLS) shows EV size distribution based on the intensity of the scattered light.81 The suspended EVs are illuminated by a laser, and the intensity of the light fluctuates over time since EV goes through Brownian motion.8183 The effective size of EVs is calculated by Strokes-Einstein equation based on the transformation between fluctuation rates and diffusivities of the EVs. However, the intensity of the scattered light is also associated with the size of the EV; larger EV size reflects higher intensity, which may influence accuracy. Nanoparticle tracking analyzer (NTA): Similar to DLS, a laser beam is used to illuminate EVs in the sample.76,84 The path of every single EV under Brownian motion is recorded by a camera,37 then a software such as NanoSight calculates the concentration and size distribution mathematically based on Strokes-Einstein equation that converts velocity and diffusivity of the EV. Unlike DLS that deals with bulk scattering intensity, NTA is capable of tracking a single EV that overcomes polydisperse problems. Both methods provide accurate and sensitive size distributions, while NTA shows concentration quantification. Tunable resistive pulse sensing (TRPS): TRPS is capable of measuring both EV concentration and size distribution.8587 Suspended EV in electrolytes generates different voltage pulse when it passes through a nanopore, and the voltage pulse information can be transferred into size and concentration based on standard calibration sets. Comparing with NTA, TRPS represents higher sensitivity and accuracy since it can measure the narrow size distribution of EVs based on the nanopores, which cover specific size ranges (eg, NP 80, ranges from 40 to 225 nm). Dynamic light scattering, NTA, and TRPS are popular in the EV field for characterizing. Comparing with NTA and TRPS, DLS lacks quantification even though it can detect very small EVs. Both NTA and TRPS provide quantification information. Compared with NTA, TRPS overcomes the detection limit caused by low EV concentration and is more precise in size distribution. But notably, TRPS may need to take effort on nanopore maintenance to avoid EV clogging and instability issues.

Protein Analysis

Pierce bicinchoninic acid protein assay kit

Bicinchoninic acid is used to quantify total protein concentration including membrane proteins and intravesicular proteins.8890 It is a highly sensitive and rapid method based on colorimetric solution but cannot characterize EV protein.91 SDS-PAGE and Western blotting: sodium dodecyl sulfate (SDS)-PAGE is a qualitative method to detect EV proteins92 that includes protein lysis, denature, and separation based on molecular weight through gel electrophoresis; protein with less mass moves faster than with greater mass. Once proteins are separated by electrophoresis, they can either use for downstream proteomics or can be transferred onto a membrane for Western blotting after staining. Western blotting is also a qualitative technique for specific protein analysis.93 Primary antibody, secondary antibody, and detection reagents are used in sequence after membrane transferring. Even though Western blotting is time-consuming, it is a powerful technique to demonstrate target proteins that are associated with EVs, and it can process multiple proteins at the same time. Enzyme-linked immunosorbent assay (ELISA): ELISA is a plate assay for membrane protein detection.94 Enzyme-linked immunosorbent assay applies a “sandwich” format, EVs with specific surface biomarkers are fixed between the support that is pretreated with antibodies that can bond with EV surface biomarkers and another detection antibody that is linked to an enzyme (horseradish peroxidase), which is incubated with a substrate to produce measurable products. Enzyme-linked immunosorbent assay is commercially available and relies on antibody–antigen interaction that can improve the specificity of detection, while it cannot quantify multiple proteins simultaneously. Flow cytometry: Flow cytometry is another technique that can quantify and characterize EV surface biomarker based on fluorescence intensity of the detection antibody.95,96 A laser beam illuminate EVs, and the scattered light is converted to an intensity-associated voltage pulse that can be quantified later.97 The advantage of flow cytometry is parallel multiple surface biomarker detection with different fluorescent antibodies; however, EV is too small for flow cytometry to capture the florescence signal since it is originally designed for cell surface expression analysis, so immunoconjugated beads are required to increase fluorescence signals since EVs can be mounted on the beads.

Nucleic Acids Analysis

Precipitation and spin columns

Both precipitation and spin columns complete total RNA extraction and are commercially available.22,98 Precipitation relies on phase separation; RNA is suspended into the aqueous phase and is recovered through ethanol precipitation.98 The spin column is based on solid-phase extraction that relied on silica and RNA binding with chaotropic agents. Amplification and sequencing: Amplification is used for detecting a target sequence by end-point electrophoresis or real-time fluorescence measurements through polymerase chain reaction.22,52,99 Sequencing RNA profiling is able to generate a wider and deeper RNA characterization of the whole transcriptome, which is able to detect unknown RNAs.47

Cancer Clinical Applications of EV

Conventional biopsies, such as tissue biopsy, are not only invasive100 but have limitations to profile tumors due to tumor heterogeneous characteristics, and they cannot reflect the whole tumor information.101,102 Hence, liquid biopsy shows advantages since it is noninvasive and on genetic level that can provide comprehensive information. Extracellular vesicles play an important role in the cancer liquid biopsy since they carry all types of genetic information from original tumors,103,104 and they also obtain attention because of preinvasive and early state diagnosis.105 Extracellular vesicle–based diagnostic relies on tumor exosomal biomarkers, so defining and discovering reliable biomarkers is vital. In this review, we will present the lasted EV diagnostic based on exosomal biomarkers among different cancers (Table 2).

Table 2.

Potential Biomarkers of EVs for Cancer Diagnostic Application.

Type Protein Biomarker Nucleic Acids Long Noncoding RNA
Breast HER2, CD82, ER, CD24, Ki67, TGF-β miR-10b, miR-21, miR-145, miR-1246, miR-105, miR-222, and miR-200c N/A
Lung (non-small cell lung cancer) EFGR, MET, PIK2CA, ALK, KRAS, MAP2K1, HER2, BRAF, AKT1, CD151, CD171, and tetra-spannin 8, CD91, CD317, ECM1, LRG1 7b-5p, let-7e-5p, miR-23a-3p, miR-486-5p lncRNA GAS5
Ovarian HER2, TrKB, CD24, EpCAM miR-375, miR-1307, miR-21, miR-200b, miR-100, miR-320, miR-141, miR-125b, miR-1246, and miR-93, miR-30a-5p, miR-145, miR25, miR148a, miR-101 N/A
Prostate N/A miR-1246, GATA2, miR-141, miR-375 SAP30L-AS1, SChLAP1
Pancreatic GPC1, MIF, EGFR, Glypican-1, ZIP4, PD-L1, CD104, Epcam miR-122-5p, miR-125b-5p, miR-1192-5p, miR-193b-3p, miR-221-3p and miR-27b-3p N/A
Bladder N/A miR-148b-3p, miR-141, miR-27a-3p, miR-100, miR-92a, miR-99a, miR-93, miR-940, miR-375, miR-146a-5p PCAT-1, SPRY4-IT1, UBC1 and SNHG16
Melanoma PD-L1, CSPG4+ N/A N/A
Brain (glioblastoma multiforme) EGFR, EFGRvIII miR-301a, miR-182-5p, miR-328-3p, miR-339-5p, miR-340-5p, miR-485-3p, miR-486-5p and miR-543, miR-22, miR-222 lncRNAs HOTAIR

Abbreviation: N/A, not applied.

Breast Cancer

Breast cancer (BC) plays the second position of cancer mortality in women.106,107 Common breast X-ray screening is invasive and radioactive, thus EV, diagnostic setting is important. Both blood samples and breast milk contain reliable EV resources, while collecting milk requires specific time point even though it is simpler than serum.108 There are several BC biomarkers; proteins such as HER2, ER, and Ki67 are highly expressed.106,109111 Yang et al said the expression of TGF-β in the breast milk increased the risk of BC.112 van’t Veer et al claimed that CD24 was abundant in patients with late-stage BC,113 and Wang et al demonstrated CD82 expression level was negatively associated with patients with BC.114 As for miRNA, miR-10b and miR-145 are abundant in patients with BC.115 Hannafon et al demonstrated both miR-21 and miR-1246 expressed in patients were higher than healthy donors.108 Recently, Zhai et al have used Au nanoflare probe to detect miR-1246 in plasma samples successfully (Figure 1A).116 Alba et al claimed miR-105 was higher in patients with metastasis BC than in healthy donors.117 Jong et al detected miR-21, miR-222, and miR-200c with high sensitivity with their surface-enhanced Ramen scattering sensor.118

Figure 1.

Figure 1.

A, Schematic of Au nanoflare probe to detect miR-1246. Fluorescence-treated probes enter the exosomes and bind to the targets after incubation with exosomes from breast cancer cells. B, Comparison of miR-1246 expression level in patients with breast cancer (n = 46) and healthy controls (n = 28). Patients with breast cancer showed higher exosomal miR-1246. P < .0001. Reprinted with permission from Zhai et al.116 Copyright © 2019 American Chemical Society. C, Lung cancer liquid biopsy–related exosomal biomarkers. Reprinted with permission from Cui et al.125 Copyright © 2019 from Elsevier Ltd.

Lung Cancer

Lung cancers (LCs) are the most common and high death leading type of cancer because the majority of LCs are at late stage and go through metastasis that cannot be cured when they are first found.119 Non-small cell lung cancer (NSCLC) is the most common type of LC and only shows symptoms at the late stage120; hence, early-stage detection is essential. EFGR, MET, PIK2CA, ALK, KRAS, MAP2K1, HER2, BRAF, AKT1, CD151, CD171, and tetraspanin 8 were revealed to be highly associated with LC.121 Ueda et al found CD91 was a powerful surface biomarker in advanced stage LCs.94 Jakobsen et al showed CD317 was able to distinguish patients with LC with 75% accuracy.122 Niu et al found patients with NSCLC expressed a high level of α-2-HS-glycoprotein (AHSG), the extracellular matrix protein 1 in the serum compared to healthy donors.123 Li et al found α-2-glycoprotein (LRG1) was strongly expressed in urinary EVs from patients with NSCLC (Figure 1B).124 Recently, Castellanos-Rizaldos et al improved the detection sensitivity and specificity of EGFR T790M from the plasma of the patients with NSCLC by combining exoRNA/DNA and circulating free tumor DNA.125 Jin et al found let-7b-5p, let-7e-5p, miR-23a-3p, and miR-486-5p were related to early-stage NSCLC.126 Xu et al demonstrated miR-21 and miR-155 were higher in patients with NSCLC with recurrence than without recurrence and healthy donors.127 Moreover, Li et al found lncRNA GAS5 was downregulated in early-stage patients with NSCLC compared with healthy donors.128

Ovarian Cancer

Ovarian cancer (OC) is difficult to be detected until it has spread within the pelvis and abdomen at the late stage, so early-stage detection is necessary.129 Symptoms of early-stage OC are rare and nonspecific even in the advanced stage, so EV diagnostic setting on genetic levels has advantages. Protein biomarkers such as claudin-4, HSP70, HER2, and TrkB derived from exosomes from patients showed different expression compared with healthy controls (Figure 2A).130133 CD24 and EpCAM are also possible biomarkers for OC.137 Exosomal miRNAs are much more powerful for OC diagnosis. Overexpressed level of miR-21, miR-200b, miR-100, miR-320 miR-141, miR-125b, miR-1246, miR-375, and miR-93 differed between OC patients and healthy donors.138 The miR-1290 also showed the possibility of diagnosis on high-grade OC.139 The miR-30a-5p was highly expressed in the urine samples of patients with OC,140 while miR-145, miR25, and miR148a were under expressed.141 Xu et al found miR-101 was expressed lesser in patients with OC than in healthy donors.132 Qiu et al found metastasis-associated lung adenocarcinoma transcript 1 (MA-LAT1) was positively associated with OC.142

Figure 2.

Figure 2.

Molecular components (long noncoding RNAs, microRNAs, and membrane proteins) in exosomes from patients with ovarian cancer. Reprinted with permission from Yang et al.130 Copyright © 1999-2019 John Wiley & Sons, Inc. B, Histogram and boxplot of fluorescence intensity of exosomes (Target: Prostate-specific membrane antigen (PSMA) positive) from patients with prostate cancer and healthy donors detected by superparamagnetic conjunctions and molecular beacons (SMC-MB) platform. Reprinted with permission from Li et al.134 Copyright © 2019 American Chemical Society. C, Comparison of glypican-1 expression level in patients with pancreatic cancer (n = 20), benign pancreatic disease (n = 7), and healthy controls (n = 11). Glypican-1 in patients with pancreatic cancer were elevated. Reprinted with permission from Lewis et al.135 Copyright © 2019 American Chemical Society. D, Quantitative reverse transcription polymerase chain reaction analysis of exosomal H19 from patients with bladder cancer, benign disease, and healthy controls. P < .001. Reprinted with permission from Wang et al.136 Copyright © International Scientific Information.

Prostate Cancer

Prostate cancer (PCa) is one of the most common types of cancer in men.143 Some types of PCa grow slowly with minimum harmful effects, while some types are aggressive.144 The early stage of the PCa that may be defined as the prostate gland is easy to cure, but they show no signs or symptoms. So the liquid biopsy based on exosomal molecular contents is important. Bhagirath et al used the nCounter technology and found miR-1246 was a promising aggressive PCa biomarker.145 Donovan found PCA3 and ERG mRNAs predicted high-grade PCa.146 Li et al used an ultrasensitive and reversible nanoplatform to detect PSA, PCA3, and mRNA successfully in urinary exosomes from patients with PCa (Figure 2B).134 However, PSA has limitation because it may not differ cancer and benign prostatic hyperplasia (BPH).146 Some miRNAs, such as miR-141 and miR-375, from serum EVs were associated with metastatic PCa, and the miR-19b distinguished PCa with 100% specificity and 93% sensitivity.147 As for long noncoding RNAs, Wang et al found SAP30L-AS1 was related to tumor invasion, and SChLAP1 was expressed higher in PCa compared with BPH and healthy controls.148

Pancreatic Cancer

Early-stage PC can only be detected in people with pancreatic cysts or family history of PCs,149 but it can seldom be detected in other conditions.150 Serum cancer antigen 19-9 (CA19-9) is a possible biomarker for PC151; however, it fails to show sensitivity and specificity of early-stage PC. Zhou et al compared 216 patients with PC with 220 healthy controls and found miR-122-5p, miR-125b-5p, miR-1192-5p, miR-193b-3p, miR-221-3p, and miR-27b-3p were significantly higher in patients with PC.152 Goto also found higher expression levels of miR-191, miR-21, miR-451a in PC.153 Lewis et al developed an AC electrokinetic microarray chip that was capable of differing 20 patients with PC from healthy donors based on glypican-1 and CD63 expression levels with 99% sensitivity (Figure 2C).135 Li et al designed an ultrasensitive polydopamine bifunctionalized Surface-Enhanced Raman Scattering (SERS) immunoassay with a detection limit of one exosome in 2 mL, and they discriminated patients with PC based on GPC1, MIF, and EGFR surface biomarkers successfully.154 Jin et al claimed ZIP4 promoted PC growth and could be a novel diagnostic biomarker for PC.155 Recently, Lux claimed the combination of CA19-9 and c-Met improved sensitivity test of patients with PC.156 They found PD-L1-positive patients showed shorter postoperative survival time that can be used as a negative prognostic factor. Moreover, the combination of CD104, Epcam, Tspan8, and some miRNAs such as miRNA-1246 improved diagnostic sensitivity and specificity of patients with PC.157

Bladder Cancer

Bladder cancer is usually in the bladder but can show in other parts that belong to the urinary tract drainage system.158 Around 50% of the patients with muscle-invasive bladder cancer will go through metastasis and die in 2 to 3 years, even patients with non-muscle-invasive BC usually have recurrence rate.159 The symptoms of bladder cancer include hematuria, pelvic pain, and urination pain,160 while these symptoms normally show at the middle or late stage; hence, the early diagnosis of bladder cancer is very important. Cystoscopy is a gold standard diagnostic tool for non-muscle invasive bladder cancer,161 but it is expensive and time-consuming since subsequent cystoscopy is necessary once the result is negative,161 and it fails to provide sensitive surveillance information. Hence, EV diagnosis with novel biomarkers is essential for patients with bladder cancer and suspected individuals. Both blood and urine provide reliable EV source for bladder cancer diagnosis; miR-148b-3p, miR-141 were increased, but miR-27a-3p, miR-100, miR-92a, and miR-99a were decreased in serum and plasma.162 Compared with blood, urine shows advantages because urine contacts with bladder directly and can be collected with various time point easily to reflect different stages of the diseases, and EVs are able to cross the basement membrane into the urine with miRNAs. The miR-375 was decreased in high-grade bladder tumors, while miR-146a-5p was increased, especially in low-grade tumors.162 Wang et al also found serum exosomal H19 expression level was higher compared to healthy controls and benign disease patients (Figure 2D).136 Zhan et al found PCAT-1 and SPRY4-IT1 were capable of the bladder cancer diagnosis.163 Zhang et al claimed UBC1 and SNHG16 identified by multivariate logistic regression model also provided high diagnostic accuracy. Moreover, the high UBC1 expression level was associated with low recurrence-free survival.164

Melanoma Cancer

Melanoma is the most serious type of skin cancers, but it can be treated successfully in the early stages.165 Melanomas can occur in any areas of the skin that are exposed to excess UV light.166 Sharma et al isolated melanoma tumor-derived exosomes successfully with an mAb 763.74 that captured the chondroitin sulfate peptidoglycan 4 (CSPG4+) that are expressed on the surface of exosomes.71 Chen et al claimed that exosomal PD-L1 was associated with anti-PD-1, which has shown promise in treating melanoma tumors. Hence, detection of PD-L1 on the exosomes is essential.166

Glioblastoma Multiforme

Glioblastoma multiforme is an aggressive type of cancer that occurs in the brain or spinal cord, and it tends to occur in older adults.167,168 Curing GBM is seldom possible but effective treatment can slow the progression of cancer and relief the symptoms.18 However, treating early-stage GBM guarantees minimum dissatisfactory effects. Normal diagnosis methods include the neurological examination, imaging tests (magnetic resonance imaging), and tissue tests. Nevertheless, compared with EV liquid biopsy, they are expensive and invasive. EGFRvIII was a well-known GBM-related biomarker, and it was highly expressed on the surface of EVs from GBM patients.22 Lan et al found miR-301a was a potential biomarker for GBM.169 Tan et al also found serum lncRNAs HOTAIR was significantly higher than in the healthy controls.170 Ebrahimkhani et al found miR-182-5p, miR-328-3p, miR-339-5p, miR-340-5p, miR-485-3p, miR-486-5p, and miR-543 were most likely stable for GBM classification after modeling and data comparisons among 26 relative microRNAs (Figure 3).171 Santangelo also claimed miR-22 and miR-222 were expressed higher in high-grade GBM patients.172

Figure 3.

Figure 3.

Hierarchical clustering of 26 microRNAs shows differences in GBM and healthy control exosomal profiles (fold change ≥2 or ≤0.5). Reprinted with permission from Ebrahimkhani et al.170 Copyright Springer Nature Publishing AG.

Conclusion and Perspective

Extracellular vesicles play an important role in tumor microenvironment; besides carrying parenting genetic information, the specific tumor-related genetic information can be used for diagnosis and immunotherapy, while size and biomolecular heterogeneities of EVs bring problems on isolating EVs that challenge the EV diagnostic setting, which requires EV isolation in a pure and rapid manner. Hence, standard sample collection, storage procedures, and efficient isolation mechanisms are important. In this review, we summarized updated isolation methods, and majorities of them enriched the bulk EVs, and immunoaffinity can isolate tumor subpopulation EV based on tumor surface biomarkers. Successful isolation is the first step of the cancer diagnosis; characterizing EVs in qualitative and quantitative way, such as microscopy, DLS, NTA, and TRPS, is also necessary. The most important thing is the molecular content characterization such as protein classification and RNA sequencing, which is the fundamental of EV-based cancer liquid biopsy because clinical trials make decision based on expression level of reliable cancer-associated RNA and protein biomarkers. However, protein biomarkers lack tumor precision and show no superiority since the same type of biomarkers can be present in multiple cancers. For example, HER2 has high expression level in breast and LCs, while CD24 is abundant in both ovarian and BCs. In that, combination of different protein biomarkers to define a specific type of cancers is necessary. Comparing with proteins, RNAs may show high specifically toward single-type cancer, but it is relatively expensive. Overall, defining and exploring specific and reliable biomarkers and compiling a cancer biomarker database are big breakthroughs for cancer diagnosis. In this review paper, we introduce EV isolation and detection separately, and we believe in the future the combination of isolation and detection methods with high efficiency is desirable and plays key role since they fulfill the point-of-care cancer diagnostic need. Moreover, even though EV-based liquid biopsy is advantageous in many aspects such as noninvasive collection, early-stage detection compared with traditional biopsy, until now, gold standard operating procedures for collecting, isolating, and detecting EVs are still missing. Also, the universality and pricing are other concerns about liquid biopsy. Up to now, only HER2 and EGFR are qualified as biomarkers tested in clinical trials. Nevertheless, EV-based liquid biopsy is still a long road ahead, and we believe it will be more reliable and standard in the future since outstanding researchers make efforts on it.

Footnotes

Author Contribution: Chunyan Ma and Fan Jiang has contributed equally to this work.

Declaration of Conflicting Interests: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding: The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was supported by Zhejiang Medical Technology Program (2018KY918).

References

  • 1. Raposo G, Stoorvogel W. Extracellular vesicles: exosomes, microvesicles, and friends. J Cell Biol. 2013;200(4):373–383. doi:10.1083/JCB.201211138. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 2. Théry C, Zitvogel L, Amigorena S. Exosomes: composition, biogenesis and function. Nat Rev Immunol. 2002;2(8):569–579. [DOI] [PubMed] [Google Scholar]
  • 3. Théry C, Ostrowski M, Segura E. Membrane vesicles as conveyors of immune responses. Nat Rev Immunol. 2009;9(8):581–593. [DOI] [PubMed] [Google Scholar]
  • 4. Heijnen HFG, Schiel AE, Fijnheer R, Geuze HJ, Sixma JJ. Activated platelets release two types of membrane vesicles: microvesicles by surface shedding and exosomes derived from exocytosis of multivesicular bodies and α-granules. Blood. 1999;94(11):3791–3799. [PubMed] [Google Scholar]
  • 5. Colombo M. Biogenesis, secretion, and intercellular interactions of exosomes and other extracellular vesicles. Annu Rev Cell Dev Biol. 2014;30:255–292. doi:10.1146/annurev-cellbio-101512-122326. [DOI] [PubMed] [Google Scholar]
  • 6. Yáñez-mó M, Siljander PR, Andreu Z, et al. Biological properties of extracellular vesicles and their physiological functions. J Extracell Vesicles. 2015;4:27066 doi:10.3402/jev.v4.27066. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7. Johnstone RM, Adam M, Hammond JR, Orr L, Turbide C. Vesicle formation during reticulocyte maturation. Association of plasma membrane activities with released vesicles (exosomes). J Biol Chem. 1987;262(19):9412–9420. [PubMed] [Google Scholar]
  • 8. Hugel B, Martínez MC, Kunzelmann C, Freyssinet JM. Membrane microparticles: two sides of the coin. Physiology. 2005;20:22–27. [DOI] [PubMed] [Google Scholar]
  • 9. Piper RC, Katzmann DJ. Biogenesis and function of multivesicular bodies. Annu Rev Cell Dev Biol. 2007;23:519–547. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10. Hurley JH, Hanson PI. Membrane budding and scission by the ESCRT machinery: it’s all in the neck. Nat Rev Mol Cell Biol. 2010;11(8):556–566. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11. Luzio JP, Gray SR, Bright NA. Endosome-lysosome fusion. Biochem Soc Trans. 2010;38:1413. [DOI] [PubMed] [Google Scholar]
  • 12. Théry C. Exosomes: secreted vesicles and intercellular communications. F1000 Biol Rep. 2011;3:15. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13. Revenfeld AL, Bæk R, Nielsen MH, Stensballe A, Varming K, Jørgensen M. Diagnostic and prognostic potential of extracellular vesicles in peripheral blood. Clin Ther. 2014;36(6):830–846. [DOI] [PubMed] [Google Scholar]
  • 14. Salih M, Zietse R, Hoorn EJ. Urinary extracellular vesicles and the kidney: biomarkers and beyond. Am J Physiol Ren Physiol. 2014;306(11):F1251–F1259. [DOI] [PubMed] [Google Scholar]
  • 15. Zonneveld MI, Brisson AR, van Herwijnen MJ, et al. Recovery of extracellular vesicles from human breast milk is influenced by sample collection and vesicle isolation procedures. J Extracell Vesicles. 2014;3:24215. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16. Pieters BC, Arntz OJ, Bennink MB, et al. Commercial cow milk contains physically stable extracellular vesicles expressing immunoregulatory TGF-B. PLoS One. 2015;10(3):e0121123. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17. Yang J, Wei F, Schafer C, Wong DT. Detection of tumor cell-specific mRNA and protein in exosome-like microvesicles from blood and saliva. PLoS One. 2014;9(11):e110641. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18. Graner MW, Alzate O, Dechkovskaia AM, et al. Proteomic and immunologic analyses of brain tumor exosomes. FASEB J. 2009;23(5):1541–1557. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19. Simpson RJ, Lim JWE, Moritz RL. Exosomes: proteomic insights and diagnostic potential. 2009;6(3):267–283. [DOI] [PubMed] [Google Scholar]
  • 20. Mathivanan S, Fahner CJ, Reid GE, Simpson RJ. ExoCarta 2012: database of exosomal proteins, RNA and lipids. Nucleic Acids Res. 2012;40:D1241–D1244. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21. Valadi H, Ekström K, Bossios A, Sjöstrand M, Lee JJ, Lötvall JO. Exosome-mediated transfer of mRNAs and microRNAs is a novel mechanism of genetic exchange between cells. Nat Cell Biol. 2007;9(6):654–659. [DOI] [PubMed] [Google Scholar]
  • 22. Skog J, Würdinger T, van Rijn S, et al. Glioblastoma microvesicles transport RNA and proteins that promote tumour growth and provide diagnostic biomarkers. Nat Cell Biol. 2008;10(12):1470–1476. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23. Rogers LK, Robbins M, Dakhlallah D, et al. Attenuation of miR-17∼92 cluster in bronchopulmonary dysplasia. Ann Am Thorac Soc. 2015;12(10):1506–1513. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24. Balaj L, Lessard R, Dai L, et al. Tumour microvesicles contain retrotransposon elements and amplified oncogene sequences. Nat Commun. 2011;2:180. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25. Azmi AS, Bao B, Sarkar FH. Exosomes in cancer development, metastasis, and drug resistance: a comprehensive review. Cancer Metastasis Rev. 2013;32(3-4):623–642. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26. Yang Z, Xie J, Zhu J, et al. Functional exosome-mimic for delivery of siRNA to cancer: in vitro and in vivo evaluation. J Control Release. 2016;243:160–171. doi:10.1016/j.jconrel.2016.10.008. [DOI] [PubMed] [Google Scholar]
  • 27. Logozzi M, De Milito A, Lugini L, et al. High levels of exosomes expressing CD63 and caveolin-1 in plasma of melanoma patients. PLoS One. 2009;4(4):e5219. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28. Pisitkun T, Shen RF, Knepper MA. Identification and proteomic profiling of exosomes in human urine. Proc Natl Acad Sci U S A. 2004. ;101(36): 13368–13373. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29. Keller S, Rupp C, Stoeck A, et al. CD24 is a marker of exosomes secreted into urine and amniotic fluid. Kidney Int. 2007; 72(9):1095–1102. [DOI] [PubMed] [Google Scholar]
  • 30. Hosseini M, Khatamianfar S, Hassanian SM, et al. Exosome-encapsulated microRNAs as potential circulating biomarkers in colon cancer. Curr Pharm Des. 2017. ;23(23): 1705–1709. [DOI] [PubMed] [Google Scholar]
  • 31. Brock G, Castellanos-rizaldos E, Hu L, Coticchia C, Skog J. Liquid biopsy for cancer screening, patient stratification and monitoring. Transl Cancer Res. 2015. ; 4(3):280–290. doi:10.3978/j.issn.2218-676X.2015.06.05. [Google Scholar]
  • 32. Shi J, Ma Y, Zhu J, Chen Y, Sun Y, Yao Y. A review on electroporation-based intracellular delivery. Molecules. 2018;23(11). doi:10.3390/molecules23113044. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33. Alix-Panabières C, Pantel K. Clinical prospects of liquid biopsies. Nat Biomed Eng. 2017. ;1(4):0065 doi:10.1038/s41551-017-0065. [Google Scholar]
  • 34. Gerlinger M, Rowan AJ, Horswell S, et al. Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. N Engl J Med. 2012;366(10):883–892. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35. Wang X, Kwak KJ, Yang Z, et al. Extracellular mRNA detected by molecular beacons in tethered lipoplex nanoparticles for diagnosis of human hepatocellular carcinoma. PLoS One. 2018;13(6):1–12. doi:10.1371/journal.pone.0198552. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 36. James Lee L, Yang Z, Rahman M, et al. Extracellular mRNA detected by tethered lipoplex nanoparticle biochip for lung adenocarcinoma detection. Am J Respir Crit Care Med. 2016;193(12):1431–1433. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 37. Shao H, Chung J, Balaj L, et al. Protein typing of circulating microvesicles allows real-time monitoring of glioblastoma therapy. Nat Med. 2012;18(12):1835–1840. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 38. Shao H, Chung J, Lee K, et al. Chip-based analysis of exosomal mRNA mediating drug resistance in glioblastoma. Nat Commun. 2015;6:6999. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 39. van der Pol E, Böing AN, Harrison P, Sturk A, Nieuwland R. Classification, functions, and clinical relevance of extracellular vesicles. Pharmacol Rev. 2012;64(3):676–705. [DOI] [PubMed] [Google Scholar]
  • 40. Raimondo F, Morosi L, Chinello C, Magni F, Pitto M. Advances in membranous vesicle and exosome proteomics improving biological understanding and biomarker discovery. Proteomics. 2011;11(4):709–720. [DOI] [PubMed] [Google Scholar]
  • 41. Baig S, Kothandaraman N, Manikandan J, et al. Proteomic analysis of human placental syncytiotrophoblast microvesicles in preeclampsia. Clin Proteomics. 2014;11(1):40. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 42. Pillay P, Maharaj N, Moodley J, Mackraj I. Placental exosomes and pre-eclampsia: maternal circulating levels in normal pregnancies and, early and late onset pre-eclamptic pregnancies. Placenta. 2016;46:18–25. [DOI] [PubMed] [Google Scholar]
  • 43. Robbins PD, Morelli AE. Regulation of immune responses by extracellular vesicles. Nat Publ Gr. 2014;14(3):195–208. doi:10.1038/nri3622. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 44. Durrani-Kolarik S, Pool CA, Gray A, et al. miR-29b supplementation decreases expression of matrix proteins and improves alveolarization in mice exposed to maternal inflammation and neonatal hyperoxia. Am J Physiol Lung Cell Mol Physiol. 2017;313(2):L339–L349. doi:10.1152/ajplung.00273.2016. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 45. Bellingham SA, Coleman BM, Hill AF. Small RNA deep sequencing reveals a distinct miRNA signature released in exosomes from prion-infected neuronal cells. Nucleic Acids Res. 2012;40(21):10937–10949. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 46. Vella LJ, Hill AF, Cheng L. Focus on extracellular vesicles: exosomes and their role in protein trafficking and biomarker potential in Alzheimer’s and Parkinson’s disease. Int J Mol Sci. 2016;17(2):173. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 47. Yang Z, Ma Y, Zhao H, Yuan Y, Kim B. Nanotechnology platforms for cancer immunotherapy. Wiley Interdiscip Rev Nanomed Nanobiotechnol. 2019. e1590 doi:10.1002/wnan.1590. [DOI] [PubMed] [Google Scholar]
  • 48. György B, Szabó TG, Pásztói M, et al. Membrane vesicles, current state-of-the-art: emerging role of extracellular vesicles. Cell Mol Life Sci. 2011;68(16):2667–2688. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 49. Ziaei P, Berkman CE, Norton MG. Review: isolation and detection of tumor-derived extracellular vesicles. ACS Appl Nano Mater. 2018;1(5):2004–2020. doi:10.1021/acsanm.8b00267. [Google Scholar]
  • 50. van Niel G, Charrin S, Simoes S, et al. The tetraspanin Cd63 regulates ESCRT-independent and -dependent endosomal sorting during melanogenesis. Dev Cell. 2011;21(4):708–721. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 51. Verweij FJ, van Eijndhoven MA, Hopmans ES, et al. Lmp1 association with Cd63 in endosomes and secretion via exosomes limits constitutive Nf-Kb activation. EMBO J. 2011;30(11):2115–2129. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 52. Conley A, Minciacchi VR, Lee DH, et al. High-throughput sequencing of two populations of extracellular vesicles provides an mRNA signature that can be detected in the circulation of breast cancer patients. RNA Biol. 2017;14(3):305–316. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 53. Jeong S, Park J, Pathania D, Castro CM, Weissleder R, Lee H. Integrated magneto-electrochemical sensor for exosome analysis. ACS Nano. 2016;10(2):1802–1809. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 54. Herlyn M, Lu Y, Chen G, et al. Exosomal PD-L1 contributes to immunosuppression and is associated with anti-PD-1 response. Nature. 2018;560(7718):382–386. doi:10.1038/s41586-018-0392-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 55. Gardiner C, Di Vizio D, Sahoo S, et al. Techniques used for the isolation and characterization of extracellular vesicles: results of a worldwide survey. J Extracell Vesicles. 2016;5(1):32945. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 56. Chen J, Xu Y, Lu Y, Xing W. Isolation and visible detection of tumor-derived exosomes from plasma. Anal Chem. 2018;90(24):14207–14215. doi:10.1021/acs.analchem.8b03031. [DOI] [PubMed] [Google Scholar]
  • 57. Busatto S, Vilanilam G, Ticer T, et al. Tangential flow filtration for highly efficient concentration of extracellular vesicles from large volumes of fluid. Cells. 2018;7(12). pii: E273 doi:10.3390/cells7120273. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 58. Koh YQ, Almughlliq FB, Vaswani K, Peiris HN, Murray D. Exosome enrichment by ultracentrifugation and size exclusion chromatography. Front Biosci (Landmark Ed). 2018;23:865–874. [DOI] [PubMed] [Google Scholar]
  • 59. Böing AN, van der Pol E, Grootemaat AE, Coumans FA, Sturk A, Nieuwland R. Single-step isolation of extracellular vesicles by size-exclusion chromatography. J Extracell Vesicles. 2014;3(1):23430. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 60. Nordin JZ, Lee Y, Vader P, et al. Ultrafiltration with size-exclusion liquid chromatography for high yield isolation of extracellular vesicles preserving intact biophysical and functional properties. Nanomedicine. 2015;11(4):879–883. [DOI] [PubMed] [Google Scholar]
  • 61. Takov K, Yellon DM, Davidson SM. Comparison of small extracellular vesicles isolated from plasma by ultracentrifugation or size-exclusion chromatography: yield, purity and functional potential. J Extracell Vesicles. 2019. ;8(1). doi:10.1080/20013078.2018.1560809. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 62. Théry C, Amigorena S, Raposo G, Clayton A. Isolation and characterization of exosomes from cell culture supernatants and biological fluids. Curr Prot Cell Biol. 2006;30(1):3.22.1–3.22.29. [DOI] [PubMed] [Google Scholar]
  • 63. Alvarez ML, Khosroheidari M, Kanchi Ravi R, DiStefano JK. Comparison of protein, microRNA, and mRNA yields using different methods of urinary exosome isolation for the discovery of kidney disease biomarkers. Kidney Int. 2012;82(9):1024–1032. [DOI] [PubMed] [Google Scholar]
  • 64. Rekker K, Saare M, Roost AM, et al. Comparison of serum exosome isolation methods for microRNA profiling. Clin Biochem. 2014. doi:10.1016/j.clinbiochem.2013.10.020. [DOI] [PubMed] [Google Scholar]
  • 65. Stranska R, Gysbrechts L, Wouters J, et al. Comparison of membrane affinity-based method with size-exclusion chromatography for isolation of exosome-like vesicles from human plasma. J Transl Med. 2018:1–9. doi:10.1186/s12967-017-1374-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 66. Macías M, Rebmann V, Mateos B, Varo N, Perez-gracia JL. Comparison of six commercial serum exosome isolation methods suitable for clinical laboratories. Effect in cytokine analysis. Clin Chem Lab Med 2019;57(10):1539–1545. [DOI] [PubMed] [Google Scholar]
  • 67. Gao F, Jiao F, Xia C, et al. A novel strategy for facile serum exosome isolation based on specific interactions between phospholipid bilayers and TiO2 . Chem Sci. 2019;10(6). doi:10.1039/c8sc04197k. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 68. Yoo CE, Kim G, Kim M, et al. A direct extraction method for microRNAs from exosomes captured by immunoaffinity beads. Anal Biochem. 2012;431(2):96–98. [DOI] [PubMed] [Google Scholar]
  • 69. Chen C, Skog J, Hsu CH, et al. Microfluidic isolation and transcriptome analysis of serum microvesicles. Lab Chip. 2010;10(4):505–511. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 70. Shao H, Chung J, Lee K, et al. Drug resistance in glioblastoma. Nat Commun. 2015;6:1–9. doi:10.1038/ncomms7999. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 71. Sharma P, Ludwig S, Muller L, et al. Immunoaffinity-based isolation of melanoma cell- derived exosomes from plasma of patients with melanoma. J Extracell Vesicles. 2018. ;7(1). doi:10.1080/20013078.2018.1435138. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 72. Masud MK, Yadav S, Islam N, Nguyen N, Hossain SA, Shiddiky MJA. Gold-loaded nanoporous ferric oxide nanocubes with peroxidase-mimicking activity for electrocatalytic and colorimetric detection of autoantibody. Anal Chem. 2017;89(20):11005–11013. doi:10.1021/acs.analchem.7b02880. [DOI] [PubMed] [Google Scholar]
  • 73. Bai Y, Lu Y, Wang K, et al. Rapid isolation and multiplexed detection of exosome tumor markers via queued beads combined with quantum dots in a microarray. Nano-Micro Lett. 2019;11:59 doi:10.1007/s40820-019-0285-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 74. Tauro BJ, Greening DW, Mathias RA, et al. Comparison of ultracentrifugation, density gradient separation, and immunoaffinity capture methods for isolating human colon cancer cell line LIM1863-derived exosomes. Methods. 2012;56(2):293–304. [DOI] [PubMed] [Google Scholar]
  • 75. Shao H, Min C, Issadore D, et al. Magnetic nanoparticles and microNMR for diagnostic applications. Theranostics. 2012;2(1):55–65. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 76. Sokolova V, Ludwig AK, Hornung S, et al. Characterisation of exosomes derived from human cells by nanoparticle tracking analysis and scanning electron microscopy. Colloids Surf, B. 2011;87(1):146–150. [DOI] [PubMed] [Google Scholar]
  • 77. Théry C, Aled C, Sebastian A, Graça R. Isolation and characterization of exosomes from cell culture supernatants. Curr Protoc Cell Biol. 2006;30(1):3.22.1–3.22.29. [DOI] [PubMed] [Google Scholar]
  • 78. Hakulinen J, Sankkila L, Sugiyama N, Lehti K, Keski-Oja J. Secretion of active membrane type 1 matrix metalloproteinase (MMP-14) into extracellular space in microvesicular exosomes. J Cell Biochem. 2008;105(5):1211–1218. [DOI] [PubMed] [Google Scholar]
  • 79. Tatischeff I, Larquet E, Falcón-Pérez JM, Turpin PY, Kruglik SG. Fast characterisation of cell-derived extracellular vesicles by nanoparticles tracking analysis, cryo-electron microscopy, and Raman tweezers microspectroscopy. J Extracell Vesicles. 2012;1:19179. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 80. Sharma S, Rasool HI, Palanisamy V, et al. Structural-mechanical characterization of nanoparticles exosomes in human saliva, using correlative AFM, FESEM and force spectroscopy. ACS Nano. 2010;4(4):1921–1926. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 81. Lawrie AS, Albanyan A, Cardigan RA, Mackie IJ, Harrison P. Microparticle sizing by dynamic light scattering in fresh-frozen plasma. Vox Sang. 2009;96(3):206–212. [DOI] [PubMed] [Google Scholar]
  • 82. Xu Y, Nakane N, Maurer-Spurej E. Novel test for microparticles in platelet-rich plasma and platelet concentrates using dynamic light scattering. Transfusion. 2011;51(2):363–370. [DOI] [PubMed] [Google Scholar]
  • 83. Sitar S, Kejžar A, Pahovnik D, et al. Size characterization and quantification of exosomes by asymmetrical-flow field-flow fractionation. Anal Chem. 2015;87(18):9225–9233. [DOI] [PubMed] [Google Scholar]
  • 84. Gardiner C, Ferreira YJ, Dragovic RA, Redman CW, Sargent IL. Extracellular vesicle sizing and enumeration by nanoparticle tracking analysis. J Extracell Vesicles. 2013;2(1):19671. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 85. Coumans FA, van der Pol E, Böing AN, et al. Reproducible extracellular vesicle size and concentration determination with tunable resistive pulse sensing. J Extracell Vesicles. 2014;3(1):25922. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 86. Vogel R, Coumans FA, Maltesen RG, et al. A standardized method to determine the concentration of extracellular vesicles using tunable resistive pulse sensing. J Extracell Vesicles. 2016;5:31242. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 87. Akers JC, Ramakrishnan V, Nolan JP, et al. Comparative analysis of technologies for quantifying extracellular vesicles (EVs) in clinical cerebrospinal fluids (CSF). PLoS One. 2016;11(2):e0149866. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 88. Ziaei P, Geruntho JJ, Marin-Flores OG, Berkman CE, Grant Norton M. Silica nanostructured platform for affinity capture of tumor-derived exosomes. J Mater Sci. 2017;52(12):6907–6916. [Google Scholar]
  • 89. He M, Crow J, Roth M, Zeng Y, Godwin AK. Integrated immunoisolation and protein analysis of circulating exosomes using microfluidic technology. Lab Chip. 2014;14(19):3773–3780. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 90. Davies RT, Kim J, Jang SC, Choi E-J, Gho YS, Park J. Microfluidic filtration system to isolate extracellular vesicles from blood. Lab Chip. 2012;12(24):5202–5210. [DOI] [PubMed] [Google Scholar]
  • 91. Krieg RC, Dong Y, Schwamborn K, Knuechel R. Protein quantification and its tolerance for different interfering reagents using the BCA-method with regard to 2D SDS PAGE. 2005;65:13–19. doi:10.1016/j.jbbm.2005.08.005. [DOI] [PubMed] [Google Scholar]
  • 92. Rath A, Glibowicka M, Nadeau VG, Chen G, Deber CM. Detergent binding explains anomalous SDS-PAGE. Proc Natl Acad Sci U S A. 2009;106(6):1760–1765. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 93. Lobb RJ, Becker M, Wen SW, et al. Optimized exosome isolation protocol for cell culture supernatant and human plasma. J Extracell Vesicles. 2015;4(1):27031. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 94. Ueda K, Ishikawa N, Tatsuguchi A, Saichi N, Fujii R, Nakagawa H. Antibody-coupled monolithic silica microtips for high throughput molecular profiling of circulating exosomes. Sci Rep. 2015;4:6232. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 95. Pospichalova V, Svoboda J, Dave Z, et al. Simplified protocol for flow cytometry analysis of fluorescently labeled exosomes and microvesicles using dedicated flow cytometer. J Extracell Vesicles. 2015;4(1):25530. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 96. Arraud N, Gounou C, Turpin D, Brisson AR. Fluorescence triggering: a general strategy for enumerating and phenotyping extracellular vesicles by flow cytometry. Cytom Part A. 2016;89(2):184–195. [DOI] [PubMed] [Google Scholar]
  • 97. Orozco AF, Lewis DE. Flow cytometric analysis of circulating microparticles in plasma. Cytom Part A. 2010;77(6):502–514. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 98. Enderle D, Spiel A, Coticchia CM, et al. Characterization of RNA from exosomes and other extracellular vesicles isolated by a novel spin column-based method. PLoS One. 2015;10(8):e0136133. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 99. Huang X, Yuan T, Tschannen M, et al. Characterization of human plasma-derived exosomal RNAs by deep sequencing. BMC Genomics. 2013;14:319. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 100. Mueller MM, Fusenig NE. Friends or foes—bipolar effects of the tumour stroma in cancer. Nat Rev Cancer. 2004;4(11):839–849. [DOI] [PubMed] [Google Scholar]
  • 101. van der Meel R, Krawczyk-Durka M, van Solinge WW, Schiffelers RM. Toward routine detection of extracellular vesicles in clinical samples. Int J Lab Hematol. 2014;36(3):244–253. [DOI] [PubMed] [Google Scholar]
  • 102. Pucci F, Garris C, Lai CP, et al. SCS macrophages suppress melanoma by restricting tumor-derived vesicle-b cell interactions. Science. 2016;352(6282):242–246. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 103. Pietras K, Ostman A. Hallmarks of cancer: interactions with the tumor stroma. Exp Cell Res. 2010;316(8):1324–1331. [DOI] [PubMed] [Google Scholar]
  • 104. Turley SJ, Cremasco V, Astarita JL. Immunological hallmarks of stromal cells in the tumour microenvironment. Nat Rev Immunol. 2015;15(11):669–682. [DOI] [PubMed] [Google Scholar]
  • 105. D’Souza-Schorey C, Clancy JW. Tumor-derived microvesicles: shedding light on novel microenvironment modulators and prospective cancer biomarkers. Genes Dev. 2012;26(12):1287–1299. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 106. Grayson M. Breast cancer. Nature. 2012;485:S49. [DOI] [PubMed] [Google Scholar]
  • 107. Duffy MJ, Evoy D, McDermott EW. Ca 15–3: uses and limitation as a biomarker for breast cancer. Clin Chim Acta. 2010;411(23-24):1869–1874. [DOI] [PubMed] [Google Scholar]
  • 108. Hannafon BN, Trigoso YD, Calloway CL, et al. Plasma exosome microRNAs are indicative of breast cancer. Breast Cancer Res. 2016;18(1):90. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 109. Admyre C, Johansson SM, Qazi KR, et al. Exosomes with immune modulatory features are present in human breast milk. J Immunol. 2007;179(3):1969–1978. [DOI] [PubMed] [Google Scholar]
  • 110. Hamed S, Zahra J, Arash S, et al. Breast cancer diagnosis: imaging techniques and biochemical markers. J Cell Physiol. 2018;233(7):5200–5213. doi:10.1002/jcp.26379. [DOI] [PubMed] [Google Scholar]
  • 111. Halvaei S, Daryani S, Eslami-s Z, et al. Exosomes in cancer liquid biopsy: a focus on breast cancer. Mol Ther Nucleic Acid. 2018;10:131–141. doi:10.1016/j.omtn.2017.11.014. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 112. Yang HP, Schneider SS, Chisholm CM, et al. Association of TGF-β2 levels in breast milk with severity of breast biopsy diagnosis. Cancer Causes Control. 2015;26(3):345–354. doi:10.1007/s10552-014-0498-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 113. van’t Veer LJ, Dai H, van de Vijver MJ, et al. Gene expression profiling predicts clinical outcome of breast cancer. Nature. 2002;415(6871):530–536. [DOI] [PubMed] [Google Scholar]
  • 114. Wang X, Zhong W, Bu J, et al. Exosomal protein CD82 as a diagnostic biomarker for precision medicine for breast cancer. Mol Carcinog. 2019;58(5):674–685. doi:10.1002/mc.22960. [DOI] [PubMed] [Google Scholar]
  • 115. Zhang J, Yang J, Zhang X, Xu J, Sun Y, Zhang P. MicroRNA-10b expression in breast cancer and its clinical association. PLoS One. 2018:13(2):e0192509. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 116. Zhai LY, Li MX, Pan WL, et al. In situ detection of plasma exosomal MicroRNA-1246 for breast cancer diagnostics by a Au nanoflare probe. ACS Appl Mater Interfaces. 2018;10(46):39478–39486. doi:10.1021/acsami.8b12725. [DOI] [PubMed] [Google Scholar]
  • 117. Rodríguez-Martínez A, Miguel-pérez D, De, Ortega FG, et al. Exosomal miRNA profile as complementary tool in the diagnostic and prediction of treatment response in localized breast cancer under neoadjuvant chemotherapy. Breast Cancer Res. 2019;21(1):210. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 118. Lee JU, Kim WH, Lee HS, Park KH, Sim SJ. Quantitative and specific detection of exosomal miRNAs for accurate diagnosis of breast cancer using a surface-enhanced Raman scattering sensor based on plasmonic head-flocked gold nanopillars. Small. 2019;15(17):1–10. doi:10.1002/smll.201804968. [DOI] [PubMed] [Google Scholar]
  • 119. Siegel R, Ma J, Zou Z, Jemal A. Cancer statistics, 2014. CA Cancer J Clin. 2014;64(1):9–29. [DOI] [PubMed] [Google Scholar]
  • 120. Shen Q, Xu L, Zhao L, et al. Specific capture and release of circulating tumor cells using aptamer-modified nanosubstrates. Adv Mater. 2013;25(16):2368–2373. doi:10.1002/adma.201300082. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 121. Flores LM, Kindelberger DW, Ligon AH, et al. Improving the yield of circulating tumour cells facilitates molecular characterisation and recognition of discordant HER2 amplification in breast cancer. Br J Cancer. 2010;102(10):1495–1502. doi:10.1038/sj.bjc.6605676. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 122. Sandfeld-Paulsen B, Jakobsen KR, Bæk R, et al. Exosomal proteins as diagnostic biomarkers in lung cancer. J Thorac Oncol. 2016;11(10):1701–1710. [DOI] [PubMed] [Google Scholar]
  • 123. Niu L, Song X, Wang N, Xue L, Song X, Xie L. Tumor-derived exosomal proteins as diagnostic biomarkers in non-small cell lung cancer. Cancer Sci. 2019;110(1):433–442. doi:10.1111/cas.13862. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 124. Cui S, Cheng Z, Qin W, Jiang L. Lung cancer exosomes as a liquid biopsy for lung cancer. Lung Cancer. 2018;116(25):46–54. doi:10.1016/j.lungcan.2017.12.012. [DOI] [PubMed] [Google Scholar]
  • 125. Castellanos-Rizaldos E, Grimm DG, Tadigotla V, et al. Exosome-based detection of EGFR T790M in plasma from non-small cell lung cancer patients. Clin Cancer Res. 2018;24(12):2944–2950. doi:10.1158/1078-0432.CCR-17-3369. [DOI] [PubMed] [Google Scholar]
  • 126. Lin J, Li J, Huang B, et al. Exosomes: novel biomarkers for clinical diagnosis. ScientificWorldJournal. 2015;2015:657086 doi:10.1155/2015/657086. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 127. Xu S, Shi L. High expression of miR-155 and miR-21 in the recurrence or metastasis of non-small cell lung cancer. Oncol Lett. 2019;18(1):758–763. doi:10.3892/ol.2019.10337. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 128. Li C, Lv Y, Fan H, Chen C. Tumor-derived exosomal lncRNA GAS5 as a biomarker for early-stage non-small-cell lung cancer diagnosis. J Cell Physiol. 2019;234(11):20721–20727. doi:10.1002/jcp.28678. [DOI] [PubMed] [Google Scholar]
  • 129. Graves LE, Ariztia E V, Navari JR, Matzel HJ, Stack MS, Fishman DA. Proinvasive properties of ovarian cancer ascites-derived membrane vesicles. Cancer Res. 2004;64(19):7045–7049. [DOI] [PubMed] [Google Scholar]
  • 130. Yang C, Kim HS. The potential role of exosomes derived from ovarian cancer cells for diagnostic and therapeutic approaches. J Cell Physiol. 2019;234(12):21493–21503. doi:10.1002/jcp.28905. [DOI] [PubMed] [Google Scholar]
  • 131. Beach A, Zhang HG, Ratajczak MZ, Kakar SS. Exosomes: an overview of biogenesis, composition and role in ovarian cancer. J Ovarian Res. 2014;7:14. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 132. Zhang H, Xu S, Liu X. MicroRNA profiling of plasma exosomes from patients with ovarian cancer using high-throughput sequencing. Oncol Lett. 2019;17(6):5601–5607. doi:10.3892/ol.2019.10220. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 133. Press D. Advances of exosome in the development of ovarian cancer and its diagnostic and therapeutic prospect. Onco Targets Ther 2018;11:2831–2841. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 134. Li P, Yu X, Han W, et al. Ultrasensitive and reversible nanoplatform of urinary exosomes for prostate cancer diagnosis. ACS Sensors. 2019;4:1433–1441. doi:10.1021/acssensors.9b00621. [DOI] [PubMed] [Google Scholar]
  • 135. Lewis JM, Vyas AD, Qiu Y, Messer KS, White R, Heller MJ. Integrated analysis of exosomal protein biomarkers on alternating current electrokinetic chips enables rapid detection of pancreatic cancer in patient blood. ACS Nano. 2018;12(4):3311–3320. doi:10.1021/acsnano.7b08199. [DOI] [PubMed] [Google Scholar]
  • 136. Wang J, Yuan W, Gao Z. Determination of serum exosomal H19 as a noninvasive biomarker for bladder cancer diagnosis and prognosis. Med Sci Monit. 2018;24:9307–9316. doi:10.12659/MSM.912018. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 137. Runz S, Keller S, Rupp C, et al. Malignant ascites-derived exosomes of ovarian carcinoma patients contain CD24 and EpCAM. Gynecol Oncol. 2007;107(3):563–571. doi:10.1016/j.ygyno.2007.08.064. [DOI] [PubMed] [Google Scholar]
  • 138. Giannopoulou L, Zavridou M, Kasimir-bauer S, Lianidou ES. Liquid biopsy in ovarian cancer: the potential. Transl Res. 2019;205:77–91. doi:10.1016/j.trsl.2018.10.003. [DOI] [PubMed] [Google Scholar]
  • 139. Kobayashi M, Sawada K, Nakamura K, et al. Exosomal miR-1290 is a potential biomarker of high-grade serous ovarian carcinoma and can discriminate patients from those with malignancies of other histological types. J Ovarian Res 2018;11(1):81. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 140. Zhou J, Gong G, Tan H, et al. Urinary microRNA-30a-5p is a potential biomarker for ovarian serous adenocarcinoma. Oncol Rep. 2015;33(6):2915–2923. doi:10.3892/or.2015.3937. [DOI] [PubMed] [Google Scholar]
  • 141. Kim S, Choi MC, Jeong J, Hwang S, Jung SG, Joo WD. Serum exosomal miRNA-145 and miRNA-200c as promising biomarkers for preoperative diagnosis of ovarian carcinomas. J Cancer. 2019;10(9):1958–1967. doi:10.7150/jca.30231. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 142. Qiu J, Lin X, Tang X, Zheng T, Lin Y. Exosomal metastasis-associated lung adenocarcinoma transcript 1 promotes angiogenesis and predicts poor prognosis in epithelial ovarian cancer. Int J Biol Sci. 2018;14(14):1960–1973. doi:10.7150/ijbs.28048. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 143. Loeb S, Bjurlin MA, Nicholson J, et al. Overdiagnosis and overtreatment of prostate cancer. Eur Urol. 2014;65(6):1046–1055. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 144. Lee DJ, Mallin K, Graves AJ, et al. Recent changes in prostate cancer screening practices and epidemiology. J Urol. 2017;198(6):1230–1240. [DOI] [PubMed] [Google Scholar]
  • 145. Bhagirath D, Yang TL, Bucay N, Sekhon K, Majid S. microRNA-1246 is an exosomal biomarker for aggressive prostate cancer. Cancer Res. 2018;78(7):1833–1844. doi:10.1158/0008-5472.CAN-17-2069. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 146. McKiernan J, Donovan MJ, O’Neill V, et al. A novel urine exosome gene expression assay to predict high-grade prostate cancer at initial biopsy. JAMA Oncol. 2016;2(7):882–889. [DOI] [PubMed] [Google Scholar]
  • 147. Bryant RJ, Pawlowski T, Catto JWF, et al. Changes in circulating microRNA levels associated with prostate cancer. Br J Cancer. 2012;106(4):768–774. doi:10.1038/bjc.2011.595. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 148. Physiology C. Tumor-derived exosomal long noncoding RNAs as promising diagnostic biomarkers for prostate cancer. Cell Physiol Biochem. 2018;430071(169):532–545. doi:10.1159/000488620. [DOI] [PubMed] [Google Scholar]
  • 149. Allenson K, Castillo J, San Lucas FA, et al. High prevalence of mutant KRAS in circulating exosome-derived DNA from early-stage pancreatic cancer patients. Ann Oncol. 2017;28(4):741–747. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 150. Nuzhat Z, Kinhal V, Sharma S, Rice GE, Joshi V, Salomon C. Tumour-derived exosomes as a signature of pancreatic cancer-liquid biopsies as indicators of tumour progression. Oncotarget. 2017;8(10):17279–17291. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 151. Ballehaninna UK, Chamberlain RS. The clinical utility of serum CA 19-9 in the diagnosis, prognosis and management of pancreatic adenocarcinoma: an evidence based appraisal. J Gastrointest Oncol. 2012;3(2):105–119. doi:10.3978/j.issn.2078-6891.2011.021. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 152. Zhou X, Lu Z, Wang T, Huang Z, Zhu W, Miao Y. Plasma miRNAs in diagnosis and prognosis of pancreatic cancer: a miRNA expression analysis. Gene. 2018;673:181–193. doi:10.1016/j.gene.2018.06.037. [DOI] [PubMed] [Google Scholar]
  • 153. Goto T, Fujiya M, Konishi H, et al. An elevated expression of serum exosomal neoplasm is considered to be efficient diagnostic marker. BMC Cancer. 2018;18:116 doi:10.1186/s12885-018-4006-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 154. Li TD, Zhang R, Chen H, et al. An ultrasensitive polydopamine bi-functionalized SERS immunoassay for exosome-based diagnosis and classification of pancreatic cancer. Chem Sci. 2018;9(24):5372–5382. doi:10.1039/c8sc01611a. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 155. Jin H, Liu P, Wu Y, et al. Exosomal zinc transporter ZIP4 promotes cancer growth and is a novel diagnostic biomarker for pancreatic cancer. Cancer Sci. 2018;109(9):2946–2956. doi:10.1111/cas.13737. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 156. Lux A, Kahlert C, Grützmann R, Pilarsky C. c-Met and PD-L1 on circulating exosomes as diagnostic and prognostic markers for pancreatic cancer. Int J Mol Sci 2019;20(13). doi:10.3390/ijms20133305. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 157. Jimenez-luna C, Torres C, Ortiz R, et al. Proteomic biomarkers in body fluids associated with pancreatic cancer. Oncotarget 2018;9(23):16573–16587. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 158. Andreu Z, Otta Oshiro R, Redruello A, et al. Extracellular vesicles as a source for non-invasive biomarkers in bladder cancer progression. Eur J Pharm Sci. 2017;98:70–79. [DOI] [PubMed] [Google Scholar]
  • 159. Welton JL, Khanna S, Giles PJ, et al. Proteomics analysis of bladder cancer exosomes. Mol Cell Proteomics. 2010;9(6):1324–1338. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 160. Maji S, Matsuda A, Yan IK, Parasramka M, Patel T. Extracellular vesicles in liver diseases. Am J Physiol Gastrointest Liver Physiol. 2017;312(3):G194–G200. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 161. Witjes JA, Lebret T, Compérat EM, et al. Updated 2016 EAU guidelines on muscle-invasive and metastatic bladder cancer. Eur Urol. 2017;71:462–475. doi:10.1016/j.eururo.2016.06.020. [DOI] [PubMed] [Google Scholar]
  • 162. Blobel CC, Jung K. Circulating miRNAs in blood and urine as diagnostic and prognostic biomarkers for bladder cancer: an update in 2017. Biomark Med. 2018;12(6):667–676. [DOI] [PubMed] [Google Scholar]
  • 163. Zhan Y, Du L, Wang L, et al. Expression signatures of exosomal long non-coding RNAs in urine serve as novel non-invasive biomarkers for diagnosis and recurrence prediction of bladder cancer. Mol Cancer. 2018;17(1):142. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 164. Zhang S, Du L, Wang L, et al. Evaluation of serum exosomal LncRNA-based biomarker panel for diagnosis and recurrence prediction of bladder cancer. J Cell Mol Med. 2019;23(2):1396–1405. doi:10.1111/jcmm.14042. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 165. Sun W, Fang M, Chen Y, et al. Delivery system of CpG oligodeoxynucleotides through eliciting an effective T cell immune response against melanoma in mice. J Cancer. 2016;7(3):241–250. doi:10.7150/jca.12899. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 166. 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(7718):382–386. doi:10.1038/s41586-018-0392-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 167. Touat M, Duran-Peña A, Alentorn A, Lacroix L, Massard C, Idbaih A. Emerging circulating biomarkers in glioblastoma: promises and challenges. Expert Rev Mol Diagn. 2015;15(10):1311–1323. [DOI] [PubMed] [Google Scholar]
  • 168. Kang C, Sun Y, Zhu J, et al. Delivery of nanoparticles for treatment of brain tumor. Curr Drug Metab. 2016;17:745–754. doi:10.2174/1389200217666160728152939. [DOI] [PubMed] [Google Scholar]
  • 169. Lan F, Qing Q, Pan Q, Hu M, Yu H, Yue X. Serum exosomal miR-301a as a potential diagnostic and prognostic biomarker for human glioma. Cell Oncol (Dordr). 2018;41(1):25–33. doi:10.1007/s13402-017-0355-3. [DOI] [PubMed] [Google Scholar]
  • 170. Tan SK, Pastori C, Penas C, et al. Serum long noncoding RNA HOTAIR as a novel diagnostic and prognostic biomarker in glioblastoma multiforme. Mol Cancer 2018;17(1):74. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 171. Ebrahimkhani S, Vafaee F, Hallal S, et al. Deep sequencing of circulating exosomal microRNA allows non-invasive glioblastoma diagnosis. NPJ Precis Oncol. 2018;2:28 doi:10.1038/s41698-018-0071-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 172. Santangelo A, Imbrucè P, Gardenghi B, et al. A microRNA signature from serum exosomes of patients with glioma as complementary diagnostic biomarker. J Neurooncol. 2018;136(1):51–62. doi:10.1007/s11060-017-2639-x. [DOI] [PubMed] [Google Scholar]

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