Extracellular vesicles in colorectal cancer

Young Il Kim; Chungyeop Lee; Hakho Lee; In Ja Park

doi:10.3393/ac.2025.00745.0106

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Review Translation/basic research Extracellular vesicles in colorectal cancer: Young Il Kim^1,2,3, Chungyeop Lee¹, Hakho Lee^2,3, In Ja Park¹; Annals of Coloproctology 2025;41(5):379-392.
DOI: https://doi.org/10.3393/ac.2025.00745.0106
Published online: October 16, 2025

¹Division of Colon and Rectal Surgery, Department of Surgery, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea

²Center for Systems Biology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA

³Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA

Correspondence to: In Ja Park, MD, PhD Division of Colon and Rectal Surgery, Department of Surgery, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea Email: ipark@amc.seoul.kr
Co-correspondence to: Hakho Lee, PhD Center for Systems Biology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA Email: HLEE@mgh.harvard.edu

• Received: June 9, 2025 • Accepted: July 5, 2025

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Abstract
INTRODUCTION
ROLE OF EVs IN CRC
EV PURIFICATION FROM PLASMA
DIAGNOSTIC BIOMARKERS FOR CRC IN EVs
THERAPEUTIC POTENTIAL OF EVs IN CRC
CONCLUSION
ARTICLE INFORMATION
REFERENCES

Abstract

Colorectal cancer (CRC) remains a major global health issue, with challenges including early detection and recurrence monitoring. While colonoscopy and fecal-based tests are standard screening tools, their limitations have driven interest in less invasive alternatives. Extracellular vesicles (EVs) present in patient liquid biopsy samples have emerged as potential biomarkers and therapeutic tools in CRC. EVs carry molecular cargo, including nucleic acids and proteins, that reflect the status of their cells of origin and can be readily accessed through minimally invasive liquid biopsy. This review outlines the role of EVs in the initiation and progression of CRC, summarizes recent advances in EV isolation techniques, and highlights candidate EV-derived biomarkers for diagnosis, prognosis, and treatment monitoring. By providing an updated synthesis of current research, this review aims to inform future studies and support clinical translation of EV-based approaches in CRC.
Keywords: Extracellular vesicles; Colorectal neoplasms; Liquid biopsy

INTRODUCTION

Colorectal cancer (CRC) is the third most common cancer worldwide, with 2 million newly diagnosed patients every year [1, 2]. Although modern medicine has continuously advanced CRC management, it is still the second leading cause of cancer-related deaths, mostly due to the inherent risk of latent metastasis and recurrence [3]. Consistent with observations in other tumor types, early-stage diagnosis and intervention in CRC are strongly correlated with improved patient survival outcomes [4]. In the United States, the 5-year relative survival rates for CRC differed significantly by stage, with 91% in localized CRC, 72% in regional CRC, and 14% in CRC with distant metastases [1]. The global trend is similar, with localized and regional CRC universally showing a better prognosis (5-year survival rate of 60%–90%) than CRC presented as a distant disease (5-year survival rate of <20%) [4]. This prognostic divergence highlights the importance of early detection. Notably, CRC has an established screening protocol followed by a colonoscopy when necessary. This process facilitates the removal of precancerous polyps or the identification of CRC at an earlier pathological stage. Populations undergoing screening colonoscopy exhibit both a reduced incidence of CRC and a higher proportion of early-stage diagnoses [5, 6].

While colonoscopy is the gold standard for CRC detection and has demonstrated clinical utility, it presents several limitations. As an invasive procedure, colonoscopy is frequently associated with patient distress before, during, and even after the procedure. The prerequisite bowel preparation involves the ingestion of substantial volumes (>2 L) of liquid, which can lead to severe diarrhea and an elevated risk of acute kidney injury. Moreover, in individuals with undiagnosed intestinal obstruction, the preparation process may result in vomiting or other acute complications. Intraprocedural interventions, such as polypectomy, carry the risk of adverse events, including bowel perforation and hemorrhage, which may manifest either acutely during the procedure or belatedly with clinical deterioration. These factors contribute to the suboptimal compliance rates (50%–87%) of individuals who were recommended for colonoscopy following positive fecal-based tests [7]. Other imaging modalities, including computed tomography (CT) and magnetic resonance imaging (MRI), exhibit lower diagnostic accuracy compared to colonoscopy and are associated with higher costs, rendering them less suitable for widespread screening applications.

This unmet clinical need has motivated the development of less invasive methodologies for CRC detection with acceptable diagnostic performance. Among them, liquid biopsies have been extensively investigated, which analyze readily accessible body fluids such as blood, urine, and feces. These biofluids contain a variety of biomarkers, such as nucleic acids, tumor cells, proteins, and metabolites, whose analysis can provide insights into pathological conditions [8]. Of note, circulating tumor DNA (ctDNA), considered to be released from dying tumor cells through apoptosis or necrosis, has demonstrated utility in the earlier detection of CRC relapse, with a lead time of up to 10 months compared to conventional CT imaging and carcinoembryonic antigen (CEA) assays. The ctDNA analysis can detect patient-specific somatic structural variants in blood following primary tumor resection [9]. In addition, fecal immunochemical tests (FIT) are commercially available as less invasive alternatives to colonoscopy, exhibiting sensitivities ranging from 65% to 86%. However, the identification of early-stage malignancy, whether through phenotypic or genetic markers, remains challenging, largely due to the limited tumor burden and consequent low concentrations of detectable biomarkers. For instance, the sensitivity of FIT for diagnosing stage I CRC is as low as 65% [10].

Over the past decade, extracellular vesicles (EVs) have emerged as promising biomarkers for cancer management. Initially considered cellular dust [11], EVs are now recognized to perform diverse functions in human physiology, acting as vehicles for transporting nucleic acids, proteins, and metabolites. The lipid bilayer membrane of EVs encapsulates these cargos, enabling the transport of sequestered tumor-derived markers into the circulatory system. These unique characteristics of EVs present encouraging prospects for cancer diagnosis and treatment; however, technical challenges persist in their detection, ranging from isolation methodologies to the standardization of processes for clinical translation.

This review aims to provide an overview of the function of EVs in CRC and further summarize the up-to-date advances and challenges in the diagnostic and therapeutic application of EVs in CRC. In particular, we highlight the molecular contents of EVs that have demonstrated potential as noninvasive biomarkers for early detection, prognosis, and monitoring of treatment response in CRC. By clarifying the current status of EV research in CRC, we hope to support the development of clinically applicable technologies and guide future investigative directions.

ROLE OF EVs IN CRC

EV definition and formation

EVs are defined as particles that are released from cells, delimited by a lipid bilayer, and cannot replicate on their own [12]. The nomenclature has been a topic of discussion since the late 1900s, with many terms used, including “microvesicles,” “microparticles,” virus-like particles,” “exosomes,” “ectosomes,” and others [13]. The growing consensus is to use a generic term “extracellular vesicle” that includes various subtypes of membrane structures secreted from the cell. Therefore, the more specific biogenesis-related terms, such as exosomes and ectosomes, are not synonymous with EVs, and the International Society for Extracellular Vesicles (ISEV) recommends that the terms not be used interchangeably unless there is strong evidence of the subcellular origin. Exosomes, microvesicles (ectosomes), and apoptotic bodies are 3 types of EVs with distinguishable biogenesis.

Exosomes are smaller EVs (30–100 nm) originating from the endosomal system, formed through the multivesicular body (MVB) pathway. The plasma membrane invaginates, forming early endosomes, which develop into late endosomes (or MVBs) containing intraluminal vesicles (ILV). Specific biomolecules are sorted into ILVs. When MVBs fuse with the plasma membrane, they eventually release ILVs as exosomes into the extracellular space [14, 15]. Microvesicles are large EVs (100–1,000 nm) formed through direct outward budding of the plasma membrane, while apoptotic bodies are the larger EVs (>1,000 nm) formed during apoptotic cell disassembly [16]. Understanding the biogenesis of EVs is important to studying their role in cancer biology.

Role of EVs in cancer

EVs are involved in intercellular communication. The conventional mechanism of cell-to-cell communication occurs through soluble factors such as secreted ligands, which cause a signaling cascade in target cells [17]. However, EVs act differently, harboring cargo from the originating cell and conveying it to distant sites for communication. In the context of cancer, this process can modulate the tumor microenvironment, enhance tumor growth, and assist with metastases. EV proteins and microRNAs (miRNAs) have been shown to transform wild-type cells [18, 19], and tumor-derived EVs can modify signaling pathways to promote tumorigenic pathways [20, 21]. Cancer invasion is known to start from signaling pathways that involve cytoskeletal dynamics in tumor cells, changes in cell-matrix, cell-to-cell junctions, and migration, in which EVs can influence by altering various tumor microenvironment components such as endothelial cells, infiltrating immune cells, and cancer-associated fibroblasts [22–24].

Role of EVs in CRC

EVs’ oncogenic functions have been observed in CRC as well (Fig. 1). Analysis from CRC tumor tissues showed that EVs can transfer miRNA-25-3p from cancer cells to endothelial cells. This miRNA modulates endothelial cells to promote vascular permeability and angiogenesis, leading to distant metastases (lung, liver) in mice [25]. Yamada et al. [26] reported a similar role of EVs: miRNA-1246 transferred to endothelial cells via EVs promoted angiogenesis. Several studies further reported macrophage polarization induced by CRC-derived EVs, which indicates EV-mediated immune modulation as another means of CRC progression [27–29]. Overall, EVs have been shown to play various functions in promoting CRC, including chemoresistance, enhanced stemness, modulation of cancer-associated fibroblasts, and activation of oncogenic pathways (Table 1) [25–41].

EV PURIFICATION FROM PLASMA

Challenges in EV isolation from plasma

Despite rapid technological advances in liquid biopsy, the routine clinical adoption of these methods has remained slow due to persistent challenges in test reproducibility, standardization, and cost-effectiveness. More noticeable in utilizing EVs is the technical issue of purification and standardization. EVs in circulation originate from many different cell types and vary widely in size and molecular composition, making it difficult to distinguish and enrich cancer-specific vesicles against a diverse background of normal vesicles. Furthermore, lipoprotein particles (LPPs), which share similar physical properties as EVs, are often co-isolated and confound the results in downstream analyses [42, 43]. Such characteristics of EVs emphasize the importance of purification methods to ensure that analyses focus on the anticipated EV content and yield reproducible results. Currently, a lack of consensus on isolation methods leads to variability between studies, making it difficult to validate EV-based biomarkers consistently.

Current techniques of EV purification

The ISEV released a set of recommendations, the “Minimum Information for Studies of Extracellular Vesicles (MISEV2023),” which contains guidelines on a rigorous and standardized framework for EV research, ranging from how the studies should be designed to execution and reporting [12]. Separation of EVs can be performed leveraging biophysical properties of EVs such as size, density, and surface composition. Methods of isolation differ in how much EVs one can obtain (yield) and specificity, according to which known characteristic of EV is targeted. Conventional techniques of EV purification include differential ultracentrifugation, density gradient/cushion, size exclusion chromatography, and other methods, each with advantages and limitations.

Differential ultracentrifugation (dUC) can enrich EV subtypes according to diameter and density. However, it also co-isolates nonvesicular extracellular particles (NVEPs) such as LPPs that have a similar sedimentation coefficient as EVs. The dUC processing is also known to cause aggregation of EVs, which may compromise their natural structure. This method may have a low yield of smaller EVs (e.g., exosomes), especially in protein-rich fluids, including blood products, which is reported to be difficult to improve [44]. Density gradients can be incorporated with dUC, the density gradient ultracentrifugation (DGU), to further separate NVEPs and proteins from EVs or distinguish small EVs from large EVs [45]. However, as fraction washing involves cleaning the isolated fractions to remove contaminants, only a certain proportion of the target EVs may be recovered; a common example of achieving purity at the expense of yield [12]. Furthermore, DGU is a time-consuming process requiring specialized equipment.

Size exclusion chromatography (SEC) is a relatively easier method of EV isolation. The process is fast (normally less than 30 minutes) and does not require complex equipment. A sample containing EVs is placed onto a column device equipped with a porous matrix that traps smaller particles and passes larger particles. Columns may be loaded with more than one matrix, designed for target EVs, and modification of the matrix can enable incorporation of other techniques such as affinity-based separation [46]. However, there are limitations, such as the difficulty in isolating EVs from other nanoparticles of similar size. Also, although the SEC technique is fast, it is unfit to process large volumes of liquid samples requiring multiple cycles for such circumstances. Similar to other conventional methods, SEC often results in diluted EV fractions.

Emerging techniques of EV purification

As conventional isolation methods (e.g., DUG, SEC, ultrafiltration) are suboptimal for pure EVs isolation from plasma, active research is underway to develop alternative technologies. Woo et al. [42] designed 2 novel approaches, one of which incorporates SEC to primarily remove particles smaller than EVs and low-density lipoproteins (LDLs), followed by a charge-based separation to remove LDLs. This dual-mode chromatography device was used to compare plasma samples from healthy donors and CRC patients, presenting higher CD63 and epithelial cell adhesion molecule (EpCAM) expressions in cancer patient plasma (Fig. 2A) [42]. Previously, Woo et al. [47] also demonstrated a centrifugal force-based filtration system (Exodisc), integrating nanoporous membranes. Using this device, EVs could be isolated in a small volume of samples (200 µL to 1 mL) in less than 30 minutes (Fig. 2B) [47]. Despite its promising potential, the device still faces limitations in high-throughput analysis, making large-scale implementation challenging.

In another study, immunomagnetic beads were used to enrich EVs directly from plasma. These bead-bound EVs were subsequently labeled with probing antibodies for signal generation, enabling simultaneous analysis of plasma samples. Using this approach, a panel of epidermal growth factor receptor (EGFR), EpCAM, CD24, and glycoprotein A33 (GPA33) was able to accurately classify CRC patients from non-CRC control individuals and predict 5-year disease-free survival [48]. Recently, Koo et al. [49] also developed a one-step EV extraction system by combining electrostatic and covalent reactions. Using this system, miRNAs could be analyzed from extracted EVs of CRC patients and healthy controls, showing significant differences in miRNA expressions (Fig. 2C) [49].

DIAGNOSTIC BIOMARKERS FOR CRC IN EVs

Several studies have documented appreciable differences in EV cargo between CRC patients and healthy individuals, including distinct profiles of noncoding RNAs (ncRNAs) such as microRNAs (miRNAs) and circular RNAs (circRNAs), messenger RNAs (mRNAs), and proteins. This field is rapidly evolving, with numerous candidate EV biomarkers demonstrating promise in clinical samples, some exhibiting high diagnostic accuracy with areas under the receiver operating characteristic curve (AUC) values approaching up to 0.9 [50]. While these EV-based diagnostics are still undergoing development and require validation in larger patient cohorts, their emergence represents progress toward improving noninvasive CRC diagnosis, prognostication, and prediction of treatment response. A summary of published studies reporting biomarkers from EVs in CRC patients is presented in Table 2 [51–93].

ncRNAs in EVs

ncRNAs, particularly miRNAs and circRNAs, have attracted attention due to their implications in oncogenesis. Ogata-Kawata et al. [51] identified a panel of 7 EV miRNAs (let-7a, miR-1229, miR-1246, miR-150, miR-21, miR-223, and miR-23a) that were elevated in early to late CRC patients compared to healthy controls. The levels of these miRNAs dropped substantially after tumor resection. Another study reported a distinct 4 EV-miRNA signature (miR-19a-3p, miR-203-3p, miR-221-3p, and let-7f-5p) capable of distinguishing CRC patients from healthy individuals with AUC values ranging from 0.71 to 0.88 [30]. Interestingly, while many oncogenic miRNAs are enriched in CRC-derived EVs, certain tumor-suppressive miRNAs were observed at lower levels. For instance, EV miR-377-3p and miR-381-3p were found to be downregulated in CRC compared to healthy controls [52]. Furthermore, miRNAs within EVs are shown to be prognostic. Yan et al. [53] reported that elevated miR-6803-5p predicted poor overall and disease-free survival in a cohort of 168 CRC patients. Conversely, another study showed a decreased EV miR-150-5p expression associated with poor differentiation, positive lymph node metastasis, and advanced TNM stage. Patients with high miR-150-5p expression showed significantly better overall survival (odds ratio, 4.36; 95% confidence interval, 2.21–6.68; P=0.02). This impact on CRC survival was comparable to other well-established prognostic factors, including lymph node metastases and TNM stage [54].

EVs’ predictive utility may extend to identifying patients with favorable treatment responses in rectal cancer. Current guidelines, informed by recent advances in neoadjuvant therapy for rectal cancer, recommend a nonsurgical approach for select patients exhibiting excellent responses to preoperative treatment, including chemotherapy with or without radiation [94]. Among patients receiving preoperative chemo(radio)therapy for rectal cancer, the proportion of patients achieving a clinically complete response has been reported to range from 10% to over 50% [95, 96]. However, it is difficult to predict clinical and pathological complete response prior to rectal resection. Current clinical practice relies on clinical examination (digital rectal exam), endoscopy, and imaging modalities, typically MRI, which exhibit limitations in accuracy due to their inherent subjectivity in interpretation. Intriguingly, detecting EV miRNAs has demonstrated potential in characterizing CRC patients with chemoresistance [55].

Beyond miRNAs, circRNAs, and long ncRNAs (lncRNAs) within EVs have also demonstrated diagnostic potential [97]. Several lncRNAs, including CCAT2, CRNDE-h, RPPH1, and GAS5, have been reported to be significantly different in CRC patients compared to healthy individuals [56, 98–100]. CircRNAs can interact with miRNAs, functioning as sponges or decoys that attenuate miRNA-mediated gene expression regulation. For instance, circLONP2 and circPNN have been found to be upregulated in CRC patients with poor prognoses [57, 101], whereas circLPAR1 was markedly downregulated [58]. In a recent study, EV circLPAR1 displayed high specificity for CRC and significantly enhanced diagnostic performance. When used in conjunction with traditional serum markers (CEA and carbohydrate antigen 19-9 [CA19-9]), the circLPAR1 assay achieved an AUC of 0.875 for CRC detection [58].

Protein-coding RNAs in EVs

EV mRNAs can also serve as informative biomarkers for CRC. Cha et al. [59] employed a two-phase approach, assessing cell lines and patient samples to identify EV mRNAs associated with CRC. Eight candidate mRNAs (MYC, VEGF, CDX2, CD133, CEA, CK19, EpCAM, and CD24) enriched in cancer-derived EVs were validated in plasma EVs from CRC patients. Combining the levels of 2 mRNAs, VEGF and CD133, achieved the best diagnostic performance (100% sensitivity, 80% specificity, and 93% accuracy; AUC, 0.96). Min et al. [102] profiled circulating EV RNAs in CRC patients and demonstrated that early-stage CRC lesions can be detected via EV mRNA signatures combined with miRNAs. Whole-transcriptome sequencing of plasma EVs revealed tens of thousands of RNAs, with over 1,600 exhibiting significant alterations even at the T1a stage of CRC compared to a healthy population. Similarly, another study employed bioinformatic selection on publicly available EV RNA datasets (exoRBase 2.0 and Gene Expression Omnibus databases) and developed a logistic regression model based on EV mRNAs (H3F3A, MYL6, FBXO7, TUBA1C, MEF2C, and BANK1). This EV mRNA signature showed good diagnostic accuracy, achieving an AUC of 0.88 [60].

Beyond diagnosis, EV-derived mRNAs also possess prognostic significance. Human telomerase reverse transcriptase (hTERT) mRNA was found to be elevated in serum EV of CRC patients, with significantly higher EV hTERT levels observed in those with metastatic disease. Nearly 30% of CRC patients showed detectable EV hTERT mRNA (vs. only 4% of healthy controls), and patients with metastases had higher EV hTERT levels [61].

Proteins in EVs

In addition to nucleic acids, classical tumor antigens carried by EVs have demonstrated enhanced diagnostic value. CEA has been detected in serum-derived EVs of CRC patients, and intriguingly, the CEA level in EVs yielded a higher diagnostic AUC than total serum CEA (0.93 vs. 0.86), suggesting that EV packaging of CEA enhances specificity [62]. Other protein markers associated with CRC-derived EVs include S100A9, an inflammatory mediator enriched in myeloid cell-derived EVs within the CRC microenvironment, with elevated plasma exosomal S100A9 levels correlating with the presence and recurrence of CRC [63]. Furthermore, glypican-1-positive EVs were found to be increased in CRC patients compared to healthy controls [64].

High-throughput proteomic studies have also identified novel candidates, such as EV-associated SERPINA1 and plasminogen, which were significantly upregulated in the plasma of CRC patients, even at early stages of the disease, compared to healthy individuals [103]. Additionally, Park et al. [48] selected a panel of markers (EpCAM, EGFR, CD24, GPA33) as EV_CRC and compared plasma from CRC patients to non-CRC controls. This EV_CRC panel achieved an AUC of 0.98 in the training cohort and an accuracy of 96% in the testing cohort. Moreover, EV_CRC levels were higher in patients with recurrence, indicating a potential association with poor prognosis.

THERAPEUTIC POTENTIAL OF EVs IN CRC

EVs have inherent advantages for potential therapeutic uses. They can encapsulate and deliver bioactive cargo and exhibit favorable pharmacokinetic properties, including biocompatibility, evasion of phagocytic clearance, stability in circulation, and protection of therapeutic molecules from degradation, thereby prolonging drug half-life and enhancing delivery efficiency [104–106]. For instance, Liang et al. [107] demonstrated that engineered EVs carrying 5-fluorouracil (5-FU) and a miR-21 inhibitor effectively reversed drug resistance and enhanced cytotoxicity in the 5-FU-resistant HCT116 cell line. In a related approach, mesenchymal stem cell-derived EVs loaded with tumor-suppressive miRNAs (miR-3940-5p and miR-34a-5p) exhibited the capacity to reduce CRC cell migration and invasion in vitro and to suppress tumor growth and metastasis in animal models [108, 109]. Similarly, milk-derived EVs, displaying an EGFR-targeting peptide (GE11) and loaded with oxaliplatin, achieved superior antitumor effects in EGFR-expressing CRC murine models compared to nontargeted therapy [110]. These encouraging findings now warrant sustained investigations with larger cohorts and enhanced reproducibility, which will pave the way for future human clinical trials (Fig. 3) [107, 109–116].

CONCLUSION

Despite their demonstrated potential, translating EV-based CRC tests into clinical practice faces significant challenges. A primary hurdle is the technical gap between research findings and clinical applications. The intrinsic heterogeneity of EV populations, combined with their presence in complex biological matrices, frequently compromises the consistency and interpretability of EV-based diagnostics. The lack of a universally accepted assay protocol further hinders the direct comparison of results across different laboratories. Establishing standardized preanalytical handling, isolation procedures, and detection platforms is crucial for enhancing the reliability of EV tests and fulfilling the necessary regulatory criteria for clinical implementation. Successful standardization efforts are also anticipated to reduce both the time and cost associated with EV analysis, thereby increasing its practicality for widespread clinical adoption.

Establishing robust biomarkers is another critical task. While numerous EV-based biomarkers for CRC have been reported, most demonstrate a correlative, rather than causal, association with CRC. Such correlative markers are inherently vulnerable to confounding variables and population-specific factors, which can limit their generalizability and reliability. Prospective and large-scale validation studies are required to verify key criteria, including the following: (1) a direct mechanistic association between the EV molecular signature and CRC pathogenesis; (2) the ability to discern CRC from benign conditions and other diseases; (3) a proportional relationship with tumor burden; and (4) a measurable reduction following effective therapeutic intervention. To date, few EV biomarkers have progressed beyond preliminary discovery phases to comprehensive validation studies that meet these stringent criteria.

EV research has advanced from exploratory studies toward practical implementation, with demonstrated potential in early-stage detection, prognostic assessment, and post-therapeutic monitoring in CRC. Focused efforts to overcome existing technical challenges and validation gaps through collaborative research are now warranted to strengthen the evidence base for EV-based diagnostics and therapeutics in CRC management.

ARTICLE INFORMATION

Conflict of interest

γIn Ja Park is the editor-in-chief of this journal and Young Il Kim is an editorial board member of this journal, but they were not involved in the peer reviewer selection, evaluation, or decision process of this article. No other potential conflict of interest relevant to this article was reported.

Funding

None.

Author contributions

Conceptualization: YIK, HL; Data curation: CL; Formal analysis: YIK; Investigation: YIK, CL; Methodology: IJP, HL; Resources: IJP, HL, YIK; Software: CL; Supervision: HL; Visualization: YIK, HL; Writing–original draft: YIK; Writing–review & editing: HL, IJP, CL. All authors read and approved the final manuscript.

Fig. 1.

Extracellular vesicles (EVs) and colorectal cancer progression.

Fig. 2.

Novel techniques for extracellular vesicle (EV) purification. (A) Enhanced dual-mode chromatography (eDMC) device for isolating EVs using size exclusion chromatography followed by ion exchange. Reprinted from Woo et al. [42], available under the Creative Commons license. (B) Disc-based microfluidic device for EV isolation using centrifugation and nanoporous membranes, exodisc. Reprinted from Woo et al. [47], with permission from the American Chemical Society. (C) Electrostatic and covalent reactions are used to isolate EVs from patient plasma. Modified from Koo et al. [49], available under the Creative Commons license. HDL, high-density lipoprotein; (V)LDL, (very) low-density lipoprotein; dia., diameter.

Fig. 3.

Modulating extracellular vesicles (EVs) for colorectal cancer treatment. 5-FU, 5-fluorouracil.

Table 1.

Published literature on EV roles in CRC progression

Study	Term	Regulation	Function
Yamada et al. [26] (2014)	Microvesicle	↑ miR-1246/TGF-β, ↓ PML	Angiogenesis (activated Smad signaling in endothelial cells)
Shao et al. [28] (2018)	Small EV	↑ miR-21-5p	Macrophage polarization, liver metastases
Zeng et al. [25] (2018)	Exosome	↑ miR-25-3p, ↓ KLF2/KLF4	Angiogenesis and vascular permeability
Ren et al. [31] (2018)	Exosome	↑ lncRNA H19	Enhanced CRC stemness and chemoresistance
Endzeliņš et al. [32] (2018)	EV	↑ HIF-induced EV release	Increased invasiveness, motility, and cancer stemness under hypoxia
Popēna et al. [27] (2018)	EV	↑ CD14, ↓ HLA-DR	Macrophage polarization
Liu et al. [33] (2020)	Exosome	↑ miR-106b-3p, ↓ DLC1	EMT induction (lung metastasis)
Xu et al. [34] (2020)	Exosome	↑ MALAT1, ↓ miR-26a/26b	Enhanced invasion and metastasis (PI3K/Akt activation via FUT4)
Jiang et al. [35] (2021)	Exosome	↑ ANGPTL1	Suppress angiogenesis, inhibit liver metastasis
Shang et al. [36] (2020)	Exosome	↑ circPACRGL	Cell proliferation, migration, invasion
Zhou et al. [37] (2021)	Exosome	↑ LINC00659, ↓ miR-342-3p	Promote proliferation, invasion, EMT via ANXA2
Lai et al. [38] (2021)	Exosome	↑ PVT1, VEGFA, EGFR, ↓ miR-152-3p	Metastasis, cancer stemness (EGFR/VEGFA pathway)
Yang et al. [29] (2021)	Exosome	↑ miR-106b-5p, ↓ PDCD4	EMT induction, macrophage polarization (M2-TAM), promoting metastasis
Dokhanchi et al. [30] (2021)	EV	↑ miR-221-3p	Endothelial cell angiogenesis
Fang et al. [39] (2022)	Exosome	↑ PCAT1	Promoted EMT, enhanced liver metastasis
Zhang et al. [40] (2022)	Exosome	↑ HSPC111, ACLY phosphorylation, ↑ CXCL5/CXCR2	Liver metastasis by reprogramming lipid metabolism in CAFs
Huang et al. [41] (2024)	Exosome	↑ SNHG3	EMT, metastasis via Wnt/β-catenin activation

EV, extracellular vesicle; CRC, colorectal cancer; miR, microRNA; TGF-β, transforming growth factor β; PML, promyelocytic leukemia protein; KLF2, Krüppel-like factor 2; KLF4, Krüppel-like factor 4; lncRNA, long noncoding RNA; HIF, hypoxia-induced factor; DLC1, deleted in liver cancer 1; EMT, epithelial-mesenchymal transition; MALAT1, metastasis-associated lung adenocarcinoma transcript 1; FUT4, fucosyltransferase 4; ANGPTL1, angiopoietin-like protein 1; PVT1, plasmacytoma variant translocation 1; VEGFA, vascular endothelial growth factor A; EGFR, epidermal growth factor receptor; PDCD4, programmed cell death 4; TAM, tumor-associated macrophage; PCAT1, prostate cancer–associated transcript 1; ACLY, ATP-citrate lyase; CAF, cancer-associated fibroblast.

Table 2.

Extracellular vesicle–derived biomarkers from liquid biopsy in colorectal cancer

Biomarker	Sample source	Clinical role	Expression	Reference
ncRNA
miR-193a-5p	Plasma	Diagnostic	Downregulated	[65]
miR-21, miR-92a, miR-222	Serum	Diagnostic and prognostic	Upregulated	[66, 67]
miR-320c	Plasma	Diagnostic and prognostic	Upregulated	[68]
miR-377-3p, miR-381-3p	Serum	Diagnostic	Downregulated	[52]
miR-181a-5p	Serum	Prognostic	Upregulated	[69]
miR-181b, miR-193b, miR-195, miR-411	Serum	Prognostic	Unavailable	[70]
miR-361-3p	Plasma	Prognostic	Upregulated	[71]
let-7a, miR-1246, miR-1229, miR-223, miR-23a	Serum	Diagnostic	Upregulated	[51, 72, 73]
miR-27a, miR-130a	Plasma	Diagnostic and prognostic	Upregulated	[74]
miR-99b-5p, miR-150-5p	Serum	Diagnostic	Downregulated	[54, 75]
miR-208b	Serum	Prediction (chemoresistance)	Upregulated	[55]
miR-301a	Serum	Diagnostic	Upregulated	[73]
miR-125a-3p	Plasma	Diagnostic	Upregulated	[76]
miR-122	Serum	Diagnostic and prognostic	Upregulated	[77]
miR-548c-5p	Serum	Prognostic	Decreased	[78]
miR-6803-5p	Serum	Diagnostic and prognostic	Elevated	[53]
miR-221	Plasma	Prognostic	Increased	[67, 79]
lncRNA (LNCV6_116109, LNCV6_98390, LNCV6_108266, LNCV6_38772, LNCV6_84003, LNCV6_98602)	Plasma	Diagnostic	Upregulated	[80]
lncRNA CRNDE-h	Serum	Diagnostic and prognostic	Upregulated	[56]
lncRNA LINC02418	Serum	Diagnostic	Upregulated	[81]
lncRNA GAS5	Plasma	Prognostic	Downregulated	[82]
lncRNA 91H	Plasma	Prognostic	Upregulated	[83]
lncRNA HOTTIP	Serum	Prognostic	Downregulated	[84]
circLPAR1	Plasma	Diagnostic and prognostic	Downregulated	[58]
circ-PNN	Serum	Diagnostic	Upregulated	[57]
hsa-circ-0004771	Serum	Diagnostic	Upregulated	[85]
Protein-coding mRNA
VEGF, CD133, MYC, CDX2, CEA, CK19, EpCAM, CD24	Serum	Diagnostic	Upregulated	[59]
H3F3A, MYL6, FBXO7, TUBA1C, MEF2C, BANK1	Serum	Diagnostic	Mixed	[60]
hTERT	Serum	Prognostic	Upregulated	[61]
Protein
SPARC, LRG1	Serum	Diagnostic and prognostic	Upregulated	[86]
QSOX1	Plasma	Diagnostic	Downregulated	[87]
CD59, TSPAN9	Plasma	Diagnostic	Upregulated	[88]
FGB, β2-GP1	Plasma	Diagnostic	Upregulated	[89]
S100A9	Plasma	Diagnostic	Upregulated	[63]
GPC1	Plasma	Diagnostic	Upregulated	[64]
CPNE3	Plasma	Diagnostic and prognostic	Upregulated	[90]
CK19, TAG72, CA125	Plasma	Diagnostic and prediction (chemoresistance)	Upregulated	[91]
CXCL7	Serum	Prediction (chemoresistance)	Upregulated	[92]
ITGBL1	Plasma	Diagnostic and prognostic	Upregulated	[93]
CEA	Serum	Diagnostic and prognostic	Upregulated	[62]

ncRNA, noncoding RNA; miR, microRNA; lncRNA, long noncoding RNA; mRNA, messenger RNA; VEGF, vascular endothelial growth factor; MYC, myelocytomatosis; CEA, carcinoembryonic antigen; CK19, cytokeratin 19; EpCAM, epithelial cell adhesion molecule; hTERT, human telomerase reverse transcriptase; SPARC, secreted protein acidic and rich in cysteine; LRG1, leucine rich alpha-2-glycoprotein 1; TSPAN9, tetraspanin 9; FGB, fibrinogen beta chain; β2-GP1, beta-2-glycoprotein 1; GPC1, glypican 1; CPNE3, copine III; TAG25, tumor-associated glycoprotein 72; CA125, carbohydrate antigen 125; CXCL7, chemokine ligand 7; ITGBL1, integrin beta-like 1; CEA, carcinoembryonic antigen.

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Extracellular vesicles in colorectal cancer

Ann Coloproctol. 2025;41(5):379-392. Published online October 16, 2025

DOI: https://doi.org/10.3393/ac.2025.00745.0106

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Extracellular vesicles in colorectal cancer

Fig. 1. Extracellular vesicles (EVs) and colorectal cancer progression.

Fig. 2. Novel techniques for extracellular vesicle (EV) purification. (A) Enhanced dual-mode chromatography (eDMC) device for isolating EVs using size exclusion chromatography followed by ion exchange. Reprinted from Woo et al. [42], available under the Creative Commons license. (B) Disc-based microfluidic device for EV isolation using centrifugation and nanoporous membranes, exodisc. Reprinted from Woo et al. [47], with permission from the American Chemical Society. (C) Electrostatic and covalent reactions are used to isolate EVs from patient plasma. Modified from Koo et al. [49], available under the Creative Commons license. HDL, high-density lipoprotein; (V)LDL, (very) low-density lipoprotein; dia., diameter.

Fig. 3. Modulating extracellular vesicles (EVs) for colorectal cancer treatment. 5-FU, 5-fluorouracil.

Fig. 1.

Fig. 2.

Fig. 3.

Extracellular vesicles in colorectal cancer

Study	Term	Regulation	Function
Yamada et al. [26] (2014)	Microvesicle	↑ miR-1246/TGF-β, ↓ PML	Angiogenesis (activated Smad signaling in endothelial cells)
Shao et al. [28] (2018)	Small EV	↑ miR-21-5p	Macrophage polarization, liver metastases
Zeng et al. [25] (2018)	Exosome	↑ miR-25-3p, ↓ KLF2/KLF4	Angiogenesis and vascular permeability
Ren et al. [31] (2018)	Exosome	↑ lncRNA H19	Enhanced CRC stemness and chemoresistance
Endzeliņš et al. [32] (2018)	EV	↑ HIF-induced EV release	Increased invasiveness, motility, and cancer stemness under hypoxia
Popēna et al. [27] (2018)	EV	↑ CD14, ↓ HLA-DR	Macrophage polarization
Liu et al. [33] (2020)	Exosome	↑ miR-106b-3p, ↓ DLC1	EMT induction (lung metastasis)
Xu et al. [34] (2020)	Exosome	↑ MALAT1, ↓ miR-26a/26b	Enhanced invasion and metastasis (PI3K/Akt activation via FUT4)
Jiang et al. [35] (2021)	Exosome	↑ ANGPTL1	Suppress angiogenesis, inhibit liver metastasis
Shang et al. [36] (2020)	Exosome	↑ circPACRGL	Cell proliferation, migration, invasion
Zhou et al. [37] (2021)	Exosome	↑ LINC00659, ↓ miR-342-3p	Promote proliferation, invasion, EMT via ANXA2
Lai et al. [38] (2021)	Exosome	↑ PVT1, VEGFA, EGFR, ↓ miR-152-3p	Metastasis, cancer stemness (EGFR/VEGFA pathway)
Yang et al. [29] (2021)	Exosome	↑ miR-106b-5p, ↓ PDCD4	EMT induction, macrophage polarization (M2-TAM), promoting metastasis
Dokhanchi et al. [30] (2021)	EV	↑ miR-221-3p	Endothelial cell angiogenesis
Fang et al. [39] (2022)	Exosome	↑ PCAT1	Promoted EMT, enhanced liver metastasis
Zhang et al. [40] (2022)	Exosome	↑ HSPC111, ACLY phosphorylation, ↑ CXCL5/CXCR2	Liver metastasis by reprogramming lipid metabolism in CAFs
Huang et al. [41] (2024)	Exosome	↑ SNHG3	EMT, metastasis via Wnt/β-catenin activation

Biomarker	Sample source	Clinical role	Expression	Reference
ncRNA
miR-193a-5p	Plasma	Diagnostic	Downregulated	[65]
miR-21, miR-92a, miR-222	Serum	Diagnostic and prognostic	Upregulated	[66, 67]
miR-320c	Plasma	Diagnostic and prognostic	Upregulated	[68]
miR-377-3p, miR-381-3p	Serum	Diagnostic	Downregulated	[52]
miR-181a-5p	Serum	Prognostic	Upregulated	[69]
miR-181b, miR-193b, miR-195, miR-411	Serum	Prognostic	Unavailable	[70]
miR-361-3p	Plasma	Prognostic	Upregulated	[71]
let-7a, miR-1246, miR-1229, miR-223, miR-23a	Serum	Diagnostic	Upregulated	[51, 72, 73]
miR-27a, miR-130a	Plasma	Diagnostic and prognostic	Upregulated	[74]
miR-99b-5p, miR-150-5p	Serum	Diagnostic	Downregulated	[54, 75]
miR-208b	Serum	Prediction (chemoresistance)	Upregulated	[55]
miR-301a	Serum	Diagnostic	Upregulated	[73]
miR-125a-3p	Plasma	Diagnostic	Upregulated	[76]
miR-122	Serum	Diagnostic and prognostic	Upregulated	[77]
miR-548c-5p	Serum	Prognostic	Decreased	[78]
miR-6803-5p	Serum	Diagnostic and prognostic	Elevated	[53]
miR-221	Plasma	Prognostic	Increased	[67, 79]
lncRNA (LNCV6_116109, LNCV6_98390, LNCV6_108266, LNCV6_38772, LNCV6_84003, LNCV6_98602)	Plasma	Diagnostic	Upregulated	[80]
lncRNA CRNDE-h	Serum	Diagnostic and prognostic	Upregulated	[56]
lncRNA LINC02418	Serum	Diagnostic	Upregulated	[81]
lncRNA GAS5	Plasma	Prognostic	Downregulated	[82]
lncRNA 91H	Plasma	Prognostic	Upregulated	[83]
lncRNA HOTTIP	Serum	Prognostic	Downregulated	[84]
circLPAR1	Plasma	Diagnostic and prognostic	Downregulated	[58]
circ-PNN	Serum	Diagnostic	Upregulated	[57]
hsa-circ-0004771	Serum	Diagnostic	Upregulated	[85]
Protein-coding mRNA
VEGF, CD133, MYC, CDX2, CEA, CK19, EpCAM, CD24	Serum	Diagnostic	Upregulated	[59]
H3F3A, MYL6, FBXO7, TUBA1C, MEF2C, BANK1	Serum	Diagnostic	Mixed	[60]
hTERT	Serum	Prognostic	Upregulated	[61]
Protein
SPARC, LRG1	Serum	Diagnostic and prognostic	Upregulated	[86]
QSOX1	Plasma	Diagnostic	Downregulated	[87]
CD59, TSPAN9	Plasma	Diagnostic	Upregulated	[88]
FGB, β2-GP1	Plasma	Diagnostic	Upregulated	[89]
S100A9	Plasma	Diagnostic	Upregulated	[63]
GPC1	Plasma	Diagnostic	Upregulated	[64]
CPNE3	Plasma	Diagnostic and prognostic	Upregulated	[90]
CK19, TAG72, CA125	Plasma	Diagnostic and prediction (chemoresistance)	Upregulated	[91]
CXCL7	Serum	Prediction (chemoresistance)	Upregulated	[92]
ITGBL1	Plasma	Diagnostic and prognostic	Upregulated	[93]
CEA	Serum	Diagnostic and prognostic	Upregulated	[62]

Table 1. Published literature on EV roles in CRC progression

Table 2. Extracellular vesicle–derived biomarkers from liquid biopsy in colorectal cancer