Serum extracellular vesicle-derived N6-methyladenosine RNA as a diagnostic and prognostic biomarker in colorectal cancer

Serum extracellular vesicle-derived N6-methyladenosine RNA as a diagnostic and prognostic biomarker in colorectal cancer | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Serum extracellular vesicle-derived N6-methyladenosine RNA as a diagnostic and prognostic biomarker in colorectal cancer You-Tong Lin, An-Chen Chang, Chian-Shiu Chien, Chun-Chi Lin, Hsin-Yi Lan, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9134647/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Background Extracellular vesicle (EV)-based liquid biopsy is increasingly recognized as a promising strategy for cancer diagnosis and prognosis, as EVs carry abundant, stable biomolecular cargo. N6-methyladenosine (m6A), the most prevalent modification in eukaryotic intracellular RNA, plays a critical role in regulating diverse cellular processes and has been implicated in tumor initiation and progression. However, the potential of EV-associated m6A-modified RNA (EV-m6A RNA) as a clinically useful biomarker for cancer detection remains unclear. Methods EV-RNA was isolated from tumor tissues and matched serum samples collected from 76 colorectal cancer (CRC) patients. Serum samples from 19 healthy donors were included as controls. EV-m6A RNA levels were quantified using an enzyme-linked immunosorbent assay (ELISA). Results Serum - EVs isolated from CRC patients and healthy donors were identified as CD9(+)/CD63(+)/ALIX(+) small extracellular vesicles (sEVs), with a mean particle diameter of 60–70 nm. The mean serum sEV concentration and sEV-RNA yield were significantly higher in CRC patients than in healthy controls (sEV concentration: 11 ± 8 × 10 12 /mL vs. 5 ± 2 × 10 12 /mL, p = 0.006; sEV-RNA yield: 57 ± 52 ng/µL vs. 18 ± 7 ng/µL, p = 0.002). Through m6A modification-specific ELISA quantification, receiver operating characteristic (ROC) curve analysis demonstrated good discriminatory performance of normalized serum sEV-m6A RNA levels for CRC detection (AUC, 0.8241; 95% CI, 0.7437–0.9045; p < 0.0001). Serum sEV-m6A RNA levels were noted to be higher in late-stage (III/IV) CRC patients than in those with early-stage (I/II) disease (0.015 ± 0.001% vs. 0.009 ± 0.005%, p < 0.001) with no significant correlation with intratumoral METTL3 expression, a m6A writer. Increased serum sEV-m6A RNA abundance further predicted a worse overall survival (OS) in CRC patients ( p = 0.0107; HR, 3.894), while m6A RNA levels in tumor tissues showed no significant prognostic value ( p = 0.3243; HR, 1.152). Conclusion These findings support serum sEV-m6A RNA levels as a feasible and noninvasive biomarker for colorectal cancer diagnosis, with additional potential for liquid biopsy-based prognostication and longitudinal disease monitoring Extracellular vesicle Liquid biopsy N6-methyladenosine (m6A) Colorectal cancer Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Extracellular vesicles (EVs) are a heterogeneous population of membrane vesicles with lipid-bilayer and nanoscale properties released from various cell types and present in body fluids. EVs can be classified into diverse subtypes based on biogenesis and size distribution. Exosomes are one of the small EVs (sEVs) with a diameter range of 30–150 nm [ 1 ]. Exosomes present several markers on the surface like tetraspanins (CD63, CD9, and CD81) [ 2 ] and major histocompatibility complex (MHC) class I [ 3 ], and carry a diverse cargo of biomolecules including proteins, lipids, and nucleic acids: DNA, messenger RNA (mRNA), noncoding RNA (ncRNA) [ 4 ], which enable them to deliver messages to recipient cells, mediating in cell-cell communication. EVs enable liquid-biopsy detection of nucleic acid and protein biomarkers, with plasma and serum favored for their higher EV abundance [ 5 ]. In colorectal cancer (CRC), EV-derived DNA (evDNA) outperforms plasma cell-free DNA (cfDNA) by droplet digital PCR (ddPCR) for KRAS G12D/G13D detection across TNM stages [ 6 ]. By next-generation sequencing (NGS), evDNA achieves higher sensitivity and specificity than cfDNA by ddPCR [ 7 ]. The non-coding EV markers include serum exosomal miR-125a-3p [ 8 ], lncRNA NAMPT-AS [ 9 ], and circ-KLHDC10 [ 10 ] have been proposed. EV protein cargo is informative: plasma EV fibrinogen α chain (FGA) rises with progression and outperforms CEA/CA19-9 for early-stage detection [ 11 ], while targeted proteomics identified EV proteins, with > 80% specificity for stage I/II [ 12 ]. EV-implemented assays complement traditional methods; notably, serum EV-microRNA (miRNA) levels exceed serum miRNA, and a higher level of EV-derived RNA (evRNA) relative to evDNA and cfDNA makes evRNA a more readily detectable source [ 13 ]. N6-methyladenosine (m6A) is a prevalent internal RNA modification across mRNA and ncRNA [ 14 ]. It is dynamically installed by writers, such as the METTL3-METTL14 complex, removed by erasers, such as FTO, and interpreted by readers, including YTHDF1 and IGF2BP2, to shape RNA fate and gene expression collectively [ 15 ]. Intracellular METTL3 drives metastasis via an m6A-IGF2BP2-SOX2 axis and is associated with worse patient survival [ 16 , 17 ]. Cell-intrinsic expression of YTHDF1 further serves as a prognostic and diagnostic factor [ 18 , 19 ]. Despite these associations, the clinical significance of m6A-modified evRNA remains unclear. In this study, we isolated m6A RNA from tumor tissue and serum-derived sEV (serum sEV) across tumor stages to investigate m6A RNA levels and compare them with those from healthy donors. The increased serum sEV-m6A RNA content suggested its potential for noninvasive CRC diagnosis. Additionally, CRC patients with high serum sEV-m6A RNA levels were associated with poor survival, supporting its potential as a biomarker for advanced CRC and its prognostic value. Methods Human specimens Paired 76 sera and tissue samples from CRC patients used in this retrospective study were obtained from the biobank of Taipei Veterans General Hospital, with approval from the institutional review board (IRB) (Approval number: 2023-01-014BC), following the ethical principles of the Declaration of Helsinki. A total of 19 healthy sera were used. 18 sera from healthy donors were collected at National Yang Ming Chiao Tung University under the IRB approval (Approval number: NYCU111182AE). Informed consent is waived. One commercially available pooled human serum was purchased from Rockland Immunochemicals (Catalog number: D119-00-0050). Serum sEV isolation sEVs were isolated from serum samples using the ExoQuick™ kit (System Biosciences, Palo Alto, CA, USA). 100 µL of serum was mixed with 25 µL of 1x phosphate-buffered saline (PBS, Bioman, New Taipei City, Taiwan) and 31.5 µL of ExoQuick reagent. The mixture was incubated on ice for 1 h. After incubation, the sample was centrifuged at 1,500 × g for 30 min at 4°C, and the supernatant was discarded. The pellet was centrifuged at 1,500 × g for an additional 5 min at 4°C, then the supernatant was removed. The resulting sEV pellet was collected for further analysis. RNA extraction The sEV pellets or CRC tissues were resuspended in 50 µL of Dulbecco's phosphate-buffered saline (DPBS) (Cytiva, Marlborough, MA, USA), added with 950 µL TRIzol reagent (Thermo Fisher Scientific, Waltham, MA, USA), and incubated at room temperature for 10 min. Following this, 200 µL of chloroform (Sigma-Aldrich/Merck KGaA, Darmstadt, Germany) was added, and the mixture was incubated at room temperature for 3 min. The mixture was centrifuged at 12,000 × g for 15 min at 4°C to obtain a transparent aqueous phase containing RNA. An equal volume of isopropanol (Sigma-Aldrich) was added, and the mixture was incubated overnight at -20°C for RNA precipitation. The sample was centrifuged at 13,000 × g for 30 min at 4°C, and the supernatant was discarded. The RNA pellet was washed with 1 mL of 75% cold ethanol and centrifuged at 13,000 × g for 10 min at 4°C. The supernatant was discarded, and the pellet was air-dried. Finally, the RNA pellet was resuspended in 30 µL of nuclease-free water (Bioman) for downstream applications. m6A RNA quantification EpiQuik™ m6A RNA Methylation Quantification Kit (EpiGentek, Farmingdale, NY, USA) was used for m6A RNA quantification. 200 ng of RNA was used as the input for each reaction. Standards of different concentrations and working solutions were prepared, and the experimental steps were carried out according to the manufacturer’s instructions. After the reactions, the absorbance was measured at 450 nm using a microplate reader (Infinite M200 Pro, Tecan, Switzerland). A standard curve was generated for the absolute quantification of m6A levels. Transmission electron microscope (TEM) sEV pellets were resuspended in DPBS and filtered through a 0.45 µm filter (Millipore, Burlington, MA, USA), fixed with an equal volume of 4% paraformaldehyde (PFA, Sigma‒Aldrich/Merck KGaA) for 30 min at room temperature. The fixed sEV samples were diluted using nuclease-free water to prepare 100× and 1000× dilutions, then loaded onto a formvar/carbon-coated grid (Pelco, Fresno, CA, USA) for 40 min at room temperature. The grids were rinsed with nuclease-free water and dried for over 5 days. Images were captured using a JEOL JEM-1400plus (JEOL, Tokyo, Japan) at magnifications of 20,000×. Tunable resistive pulse sensing (TRPS) particle analysis sEV pellets isolated from 50 µL of serum were resuspended in DPBS and filtered through a 0.45 µm filter before analysis. Tunable resistive pulse sensing (TRPS) was conducted by an Exoid system employing an NP100 Nanopore and CPC100 standard calibration beads (Izon Bioscience, Christchurch, New Zealand). The Nanopore was stretched to 47 mm and wetted to verify the baseline current at 100 mV. It was then coated and rinsed with various buffers from the supplied Izon reagent kit. Before analysis, the NP100 Nanopore was calibrated with diluted CPC100 standard calibration beads (1500×) provided in the kit. Sample exosomes were diluted in 2× PBS, loaded into the Nanopore, and analyzed under the same pressure and voltage conditions as the calibration beads. The measurement conditions were as follows: stretch 47 mm, pressure 1000 cm H 2 O, and a current of about 110–130 nA. Data recording was performed using the Exoid Control Suite software (version V1.0.0.181), and particle size ranges and concentrations were calculated using the Izon Data Suite (version V1.0.2.32). Immunohistochemistry (IHC) staining. Paraffin-embedded tissue slides were deparaffinized by heating at 65°C for 10 min, followed by immersion in ultra-clear solution (J.T. Baker, Phillipsburg, NJ, USA) three times, each for 10 min. The slides were rehydrated through a graded ethanol series: 100% ethanol (Nihon Shiyaku, Japan) for 5 min, twice; 90% ethanol for 5 min; 70% ethanol for 5 min; and 50% ethanol for 5 min, followed by rinsing with ddH₂O. For antigen retrieval, the slides were autoclaved at 120°C for 10 min and immersed in 10 mM citrate buffer (Honeywell, Morris Plains, NJ, USA). After cooling, the slides were washed with 1× PBS. The tissue area intended for staining was encircled with a hydrophobic pen. Blocking was performed by treating the tissues with 3% hydrogen peroxide for 10 min, followed by two 5-minute washes with 1× PBS. Cell membrane permeabilization was achieved using 0.1% Triton X-100 (Bionovas, North York, ON, Canada) for 5 min, followed by washing with 1× PBS twice for 5 min. The tissues were then incubated overnight at 4°C with the anti-METTL3 antibody (ABclonal, cat A8370) diluted 1:400 in antibody dilution buffer (Ventana). The tissues were washed twice with 1× PBS for 10 min each before incubation with anti-rabbit immunoglobulin (BioGenex, Fremont, CA, USA) for 30 min at room temperature. Next, the tissues were washed with 1× PBS for 10 min, twice, before being applied with streptavidin peroxidase (BioGenex, Fremont, CA, USA) for 20 min at room temperature, followed by 1× PBS for 10 min, twice. Next, tissues were treated with DAB solution (Epredia, Kalamazoo, MI, USA) for 45 seconds for visualization, and the reaction was stopped with 1× PBS. Finally, the tissues were counterstained with Mayer’s hemalum solution (Sigma-Aldrich/Merck KGaA) for 20 seconds and rinsed with ddH₂O. The slides were then mounted using Kaiser’s glycerol gelatin (Sigma-Aldrich/Merck KGaA). Images were captured under an Olympus BX43 microscope equipped with a DP22 CCD camera (Olympus, Tokyo, Japan), and the histology score (H-score) was calculated to quantify staining intensity. The H score was defined as the percentage of the METTL3-positive immunostained region (0 to 100) multiplied by the degree of METTL3 staining (0, 1, 2, and 3). Western blot sEV pellets isolated from 20 µL of serum were lysed with 40 µL of 1× radioimmunoprecipitation assay (RIPA) lysis buffer prepared from ddH 2 O diluted 5× RIPA lysis buffer (T-Pro Biotechnology) containing 1% protease inhibitor (ThermoFisher, Waltham, MA, USA) on ice for 1 h, then centrifuged at 13,000 rpm for 15 min at 4°C to extract protein supernatant. Subsequently, 40 µL of 2× sample buffer was added to the supernatant. The mixture was heated at 99°C for 10 min. SDS-PAGE-separated samples were then transferred to a PVDF membrane (Millipore, Burlington, MA, USA). The membranes were blocked with 5% skim milk for 1 h, followed by three washes with 1× PBS containing 0.1% Tween 20 (Bioshop, Burlington, ON, Canada). The membranes were then incubated with the primary antibodies at 4°C overnight in PBS-Tween20 (0.1%), followed by incubation with the relevant HRP-conjugated secondary antibodies (GeneTex, Irvine, CA, USA) at room temperature for 1 h on an orbital shaker. The membranes were soaked with chemiluminescent HRP substrate (Bioshop, Burlington, ON, Canada) and visualized using an ImageQuant LAS 4000 chemiluminescence detection system (GE Healthcare Bio-Sciences, Pittsburgh, PA, USA). The primary antibodies used are as follows: Alix (Cell Signaling, cat 2171), CD9 (Abcam, cat ab92726), CD63 (EMD Millipore, cat CBL551), Calreticulin (Abcam, cat ab39897), and TOM20 (ABclonal, cat A16896). Statistical analysis GraphPad Prism (version 10.1.2) was used for statistical analysis, including two-tailed unpaired Student’s t-test, Pearson correlation analysis, ROC curve analysis, chi-square test for correlation of clinicopathological variable and m6A abundance, and Log-rank (Mantel-Cox) test for Kaplan-Meier survival analysis. The Wilson/Brown test performed 95% confidence intervals (95% CI). A p-value less than 0.05 was considered statistically significant. Python visualized the confusion matrix for a cancer prediction model on Google Colab. The calculating formula was following: accuracy = (TP + TN)/(TP + TN+FP + FN), precision = TP/(TP + FP), sensitivity = TP/(TP + FN), specificity = TN/(TN + FP). Result Characterization of serum sEV from CRC patients and healthy donors A total of 76 CRC patients were included in the analysis. The early-stage group contained 16 of stage Ⅰ and 20 of stage Ⅱ; the late-stage group contained 20 of stage Ⅲ and 20 of stage Ⅳ; and their median ages were 66 years (range, 22–84 years) and 62.5 years (range, 34–84 years), respectively. All serum sEVs were isolated using ExoQuick™ precipitation solution and characterized according to MISEV2023 guidelines, including quantification, protein markers, and single-vesicle analysis [ 20 ]. Two healthy controls and four tumor samples were randomly selected for sEV characterization. For protein marker identification, sEV markers, including ALIX, CD9, and CD63, were enriched in serum sEVs from patients and healthy controls. Serum sEVs were free from organelle contamination, including mitochondria (TOM20) and endoplasmic reticulum (Calreticulin) ( Fig. 1 A ) . Under the transmission electron microscope (TEM), the images showed that serum sEVs were about 60 nm in diameter and had bilayer membranous structures ( Fig. 1 B ) . Single-vesicle analysis of serum sEVs from representative samples using tunable resistive pulse sensing (TRPS) showed a size distribution of 50–70 nm, consistent with TEM results ( Fig. 1 C ) . The mean particle diameters of serum sEVs from CRC patients and healthy donors were about 70 nm ( Fig. 1 D ) , and the mode particle diameters were about 60 nm ( Fig. 1 E ) . The d90/d10 ratio showed no statistically significant differences between the two groups ( Fig. 1 F ) . The particle concentration of serum sEVs from CRC patients was higher than that from healthy donors ( Fig. 1 G ) . Moreover, sEV RNA amounts were elevated in CRC patients ( Fig. 1 H ) . These findings support serum sEV RNA as a viable target for detection. Detection of serum sEV-derived m6A RNA abundance as diagnostics To investigate the clinical significance of serum sEV-m6A RNA in CRC, the abundance of serum sEV-m6A RNA was quantified in all stages of CRC and in healthy controls, and receiver operating characteristic (ROC) curves were used to assess sensitivity and specificity across all possible threshold values [ 21 ]. The confusion matrix calculated the accuracy and precision at a fixed threshold [ 22 ]. Regardless of tumor staging, when the cut-off value of normalized serum sEV-m6A RNA level was 0.00867%, the serum sEV-m6A RNA level could differentiate CRC patients and healthy controls with an area under curve (AUC) of 0.8241 (95% CI, 0.7437–0.9045), a sensitivity of 78.95% (95% CI, 68.50%–86.60%), a specificity of 73.68% (95% CI, 51.21% – 88.19%) ( Fig. 2 A ) . An accuracy of 77.89% (95% CI, 68.56% – 85.06%), and a precision of 92.31% (95% CI, 83.22% – 96.67%) were observed under the setting ( Fig. 2 B ) , suggesting serum sEV-m6A RNA level could be considered a diagnostic molecule Furthermore, the serum sEV-m6A RNA level of CRC was increased from early-stage (I/II) to late-stage (III/IV) ( Fig. 2 C ) . Correlation between m6A RNA levels in CRC tissues and serum-sEVs Given the increased abundance of serum sEV-m6A RNA in CRC patients, we sought to elucidate potential sources. As METTL3 is the dominant m6A RNA writer [ 16 , 17 ], we examined METTL3 protein expression by immunohistochemistry (IHC) ( Fig. 3 A ) . We found that in situ METTL3 expression positively correlated with m6A RNA levels in late-stage (III/IV) tumor tissues, but not in early-stage (I/II) CRC tissues ( Fig. 3 B ) . Interestingly, in early-stage CRC patients, METTL3 expression was positively correlated with serum sEV-m6A RNA levels, but not in late-stage samples ( Fig. 3 C ) . Furthermore, no significant association between m6A RNA in CRC tissues and serum-sEV was noted ( Fig. 3 D ) , suggesting highly dynamic m6A RNA biogenesis and transportation during cancer progression. Evaluate the prognostic significance of serum sEV-derived m6A RNA abundance Next, we stratified CRC patients into low- and high-m6A RNA groups based on the normalized median m6A level in CRC tissues or serum sEVs for clinicopathological characterization. It was found that m6A RNA levels in CRC tissues in situ showed no significant association with age, gender, tumor location, and tumor stage ( Table 1 ) . In contrast, CRC patients with high m6A RNA levels in serum sEVs were more likely to have late-stage CRC ( p < 0.001), with no association with age, gender, or tumor location ( Table 2 ) . Moreover, CRC patients with high m6A RNA levels in serum sEVs had worse overall survival (OS) than those with low m6A RNA levels ( Fig. 4 A ) . In contrast, CRC patients with high m6A RNA levels in CRC tissues showed no difference in OS compared with those with low m6A RNA levels ( Fig. 4 B ) , highlighting the prognostic potential of serum sEV-m6A RNA. Table 1 Association between clinicopathological characteristics and tumor tissue m6A RNA abundance in CRC patients (N = 76). Tumor tissue m6A RNA abundance (by median) N = 76 Low m6A (N = 38) High m6A (N = 38) characteristics N % N % p value* Age (y/o) < 65 19 50 20 52.6 0.8185 ≧ 65 19 50 18 47.3 Gender Male 18 47.4 24 63.2 0.1663 Female 20 52.6 14 36.8 Location Right/ proximal 8 21.1 7 18.4 0.7732 Left/ distal 30 78.9 31 81.6 Stage (AJCC Ⅶ ) Early stage (Ⅰ/Ⅱ) 18 47.4 18 47.4 > 0.9999 Late stage (Ⅲ/Ⅳ) 20 52.6 20 52.6 *p value is estimated by a chi-square test. Table 2 Association between clinicopathological characteristics and serum-sEV m6A RNA abundance in CRC patients (N = 76). *p value is estimated by a chi-square test. Serum-sEV m6A RNA abundance (by median) N = 76 Low m6A (N = 37) High m6A (N = 39) characteristics N % N % p value* Age (y/o) < 65 17 45.9 22 56.4 0.3616 ≧ 65 20 54.1 17 43.6 Gender Male 18 48.6 24 61.5 0.2586 Female 19 51.4 15 38.5 Location Right/ proximal 4 10.8 11 28.2 0.0569 Left/ distal 33 89.2 28 71.8 Stage (AJCC Ⅶ ) Early stage (Ⅰ/Ⅱ) 29 78.4 7 17.9 < 0.001 Late stage (Ⅲ/Ⅳ) 8 21.6 32 82.1 Discussion Our study firstly compares m6A RNA abundance in CRC tumor tissue and paired serum sEVs. CRC patients with high serum sEV-m6A levels were markedly enriched in advanced stages, and associated with poorer survival, indicating its prognostic value. This study highlights the potential of serum sEV-m6A RNA as a minimally invasive biomarker for cancer diagnosis, with the findings that serum sEV-m6A RNA abundance was significantly elevated in CRC patients compared with healthy controls, with a sensitivity of 78.95%, specificity of 73.68%, and an AUC of 0.8241, fostering the development of a reliable, high-efficiency, high-specificity, and high-recovery-rate automated pipeline integrating EV isolation, RNA extraction, and m6A detection for point-of-care testing (POCT) and home care ( Fig. 4 C ) . Although multiple studies have demonstrated that blood EVs are promising candidates for liquid biopsy, a standardized protocol for their isolation remains poorly defined. According to the MISEV2023 guideline, improper blood collection, processing, and storage can introduce significant variability and affect EV quality. Freeze-thaw cycles of the blood sample may disrupt EV membranes or generate cellular debris of similar size to EVs. At the same time, platelet activation upon stimulation leads to the release of many platelet EVs, which can alter the overall EV profile. Second, the presence of abundant soluble proteins and lipoproteins, which share similar size or density with EVs, makes separation challenging [ 20 ]. Different EV isolation methods introduce varying degrees of co-isolate contamination. For example, plasma EVs isolated by ultracentrifugation alone often retained albumin, whereas those isolated by size-exclusion chromatography (SEC) alone tended to retain lipoproteins [ 23 ]. Although combining more than two isolation techniques can significantly reduce these common contaminants, it often results in a substantial loss of EV yield [ 24 ]. Moreover, to avoid excess chylomicrons, an overnight fasting period before blood collection is recommended [ 25 , 26 ]. Given the trade-offs among purity, yield, and isolation efficiency, selecting an optimal procedure for blood collection and EV isolation requires careful tailoring to specific experimental needs to ensure the most reliable results. A highly efficient, automated pipeline for EV isolation, RNA extraction, and m6A detection can bridge the gap in clinical translation. A closed, automated system suffices to address the challenges posed by time-consuming procedures, human error, analyte loss, and interference in RNA analysis. An automated EV-miRNA extraction technology utilizing Fe 3 O 4 @TiO 2 beads relies on the interaction between Ti 4+ and phosphate groups on the EV membrane, followed by magnetic capture and heat lysis for RNA extraction [ 27 ]. In automatic digital microfluidics, magnetic particles capture EVs and move EVs on a chip via an electrode system for RNA preparation [ 28 ]. Both automated platforms require only 50 µL of plasma and can be completed within 30 minutes, offering a convenient, rapid solution. Establishing automated EV RNA isolation pipelines with enhanced standardization and yield stability will be essential for future clinical implementation [ 29 ]. To quantify total serum sEV-m6A RNA levels, m6A-ELISA appeared to be the most suitable platform for hospital-based automated detection, given its lower cost, rapid turnaround, simplicity, and scalability compared to LC/MS [ 30 – 32 ]. Although nanopore direct RNA sequencing enabled precise detection of total m6A levels in total RNA via current signal variations, its high cost and complex algorithms limit broad clinical applicability [ 33 , 34 ]. There were some limitations in our study. Since the serum samples had been stored for an extended period, freeze-thaw cycles may have disrupted EVs. Additionally, variations in EV isolation methods can impact RNA analysis outcomes [ 35 ]. Manual handling of RNA extraction and ELISA procedures also introduced batch-to-batch variability. Therefore, the performance of an optimal cut-off for fresh serum sEV-m6A RNA in CRC detection warrants further large-scale validation. Conclusion EVs in liquid biopsy have been considered potential tools for cancer detection. This study demonstrated the value of serum sEV-m6A RNA as a biomarker for CRC diagnosis and prognosis, facilitating cancer management ( Fig. 4 C ) . Abbreviations AUC area under curve CRC colorectal cancer ELISA enzyme-linked immunosorbent assay EV extracellular vesicle FN false negative FP false positive IHC immunohistochemistry IRB institutional review board m6A N6-methyladenosine OS overall survival POCT point-of-care testing ROC receiver operating characteristic sEV small extracellular vesicle TEM transmission electron microscope TN true negative TP true positive TRPS tunable resistive pulse sensing. Declarations Ethics approval and consent to participate: This study conforms to the principles of the Declaration of Helsinki and has been approved by the Institutional Review Board of Taipei Veterans General Hospital (2023-01-014BC) and the Institutional Review Board of National Yang Ming Chiao Tung University (NYCU111182AE). The informed consent has been waived. Competing interests: The authors declare no conflicts of interest. Funding: National Science and Technology Council (112-2314-B-038-146-MY3 to A-C.C., 113-2314-B-075-023 to H-W.T., and 112-2326-B-A49-002-MY3 and 111-2628-B-A49-017 to W-L.H). Ministry of Education, Higher Education SPROUT Project for Cancer and Immunology Research Center (114W031101 and 115W031101). A grant from the Yen Tjing Ling Medical Foundation (CI-111-15 and CI-115-24). Shin Kong Wu Ho-Su Memorial Hospital (2023SKHAND007). Author Contribution ACC, HWT, and WLH conceptualized this study and directed the research; YTL and HYL conducted the experiments and analyzed the data with the support of CSC, CCL, HWT, and WLH. YTL wrote the original manuscript. ACC, CSC, CCL, HWT, and WLH revised the manuscript. Acknowledgement We thank the Institute of Anatomy and Cell Biology at National Yang Ming Chiao Tung University for assistance with TEM imaging. This work was supported by the Higher Education SPROUT Project of the National Yang Ming Chiao Tung University and the Ministry of Education (MOE), Taiwan, for Cancer and Immunology Research Center and YEN TJING LAING MEDICAL FOUNDATION. Data Availability Data supporting the findings of this study are available within this article. References Yokoi A, Ochiya T. Exosomes and extracellular vesicles: Rethinking the essential values in cancer biology. Sem Cancer Biol. 2021;74:79–91. Mathieu M, Névo N, Jouve M, Valenzuela JI, Maurin M, Verweij FJ, Palmulli R, Lankar D, Dingli F, Loew D, et al. Specificities of exosome versus small ectosome secretion revealed by live intracellular tracking of CD63 and CD9. Nat Commun. 2021;12(1):4389. Synowsky SA, Shirran SL, Cooke FGM, Antoniou AN, Botting CH, Powis SJ. The major histocompatibility complex class I immunopeptidome of extracellular vesicles. J Biol Chem 2017, 292(41):17084–92. Mathivanan S, Fahner CJ, Reid GE, Simpson RJ. ExoCarta 2012: database of exosomal proteins, RNA and lipids. Nucleic Acids Res. 2012;40(Database issue):D1241–1244. Gardiner C, Di Vizio D, Sahoo S, Théry C, Witwer KW, Wauben M, Hill AF. Techniques used for the isolation and characterization of extracellular vesicles: results of a worldwide survey. J Extracell vesicles. 2016;5:32945. Choi J, Cho HY, Jeon J, Kim KA, Han YD, Ahn JB, Wortzel I, Lyden D, Kim HS. Detection of circulating KRAS mutant DNA in extracellular vesicles using droplet digital PCR in patients with colon cancer. Front Oncol. 2022;12:1067210. Möhrmann L, Huang HJ, Hong DS, Tsimberidou AM, Fu S, Piha-Paul SA, Subbiah V, Karp DD, Naing A, Krug A, et al. Liquid Biopsies Using Plasma Exosomal Nucleic Acids and Plasma Cell-Free DNA Compared with Clinical Outcomes of Patients with Advanced Cancers. Clin cancer research: official J Am Association Cancer Res. 2018;24(1):181–8. Wang J, Yan F, Zhao Q, Zhan F, Wang R, Wang L, Zhang Y, Huang X. Circulating exosomal miR-125a-3p as a novel biomarker for early-stage colon cancer. Sci Rep. 2017;7(1):4150. Rizk NI, Kassem DH, Abulsoud AI, AbdelHalim S, Yasser MB, Kamal MM, Hamdy NM. Revealing the role of serum exosomal novel long non-coding RNA NAMPT-AS as a promising diagnostic/prognostic biomarker in colorectal cancer patients. Life Sci. 2024;352:122850. Li Y, Zheng Q, Bao C, Li S, Guo W, Zhao J, Chen D, Gu J, He X, Huang S. Circular RNA is enriched and stable in exosomes: a promising biomarker for cancer diagnosis. Cell Res. 2015;25(8):981–4. Zheng X, Xu K, Zhou B, Chen T, Huang Y, Li Q, Wen F, Ge W, Wang J, Yu S, et al. A circulating extracellular vesicles-based novel screening tool for colorectal cancer revealed by shotgun and data-independent acquisition mass spectrometry. J Extracell vesicles. 2020;9(1):1750202. Shiromizu T, Kume H, Ishida M, Adachi J, Kano M, Matsubara H, Tomonaga T. Quantitation of putative colorectal cancer biomarker candidates in serum extracellular vesicles by targeted proteomics. Sci Rep. 2017;7(1):12782. Casanova-Salas I, Aguilar D, Cordoba-Terreros S, Agundez L, Brandariz J, Herranz N, Mas A, Gonzalez M, Morales-Barrera R, Sierra A, et al. Circulating tumor extracellular vesicles to monitor metastatic prostate cancer genomics and transcriptomic evolution. Cancer Cell. 2024;42(7):1301–e13121307. Yan Y, Peng J, Liang Q, Ren X, Cai Y, Peng B, Chen X, Wang X, Yi Q, Xu Z. Dynamic m6A-ncRNAs association and their impact on cancer pathogenesis, immune regulation and therapeutic response. Genes Dis. 2023;10(1):135–50. Huang H, Weng H, Chen J. m(6)A Modification in Coding and Non-coding RNAs: Roles and Therapeutic Implications in Cancer. Cancer Cell. 2020;37(3):270–88. Li T, Hu PS, Zuo Z, Lin JF, Li X, Wu QN, Chen ZH, Zeng ZL, Wang F, Zheng J, et al. METTL3 facilitates tumor progression via an m(6)A-IGF2BP2-dependent mechanism in colorectal carcinoma. Mol Cancer. 2019;18(1):112. Li H, Liu Z, Wang H. Expression and clinical significance of METTL3 in colorectal cancer. Medicine. 2023;102(37):e34658. Nishizawa Y, Konno M, Asai A, Koseki J, Kawamoto K, Miyoshi N, Takahashi H, Nishida N, Haraguchi N, Sakai D, et al. Oncogene c-Myc promotes epitranscriptome m(6)A reader YTHDF1 expression in colorectal cancer. Oncotarget. 2018;9(7):7476–86. Zhu Y, Li J, Yang H, Yang X, Zhang Y, Yu X, Li Y, Chen G, Yang Z. The potential role of m6A reader YTHDF1 as diagnostic biomarker and the signaling pathways in tumorigenesis and metastasis in pan-cancer. Cell death discovery. 2023;9(1):34. Welsh JA, Goberdhan DCI, O'Driscoll L, Buzas EI, Blenkiron C, Bussolati B, Cai H, Di Vizio D, Driedonks TAP, Erdbrügger U, et al. Minimal information for studies of extracellular vesicles (MISEV2023): From basic to advanced approaches. J Extracell vesicles. 2024;13(2):e12404. Safari S, Baratloo A, Elfil M, Negida A. Evidence Based Emergency Medicine; Part 5 Receiver Operating Curve and Area under the Curve. Emerg (Tehran Iran). 2016;4(2):111–3. Hadisurya M, Li L, Kuwaranancharoen K, Wu X, Lee ZC, Alcalay RN, Padmanabhan S, Tao WA, Iliuk A. Quantitative proteomics and phosphoproteomics of urinary extracellular vesicles define putative diagnostic biosignatures for Parkinson's disease. Commun Med. 2023;3(1):64. Askeland A, Borup A, Østergaard O, Olsen JV, Lund SM, Christiansen G, Kristensen SR, Heegaard NHH, Pedersen S. Mass-Spectrometry Based Proteome Comparison of Extracellular Vesicle Isolation Methods: Comparison of ME-kit, Size-Exclusion Chromatography, and High-Speed Centrifugation. Biomedicines 2020, 8(8). Brennan K, Martin K, FitzGerald SP, O'Sullivan J, Wu Y, Blanco A, Richardson C, Mc Gee MM. A comparison of methods for the isolation and separation of extracellular vesicles from protein and lipid particles in human serum. Sci Rep. 2020;10(1):1039. Nouvel J, Bustos-Quevedo G, Prinz T, Masood R, Daaboul G, Gainey-Schleicher T, Wittel U, Chikhladze S, Melykuti B, Helmstaedter M, et al. Separation of small extracellular vesicles (sEV) from human blood by Superose 6 size exclusion chromatography. J Extracell vesicles. 2024;13(10):e70008. Sáenz-Cuesta M, Arbelaiz A, Oregi A, Irizar H, Osorio-Querejeta I, Muñoz-Culla M, Banales JM, Falcón-Pérez JM, Olascoaga J, Otaegui D. Methods for extracellular vesicles isolation in a hospital setting. Front Immunol. 2015;6:50. Di K, Fan B, Gu X, Huang R, Khan A, Liu C, Shen H, Li Z. Highly efficient and automated isolation technology for extracellular vesicles microRNA. Front Bioeng Biotechnol. 2022;10:948757. Tong Z, Yang D, Shen C, Li C, Xu X, Li Q, Wu Z, Ma H, Chen F, Mao H. Rapid automated extracellular vesicle isolation and miRNA preparation on a cost-effective digital microfluidic platform. Anal Chim Acta. 2024;1296:342337. Khanabdali R, Mandrekar M, Grygiel R, Vo PA, Palma C, Nikseresht S, Barton S, Shojaee M, Bhuiyan S, Asari K, et al. High-throughput surface epitope immunoaffinity isolation of extracellular vesicles and downstream analysis. Biology methods protocols. 2024;9(1):bpae032. Ensinck I, Sideri T, Modic M, Capitanchik C, Vivori C, Toolan-Kerr P, van Werven FJ. m6A-ELISA, a simple method for quantifying N6-methyladenosine from mRNA populations. RNA (New York NY). 2023;29(5):705–12. Mathur L, Jung S, Jang C, Lee G. Quantitative analysis of m(6)A RNA modification by LC-MS. STAR protocols. 2021;2(3):100724. Yang Y, Lu Y, Wang Y, Wen X, Qi C, Piao W, Jin H. Current progress in strategies to profile transcriptomic m(6)A modifications. Front cell Dev biology. 2024;12:1392159. Liu H, Begik O, Novoa EM. EpiNano: Detection of m(6)A RNA Modifications Using Oxford Nanopore Direct RNA Sequencing. Methods Mol biology (Clifton NJ). 2021;2298:31–52. Zhong ZD, Xie YY, Chen HX, Lan YL, Liu XH, Ji JY, Wu F, Jin L, Chen J, Mak DW, et al. Systematic comparison of tools used for m(6)A mapping from nanopore direct RNA sequencing. Nat Commun. 2023;14(1):1906. Van Deun J, Mestdagh P, Sormunen R, Cocquyt V, Vermaelen K, Vandesompele J, Bracke M, De Wever O, Hendrix A. The impact of disparate isolation methods for extracellular vesicles on downstream RNA profiling. J Extracell vesicles 2014, 3. Additional Declarations No competing interests reported. Supplementary Files supplementary1.tif supplementary2.tif Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 30 Apr, 2026 Reviews received at journal 20 Apr, 2026 Reviewers agreed at journal 11 Apr, 2026 Reviews received at journal 10 Apr, 2026 Reviewers agreed at journal 09 Apr, 2026 Reviewers agreed at journal 09 Apr, 2026 Reviewers invited by journal 23 Mar, 2026 Editor assigned by journal 17 Mar, 2026 Submission checks completed at journal 17 Mar, 2026 First submitted to journal 16 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9134647","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":610815812,"identity":"c2d85e4c-c879-4c5a-9a3f-71cd50b8b2e9","order_by":0,"name":"You-Tong Lin","email":"","orcid":"","institution":"National Yang Ming Chiao Tung University","correspondingAuthor":false,"prefix":"","firstName":"You-Tong","middleName":"","lastName":"Lin","suffix":""},{"id":610815813,"identity":"38037f01-457e-4e85-ad70-0256d7d86283","order_by":1,"name":"An-Chen Chang","email":"","orcid":"","institution":"Taipei Medical University","correspondingAuthor":false,"prefix":"","firstName":"An-Chen","middleName":"","lastName":"Chang","suffix":""},{"id":610815814,"identity":"77633605-c9cb-4a30-9e01-95812bd61fb2","order_by":2,"name":"Chian-Shiu Chien","email":"","orcid":"","institution":"National Yang Ming Chiao Tung University","correspondingAuthor":false,"prefix":"","firstName":"Chian-Shiu","middleName":"","lastName":"Chien","suffix":""},{"id":610815815,"identity":"e331896f-c85b-4ae4-a411-93943d0f364a","order_by":3,"name":"Chun-Chi Lin","email":"","orcid":"","institution":"Taipei Veterans General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Chun-Chi","middleName":"","lastName":"Lin","suffix":""},{"id":610815816,"identity":"60ccbd65-0913-4af2-8f7a-db5d7940440b","order_by":4,"name":"Hsin-Yi Lan","email":"","orcid":"","institution":"National Yang Ming Chiao Tung University","correspondingAuthor":false,"prefix":"","firstName":"Hsin-Yi","middleName":"","lastName":"Lan","suffix":""},{"id":610815817,"identity":"bd2ae57f-2eb2-4527-8e5e-09b11af61da5","order_by":5,"name":"Hao-Wei Teng","email":"","orcid":"","institution":"Taipei Veterans General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Hao-Wei","middleName":"","lastName":"Teng","suffix":""},{"id":610815818,"identity":"b53539de-5b13-4ddf-89f7-37178b46065f","order_by":6,"name":"Wei-Lun Hwang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1ElEQVRIiWNgGAWjYDACdh4wJQflMhOhhRmixRihhY1ILYkNRGsxOMx7TOLnjtr07RLJzyQYKqwTG+R7DAho4UuT7D1zPHfnjDQzCYYz6YkNbDz4tZgd5jG7wdt2LHfDjRw2Cca2w0AtvBsIarn5t+1YugFYyz8itdzmbatJgGhpIEKL/WEe89+ybQcMN5x5ZmyRcCzduI0t/wNeLZLtPcaGb9vq5A2OJz+88aHGWraf+VgCXi1QcJiBQQCoEKSWYExCQR0DA/8BItWOglEwCkbBiAMA6LJE6CjpU64AAAAASUVORK5CYII=","orcid":"","institution":"National Yang Ming Chiao Tung University","correspondingAuthor":true,"prefix":"","firstName":"Wei-Lun","middleName":"","lastName":"Hwang","suffix":""}],"badges":[],"createdAt":"2026-03-16 07:54:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9134647/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9134647/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105566622,"identity":"678c3086-6caf-486f-98d8-03913cd3f3ea","added_by":"auto","created_at":"2026-03-27 12:56:50","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":473061,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eBiochemical characterization of purified serum sEVs. A:\u003c/strong\u003e Western blot analysis shows the expression of sEV markers (ALIX, CD9, and CD63) and the organelle markers, including calreticulin (endoplasmic reticulum) and TOM20 (mitochondria), in serum sEVs. 10 μg of whole-cell lysate (WCL) from 293T cells is used as a control. The equivalent volume of serum sEV proteins is taken for each lane. H1 and H2, healthy sEVs; T1 to T4, tumor sEVs. M.W., molecular weight. \u003cstrong\u003eB:\u003c/strong\u003eRepresentative TEM images of serum sEVs from two cases. Scale bar: 100 nm. \u003cstrong\u003eC:\u003c/strong\u003eThe particle size distribution of serum sEVs was analyzed using TRPS with Exoid. Mean, average value of recorded particle diameter. Mode is the particle diameter that occurs most frequently. D90, the particle diameter at the 90th percentile of the distribution. D10 is the particle diameter at the 10th percentile of the distribution; D90 / D10 index is the ratio of the 90th percentile diameter to the 10th percentile diameter, representing the relative distribution range. \u003cstrong\u003eD-F:\u003c/strong\u003e Histograms demonstrate the mean diameter \u003cstrong\u003e(D)\u003c/strong\u003e, the mode diameter \u003cstrong\u003e(E)\u003c/strong\u003e, and the D90 / D10 index \u003cstrong\u003e(F)\u003c/strong\u003e of serum sEVs. \u003cstrong\u003eG:\u003c/strong\u003e Histogram exhibits the particle number of serum sEVs. \u003cstrong\u003eH:\u003c/strong\u003eHistogram displays the RNA amount of serum sEVs. Data represented mean ± SD. *\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, **\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01; ns, not significant (Student’s t-test).\u003c/p\u003e","description":"","filename":"OnlineFigure1.png","url":"https://assets-eu.researchsquare.com/files/rs-9134647/v1/d7f04ae9760d8d7842d505ac.png"},{"id":105566971,"identity":"ee877ffb-50b5-43f2-aafc-ca38dda6d9f5","added_by":"auto","created_at":"2026-03-27 12:57:53","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":110339,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIncreased serum sEV-m6A RNA abundance in CRC patients.\u003c/strong\u003e \u003cstrong\u003eA:\u003c/strong\u003e ROC curve analysis for distinguishing serum sEV-m6A RNA-positive and -negative patients. \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.0001, standard approximation method. \u003cstrong\u003eB:\u003c/strong\u003e A confusion matrix to visualize calculated accuracy, precision, sensitivity, and specificity. TN, true negative. FN, false negative. TP, true positive. TN, true negative. \u003cstrong\u003eC:\u003c/strong\u003e The scatter blot shows the serum sEV-m6A RNA contents of CRC patients and healthy donors. Data represented mean ± SD; *\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, ***\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001 (Student’s t-test).\u003c/p\u003e","description":"","filename":"OnlineFigure2.png","url":"https://assets-eu.researchsquare.com/files/rs-9134647/v1/ac9a243db89e3d20fd32145d.png"},{"id":105566341,"identity":"6066b510-b3a3-4477-8cad-9fd53a61fc64","added_by":"auto","created_at":"2026-03-27 12:56:13","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":859481,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelation between m6A RNA abundance and METTL3 immunoreactivity in CRC patients. A:\u003c/strong\u003e Representative images of METTL3 IHC staining. The photos on the upper right show enlarged views of the representative areas. Scale bar: 100 μm. \u003cstrong\u003eB:\u003c/strong\u003eThe histogram depicts the correlation between m6A RNA contents in CRC tissues and METTL3. \u003cstrong\u003eC:\u003c/strong\u003e The histogram illustrates the correlation between serum sEV-m6A RNA contents and the METTL3 expression. \u003cstrong\u003eD:\u003c/strong\u003e The histogram shows the correlation between m6A RNA contents in serum sEVs and CRC tissues. *\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, t-test. r: Pearson correlation coefficient.\u003c/p\u003e","description":"","filename":"OnlineFigure3.png","url":"https://assets-eu.researchsquare.com/files/rs-9134647/v1/6899bbdc85d4716fe08a1588.png"},{"id":105457187,"identity":"c0fdd700-1efc-4abe-af43-3276cb87a5ef","added_by":"auto","created_at":"2026-03-26 09:20:54","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":221262,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCRC patients with high serum sEV-m6A RNA abundance are associated with poor overall survival.\u003c/strong\u003e \u003cstrong\u003eA: \u003c/strong\u003eKaplan-Meier survival plot showing the overall survival rate of 76 CRC patients with high and low serum sEV-m6A RNA abundance. HR, hazard ratio. \u003cstrong\u003eB:\u003c/strong\u003e The overall survival rate of CRC patients with indicated tumor m6A RNA abundance. \u003cstrong\u003eC: \u003c/strong\u003eUtilization of liquid biopsy-based sEV-m6A RNA for colorectal cancer detection\u003cstrong\u003e.\u003c/strong\u003e Serum sEV-m6A RNAs serve as a minimally invasive biomarker for cancer detection, enabling cancer diagnosis and post-treatment cancer management. All components in the schematic are sourced from BioRender: sEV, small extracellular vesicle.\u003c/p\u003e","description":"","filename":"OnlineFigure4.png","url":"https://assets-eu.researchsquare.com/files/rs-9134647/v1/6a66b03529c545d303e56114.png"},{"id":105571241,"identity":"6776dd44-7935-4a00-8bfb-e75f6c383936","added_by":"auto","created_at":"2026-03-27 13:22:12","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3258630,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9134647/v1/6b5e4f5c-a8c5-4298-ac24-c26a54c91c8d.pdf"},{"id":105566537,"identity":"b76cc27d-04de-4386-9bfd-e7028d9bf8b6","added_by":"auto","created_at":"2026-03-27 12:56:39","extension":"tif","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":10056178,"visible":true,"origin":"","legend":"","description":"","filename":"supplementary1.tif","url":"https://assets-eu.researchsquare.com/files/rs-9134647/v1/33b29c1b969832129ab3e7d1.tif"},{"id":105567371,"identity":"0768aadd-a37d-4f21-8fe3-1c8f2b793012","added_by":"auto","created_at":"2026-03-27 12:59:12","extension":"tif","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":18845988,"visible":true,"origin":"","legend":"","description":"","filename":"supplementary2.tif","url":"https://assets-eu.researchsquare.com/files/rs-9134647/v1/77ac7c0d5aecc1e9ec9a5ca1.tif"}],"financialInterests":"No competing interests reported.","formattedTitle":"Serum extracellular vesicle-derived N6-methyladenosine RNA as a diagnostic and prognostic biomarker in colorectal cancer","fulltext":[{"header":"Introduction","content":"\u003cp\u003eExtracellular vesicles (EVs) are a heterogeneous population of membrane vesicles with lipid-bilayer and nanoscale properties released from various cell types and present in body fluids. EVs can be classified into diverse subtypes based on biogenesis and size distribution. Exosomes are one of the small EVs (sEVs) with a diameter range of 30\u0026ndash;150 nm [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Exosomes present several markers on the surface like tetraspanins (CD63, CD9, and CD81) [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e] and major histocompatibility complex (MHC) class I [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], and carry a diverse cargo of biomolecules including proteins, lipids, and nucleic acids: DNA, messenger RNA (mRNA), noncoding RNA (ncRNA) [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], which enable them to deliver messages to recipient cells, mediating in cell-cell communication.\u003c/p\u003e \u003cp\u003eEVs enable liquid-biopsy detection of nucleic acid and protein biomarkers, with plasma and serum favored for their higher EV abundance [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. In colorectal cancer (CRC), EV-derived DNA (evDNA) outperforms plasma cell-free DNA (cfDNA) by droplet digital PCR (ddPCR) for KRAS G12D/G13D detection across TNM stages [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. By next-generation sequencing (NGS), evDNA achieves higher sensitivity and specificity than cfDNA by ddPCR [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. The non-coding EV markers include serum exosomal miR-125a-3p [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], lncRNA NAMPT-AS [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], and circ-KLHDC10 [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] have been proposed. EV protein cargo is informative: plasma EV fibrinogen α chain (FGA) rises with progression and outperforms CEA/CA19-9 for early-stage detection [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], while targeted proteomics identified EV proteins, with \u0026gt;\u0026thinsp;80% specificity for stage I/II [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. EV-implemented assays complement traditional methods; notably, serum EV-microRNA (miRNA) levels exceed serum miRNA, and a higher level of EV-derived RNA (evRNA) relative to evDNA and cfDNA makes evRNA a more readily detectable source [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eN6-methyladenosine (m6A) is a prevalent internal RNA modification across mRNA and ncRNA [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. It is dynamically installed by writers, such as the METTL3-METTL14 complex, removed by erasers, such as FTO, and interpreted by readers, including YTHDF1 and IGF2BP2, to shape RNA fate and gene expression collectively [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Intracellular METTL3 drives metastasis via an m6A-IGF2BP2-SOX2 axis and is associated with worse patient survival [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Cell-intrinsic expression of YTHDF1 further serves as a prognostic and diagnostic factor [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Despite these associations, the clinical significance of m6A-modified evRNA remains unclear.\u003c/p\u003e \u003cp\u003eIn this study, we isolated m6A RNA from tumor tissue and serum-derived sEV (serum sEV) across tumor stages to investigate m6A RNA levels and compare them with those from healthy donors. The increased serum sEV-m6A RNA content suggested its potential for noninvasive CRC diagnosis. Additionally, CRC patients with high serum sEV-m6A RNA levels were associated with poor survival, supporting its potential as a biomarker for advanced CRC and its prognostic value.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eHuman specimens\u003c/h2\u003e \u003cp\u003e Paired 76 sera and tissue samples from CRC patients used in this retrospective study were obtained from the biobank of Taipei Veterans General Hospital, with approval from the institutional review board (IRB) (Approval number: 2023-01-014BC), following the ethical principles of the Declaration of Helsinki. A total of 19 healthy sera were used. 18 sera from healthy donors were collected at National Yang Ming Chiao Tung University under the IRB approval (Approval number: NYCU111182AE). Informed consent is waived. One commercially available pooled human serum was purchased from Rockland Immunochemicals (Catalog number: D119-00-0050).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSerum sEV isolation\u003c/h3\u003e\n\u003cp\u003esEVs were isolated from serum samples using the ExoQuick™ kit (System Biosciences, Palo Alto, CA, USA). 100 µL of serum was mixed with 25 µL of 1x phosphate-buffered saline (PBS, Bioman, New Taipei City, Taiwan) and 31.5 µL of ExoQuick reagent. The mixture was incubated on ice for 1 h. After incubation, the sample was centrifuged at 1,500 × g for 30 min at 4°C, and the supernatant was discarded. The pellet was centrifuged at 1,500 × g for an additional 5 min at 4°C, then the supernatant was removed. The resulting sEV pellet was collected for further analysis.\u003c/p\u003e\n\u003ch3\u003eRNA extraction\u003c/h3\u003e\n\u003cp\u003eThe sEV pellets or CRC tissues were resuspended in 50 µL of Dulbecco's phosphate-buffered saline (DPBS) (Cytiva, Marlborough, MA, USA), added with 950 µL TRIzol reagent (Thermo Fisher Scientific, Waltham, MA, USA), and incubated at room temperature for 10 min. Following this, 200 µL of chloroform (Sigma-Aldrich/Merck KGaA, Darmstadt, Germany) was added, and the mixture was incubated at room temperature for 3 min. The mixture was centrifuged at 12,000 × g for 15 min at 4°C to obtain a transparent aqueous phase containing RNA. An equal volume of isopropanol (Sigma-Aldrich) was added, and the mixture was incubated overnight at -20°C for RNA precipitation. The sample was centrifuged at 13,000 × g for 30 min at 4°C, and the supernatant was discarded. The RNA pellet was washed with 1 mL of 75% cold ethanol and centrifuged at 13,000 × g for 10 min at 4°C. The supernatant was discarded, and the pellet was air-dried. Finally, the RNA pellet was resuspended in 30 µL of nuclease-free water (Bioman) for downstream applications.\u003c/p\u003e\n\u003ch3\u003em6A RNA quantification\u003c/h3\u003e\n\u003cp\u003eEpiQuik™ m6A RNA Methylation Quantification Kit (EpiGentek, Farmingdale, NY, USA) was used for m6A RNA quantification. 200 ng of RNA was used as the input for each reaction. Standards of different concentrations and working solutions were prepared, and the experimental steps were carried out according to the manufacturer’s instructions. After the reactions, the absorbance was measured at 450 nm using a microplate reader (Infinite M200 Pro, Tecan, Switzerland). A standard curve was generated for the absolute quantification of m6A levels.\u003c/p\u003e\n\u003ch3\u003eTransmission electron microscope (TEM)\u003c/h3\u003e\n\u003cp\u003esEV pellets were resuspended in DPBS and filtered through a 0.45 µm filter (Millipore, Burlington, MA, USA), fixed with an equal volume of 4% paraformaldehyde (PFA, Sigma‒Aldrich/Merck KGaA) for 30 min at room temperature. The fixed sEV samples were diluted using nuclease-free water to prepare 100× and 1000× dilutions, then loaded onto a formvar/carbon-coated grid (Pelco, Fresno, CA, USA) for 40 min at room temperature. The grids were rinsed with nuclease-free water and dried for over 5 days. Images were captured using a JEOL JEM-1400plus (JEOL, Tokyo, Japan) at magnifications of 20,000×.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eTunable resistive pulse sensing (TRPS) particle analysis\u003c/h2\u003e \u003cp\u003esEV pellets isolated from 50 µL of serum were resuspended in DPBS and filtered through a 0.45 µm filter before analysis. Tunable resistive pulse sensing (TRPS) was conducted by an Exoid system employing an NP100 Nanopore and CPC100 standard calibration beads (Izon Bioscience, Christchurch, New Zealand). The Nanopore was stretched to 47 mm and wetted to verify the baseline current at 100 mV. It was then coated and rinsed with various buffers from the supplied Izon reagent kit. Before analysis, the NP100 Nanopore was calibrated with diluted CPC100 standard calibration beads (1500×) provided in the kit. Sample exosomes were diluted in 2× PBS, loaded into the Nanopore, and analyzed under the same pressure and voltage conditions as the calibration beads. The measurement conditions were as follows: stretch 47 mm, pressure 1000 cm H\u003csub\u003e2\u003c/sub\u003eO, and a current of about 110–130 nA. Data recording was performed using the Exoid Control Suite software (version V1.0.0.181), and particle size ranges and concentrations were calculated using the Izon Data Suite (version V1.0.2.32).\u003c/p\u003e \u003cp\u003e \u003cb\u003eImmunohistochemistry (IHC) staining.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eParaffin-embedded tissue slides were deparaffinized by heating at 65°C for 10 min, followed by immersion in ultra-clear solution (J.T. Baker, Phillipsburg, NJ, USA) three times, each for 10 min. The slides were rehydrated through a graded ethanol series: 100% ethanol (Nihon Shiyaku, Japan) for 5 min, twice; 90% ethanol for 5 min; 70% ethanol for 5 min; and 50% ethanol for 5 min, followed by rinsing with ddH₂O. For antigen retrieval, the slides were autoclaved at 120°C for 10 min and immersed in 10 mM citrate buffer (Honeywell, Morris Plains, NJ, USA). After cooling, the slides were washed with 1× PBS. The tissue area intended for staining was encircled with a hydrophobic pen. Blocking was performed by treating the tissues with 3% hydrogen peroxide for 10 min, followed by two 5-minute washes with 1× PBS. Cell membrane permeabilization was achieved using 0.1% Triton X-100 (Bionovas, North York, ON, Canada) for 5 min, followed by washing with 1× PBS twice for 5 min. The tissues were then incubated overnight at 4°C with the anti-METTL3 antibody (ABclonal, cat A8370) diluted 1:400 in antibody dilution buffer (Ventana). The tissues were washed twice with 1× PBS for 10 min each before incubation with anti-rabbit immunoglobulin (BioGenex, Fremont, CA, USA) for 30 min at room temperature. Next, the tissues were washed with 1× PBS for 10 min, twice, before being applied with streptavidin peroxidase (BioGenex, Fremont, CA, USA) for 20 min at room temperature, followed by 1× PBS for 10 min, twice. Next, tissues were treated with DAB solution (Epredia, Kalamazoo, MI, USA) for 45 seconds for visualization, and the reaction was stopped with 1× PBS. Finally, the tissues were counterstained with Mayer’s hemalum solution (Sigma-Aldrich/Merck KGaA) for 20 seconds and rinsed with ddH₂O. The slides were then mounted using Kaiser’s glycerol gelatin (Sigma-Aldrich/Merck KGaA). Images were captured under an Olympus BX43 microscope equipped with a DP22 CCD camera (Olympus, Tokyo, Japan), and the histology score (H-score) was calculated to quantify staining intensity. The H score was defined as the percentage of the METTL3-positive immunostained region (0 to 100) multiplied by the degree of METTL3 staining (0, 1, 2, and 3).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eWestern blot\u003c/h3\u003e\n \u003cp\u003esEV pellets isolated from 20 µL of serum were lysed with 40 µL of 1× radioimmunoprecipitation assay (RIPA) lysis buffer prepared from ddH\u003csub\u003e2\u003c/sub\u003eO diluted 5× RIPA lysis buffer (T-Pro Biotechnology) containing 1% protease inhibitor (ThermoFisher, Waltham, MA, USA) on ice for 1 h, then centrifuged at 13,000 rpm for 15 min at 4°C to extract protein supernatant. Subsequently, 40 µL of 2× sample buffer was added to the supernatant. The mixture was heated at 99°C for 10 min. SDS-PAGE-separated samples were then transferred to a PVDF membrane (Millipore, Burlington, MA, USA). The membranes were blocked with 5% skim milk for 1 h, followed by three washes with 1× PBS containing 0.1% Tween 20 (Bioshop, Burlington, ON, Canada). The membranes were then incubated with the primary antibodies at 4°C overnight in PBS-Tween20 (0.1%), followed by incubation with the relevant HRP-conjugated secondary antibodies (GeneTex, Irvine, CA, USA) at room temperature for 1 h on an orbital shaker. The membranes were soaked with chemiluminescent HRP substrate (Bioshop, Burlington, ON, Canada) and visualized using an ImageQuant LAS 4000 chemiluminescence detection system (GE Healthcare Bio-Sciences, Pittsburgh, PA, USA). The primary antibodies used are as follows: Alix (Cell Signaling, cat 2171), CD9 (Abcam, cat ab92726), CD63 (EMD Millipore, cat CBL551), Calreticulin (Abcam, cat ab39897), and TOM20 (ABclonal, cat A16896).\u003c/p\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eGraphPad Prism (version 10.1.2) was used for statistical analysis, including two-tailed unpaired Student’s t-test, Pearson correlation analysis, ROC curve analysis, chi-square test for correlation of clinicopathological variable and m6A abundance, and Log-rank (Mantel-Cox) test for Kaplan-Meier survival analysis. The Wilson/Brown test performed 95% confidence intervals (95% CI). A p-value less than 0.05 was considered statistically significant. Python visualized the confusion matrix for a cancer prediction model on Google Colab. The calculating formula was following: accuracy = (TP + TN)/(TP + TN+FP + FN), precision = TP/(TP + FP), sensitivity = TP/(TP + FN), specificity = TN/(TN + FP).\u003c/p\u003e \u003c/div\u003e "},{"header":"Result","content":"\u003ch2\u003eCharacterization of serum sEV from CRC patients and healthy donors\u003c/h2\u003e\u003cp\u003eA total of 76 CRC patients were included in the analysis. The early-stage group contained 16 of stage Ⅰ and 20 of stage Ⅱ; the late-stage group contained 20 of stage Ⅲ and 20 of stage Ⅳ; and their median ages were 66 years (range, 22–84 years) and 62.5 years (range, 34–84 years), respectively.\u003c/p\u003e\u003cp\u003eAll serum sEVs were isolated using ExoQuick™ precipitation solution and characterized according to MISEV2023 guidelines, including quantification, protein markers, and single-vesicle analysis [\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e]. Two healthy controls and four tumor samples were randomly selected for sEV characterization. For protein marker identification, sEV markers, including ALIX, CD9, and CD63, were enriched in serum sEVs from patients and healthy controls. Serum sEVs were free from organelle contamination, including mitochondria (TOM20) and endoplasmic reticulum (Calreticulin) \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eA\u003cb\u003e)\u003c/b\u003e. Under the transmission electron microscope (TEM), the images showed that serum sEVs were about 60 nm in diameter and had bilayer membranous structures \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eB\u003cb\u003e)\u003c/b\u003e. Single-vesicle analysis of serum sEVs from representative samples using tunable resistive pulse sensing (TRPS) showed a size distribution of 50–70 nm, consistent with TEM results \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eC\u003cb\u003e)\u003c/b\u003e. The mean particle diameters of serum sEVs from CRC patients and healthy donors were about 70 nm \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eD\u003cb\u003e)\u003c/b\u003e, and the mode particle diameters were about 60 nm \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eE\u003cb\u003e)\u003c/b\u003e. The d90/d10 ratio showed no statistically significant differences between the two groups \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eF\u003cb\u003e)\u003c/b\u003e. The particle concentration of serum sEVs from CRC patients was higher than that from healthy donors \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eG\u003cb\u003e)\u003c/b\u003e. Moreover, sEV RNA amounts were elevated in CRC patients \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eH\u003cb\u003e)\u003c/b\u003e. These findings support serum sEV RNA as a viable target for detection.\u003c/p\u003e\u003ch2\u003eDetection of serum sEV-derived m6A RNA abundance as diagnostics\u003c/h2\u003e\u003cp\u003eTo investigate the clinical significance of serum sEV-m6A RNA in CRC, the abundance of serum sEV-m6A RNA was quantified in all stages of CRC and in healthy controls, and receiver operating characteristic (ROC) curves were used to assess sensitivity and specificity across all possible threshold values [\u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e]. The confusion matrix calculated the accuracy and precision at a fixed threshold [\u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e]. Regardless of tumor staging, when the cut-off value of normalized serum sEV-m6A RNA level was 0.00867%, the serum sEV-m6A RNA level could differentiate CRC patients and healthy controls with an area under curve (AUC) of 0.8241 (95% CI, 0.7437–0.9045), a sensitivity of 78.95% (95% CI, 68.50%–86.60%), a specificity of 73.68% (95% CI, 51.21% – 88.19%) \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eA\u003cb\u003e)\u003c/b\u003e. An accuracy of 77.89% (95% CI, 68.56% – 85.06%), and a precision of 92.31% (95% CI, 83.22% – 96.67%) were observed under the setting \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eB\u003cb\u003e)\u003c/b\u003e, suggesting serum sEV-m6A RNA level could be considered a diagnostic molecule Furthermore, the serum sEV-m6A RNA level of CRC was increased from early-stage (I/II) to late-stage (III/IV) \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eC\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e\u003ch2\u003eCorrelation between m6A RNA levels in CRC tissues and serum-sEVs\u003c/h2\u003e\u003cp\u003eGiven the increased abundance of serum sEV-m6A RNA in CRC patients, we sought to elucidate potential sources. As METTL3 is the dominant m6A RNA writer [\u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e], we examined METTL3 protein expression by immunohistochemistry (IHC) \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eA\u003cb\u003e)\u003c/b\u003e. We found that in situ METTL3 expression positively correlated with m6A RNA levels in late-stage (III/IV) tumor tissues, but not in early-stage (I/II) CRC tissues \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eB\u003cb\u003e)\u003c/b\u003e. Interestingly, in early-stage CRC patients, METTL3 expression was positively correlated with serum sEV-m6A RNA levels, but not in late-stage samples \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eC\u003cb\u003e)\u003c/b\u003e. Furthermore, no significant association between m6A RNA in CRC tissues and serum-sEV was noted \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eD\u003cb\u003e)\u003c/b\u003e, suggesting highly dynamic m6A RNA biogenesis and transportation during cancer progression.\u003c/p\u003e\u003ch2\u003eEvaluate the prognostic significance of serum sEV-derived m6A RNA abundance\u003c/h2\u003e\u003cp\u003eNext, we stratified CRC patients into low- and high-m6A RNA groups based on the normalized median m6A level in CRC tissues or serum sEVs for clinicopathological characterization. It was found that m6A RNA levels in CRC tissues in situ showed no significant association with age, gender, tumor location, and tumor stage \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. In contrast, CRC patients with high m6A RNA levels in serum sEVs were more likely to have late-stage CRC (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001), with no association with age, gender, or tumor location \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. Moreover, CRC patients with high m6A RNA levels in serum sEVs had worse overall survival (OS) than those with low m6A RNA levels \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eA\u003cb\u003e)\u003c/b\u003e. In contrast, CRC patients with high m6A RNA levels in CRC tissues showed no difference in OS compared with those with low m6A RNA levels \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eB\u003cb\u003e)\u003c/b\u003e, highlighting the prognostic potential of serum sEV-m6A RNA.\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" class=\"colspec\"\u003e\u003c/div\u003e\u003ctable id=\"Tab1\" border=\"1\"\u003e \u003ccaption\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociation between clinicopathological characteristics and tumor tissue m6A RNA abundance in CRC patients (N = 76).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003c/colgroup\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"5\"\u003e \u003cp\u003eTumor tissue m6A RNA abundance (by median)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\"\u003e \u003cp\u003eN = 76\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\"\u003e \u003cp\u003eLow m6A (N = 38)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\"\u003e \u003cp\u003eHigh m6A (N = 38)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\"\u003e \u003cp\u003echaracteristics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003eAge (y/o)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u0026lt; 65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e52.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.8185\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e≧ 65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e47.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003eGender\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e47.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e63.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.1663\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e52.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e36.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003eLocation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eRight/ proximal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e21.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e18.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.7732\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eLeft/ distal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e78.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e81.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003eStage (AJCC Ⅶ )\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eEarly stage (Ⅰ/Ⅱ)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e47.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e47.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e\u0026gt; 0.9999\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eLate stage (Ⅲ/Ⅳ)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e52.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e52.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003cem\u003e*p\u003c/em\u003e value is estimated by a chi-square test.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e\u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" class=\"colspec\"\u003e\u003c/div\u003e\u003ctable id=\"Tab2\" border=\"1\"\u003e \u003ccaption\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociation between clinicopathological characteristics and serum-sEV m6A RNA abundance in CRC patients (N = 76). \u003cem\u003e*p\u003c/em\u003e value is estimated by a chi-square test.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003c/colgroup\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"5\"\u003e \u003cp\u003eSerum-sEV m6A RNA abundance (by median)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\"\u003e \u003cp\u003eN = 76\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\"\u003e \u003cp\u003eLow m6A (N = 37)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\"\u003e \u003cp\u003eHigh m6A (N = 39)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\"\u003e \u003cp\u003echaracteristics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003eAge (y/o)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u0026lt; 65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e45.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e56.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.3616\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e≧ 65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e54.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e43.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003eGender\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e48.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e61.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.2586\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e51.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e38.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003eLocation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eRight/ proximal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e10.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e28.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.0569\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eLeft/ distal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e89.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e71.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003eStage (AJCC Ⅶ )\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eEarly stage (Ⅰ/Ⅱ)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e78.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e17.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eLate stage (Ⅲ/Ⅳ)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e21.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e82.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/table\u003e\u003c/div\u003e\u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur study firstly compares m6A RNA abundance in CRC tumor tissue and paired serum sEVs. CRC patients with high serum sEV-m6A levels were markedly enriched in advanced stages, and associated with poorer survival, indicating its prognostic value. This study highlights the potential of serum sEV-m6A RNA as a minimally invasive biomarker for cancer diagnosis, with the findings that serum sEV-m6A RNA abundance was significantly elevated in CRC patients compared with healthy controls, with a sensitivity of 78.95%, specificity of 73.68%, and an AUC of 0.8241, fostering the development of a reliable, high-efficiency, high-specificity, and high-recovery-rate automated pipeline integrating EV isolation, RNA extraction, and m6A detection for point-of-care testing (POCT) and home care \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003eAlthough multiple studies have demonstrated that blood EVs are promising candidates for liquid biopsy, a standardized protocol for their isolation remains poorly defined. According to the MISEV2023 guideline, improper blood collection, processing, and storage can introduce significant variability and affect EV quality. Freeze-thaw cycles of the blood sample may disrupt EV membranes or generate cellular debris of similar size to EVs. At the same time, platelet activation upon stimulation leads to the release of many platelet EVs, which can alter the overall EV profile. Second, the presence of abundant soluble proteins and lipoproteins, which share similar size or density with EVs, makes separation challenging [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Different EV isolation methods introduce varying degrees of co-isolate contamination. For example, plasma EVs isolated by ultracentrifugation alone often retained albumin, whereas those isolated by size-exclusion chromatography (SEC) alone tended to retain lipoproteins [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Although combining more than two isolation techniques can significantly reduce these common contaminants, it often results in a substantial loss of EV yield [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Moreover, to avoid excess chylomicrons, an overnight fasting period before blood collection is recommended [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Given the trade-offs among purity, yield, and isolation efficiency, selecting an optimal procedure for blood collection and EV isolation requires careful tailoring to specific experimental needs to ensure the most reliable results.\u003c/p\u003e \u003cp\u003eA highly efficient, automated pipeline for EV isolation, RNA extraction, and m6A detection can bridge the gap in clinical translation. A closed, automated system suffices to address the challenges posed by time-consuming procedures, human error, analyte loss, and interference in RNA analysis. An automated EV-miRNA extraction technology utilizing Fe\u003csub\u003e3\u003c/sub\u003eO\u003csub\u003e4\u003c/sub\u003e@TiO\u003csub\u003e2\u003c/sub\u003e beads relies on the interaction between Ti\u003csup\u003e4+\u003c/sup\u003e and phosphate groups on the EV membrane, followed by magnetic capture and heat lysis for RNA extraction [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. In automatic digital microfluidics, magnetic particles capture EVs and move EVs on a chip via an electrode system for RNA preparation [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Both automated platforms require only 50 \u0026micro;L of plasma and can be completed within 30 minutes, offering a convenient, rapid solution. Establishing automated EV RNA isolation pipelines with enhanced standardization and yield stability will be essential for future clinical implementation [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. To quantify total serum sEV-m6A RNA levels, m6A-ELISA appeared to be the most suitable platform for hospital-based automated detection, given its lower cost, rapid turnaround, simplicity, and scalability compared to LC/MS [\u003cspan additionalcitationids=\"CR31\" citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Although nanopore direct RNA sequencing enabled precise detection of total m6A levels in total RNA via current signal variations, its high cost and complex algorithms limit broad clinical applicability [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThere were some limitations in our study. Since the serum samples had been stored for an extended period, freeze-thaw cycles may have disrupted EVs. Additionally, variations in EV isolation methods can impact RNA analysis outcomes [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Manual handling of RNA extraction and ELISA procedures also introduced batch-to-batch variability. Therefore, the performance of an optimal cut-off for fresh serum sEV-m6A RNA in CRC detection warrants further large-scale validation.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eEVs in liquid biopsy have been considered potential tools for cancer detection. This study demonstrated the value of serum sEV-m6A RNA as a biomarker for CRC diagnosis and prognosis, facilitating cancer management \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAUC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003earea under curve\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCRC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ecolorectal cancer\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eELISA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eenzyme-linked immunosorbent assay\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eEV\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eextracellular vesicle\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFN\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003efalse negative\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003efalse positive\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIHC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eimmunohistochemistry\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIRB\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003einstitutional review board\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003em6A\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eN6-methyladenosine\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eOS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eoverall survival\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePOCT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003epoint-of-care testing\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eROC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ereceiver operating characteristic\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003esEV\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003esmall extracellular vesicle\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTEM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003etransmission electron microscope\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTN\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003etrue negative\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003etrue positive\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTRPS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003etunable resistive pulse sensing.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003cstrong\u003eEthics approval and consent to participate:\u003c/strong\u003e \u003cp\u003e This study conforms to the principles of the Declaration of Helsinki and has been approved by the Institutional Review Board of Taipei Veterans General Hospital (2023-01-014BC) and the Institutional Review Board of National Yang Ming Chiao Tung University (NYCU111182AE). The informed consent has been waived.\u003c/p\u003e \u003ch2\u003eCompeting interests:\u003c/h2\u003e \u003cp\u003eThe authors declare no conflicts of interest.\u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e \u003cp\u003eNational Science and Technology Council (112-2314-B-038-146-MY3 to A-C.C., 113-2314-B-075-023 to H-W.T., and 112-2326-B-A49-002-MY3 and 111-2628-B-A49-017 to W-L.H). Ministry of Education, Higher Education SPROUT Project for Cancer and Immunology Research Center (114W031101 and 115W031101). A grant from the Yen Tjing Ling Medical Foundation (CI-111-15 and CI-115-24). Shin Kong Wu Ho-Su Memorial Hospital (2023SKHAND007).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eACC, HWT, and WLH conceptualized this study and directed the research; YTL and HYL conducted the experiments and analyzed the data with the support of CSC, CCL, HWT, and WLH. YTL wrote the original manuscript. ACC, CSC, CCL, HWT, and WLH revised the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe thank the Institute of Anatomy and Cell Biology at National Yang Ming Chiao Tung University for assistance with TEM imaging. This work was supported by the Higher Education SPROUT Project of the National Yang Ming Chiao Tung University and the Ministry of Education (MOE), Taiwan, for Cancer and Immunology Research Center and YEN TJING LAING MEDICAL FOUNDATION.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eData supporting the findings of this study are available within this article.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eYokoi A, Ochiya T. Exosomes and extracellular vesicles: Rethinking the essential values in cancer biology. Sem Cancer Biol. 2021;74:79\u0026ndash;91.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMathieu M, N\u0026eacute;vo N, Jouve M, Valenzuela JI, Maurin M, Verweij FJ, Palmulli R, Lankar D, Dingli F, Loew D, et al. Specificities of exosome versus small ectosome secretion revealed by live intracellular tracking of CD63 and CD9. Nat Commun. 2021;12(1):4389.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSynowsky SA, Shirran SL, Cooke FGM, Antoniou AN, Botting CH, Powis SJ. The major histocompatibility complex class I immunopeptidome of extracellular vesicles. J Biol Chem 2017, 292(41):17084\u0026ndash;92.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMathivanan S, Fahner CJ, Reid GE, Simpson RJ. ExoCarta 2012: database of exosomal proteins, RNA and lipids. Nucleic Acids Res. 2012;40(Database issue):D1241\u0026ndash;1244.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGardiner C, Di Vizio D, Sahoo S, Th\u0026eacute;ry C, Witwer KW, Wauben M, Hill AF. Techniques used for the isolation and characterization of extracellular vesicles: results of a worldwide survey. J Extracell vesicles. 2016;5:32945.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChoi J, Cho HY, Jeon J, Kim KA, Han YD, Ahn JB, Wortzel I, Lyden D, Kim HS. Detection of circulating KRAS mutant DNA in extracellular vesicles using droplet digital PCR in patients with colon cancer. Front Oncol. 2022;12:1067210.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eM\u0026ouml;hrmann L, Huang HJ, Hong DS, Tsimberidou AM, Fu S, Piha-Paul SA, Subbiah V, Karp DD, Naing A, Krug A, et al. Liquid Biopsies Using Plasma Exosomal Nucleic Acids and Plasma Cell-Free DNA Compared with Clinical Outcomes of Patients with Advanced Cancers. Clin cancer research: official J Am Association Cancer Res. 2018;24(1):181\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang J, Yan F, Zhao Q, Zhan F, Wang R, Wang L, Zhang Y, Huang X. Circulating exosomal miR-125a-3p as a novel biomarker for early-stage colon cancer. Sci Rep. 2017;7(1):4150.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRizk NI, Kassem DH, Abulsoud AI, AbdelHalim S, Yasser MB, Kamal MM, Hamdy NM. Revealing the role of serum exosomal novel long non-coding RNA NAMPT-AS as a promising diagnostic/prognostic biomarker in colorectal cancer patients. Life Sci. 2024;352:122850.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi Y, Zheng Q, Bao C, Li S, Guo W, Zhao J, Chen D, Gu J, He X, Huang S. Circular RNA is enriched and stable in exosomes: a promising biomarker for cancer diagnosis. Cell Res. 2015;25(8):981\u0026ndash;4.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZheng X, Xu K, Zhou B, Chen T, Huang Y, Li Q, Wen F, Ge W, Wang J, Yu S, et al. A circulating extracellular vesicles-based novel screening tool for colorectal cancer revealed by shotgun and data-independent acquisition mass spectrometry. J Extracell vesicles. 2020;9(1):1750202.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShiromizu T, Kume H, Ishida M, Adachi J, Kano M, Matsubara H, Tomonaga T. Quantitation of putative colorectal cancer biomarker candidates in serum extracellular vesicles by targeted proteomics. Sci Rep. 2017;7(1):12782.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCasanova-Salas I, Aguilar D, Cordoba-Terreros S, Agundez L, Brandariz J, Herranz N, Mas A, Gonzalez M, Morales-Barrera R, Sierra A, et al. Circulating tumor extracellular vesicles to monitor metastatic prostate cancer genomics and transcriptomic evolution. Cancer Cell. 2024;42(7):1301\u0026ndash;e13121307.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYan Y, Peng J, Liang Q, Ren X, Cai Y, Peng B, Chen X, Wang X, Yi Q, Xu Z. Dynamic m6A-ncRNAs association and their impact on cancer pathogenesis, immune regulation and therapeutic response. Genes Dis. 2023;10(1):135\u0026ndash;50.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuang H, Weng H, Chen J. m(6)A Modification in Coding and Non-coding RNAs: Roles and Therapeutic Implications in Cancer. Cancer Cell. 2020;37(3):270\u0026ndash;88.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi T, Hu PS, Zuo Z, Lin JF, Li X, Wu QN, Chen ZH, Zeng ZL, Wang F, Zheng J, et al. METTL3 facilitates tumor progression via an m(6)A-IGF2BP2-dependent mechanism in colorectal carcinoma. Mol Cancer. 2019;18(1):112.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi H, Liu Z, Wang H. Expression and clinical significance of METTL3 in colorectal cancer. Medicine. 2023;102(37):e34658.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNishizawa Y, Konno M, Asai A, Koseki J, Kawamoto K, Miyoshi N, Takahashi H, Nishida N, Haraguchi N, Sakai D, et al. Oncogene c-Myc promotes epitranscriptome m(6)A reader YTHDF1 expression in colorectal cancer. Oncotarget. 2018;9(7):7476\u0026ndash;86.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhu Y, Li J, Yang H, Yang X, Zhang Y, Yu X, Li Y, Chen G, Yang Z. The potential role of m6A reader YTHDF1 as diagnostic biomarker and the signaling pathways in tumorigenesis and metastasis in pan-cancer. Cell death discovery. 2023;9(1):34.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWelsh JA, Goberdhan DCI, O'Driscoll L, Buzas EI, Blenkiron C, Bussolati B, Cai H, Di Vizio D, Driedonks TAP, Erdbr\u0026uuml;gger U, et al. Minimal information for studies of extracellular vesicles (MISEV2023): From basic to advanced approaches. J Extracell vesicles. 2024;13(2):e12404.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSafari S, Baratloo A, Elfil M, Negida A. Evidence Based Emergency Medicine; Part 5 Receiver Operating Curve and Area under the Curve. Emerg (Tehran Iran). 2016;4(2):111\u0026ndash;3.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHadisurya M, Li L, Kuwaranancharoen K, Wu X, Lee ZC, Alcalay RN, Padmanabhan S, Tao WA, Iliuk A. Quantitative proteomics and phosphoproteomics of urinary extracellular vesicles define putative diagnostic biosignatures for Parkinson's disease. Commun Med. 2023;3(1):64.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAskeland A, Borup A, \u0026Oslash;stergaard O, Olsen JV, Lund SM, Christiansen G, Kristensen SR, Heegaard NHH, Pedersen S. Mass-Spectrometry Based Proteome Comparison of Extracellular Vesicle Isolation Methods: Comparison of ME-kit, Size-Exclusion Chromatography, and High-Speed Centrifugation. Biomedicines 2020, 8(8).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrennan K, Martin K, FitzGerald SP, O'Sullivan J, Wu Y, Blanco A, Richardson C, Mc Gee MM. A comparison of methods for the isolation and separation of extracellular vesicles from protein and lipid particles in human serum. Sci Rep. 2020;10(1):1039.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNouvel J, Bustos-Quevedo G, Prinz T, Masood R, Daaboul G, Gainey-Schleicher T, Wittel U, Chikhladze S, Melykuti B, Helmstaedter M, et al. Separation of small extracellular vesicles (sEV) from human blood by Superose 6 size exclusion chromatography. J Extracell vesicles. 2024;13(10):e70008.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eS\u0026aacute;enz-Cuesta M, Arbelaiz A, Oregi A, Irizar H, Osorio-Querejeta I, Mu\u0026ntilde;oz-Culla M, Banales JM, Falc\u0026oacute;n-P\u0026eacute;rez JM, Olascoaga J, Otaegui D. Methods for extracellular vesicles isolation in a hospital setting. Front Immunol. 2015;6:50.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDi K, Fan B, Gu X, Huang R, Khan A, Liu C, Shen H, Li Z. Highly efficient and automated isolation technology for extracellular vesicles microRNA. Front Bioeng Biotechnol. 2022;10:948757.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTong Z, Yang D, Shen C, Li C, Xu X, Li Q, Wu Z, Ma H, Chen F, Mao H. Rapid automated extracellular vesicle isolation and miRNA preparation on a cost-effective digital microfluidic platform. Anal Chim Acta. 2024;1296:342337.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKhanabdali R, Mandrekar M, Grygiel R, Vo PA, Palma C, Nikseresht S, Barton S, Shojaee M, Bhuiyan S, Asari K, et al. High-throughput surface epitope immunoaffinity isolation of extracellular vesicles and downstream analysis. Biology methods protocols. 2024;9(1):bpae032.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEnsinck I, Sideri T, Modic M, Capitanchik C, Vivori C, Toolan-Kerr P, van Werven FJ. m6A-ELISA, a simple method for quantifying N6-methyladenosine from mRNA populations. RNA (New York NY). 2023;29(5):705\u0026ndash;12.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMathur L, Jung S, Jang C, Lee G. Quantitative analysis of m(6)A RNA modification by LC-MS. STAR protocols. 2021;2(3):100724.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang Y, Lu Y, Wang Y, Wen X, Qi C, Piao W, Jin H. Current progress in strategies to profile transcriptomic m(6)A modifications. Front cell Dev biology. 2024;12:1392159.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu H, Begik O, Novoa EM. EpiNano: Detection of m(6)A RNA Modifications Using Oxford Nanopore Direct RNA Sequencing. Methods Mol biology (Clifton NJ). 2021;2298:31\u0026ndash;52.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhong ZD, Xie YY, Chen HX, Lan YL, Liu XH, Ji JY, Wu F, Jin L, Chen J, Mak DW, et al. Systematic comparison of tools used for m(6)A mapping from nanopore direct RNA sequencing. Nat Commun. 2023;14(1):1906.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVan Deun J, Mestdagh P, Sormunen R, Cocquyt V, Vermaelen K, Vandesompele J, Bracke M, De Wever O, Hendrix A. The impact of disparate isolation methods for extracellular vesicles on downstream RNA profiling. J Extracell vesicles 2014, 3.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcan","sideBox":"Learn more about [BMC Cancer](http://bmccancer.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcan/default.aspx","title":"BMC Cancer","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Extracellular vesicle, Liquid biopsy, N6-methyladenosine (m6A), Colorectal cancer","lastPublishedDoi":"10.21203/rs.3.rs-9134647/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9134647/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eExtracellular vesicle (EV)-based liquid biopsy is increasingly recognized as a promising strategy for cancer diagnosis and prognosis, as EVs carry abundant, stable biomolecular cargo. N6-methyladenosine (m6A), the most prevalent modification in eukaryotic intracellular RNA, plays a critical role in regulating diverse cellular processes and has been implicated in tumor initiation and progression. However, the potential of EV-associated m6A-modified RNA (EV-m6A RNA) as a clinically useful biomarker for cancer detection remains unclear.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eEV-RNA was isolated from tumor tissues and matched serum samples collected from 76 colorectal cancer (CRC) patients. Serum samples from 19 healthy donors were included as controls. EV-m6A RNA levels were quantified using an enzyme-linked immunosorbent assay (ELISA).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eSerum\u003cb\u003e-\u003c/b\u003eEVs isolated from CRC patients and healthy donors were identified as CD9(+)/CD63(+)/ALIX(+) small extracellular vesicles (sEVs), with a mean particle diameter of 60\u0026ndash;70 nm. The mean serum sEV concentration and sEV-RNA yield were significantly higher in CRC patients than in healthy controls (sEV concentration: 11\u0026thinsp;\u0026plusmn;\u0026thinsp;8 \u0026times; 10\u003csup\u003e12\u003c/sup\u003e/mL vs. 5\u0026thinsp;\u0026plusmn;\u0026thinsp;2 \u0026times; 10\u003csup\u003e12\u003c/sup\u003e/mL, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.006; sEV-RNA yield: 57\u0026thinsp;\u0026plusmn;\u0026thinsp;52 ng/\u0026micro;L vs. 18\u0026thinsp;\u0026plusmn;\u0026thinsp;7 ng/\u0026micro;L, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002). Through m6A modification-specific ELISA quantification, receiver operating characteristic (ROC) curve analysis demonstrated good discriminatory performance of normalized serum sEV-m6A RNA levels for CRC detection (AUC, 0.8241; 95% CI, 0.7437\u0026ndash;0.9045; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). Serum sEV-m6A RNA levels were noted to be higher in late-stage (III/IV) CRC patients than in those with early-stage (I/II) disease (0.015\u0026thinsp;\u0026plusmn;\u0026thinsp;0.001% vs. 0.009\u0026thinsp;\u0026plusmn;\u0026thinsp;0.005%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) with no significant correlation with intratumoral METTL3 expression, a m6A writer. Increased serum sEV-m6A RNA abundance further predicted a worse overall survival (OS) in CRC patients (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0107; HR, 3.894), while m6A RNA levels in tumor tissues showed no significant prognostic value (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.3243; HR, 1.152).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThese findings support serum sEV-m6A RNA levels as a feasible and noninvasive biomarker for colorectal cancer diagnosis, with additional potential for liquid biopsy-based prognostication and longitudinal disease monitoring\u003c/p\u003e","manuscriptTitle":"Serum extracellular vesicle-derived N6-methyladenosine RNA as a diagnostic and prognostic biomarker in colorectal cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-26 09:20:42","doi":"10.21203/rs.3.rs-9134647/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-30T10:35:04+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-20T20:07:25+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"178137165110829466790406600456225016572","date":"2026-04-11T09:57:19+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-10T06:44:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"132538391693300886717672246325271642267","date":"2026-04-10T02:27:00+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"111360389638041763543867012926570074639","date":"2026-04-09T11:08:12+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-23T15:20:37+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-17T14:03:38+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-17T14:02:42+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Cancer","date":"2026-03-16T07:45:17+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcan","sideBox":"Learn more about [BMC Cancer](http://bmccancer.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcan/default.aspx","title":"BMC Cancer","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"307614ca-c0b9-4ad1-9f56-47083dddde89","owner":[],"postedDate":"March 26th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Revision requested","date":"2026-04-30T10:35:04+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-15T10:38:10+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-26 09:20:42","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9134647","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9134647","identity":"rs-9134647","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}