Characterization and proteomic analysis of plasma-derived small extracellular vesicles in locally advanced rectal cancer patients

Characterization and proteomic analysis of plasma-derived small extracellular vesicles in locally advanced rectal cancer patients | 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 Characterization and proteomic analysis of plasma-derived small extracellular vesicles in locally advanced rectal cancer patients Haiyan Chen, Yimin Fang, Siqi Dai, Kai Jiang, Li Shen, Jian Zhao, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4539832/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 20 Aug, 2024 Read the published version in Cellular Oncology → Version 1 posted 12 You are reading this latest preprint version Abstract Background Neoadjuvant chemoradiotherapy (nCRT) stands as a pivotal therapeutic approach for locally advanced rectal cancer (LARC), yet the absence of a reliable biomarker to forecast its efficacy remains a challenge. Thus, this study aimed to assess whether the proteomic compositions of small extracellular vesicles (sEVs) might offer predictive insights into nCRT response among patients with LARC, while also delving into the proteomic alterations within sEVs post nCRT. Methods Plasma samples were obtained from LARC patients both pre- and post-nCRT. Plasma-derived sEVs were isolated utilizing the TIO 2 -based method, followed by LC-MS/MS-based proteomic analysis. Subsequently, pathway enrichment analysis were performed to the Differentially Expressed Proteins (DEPs). Additionally, ROC curves were generated to evaluate the predictive potential of sEV proteins in determining nCRT response. Public databases were interrogated to identify sEV protein-associated genes that are correlated with the response to nCRT in LARC. Results A total of 16 patients were enrolled. Among them, 8 patients achieved a pathological complete response (good responders, GR), while the remaining 8 did not achieve a complete response (poor responders, PR). Our analysis of pretreatment plasma-derived sEVs revealed 67 significantly up-regulated DEPs and 9 significantly down-regulated DEPs. Notably, PROC (AUC: 0.922), F7 (AUC: 0.953) and AZU1 (AUC: 0.906) demonstrated high AUC values and significant differences (P value < 0.05) in discriminating between GR and PR patients. Furthermore, a signature consisting of 5 sEV protein-associated genes (S100A6, ENO1, MIF, PRDX6 and MYL6) was capable of predicting the response to nCRT, yielding an AUC of 0.621(95% CI: 0.454–0.788). Besides, this 5-sEV protein-associated gene signature enabled stratification of patients into low- and high-risk group, with the low-risk group demonstrating a longer overall survival in the testing set (P = 0.048). Moreover, our investigation identified 11 significantly up-regulated DEPs and 31 significantly down-regulated DEPs when comparing pre- and post-nCRT proteomic profiles. GO analysis unveiled enrichment in the regulation of phospholipase A2 activity. Conclusions Differential expression of sEV proteins distinguishes between GR and PR patients and holds promise as predictive markers for nCRT response and prognosis in patients with LARC. Furthermore, our findings highlight substantial alterations in sEV protein composition following nCRT. Locally advanced rectal cancer Neoadjuvant chemoradiotherapy Small Extracellular vesicles Proteomics Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Colorectal cancer (CRC) ranks as the third most prevalent cancer type globally and is the second leading cause of cancer-related deaths 1 . Its incidence is rising, particularly among younger adults and in transitioning countries 2 . Locally advanced rectal cancer (LARC), which encompass T3 and T4 tumors and/or tumors involving nearby lymph nodes within the rectum, account for approximately 15% of CRC cases 3 . Due to anatomical constraints and the imperative to preserve sphincter and nerve function, treating LARC presents a significant challenge in colorectal surgery 4 . Neoadjuvant chemoradiotherapy (nCRT) has emerged as the standard treatment for LARC to improve the resection rate of tumors and reduce the risk of local recurrence 5 . Studies have shown that LARC patients who achieve a pathological complete response (pCR) following nCRT experience significantly increased disease-free survival (DFS) and overall survival (OS) compared to those who do not achieve pCR 5 . Additionally, patients with low rectal cancer who exhibit clinical complete response (cCR) to nCRT can opt for watchful waiting, avoiding the complications associated with surgery while preserving the affected organ 6 . However, only a limited percentage of patients (approximately 15%-27%) attain pCR when treated with conventional radiation therapy doses 7 . Efforts are underway to explore approaches to enhance the efficacy of radiation therapy and augment curative effects for patients with low rectal cancer. Additionally, for individuals who do not benefit from nCRT, alternative treatment modalities such as neoadjuvant immunotherapy and comprehensive chemotherapy are necessary to achieve improved treatment outcomes 8 . In the era of personalized medicine, accurately predicting an individual's response to nCRT and distinguishing between patients likely to be cured by nCRT and those who may be resistant or unresponsive to the treatment is vital 9 . Unfortunately, there is currently no effective biomarker available to predict the efficacy of nCRT in LARC. Therefore, the identification of novel predictive biomarkers holds considerable importance in tailoring LARC treatment and enhancing patient outcomes. Liquid biopsy refers to the utilization of biomarkers present in bodily fluids, primarily blood, for diagnostic and prognostic purposes 10 . While the technology behind liquid biopsies is still evolving, its non-invasive nature holds immense potential and ample opportunities for application in clinical oncology, particularly in scenarios where tissue acquisition poses challenges 11,12 . Notably, liquid biopsies are already employed to assess disease response and monitor relapse. The primary targets of liquid biopsy encompass circulating tumor DNA, circulating tumor cells, small extracellular vesicles (sEVs), circulating cell-free RNA, and tumor-educated platelets 12 . SEVs, which are small vesicles released by living cells and contain various bioactive substances like proteins, nucleic acids, lipids, and metabolites 13 , exhibit greater stability and sufficient concentration in the bloodstream compared to circulating tumor DNA and circulating tumor cells 14 . This grants sEVs advantages in the field of liquid biopsy, making them promising tools for monitoring tumors and therapeutic responses. For instance, studies have shown that the levels of sEV PD-L1 correlate with tumor burden and treatment response in melanoma patients undergoing PD-1 checkpoint inhibitor therapy 15,16 . Moreover, sEVs can act as carriers for intercellular communication and play a role in regulating tumor development and treatment sensitivity 17 . A notable example is the discovery that ALK protein present in sEVs can drive tumor growth and compromise the effectiveness of ALK inhibitors in ALK-positive non-small cell lung cancer 18 . The nucleic acids and proteins present in sEVs found in the bloodstream hold potential as biomarkers for CRC diagnosis and monitoring 19 . Notably, recent study has identified mutant KRAS and BRAF DNA on the surface of sEVs isolated from the plasma of CRC patients 20 . The quantity of either wild-type or mutant KRAS associated with sEVs has been identified as a prognostic marker for CRC 21 . Additionally, combining multiple markers such as EGFR, EpCAM, GPA33, and CD24 has demonstrated a diagnostic accuracy of 98% for CRC 22 . Overall, profiling sEVs provides a feasible approach for minimally invasive liquid biopsies, and further analysis of sEV populations shows promise as a strategy to identify potential biomarkers for diagnosis prognosis, and anti-cancer treatment monitoring 19 . While Strybel et al. 23 have reported an association between molecular components of sEVs and the response of patients with LARC to nCRT, our understanding of the predictive capabilities of sEVs in the context of nCRT response for LARC remains considerably limited. Hence, the objective of this study was to test the hypothesis that the proteomic components within sEVs are correlated with the response of LARC patients to nCRT. We aimed to determine whether these proteomic profiles could serve as predictive markers for the response to nCRT in LARC patients and investigate the proteomic alterations within sEVs post nCRT. Methods Plasma samples collection Plasma samples were obtained from patients diagnosed with LARC at the Second Affiliated Hospital of Zhejiang University School of Medicine. Inclusion criteria comprised: ( 1 ) histological confirmation of rectal adenocarcinoma; ( 2 ) tumor staging as T3-T4, and/or lymph node involvement confirmed by MRI; ( 3 ) absence of distant metastasis; ( 4 ) no prior anti-tumor treatment; ( 5 ) undergoing nCRT followed by total mesorectal excision surgical resection. The treatment regimen involved intensity-modulated radiation therapy, with a total radiation dose of either 50 Gy (administered in daily fractions of 2.0 Gy) or 57.5 Gy (administered in daily fractions of 2.3 Gy), given 5 days a week over 5 weeks. Concurrently, chemotherapy was administered using capecitabine at a dose of 825 mg/m 2 orally twice daily on radiation therapy days 24 . Plasma samples were collected before commencement of nCRT and at the completion of nCRT. Additionally, patients were categorized as either good responders (GR), defined by achieving pathological complete response (pCR), or poor responders (PR), indicating those without pCR. This project was approved by the Independent Ethics Committee of the Second Affiliated Hospital of Zhejiang University, and we got the informed consent from all patients. Isolation of sEVs from plasma SEVs were extracted from plasma utilizing TiO 2 enrichment technology 25,26 . Initially, cells and dead cells of plasma were eliminated via centrifugation at 2000g for 30 minutes, followed by a subsequent removal of cells debris through centrifugation at 12,000g for 45 minutes. Subsequently, 200 µL of plasma was combined with 20 mg of TiO 2 microspheres (Alson Scientific Instruments, Guangzhou, China) and incubated for 10 minutes on a shaker. Following this, exosomes adhering to the surface of the TiO 2 microspheres were lysed using 40 µL of lysis buffer (composed of 2% SDS, EDTA-free protease inhibitor cocktail, and 0.1 M Tris-HCl, pH 7.5), followed by ultrasonication on ice for 20 minutes. The sEV proteins were then harvested through centrifugation at 20,000g for 5 minutes. Besides, transmission electron microscopy (TEM) (Hitachi HT-7700, Tokyo, Japan) was used to confirm the morphology of sEVs, and sEV concentration and size were determined via Nano-Flow Cytometry device (NanoFCM Inc. N30E, China) 27 . Western blot (WB) analysis WB was performed as depicted previously 27,28 . The sEV protein samples underwent separation through 12% SDS-PAGE, followed by transfer onto a PVDF membrane (Bio-Rad, Hercules, CA, USA). Detection was carried out using primary antibody and peroxidase-conjugated secondary antibody (1:5000, Huabio, Hangzhou, China). Bands were then visualized utilizing enhanced chemiluminescence reagents (YEASEN, Shanghai, China) and captured by scanning with a Tanon 5200 Chemiluminescent Imaging System (Tanon, Shanghai, China). The primary antibodies used were Alix (1:1000, Cell Signaling Technology, Beverly, MA, USA), Calnexin (1:1000, Cell Signaling Technology, Beverly, MA, USA) and TSG101(1:1000, Cell Signaling Technology, Beverly, MA, USA). LC-MS/MS-based proteomic analysis SEV proteins were first treated with 5 mM dithiothreitol (DTT) to reduce disulfide bonds, followed by alkylation with 10 mM iodoacetamide (IAA) to prevent reformation of disulfide bonds. Subsequently, the proteins underwent digestion by trypsin at an enzyme/substrate ratio of 1:100, incubated overnight at 37°C. The resulting peptides were then subjected to desalting and concentration using C18 StageTip. Fractionation of the peptide samples was performed using a homemade capillary column (75 µm i.d. × 12 cm; ReproSil-Pur C18-AQ, 3 µm) on a Q Exactive HF-X mass spectrometer (Thermo Fisher Scientific, San Jose, CA) equipped with an UltiMate 3000 high-pressure liquid chromatography (UHPLC) system (Thermo Fisher Scientific, San Jose, CA) operating in data independent acquisition (DIA) mode. For the generation of the spectral library, DIA raw data were analyzed using Spectronaut software and a mass spectrometer vendor-independent software from Biognosys. The DIA files were searched against the Swiss-Prot human database (20,353 entries released on Aug 10, 2020). Functional analysis Differentially expressed proteins (DEPs) were identified as proteins showing a fold change > 1.5 and a statistically significant difference (p < 0.05, Student’s t-test) using the "MSstats" R package between the two groups. Volcano plots, heatmaps, and partial least squares discriminant analysis (PLS-DA) were performed. For the DEPs, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment were annotated and visualized using the “clusterProfiler” R package. The pathway-level p-value was calculated using a hypergeometric test, and values with p < 0.05 were deemed significantly enriched. Public database mining Expression data for sEV protein-associated genes in LARC were sourced from both the Memorial Sloan Kettering Cancer Center (MSKCC) database 29 accessible via http://www.cbioportal.org and the Gene Expression Omnibus 30,31 (GSE3843) available at http://www.ncbi.nlm.nih.gov/geo/ . Access to these databases was granted in accordance with the freedom-to-publish criteria outlined by The Cancer Genome Atlas and the National Center for Biotechnology Information. Patients were randomly assigned to training and testing cohorts at a ratio of 7:3. The Lasso-Logistic regression analysis was employed to identify sEV protein-associated genes correlated with the response to nCRT in LARC. The LASSO algorithm utilized the "glmnet" R package for variable selection and contraction. In the regression model, the independent variable consisted of the normalized expression matrix of sEV protein-associated genes, while the response variable indicated whether patients achieved pCR following nCRT. Statistical analysis Receiver Operating Characteristic (ROC) curves were utilized to assess the specificity and sensitivity of DEPs, with the Area Under the ROC Curve (AUC) calculated for each DEP 32 . These curves and AUC values were generated through the “timeROC” package in R. Statistical significance was established at a p-value of < 0.05. All analyses were two-sided, with 95% confidence intervals (CIs) employed. Results Isolation and proteomic analysis of plasma-derived sEVs in LARC The procedure for conducting sEV proteomics from plasma samples is depicted in Fig. 1 A. The study included a total of 16 patients diagnosed with LARC, of whom 8 patients achieved a pathological complete response (GR) and the remaining 8 did not achieve a complete response (PR). No statistically significant differences were observed in terms of age, gender, T stage, and N stage between the two groups (Fig. 1 B). Plasma samples were collected from these patients both before and after undergoing nCRT. Then, plasma-derived sEVs were isolated using the TIO 2 -based method and validated by TEM, WB and NTA. TEM results revealed round and cup-shaped vesicles with diameter ranging from 50 to 150 nm (supplementary Fig. 1A), correspond to size of sEVs. WB analysis demonstrated significant expression of traditional exosomal marker proteins, such as Alix and TSG101, while the intracellular protein contamination marker, calnexin, was absent (supplementary Fig. 1B). Additionally, Nano-Flow analysis showed that the mean size of the purified sEVs was 171.8 ± 11.76 nm (supplementary Fig. 1C). Taken together, these findings collectively indicate the successful isolation of sEVs from plasma samples obtained from patients with LARC. We next conducted LC-MS/MS-based proteomics of plasma-derived sEVs to identify potentially predictive biomarkers and investigate the proteomic alterations within sEVs post nCRT. Collectively, we quantified 6050, 6288, 6289, and 6359 peptides in sEV samples from GR and PR patients before and after nCRT (supplementary Fig. 2A). Then, a total of 705, 748, 736 and 752 proteins were identified in each respective group (supplementary Fig. 2B). The results for the coefficient of variation (CV%) were shown in supplementary Fig. 2B, illustrating favorable stability and consistency across the samples within each group. We compared the results of GR and PR patients before and after nCRT, and identified 827 sEV proteins that were common to all four groups, which was depicted in Venn diagram (supplementary Fig. 2C). To visualize the overall expression patterns among the groups, a heat map was created to visualize the overall expression patterns among the groups (supplementary Fig. 2D). Moreover, we detected 12 out of 13 (92.3%) sEV proteins (e.g., A2M, B2M, FLNA, FN1, GSN) that are reported by Ayuko et al. 43 , which are expressed at high frequency in human-derived sEVs and likely markers of endocytosis/exocytosis (Fig. 1 C). Identification of differentially-expressed sEV proteins in LARC patients To identify sEV proteins that could be used as predictive biomarkers for nCRT response in LARC, we first sought to identify DEPs by comparison between GR sEV proteomes and PR sEV proteomes before nCRT. PCA analysis revealed a distinction between GR and PR sEV proteins (Fig. 2 A). In total, we identified 67 significantly up-regulated DEPs (e.g., TTC9C, ROA1, UFC1, IGHG2 and SUMO3) and 9 significantly down-regulated DEPs (e.g., Q8IUL9, KV224, GUC2A and CDN1A) (Fig. 2 B) (supplementary Table 1). The expression of DEPs was displayed in a heatmap (Fig. 2 C). KEGG pathway enrichment analysis demonstrated that DEPs were enriched in pathways including the riboflavin metabolism (ACP1 and BLVRB), malaria (TGFB1, THBS1 and HBB) and TGF-beta signaling pathway (TGFB1, THBS1 and LTBP1) (Fig. 2 D). In addition, GO analysis of biological processes showed that DEPs were enriched in cellular process, single-organism process and biological regulation. GO analysis of cellular components revealed that most of the DEPs were associated with organelle, cell and cell part. At the molecular function level, DEPs exhibited enrichment in binding, catalytic activity and molecular function regulator (Fig. 2 E). We identified sEV proteins that were present in ≥ 50% of the GR samples and examined their baseline expression association with the likelihood of being a good responder. PROC (AUC: 0.922), F7 (AUC: 0.953), AZU1 (AUC: 0.906), ALB (AUC: 0.875), ENO1 (AUC: 0.828), APOC4 (AUC: 0.844), LCN2 (AUC: 0.875), CRNKL1 (AUC: 0.859), and HBB (AUC: 0.812) demonstrated statistically significant differences (P value < 0.05) and exhibited high AUC values in discriminating between GR and PR patients in the univariate logistic analysis (Fig. 2 F) (supplementary Fig. 3). These findings collectively indicate that the expression of sEV proteins differs between GR and PR patients and can be used to distinguish between different response types. SEV protein-associated genes predicted therapeutic response to nCRT in LARC Plasma-derived SEVs originate from various sources, including tumor, normal tissues and elsewhere. We have identified 76 differentially-expressed sEV proteins between GR and PR patients before nCRT, and then we investigated whether these sEV proteins originated from the tumor. To identify the likely source of differentially-expressed sEV proteins, we compared our plasma-derived sEV proteins with reported CRC cell-derived 33,34,35,36,37 and fibroblast cell-derived 35 sEV proteomes based on the public database (supplementary Table 2). Interestingly, 30 proteins such as SH3BGRL, SOD1, CA2 were present in both plasma- and cancer cell-derived CRC sEVs, but were undetectable in all of the fibroblast cell-derived sEV samples, suggesting that these proteins most likely originate from CRC (supplementary Table 3). Other 46 proteins were absent in cancer cell-derived sEVs, suggesting that these sEV proteins might originate from other normal cells or distant organs. These results reinforce the idea that all the cells can secret sEVs and nCRT response is a systemic procedure that requires not only cancer but also normal organs’ participation. To investigate the predictive role of cancer source-sEV protein-associated genes in determining radiosensitivity in CRC, we collected gene expression and clinical data of CRC from a public MSKCC database. Our objective was to assess the predictive value of these genes for response to nCRT. In the training cohort, we utilized lasso-logistic regression analysis to develop a signature consisting of 5 sEV protein-associated genes, including S100A6, ENO1, MIF, PRDX6 and MYL6 (Fig. 3 A). The predictive performance of this signature was evaluated using ROC curves, yielding an AUC of 0.692 (95% CI: 0.535–0.848) in the training cohort (Fig. 3 B), and 0.823 (95% CI: 0.628-1.000) in the testing cohort (Fig. 3 C). Additionally, we validated the predictive capability of this signature in an external dataset (GSE3843), where the area under the ROC curve was found to be 0.621(95% CI: 0.454–0.788), indicating favorable predictive value (Fig. 3 D). Concludely, sEV protein-associated genes can predict response to nCRT in LARC patients. In addition, based on this 5-sEV protein-associated gene signature, patients can be stratified into low- and high-risk group according to the optimal cut-off value selected by the ROC curve. The low-risk group had a longer OS in both training (P = 0.057) (Fig. 3 E) and testing sets (P = 0.048) (Fig. 3 F). In conclusion, our findings suggest that sEV protein-associated genes can serve as predictors of response to chemoradiotherapy and prognosis in LARC patients. nCRT-induced alteration of sEV proteins in LARC patients Afterward, we delved into the proteomic changes pre- and post-treatment among 16 patients diagnosed with LARC. We pinpointed 11 significantly up-regulated DEPs, such as IGLV3-22, RAB3IL1, and HPSE, alongside 31 significantly down-regulated DEPs, including IGKV1-9, IGHA2, and AHSP (Fig. 4 A) (supplementary Table 4). GO analysis of molecular function unveiled enrichment in the regulation of phospholipase A2 (PLA2) activity, 1-acylglycerophosphocholine O-acyltransferase activity, and calcium-independent PLA2 activity among the DEPs (Fig. 4 B). Within the GR cohort, we identified 16 significantly down-regulated DEPs (e.g., Q5NV74, ADH4, and HPSE) and 23 significantly up-regulated DEPs (e.g., OLFL3, CYTB, and HV70D) (Fig. 4 C) (supplementary Table 5). Conversely, in the PR cohort, we noted 19 significantly down-regulated DEPs (e.g., KI67, AEC1, and SBSN) and 89 significantly up-regulated DEPs (e.g., CPNE3, ROA1, and HNPRK) (Fig. 4 D) (supplementary Table 6). Subsequently, a comparison of the GR-associated DEPs with the PR-associated DEPs revealed 13 common DEPs across both groups. Furthermore, 26 DEPs were exclusive to the GR group, while 95 DEPs were unique to the PR group (Fig. 4 E). GO analysis of molecular function for the unique DEPs in the GR group unveiled significant enrichment in pathways such as protein self-association, extracellular matrix structural constituent, NADPH:quinone reductase activity, and alcohol dehydrogenase (NAD) activity (Fig. 4 F). Additionally, GO analysis of molecular function highlighted pathways enriched in the unique DEPs within the PR group, including magnesium ion binding, arylesterase activity, phospholipase A2 activity, and lyase activity (Fig. 4 G). Discussion SEVs are secreted by cells and released into various body fluids, including serum, urine, and saliva. They carry specific molecular information inherited from their parent cells, making them promising cancer biomarkers for liquid biopsy 38 . The enrichment and stability of sEVs render them a valuable diagnostic tool, particularly in the context of CRC. Currently, miRNAs are the most commonly investigated molecules in sEVs for CRC detection. MiRNAs contained within sEVs have been identified in the circulation of CRC patients, exhibiting dysregulation compared to healthy individuals. Specific miRNA candidates such as miR-21, miR-23a, miR-1246, and miR-92a have been suggested as potential diagnostic markers 39 . Furthermore, a signature based on long RNAs within plasma-derived sEVs has demonstrated promise in discerning CRC patients from healthy counterparts 40 . Notably, Zhang et al. 38 observed significantly elevated levels of CD147 and A33 on SEVs isolated from fecal samples of CRC patients, facilitating the discrimination between CRC patients and healthy individuals. SEVs thus represent an innovative generation of biomarkers for CRC, offering a non-invasive screening method on a large scale. In addition to their diagnostic utility in early detection of CRC, sEVs also hold prognostic significance in the disease. For instance, the expression of miR-17-92 in sEVs was notably elevated in CRC patients with poorer prognoses compared to healthy individuals 41 . Besides, sEVs harboring miR-122 represent another promising biomarker for predicting the survival outcomes of CRC, and the heightened expression of miR-122 within sEVs is closely linked to the presence of liver metastasis in CRC patients 42 . While many studies on sEVs from human samples primarily focus on nucleic acids, it's noteworthy that the proteome within sEVs is also enriched and emerging as a potential source of biomarkers. Park et al. 22 discovered that the expression of sEV proteins such as EGFR, EpCAM, CD24, and GPA33 could differentiate plasma samples from patients with CRC and non-CRC controls with accuracies exceeding 96%. Furthermore, the abundance of sEVs was predictive of five-year disease-free survival and strongly associated with tumor burden, showing a decrease after surgery and an increase upon relapse. The aforementioned findings indicate that sEVs have demonstrated excellent predictive capabilities in CRC. Additionally, mass spectrometry-based proteomic profiling is emerging as a strategy to gain insight into the biology and clinical potential of circulating sEVs 43 . However, whether they can forecast the efficacy of nCRT in LARC has not been reported in the literature thus far. Hence, our study aims to explore whether the proteomic profiles of sEVs could function as predictive markers for nCRT response in LARC patients and to investigate proteomic alterations within sEVs following nCRT. SEVs are actively released into the peripheral circulation by cancer cells, and can reflect the compositions and dynamic changes occurring within cancer cells, even participating in response to treatment 44 . In our study, we identified differential expression of sEV proteins before nCRT that distinguishes between patients categorized as GR and PR. Notably, PROC, F7, and AZU1 exhibited high AUC values (> 0.9) and significant differences (P value < 0.05). AZU1, functioning as a tumor suppressor, not only inhibits tumor cell proliferation but also facilitates tissue reorganization 45 . Interestingly, we found significantly higher levels of AZU1 in sEVs from GR patients compared to PR patients. The observed differential expression of these proteins in sEVs can serve as a valuable tool in distinguishing between GR and PR patients in LARC. Furthermore, considering that plasma-derived sEVs originate from tissues, and tumors are particularly prone to releasing exosomes on a large scale, sEV-associated genes were found to be dysregulated in tumor tissues among patients with varying response rates. Consequently, we investigated the expression of cancer-source sEV protein-associated genes in LARC tissues. Our analysis revealed that a signature consisting of five sEV protein-associated genes (S100A6, ENO1, MIF, PRDX6, and MYL6) could reliably predict the response to nCRT and contribute to the prediction of overall survival. Wu et al. 46 utilized tumor-derived sEV-associated genes to establish a TEXscore for predicting prognosis across various cancer types and in patients undergoing immunotherapy. They found that TEXscore serves as a robust biomarker for prognosis and treatment responses in independent cohorts. In summary, sEVs demonstrate significant potential as predictive markers for both nCRT response and prognosis in patients with LARC. In addition to their predictive role in patients with LARC, we also investigated the proteomic changes following nCRT. It has been demonstrated that radiation can alter the cargo of sEVs. We identified 11 significantly up-regulated DEPs alongside 31 significantly down-regulated DEPs after nCRT. GO analysis of molecular function revealed enrichment in the regulation of PLA2 activity. Currently, PLA2 enzymes have emerged as targets in cancer therapy, with elevated PLA2 activities detected in plasma from patients with CRC, exceeding those of healthy controls. Moreover, PLA2 levels were found to be correlated with CRC tumor stage 47 . Additionally, radiation can induce alterations in PLA2 activity, which can mediate critical biological processes such as inflammation, senescence, and apoptosis 48 . Our data demonstrate that radiation-induced alterations in PLA2 activity might be associated with radiotherapy-related toxicity. Furthermore, when stratified by tumor response, GO analysis of unique DEPs within the PR group showed enrichment in magnesium ion binding. Magnesium is an essential cofactor in almost all enzymatic systems involved in DNA processing and is highly required to maintain genomic stability. Apart from its stabilizing effect on DNA and chromatin structure, magnesium is involved in the removal of DNA damage and is required for repairing double-strand breaks arising after radiotherapy 49 . The alterations in the proteome of sEVs in the PR group might be associated with resistance to radiotherapy. However, as pointed out by Jelonek et al., there is a significant gap in understanding how radiation-induced changes in the composition of sEVs translate into their functional importance 50 . Further investigation is needed to elucidate how altered sEVs affect the response to nCRT. In conclusion, differential expression of sEV proteins distinguishes between GR and PR patients and shows promise as predictive markers for nCRT response and prognosis in LARC patients. Furthermore, our findings underscore significant alterations in sEV protein composition following nCRT. However, it is important to note that further studies are necessary to validate and verify these sEV proteins in larger clinical sample sets, and to explore their underlying functions and mechanisms. Declarations Ethics declarations This project was approved by the Independent Ethics Committee of the Second Affiliated Hospital of Zhejiang University, and we got the informed consent from all patients. Consent for publication Not applicable. Competing interests The authors declare no conflict of interest. Availability of data and material All data associated with this study are present in the paper or the supplementary materials. Any additional information required to reanalyze the data reported in this paper is available from the lead contact upon request. Contributions H.Y.C. and K.F.D. conceived and designed the study; Y.M.F. and S.Q.D. performed all the experiments with help from J.Z., L.S., K.H.W. and X.F.Z. H.Y.C. wrote the manuscript. All authors approved the manuscript. Funding This study was supported by the National Natural Science Foundation of China (Grant No. 82103498, No.82072624, No.82203704) and CSCO-Roche research funding (Y-Roche2019/2-0088). Acknowledgements Not applicable. References R.L. Siegel, K.D. Miller, N.S. Wagle, A. Jemal, Cancer Statistics, 2023. CA. Cancer J. Clin. 2023, 73 (1), 17–48. https://doi.org/10.3322/caac.21763 E. Morgan, M. Arnold, A. Gini, V. Lorenzoni, C.J. Cabasag, M. Laversanne, J. Vignat, J. Ferlay, N. Murphy, F. Bray, Global Burden of Colorectal Cancer in 2020 and 2040: Incidence and Mortality Estimates from GLOBOCAN. Gut. 72 (2), 338–344 (2023). https://doi.org/10.1136/gutjnl-2022-327736 K. He, L. Li, A. Li, Y. Xu, J. Pang, D. Mu, J. Ma, H. Ge, A. Maleki, X. Qin, X. Zhang, Q. Ou, Y. Shao, J. Yu, S. Yuan, Genomic Features, Evolutionary Patterns and Minimal Residual Disease at Surgical Margins as Novel Prognostic/Predictive Biomarkers in Locally Advanced Rectal Cancer. Clin. Transl Med. 13 (6), e1286 (2023). https://doi.org/10.1002/ctm2.1286 B.Y. Oh, Advances in Surgery for Locally Advanced Rectal Cancer. Ann. Coloproctology. 38 (4), 279–280 (2022). https://doi.org/10.3393/ac.2022.00493.0070 W.H. Zwart, A. Hotca, G.A.P. Hospers, K.A. Goodman, J. Garcia-Aguilar, The Multimodal Management of Locally Advanced Rectal Cancer: Making Sense of the New Data. Am. Soc. Clin. Oncol. Educ. Book. No. 42 , 264–277 (2022). https://doi.org/10.1200/EDBK_351411 High-dose chemoradiotherapy and watchful waiting for distal rectal cancer: a prospective observational study - ScienceDirect . https://www.sciencedirect.com/science/article/pii/S1470204515001205 (accessed 2023-06-16) T. Millard, P.R. Kunk, E. Ramsdale, O.E. Rahma, Current Debate in the Oncologic Management of Rectal Cancer. World J. Gastrointest. Oncol. 8 (10), 715–724 (2016). https://doi.org/10.4251/wjgo.v8.i10.715 D. Schrag, Q. Shi, M.R. Weiser, M.J. Gollub, L.B. Saltz, B.L. Musher, J. Goldberg, T. Al Baghdadi, K.A. Goodman, R.R. McWilliams, J.M. Farma, T.J. George, H.F. Kennecke, A. Shergill, M. Montemurro, G.D. Nelson, B. Colgrove, V. Gordon, A.P. Venook, E.M. O’Reilly, J.A. Meyerhardt, A.C. Dueck, E. Basch, G.J. Chang, H.J. Mamon, Preoperative Treatment of Locally Advanced Rectal Cancer. N Engl. J. Med. (2023). https://doi.org/10.1056/NEJMoa2303269 C. Koulis, R. Yap, R. Engel, T. Jardé, S. Wilkins, G. Solon, J.D. Shapiro, H. Abud, P. McMurrick, Personalized Medicine—Current and Emerging Predictive and Prognostic Biomarkers in Colorectal Cancer. Cancers. 12 (4), 812 (2020). https://doi.org/10.3390/cancers12040812 A. Bardelli, K. Pantel, Liquid Biopsies, What We Do Not Know (Yet). Cancer Cell. 31 (2), 172–179 (2017). https://doi.org/10.1016/j.ccell.2017.01.002 M. Ignatiadis, G.W. Sledge, S.S. Jeffrey, Liquid Biopsy Enters the Clinic — Implementation Issues and Future Challenges. Nat. Rev. Clin. Oncol. 18 (5), 297–312 (2021). https://doi.org/10.1038/s41571-020-00457-x C. Alix-Panabières, K. Pantel, Liquid Biopsy: From Discovery to Clinical Application. Cancer Discov. 11 (4), 858–873 (2021). https://doi.org/10.1158/2159-8290.CD-20-1311 G. Wang, J. Li, L. Bojmar, H. Chen, Z. Li, G.C. Tobias, M. Hu, E.A. Homan, S. Lucotti, F. Zhao, V. Posada, P.R. Oxley, M. Cioffi, H.S. Kim, H. Wang, P. Lauritzen, N. Boudreau, Z. Shi, C.E. Burd, J.H. Zippin, J.C. Lo, G.S. Pitt, J. Hernandez, C.P. Zambirinis, M.A. Hollingsworth, P.M. Grandgenett, M. Jain, S.K. Batra, D.J. DiMaio, J.L. Grem, K.A. Klute, T.M. Trippett, M. Egeblad, D. Paul, J. Bromberg, D. Kelsen, V.K. Rajasekhar, J.H. Healey, I.R. Matei, W.R. Jarnagin, R.E. Schwartz, H. Zhang, Lyden, D. Tumour Extracellular Vesicles and Particles Induce Liver Metabolic Dysfunction. Nature. 618 (7964), 374–382 (2023). https://doi.org/10.1038/s41586-023-06114-4 D. Yu, Y. Li, M. Wang, J. Gu, W. Xu, H. Cai, X. Fang, X. Zhang, Exosomes as a New Frontier of Cancer Liquid Biopsy. Mol. Cancer. 21 (1), 56 (2022). https://doi.org/10.1186/s12943-022-01509-9 S.M. Morrissey, J. Yan, Exosomal, PD-L1: Roles in Tumor Progression and Immunotherapy. Trends Cancer. 6 (7), 550–558 (2020). https://doi.org/10.1016/j.trecan.2020.03.002 S. Serratì, M. Guida, Di R. Fonte, De S. Summa, S. Strippoli, R.M. Iacobazzi, A. Quarta, De I. Risi, G. Guida, A. Paradiso, L. Porcelli, A. Azzariti, Circulating Extracellular Vesicles Expressing PD1 and PD-L1 Predict Response and Mediate Resistance to Checkpoint Inhibitors Immunotherapy in Metastatic Melanoma. Mol. Cancer. 21 (1), 20 (2022). https://doi.org/10.1186/s12943-021-01490-9 S.N. Lone, S. Nisar, T. Masoodi, M. Singh, A. Rizwan, S. Hashem, W. El-Rifai, D. Bedognetti, S.K. Batra, M. Haris, A.A. Bhat, M.A. Macha, Liquid Biopsy: A Step Closer to Transform Diagnosis, Prognosis and Future of Cancer Treatments. Mol. Cancer. 21 (1), 79 (2022). https://doi.org/10.1186/s12943-022-01543-7 H. Wu, C. Zeng, Y. Ye, J. Liu, Z. Mu, Y. Xie, B. Chen, Q. Nong, D. Wu, Exosomes from Irradiated Nonsmall Cell Lung Cancer Cells Reduced Sensitivity of Recipient Cells to Anaplastic Lymphoma Kinase Inhibitors. Mol. Pharm. 15 (5), 1892–1900 (2018). https://doi.org/10.1021/acs.molpharmaceut.8b00059 S.E. Glass, R.J. Coffey, Recent Advances in the Study of Extracellular Vesicles in Colorectal Cancer. Gastroenterology. 163 (5), 1188–1197 (2022). https://doi.org/10.1053/j.gastro.2022.06.039 S. Galbiati, F. Damin, D. Brambilla, L. Ferraro, N. Soriani, A.M. Ferretti, V. Burgio, M. Ronzoni, R. Vago, L. Sola, M. Chiari, Small EVs-Associated DNA as Complementary Biomarker to Circulating Tumor DNA in Plasma of Metastatic Colorectal Cancer Patients. Pharm. Basel Switz. 14 (2), 128 (2021). https://doi.org/10.3390/ph14020128 D. Lucchetti, I.V. Zurlo, F. Colella, C. Ricciardi-Tenore, Di M. Salvatore, G. Tortora, De R. Maria, F. Giuliante, A. Cassano, M. Basso, A. Crucitti, I. Laurenzana, G. Artemi, A. Sgambato, Mutational Status of Plasma Exosomal KRAS Predicts Outcome in Patients with Metastatic Colorectal Cancer. Sci. Rep. 11 (1), 22686 (2021). https://doi.org/10.1038/s41598-021-01668-7 J. Park, J.S. Park, C.-H. Huang, A. Jo, K. Cook, R. Wang, H.-Y. Lin, Van J. Deun, H. Li, J. Min, L. Wang, G. Yoon, B.S. Carter, L. Balaj, G.-S. Choi, C.M. Castro, R. Weissleder, H. Lee, An Integrated Magneto-Electrochemical Device for the Rapid Profiling of Tumour Extracellular Vesicles from Blood Plasma. Nat. Biomed. Eng. 5 (7), 678–689 (2021). https://doi.org/10.1038/s41551-021-00752-7 U. Strybel, L. Marczak, M. Zeman, K. Polanski, Ł. Mielańczyk, O. Klymenko, A. Samelak-Czajka, P. Jackowiak, M. Smolarz, M. Chekan, E. Zembala-Nożyńska, P. Widlak, M. Pietrowska, A. Wojakowska, Molecular Composition of Serum Exosomes Could Discriminate Rectal Cancer Patients with Different Responses to Neoadjuvant Radiotherapy. Cancers. 14 (4), 993 (2022). https://doi.org/10.3390/cancers14040993 C. Li, H. Chen, B. Zhang, Y. Fang, W. Sun, D. Wu, Z. Su, L. Shen, Q. Wei, Radiomics Signature Based on Support Vector Machines for the Prediction of Pathological Complete Response to Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer. Cancers. 15 (21), 5134 (2023). https://doi.org/10.3390/cancers15215134 F. Gao, F. Jiao, C. Xia, Y. Zhao, W. Ying, Y. Xie, X. Guan, M. Tao, Y. Zhang, W. Qin, X. Qian, A Novel Strategy for Facile Serum Exosome Isolation Based on Specific Interactions between Phospholipid Bilayers and TiO2. Chem. Sci. 10 (6), 1579 (2019). https://doi.org/10.1039/c8sc04197k L. Zhao, J. Shi, L. Chang, Y. Wang, S. Liu, Y. Li, T. Zhang, T. Zuo, B. Fu, G. Wang, Y. Ruan, Y. Zhang, P. Xu, Serum-Derived Exosomal Proteins as Potential Candidate Biomarkers for Hepatocellular Carcinoma. ACS Omega. 6 (1), 827–835 (2021). https://doi.org/10.1021/acsomega.0c05408 K. Jiang, H. Chen, Y. Fang, L. Chen, C. Zhong, T. Bu, S. Dai, X. Pan, D. Fu, Y. Qian, J. Wei, K. Ding, Exosomal ANGPTL1 Attenuates Colorectal Cancer Liver Metastasis by Regulating Kupffer Cell Secretion Pattern and Impeding MMP9 Induced Vascular Leakiness. J. Exp. Clin. Cancer Res. 40 (1), 21 (2021). https://doi.org/10.1186/s13046-020-01816-3 H. Chen, Q. Xiao, Y. Hu, L. Chen, K. Jiang, Y. Tang, Y. Tan, W. Hu, Z. Wang, J. He, Y. Liu, Y. Cai, Q. Yang, K. Ding, ANGPTL1 Attenuates Colorectal Cancer Metastasis by Up-Regulating microRNA-138. J. Exp. Clin. Cancer Res. 36 (1), 78 (2017). https://doi.org/10.1186/s13046-017-0548-7 W.K. Chatila, J.K. Kim, H. Walch, M.R. Marco, C.-T. Chen, F. Wu, D.M. Omer, D.N. Khalil, K. Ganesh, X. Qu, A. Luthra, S.-H. Choi, Y.-J. Ho, R. Kundra, K.I. Groves, O.S. Chow, A. Cercek, M.R. Weiser, M. Widmar, I.H. Wei, E.P. Pappou, G.M. Nash, P.B. Paty, Q. Shi, E. Vakiani, S. Duygu Selcuklu, M.T.A. Donoghue, D.B. Solit, M.F. Berger, J. Shia, R. Pelossof, P.B. Romesser, R. Yaeger, J.J. Smith, N. Schultz, F. Sanchez-Vega, J. Garcia-Aguilar, Genomic and Transcriptomic Determinants of Response to Neoadjuvant Therapy in Rectal Cancer. Nat. Med. 28 (8), 1646–1655 (2022). https://doi.org/10.1038/s41591-022-01930-z J. Gao, B.A. Aksoy, U. Dogrusoz, G. Dresdner, B. Gross, S.O. Sumer, Y. Sun, A. Jacobsen, R. Sinha, E. Larsson, E. Cerami, C. Sander, N. Schultz, Integrative Analysis of Complex Cancer Genomics and Clinical Profiles Using the cBioPortal. Sci. Signal. 6 (269), pl1 (2013). https://doi.org/10.1126/scisignal.2004088 E. Cerami1, J. Gao, U. Dogrusoz, B.E. Gross, S.O. Sumer, B.A. Aksoy, A. Jacobsen, C.J. Byrne, M.L. Heuer, E. Larsson, Y. Antipin, B. Reva, A.P. Goldberg, C. Sander, N. Schultz, The cBio Cancer Genomics Portal: An Open Platform for Exploring Multidimensional Cancer Genomics Data. Cancer Discov. 2 (5), 401–404 (2012). https://doi.org/10.1158/2159-8290.CD-12-0095 H. Chen, C. Li, L. Zheng, W. Lu, Y. Li, Q.A. Wei, Machine Learning-Based Survival Prediction Model of High Grade Glioma by Integration of Clinical and Dose-Volume Histogram Parameters. Cancer Med. 10 (8), 2774–2786 (2021). https://doi.org/10.1002/cam4.3838 D.-S. Choi, J.-S. Yang, E.-J. Choi, S.C. Jang, S. Park, O.Y. Kim, D. Hwang, K.P. Kim, Y.-K. Kim, S. Kim, Y.S. Gho, The Protein Interaction Network of Extracellular Vesicles Derived from Human Colorectal Cancer Cells. J. Proteome Res. 11 (2), 1144–1151 (2012). https://doi.org/10.1021/pr200842h B.J. Tauro, D.W. Greening, R.A. Mathias, S. Mathivanan, H. Ji, R.J. Simpson, Two Distinct Populations of Exosomes Are Released from LIM1863 Colon Carcinoma Cell-Derived Organoids *. Mol. Cell. Proteom. 12 (3), 587–598 (2013). https://doi.org/10.1074/mcp.M112.021303 C.-H. Lee, E.-J. Im, P.-G. Moon, M.-C. Baek, Discovery of a Diagnostic Biomarker for Colon Cancer through Proteomic Profiling of Small Extracellular Vesicles. BMC Cancer. 18 (1), 1058 (2018). https://doi.org/10.1186/s12885-018-4952-y W. Suwakulsiri, A. Rai, R. Xu, M. Chen, D.W. Greening, R.J. Simpson, Proteomic Profiling Reveals Key Cancer Progression Modulators in Shed Microvesicles Released from Isogenic Human Primary and Metastatic Colorectal Cancer Cell Lines. Biochim. Biophys. Acta BBA - Proteins Proteom. 1867 (12), 140171 (2019). https://doi.org/10.1016/j.bbapap.2018.11.008 A. Montero-Calle, I. Aranguren-Abeigon, M. Garranzo-Asensio, C. Poves, M.J. Fernández-Aceñero, J. Martínez-Useros, R. Sanz, J. Dziaková, J. Rodriguez-Cobos, G. Solís-Fernández, E. Povedano, M. Gamella, R.M. Torrente-Rodríguez, M. Alonso-Navarro, de los V. Ríos, J.I. Casal, G. Domínguez, A. Guzman-Aranguez, A. Peláez-García, J.M. Pingarrón, S. Campuzano, R. Barderas, Multiplexed Biosensing Diagnostic Platforms Detecting Autoantibodies to Tumor-Associated Antigens from Exosomes Released by CRC Cells and Tissue Samples Showed High Diagnostic Ability for Colorectal Cancer. Engineering. 7 (10), 1393–1412 (2021). https://doi.org/10.1016/j.eng.2021.04.026 Z. Zhang, X. Liu, X. Yang, Y. Jiang, A. Li, J. Cong, Y. Li, Q. Xie, C. Xu, D. Liu, Identification of Faecal Extracellular Vesicles as Novel Biomarkers for the Non-Invasive Diagnosis and Prognosis of Colorectal Cancer. J. Extracell. Vesicles. 12 (1), 12300 (2023). https://doi.org/10.1002/jev2.12300 B.J. Desmond, E.R. Dennett, K.M. Danielson, Circulating Extracellular Vesicle MicroRNA as Diagnostic Biomarkers in Early Colorectal Cancer—A Review. Cancers. 12 (1), 52 (2020). https://doi.org/10.3390/cancers12010052 T.-A. Guo, H.-Y. Lai, C. Li, Y. Li, Y.-C. Li, Y.-T. Jin, Z.-Z. Zhang, H.-B. Huang, S.-L. Huang, Y. Xu, Plasma Extracellular Vesicle Long RNAs Have Potential as Biomarkers in Early Detection of Colorectal Cancer. Front. Oncol. 2022, 12 . https://doi.org/10.3389/fonc.2022.829230 T. Matsumura, K. Sugimachi, H. Iinuma, Y. Takahashi, J. Kurashige, G. Sawada, M. Ueda, R. Uchi, H. Ueo, Y. Takano, Y. Shinden, H. Eguchi, H. Yamamoto, Y. Doki, M. Mori, T. Ochiya, K. Mimori, Exosomal microRNA in Serum Is a Novel Biomarker of Recurrence in Human Colorectal Cancer. Br. J. Cancer. 113 (2), 275–281 (2015). https://doi.org/10.1038/bjc.2015.201 L. Sun, X. Liu, B. Pan, X. Hu, Y. Zhu, Y. Su, Z. Guo, G. Zhang, M. Xu, X. Xu, H. Sun, S. Wang, Serum Exosomal miR-122 as a Potential Diagnostic and Prognostic Biomarker of Colorectal Cancer with Liver Metastasis. J. Cancer. 11 (3), 630–637 (2020). https://doi.org/10.7150/jca.33022 A. Hoshino, H.S. Kim, L. Bojmar, K.E. Gyan, M. Cioffi, J. Hernandez, C.P. Zambirinis, G. Rodrigues, H. Molina, S. Heissel, M.T. Mark, L. Steiner, A. Benito-Martin, S. Lucotti, Di A. Giannatale, K. Offer, M. Nakajima, C. Williams, L. Nogués, F.A. Pelissier Vatter, A. Hashimoto, A.E. Davies, D. Freitas, C.M. Kenific, Y. Ararso, W. Buehring, P. Lauritzen, Y. Ogitani, K. Sugiura, N. Takahashi, M. Alečković, K.A. Bailey, J.S. Jolissant, H. Wang, A. Harris, L.M. Schaeffer, G. García-Santos, Z. Posner, V.P. Balachandran, Y. Khakoo, G.P. Raju, A. Scherz, I. Sagi, R. Scherz-Shouval, Y. Yarden, M. Oren, M. Malladi, M. Petriccione, De K.C. Braganca, M. Donzelli, C. Fischer, S. Vitolano, G.P. Wright, L. Ganshaw, M. Marrano, A. Ahmed, J. DeStefano, E. Danzer, M.H.A. Roehrl, N.J. Lacayo, T.C. Vincent, M.R. Weiser, M.S. Brady, P.A. Meyers, L.H. Wexler, S.R. Ambati, A.J. Chou, E.K. Slotkin, S. Modak, S.S. Roberts, E.M. Basu, D. Diolaiti, B.A. Krantz, F. Cardoso, A.L. Simpson, M. Berger, C.M. Rudin, D.M. Simeone, M. Jain, C.M. Ghajar, S.K. Batra, B.Z. Stanger, J. Bui, K.A. Brown, V.K. Rajasekhar, J.H. Healey, de M. Sousa, K. Kramer, S. Sheth, J. Baisch, V. Pascual, T.E. Heaton, La M.P. Quaglia, D.J. Pisapia, R. Schwartz, H. Zhang, Y. Liu, A. Shukla, L. Blavier, Y.A. DeClerck, M. LaBarge, M.J. Bissell, T.C. Caffrey, P.M. Grandgenett, M.A. Hollingsworth, J. Bromberg, B. Costa-Silva, H. Peinado, Y. Kang, B.A. Garcia, E.M. O’Reilly, D. Kelsen, T.M. Trippett, Jones, D. R.; Matei, I. R.; Jarnagin, W. R.; Lyden, D. Extracellular Vesicle and Particle Biomarkers Define Multiple Human Cancers. Cell 2020, 182 (4), 1044–1061.e18. https://doi.org/10.1016/j.cell.2020.07.009 E. Zhou, Y. Li, F. Wu, M. Guo, J. Xu, S. Wang, Q. Tan, P. Ma, S. Song, Y. Jin, Circulating Extracellular Vesicles Are Effective Biomarkers for Predicting Response to Cancer Therapy. eBioMedicine 2021, 67 . https://doi.org/10.1016/j.ebiom.2021.103365 AZU-1:, A Candidate Breast Tumor Suppressor and Biomarker for Tumor Progression | Molecular Biology of the Cell . https://www.molbiolcell.org/doi/full/ 10.1091/mbc.11.4.1357 (accessed 2024-04-02) J. Wu, D. Zeng, S. Zhi, Z. Ye, W. Qiu, N. Huang, L. Sun, C. Wang, Z. Wu, J. Bin, Y. Liao, M. Shi, W. Liao, Single-Cell Analysis of a Tumor-Derived Exosome Signature Correlates with Prognosis and Immunotherapy Response. J. Transl Med. 19 (1), 381 (2021). https://doi.org/10.1186/s12967-021-03053-4 H. Cai, E.G. Chiorean, M.V. Chiorean, D.K. Rex, B.W. Robb, N.M. Hahn, Z. Liu, P.J. Loehrer, M.L. Harrison, Y. Xu, Elevated Phospholipase A2 Activities in Plasma Samples from Multiple Cancers. PLOS ONE. 8 (2), e57081 (2013). https://doi.org/10.1371/journal.pone.0057081 S.-R. Kim, J.-I. Heo, J.-W. Park, C.-M. Kang, K.S. Kim, Radiation-Induced Lipoprotein-Associated Phospholipase A2 Increases Lysophosphatidylcholine and Induces Endothelial Cell Damage. Toxicology. 458 , 152841 (2021). https://doi.org/10.1016/j.tox.2021.152841 Role of magnesium in genomic stability - ScienceDirect . https://www.sciencedirect.com/science/article/abs/pii/S0027510701000744 (accessed 2024-04-02) K. Jelonek, P. Widlak, M. Pietrowska, The Influence of Ionizing Radiation on Exosome Composition, Secretion and Intercellular Communication. Protein Pept. Lett. 23 (7), 656–663 (2016). https://doi.org/10.2174/0929866523666160427105138 Additional Declarations No competing interests reported. Supplementary Files FigS100.tif FigS200.tif FigS300.tif supplementarytable1RbeforeIRVSNRbeforeIR.diff.xls supplementarytable2.xlsx supplementarytable3.xlsx supplementarytable420240408.xls supplementarytable5RbeforeIRVSRafterIR.diff.xls supplementarytable6NRbeforeIRVSNRafterIR.diff.xls Cite Share Download PDF Status: Published Journal Publication published 20 Aug, 2024 Read the published version in Cellular Oncology → Version 1 posted Editorial decision: Revision requested 25 Jun, 2024 Reviews received at journal 24 Jun, 2024 Reviews received at journal 22 Jun, 2024 Reviews received at journal 19 Jun, 2024 Reviewers agreed at journal 08 Jun, 2024 Reviewers agreed at journal 07 Jun, 2024 Reviewers agreed at journal 07 Jun, 2024 Reviewers agreed at journal 07 Jun, 2024 Reviewers invited by journal 07 Jun, 2024 Editor assigned by journal 06 Jun, 2024 Submission checks completed at journal 06 Jun, 2024 First submitted to journal 06 Jun, 2024 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-4539832","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":316321701,"identity":"322bd227-d4f7-4447-bd75-90ef2c46173b","order_by":0,"name":"Haiyan Chen","email":"","orcid":"","institution":"Second Affiliated Hospital of Zhejiang University","correspondingAuthor":false,"prefix":"","firstName":"Haiyan","middleName":"","lastName":"Chen","suffix":""},{"id":316321702,"identity":"21715bd0-91db-4dc6-956f-af9eb4c2b68c","order_by":1,"name":"Yimin Fang","email":"","orcid":"","institution":"Second Affiliated Hospital of Zhejiang University","correspondingAuthor":false,"prefix":"","firstName":"Yimin","middleName":"","lastName":"Fang","suffix":""},{"id":316321703,"identity":"50ca9075-adda-4c4d-8c3b-d27e64b062db","order_by":2,"name":"Siqi Dai","email":"","orcid":"","institution":"Second Affiliated Hospital of Zhejiang University","correspondingAuthor":false,"prefix":"","firstName":"Siqi","middleName":"","lastName":"Dai","suffix":""},{"id":316321704,"identity":"55039ed6-7a32-4643-ac47-b3c3d8b54c63","order_by":3,"name":"Kai Jiang","email":"","orcid":"","institution":"Second Affiliated Hospital of Zhejiang University","correspondingAuthor":false,"prefix":"","firstName":"Kai","middleName":"","lastName":"Jiang","suffix":""},{"id":316321705,"identity":"6ecfc787-0817-4c17-bf8f-7188f3b79732","order_by":4,"name":"Li Shen","email":"","orcid":"","institution":"Second Affiliated Hospital of Zhejiang University","correspondingAuthor":false,"prefix":"","firstName":"Li","middleName":"","lastName":"Shen","suffix":""},{"id":316321706,"identity":"aafce12d-62e0-4c00-b2e0-3fbea7f9bb7b","order_by":5,"name":"Jian Zhao","email":"","orcid":"","institution":"First Affiliated Hospital of Bengbu Medical College","correspondingAuthor":false,"prefix":"","firstName":"Jian","middleName":"","lastName":"Zhao","suffix":""},{"id":316321707,"identity":"17ef2670-3e11-4cd6-a725-d8ea84c8f5ae","order_by":6,"name":"Kanghua Huang","email":"","orcid":"","institution":"Second Affiliated Hospital of Zhejiang University","correspondingAuthor":false,"prefix":"","firstName":"Kanghua","middleName":"","lastName":"Huang","suffix":""},{"id":316321708,"identity":"6ca4150a-c0c5-4c1a-aabe-75143cca36be","order_by":7,"name":"Xiaofeng Zhou","email":"","orcid":"","institution":"Second Affiliated Hospital of Zhejiang University","correspondingAuthor":false,"prefix":"","firstName":"Xiaofeng","middleName":"","lastName":"Zhou","suffix":""},{"id":316321709,"identity":"bdffae2d-d828-49e9-b1a5-a133e266d923","order_by":8,"name":"Kefeng Ding","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9UlEQVRIie3RsUrFMBSA4RMCnWrumnKh9xVOCSgFHyYiODuJg3ALQqf7APUdHOIbRALepdo1o8XJQah0FfHY4Y5pR8H8Sw4hHxkOQCz2F3MArIKcJm5B/t7YZUTRlOiFxB5IioebYGJ/5N6ba8xPVvfjUNaQC6/ZeBkgmRMXpWlRlc2HwawGlXnN102AoEuPi77+OjP+2WgiNOiEp7PkG7fGt6+WyHYJUf1DhRq7HauIaJwjGf3CmicsjE8UyBdZ3LX97TpERNeqz90NbrBzb6O8Ot2I/fnjGCJUMi0QpAYup2XSnmbiw3SsLLBh7m0sFov9y34A4k1Rjf/hgTYAAAAASUVORK5CYII=","orcid":"","institution":"Second Affiliated Hospital of Zhejiang University","correspondingAuthor":true,"prefix":"","firstName":"Kefeng","middleName":"","lastName":"Ding","suffix":""}],"badges":[],"createdAt":"2024-06-06 10:59:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4539832/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4539832/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s13402-024-00983-1","type":"published","date":"2024-08-20T15:57:15+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":59214969,"identity":"730eb3b7-da49-4431-a2b3-4bf87456a1a9","added_by":"auto","created_at":"2024-06-27 18:51:34","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":226900,"visible":true,"origin":"","legend":"\u003cp\u003e(A) The methodology for performing sEV proteomics on plasma samples from patients diagnosed with LARC, categorized into GR and PR groups. (B) Comparative analysis of age, gender, T stage, and N stage across the GR and PR groups. (C) Heatmap depicting the expression of sEV proteins frequently found in human-derived sEVs.\u003c/p\u003e","description":"","filename":"Fig100.png","url":"https://assets-eu.researchsquare.com/files/rs-4539832/v1/852bf7231dea4312aedb767c.png"},{"id":59214966,"identity":"b015871e-42f3-46de-a73f-f548fe29f044","added_by":"auto","created_at":"2024-06-27 18:51:34","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":304726,"visible":true,"origin":"","legend":"\u003cp\u003e(A) PCA analysis of sEV proteins from GR and PR groups before nCRT. (B) Significantly upregulated and downregulated DEPs in sEV proteins from GR and PR groups prior to nCRT. (C) Heatmap showing the expression levels of DEPs in the GR and PR groups before nCRT. (D-E) KEGG pathway and GO enrichment analysis of the identified DEPs. (F) DEPs including PROC (AUC: 0.922), F7 (AUC: 0.953), and AZU1 (AUC: 0.906) demonstrated statistically significant differences (P value \u0026lt; 0.05) and high AUC values, effectively distinguishing between GR and PR patients.\u003c/p\u003e","description":"","filename":"Fig200.png","url":"https://assets-eu.researchsquare.com/files/rs-4539832/v1/9cd2d3cea6ad9189c3e25d86.png"},{"id":59214973,"identity":"56281345-9063-430a-9a72-58e49fbd1a98","added_by":"auto","created_at":"2024-06-27 18:51:34","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":170623,"visible":true,"origin":"","legend":"\u003cp\u003e(A) Establishment of the sEV protein-associated gene signature for predicting the response to nCRT. (B) The 5-sEV protein-associated gene signature achieved an AUC of 0.692 (95% CI: 0.535-0.848) in the training cohort of the MSKCC dataset. (C) The 5-sEV protein-associated gene signature achieved an AUC of 0.823 (95% CI: 0.628-1.000) in the testing cohort of the MSKCC dataset. (D) The 5-sEV protein-associated gene signature achieved an AUC of 0.621 (95% CI: 0.454-0.788) in an external dataset (GSE3843). (E-F) The 5-sEV protein-associated gene signature effectively stratified patients into low- and high-risk groups. The low-risk group exhibited longer overall survival (OS) in both the training (P = 0.057) and testing sets (P = 0.048).\u003c/p\u003e","description":"","filename":"Fig300.png","url":"https://assets-eu.researchsquare.com/files/rs-4539832/v1/d2cf7a1fae55e4917f7385f7.png"},{"id":59214974,"identity":"02e9c3ea-cf65-4948-86f1-cefc5f059436","added_by":"auto","created_at":"2024-06-27 18:51:34","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":408805,"visible":true,"origin":"","legend":"\u003cp\u003e(A) Identification of significantly upregulated and downregulated DEPs in altered sEV proteins after nCRT. (B) GO analysis of molecular functions associated with DEPs in altered sEV proteins after nCRT.(C) Analysis of significantly upregulated and downregulated DEPs in the GR group among altered sEV proteins before and after nCRT. (D) Analysis of significantly upregulated and downregulated DEPs in the PR group among altered sEV proteins before and after nCRT. (E) Venn diagram showing the overlap of altered sEV proteins after nCRT between GR and PR patients. (F) GO analysis of molecular functions for the unique DEPs identified in the GR group. (G) GO analysis of molecular functions for the unique DEPs identified in the PR group.\u003c/p\u003e","description":"","filename":"Fig400.png","url":"https://assets-eu.researchsquare.com/files/rs-4539832/v1/787c4c87526a2b399c709357.png"},{"id":63300143,"identity":"4d301e95-cc9f-4d1f-aa5f-dc939b724c46","added_by":"auto","created_at":"2024-08-26 16:11:45","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1654303,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4539832/v1/74b81beb-8847-48ee-9bd3-f7cbb04423aa.pdf"},{"id":59215646,"identity":"f014f6f6-b649-4f8a-b5f1-f18a9fd9698e","added_by":"auto","created_at":"2024-06-27 18:59:34","extension":"tif","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":7108234,"visible":true,"origin":"","legend":"","description":"","filename":"FigS100.tif","url":"https://assets-eu.researchsquare.com/files/rs-4539832/v1/6b3debef166693876d494e52.tif"},{"id":59215644,"identity":"a35acde9-4992-46dc-b7e7-a9b8e01d4a6e","added_by":"auto","created_at":"2024-06-27 18:59:34","extension":"tif","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":7108234,"visible":true,"origin":"","legend":"","description":"","filename":"FigS200.tif","url":"https://assets-eu.researchsquare.com/files/rs-4539832/v1/c09bc7b866ca5f07990bf5c8.tif"},{"id":59214976,"identity":"2a19f6b6-aa10-4d73-b01e-22aa53b72d80","added_by":"auto","created_at":"2024-06-27 18:51:35","extension":"tif","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":5332234,"visible":true,"origin":"","legend":"","description":"","filename":"FigS300.tif","url":"https://assets-eu.researchsquare.com/files/rs-4539832/v1/e00bb681d750868442865515.tif"},{"id":59214977,"identity":"7dc87f89-41f0-48e0-b7ab-2d07f61affc7","added_by":"auto","created_at":"2024-06-27 18:51:35","extension":"xls","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":74795,"visible":true,"origin":"","legend":"","description":"","filename":"supplementarytable1RbeforeIRVSNRbeforeIR.diff.xls","url":"https://assets-eu.researchsquare.com/files/rs-4539832/v1/3b8dc08cce886bf8e34f35be.xls"},{"id":59215647,"identity":"1ebdafa0-b8aa-4dd5-a384-c7d908734a13","added_by":"auto","created_at":"2024-06-27 18:59:35","extension":"xlsx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":10690,"visible":true,"origin":"","legend":"","description":"","filename":"supplementarytable2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4539832/v1/48e06a5f1b0f9d1fe3d35d18.xlsx"},{"id":59214972,"identity":"5cd48a23-fc80-4654-9aa8-930c03c949bb","added_by":"auto","created_at":"2024-06-27 18:51:34","extension":"xlsx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":34113,"visible":true,"origin":"","legend":"","description":"","filename":"supplementarytable3.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4539832/v1/910844b0e9bc1dfd7ce49055.xlsx"},{"id":59214968,"identity":"f94b964b-52e9-41d2-a890-01495a5642f5","added_by":"auto","created_at":"2024-06-27 18:51:34","extension":"xls","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":49079,"visible":true,"origin":"","legend":"","description":"","filename":"supplementarytable420240408.xls","url":"https://assets-eu.researchsquare.com/files/rs-4539832/v1/c3e404a7dc6d152ddc2d5f63.xls"},{"id":59214975,"identity":"05f19849-daab-4778-970d-76f08d20fdb2","added_by":"auto","created_at":"2024-06-27 18:51:35","extension":"xls","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":44493,"visible":true,"origin":"","legend":"","description":"","filename":"supplementarytable5RbeforeIRVSRafterIR.diff.xls","url":"https://assets-eu.researchsquare.com/files/rs-4539832/v1/692ba40efd905f9ea9827206.xls"},{"id":59214978,"identity":"a7d0917c-a1c8-4e91-a7ba-0fb3433bd0b6","added_by":"auto","created_at":"2024-06-27 18:51:35","extension":"xls","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":106362,"visible":true,"origin":"","legend":"","description":"","filename":"supplementarytable6NRbeforeIRVSNRafterIR.diff.xls","url":"https://assets-eu.researchsquare.com/files/rs-4539832/v1/ab0a68350d0c92823392585d.xls"}],"financialInterests":"No competing interests reported.","formattedTitle":"Characterization and proteomic analysis of plasma-derived small extracellular vesicles in locally advanced rectal cancer patients","fulltext":[{"header":"Introduction","content":"\u003cp\u003eColorectal cancer (CRC) ranks as the third most prevalent cancer type globally and is the second leading cause of cancer-related deaths\u003csup\u003e1\u003c/sup\u003e. Its incidence is rising, particularly among younger adults and in transitioning countries\u003csup\u003e2\u003c/sup\u003e. Locally advanced rectal cancer (LARC), which encompass T3 and T4 tumors and/or tumors involving nearby lymph nodes within the rectum, account for approximately 15% of CRC cases\u003csup\u003e3\u003c/sup\u003e. Due to anatomical constraints and the imperative to preserve sphincter and nerve function, treating LARC presents a significant challenge in colorectal surgery\u003csup\u003e4\u003c/sup\u003e. Neoadjuvant chemoradiotherapy (nCRT) has emerged as the standard treatment for LARC to improve the resection rate of tumors and reduce the risk of local recurrence\u003csup\u003e5\u003c/sup\u003e. Studies have shown that LARC patients who achieve a pathological complete response (pCR) following nCRT experience significantly increased disease-free survival (DFS) and overall survival (OS) compared to those who do not achieve pCR\u003csup\u003e5\u003c/sup\u003e. Additionally, patients with low rectal cancer who exhibit clinical complete response (cCR) to nCRT can opt for watchful waiting, avoiding the complications associated with surgery while preserving the affected organ\u003csup\u003e6\u003c/sup\u003e. However, only a limited percentage of patients (approximately 15%-27%) attain pCR when treated with conventional radiation therapy doses\u003csup\u003e7\u003c/sup\u003e. Efforts are underway to explore approaches to enhance the efficacy of radiation therapy and augment curative effects for patients with low rectal cancer. Additionally, for individuals who do not benefit from nCRT, alternative treatment modalities such as neoadjuvant immunotherapy and comprehensive chemotherapy are necessary to achieve improved treatment outcomes\u003csup\u003e8\u003c/sup\u003e. In the era of personalized medicine, accurately predicting an individual's response to nCRT and distinguishing between patients likely to be cured by nCRT and those who may be resistant or unresponsive to the treatment is vital\u003csup\u003e9\u003c/sup\u003e. Unfortunately, there is currently no effective biomarker available to predict the efficacy of nCRT in LARC. Therefore, the identification of novel predictive biomarkers holds considerable importance in tailoring LARC treatment and enhancing patient outcomes.\u003c/p\u003e \u003cp\u003eLiquid biopsy refers to the utilization of biomarkers present in bodily fluids, primarily blood, for diagnostic and prognostic purposes\u003csup\u003e10\u003c/sup\u003e. While the technology behind liquid biopsies is still evolving, its non-invasive nature holds immense potential and ample opportunities for application in clinical oncology, particularly in scenarios where tissue acquisition poses challenges\u003csup\u003e11,12\u003c/sup\u003e. Notably, liquid biopsies are already employed to assess disease response and monitor relapse. The primary targets of liquid biopsy encompass circulating tumor DNA, circulating tumor cells, small extracellular vesicles (sEVs), circulating cell-free RNA, and tumor-educated platelets\u003csup\u003e12\u003c/sup\u003e. SEVs, which are small vesicles released by living cells and contain various bioactive substances like proteins, nucleic acids, lipids, and metabolites\u003csup\u003e13\u003c/sup\u003e, exhibit greater stability and sufficient concentration in the bloodstream compared to circulating tumor DNA and circulating tumor cells \u003csup\u003e14\u003c/sup\u003e. This grants sEVs advantages in the field of liquid biopsy, making them promising tools for monitoring tumors and therapeutic responses. For instance, studies have shown that the levels of sEV PD-L1 correlate with tumor burden and treatment response in melanoma patients undergoing PD-1 checkpoint inhibitor therapy\u003csup\u003e15,16\u003c/sup\u003e. Moreover, sEVs can act as carriers for intercellular communication and play a role in regulating tumor development and treatment sensitivity\u003csup\u003e17\u003c/sup\u003e. A notable example is the discovery that ALK protein present in sEVs can drive tumor growth and compromise the effectiveness of ALK inhibitors in ALK-positive non-small cell lung cancer\u003csup\u003e18\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe nucleic acids and proteins present in sEVs found in the bloodstream hold potential as biomarkers for CRC diagnosis and monitoring\u003csup\u003e19\u003c/sup\u003e. Notably, recent study has identified mutant KRAS and BRAF DNA on the surface of sEVs isolated from the plasma of CRC patients\u003csup\u003e20\u003c/sup\u003e. The quantity of either wild-type or mutant KRAS associated with sEVs has been identified as a prognostic marker for CRC\u003csup\u003e21\u003c/sup\u003e. Additionally, combining multiple markers such as EGFR, EpCAM, GPA33, and CD24 has demonstrated a diagnostic accuracy of 98% for CRC\u003csup\u003e22\u003c/sup\u003e. Overall, profiling sEVs provides a feasible approach for minimally invasive liquid biopsies, and further analysis of sEV populations shows promise as a strategy to identify potential biomarkers for diagnosis prognosis, and anti-cancer treatment monitoring\u003csup\u003e19\u003c/sup\u003e. While Strybel et al.\u003csup\u003e23\u003c/sup\u003e have reported an association between molecular components of sEVs and the response of patients with LARC to nCRT, our understanding of the predictive capabilities of sEVs in the context of nCRT response for LARC remains considerably limited. Hence, the objective of this study was to test the hypothesis that the proteomic components within sEVs are correlated with the response of LARC patients to nCRT. We aimed to determine whether these proteomic profiles could serve as predictive markers for the response to nCRT in LARC patients and investigate the proteomic alterations within sEVs post nCRT.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePlasma samples collection\u003c/h2\u003e \u003cp\u003ePlasma samples were obtained from patients diagnosed with LARC at the Second Affiliated Hospital of Zhejiang University School of Medicine. Inclusion criteria comprised: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) histological confirmation of rectal adenocarcinoma; (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) tumor staging as T3-T4, and/or lymph node involvement confirmed by MRI; (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) absence of distant metastasis; (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) no prior anti-tumor treatment; (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) undergoing nCRT followed by total mesorectal excision surgical resection. The treatment regimen involved intensity-modulated radiation therapy, with a total radiation dose of either 50 Gy (administered in daily fractions of 2.0 Gy) or 57.5 Gy (administered in daily fractions of 2.3 Gy), given 5 days a week over 5 weeks. Concurrently, chemotherapy was administered using capecitabine at a dose of 825 mg/m\u003csup\u003e2\u003c/sup\u003e orally twice daily on radiation therapy days\u003csup\u003e24\u003c/sup\u003e. Plasma samples were collected before commencement of nCRT and at the completion of nCRT. Additionally, patients were categorized as either good responders (GR), defined by achieving pathological complete response (pCR), or poor responders (PR), indicating those without pCR. This project was approved by the Independent Ethics Committee of the Second Affiliated Hospital of Zhejiang University, and we got the informed consent from all patients.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eIsolation of sEVs from plasma\u003c/h2\u003e \u003cp\u003eSEVs were extracted from plasma utilizing TiO\u003csub\u003e2\u003c/sub\u003e enrichment technology\u003csup\u003e25,26\u003c/sup\u003e. Initially, cells and dead cells of plasma were eliminated via centrifugation at 2000g for 30 minutes, followed by a subsequent removal of cells debris through centrifugation at 12,000g for 45 minutes. Subsequently, 200 \u0026micro;L of plasma was combined with 20 mg of TiO\u003csub\u003e2\u003c/sub\u003e microspheres (Alson Scientific Instruments, Guangzhou, China) and incubated for 10 minutes on a shaker. Following this, exosomes adhering to the surface of the TiO\u003csub\u003e2\u003c/sub\u003e microspheres were lysed using 40 \u0026micro;L of lysis buffer (composed of 2% SDS, EDTA-free protease inhibitor cocktail, and 0.1 M Tris-HCl, pH 7.5), followed by ultrasonication on ice for 20 minutes. The sEV proteins were then harvested through centrifugation at 20,000g for 5 minutes. Besides, transmission electron microscopy (TEM) (Hitachi HT-7700, Tokyo, Japan) was used to confirm the morphology of sEVs, and sEV concentration and size were determined via Nano-Flow Cytometry device (NanoFCM Inc. N30E, China) \u003csup\u003e27\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eWestern blot (WB) analysis\u003c/h2\u003e \u003cp\u003eWB was performed as depicted previously\u003csup\u003e27,28\u003c/sup\u003e. The sEV protein samples underwent separation through 12% SDS-PAGE, followed by transfer onto a PVDF membrane (Bio-Rad, Hercules, CA, USA). Detection was carried out using primary antibody and peroxidase-conjugated secondary antibody (1:5000, Huabio, Hangzhou, China). Bands were then visualized utilizing enhanced chemiluminescence reagents (YEASEN, Shanghai, China) and captured by scanning with a Tanon 5200 Chemiluminescent Imaging System (Tanon, Shanghai, China). The primary antibodies used were Alix (1:1000, Cell Signaling Technology, Beverly, MA, USA), Calnexin (1:1000, Cell Signaling Technology, Beverly, MA, USA) and TSG101(1:1000, Cell Signaling Technology, Beverly, MA, USA).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eLC-MS/MS-based proteomic analysis\u003c/h2\u003e \u003cp\u003eSEV proteins were first treated with 5 mM dithiothreitol (DTT) to reduce disulfide bonds, followed by alkylation with 10 mM iodoacetamide (IAA) to prevent reformation of disulfide bonds. Subsequently, the proteins underwent digestion by trypsin at an enzyme/substrate ratio of 1:100, incubated overnight at 37\u0026deg;C. The resulting peptides were then subjected to desalting and concentration using C18 StageTip. Fractionation of the peptide samples was performed using a homemade capillary column (75 \u0026micro;m i.d. \u0026times; 12 cm; ReproSil-Pur C18-AQ, 3 \u0026micro;m) on a Q Exactive HF-X mass spectrometer (Thermo Fisher Scientific, San Jose, CA) equipped with an UltiMate 3000 high-pressure liquid chromatography (UHPLC) system (Thermo Fisher Scientific, San Jose, CA) operating in data independent acquisition (DIA) mode. For the generation of the spectral library, DIA raw data were analyzed using Spectronaut software and a mass spectrometer vendor-independent software from Biognosys. The DIA files were searched against the Swiss-Prot human database (20,353 entries released on Aug 10, 2020).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eFunctional analysis\u003c/h2\u003e \u003cp\u003eDifferentially expressed proteins (DEPs) were identified as proteins showing a fold change\u0026thinsp;\u0026gt;\u0026thinsp;1.5 and a statistically significant difference (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Student\u0026rsquo;s t-test) using the \"MSstats\" R package between the two groups. Volcano plots, heatmaps, and partial least squares discriminant analysis (PLS-DA) were performed. For the DEPs, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment were annotated and visualized using the \u0026ldquo;clusterProfiler\u0026rdquo; R package. The pathway-level p-value was calculated using a hypergeometric test, and values with p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were deemed significantly enriched.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003ePublic database mining\u003c/h2\u003e \u003cp\u003eExpression data for sEV protein-associated genes in LARC were sourced from both the Memorial Sloan Kettering Cancer Center (MSKCC) database\u003csup\u003e29\u003c/sup\u003e accessible via \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.cbioportal.org\u003c/span\u003e\u003cspan address=\"http://www.cbioportal.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e and the Gene Expression Omnibus\u003csup\u003e30,31\u003c/sup\u003e (GSE3843) available at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.ncbi.nlm.nih.gov/geo/\u003c/span\u003e\u003cspan address=\"http://www.ncbi.nlm.nih.gov/geo/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Access to these databases was granted in accordance with the freedom-to-publish criteria outlined by The Cancer Genome Atlas and the National Center for Biotechnology Information. Patients were randomly assigned to training and testing cohorts at a ratio of 7:3. The Lasso-Logistic regression analysis was employed to identify sEV protein-associated genes correlated with the response to nCRT in LARC. The LASSO algorithm utilized the \"glmnet\" R package for variable selection and contraction. In the regression model, the independent variable consisted of the normalized expression matrix of sEV protein-associated genes, while the response variable indicated whether patients achieved pCR following nCRT.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eReceiver Operating Characteristic (ROC) curves were utilized to assess the specificity and sensitivity of DEPs, with the Area Under the ROC Curve (AUC) calculated for each DEP\u003csup\u003e32\u003c/sup\u003e. These curves and AUC values were generated through the \u0026ldquo;timeROC\u0026rdquo; package in R. Statistical significance was established at a p-value of \u0026lt;\u0026thinsp;0.05. All analyses were two-sided, with 95% confidence intervals (CIs) employed.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eIsolation and proteomic analysis of plasma-derived sEVs in LARC\u003c/h2\u003e \u003cp\u003eThe procedure for conducting sEV proteomics from plasma samples is depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA. The study included a total of 16 patients diagnosed with LARC, of whom 8 patients achieved a pathological complete response (GR) and the remaining 8 did not achieve a complete response (PR). No statistically significant differences were observed in terms of age, gender, T stage, and N stage between the two groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). Plasma samples were collected from these patients both before and after undergoing nCRT. Then, plasma-derived sEVs were isolated using the TIO\u003csub\u003e2\u003c/sub\u003e-based method and validated by TEM, WB and NTA. TEM results revealed round and cup-shaped vesicles with diameter ranging from 50 to 150 nm (supplementary Fig.\u0026nbsp;1A), correspond to size of sEVs. WB analysis demonstrated significant expression of traditional exosomal marker proteins, such as Alix and TSG101, while the intracellular protein contamination marker, calnexin, was absent (supplementary Fig.\u0026nbsp;1B). Additionally, Nano-Flow analysis showed that the mean size of the purified sEVs was 171.8\u0026thinsp;\u0026plusmn;\u0026thinsp;11.76 nm (supplementary Fig.\u0026nbsp;1C). Taken together, these findings collectively indicate the successful isolation of sEVs from plasma samples obtained from patients with LARC.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe next conducted LC-MS/MS-based proteomics of plasma-derived sEVs to identify potentially predictive biomarkers and investigate the proteomic alterations within sEVs post nCRT. Collectively, we quantified 6050, 6288, 6289, and 6359 peptides in sEV samples from GR and PR patients before and after nCRT (supplementary Fig.\u0026nbsp;2A). Then, a total of 705, 748, 736 and 752 proteins were identified in each respective group (supplementary Fig.\u0026nbsp;2B). The results for the coefficient of variation (CV%) were shown in supplementary Fig.\u0026nbsp;2B, illustrating favorable stability and consistency across the samples within each group. We compared the results of GR and PR patients before and after nCRT, and identified 827 sEV proteins that were common to all four groups, which was depicted in Venn diagram (supplementary Fig.\u0026nbsp;2C). To visualize the overall expression patterns among the groups, a heat map was created to visualize the overall expression patterns among the groups (supplementary Fig.\u0026nbsp;2D). Moreover, we detected 12 out of 13 (92.3%) sEV proteins (e.g., A2M, B2M, FLNA, FN1, GSN) that are reported by Ayuko et al.\u003csup\u003e43\u003c/sup\u003e, which are expressed at high frequency in human-derived sEVs and likely markers of endocytosis/exocytosis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of differentially-expressed sEV proteins in LARC patients\u003c/h2\u003e \u003cp\u003eTo identify sEV proteins that could be used as predictive biomarkers for nCRT response in LARC, we first sought to identify DEPs by comparison between GR sEV proteomes and PR sEV proteomes before nCRT. PCA analysis revealed a distinction between GR and PR sEV proteins (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). In total, we identified 67 significantly up-regulated DEPs (e.g., TTC9C, ROA1, UFC1, IGHG2 and SUMO3) and 9 significantly down-regulated DEPs (e.g., Q8IUL9, KV224, GUC2A and CDN1A) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB) (supplementary Table\u0026nbsp;1). The expression of DEPs was displayed in a heatmap (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). KEGG pathway enrichment analysis demonstrated that DEPs were enriched in pathways including the riboflavin metabolism (ACP1 and BLVRB), malaria (TGFB1, THBS1 and HBB) and TGF-beta signaling pathway (TGFB1, THBS1 and LTBP1) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD). In addition, GO analysis of biological processes showed that DEPs were enriched in cellular process, single-organism process and biological regulation. GO analysis of cellular components revealed that most of the DEPs were associated with organelle, cell and cell part. At the molecular function level, DEPs exhibited enrichment in binding, catalytic activity and molecular function regulator (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe identified sEV proteins that were present in \u0026ge;\u0026thinsp;50% of the GR samples and examined their baseline expression association with the likelihood of being a good responder. PROC (AUC: 0.922), F7 (AUC: 0.953), AZU1 (AUC: 0.906), ALB (AUC: 0.875), ENO1 (AUC: 0.828), APOC4 (AUC: 0.844), LCN2 (AUC: 0.875), CRNKL1 (AUC: 0.859), and HBB (AUC: 0.812) demonstrated statistically significant differences (P value\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and exhibited high AUC values in discriminating between GR and PR patients in the univariate logistic analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF) (supplementary Fig.\u0026nbsp;3). These findings collectively indicate that the expression of sEV proteins differs between GR and PR patients and can be used to distinguish between different response types.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eSEV protein-associated genes predicted therapeutic response to nCRT in LARC\u003c/h2\u003e \u003cp\u003ePlasma-derived SEVs originate from various sources, including tumor, normal tissues and elsewhere. We have identified 76 differentially-expressed sEV proteins between GR and PR patients before nCRT, and then we investigated whether these sEV proteins originated from the tumor. To identify the likely source of differentially-expressed sEV proteins, we compared our plasma-derived sEV proteins with reported CRC cell-derived\u003csup\u003e33,34,35,36,37\u003c/sup\u003e and fibroblast cell-derived\u003csup\u003e35\u003c/sup\u003e sEV proteomes based on the public database (supplementary Table\u0026nbsp;2). Interestingly, 30 proteins such as SH3BGRL, SOD1, CA2 were present in both plasma- and cancer cell-derived CRC sEVs, but were undetectable in all of the fibroblast cell-derived sEV samples, suggesting that these proteins most likely originate from CRC (supplementary Table\u0026nbsp;3). Other 46 proteins were absent in cancer cell-derived sEVs, suggesting that these sEV proteins might originate from other normal cells or distant organs. These results reinforce the idea that all the cells can secret sEVs and nCRT response is a systemic procedure that requires not only cancer but also normal organs\u0026rsquo; participation.\u003c/p\u003e \u003cp\u003eTo investigate the predictive role of cancer source-sEV protein-associated genes in determining radiosensitivity in CRC, we collected gene expression and clinical data of CRC from a public MSKCC database. Our objective was to assess the predictive value of these genes for response to nCRT. In the training cohort, we utilized lasso-logistic regression analysis to develop a signature consisting of 5 sEV protein-associated genes, including S100A6, ENO1, MIF, PRDX6 and MYL6 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). The predictive performance of this signature was evaluated using ROC curves, yielding an AUC of 0.692 (95% CI: 0.535\u0026ndash;0.848) in the training cohort (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB), and 0.823 (95% CI: 0.628-1.000) in the testing cohort (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). Additionally, we validated the predictive capability of this signature in an external dataset (GSE3843), where the area under the ROC curve was found to be 0.621(95% CI: 0.454\u0026ndash;0.788), indicating favorable predictive value (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD). Concludely, sEV protein-associated genes can predict response to nCRT in LARC patients. In addition, based on this 5-sEV protein-associated gene signature, patients can be stratified into low- and high-risk group according to the optimal cut-off value selected by the ROC curve. The low-risk group had a longer OS in both training (P\u0026thinsp;=\u0026thinsp;0.057) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE) and testing sets (P\u0026thinsp;=\u0026thinsp;0.048) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF). In conclusion, our findings suggest that sEV protein-associated genes can serve as predictors of response to chemoradiotherapy and prognosis in LARC patients.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003enCRT-induced alteration of sEV proteins in LARC patients\u003c/h2\u003e \u003cp\u003eAfterward, we delved into the proteomic changes pre- and post-treatment among 16 patients diagnosed with LARC. We pinpointed 11 significantly up-regulated DEPs, such as IGLV3-22, RAB3IL1, and HPSE, alongside 31 significantly down-regulated DEPs, including IGKV1-9, IGHA2, and AHSP (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA) (supplementary Table\u0026nbsp;4). GO analysis of molecular function unveiled enrichment in the regulation of phospholipase A2 (PLA2) activity, 1-acylglycerophosphocholine O-acyltransferase activity, and calcium-independent PLA2 activity among the DEPs (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWithin the GR cohort, we identified 16 significantly down-regulated DEPs (e.g., Q5NV74, ADH4, and HPSE) and 23 significantly up-regulated DEPs (e.g., OLFL3, CYTB, and HV70D) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC) (supplementary Table\u0026nbsp;5). Conversely, in the PR cohort, we noted 19 significantly down-regulated DEPs (e.g., KI67, AEC1, and SBSN) and 89 significantly up-regulated DEPs (e.g., CPNE3, ROA1, and HNPRK) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD) (supplementary Table\u0026nbsp;6). Subsequently, a comparison of the GR-associated DEPs with the PR-associated DEPs revealed 13 common DEPs across both groups. Furthermore, 26 DEPs were exclusive to the GR group, while 95 DEPs were unique to the PR group (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE). GO analysis of molecular function for the unique DEPs in the GR group unveiled significant enrichment in pathways such as protein self-association, extracellular matrix structural constituent, NADPH:quinone reductase activity, and alcohol dehydrogenase (NAD) activity (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eF). Additionally, GO analysis of molecular function highlighted pathways enriched in the unique DEPs within the PR group, including magnesium ion binding, arylesterase activity, phospholipase A2 activity, and lyase activity (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eG).\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eSEVs are secreted by cells and released into various body fluids, including serum, urine, and saliva. They carry specific molecular information inherited from their parent cells, making them promising cancer biomarkers for liquid biopsy\u003csup\u003e38\u003c/sup\u003e. The enrichment and stability of sEVs render them a valuable diagnostic tool, particularly in the context of CRC. Currently, miRNAs are the most commonly investigated molecules in sEVs for CRC detection. MiRNAs contained within sEVs have been identified in the circulation of CRC patients, exhibiting dysregulation compared to healthy individuals. Specific miRNA candidates such as miR-21, miR-23a, miR-1246, and miR-92a have been suggested as potential diagnostic markers\u003csup\u003e39\u003c/sup\u003e. Furthermore, a signature based on long RNAs within plasma-derived sEVs has demonstrated promise in discerning CRC patients from healthy counterparts\u003csup\u003e40\u003c/sup\u003e. Notably, Zhang et al.\u003csup\u003e38\u003c/sup\u003e observed significantly elevated levels of CD147 and A33 on SEVs isolated from fecal samples of CRC patients, facilitating the discrimination between CRC patients and healthy individuals. SEVs thus represent an innovative generation of biomarkers for CRC, offering a non-invasive screening method on a large scale. In addition to their diagnostic utility in early detection of CRC, sEVs also hold prognostic significance in the disease. For instance, the expression of miR-17-92 in sEVs was notably elevated in CRC patients with poorer prognoses compared to healthy individuals\u003csup\u003e41\u003c/sup\u003e. Besides, sEVs harboring miR-122 represent another promising biomarker for predicting the survival outcomes of CRC, and the heightened expression of miR-122 within sEVs is closely linked to the presence of liver metastasis in CRC patients\u003csup\u003e42\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eWhile many studies on sEVs from human samples primarily focus on nucleic acids, it's noteworthy that the proteome within sEVs is also enriched and emerging as a potential source of biomarkers. Park et al.\u003csup\u003e22\u003c/sup\u003e discovered that the expression of sEV proteins such as EGFR, EpCAM, CD24, and GPA33 could differentiate plasma samples from patients with CRC and non-CRC controls with accuracies exceeding 96%. Furthermore, the abundance of sEVs was predictive of five-year disease-free survival and strongly associated with tumor burden, showing a decrease after surgery and an increase upon relapse. The aforementioned findings indicate that sEVs have demonstrated excellent predictive capabilities in CRC. Additionally, mass spectrometry-based proteomic profiling is emerging as a strategy to gain insight into the biology and clinical potential of circulating sEVs\u003csup\u003e43\u003c/sup\u003e. However, whether they can forecast the efficacy of nCRT in LARC has not been reported in the literature thus far. Hence, our study aims to explore whether the proteomic profiles of sEVs could function as predictive markers for nCRT response in LARC patients and to investigate proteomic alterations within sEVs following nCRT.\u003c/p\u003e \u003cp\u003eSEVs are actively released into the peripheral circulation by cancer cells, and can reflect the compositions and dynamic changes occurring within cancer cells, even participating in response to treatment\u003csup\u003e44\u003c/sup\u003e. In our study, we identified differential expression of sEV proteins before nCRT that distinguishes between patients categorized as GR and PR. Notably, PROC, F7, and AZU1 exhibited high AUC values (\u0026gt;\u0026thinsp;0.9) and significant differences (P value\u0026thinsp;\u0026lt;\u0026thinsp;0.05). AZU1, functioning as a tumor suppressor, not only inhibits tumor cell proliferation but also facilitates tissue reorganization\u003csup\u003e45\u003c/sup\u003e. Interestingly, we found significantly higher levels of AZU1 in sEVs from GR patients compared to PR patients. The observed differential expression of these proteins in sEVs can serve as a valuable tool in distinguishing between GR and PR patients in LARC. Furthermore, considering that plasma-derived sEVs originate from tissues, and tumors are particularly prone to releasing exosomes on a large scale, sEV-associated genes were found to be dysregulated in tumor tissues among patients with varying response rates. Consequently, we investigated the expression of cancer-source sEV protein-associated genes in LARC tissues. Our analysis revealed that a signature consisting of five sEV protein-associated genes (S100A6, ENO1, MIF, PRDX6, and MYL6) could reliably predict the response to nCRT and contribute to the prediction of overall survival. Wu et al.\u003csup\u003e46\u003c/sup\u003e utilized tumor-derived sEV-associated genes to establish a TEXscore for predicting prognosis across various cancer types and in patients undergoing immunotherapy. They found that TEXscore serves as a robust biomarker for prognosis and treatment responses in independent cohorts. In summary, sEVs demonstrate significant potential as predictive markers for both nCRT response and prognosis in patients with LARC.\u003c/p\u003e \u003cp\u003eIn addition to their predictive role in patients with LARC, we also investigated the proteomic changes following nCRT. It has been demonstrated that radiation can alter the cargo of sEVs. We identified 11 significantly up-regulated DEPs alongside 31 significantly down-regulated DEPs after nCRT. GO analysis of molecular function revealed enrichment in the regulation of PLA2 activity. Currently, PLA2 enzymes have emerged as targets in cancer therapy, with elevated PLA2 activities detected in plasma from patients with CRC, exceeding those of healthy controls. Moreover, PLA2 levels were found to be correlated with CRC tumor stage\u003csup\u003e47\u003c/sup\u003e. Additionally, radiation can induce alterations in PLA2 activity, which can mediate critical biological processes such as inflammation, senescence, and apoptosis\u003csup\u003e48\u003c/sup\u003e. Our data demonstrate that radiation-induced alterations in PLA2 activity might be associated with radiotherapy-related toxicity. Furthermore, when stratified by tumor response, GO analysis of unique DEPs within the PR group showed enrichment in magnesium ion binding. Magnesium is an essential cofactor in almost all enzymatic systems involved in DNA processing and is highly required to maintain genomic stability. Apart from its stabilizing effect on DNA and chromatin structure, magnesium is involved in the removal of DNA damage and is required for repairing double-strand breaks arising after radiotherapy\u003csup\u003e49\u003c/sup\u003e. The alterations in the proteome of sEVs in the PR group might be associated with resistance to radiotherapy. However, as pointed out by Jelonek et al., there is a significant gap in understanding how radiation-induced changes in the composition of sEVs translate into their functional importance\u003csup\u003e50\u003c/sup\u003e. Further investigation is needed to elucidate how altered sEVs affect the response to nCRT.\u003c/p\u003e \u003cp\u003eIn conclusion, differential expression of sEV proteins distinguishes between GR and PR patients and shows promise as predictive markers for nCRT response and prognosis in LARC patients. Furthermore, our findings underscore significant alterations in sEV protein composition following nCRT. However, it is important to note that further studies are necessary to validate and verify these sEV proteins in larger clinical sample sets, and to explore their underlying functions and mechanisms.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis project was approved by the Independent Ethics Committee of the Second Affiliated Hospital of Zhejiang University, and we got the informed consent from all patients.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data associated with this study are present in the paper or the supplementary materials. Any additional information required to reanalyze the data reported in this paper is available from the lead contact upon request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eH.Y.C. and K.F.D. conceived and designed the study; Y.M.F. and S.Q.D. performed all the experiments with help from J.Z., L.S., K.H.W. and X.F.Z. H.Y.C. wrote the manuscript. All authors approved the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the National Natural Science Foundation of China (Grant No. 82103498, No.82072624, No.82203704) and CSCO-Roche research funding (Y-Roche2019/2-0088).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eR.L. Siegel, K.D. Miller, N.S. Wagle, A. Jemal, Cancer Statistics, 2023. \u003cem\u003eCA. Cancer J. Clin.\u003c/em\u003e 2023, \u003cem\u003e73\u003c/em\u003e (1), 17\u0026ndash;48. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3322/caac.21763\u003c/span\u003e\u003cspan address=\"10.3322/caac.21763\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eE. Morgan, M. Arnold, A. Gini, V. Lorenzoni, C.J. Cabasag, M. Laversanne, J. Vignat, J. Ferlay, N. Murphy, F. Bray, Global Burden of Colorectal Cancer in 2020 and 2040: Incidence and Mortality Estimates from GLOBOCAN. Gut. \u003cb\u003e72\u003c/b\u003e(2), 338\u0026ndash;344 (2023). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1136/gutjnl-2022-327736\u003c/span\u003e\u003cspan address=\"10.1136/gutjnl-2022-327736\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eK. He, L. Li, A. Li, Y. Xu, J. Pang, D. Mu, J. Ma, H. Ge, A. Maleki, X. Qin, X. Zhang, Q. Ou, Y. Shao, J. Yu, S. Yuan, Genomic Features, Evolutionary Patterns and Minimal Residual Disease at Surgical Margins as Novel Prognostic/Predictive Biomarkers in Locally Advanced Rectal Cancer. Clin. Transl Med. \u003cb\u003e13\u003c/b\u003e(6), e1286 (2023). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/ctm2.1286\u003c/span\u003e\u003cspan address=\"10.1002/ctm2.1286\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eB.Y. Oh, Advances in Surgery for Locally Advanced Rectal Cancer. Ann. Coloproctology. \u003cb\u003e38\u003c/b\u003e(4), 279\u0026ndash;280 (2022). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3393/ac.2022.00493.0070\u003c/span\u003e\u003cspan address=\"10.3393/ac.2022.00493.0070\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eW.H. Zwart, A. Hotca, G.A.P. Hospers, K.A. Goodman, J. Garcia-Aguilar, The Multimodal Management of Locally Advanced Rectal Cancer: Making Sense of the New Data. Am. Soc. Clin. Oncol. Educ. Book. No. \u003cb\u003e42\u003c/b\u003e, 264\u0026ndash;277 (2022). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1200/EDBK_351411\u003c/span\u003e\u003cspan address=\"10.1200/EDBK_351411\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e\u003cem\u003eHigh-dose chemoradiotherapy and watchful waiting for distal rectal cancer: a prospective observational study - ScienceDirect\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.sciencedirect.com/science/article/pii/S1470204515001205\u003c/span\u003e\u003cspan address=\"https://www.sciencedirect.com/science/article/pii/S1470204515001205\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (accessed 2023-06-16)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eT. Millard, P.R. Kunk, E. Ramsdale, O.E. Rahma, Current Debate in the Oncologic Management of Rectal Cancer. World J. Gastrointest. Oncol. \u003cb\u003e8\u003c/b\u003e(10), 715\u0026ndash;724 (2016). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.4251/wjgo.v8.i10.715\u003c/span\u003e\u003cspan address=\"10.4251/wjgo.v8.i10.715\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eD. Schrag, Q. Shi, M.R. Weiser, M.J. Gollub, L.B. Saltz, B.L. Musher, J. Goldberg, T. Al Baghdadi, K.A. Goodman, R.R. McWilliams, J.M. Farma, T.J. George, H.F. Kennecke, A. Shergill, M. Montemurro, G.D. Nelson, B. Colgrove, V. Gordon, A.P. Venook, E.M. O\u0026rsquo;Reilly, J.A. Meyerhardt, A.C. Dueck, E. Basch, G.J. Chang, H.J. Mamon, Preoperative Treatment of Locally Advanced Rectal Cancer. N Engl. J. Med. (2023). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1056/NEJMoa2303269\u003c/span\u003e\u003cspan address=\"10.1056/NEJMoa2303269\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eC. Koulis, R. Yap, R. Engel, T. Jard\u0026eacute;, S. Wilkins, G. Solon, J.D. Shapiro, H. Abud, P. McMurrick, Personalized Medicine\u0026mdash;Current and Emerging Predictive and Prognostic Biomarkers in Colorectal Cancer. Cancers. \u003cb\u003e12\u003c/b\u003e(4), 812 (2020). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/cancers12040812\u003c/span\u003e\u003cspan address=\"10.3390/cancers12040812\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eA. Bardelli, K. Pantel, Liquid Biopsies, What We Do Not Know (Yet). Cancer Cell. \u003cb\u003e31\u003c/b\u003e(2), 172\u0026ndash;179 (2017). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ccell.2017.01.002\u003c/span\u003e\u003cspan address=\"10.1016/j.ccell.2017.01.002\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eM. Ignatiadis, G.W. Sledge, S.S. Jeffrey, Liquid Biopsy Enters the Clinic \u0026mdash; Implementation Issues and Future Challenges. Nat. Rev. Clin. Oncol. \u003cb\u003e18\u003c/b\u003e(5), 297\u0026ndash;312 (2021). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41571-020-00457-x\u003c/span\u003e\u003cspan address=\"10.1038/s41571-020-00457-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eC. Alix-Panabi\u0026egrave;res, K. Pantel, Liquid Biopsy: From Discovery to Clinical Application. Cancer Discov. \u003cb\u003e11\u003c/b\u003e(4), 858\u0026ndash;873 (2021). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1158/2159-8290.CD-20-1311\u003c/span\u003e\u003cspan address=\"10.1158/2159-8290.CD-20-1311\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eG. Wang, J. Li, L. Bojmar, H. Chen, Z. Li, G.C. Tobias, M. Hu, E.A. Homan, S. Lucotti, F. Zhao, V. Posada, P.R. Oxley, M. Cioffi, H.S. Kim, H. Wang, P. Lauritzen, N. Boudreau, Z. Shi, C.E. Burd, J.H. Zippin, J.C. Lo, G.S. Pitt, J. Hernandez, C.P. Zambirinis, M.A. Hollingsworth, P.M. Grandgenett, M. Jain, S.K. Batra, D.J. DiMaio, J.L. Grem, K.A. Klute, T.M. Trippett, M. Egeblad, D. Paul, J. Bromberg, D. Kelsen, V.K. Rajasekhar, J.H. Healey, I.R. Matei, W.R. Jarnagin, R.E. Schwartz, H. Zhang, Lyden, D. Tumour Extracellular Vesicles and Particles Induce Liver Metabolic Dysfunction. Nature. \u003cb\u003e618\u003c/b\u003e(7964), 374\u0026ndash;382 (2023). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41586-023-06114-4\u003c/span\u003e\u003cspan address=\"10.1038/s41586-023-06114-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eD. Yu, Y. Li, M. Wang, J. Gu, W. Xu, H. Cai, X. Fang, X. Zhang, Exosomes as a New Frontier of Cancer Liquid Biopsy. Mol. Cancer. \u003cb\u003e21\u003c/b\u003e(1), 56 (2022). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12943-022-01509-9\u003c/span\u003e\u003cspan address=\"10.1186/s12943-022-01509-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eS.M. Morrissey, J. Yan, Exosomal, PD-L1: Roles in Tumor Progression and Immunotherapy. Trends Cancer. \u003cb\u003e6\u003c/b\u003e(7), 550\u0026ndash;558 (2020). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.trecan.2020.03.002\u003c/span\u003e\u003cspan address=\"10.1016/j.trecan.2020.03.002\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eS. Serrat\u0026igrave;, M. Guida, Di R. Fonte, De S. Summa, S. Strippoli, R.M. Iacobazzi, A. Quarta, De I. Risi, G. Guida, A. Paradiso, L. Porcelli, A. Azzariti, Circulating Extracellular Vesicles Expressing PD1 and PD-L1 Predict Response and Mediate Resistance to Checkpoint Inhibitors Immunotherapy in Metastatic Melanoma. Mol. Cancer. \u003cb\u003e21\u003c/b\u003e(1), 20 (2022). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12943-021-01490-9\u003c/span\u003e\u003cspan address=\"10.1186/s12943-021-01490-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eS.N. Lone, S. Nisar, T. Masoodi, M. Singh, A. Rizwan, S. Hashem, W. El-Rifai, D. Bedognetti, S.K. Batra, M. Haris, A.A. Bhat, M.A. Macha, Liquid Biopsy: A Step Closer to Transform Diagnosis, Prognosis and Future of Cancer Treatments. Mol. Cancer. \u003cb\u003e21\u003c/b\u003e(1), 79 (2022). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12943-022-01543-7\u003c/span\u003e\u003cspan address=\"10.1186/s12943-022-01543-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eH. Wu, C. Zeng, Y. Ye, J. Liu, Z. Mu, Y. Xie, B. Chen, Q. Nong, D. Wu, Exosomes from Irradiated Nonsmall Cell Lung Cancer Cells Reduced Sensitivity of Recipient Cells to Anaplastic Lymphoma Kinase Inhibitors. Mol. Pharm. \u003cb\u003e15\u003c/b\u003e(5), 1892\u0026ndash;1900 (2018). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1021/acs.molpharmaceut.8b00059\u003c/span\u003e\u003cspan address=\"10.1021/acs.molpharmaceut.8b00059\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eS.E. Glass, R.J. Coffey, Recent Advances in the Study of Extracellular Vesicles in Colorectal Cancer. Gastroenterology. \u003cb\u003e163\u003c/b\u003e(5), 1188\u0026ndash;1197 (2022). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1053/j.gastro.2022.06.039\u003c/span\u003e\u003cspan address=\"10.1053/j.gastro.2022.06.039\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eS. Galbiati, F. Damin, D. Brambilla, L. Ferraro, N. Soriani, A.M. Ferretti, V. Burgio, M. Ronzoni, R. Vago, L. Sola, M. Chiari, Small EVs-Associated DNA as Complementary Biomarker to Circulating Tumor DNA in Plasma of Metastatic Colorectal Cancer Patients. Pharm. Basel Switz. \u003cb\u003e14\u003c/b\u003e(2), 128 (2021). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/ph14020128\u003c/span\u003e\u003cspan address=\"10.3390/ph14020128\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eD. Lucchetti, I.V. Zurlo, F. Colella, C. Ricciardi-Tenore, Di M. Salvatore, G. Tortora, De R. Maria, F. Giuliante, A. Cassano, M. Basso, A. Crucitti, I. Laurenzana, G. Artemi, A. Sgambato, Mutational Status of Plasma Exosomal KRAS Predicts Outcome in Patients with Metastatic Colorectal Cancer. Sci. Rep. \u003cb\u003e11\u003c/b\u003e(1), 22686 (2021). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41598-021-01668-7\u003c/span\u003e\u003cspan address=\"10.1038/s41598-021-01668-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJ. Park, J.S. Park, C.-H. Huang, A. Jo, K. Cook, R. Wang, H.-Y. Lin, Van J. Deun, H. Li, J. Min, L. Wang, G. Yoon, B.S. Carter, L. Balaj, G.-S. Choi, C.M. Castro, R. Weissleder, H. Lee, An Integrated Magneto-Electrochemical Device for the Rapid Profiling of Tumour Extracellular Vesicles from Blood Plasma. Nat. Biomed. Eng. \u003cb\u003e5\u003c/b\u003e(7), 678\u0026ndash;689 (2021). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41551-021-00752-7\u003c/span\u003e\u003cspan address=\"10.1038/s41551-021-00752-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eU. Strybel, L. Marczak, M. Zeman, K. Polanski, Ł. Mielańczyk, O. Klymenko, A. Samelak-Czajka, P. Jackowiak, M. Smolarz, M. Chekan, E. Zembala-Nożyńska, P. Widlak, M. Pietrowska, A. Wojakowska, Molecular Composition of Serum Exosomes Could Discriminate Rectal Cancer Patients with Different Responses to Neoadjuvant Radiotherapy. Cancers. \u003cb\u003e14\u003c/b\u003e(4), 993 (2022). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/cancers14040993\u003c/span\u003e\u003cspan address=\"10.3390/cancers14040993\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eC. Li, H. Chen, B. Zhang, Y. Fang, W. Sun, D. Wu, Z. Su, L. Shen, Q. Wei, Radiomics Signature Based on Support Vector Machines for the Prediction of Pathological Complete Response to Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer. Cancers. \u003cb\u003e15\u003c/b\u003e(21), 5134 (2023). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/cancers15215134\u003c/span\u003e\u003cspan address=\"10.3390/cancers15215134\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eF. Gao, F. Jiao, C. Xia, Y. Zhao, W. Ying, Y. Xie, X. Guan, M. Tao, Y. Zhang, W. Qin, X. Qian, A Novel Strategy for Facile Serum Exosome Isolation Based on Specific Interactions between Phospholipid Bilayers and TiO2. Chem. Sci. \u003cb\u003e10\u003c/b\u003e(6), 1579 (2019). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1039/c8sc04197k\u003c/span\u003e\u003cspan address=\"10.1039/c8sc04197k\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eL. Zhao, J. Shi, L. Chang, Y. Wang, S. Liu, Y. Li, T. Zhang, T. Zuo, B. Fu, G. Wang, Y. Ruan, Y. Zhang, P. Xu, Serum-Derived Exosomal Proteins as Potential Candidate Biomarkers for Hepatocellular Carcinoma. ACS Omega. \u003cb\u003e6\u003c/b\u003e(1), 827\u0026ndash;835 (2021). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1021/acsomega.0c05408\u003c/span\u003e\u003cspan address=\"10.1021/acsomega.0c05408\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eK. Jiang, H. Chen, Y. Fang, L. Chen, C. Zhong, T. Bu, S. Dai, X. Pan, D. Fu, Y. Qian, J. Wei, K. Ding, Exosomal ANGPTL1 Attenuates Colorectal Cancer Liver Metastasis by Regulating Kupffer Cell Secretion Pattern and Impeding MMP9 Induced Vascular Leakiness. J. Exp. Clin. Cancer Res. \u003cb\u003e40\u003c/b\u003e(1), 21 (2021). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s13046-020-01816-3\u003c/span\u003e\u003cspan address=\"10.1186/s13046-020-01816-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eH. Chen, Q. Xiao, Y. Hu, L. Chen, K. Jiang, Y. Tang, Y. Tan, W. Hu, Z. Wang, J. He, Y. Liu, Y. Cai, Q. Yang, K. Ding, ANGPTL1 Attenuates Colorectal Cancer Metastasis by Up-Regulating microRNA-138. J. Exp. Clin. Cancer Res. \u003cb\u003e36\u003c/b\u003e(1), 78 (2017). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s13046-017-0548-7\u003c/span\u003e\u003cspan address=\"10.1186/s13046-017-0548-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eW.K. Chatila, J.K. Kim, H. Walch, M.R. Marco, C.-T. Chen, F. Wu, D.M. Omer, D.N. Khalil, K. Ganesh, X. Qu, A. Luthra, S.-H. Choi, Y.-J. Ho, R. Kundra, K.I. Groves, O.S. Chow, A. Cercek, M.R. Weiser, M. Widmar, I.H. Wei, E.P. Pappou, G.M. Nash, P.B. Paty, Q. Shi, E. Vakiani, S. Duygu Selcuklu, M.T.A. Donoghue, D.B. Solit, M.F. Berger, J. Shia, R. Pelossof, P.B. Romesser, R. Yaeger, J.J. Smith, N. Schultz, F. Sanchez-Vega, J. Garcia-Aguilar, Genomic and Transcriptomic Determinants of Response to Neoadjuvant Therapy in Rectal Cancer. Nat. Med. \u003cb\u003e28\u003c/b\u003e(8), 1646\u0026ndash;1655 (2022). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41591-022-01930-z\u003c/span\u003e\u003cspan address=\"10.1038/s41591-022-01930-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJ. Gao, B.A. Aksoy, U. Dogrusoz, G. Dresdner, B. Gross, S.O. Sumer, Y. Sun, A. Jacobsen, R. Sinha, E. Larsson, E. Cerami, C. Sander, N. Schultz, Integrative Analysis of Complex Cancer Genomics and Clinical Profiles Using the cBioPortal. Sci. Signal. \u003cb\u003e6\u003c/b\u003e(269), pl1 (2013). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1126/scisignal.2004088\u003c/span\u003e\u003cspan address=\"10.1126/scisignal.2004088\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eE. Cerami1, J. Gao, U. Dogrusoz, B.E. Gross, S.O. Sumer, B.A. Aksoy, A. Jacobsen, C.J. Byrne, M.L. Heuer, E. Larsson, Y. Antipin, B. Reva, A.P. Goldberg, C. Sander, N. Schultz, The cBio Cancer Genomics Portal: An Open Platform for Exploring Multidimensional Cancer Genomics Data. Cancer Discov. \u003cb\u003e2\u003c/b\u003e(5), 401\u0026ndash;404 (2012). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1158/2159-8290.CD-12-0095\u003c/span\u003e\u003cspan address=\"10.1158/2159-8290.CD-12-0095\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eH. Chen, C. Li, L. Zheng, W. Lu, Y. Li, Q.A. Wei, Machine Learning-Based Survival Prediction Model of High Grade Glioma by Integration of Clinical and Dose-Volume Histogram Parameters. Cancer Med. \u003cb\u003e10\u003c/b\u003e(8), 2774\u0026ndash;2786 (2021). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/cam4.3838\u003c/span\u003e\u003cspan address=\"10.1002/cam4.3838\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eD.-S. Choi, J.-S. Yang, E.-J. Choi, S.C. Jang, S. Park, O.Y. Kim, D. Hwang, K.P. Kim, Y.-K. Kim, S. Kim, Y.S. Gho, The Protein Interaction Network of Extracellular Vesicles Derived from Human Colorectal Cancer Cells. J. Proteome Res. \u003cb\u003e11\u003c/b\u003e(2), 1144\u0026ndash;1151 (2012). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1021/pr200842h\u003c/span\u003e\u003cspan address=\"10.1021/pr200842h\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eB.J. Tauro, D.W. Greening, R.A. Mathias, S. Mathivanan, H. Ji, R.J. Simpson, Two Distinct Populations of Exosomes Are Released from LIM1863 Colon Carcinoma Cell-Derived Organoids *. Mol. Cell. Proteom. \u003cb\u003e12\u003c/b\u003e(3), 587\u0026ndash;598 (2013). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1074/mcp.M112.021303\u003c/span\u003e\u003cspan address=\"10.1074/mcp.M112.021303\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eC.-H. Lee, E.-J. Im, P.-G. Moon, M.-C. Baek, Discovery of a Diagnostic Biomarker for Colon Cancer through Proteomic Profiling of Small Extracellular Vesicles. BMC Cancer. \u003cb\u003e18\u003c/b\u003e(1), 1058 (2018). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12885-018-4952-y\u003c/span\u003e\u003cspan address=\"10.1186/s12885-018-4952-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eW. Suwakulsiri, A. Rai, R. Xu, M. Chen, D.W. Greening, R.J. Simpson, Proteomic Profiling Reveals Key Cancer Progression Modulators in Shed Microvesicles Released from Isogenic Human Primary and Metastatic Colorectal Cancer Cell Lines. Biochim. Biophys. Acta BBA - Proteins Proteom. \u003cb\u003e1867\u003c/b\u003e(12), 140171 (2019). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.bbapap.2018.11.008\u003c/span\u003e\u003cspan address=\"10.1016/j.bbapap.2018.11.008\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eA. Montero-Calle, I. Aranguren-Abeigon, M. Garranzo-Asensio, C. Poves, M.J. Fern\u0026aacute;ndez-Ace\u0026ntilde;ero, J. Mart\u0026iacute;nez-Useros, R. Sanz, J. Dziakov\u0026aacute;, J. Rodriguez-Cobos, G. Sol\u0026iacute;s-Fern\u0026aacute;ndez, E. Povedano, M. Gamella, R.M. Torrente-Rodr\u0026iacute;guez, M. Alonso-Navarro, de los V. R\u0026iacute;os, J.I. Casal, G. Dom\u0026iacute;nguez, A. Guzman-Aranguez, A. Pel\u0026aacute;ez-Garc\u0026iacute;a, J.M. Pingarr\u0026oacute;n, S. Campuzano, R. Barderas, Multiplexed Biosensing Diagnostic Platforms Detecting Autoantibodies to Tumor-Associated Antigens from Exosomes Released by CRC Cells and Tissue Samples Showed High Diagnostic Ability for Colorectal Cancer. Engineering. \u003cb\u003e7\u003c/b\u003e(10), 1393\u0026ndash;1412 (2021). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.eng.2021.04.026\u003c/span\u003e\u003cspan address=\"10.1016/j.eng.2021.04.026\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZ. Zhang, X. Liu, X. Yang, Y. Jiang, A. Li, J. Cong, Y. Li, Q. Xie, C. Xu, D. Liu, Identification of Faecal Extracellular Vesicles as Novel Biomarkers for the Non-Invasive Diagnosis and Prognosis of Colorectal Cancer. J. Extracell. Vesicles. \u003cb\u003e12\u003c/b\u003e(1), 12300 (2023). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/jev2.12300\u003c/span\u003e\u003cspan address=\"10.1002/jev2.12300\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eB.J. Desmond, E.R. Dennett, K.M. Danielson, Circulating Extracellular Vesicle MicroRNA as Diagnostic Biomarkers in Early Colorectal Cancer\u0026mdash;A Review. Cancers. \u003cb\u003e12\u003c/b\u003e(1), 52 (2020). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/cancers12010052\u003c/span\u003e\u003cspan address=\"10.3390/cancers12010052\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eT.-A. Guo, H.-Y. Lai, C. Li, Y. Li, Y.-C. Li, Y.-T. Jin, Z.-Z. Zhang, H.-B. Huang, S.-L. Huang, Y. Xu, Plasma Extracellular Vesicle Long RNAs Have Potential as Biomarkers in Early Detection of Colorectal Cancer. \u003cem\u003eFront. Oncol.\u003c/em\u003e 2022, \u003cem\u003e12\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fonc.2022.829230\u003c/span\u003e\u003cspan address=\"10.3389/fonc.2022.829230\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eT. Matsumura, K. Sugimachi, H. Iinuma, Y. Takahashi, J. Kurashige, G. Sawada, M. Ueda, R. Uchi, H. Ueo, Y. Takano, Y. Shinden, H. Eguchi, H. Yamamoto, Y. Doki, M. Mori, T. Ochiya, K. Mimori, Exosomal microRNA in Serum Is a Novel Biomarker of Recurrence in Human Colorectal Cancer. Br. J. Cancer. \u003cb\u003e113\u003c/b\u003e(2), 275\u0026ndash;281 (2015). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/bjc.2015.201\u003c/span\u003e\u003cspan address=\"10.1038/bjc.2015.201\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eL. Sun, X. Liu, B. Pan, X. Hu, Y. Zhu, Y. Su, Z. Guo, G. Zhang, M. Xu, X. Xu, H. Sun, S. Wang, Serum Exosomal miR-122 as a Potential Diagnostic and Prognostic Biomarker of Colorectal Cancer with Liver Metastasis. J. Cancer. \u003cb\u003e11\u003c/b\u003e(3), 630\u0026ndash;637 (2020). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.7150/jca.33022\u003c/span\u003e\u003cspan address=\"10.7150/jca.33022\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eA. Hoshino, H.S. Kim, L. Bojmar, K.E. Gyan, M. Cioffi, J. Hernandez, C.P. Zambirinis, G. Rodrigues, H. Molina, S. Heissel, M.T. Mark, L. Steiner, A. Benito-Martin, S. Lucotti, Di A. Giannatale, K. Offer, M. Nakajima, C. Williams, L. Nogu\u0026eacute;s, F.A. Pelissier Vatter, A. Hashimoto, A.E. Davies, D. Freitas, C.M. Kenific, Y. Ararso, W. Buehring, P. Lauritzen, Y. Ogitani, K. Sugiura, N. Takahashi, M. Alečković, K.A. Bailey, J.S. Jolissant, H. Wang, A. Harris, L.M. Schaeffer, G. Garc\u0026iacute;a-Santos, Z. Posner, V.P. Balachandran, Y. Khakoo, G.P. Raju, A. Scherz, I. Sagi, R. Scherz-Shouval, Y. Yarden, M. Oren, M. Malladi, M. Petriccione, De K.C. Braganca, M. Donzelli, C. Fischer, S. Vitolano, G.P. Wright, L. Ganshaw, M. Marrano, A. Ahmed, J. DeStefano, E. Danzer, M.H.A. Roehrl, N.J. Lacayo, T.C. Vincent, M.R. Weiser, M.S. Brady, P.A. Meyers, L.H. Wexler, S.R. Ambati, A.J. Chou, E.K. Slotkin, S. Modak, S.S. Roberts, E.M. Basu, D. Diolaiti, B.A. Krantz, F. Cardoso, A.L. Simpson, M. Berger, C.M. Rudin, D.M. Simeone, M. Jain, C.M. Ghajar, S.K. Batra, B.Z. Stanger, J. Bui, K.A. Brown, V.K. Rajasekhar, J.H. Healey, de M. Sousa, K. Kramer, S. Sheth, J. Baisch, V. Pascual, T.E. Heaton, La M.P. Quaglia, D.J. Pisapia, R. Schwartz, H. Zhang, Y. Liu, A. Shukla, L. Blavier, Y.A. DeClerck, M. LaBarge, M.J. Bissell, T.C. Caffrey, P.M. Grandgenett, M.A. Hollingsworth, J. Bromberg, B. Costa-Silva, H. Peinado, Y. Kang, B.A. Garcia, E.M. O\u0026rsquo;Reilly, D. Kelsen, T.M. Trippett, Jones, D. R.; Matei, I. R.; Jarnagin, W. R.; Lyden, D. Extracellular Vesicle and Particle Biomarkers Define Multiple Human Cancers. \u003cem\u003eCell\u003c/em\u003e 2020, \u003cem\u003e182\u003c/em\u003e (4), 1044\u0026ndash;1061.e18. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.cell.2020.07.009\u003c/span\u003e\u003cspan address=\"10.1016/j.cell.2020.07.009\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eE. Zhou, Y. Li, F. Wu, M. Guo, J. Xu, S. Wang, Q. Tan, P. Ma, S. Song, Y. Jin, Circulating Extracellular Vesicles Are Effective Biomarkers for Predicting Response to Cancer Therapy. \u003cem\u003eeBioMedicine\u003c/em\u003e 2021, \u003cem\u003e67\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ebiom.2021.103365\u003c/span\u003e\u003cspan address=\"10.1016/j.ebiom.2021.103365\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAZU-1:, \u003cem\u003eA Candidate Breast Tumor Suppressor and Biomarker for Tumor Progression | Molecular Biology of the Cell\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.molbiolcell.org/doi/full/\u003c/span\u003e\u003cspan address=\"https://www.molbiolcell.org/doi/full/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1091/mbc.11.4.1357\u003c/span\u003e\u003cspan address=\"10.1091/mbc.11.4.1357\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (accessed 2024-04-02)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJ. Wu, D. Zeng, S. Zhi, Z. Ye, W. Qiu, N. Huang, L. Sun, C. Wang, Z. Wu, J. Bin, Y. Liao, M. Shi, W. Liao, Single-Cell Analysis of a Tumor-Derived Exosome Signature Correlates with Prognosis and Immunotherapy Response. J. Transl Med. \u003cb\u003e19\u003c/b\u003e(1), 381 (2021). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12967-021-03053-4\u003c/span\u003e\u003cspan address=\"10.1186/s12967-021-03053-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eH. Cai, E.G. Chiorean, M.V. Chiorean, D.K. Rex, B.W. Robb, N.M. Hahn, Z. Liu, P.J. Loehrer, M.L. Harrison, Y. Xu, Elevated Phospholipase A2 Activities in Plasma Samples from Multiple Cancers. PLOS ONE. \u003cb\u003e8\u003c/b\u003e(2), e57081 (2013). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pone.0057081\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0057081\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eS.-R. Kim, J.-I. Heo, J.-W. Park, C.-M. Kang, K.S. Kim, Radiation-Induced Lipoprotein-Associated Phospholipase A2 Increases Lysophosphatidylcholine and Induces Endothelial Cell Damage. Toxicology. \u003cb\u003e458\u003c/b\u003e, 152841 (2021). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.tox.2021.152841\u003c/span\u003e\u003cspan address=\"10.1016/j.tox.2021.152841\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e\u003cem\u003eRole of magnesium in genomic stability - ScienceDirect\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.sciencedirect.com/science/article/abs/pii/S0027510701000744\u003c/span\u003e\u003cspan address=\"https://www.sciencedirect.com/science/article/abs/pii/S0027510701000744\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (accessed 2024-04-02)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eK. Jelonek, P. Widlak, M. Pietrowska, The Influence of Ionizing Radiation on Exosome Composition, Secretion and Intercellular Communication. Protein Pept. Lett. \u003cb\u003e23\u003c/b\u003e(7), 656\u0026ndash;663 (2016). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2174/0929866523666160427105138\u003c/span\u003e\u003cspan address=\"10.2174/0929866523666160427105138\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\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":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"cellular-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ceon","sideBox":"Learn more about [Cellular Oncology](http://link.springer.com/journal/13402)","snPcode":"13402","submissionUrl":"https://submission.nature.com/new-submission/13402/3","title":"Cellular Oncology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Locally advanced rectal cancer, Neoadjuvant chemoradiotherapy, Small Extracellular vesicles, Proteomics","lastPublishedDoi":"10.21203/rs.3.rs-4539832/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4539832/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003e Neoadjuvant chemoradiotherapy (nCRT) stands as a pivotal therapeutic approach for locally advanced rectal cancer (LARC), yet the absence of a reliable biomarker to forecast its efficacy remains a challenge. Thus, this study aimed to assess whether the proteomic compositions of small extracellular vesicles (sEVs) might offer predictive insights into nCRT response among patients with LARC, while also delving into the proteomic alterations within sEVs post nCRT.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003ePlasma samples were obtained from LARC patients both pre- and post-nCRT. Plasma-derived sEVs were isolated utilizing the TIO\u003csub\u003e2\u003c/sub\u003e-based method, followed by LC-MS/MS-based proteomic analysis. Subsequently, pathway enrichment analysis were performed to the Differentially Expressed Proteins (DEPs). Additionally, ROC curves were generated to evaluate the predictive potential of sEV proteins in determining nCRT response. Public databases were interrogated to identify sEV protein-associated genes that are correlated with the response to nCRT in LARC.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA total of 16 patients were enrolled. Among them, 8 patients achieved a pathological complete response (good responders, GR), while the remaining 8 did not achieve a complete response (poor responders, PR). Our analysis of pretreatment plasma-derived sEVs revealed 67 significantly up-regulated DEPs and 9 significantly down-regulated DEPs. Notably, PROC (AUC: 0.922), F7 (AUC: 0.953) and AZU1 (AUC: 0.906) demonstrated high AUC values and significant differences (P value\u0026thinsp;\u0026lt;\u0026thinsp;0.05) in discriminating between GR and PR patients. Furthermore, a signature consisting of 5 sEV protein-associated genes (S100A6, ENO1, MIF, PRDX6 and MYL6) was capable of predicting the response to nCRT, yielding an AUC of 0.621(95% CI: 0.454\u0026ndash;0.788). Besides, this 5-sEV protein-associated gene signature enabled stratification of patients into low- and high-risk group, with the low-risk group demonstrating a longer overall survival in the testing set (P\u0026thinsp;=\u0026thinsp;0.048). Moreover, our investigation identified 11 significantly up-regulated DEPs and 31 significantly down-regulated DEPs when comparing pre- and post-nCRT proteomic profiles. GO analysis unveiled enrichment in the regulation of phospholipase A2 activity.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eDifferential expression of sEV proteins distinguishes between GR and PR patients and holds promise as predictive markers for nCRT response and prognosis in patients with LARC. Furthermore, our findings highlight substantial alterations in sEV protein composition following nCRT.\u003c/p\u003e","manuscriptTitle":"Characterization and proteomic analysis of plasma-derived small extracellular vesicles in locally advanced rectal cancer patients","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-27 18:51:29","doi":"10.21203/rs.3.rs-4539832/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-06-26T00:20:14+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-06-24T09:07:00+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-06-22T11:41:34+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-06-19T09:36:08+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"2310082928281838280986468021775386167","date":"2024-06-08T05:08:29+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"323073078132778122197459331639582620482","date":"2024-06-08T03:39:25+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"99408911118032259109729685200111186926","date":"2024-06-08T03:32:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"103099288189485013547012478324382692178","date":"2024-06-08T03:27:49+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-06-08T03:22:13+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-06-06T22:14:22+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-06-06T22:13:14+00:00","index":"","fulltext":""},{"type":"submitted","content":"Cellular Oncology","date":"2024-06-06T10:57:07+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"cellular-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ceon","sideBox":"Learn more about [Cellular Oncology](http://link.springer.com/journal/13402)","snPcode":"13402","submissionUrl":"https://submission.nature.com/new-submission/13402/3","title":"Cellular Oncology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"8e27b524-ae26-4917-b7dc-9086ecab78d6","owner":[],"postedDate":"June 27th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-08-26T16:01:00+00:00","versionOfRecord":{"articleIdentity":"rs-4539832","link":"https://doi.org/10.1007/s13402-024-00983-1","journal":{"identity":"cellular-oncology","isVorOnly":false,"title":"Cellular Oncology"},"publishedOn":"2024-08-20 15:57:15","publishedOnDateReadable":"August 20th, 2024"},"versionCreatedAt":"2024-06-27 18:51:29","video":"","vorDoi":"10.1007/s13402-024-00983-1","vorDoiUrl":"https://doi.org/10.1007/s13402-024-00983-1","workflowStages":[]},"version":"v1","identity":"rs-4539832","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4539832","identity":"rs-4539832","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}