Proteomic analysis of altered urinary extracellular vesicle levels in breast cancer patients pre- and post-surgery

Introduction

Breast cancer (BC) is a widespread disease characterized by abnormal growth of breast tissue. Notably, BC is one of the most common cancers affecting women worldwide, with significant variations in incidence and mortality rates (1).

Extracellular vesicles (EVs) are released from cells, enclosed in a lipid bilayer, and are incapable of self-replication. During formation, EVs assimilate various bioactive molecules from their cells of origin, such as membrane receptors, soluble proteins, nucleic acids (mRNAs and microRNAs), and lipids, which can then be transmitted to target cells. Importantly, the diverse cargos carried by EVs reflect the state of their originating cells and can influence the functions and characteristics of other cells. EVs are present in bodily fluids, such as blood, urine, and saliva, and their levels are elevated in patients with cancer compared to healthy individuals. Collectively, these findings highlight the potential of EVs as a valuable source of diagnostic, predictive, and prognostic biomarkers for cancer (2,3). Inubushi et al. (4) reported that serum exosomes co-expressing CD63/CD9 and CD9/HER2 decreased after surgery in patients with HER2-expressing BC, indicating an association with tumor burden. Given that more than 80% of primary BC exhibit HER2 expression at varying immunohistochemical levels (IHC 1+, 2+, or 3+), HER2 presented on the surface of serum exosomes represents a promising biomarker for BC detection and disease monitoring. Rodríguez et al. (5) observed a significant decrease in the plasma levels of exosomes expressing CD63 1-week post-surgery, suggesting that the tumor mass was responsible for the higher levels of circulating exosomes in patients with cancer. Moreover, lower plasma exosome levels pre- and post-surgery are associated with better life expectancies in patients with oral squamous cell carcinoma. Theodoraki et al. (6) suggested that monitoring plasma exosomes during post-therapy follow-up could reveal changes in the patient’s immune status and detect treatment failure and the risk of recurrence in patients with head and neck squamous cell carcinoma.

Urinary extracellular vesicles (uEVs) have emerged as potential non-invasive biomarkers for various urological cancers (kidney, bladder, and prostate cancer) (7), non-urological cancers (lung cancer) (8), and neurological diseases such as glioblastoma (GBM) (9), Alzheimer’s disease (10), and Parkinson’s disease (11). Hallal et al. (9) identified specific changes in uEV proteomes associated with primary GBM, treatment-resistant GBM, or GBM recurrence. Notably, alterations in these proteins may be related to surgical procedures or may represent common markers associated with both primary and recurrent GBM, suggesting that some protein alterations may reflect surgical effects or represent shared biomarkers across disease stages. Considering that the urinary system serves as a primary route for EV clearance, uEVs may provide valuable insights into postoperative physiological states. Among the diverse cargoes contained in uEVs, the proteome offers distinct advantages for biomarker discovery and functional interpretation. Proteins are the primary effectors of cellular functions and directly reflect the physiological or pathological status of their cells of origin. Compared with nucleic acids, uEV proteins are more stable under storage and processing conditions, allowing consistent detection and quantification. Furthermore, proteomic profiling enables comprehensive assessment of biological pathways involved in immune responses, wound healing, and tissue remodeling, which are highly relevant to postoperative recovery in patients with BC. Therefore, focusing on uEV proteomic alterations can provide functional insights into molecular mechanisms underlying surgical outcomes and may help identify novel protein biomarkers associated with disease progression or tissue repair. However, little is known about the relationship between uEV levels and their proteomic changes following BC surgery. Therefore, this study aimed to investigate changes in uEV levels and proteomes in patients with BC pre- and post-surgery. We present this article in accordance with the MDAR reporting checklist (available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-1215/rc).

Methods

Sample collection and preparation

Midstream urine samples were collected from patients with BC (n=30) prior to surgery and within 2–3 weeks post-surgery. All patients with BC (aged ≥18 years) were enrolled at Songklanagarind Hospital. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by the Human Research Ethics Committee of the Faculty of Medicine, Prince of Songkla University, Thailand (No. REC.67-120-38-2). Informed consent was obtained from all subjects involved in the study. The clinical characteristics of the patients were summarized in Table 1. For urine preparation, urine samples were centrifuged at 2,500 ×g for 15 min at 4 ℃ to remove cells, cell debris, and bacteria. Thereafter, the supernatants were collected and stored at −80 ℃ until use (Figure 1).

Figure 1 Workflow of uEVs isolation and data analysis. BC, breast cancer; DTT, dithiothreitol; LC-MS/MS, liquid chromatography-tandem mass spectrometry; NTA, nanoparticle tracking analysis; TEM, transmission electron microscopy; uEVs, urinary extracellular vesicles; WB, western blot.

uEV Isolation via differential ultracentrifugation

The uEV isolation procedure was modified from previously reported methods (12,13). In brief, 9 mL of urine was initially centrifuged at 2,500 ×g for 15 min at 4 ℃, and the resulting supernatant was further centrifuged at 20,000 ×g for 20 min at 4 ℃. This supernatant, referred to as SN1, was reserved for subsequent steps, while the pellets, expected to contain large particles and uEVs trapped within the network-like structure of Tamm-Horsfall protein (THP), were processed further. To release the entrapped uEVs, the pellets were incubated in an isolation solution containing 10 mM triethanolamine and 250 mM sucrose, vortexed for 30 seconds, followed by the addition of 200 mg/mL dithiothreitol (DTT), and incubated at 37 ℃ for 10 minutes. The mixture was then centrifuged at 20,000 ×g for 20 min at 4 ℃, and the resulting supernatant was combined with SN1. The pooled supernatant (SN2) was subsequently ultracentrifuged at 120,000 ×g for 90 min at 4 ℃ (Optima MAX-XP Ultracentrifuge, Beckman Coulter, Bera, CA, USA). The resulting pellets were resuspended and washed with Dulbecco’s Phosphate-Buffered Saline (DPBS), followed by a second ultracentrifugation at 120,000 ×g for 90 min at 4 ℃. Finally, the uEV pellets were resuspended in DPBS and stored at −80 ℃ until use (Figure 1).

Quantification of uEVs using nanoparticle tracking analysis (NTA)

The concentration and size distribution of uEVs were determined using a NanoSight NS300 instrument (Malvern Panalytical Ltd, Malvern, UK). Each uEV sample was independently diluted in sterile water for two replicates to achieve the recommended concentration range of 1×10⁷ to 10⁹ particles/mL, corresponding to 20–100 particles per frame. The diluted samples were injected into the nanoparticle tracking analyzer using a syringe pump at a speed of 50. Five 30-second videos were recorded at 25 ℃, with a camera level of 14 and a detection threshold of 6. The resulting data were analyzed using NanoSight NTA 3.0 software.

Western blot analysis

Ten microliters of uEV samples were mixed with non-reducing loading dye and denatured by heating at 95 ℃ for 5 minutes. The proteins were then resolved by SDS-PAGE using a 12% TGX Stain-Free^TM FastCast^TM Acrylamide Gel (cat#161-0185, Bio-Rad Laboratories, Hercules, CA, USA) and transferred onto polyvinylidene difluoride (PVDF) membranes. The membranes were blocked with 5% nonfat milk in Tris-buffered saline containing 0.1% Tween 20 (TBS-T) for 1 h at room temperature. After washing with TBS-T, the membranes were incubated overnight at 4 ℃ with primary antibodies at a 1:1,000 dilution, including CD9 (cat#13174; Cell Signaling Technology, Inc., Danvers, MA, USA), Alix (cat#2171; Cell Signaling Technology, Inc., Danvers, MA, USA), TSG101 (cat#72312; Cell Signaling Technology, Inc., Danvers, MA, USA), THP (cat#sc-271022; Santa Cruz Biotechnology, Dallas, TX, USA), and cytochrome C (cat#4272T; Cell Signaling Technology, Inc., Danvers, MA, USA). The membranes were washed with TBS-T and then incubated for 2 hours with horseradish peroxidase (HRP)-conjugated secondary antibodies (1:2,000). Protein expression was detected using the SuperSignal^TM West Dura Extended Duration Substrate (cat#34075; Thermo Fisher Scientific, Waltham, MA, USA) and visualized with a chemiluminescence imaging system (Alliance Q9 Advanced, UVITEC).

Transmission electron microscopy (TEM)

uEV pellets were fixed in 2.5% glutaraldehyde for 30 minutes at room temperature. A 3 µL aliquot of the sample was applied onto a carbon/formvar-coated grid and incubated for 10 minutes. Excess sample was then removed, and the grid was washed sequentially with PBS and distilled water. For negative staining, the sample was incubated with 2.5% uranyl acetate in the dark for 10 minutes and allowed to air-dry overnight at room temperature. The uEVs were ultimately visualized at 50,000× magnification using a JEOL JEM 2010 transmission electron microscope at the Scientific Equipment Center, Prince of Songkla University.

Proteomic analysis by liquid chromatography-tandem mass spectrometry (LC-MS/MS)

The total protein content of each uEV sample was measured using the Lowry assay with bovine serum albumin as the standard (14). Five micrograms of protein were reduced with 5 mM DTT, alkylated with 15 mM iodoacetamide, and digested with sequencing-grade porcine trypsin for 16 h at 37 ℃. Tryptic peptides were dried, resuspended in 0.1% formic acid, and analyzed by LC-MS/MS using an Ultimate3000 Nano/Capillary LC System coupled to a Q-TOF impact II (Bruker Daltonics) with a CaptiveSpray ion source. Peptides were enriched on a µ-Precolumn C18 Pepmap 100 (Thermo Scientific) and separated on an Acclaim PepMap RSLC C18 column, nanoViper (Thermo Scientific). A CaptiveSpray was used for electrospray ionization at 1.6 kV. Mass spectra (3) (m/z 150–2200) were acquired in positive-ion mode at 2 Hz, with collision energy adjusted to 10 eV relative to m/z. Each sample was analyzed in triplicate.

Proteins were identified by matching MS/MS spectra to the UniProt Homo sapiens database using MaxQuant 2.1.0.0 with the Andromeda search engine (15). Label-free quantitation was performed using standard MaxQuant settings: trypsin digestion with up to two missed cleavages, carbamidomethylation of cysteine as a fixed modification, and oxidation of methionine and N-terminal acetylation as variable modifications. Protein identification required at least seven amino acids and one unique peptide, with a 1% FDR and a maximum of five modifications per peptide.

Statistical analysis

The data were analyzed using chi-square tests in IBM SPSS Statistics V.30 to compare clinical characteristics based on the trend of uEV levels after surgery. A Venn diagram of the identified proteins in the sample groups was constructed using the Venn software (https://jvenn.toulouse.srae.fr/app/index.html). Differential expression analysis to identify differentially expressed proteins (DEPs), heatmap visualization of protein expression, and principal component analysis (PCA) were performed using MetaboAnalyst 6.0 (https://www.metaboanalyst.ca/). Notably, we removed missing features from more than 50% of the samples and estimated the remaining missing values based on the limit of determination (LoD). Data filtering was based on the median intensity values. The data were normalized using quantile normalization and autoscaling. Data were analyzed to identify DEPs using paired two-sample t-tests, with fold change (FC) cut-off >2 and P value <0.05 considered statistically significant. Pathway analysis was performed using FunRich Version 3.1.3. Additionally, the protein-protein interactions (PPI) of the DEPs were analyzed using STRING (version 12.0; https://string-db.org/). CytoHubba was utilized to identify hub proteins of the DEPs-PPI, which were calculated in the top 10 nodes ranked by mutant cancer protein (MCC) and then retrieved for functional enrichment using string App, as the Cytoscape plugin.

Results

Characterization of uEVs

Transmission electron microscopy showed that the uEVs had a spherical shape and morphology, with sizes of approximately 50–100 nm (Figure 2A). Additionally, western blotting indicated that the uEVs expressed typical EV markers, including CD9, Alix, and TSG101, whereas cytochrome C was not detected. However, THP was still present in most uEVs samples (Figure 2B). It is the most abundant urinary protein and is difficult to completely isolate from urine samples. Although the uEVs pellets were successfully isolated, these results raise concerns about the purity of the uEVs. Notably, further steps and methods, such as size exclusion chromatography (3), may be employed to generate high-purity uEVs.

Figure 2 Characterization of uEVs. (A) TEM showed the size and morphology of the uEVs pellets (magnification: ×50,000). (B) Western blot analysis of uEVs samples pre- (n=5) and post-surgery (n=5). TEM, transmission electron microscopy; uEVs, urinary extracellular vesicles.

Concentrations of uEVs pre- and post-surgery

In the present study, we measured the concentrations of uEVs in patients with BC before and after surgery. As shown in Figure 3A, the fluctuating uEV levels post-surgery could be used to classify patients with BC into two groups (increasing and decreasing uEV levels). Notably, the increasing and decreasing trend groups included 8 and 22 patients, respectively (Figure 3B,3C). The uEV size distribution showed that the major population of uEVs, ranging from 101 to 150 nm, was present in both pre- and post-surgery samples (Figure 3D), which is consistent with the TEM image (Figure 2A). Furthermore, the mode size of uEVs in the pre- and post-surgery samples was significantly different (P=0.02), with sizes of 94.65±18.17 nm and 104±25.4 nm, respectively (Figure 3E).

Figure 3 NTA of uEVs. (A) The concentration of uEVs in patients with BC (n=30) was measured pre- and post-surgery. Based on the uEVs levels after surgery, patients with BC were categorized into two groups: (B) increasing (n=8) and (C) decreasing (n=22) trend groups. (D) Size distribution of uEVs samples pre- and post-surgery. (E) Mode size of uEVs samples pre- and post-surgery (median ± IQR). *, P<0.05; ****, P<0.001; determined by the Wilcoxon matched-pairs signed-rank test. BC, breast cancer; IQR, interquartile range; NTA, nanoparticle tracking analysis; uEVs, urinary extracellular vesicles.

As summarized in Table 1, BC subtypes and progesterone receptor (PR) status differed significantly between the post-surgical uEVs increasing and decreasing groups (P=0.01 and P=0.04, respectively). The uEVs increasing group was enriched with triple negative breast cancer (TNBC) and PR-negative cases, both of which are considered more aggressive BC subtypes. In contrast, the decreasing group was enriched with Luminal A and PR-positive cases, which are less aggressive and generally associated with a better prognosis. Other characteristics, including age, stage, and estrogen receptor (ER) status, did not differ significantly between the groups.

Changes in the proteome of uEVs pre- and post-surgery

Importantly, we compared the levels of specific proteins in each group pre- and post-surgery. In the increasing trend group, 2,175 and 2,556 unique uEVs proteins were identified pre- and post-surgery, respectively, with 10,463 proteins shared by both groups (Figure 4A). In the decreasing group, 706 and 3,055 unique uEV proteins were identified pre- and post-surgery, respectively, with 12,962 shared by both groups (Figure 4B). Additionally, the top 25 proteins with the highest variation in expression patterns post- versus pre-surgery in the increasing and decreasing trend groups are shown using heatmaps (Figure 4C,4D). Moreover, PCA was performed to group the samples into clusters based on protein expression profiles. In the increasing trend group, uEVs samples showed similar protein profiles, with pre- and post-surgery samples overlapping (Figure 4E). In contrast, pre- and post-surgery uEV samples tended to form two distinct clusters based on the proteome profiles in the decreasing trend group (Figure 4F).

Figure 4 Comparison of the proteome of uEVs pre- and post-surgery. Venn diagram of the total proteins identified in the uEVs samples in the (A) increasing and (B) decreasing trend groups. Heatmap analysis of proteomic profiles of uEVs samples in the (C) increasing and (D) decreasing trend groups. PCA of the proteomic data of uEVs samples in the (E) increasing and (F) decreasing trend groups. BC, breast cancer; PCA, principal component analysis; uEVs, urinary extracellular vesicles.

DEPs in uEVs post- and pre-surgery, and biological pathway analysis

Differential expression analysis of the proteomic data was conducted to identify DEPs in uEVs exhibiting increasing or decreasing trends between post- and pre-surgery patients. In the increasing trend group, 35 proteins were significantly upregulated and 26 were downregulated (Figure 5A, Table S1). Meanwhile, the decreasing trend group exhibited 121 significantly upregulated and 121 downregulated proteins (Figure 5B, Table S2).

Figure 5 Volcano plots of DEPs in the uEVs of patients with BC post-surgery versus pre-surgery. (A) DEPs in the increasing trend group (35 significantly upregulated and 26 significantly downregulated) and (B) DEPs in the decreasing trend group (121 significantly upregulated and 121 significantly downregulated). Red and blue dots represent upregulated and downregulated proteins, respectively. DEPs were considered significant at FC >2 and P<0.05. BC, breast cancer; DEP, differentially expressed proteins; FC, fold change; uEVs, urinary extracellular vesicles.

Pathway enrichment analysis was subsequently performed using FunRich (Version 3.1.3). The upregulated DEPs in the increasing trend group were predominantly enriched in the TNF-α/NF-κB signaling pathway, followed by integrin cell surface interactions, aurora A signaling, notch signaling pathway, and polo-like kinase signaling events in the cell cycle. Additionally, the downregulated DEPs were enriched in the cell cycle, mitotic signaling pathway, mesenchymal-to-epithelial transition, and polo-like kinase signaling events in the cell cycle (Figure 6, Table 2).

Figure 6 Biological pathways of DEPs in the uEVs increasing and decreasing trend groups, annotated by FunRich. De-Sig down, decreasing trend group—significantly downregulated proteins; De-Sig up, decreasing trend group—significantly upregulated proteins; In-Sig down, increasing trend group—significantly downregulated proteins; In-Sig up, increasing trend group—significantly upregulated proteins. DEP, differentially expressed proteins; uEVs, urinary extracellular vesicles.

Conversely, in the decreasing trend group, the upregulated DEPs were primarily enriched in the vascular endothelial growth factor (VEGF) and vascular endothelial growth factor receptor (VEGFR) signaling networks, interferon (IFN)-gamma pathway, and Class I PI3K signaling events mediated by Akt. Notably, these proteins are involved in the Wnt signaling network, mesenchymal-to-epithelial transition, epithelial-to-mesenchymal transition, and integrin signaling pathways, such as integrins in angiogenesis, integrin-linked kinase signaling, and integrin cell surface interactions. Most downregulated DEPs were mapped to the cell cycle, mitosis, integrin-linked kinase signaling, VEGF and VEGFR signaling networks, IFN-gamma pathway, and Class I PI3K signaling events mediated by Akt. Importantly, downregulated proteins linked to p53-, p38 MAPK-, and IL23-mediated signaling events were identified only in patients with BC whose uEV levels decreased following surgery (Figure 6, Table 2).

Furthermore, different proteins in the four datasets may be involved in the same pathway, such as polo-like kinase signaling events in the cell cycle, signal transduction, and signaling by GPCR (Table 2, more Supplementary Material available at https://cdn.amegroups.cn/static/public/tcr-2025-1215-1.xlsx). However, depending on their functions, the proteins may participate in these pathways through different mechanisms.

PPI networks, hub proteins, and STRING enrichment of up- and down-regulated DEPs

In this study, we predicted PPIs using the STRING network and identified hub proteins. Additionally, we performed functional annotation of the hub proteins on the Gene Ontology (GO) database, using STRING.

In the increasing trend group, the PPI network showed interactions between proteins in the database and the 35 upregulated DEPs (Figure 7A), and the critical hub proteins were SF3A3 and cell cycle and apoptosis regulator 2 (CCAR2), linked colorectal MCC (Figure 7B). Importantly, this result was consistent with the findings of the pathway analysis, which showed that CCAR2 and MCC are involved in the TNF alpha/NF-kB pathway (Table 2). In contrast, the 26 downregulated DEPs did not interact with these proteins, and thus, no hub protein was selected for GO functional annotation (Figure 7C).

Figure 7 PPI networks and the hub proteins. (A) PPI network of 35 upregulated DEPs in the uEVs increasing trend group, with (B) 8 hub proteins calculated. (C) PPI network of 26 downregulated DEPs. Hub protein interpreted color scales ranging from red (the most important) to yellow (less critical). DEP, differentially expressed proteins; PPI, protein-protein interaction; uEVs, urinary extracellular vesicles.

In the decreasing trend group, the PPI network showed that the 121 upregulated DEPs interacted with proteins in the database, and the top 10 hub proteins were KRT1, KRT2, KRT10, KRT5, FN1, EPB41, ROCK1, EEF2, KRT9, and ROCK2 (Figure 8A). The GO enrichment analysis revealed that most hub proteins were involved in tissue development and cell differentiation (Figure 8B). KRT1, KRT2, KRT10, KRT5, and KRT9, which are keratin filament proteins, were correlated with skin and epidermal development. FN1, ROCK1, and ROCK2 mediated the regulation of cell-substrate adhesion, mesenchymal cell differentiation, and the angiotensin-activated signaling pathway (Figure 8B, Table S3) and were linked to integrins in the angiogenesis pathway (Table 2). Additionally, the PPI network of 121 downregulated DEPs is shown in Figure 9A, and the top 10 hub proteins were BUB1B, CKAP5, KIF20B, SPAG5, ATAD2, ATM, CDT1, PCID2, BAZ1B, and SMC1B. The GO functional annotation showed that most of the hub proteins were related to cell cycle and chromosome organization processes and were involved in cell cycle checkpoint signaling (Figure 9B).

Figure 8 PPI networks and the hub proteins. (A) PPI network of 121 upregulated DEPs in the uEVs decreasing trend group and (B) biological processes annotation by the top 10 hub proteins. Hub proteins are interpreted by color scale ranging from red (the most important) to yellow (less important). DEP, differentially expressed proteins; PPI, protein-protein interaction; uEVs, urinary extracellular vesicles.

Figure 9 PPI networks and the hub proteins. (A) PPI network of 121 downregulated DEPs in the uEVs decreasing trend group and (B) the biological processes annotation by the top 10 hub proteins. Hub proteins were represented on a color scale ranging from red (most important) to yellow (less important). DEP, differentially expressed proteins; PPI, protein-protein interaction; uEVs, urinary extracellular vesicles.

Discussion

In this study, we hypothesized that patients with BC with higher uEV levels after surgery might have poorer outcomes than those with low uEV levels. Additionally, we performed functional annotation and pathway analysis of DEPs in uEVs from patients with BC to investigate their role in disease progression. The results are summarized in Figure 10.

Figure 10 Summary of uEVs levels post-surgery, indicating patient outcomes through uEVs proteomes. The figure is created via BioRender. BC, breast cancer; uEVs, urinary extracellular vesicles.

Notably, there was no consistent trend in the expression profiles of cancer-related proteins in uEVs from patients with BC, even in patients with increased uEVs concentrations after surgery. Therefore, we speculated that the local BC tumor produces some proteins (downregulated proteins) pre-surgery, and that any residual or metastatic BC cells may increase the production of some proteins (upregulated proteins) after surgery to promote cancer and recurrence (two of eight patients) and even death (one patient death, 2 years after resection). In a previous study, we identified upregulated DEPs associated with cancer progression, including CCAR2, MCC, JAG1, and TPX2. CCAR2, also known as DBC1 or KIAA1967, plays a pivotal role as tumor suppressor and promoter. It mediates the transcription that promotes cancer growth and survival by binding to β-catenin. Additionally, CCAR2 plays an essential role in tumor suppression by promoting p53-mediated apoptosis and interacting with MCC to inhibit tumor growth (16,17). MCC acts as a tumor suppressor. When overexpressed, MCC re-localizes CCAR2 from the nucleus to the cytoplasm and binds β-catenin to convert it to its deacetylated form, which negatively regulates Wnt signaling in cancer cell lines and inhibits cell proliferation (18). JAG1 is a primary activator of the Notch signaling pathway and is often overexpressed in metastatic BC. JAG1 promotes cell migration and invasion and enhances angiogenesis in TNBC (19,20). TPX2 is a cofactor of Aurora A kinase and is associated with microtubules. TPX2 overexpression increases the proliferation, migration, and invasion of BC cells. Additionally, higher levels of TPX2 have been linked to tumor development and poor prognosis in various types of cancers, such as lung (21), colon (22), liver (23), prostate (24) and gastric cancers (25).

However, we did not identify any hub proteins or node interactions among the downregulated DEPs, making it difficult to confirm the biological processes and pathways through which they regulate cancer progression. Additionally, BUB1B, which is involved in the cell cycle pathway, was downregulated in patients with BC, with a decreasing trend in uEV levels.

Compared with patients who had increased uEV levels, patients with BC who had decreased uEV levels had better overall survival (OS) and recurrence-free survival (RFS) after surgery. Importantly, there was a decrease in the levels of uEV proteins involved in BC cell division and progression and an increase in the expression of proteins related to tissue development, possibly due to tissue injury postoperatively. Additionally, we identified 121 upregulated DEPs and 10 hub proteins (KRT1, KRT2, KRT10, KRT5, FN1, EPB41, ROCK1, EEF2, KRT9, and ROCK2) that were highly correlated with tissue and epidermal development, cell differentiation, and regulation of cell-substrate adhesion (Table S3). KRT1, KRT2, KRT10, KRT5, and KRT9 are keratin filament proteins, whereas ROCK1 and ROCK2 are involved in regulating keratinocyte differentiation and endothelial barrier establishment. Moreover, FN1, ROCK1, and ROCK2 mediate the regulation of cell-substrate adhesion and mesenchymal cell differentiation and are linked to integrins in the angiogenesis pathway (Table 2). Collectively, these results suggest that these proteins may be associated with tissue regeneration and wound healing after surgery. The wound healing process consists of four partially overlapping phases: the hemostatic phase (within 1 h after injury), inflammatory phase (3–5 days), differentiation or proliferation phase (3 weeks), and remodeling phase (over a year). Importantly, the proliferation phase is characterized by the formation of granulation, new blood vessel networks, collagen, and extracellular matrix (ECM) proteins, including fibronectin, collagens, and laminins (26,27). Early differentiation begins in the suprabasal layer (18), where K10/K1 is expressed in differentiated suprabasal cells and K14/K5 is expressed only in the basal layer. In the later phase of differentiation, K2 is another type II keratin protein co-expressed with K1/K10 in differentiated keratinocytes (28,29). ROCK1 and ROCK2 are vital components of the RhoA/ROCK signaling pathway, which is involved in regulating human keratinocyte proliferation and differentiation (30). ROCK1 depletion reduces keratinocyte adhesion to fibronectin and promotes terminal differentiation, whereas ROCK2 depletion increases keratinocyte adhesion to fibronectin and inhibits terminal differentiation (31). Additionally, the roles of the keratins FN1, ROCK1, and ROCK2 in cancer development have been described previously. The overexpression of keratin family proteins and fibronectin acts as a cancer promoter and marker in various types of cancer. KRT1 is overexpressed in BC cells compared with the expression level in normal cells (32). High KRT1/5 expression was significantly associated with poor OS in patients with melanoma (33). Keratin 5 overexpression is associated with serous ovarian cancer recurrence and chemotherapy resistance (34). FN1 stimulates the progression of various types of cancers. For example, FN1 is highly expressed in breast carcinoma and is closely related to the invasion and migration of ovarian cancer. FN1 has been identified as a potential marker for detecting breast and ovarian cancer (35-37). Overall, it is possible that these proteins and the downregulated proteins are not responsible for promoting cancer.

Furthermore, the downregulated DEPs confirmed our previous hypothesis. In the present study, we identified 121 downregulated DEPs, and the top 10 hub proteins included BUB1B, CKAP5, KIF20B, SPAG5, ATAD2, ATM, CDT1, PCID2, BAZ1B, and SMC1B. Notably, these proteins are related to cell division, the cell cycle, and chromosome organization processes, and are involved in cell cycle checkpoint signaling, which is relevant to cancer promotion (Table S3). Previous findings indicate that BUB1B, a member of the spindle assembly checkpoint protein family, plays an oncogenic role in cancer progression and is overexpressed in various cancers. BUB1B overexpression is correlated with poor prognosis in patients with BC (38), hepatocellular carcinoma (HCC) (39), prostate cancer (40) and bladder cancer (41). CKAP5 is a vital microtubule-associated protein that regulates microtubule dynamics and organization and is essential for processes that rely on the cytoskeleton, such as cell migration and differentiation (42). CKAP5, among other genes, has the potential to serve as a valuable prognostic biomarker in patients with HCC (43) and non-small cell lung cancer (44), and could be a target for ovarian cancer therapy (45). KIF20B is an M-phase microtubule-associated protein that plays a crucial role in cytokinesis. Additionally, KIF20B plays a pivotal role in the proliferation, survival, invasion, and metastasis of cancer cells. KIF20B overexpression suggests poor prognosis, and the protein could be a potential therapeutic target in breast (46), lung (47), pancreatic (48), and colorectal cancers (49). SPAG5 is a critical component of the mitotic spindle and is essential for normal chromosomal segregation and progression to anaphase. SPAG5 is an important oncogene in various cancers. SPAG5 overexpression promotes the progression of BC (50), lung adenocarcinoma (51), and colorectal cancer (52), and promotes chemoresistance in gastric cancer (53). ATAD2 is associated with estrogen-induced cell proliferation and cell cycle progression in BC cells, and promotes cell proliferation, invasion, and migration in several cancers, including cervical (54) and gastric (55) cancers. ATAD2 overexpression is linked to poor outcomes in breast, lung (56), ovarian (57) and colorectal cancers (58).

Conclusions

Post-surgical patients with elevated uEV levels were more often associated with aggressive BC and showed a higher risk of recurrence and cancer progression than those with reduced levels. While cancer-associated proteins were also significantly reduced after tumor removal, consistent with the decreasing uEV trend. The main limitation of this study is the small sample size, which limited the ability to detect significant differences between groups. These findings suggest that the observed trend could serve as a potential predictor of patient outcomes and should be confirmed in larger cohorts.

Acknowledgments

We thank Prof. Dr. Hutcha Sriplung (Epidemiology Unit, Faculty of Medicine, Prince of Songkla University) for supplying the urine samples, and the National Center for Genetic Engineering and Biotechnology (BIOTEC) for conducting the LC-MS/MS analysis.

Footnote

Reporting Checklist: The authors have completed the MDAR reporting checklist. Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-1215/rc

Data Sharing Statement: Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-1215/dss

Peer Review File: Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-1215/prf

Funding: This work was supported by a grant from the Faculty of Medicine, Prince of Songkla University (No. MR PSU-663823-267, to R.N.). The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-1215/coif). R.N. reports receiving research funding and support for the article processing charges from the Faculty of Medicine, Prince of Songkla University. The other authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by the Human Research Ethics Committee of the Faculty of Medicine, Prince of Songkla University, Thailand (No. REC.67-120-38-2). Informed consent was obtained from all subjects involved in the study.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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