Urinary exosomal RAB11A serves as a novel non-invasive biomarker for diagnosis, treatment response monitoring, and prognosis in small cell lung cancer

Abstract

Background

Small cell lung cancer (SCLC) is an aggressive malignancy with a poor prognosis. This study aimed to analyze the urinary exosomal proteome of SCLC patients to identify and validate potential non-invasive biomarkers for improving diagnosis, treatment response monitoring, and prognosis prediction.

Methods

We analyzed 90 urine samples from SCLC patients, divided into training (n = 38) and validation (n = 52) sets, including untreated, partial/complete remission, and relapsed groups. Ten healthy controls were included. Urinary exosomes were isolated by ultracentrifugation. The proteomic analysis employed data-independent acquisition mass spectrometry (DIA-MS) and parallel reaction monitoring (PRM). Immunohistochemistry was performed on 30 pairs of SCLC and adjacent normal tissues.

Results

Proteomic analysis revealed distinct exosomal protein expression patterns across SCLC stages. RAB11A emerged as a key differentially expressed protein. PRM validation confirmed significant changes in RAB11A levels across disease stages. ROC curve analysis demonstrated excellent diagnostic performance of RAB11A in distinguishing SCLC patients from healthy controls (AUC = 0.91, 95% CI 0.79–1.00, P = 0.0004), with a sensitivity of 85% and specificity of 92%. RAB11A also showed significant potential in monitoring treatment response (AUC = 0.86, 95% CI 0.69–1.00, P = 0.0019) and disease relapse (AUC = 0.90, 95% CI 0.76–1.00, P = 0.0005). Immunohistochemistry showed significantly higher RAB11A expression in SCLC tissues compared to adjacent normal tissues (70% vs. 33% positive expression, P = 0.043).

Conclusion

Urinary exosomal RAB11A shows promise as a non-invasive biomarker for SCLC diagnosis, treatment response monitoring, and early detection of relapse, potentially improving clinical management of SCLC patients. The findings provide insights into SCLC pathogenesis and offer a non-invasive approach for patient monitoring, which could improve clinical management strategies.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12014-025-09554-4.

Introduction

Small Cell Lung Cancer (SCLC) remains one of the most aggressive and lethal forms of lung cancer, accounting for approximately 10–15% of all lung cancer cases [1]. Characterized by rapid tumor growth and early metastatic spread, SCLC presents a significant challenge in oncology [2]. Despite initial responsiveness to chemotherapy and radiotherapy, frequent relapses and treatment resistance result in a dismally low 5-year survival rate of less than 7%, a statistic that has not significantly improved in four decades [3]. Recognizing the urgency of this situation, the National Cancer Institute (NCI) has designated SCLC as a recalcitrant malignancy, emphasizing the critical need for intensified research efforts [4].

Current diagnostic and monitoring strategies for SCLC face several limitations. Conventional imaging techniques such as computed tomography (CT) scans and positron emission tomography—computed tomography (PET-CT) expose patients to ionizing radiation, which is concerning for frequent monitoring [5]. Biopsies, while providing crucial diagnostic information, are invasive and carry risks such as bleeding, infection, and rare tumor seeding [6]. Serum biomarkers like neuron-specific enolase and pro-gastrin-releasing peptide show some utility but lack sufficient accuracy and reliability for comprehensive disease management [7]. These limitations underscore the urgent need for novel, non-invasive biomarkers to enhance SCLC management throughout the disease course while minimizing patient risk and discomfort.

Exosomes are small extracellular vesicles (30–150 nm in diameter) that play a crucial role in intercellular communication and signal transduction. These vesicles, secreted by most cell types under both physiological and pathological conditions, contain a diverse cargo of proteins, lipids, and genetic materials (DNA, RNA, and microRNA) [8]. Exosomes function as important mediators in various biological processes, including morphogen signaling, immune regulation, cell recruitment, and horizontal transfer of genetic material. In the context of cancer, tumor cells actively release large quantities of exosomes, which significantly influence cancer biology. These tumor-derived exosomes participate in crucial processes such as tumor growth, tumorigenesis, immune escape, angiogenesis, metastasis, and therapy resistance [9]. Their ability to reflect the physiological or pathological state of their cell of origin has led to increasing interest in exosomes as a promising source of cancer biomarkers [10]. Urinary exosomes, in particular, offer several advantages for cancer research and clinical management, especially in SCLC. They allow for non-invasive and frequent sampling without the risks associated with radiation exposure or biopsies. These exosomes have the potential to provide valuable insights into tumor biology and treatment response by reflecting systemic changes associated with cancer [10–13]. Moreover, the relatively low complexity of the urinary proteome compared to blood plasma may facilitate the detection of low-abundance cancer-specific proteins [14], making urinary exosomes an attractive target for biomarker discovery and validation in SCLC.

Recent advancements in mass spectrometry-based proteomics have enhanced our ability to profile and quantify complex protein mixtures. Data-independent acquisition mass spectrometry (DIA-MS) offers high reproducibility and quantitative accuracy in discovery proteomics [15], while parallel reaction monitoring (PRM) provides precise targeted quantification for candidate biomarker validation [16]. The combination of these techniques presents a powerful approach for biomarker discovery and validation in SCLC.

While studies have highlighted the potential of exosomal proteins as biomarkers in various cancers, including lung cancer, the specific role of urinary exosomal proteins in SCLC diagnosis, treatment monitoring, and prognosis remains largely unexplored. Among the various exosomal proteins, RAB11A, a member of the RAB family of small GTPases, has emerged as a protein of interest due to its involvement in vesicle trafficking and potential role in cancer progression [17].

In this study, we aim to conduct a comprehensive analysis of urinary exosomal proteins in SCLC patients at different stages of disease treatment response and relapse. Our objectives are to employ a multi-omics approach, combining DIA-MS for initial protein profiling, PRM for targeted validation, and immunohistochemistry for tissue-level confirmation. We seek to identify and validate potential biomarkers, with particular attention to RAB11A, for non-invasive diagnosis, treatment response monitoring, and early detection of disease progression in SCLC. By leveraging urinary exosome analysis and advanced proteomic techniques, we aim to develop a novel, non-invasive tool for improving the clinical management of SCLC patients.

Materials and methods

Patient selection and sample collection

This study was approved by the ethics committee of Beijing Shijitan Hospital, Capital Medical University (Research Ethics Review No. 5, 2017). Patients were recruited from the Departments of Respiratory and Critical Care Medicine, Oncology, and Thoracic Surgery at Beijing Shijitan Hospital between January 2017 and January 2024. Healthy controls were selected from individuals with normal physical examinations.

Patients were categorized into three groups based on treatment status: pre-treated SCLC (n = 13), partial/complete remission SCLC (n = 12), and relapse SCLC (n = 12). A validation cohort was established, comprising health (n = 13), pre-treated SCLC (n = 13), partial/complete remission SCLC (n = 13), and relapse SCLC (n = 13). Additionally, 30 pairs of SCLC tissue samples and adjacent healthy tissues were utilized. Inclusion criteria were histologically confirmed SCLC. Baseline patient demographics and clinical characteristics were comprehensively documented, encompassing key parameters such as age, sex, smoking history, tumor stage and Eastern Cooperative Oncology Group (ECOG) performance status. Tumor staging was based on Veterans Administration Lung Study Group (VALSG) system and the seventh edition of American Joint Committee on Cancer (7th AJCC) TNM staging system, using chest CT, brain magnetic resonance imaging (MRI), and PET-CT. Treatment response was evaluated according to Response Evaluation Criteria in Solid Tumors (RECIST) 1.1 criteria [18]. Exclusion criteria included hematuresis, urinary albumin/creatinine ratio ≥ 30 mg/g, and concomitant malignancies. The study workflow is illustrated in Fig. 1.

Fig. 1.

The study workflow was displayed

Urine exosome isolation

Urinary exosomes were isolated from 50 mL of first morning urine using differential ultracentrifugation. After thawing at 37 °C, samples underwent differential centrifugation steps in fresh tubes. Initial centrifugation was performed at 2000 × g for 30 min at 4 °C to pellet cell debris. The collected supernatant was further centrifuged at 10,000 × g for 45 min at 4 °C to remove large vesicles. Following filtration through a 0.45 μm membrane (Millipore, R6BA09493), the filtrate underwent ultracentrifugation at 100,000 × g for 70 min at 4 °C. To enhance purity, the pellet was washed in 10 mL pre-chilled PBS and ultracentrifuged again using identical conditions. The final pellet was reconstituted in 100 μL pre-chilled PBS and stored at −80 °C.

Characterization of urinary exosomes

Exosomes were characterized using transmission electron microscopy (TEM), nanoparticle tracking analysis (NTA), and Western blotting. TEM visualized size and morphology, NTA determined concentration and size distribution, and Western blotting assessed exosomal marker TSG101 (ab125011, Abcam), confirming the presence of exosomes in the isolated samples.

Liquid chromatography tandem mass spectrometry

Proteins from urinary exosomes were extracted using the acetone precipitation method. Equal protein amounts (200 μg each) of pooled urinary exosome samples were processed for mass spectrometry analysis. Proteins were diluted in 50 mmol/L Ammonium bicarbonate (pH ≥ 8, urea concentration ≤ 1 mol/L), reduced with 50 mmol/L dithiothreitol for one hour at 37 °C, alkylated with 10 mmol/L iodoacetamide for 45 min at room temperature in dark, and digested with trypsin (1:50 ratio) overnight at 37 °C. The reaction was stopped by adding formic acid to adjust the pH to below 3. Peptides were desalted using Ziptip C18 columns (Millipore) and analyzed by mass spectrometry.

Mass spectrometry analysis was performed using an EASY-nLC 1200 UHPLC system coupled with an Orbitrap Eclipse mass spectrometer (Thermo Scientific, USA). Two mobile phase solutions were prepared: Solution A (100% water with 0.1% formic acid) and Solution B (80% acetonitrile with 0.1% formic acid). Peptides were separated on a 25-cm column (100 μm inner diameter) packed with ReproSil-Pur C18-AQ 1.5-μm silica beads (Dr. Maisch GmbH), using a 65-min linear gradient from 6 to 95% acetonitrile in 0.1% formic acid at a flow rate of 600 nL/min. Initially, data-dependent acquisition (DDA) mode was used to build a spectral library. For DDA, the mass spectrum (MS) was acquired in the Orbitrap (350–1500 m/z) at a resolution of 120,000, followed by data-dependent MS/MS scans in an Ion Routing Multipole at 27% normalized collision energy. Subsequently, Data-Independent Acquisition (DIA) mode was employed for protein quantification. In DIA mode, MS1 scans were performed with a resolution of 120,000 over a range of 350–1500 m/z, followed by MS2 scans using 45 isolation windows across the precursor mass range.

Proteomic data analysis.

Mass spectrometry data were processed using Spectronaut software (Biognosys, version 16.0). The DDA data were used to generate a spectral library, which was then employed for analyzing the DIA data for protein identification and quantitation. Searches were performed against the UniProt Homo sapiens Swiss-Prot (SP) proteome database (20,361 target sequences, downloaded on 2022-03-17). Search parameters included carbamidomethylation as a fixed modification, acetylation of protein N—terminus and oxidation of methionines as variable modifications, trypsin/P proteolytic cleavage rule with maximum two miscleavages, peptide length of 7–52 amino acids, and protein and precursor false discovery rate (FDR) of 1%.

For DIA data analysis, MS1 and MS2 mass tolerances were set to 10 ppm. Retention time prediction type was set to dynamic indexed Retention Time. Protein inference was performed using the ID Picker algorithm. Protein intensities were normalized using the “Local Normalization” algorithm based on a local regression model. Differential protein expression analysis was performed with the following criteria: expression difference multiple fold change ≥ 1.2 or fold change ≤ 1/1.2, and significant P value ≤ 0.05.

The functional analysis of protein and DEP

Functional annotation and enrichment analysis utilized multiple bioinformatics resources: Gene Ontology (GO) analysis via QuickGO database for molecular function, biological process, and cellular component categorization; pathway analysis through Kyoto Encyclopedia of Genes and Genomes (KEGG) and Reactome databases. Enrichment analyses employed hypergeometric tests with Benjamini–Hochberg correction, considering terms significant at adjusted P-value < 0.05. Data analysis and visualization were performed using R (version 4.1.0) and Bioconductor packages, with custom scripts for data integration and figure generation. This comprehensive approach provided insights into the functional implications of identified proteins in the context of small cell lung cancer biology.

Protein validation by PRM

PRM analysis was used to validate candidate biomarkers. The process involved transition library construction using shotgun proteomics on a Q Exactive HF-X mass spectrometer, followed by PRM acquisition on a U3000 UHPLC system coupled with a Q Exactive HF-X mass spectrometer. Data analysis was performed using Skyline 22.2 software. Detailed MS parameters for both library construction and PRM acquisition were carefully controlled to ensure accurate quantification of target proteins.

Immunohistochemistry

Immunohistochemistry (IHC) was performed on SCLC tissues and adjacent normal lung tissues as controls. Tissue samples were fixed in 10% neutral buffered formalin for 24 h, embedded in paraffin, and sectioned at 4 μm thickness. Sections were deparaffinized in xylene and rehydrated through a graded ethanol series. Antigen retrieval was performed using citrate buffer (pH 6.0) in a pressure cooker for 3 min. Endogenous peroxidase activity was blocked with 3% hydrogen peroxide for 10 min. Sections were then incubated with primary antibody against RAB11A (Santa Cruz, sc-166912, 1:100 dilution) overnight at 4 °C. After washing, sections were incubated with HRP-conjugated secondary antibody for 30 min at room temperature. The immunoreaction was visualized using DAB chromogen, and sections were counterstained with hematoxylin. Staining was evaluated independently by two pathologists blinded to the clinical data. A scoring system combining staining intensity (0–3) and percentage of positive cells (0–4) was used, with a total score of 4–12 considered positive expression [19]. The comparison between SCLC tissues and adjacent normal tissues provided insights into the differential expression of RAB11A in cancerous versus non-cancerous lung tissue.

Statistical analysis

All quantitative data are presented as mean ± standard deviation for normally distributed data or as median with interquartile range for non-normally distributed data. Categorical variables are expressed as numbers (proportions). For comparison of continuous variables between two groups, a t-test was used for normally distributed data, while the Mann–Whitney U test was employed for non-normally distributed data. Categorical variables between groups were compared using Chi-squared tests. All statistical analyses were conducted software version 27.0 (SPSS Inc., Chicago, IL, USA) and GraphPad Prism software version 9.5.1 (GraphPad, La Jolla, CA, USA). P < 0.05 was considered statistically significant.

Results

Patient demographics and clinical characteristics

A total of 120 participants were enrolled in this study, including 90 cases in the urine set and 30 cases in the tissue set. The urine set was further divided into a training set (n = 38) and a validation set (n = 52). All participants were male. The mean age was comparable among all groups in both training set (61.8 ± 10.8, 63.6 ± 10.7, and 59.2 ± 9.9 years for pre-treatment, remission, and relapse groups, respectively; P = 0.83) and validation set (69.1 ± 4.5, 69.1 ± 5.9, 66.8 ± 9.1, and 65.0 ± 8.4 years, respectively; P = 0.64). The majority of patients were ever smokers (75–83%) with extended—stage disease (75–88%) and good performance status (ECOG 0–1: 75–89%). No significant differences were observed in smoking status, clinical stage, or ECOG performance status among groups (all P > 0.05). Notably, serum NSE levels showed significant variations across different disease states in both training set (P = 0.0007) and validation set (P = 0.0068), with the highest levels observed in pre-treatment groups and lowest in remission groups. The patients’ baseline characteristics are detailed in Table 1.

Table 1.

Demographics of the study groups

	Urine set (n = 90)									Tissue set (n = 30)
	Training set (n = 38)					Validation set (n = 52)
	Pre-treatment (n = 13)	Remission (n = 13)	Relapse (n = 12)	P	HC (n = 13)	Pre-treatment (n = 13)	Remission (n = 13)	Relapse (n = 13)	P	Tumor/adjacent normal pairs (n = 30)
Age	61.8 ± 10.8	63.6 ± 10.7	59.2 ± 9.9	P = 0.83	69.1 ± 4.5	69.1 ± 5.9	66.8 ± 9.1	65.0 ± 8.4	P = 0.64	65.3 ± 10.6
Gender
Male	13 (100%)	13 (100%)	12 (100%)		10 (100%)	19 (100%)	8 (100%)	8 (100%)		30 (100%)
Smoking habit
Nonsmoker	3 (23%)	3 (23%)	3 (25%)	P = 1.00	3 (30%)	2 (22%)	2 (25%)	2 (25%)	P = 0.99	5 (17%)
Ever smoker	10 (77%)	10 (77%)	9 (75%)		7 (70%)	7 (78%)	6 (75%)	6 (75%)		25 (83%)
Clinical stage
Limited stage	2 (15%)	2 (15%)	2 (17%)	P = 1.00		2 (22%)	2 (25%)	1 (13%)	P = 0.84	30 (100%)
Extended stage	11 (85%)	11 (85%)	10 (83%)			7 (78%)	6 (75%)	7 (88%)		0 (0%)
ECOG
0–1	10 (77%)	11 (85%)	9 (75%)	P = 0.86		8 (89%)	7 (88%)	6 (75%)	P = 0.74	30 (100%)
≥ 2	3 (23%)	2 (15%)	3 (25%)			1 (11%)	1 (13%)	2 (25%)		0 (0%)
NSE (μ/L)	41.48 (32.81 ~ 88.22)	10.67 (9.68 ~ 14.69)	24.20 (15.43 ~ 87.79)	P = 0.0007	13.31 ± 3.8	33.45 (22.34 ~ 96.98)	10.23 (8.29 ~ 18.84)	18.29 (12.65 ~ 29.73)	P = 0.0068	35.67 (24.82 ~ 83.45)

Characterization of urine exosomes in SCLC

Transmission electron microscopy (TEM), nanoparticle tracking analysis (NTA), and Western blotting collectively confirmed the presence of exosomes in urine samples from SCLC patients. These techniques demonstrated that the morphology, dimensions, and specific marker expression (TSG101) of the exosomes were consistent with established characteristics. The exosomes found in urine are spherical bilayer membrane structures and TSG101 level (Fig. 2A–B) with an average diameter of 141.4 nm and an average count of 4.5 × 10¹⁰ per ml urine (Fig. 2C–D).

Fig. 2.

Characterization of urine exosomes in SCLC. A The spherical bilayer membrane structures of exosomes were found through transmission electron microscopy (TEM) B The protein expression of CD9 was evaluated through western blot. C–D The average diameter and the average count were confirmed through nanoparticle tracking analysis (NTA)

Comparative analysis of proteins in urinary exosomes

In our study investigating urinary exosomal proteins as potential biomarkers for monitoring treatment efficacy and disease progression in SCLC patients, we conducted comparative analyses across distinct clinical stages: pre-treatment, partial/complete remission, and relapse. The results, as illustrated in the heatmap (Fig. 3A), reveal distinct protein expression patterns across these stages. Volcano plots (Fig. 3B–C) demonstrate significant differences between pre-treatment and remission groups, as well as between remission and relapse groups. These findings demonstrate that urinary exosomal protein expression changes in accordance with SCLC disease status. Compared to pre-treatment levels, 34 urinary exosomal proteins were downregulated in patients achieving partial remission. Upon relapse, 7 urinary exosomal proteins showed increased expression relative to the partial/complete remission group.

Fig. 3.

Comparative analysis of proteins in urinary exosomes. A The heatmap of distinct protein expression patterns across these stages. B–C Volcano plots of significant differences between pre-treatment and remission groups, as well as between remission and relapse groups. D GO analysis for comparison of post-treatment partial/complete remission to pre-treatment samples. E KEGG pathway analysis. F Reactome analysis. G GO analysis for comparison between disease relapse and post-treatment remission samples. H KEGG pathway analysis. I Reactome analysis

Functional assessment of 41 differentially expressed urinary exosome proteins associated with SCLC treatment response and relapse

Urinary exosome proteomics analysis revealed distinct molecular signatures associated with SCLC treatment efficacy and disease progression. Comparison of post-treatment partial/complete remission to pre-treatment samples highlighted enrichment in endocytosis, lung development, and cellular response to oxidative stress (GO analysis, Fig. 3D), as well as pathways related to calcium reabsorption, oxytocin signaling, and ABC transporters (KEGG analysis Fig. 3E). Reactome analysis (Fig. 3F) further identified changes in selenium metabolism and vasopressin-regulated water homeostasis. In contrast, the comparison between disease relapse and post-treatment remission samples revealed alterations in protein phosphorylation, cell volume homeostasis, and xenobiotic detoxification (GO analysis, Fig. 3G). KEGG pathway analysis of this comparison emphasized changes in thyroid hormone signaling, cGMP-PKG signaling, and aldosterone synthesis and secretion (Fig. 3H). Reactome analysis for this comparison highlighted modifications in glycosphingolipid metabolism, Netrin-1 signaling, and interleukin signaling pathways (Fig. 3I). These findings provide insights into the molecular mechanisms underlying SCLC treatment response and disease relapse, potentially identifying novel therapeutic targets and biomarkers for monitoring treatment efficacy and disease status.

Identification and evaluation of the clinical value for RAB11A in urinary exosome

Notably, the Venn diagram (Fig. 4A) highlights RAB11A as the sole differentially expressed protein common to all comparisons, showing a decrease during remission and an increase upon relapse, suggesting its potential as a key biomarker for SCLC relapse and treatment response. Furthermore, different expression of RAB11A was found in patients with different stage of SCLC (Fig. 4B).

Fig. 4.

Identification and Evaluation of the Clinical Value for RAB11A. A Venn diagram. B The protein expression of RAB11A among different groups in the training cohort. *P < 0.05, **P < 0.05

Validation of RAB11A in urinary exosome using PRM-based targeted proteomics

To validate the findings from our differential proteomics analysis, we employed PRM-based targeted proteomics to examine RAB11A levels in urinary exosomes. We collected samples from three distinct groups of SCLC patients: pre-treatment, post-treatment with partial/complete remission, and relapsed cases, as well as from healthy controls (n = 13 for each group). Our results confirmed that RAB11A levels in urinary exosomes were significantly elevated in SCLC patients compared to healthy controls. Furthermore, we observed a marked decrease in RAB11A levels in patients who achieved partial/complete remission following treatment, relative to their pre-treatment levels. Notably, upon relapse, RAB11A levels in urinary exosomes increased again, surpassing the levels observed during remission. These findings corroborate our initial proteomics results and suggest that RAB11A in urinary exosomes may serve as a potential biomarker for SCLC treatment response and relapse. The protein concentrations among the groups are shown in Fig. 5A–C.

Fig. 5.

Validation of RAB11A in urinary exosome using PRM-based targeted proteomics. A–C The protein expression of RAB11A among different groups in the validation cohort D The RAB11A expression in SCLC tissues and adjacent normal lung tissues was measured through immunohistochemistry (IHC) assay. *P < 0.05, ***P < 0.001

Immunohistochemical analysis of RAB11A in SCLC tissue

Immunohistochemical analysis of RAB11A expression was performed on 30 pairs SCLC tissues and adjacent normal lung tissues. RAB11A staining was predominantly observed in the cytoplasm. SCLC tissues exhibited significantly higher RAB11A expression compared to adjacent normal tissues (P = 0.043). In SCLC tissues, 70% (21/30) showed positive RAB11A expression, compared to only 33% (10/20) in adjacent normal tissues, shown in Fig. 5D. These results suggest that RAB11A is overexpressed in SCLC and may play a role in its pathogenesis.

Discussion

This study investigated the potential of urinary exosomal RAB11A as a biomarker for diagnosis, treatment response monitoring, and prognosis prediction in SCLC. Through a multi-platform proteomics approach integrating discovery proteomics (DIA-MS) and targeted validation (PRM), complemented by IHC, we demonstrated that RAB11A expression in urinary exosomes is significantly elevated in SCLC patients compared to healthy controls, with levels dynamically changing in response to treatment and disease progression. These findings establish urinary exosomal RAB11A as a promising biomarker for SCLC diagnosis and monitoring, while providing insights into disease mechanisms.

Our findings align with some previous studies while also presenting certain differences. Dong et al. found that RAB11A is overexpressed in non-small cell lung cancer (NSCLC) and associated with advanced TNM stage, positive lymph node metastasis, and poor prognosis [20]. This is consistent with our observations in SCLC, suggesting a common oncogenic role of RAB11A across different lung cancer subtypes. However, our study is the first to reveal the dynamic changes of RAB11A in urinary exosomes during SCLC treatment response and relapse, a finding not previously reported in lung cancer research.

Critically, immunohistochemical analysis revealed significantly elevated RAB11A expression in SCLC tissues compared to adjacent normal lung tissue. This tissue-level overexpression aligns robustly with the increased RAB11A levels observed in urinary exosomes. This concordance supports the hypothesis that extracellular vesicles detected in urine originate, at least partially, from tumor tissue. These tumor-derived extracellular vesicles likely enter circulation and undergo renal filtration, carrying a proteomic signature reflective of their source. The observed correlation between urinary exosomal RAB11A and tissue IHC staining strengthens this premise, positioning urinary exosomal RAB11A as a non-invasive proxy for tumor biology. Further studies are warranted to fully elucidate the underlying biological mechanisms governing this relationship.

Mechanistically, RAB11A, a member of the Rab family of small GTPases, plays crucial roles in intracellular protein trafficking, secretion, and signal transduction [21]. Its overexpression has been documented in various cancers, including ovarian [22], breast cancer [23], gastric [24], and prostate cancers [25], suggesting its importance in cancer progression. In the context of SCLC, our study reveals a significant upregulation of RAB11A expression in urinary exosomes compared to healthy controls. Notably, RAB11A levels exhibit dynamic fluctuations in response to treatment interventions and disease progression. This finding is corroborated by our immunohistochemical analysis, which demonstrates a marked overexpression of RAB11A in SCLC tissue specimens relative to adjacent normal lung tissue. The concordance between elevated RAB11A levels in urinary exosomes and its overexpression in tumor tissues underscores the potential utility of urinary exosomal RAB11A as a non-invasive biomarker reflective of SCLC pathophysiology. These aligns with findings in other cancers, where RAB11A overexpression correlates with poor prognosis and cancer progression [20].

Interestingly, our findings differ from previous studies of RAB11A in other cancers, suggesting its distinct regulatory functions across different tumor types. For instance, Xu et al.[26] reported a downregulation trend of RAB11A in esophageal squamous cell carcinoma,, while Rab11A has been shown to suppress head and neck carcinoma by regulating epidermal growth factor receptor (EGFR) recycling and Epithelial cell adhesion molecule exosome secretion [27]. These discrepancies underscore the importance of cancer-specific biomarker validation and the complex, context-dependent roles of RAB11A in different cancer types.

In the context of SCLC, RAB11A may contribute to disease progression and drug resistance through multiple mechanisms. Previous work by Gombodorj et al. demonstrated RAB11A’s control of EGFR and fibroblast growth factor receptor (FGFR) recycling in lung squamous cell carcinoma, which corresponds to our observed changes in RAB11A expression during SCLC treatment and relapse [28]. Zhang et al. [29] showed that RAB11A drives metalloproteinase 2 (MMP2) expression through phosphatidylinositol 3-kinase (PI3K) / ATP-dependent tyrosine kinase (AKT) signaling in hepatocellular carcinoma, while similar mechanisms were found in prostate cancer where RAB11A activates the focal adhesion kinase (FAK) / AKT pathways to promote tumor growth [25]. These findings suggest common oncogenic pathways regulated by RAB11A across different cancers. Zhao et al. [30] found that RAB11A promotes cancer cell proliferation and migration through the WNT signaling pathway in esophageal squamous cell carcinoma. This observation is further corroborated by Yu et al., who reported that RAB11A promoted cell growth, invasion, and cell cycle progression by activating the glycogen synthase kinase-3β (GSK3β) / wingless/integrated (WNT) / β-catenin signaling pathway in pancreatic cancer [31]. Interestingly, in ovarian cancer, Rab11a has been shown to promote malignant progression by inducing autophagy [22], revealing yet another mechanism by which this protein may contribute to cancer development. RAB11A may affect tumor cell migration and invasion by regulating integrin recycling [32]. Additionally, RAB11A may influence the tumor microenvironment by regulating exosome secretion [33]. While we did not directly investigate these mechanisms in SCLC, our functional analysis of differentially expressed urinary exosome proteins revealed enrichment in pathways related to endocytosis, cellular response to oxidative stress, and protein phosphorylation, which are consistent with the known functions of RAB11A. These findings provide a foundation for future mechanistic studies in SCLC.

The comparison of post-treatment partial/complete remission samples to pre-treatment samples revealed enrichment in pathways related to endocytosis [34], lung development [35], and cellular response to oxidative stress [36]. These findings suggest that successful treatment may enhance the clearance of cellular waste and toxins, promote lung tissue repair and regeneration, and strengthen the antioxidant defense system-all of which could contribute to the favorable treatment outcome. Notably, the analysis also highlighted changes in calcium reabsorption [37], oxytocin signaling [38], and ATP-binding cassette (ABC) transporter pathways [39], which are known to play important roles in fluid and electrolyte homeostasis, cell volume regulation, and drug efflux-processes that may influence the tumor microenvironment and treatment response.

In contrast, the comparison between disease relapse and post-treatment remission samples revealed alterations in protein phosphorylation [40], cell volume homeostasis [41], and xenobiotic detoxification [42]. These changes suggest that disease relapse may be associated with dysregulated cell signaling, impaired cellular stress response, and reduced drug sensitivity-potentially driving the resurgence of the tumor. Further analysis identified modifications in thyroid hormone signaling [43], cyclic guanosine monophosphate (cGMP)-protein kinase G (PKG) signaling [44], and aldosterone synthesis and secretion pathways [45], which are involved in various physiological processes, including metabolism, vascular function, and fluid-electrolyte balance, and could impact tumor progression and treatment resistance. Collectively, these findings provide important mechanistic insights into the molecular events underlying SCLC treatment response and disease relapse, and may serve as potential therapeutic targets and biomarkers for monitoring treatment efficacy and disease status.

Our study has several strengths, including its longitudinal design capturing multiple stages of SCLC progression and the application of advanced mass spectrometry techniques. However, this work represents a preliminary, exploratory investigation specifically evaluating the utility of proteins within urinary exosomes as biomarkers for the diagnosis and therapeutic monitoring of SCLC. Importantly, the requirement for a relatively large urine volume for urinary exosomes isolation and the labor-intensive nature of the current isolation protocols represent significant practical limitations for large-scale clinical implementation. Although it yields initial insights, these findings must be interpreted cautiously. The practical challenges associated with sample collection banking and processing throughput highlight critical hurdles that need to be addressed. Large-scale, multi-center, independent clinical trials with rigorous external validation, alongside the development of more efficient, standardized, and less sample-demanding urinary exosomes isolation and detection methods, are imperative before considering their clinical application.

Conclusions

In conclusion, our study provides evidence for the potential of urinary exosomal RAB11A as a novel biomarker in SCLC. The dynamic changes in RAB11A expression during treatment and disease progression offer new insights into SCLC biology and open up possibilities for improved patient management. While further validation is needed, these findings represent a significant step towards developing non-invasive, personalized approaches for SCLC diagnosis, treatment monitoring, and relapse prediction. Future studies should investigate the exact mechanisms through which RAB11A influences SCLC progression and drug resistance, as well as explore the potential of RAB11A-targeted therapies alongside current treatments.

Author contributions

MZ, LP: conceptualization; formal analysis; funding acquisition; methodology; project administration; resources. WWW: investigation; methodology; data curation; software; writing-original draft; writing-review & editing. NL, SSW: investigation; methodology; supervision. CKY: investigation; methodology. All authors read and approved the final manuscript.

Funding

This work was supported by the Beijing Key Laboratory of Urinary Cellular Molecular Diagnostics and Beijing Shijtan Hospital’s “14th 5-Year Plan” Leading Talent Training Project (2023LJRCPL).

Data availability

All data are available from the corresponding author upon reasonable request.

Declarations

Competing interests

The authors declare no competing interests.