Liquid Biopsy in Lung Cancer: Nano-Flow Cytometry Detection of Non-Small Cell Lung Cancer in Blood

Liquid Biopsy in Lung Cancer: Nano-Flow Cytometry Detection of Non-Small Cell Lung Cancer in Blood | 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 Liquid Biopsy in Lung Cancer: Nano-Flow Cytometry Detection of Non-Small Cell Lung Cancer in Blood Andong Zhang, Qiqi Gao, Chen Tian, Wentao Chen, Catherine Pan, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4241602/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Non-small cell lung cancer (NSCLC) remains a leading cause of global mortality, with current screening and diagnostic methods often lacking in sensitivity and specificity. In our endeavor to develop precise, objective, and easily accessible diagnostic biomarkers for NSCLC, this study aimed to leverage rapidly evolving liquid biopsy techniques to differentiate NSCLC patients from healthy controls by isolating peripheral blood samples and enriching extracellular vesicles (EVs) containing lung-derived proteins (TTF-1 and SFTPB), along with the cancer-associated protein CD151 + EVs. Additionally, we established a nano-flow cytometry assay for plasma EVs detection. NSCLC patients demonstrated significantly reduced counts of TTF-1 + EVs and CD151 + EVs in plasma compared to healthy controls (P 0.05). However, integrated analysis of TTF-1 + , CD151 + , and SFTPB + EVs yielded area under the curve (AUC) values of 0.917 and 0.845 in the discovery and validation cohorts, respectively. Thus, while further validation is essential, the advanced technologies mentioned above are of great significance for the detection of NSCLC biomarkers. Nano-Flow Cytometry Detection Liquid biopsy Biomarker NSCLC SFTPB TTF-1 CD151 Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Lung cancer, a leading cause of cancer-related deaths worldwide 1 , can be classified pathologically into small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC), which accounts for approximately 85% of cases 2 . NSCLC further comprises three main subtypes: adenocarcinoma (40%), squamous cell carcinoma (30%), and large cell carcinoma (10%) 3 . Currently, the pathogenesis of NSCLC remains elusive, with most patients being diagnosed at an advanced or late stage, often at stage 4 4 . Early detection significantly enhances survival rate 5 , 6 , yet current screening methods, including sputum cytology and low-dose computed tomography (LDCT), exhibit considerable false sensitivity and specificity 7 , 8 . Furthermore, effective monitoring NSCLC patients undergoing surgical treatment is necessary, given that at least one-third experience recurrence within 5 years post complete surgical resection 9 . To enhance the efficacy and convenience of lung cancer screening and post-surgical monitoring, the field of liquid biopsy, particularly utilizing blood samples, is rapidly advancing. However, conventional free-form biomarkers, such as carbohydrate antigen 125 (CA125) and cytokeratin-19 fragment antigen 21 − 1 (CYFRA21-1), lack specificity and are susceptible to various internal environment factors. More recently, the subdomain of liquid biopsy, focusing on extracellular vesicles (EVs), including exosomes 10 , has emerged as a promising avenue for early cancer detection. EVs widely distributed in bodily fluids, comprising plasma, saliva, breast milk, cerebrospinal fluid, and others 11 . In terms of biomarker discovery, EVs are advantageous because they not only carry various critical biomarkers such as nucleic acids and proteins 12 but also express specific marker proteins allowing for identifying the cell types from which they originate. Indeed, several previous studies have demonstrated differences in EVs markers, including CD151 + , a tetraspanin superfamily protein with four transmembrane domains, that were increased in the EVs of cancer patients, including NSCLC 13 , 14 . However, most of these studies were conducted in small cohorts, and typically did not use tissue-specific methods for EVs enrichment 15 – 17 . In this study, we have developed a novel strategy, i.e. nano-flow cytometry, for measuring biomarkers of NSCLC, primarily through targeted enrichment of EVs carrying lung-derived proteins, specifically thyroid transcription factor-1 (TTF-1) and surfactant protein B (SFTPB) 18 – 20 , along with nonspecific CD151 cancer biomarkers, for NSCLC detection. Combining TTF1 + and SFTPB + with CD151 + EVs effectively differentiated NSCLC in both discovery and validation cohorts. Materials and Methods 1. Study design This study was a multicenter research involving a discovery cohort and a validation cohort. The discovery cohort consisted of 39 NSCLC patients and 40 age- and gender-matched healthy controls (HC). The validation cohort included 78 NSCLC patients and 77 HC with similar age and gender distributions. Samples from both cohorts were collected from the First Affiliated Hospital, Zhejiang University School of Medicine and the First Affiliated Hospital of Soochow University, with the study populations randomly distributed between the two cohorts. Only individuals clinically characterized by radiological features indicative of NSCLC and subsequently confirmed with pathological diagnosis were included in the "NSCLC" group, while subjects lacking clinical features of NSCLC were classified as the "HC" group. Initially, we identified the NSCLC-associated markers TTF-1 and SFTPB, as well as the broad-spectrum tumor marker CD151. Using Western Blot, we confirmed the expression of these biomarkers on EVs. These tumor markers were further validated to be present on the surface of EVs using stochastic optical reconstruction microscopy (STORM), a microscopic technique capable of imaging individual EVs. By imaging TTF-1, CD151, and SFTPB in conjunction with general markers of EVs, we analyzed super-centrifuged plasma EVs. The localization of these markers closely aligned with the a forementioned tumor markers, providing evidence that these NSCLC-related tumor markers are indeed present on EVs. And then, we employed Beckman coulter CytoFLEX S flow Systems (CytoFLEX S) to quantify TTF-1 + , CD151 + , and SFTPB + EVs in plasma samples. The quantities of EVs carrying each marker were quantified as a proportion of all EVs, and the levels between different groups were compared. 2. Methods 2.1 Participants information and sample collection The samples in both the discovery and validation cohorts were collected from the First Affiliated Hospital, Zhejiang University School of Medicine and the First Affiliated Hospital of Soochow University. All procedures were reviewed and approved by their respective ethics committees, and informed consent was obtained from all participants. The study population included individuals who had undergone extensive clinical imaging and pathological evaluations, ensuring that the collected patient cohorts were diagnosed with primary NSCLC. 2.2 Blood sample processing After obtaining informed consent from all patients, 5 ml of fasting peripheral venous blood was collected using EDTA anticoagulant tubes, and the patient's name (abbreviation), identification number, and sampling time were recorded. The collected blood samples were gently inverted 8–10 times immediately after blood collection to ensure proper mixing with the anticoagulant without hemolysis. Within 60 minutes of collection, the samples were centrifuged at 1500 g for 15 minutes at room temperature. After centrifugation, the upper plasma layer was carefully transferred to a 15 ml centrifuge tube at room temperature, and the mixture was gently mixed 3–4 times. The tube containing the plasma was centrifuged at 3200 g for 15 minutes at room temperature. The supernatant was aspirated and divided into 0.5 ml aliquots in low-binding centrifuge tubes. The aliquots were stored at -80°C until further use. 2.3 Isolation of Plasma Extracellular Vesicle Thaw 0.5 ml of plasma stored at -80°C at room temperature (28°C) and centrifuge at 3000 g for 30 minutes at 4°C. Collect the supernatant and centrifuge at 10,000 g for 30 minutes at 4°C. Take 100µl of the supernatant and add it to an ultra-centrifuge tube containing 900µl of phosphate-buffered saline (PBS) (filtered through a 0.22µm membrane) to balance the sample. Centrifuge at 100,000 g for 60 minutes at 4°C. Discard 800µl of the supernatant and gently flick the remaining liquid 200 times. Add 800µl of PBS (filtered through a 0.22µm membrane) to the tube, balance the sample, and centrifuge at 100,000 g for 60 minutes at 4°C. Discard 800µl of the supernatant and flick the remaining liquid 200 times. The resulting 200µl is the processed plasma extracellular vesicle sample. 2.4 Stochastic optical reconstruction microscopy All images were acquired on the Nikon N-STORM super-resolution system (Nikon Instruments Inc.), using a Nikon Eclipse Ti inverted microscope with a 100×TIRF objective (numerical aperture 1.49). The Alexa 561 fluorescent dye was excited with a 561 nm semiconductor laser, Alexa 488 was excited with a 488 nm semiconductor laser, and Alexa 647 was excited with a 647 nm semiconductor laser. Additionally, a lower-power 405 nm laser was employed to enhance the excitation of certain fluorescent dyes. Electron-multiplying charge-coupled device (EMCCD) cameras (Andor ixon DU897) recorded 2000 frames with an exposure time of 60 milliseconds for imaging a single cell. The capture time for a single cell typically lasted around 20 minutes. During fluorescence acquisition, the Nikon microscopy setup employed a perfect focus system (PFS) to perform real-time correction for Z-axis focus drift. Plasma-derived extracellular vesicles (EVs) were washed three times with PBS by ultracentrifugation and then immersed in 200µL of specialized STORM imaging buffer (7µL of glucose oxidase-free GLOX buffer [14 mg glucose oxidase, 50µL of 17 mg/mL catalase, 200µL of 10 mM Tris, 50 mM NaCl, pH 8.0], 70µL of MEA buffer [1 M]). The EVs were then combined with 620µL of buffer B (50 mM Tris-HCl [pH 8.0], 10 mM NaCl, 10% glucose) before data acquisition. 2.5 Electron microscopy Take 5µl of the sample and apply it to an electron microscopy grid coated with a porous carbon film. Incubate for 30 minutes, remove the liquid from the back of the grid, and immerse the grid in liquid ethane using a Leica cryo plunge freezer. Observations of the sample after processing provide data on the size and morphology of extracellular vesicles using TEM. 2.6 Nanoparticle tracking analysis Take 10µl of the sample and dilute it 1:100 in PBS filtered through a 0.22µm membrane to obtain a 1ml solution. Direct light scattering measurements were performed using a purple laser (405 nm). Pre-testing was conducted to determine the ideal number of particles per frame (20–100 particles/frame) for accurate concentration measurement. (Cell temperature: 25°C; Injection speed: 40µl/s). Once the sample has completed the analysis, data on the size and concentration of extracellular vesicles can be obtained. 2.7 EV analysis with flow System According to the manufacturer's protocol, Zenon IgG Labeling Kits (Invitrogen/Life Technologies) was used to generate fluorescently conjugated antibodies. Zenon Alexa Fluor 405 Mouse IgG Labeling Kit was utilized to label monoclonal antibodies against mouse anti-CD151(10418-1-AP, Proteintech), mouse anti-TTF-1(ab227652, Abcam), mouse anti-SFTPB(13664-1-AP, Proteintech). Isotype controls for the respective species were also labeled at the same final concentration as all the antibodies. Another negative control (no antibody "blank," i.e., staining only) was achieved by substituting specific antibodies with the same volume of PBS in the labeling reaction. The labeled anti-CD151, TTF-1, SFTPB (Zenon Alexa Fluor 405 labeled) was added to 10µL of ultra-centrifuged plasma samples (equivalent antibody amount per sample: 0.1µg), and incubated overnight at 4°C. Fixation was performed using 20µL of 4% paraformaldehyde filtered through a 0.22µm filter, and left at room temperature for 20 minutes. The samples were analyzed using Beckman coulter CytoFLEX S flow Systems (CytoFLEX S), as mentioned earlier. The system was set up with a 70mW 405 nm laser for forward and side scatter as well as blue fluorescence, and a 200mW 488 nm laser for green fluorescence. The optimized microfluidic flow cytometer setup ensured stable particle counts and prevented aggregation. After dilution, the apparent concentration of the samples showed linear dilution. Reference beads and EV samples were run with high threshold settings to minimize background noise: threshold values for the 405-LALS and 405-Blue lasers were set at 17 and 25, respectively; the voltage for the 405-Blue laser was set to 1V with a voltage of 450V. The sheath fluid pressure was maintained at 150 mbar, and samples were injected at a flow rate of 1.5µL/min. All samples were stored at 4°C and analyzed within 8 hours of labeling, ensuring stable labeling under these conditions. For each batch, clinical samples were analyzed within 2 days, with samples from different diagnostic groups distributed across days. Two reference plasma samples (taken from approximately 30 HC individuals) were included in each day's measurements to help assess daily variations (< 5%). 2.8 Statistical analysis Non-interventional research methods were used to compare the basic characteristics of the two sample groups. The identification of exosomes was performed using TEM, NTA, and Western blot methods, and the exosome samples from both groups were analyzed using flow cytometry. Statistical analysis and generation of corresponding charts were conducted using SPSS 23.0 software and GraphPad Prism V8. Descriptive statistics were presented as mean ± standard deviation (x ± s) for continuous variables, and rates were used for categorical and ordinal data. For normally distributed quantitative data, t-tests were performed, while non-normally distributed data were analyzed using non-parametric tests (Mann-Whitney U). Chi-square test and rank sum test were employed for the comparison of qualitative data. Logistic regression analysis was conducted to assess the statistical significance of multivariate variables, and linear regression was performed to evaluate the linear relationship between single-factor variables and clinical data. ROC curve analysis was used to determine the sensitivity and specificity of diagnostic indicators and to identify the optimal cutoff values. A significance level of P < 0.05 was considered statistically significant. Results 1. Characteristics of the clinical cohorts Table 1 contains the pertinent clinical information, including pathological diagnoses. Additionally, characteristic radiographic demonstrations and pathological images for each cancer subtype are illustrated in their respective Figures (Fig. 1 ). Table 1 The characteristics of clinic cohorts Discovery cohort Validation cohort HC NSCLC HC NSCLC Case 40 39 77 78 Male 23 (57.5%) 24 (61.5%) 33(42.9%) 46(56.4%) Female 17 (42.5%) 15 (38.5%) 44(57.1%) 32(43.6%) Age(SD) 55.03 ± 16.20 63.55 ± 12.51 54.13 ± 14.66 60.50 ± 12.92 Range 24–85 24–94 25–86 26–86 Adenocarcinoma - 33 (84.6%) - 61 (78.2%) Squamous cell carcinoma - 6 (15.4%) - 14 (17.9%) Large cell carcinoma - 0 (0%) - 1 (1.3%) Others - 0 (0%) - 2 (2.6%) Clinical stage Stage I - 0 (0%) - 0 (0%) Stage II - 1 (2.5%) - 3 (3.8%) Stage III - 5 (15.4%) - 4 (5.2%) Stage IV - 33 (82.1%) - 71 91.0%) 2. Define NSCLC EVs in blood To characterize the EVs associated with NSCLC according to the recommendations of MISEV2018, EVs were enriched by ultracentrifugation. For the analysis of the size and morphological features of EVs, cryo-electron microscopy was utilized, demonstrating that the EVs were double-membrane structures with a diameter of approximately 100nm. Nanoparticle tracking analysis (NTA) further determined the size and distribution of EVs, showing a broad peak with a maximum around 100nm. We also performed Western blot (WB) analysis on EV-specific markers. As expected, WB results indicated that the post-ultracentrifugation EV samples were rich in CD151, TTF-1, SFTPB, as well as general EV markers TSG101 and Alix proteins. (Fig. 2 ) In order to characterize EVs originating from the lungs specifically, we investigated two commonly used histological markers for lung cancers, TTF1 and SFTPB, alongside CD151, a marker previously identified in EVs derived from lung cancer using STORM super-resolution single-molecule imaging microscopy 14 , 21 . To enhance the sensitivity of the experimental measurement, we have introduced a novel PE probe (Cy5 probe), which potentially enables a more precise determination of specific proteins. We confirmed that CD151, TTF-1, SFTPB and Cy5 Probe coexist with the general EV marker CD9 on the EV membrane. Nanoscale fluorescent beads with a diameter of 100nm were used as a scale standard. To confirm the identity of the detected signals in the post-ultracentrifugation plasma samples as EVs, we compared CD151, TTF-1, SFTPB and Cy5 Probe with the general EV marker CD9 on EV membranes. It was observed that CD151, TTF-1, SFTPB and Cy5 Probe along with the general EV marker (CD9), could be detected on the same plasma EVs (Fig. 3 ). 3. Development of a sensitive assay for EVs quantification We optimized the measurement of CD151 + , TTF-1 + , and SFTPB + EVs in plasma using the previously developed nanoscale flow cytometry technique (CytoFLEX S)(Fig. 4 ). We confirmed detection specificity and dilution linearity. A single reference plasma sample was run repeatedly for 7 days to demonstrate daily stability. The coefficient of variation for all markers was ≤ 10%. The relationship between gating positive particles and sample dilution to avoid coincidental events is illustrated. 4. Quantification of EVs in discovery and validation cohorts The next step involved detecting the quantity of extracellular vesicles expressing CD151, TTF-1, and SFTPB, in order to distinguish between true NSCLC and HC. For ease of comparison, we use the positivity rate for comparison. (Table 2 ) Table 2 Biomarker levels for two cohorts Discovery cohort Validation cohort EV markers (percent positive ± SD) HC NSCLC HC NSCLC CD151 5.03 ± 1.22 3.43 ± 1.55 5.12 ± 2.73 3.04 ± 1.17 TTF-1 7.40 ± 2.79 3.26 ± 2.14 6.71 ± 3.90 2.86 ± 1.73 SFTPB 7.12 ± 2.64 7.02 ± 4.59 6.72 ± 3.62 6.76 ± 4.68 Table 2 ANOVA followed by Dunnett’s multiple comparisons test. Abbreviation: HC, healthy control; NSCLC, non-small cell lung cancer; ANOVA, analysis of variance; EV, extracellular vesicle; CD151, CD151 Protein; TTF-1, transcription termination factor 1; SFTPB, Pulmonary surfactant-associated protein B. In the discovery cohort, levels of TTF-1 + and CD151 + EVs in NSCLC patients were found to be lower than those in the HC group (Table 2 , Fig. 5 ). The marker levels were not influenced by age; however, correlations were observed between the markers. Subsequently, ROC analysis was conducted to evaluate diagnostic performance. When comparing NSCLC and HC, CD151 + EVs exhibited a sensitivity of 70.0% and specificity of 84.6% (AUC = 0.825, 95%CI = 0.735–0.915), while TTF-1 + EVs showed a sensitivity of 80.0% and specificity of 87.2% (AUC = 0.878, 95%CI = 0.802–0.954). SFTPB + EVs, on the other hand, demonstrated moderate performance (AUC = 0.521, 95%CI = 0.391–0.651). Besides, the combined model of the three EV markers for differentiation between NSCLC and HC showed an AUC of 0.913 (95%CI = 0.854–0.973, sensitivity = 84.6%, specificity = 95.0%). To validate the results, we collected an additional 78 confirmed NSCLC samples and confirmed the differences in total particles and all markers between NSCLC and HC groups (Table 1 , Fig. 6 ). The sensitivity and specificity for CD151 + EVs were 70.1% and 73.1%, respectively (AUC = 0.778, 95%CI = 0.706–0.851), for TTF-1 + EVs were 75.3% and 79.5%, respectively (AUC = 0.841, 95%CI = 0.780–0.902). The AUC for SFTPB + EVs was 0.574 (95%CI = 0.483–0.666), indicating moderate discrimination between NSCLC and HC. Additionally, the composite model of TTF-1, CD151, and SFTPB combined analysis with an AUC of 0.854 (95%CI = 0.796–0.913, sensitivity = 85.9%, specificity = 74.0%). Discussion The current investigations have yielded two significant discoveries. Firstly, we have identified a distinct set of markers capable of detecting blood EVs originating specifically from the lungs. Secondly, we have successfully utilized these markers to readily identify NSCLC patients using a sensitive nano-flow cytometry technology. A critical aspect of EVs research in pulmonary neoplasia’s diseases revolves around delineating the targeted population, particularly in enriching EVs originating from the lungs. To accomplish this objective, we initially demonstrated the presence of TTF-1 and SFTPB in healthy lung tissue and tissues affected by NSCLC (Fig. 1 ). Subsequently, we employed various technologies to indicate that the EVs positive for TTF-1 and SFTPB were colocalized with CD151, which is a nonspecific protein marker detected in many other cancers (Fig. 3 ) 18 – 22 . TTF-1 is a transcription factor highly expressed in lung cancers, although its expression is also detected in a few other organs, e.g. the thyroid 23 . SFTPB is a pulmonary surfactant protein mainly expressed in alveolar epithelial cells, contributing to the regulation of pulmonary surfactant tension and maintaining lung stability and function. In lung cancer, the expression of SFTPB is typically regulated, but the specific expression levels and its role in lung cancer development remain somewhat controversial 20 , 25 . To develop a robust assay for NSCLC, we took advantages of nano-flowcytometry to quantify the numbers of TTF-1 + and SFTPB + , along with CD151 + , EVs in peripheral blood of healthy individuals and patients with NSCLC. Of note, CD151 has been reported to be increased in NSCLC and other cancers 21 . The results indicate that, while SFTPB + -EVs was not significant different between NSCLC patients and healthy controls, the numbers of CD151 + and TTF-1 + - EVs in the peripheral blood of NSCLC patients were significantly reduced compared to healthy individuals. Combined statistical analysis of the numbers of SFTPB + , TTF-1 + and D151 + EVs provided a more accurate assessment compared to measuring a single protein . Prior studies have reported increased expression of CD151 in the EVs isolated from NSCLC 14 , 15 . It's important to note that each of these previous studies quantified the concentration of CD151 from immunocaptured and lysed EV samples in plasma. In contrast, our study employs fluorescent labeling and flow cytometry to quantify the number of EVs carrying the target markers. As to the causes of a reduced number of CD151 + EVs in NSCLC patients, several explanations may align with this observation; for instance, NSCLC might impact the mechanisms of EV generation, or the cellular processes involved in transferring pathological proteins from tumor cells to peripheral circulating cells. To probe these hypotheses further, a thorough examination of EV-related processes in the context of NSCLC progression is warranted to fully elucidate the underlying reasons for the observed changes in EV populations. There are a few significant shortcomings in our investigations. First, our diagnostic cohorts all utilized radiological examinations and pathological biopsies, enabling us to identify the most reliable NSCLC cases. While this is a strength of our study, allowing evaluation and analysis within the targeted NSCLC population, performance the biomarkers in other types of lung cancers remains to be determined, ideally in a large-scale, multi-site manner using robust sampling protocols in well-defined subjects. Equally important is whether these EV markers can serve as clinically meaningful indicators for early detection of NSCLC or effective indicator of recurrence of NSCLC after surgical resection of the primary neoplasm, a process involves longitudinal follow up for many years. Additionally, several technical issues must be considered regarding these detailed experimental results. The current CytoFLEX S system might not accurately detect EVs smaller than 100nm. Furthermore, the separation technique used in this study to enrich EVs from plasma, namely ultracentrifugation, is known to sometimes induce particle aggregation. As a result, imaging data showing co-localization of these NSCLC markers with common EV markers might actually represent two or more independent EV populations closely related to each other. These possibilities can be differentiated in future experiments using internal labeling of EVs in contrast to the membrane labeling used in this study. Lastly, the variability in total EVs quantities between subjects, coupled with the challenge of ensuring EVs specificity in plasma ion measurements, necessitates both standardization and poses challenges in marking positive EVs. In this study, we addressed this issue by attributing positive EVs to all detectable particles and interpreted changes through reporting positive ratios. A more stringent control should involve the normalization of all EVs (which are also exclusively EVs) via fluorescence labeling. However, such specificity proteins are limited. Tetraspanin proteins, such as CD9 used in our STORM study, are the most widespread EVs markers, yet they label only a subset of potential EV particles and are not suitable as markers for total EVs 26 . Therefore, the identification of these ubiquitous EV markers will provide a foundation for further EV research. Conclusion In summary, our study discovered the surface expression of SFTPB, TTF-1, and CD151 proteins on EVs of NSCLC patients. Using flow cytometry experiments, we demonstrated that through combined analysis of SFTPB + , TTF-1 + and CD151 + EVs, a robust differentiation between NSCLC and HC was achieved. While randomized and large-sample follow-up studies are necessary to validate these experimental findings, our results offer a proof of concept for detecting NSCLC patients through a convenient flow cytometry method in the field of liquid biopsy. Abbreviations AUC Area under the curve CA125 Carbohydrate antigen 125 CYFRA21-1 Cytokeratin-19 fragment antigen 21 − 1 EVs Extracellular vesicles LDCT Low-dose computed tomography NSCLC Non-small cell lung cancer NTA Nanoparticle tracking analysis ROC Receiver Operating Characteristic SFTPB Surfactant protein B STORM Stochastic optical reconstruction microscopy SCLC Small cell lung cancer TTF-1 Thyroid transcription factor-1 TEM Transmission Electron Microscope WB Western blot Declarations Availability of data and materials The datasets supporting the conclusions of this article are included within the article and its additional files. Acknowledgements Not applicable. Contributions Andong Zhang: Conceptualization, Methodology, Data curation and Writing-Original draft preparation. Qiqi Gao& Chen Tian: Methodology, Funding acquisition, Data curation, and Writing-Original draft preparation. Wentao Chen& Catherine Pan: Investigation and Validation. Ling Wang, Jie Huang and Jing Zhang: Conceptualization, Supervision, Funding acquisition and Writing- Reviewing and Editing. All authors have read and approved the final manuscript. Funding This study was supported by Youth Program of National Natural Science Foundation of China (82102183) to Qiqi Gao, National Natural Science Foundation of China (82201560) to Chen Tian, National Natural Science Foundation of China(NSFC), International Cooperation and Exchange Project (82020108012) to Jing Zhang. Availability of data and materials The datasets supporting the conclusions of this article are included within the article and available from the corresponding author on reasonable request. Ethics approval and consent to participate This research was approved by The First Affiliated Hospital of Soochow University and The First Affiliated Hospital, Zhejiang University School of Medicine. Consent for publication All authors have agreed to publish this manuscript. Competing interests The authors declare no competing interests. Author information Andong Zhang , Qiqi Gao and Chen Tian have contributed equally to the work. Authors and Affiliations 1 Department of General Medicine, The Affiliated Hospital of Jiaxing University, Zhejiang, Jiaxing, China Andong Zhang& Jie Huang 2 Department of Pathology, First Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang, Hangzhou, China Qiqi Gao, Chen Tian, Wentao Chen, Catherine Pan & Jing Zhang 3 National Human Brain Bank for Health and Disease, Zhejiang University, Zhejiang, Hangzhou, China Jing Zhang 4 Department of General Medicine, The First Affiliated Hospital of Soochow University, Jiangsu, Suzhou, China Ling Wang 5 Department of General Medicine, The Fourth Affiliated Hospital of Soochow University, Jiangsu, Suzhou, China Ling Wang Corresponding authors Correspondence to Ling Wang, Jie Huang or Jing Zhang. 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Kong R, Patel AS, Sato T, Jiang F, Yoo S, Bao L, et al. Transcriptional Circuitry of NKX2-1 and SOX1 Defines an Unrecognized Lineage Subtype of Small-Cell Lung Cancer. Am J Respir Crit Care Med. 2022;206(12):1480–94. Sin DD, Tammemagi CM, Lam S, Barnett MJ, Duan X, Tam A, et al. Pro-surfactant protein B as a biomarker for lung cancer prediction. J Clin Oncol. 2013;31(36):4536–43. Karimi N, Dalirfardouei R, Dias T, Lotvall J, Lasser C. Tetraspanins distinguish separate extracellular vesicle subpopulations in human serum and plasma - Contributions of platelet extracellular vesicles in plasma samples. J Extracell Vesicles. 2022;11(5):e12213. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4241602","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":291220503,"identity":"a85f63b6-8941-41e0-8dde-43c3c4e09f20","order_by":0,"name":"Andong Zhang","email":"","orcid":"","institution":"First Hospital of Jiaxing","correspondingAuthor":false,"prefix":"","firstName":"Andong","middleName":"","lastName":"Zhang","suffix":""},{"id":291220504,"identity":"74a7a5e4-4714-4926-b1d2-97952c797b48","order_by":1,"name":"Qiqi Gao","email":"","orcid":"","institution":"First Affiliated Hospital Zhejiang University","correspondingAuthor":false,"prefix":"","firstName":"Qiqi","middleName":"","lastName":"Gao","suffix":""},{"id":291220505,"identity":"b05f2ec6-ab38-4f33-9ae0-b576d13ccb9d","order_by":2,"name":"Chen Tian","email":"","orcid":"","institution":"First Affiliated Hospital Zhejiang University","correspondingAuthor":false,"prefix":"","firstName":"Chen","middleName":"","lastName":"Tian","suffix":""},{"id":291220506,"identity":"22b1be02-66e8-4e19-80c1-639eb4fedd57","order_by":3,"name":"Wentao Chen","email":"","orcid":"","institution":"First Affiliated Hospital Zhejiang University","correspondingAuthor":false,"prefix":"","firstName":"Wentao","middleName":"","lastName":"Chen","suffix":""},{"id":291220507,"identity":"62e515e9-4a16-4e87-b487-1e0c755f31b8","order_by":4,"name":"Catherine Pan","email":"","orcid":"","institution":"First Affiliated Hospital Zhejiang University","correspondingAuthor":false,"prefix":"","firstName":"Catherine","middleName":"","lastName":"Pan","suffix":""},{"id":291220508,"identity":"fb7175e8-9a5e-4ac1-87c3-676aff6204f9","order_by":5,"name":"Ling Wang","email":"","orcid":"","institution":"First Affiliated Hospital of Soochow University","correspondingAuthor":false,"prefix":"","firstName":"Ling","middleName":"","lastName":"Wang","suffix":""},{"id":291220509,"identity":"cda8f113-7a5a-4d7e-a1bf-aea30112dc6a","order_by":6,"name":"Jie Huang","email":"","orcid":"","institution":"First Hospital of Jiaxing","correspondingAuthor":false,"prefix":"","firstName":"Jie","middleName":"","lastName":"Huang","suffix":""},{"id":291220510,"identity":"b90a07f4-604e-4e83-be03-d732e8060f58","order_by":7,"name":"Jing Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/ElEQVRIiWNgGAWjYBACxgYeECUhx8/efIBBAsQ+QJwWC2PJnmMJxGlhYABrqUjccCPHACJASAtz/9mDnwt+SSTOnJHz+YNlG4Mc340Exs8FeB12Lll6Zp+EcT/P2w0Gkm0MxpI3EpilZ+DT0thjIM3bIyE7sz13QwJQC9CFCWzMPPi0NPMY/wZqYdxwIOfBAaCWesJa2njMpHl+SChuOJHD2ADUkmBAUEsPj5k1b4MEKJCNGSTOSRjOPPOwWRqfFsP+M8a3ef7UgaLy8WeJMht5vuPJBz/j1dIAdh2EwywBjkzGBjwaGBjkweQfqCs/4FU7CkbBKBgFIxUAAC48TMUP2nhVAAAAAElFTkSuQmCC","orcid":"","institution":"First Affiliated Hospital Zhejiang University","correspondingAuthor":true,"prefix":"","firstName":"Jing","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2024-04-09 11:43:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4241602/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4241602/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":55005947,"identity":"b59a4ada-4715-44a3-a875-9b4b17ef94ff","added_by":"auto","created_at":"2024-04-19 18:55:29","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":3750731,"visible":true,"origin":"","legend":"\u003cp\u003eparticipants clinical information. A-B, Images of lung cancer under LDCT, A, A nodule located in the posterior segment of the right lobe, measuring approximately 5.5x6.4 centimeters, presenting with spiculation and lobulation, demonstrating moderate heterogeneous enhancement, and causing adjacent pleural traction. B, A nodule located in the left lobe of the mediastinum, measuring approximately 2.3x3.1 centimeters, exhibiting spiculation and lobulation, with interference to adjacent blood vessels. C, Pathological images of lung cancer. Non-small cell lung malignant tumor cells are arranged in glandular, papillary, and micropapillary patterns; Cells show pleomorphism, eosinophilic cytoplasm, coarse chromatin, and visible nucleoli. D-E, The specific proteins are expressed in lung tissue. D, The lung cells show positive nuclear staining for TTF1, appearing as brown coloration. E, The lung cells exhibit positive cytoplasmic staining for SFTPB, appearing as brown coloration.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-4241602/v1/b19abe8466e5cd874f17ca34.png"},{"id":55004779,"identity":"b88bdebb-e921-4a2b-916c-c96d2ae0cabc","added_by":"auto","created_at":"2024-04-19 18:47:30","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":556914,"visible":true,"origin":"","legend":"\u003cp\u003eCharacterization of extracellular vesicles (EVs) enriched by ultracentrifugation. A, EV structure revealed by TEM showed double layered membrane-bound vesicles with a diameter≈100nm. B, Nanoparticle tracking analysis showed a population of EVs with a peak≈100 nm. C, EV and specific proteins were present in the EV fraction obtained by ultracentrifugation.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-4241602/v1/73ead08da4fa41655ac84a83.png"},{"id":55004778,"identity":"f116a90e-db00-4b5e-aeb4-53366eb599ff","added_by":"auto","created_at":"2024-04-19 18:47:30","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":36398,"visible":true,"origin":"","legend":"\u003cp\u003eCD151, TTF-1, SFTPB and Cy5 Probe (PE Probe) are present on extracellular vesicle (EV) membranes. Stochastic optical reconstruction microscopy (STORM) was performed to confirm CD151 or TTF-1 or SFTPB and PE presence of EV membranes together. To confirm the EV identity of the detected signal in ultracentrifuged plasma samples, CD151, TTF-1, SFTPB or PE (green) was compared to the EV marker CD9 (red) on the EV membranes together. Overlap of three markers with markers with CD9 indicates their presence on EV membranes. Scale bar=0.1μm.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-4241602/v1/87ff19dbd54d76be510ce4c6.png"},{"id":55004781,"identity":"d5d1d15f-127f-4b10-a780-eaa894294ea8","added_by":"auto","created_at":"2024-04-19 18:47:30","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1128419,"visible":true,"origin":"","legend":"\u003cp\u003eDevelopment of flow cytometry-based assays for NSCLC markers on plasma extracellular vesicles (EVs). A, Example histograms showing populations of EVs positive for each marker after labeling with fluorophore-conjugated antibody. B, Histograms of plasma samples labeled using fluorophore-conjugated immunoglobulin G isotype control for the indicated marker target antibody. C, Histogram showing blank (fluorophore only, no antibody) control experiment. D, Histogram of remaining particles after depletion of EVs from plasma by ultracentrifugation (UC). E-G, ,Summary data from experiments demonstrating specificity of EV assays, linearity in different dilutions of EV plasma samples, and stability of reference plasma (three replicates run each day on 7 separate days of the experiment) for (E) TTF-1, (F) CD151, (G) SFTPB\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-4241602/v1/2d130d45456a41c189f3ff29.png"},{"id":55004777,"identity":"247c4e53-7c96-4180-981f-89b3be3bf5c8","added_by":"auto","created_at":"2024-04-19 18:47:30","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":82002,"visible":true,"origin":"","legend":"\u003cp\u003eThe performance of extracellular vesicle (EV) markers in peripheral blood plasma in the discovery cohort is as follows: A, The percentage of CD151\u003csup\u003e+\u003c/sup\u003e EVs in NSCLC patients was significantly lower than in the healthy control group. B, The percentage of TTF-1\u003csup\u003e+\u003c/sup\u003e EVs in NSCLC patients was significantly lower than in the healthy control group. C, The percentage of SFTPB+ EVs in NSCLC patients showed no significant difference compared to the healthy control group. D, When individual markers were used to distinguish NSCLC from the control group, their performance was moderate. The use of EVs carrying TTF-1, CD151 showed separation between NSCLC and the control group, as indicated by the Receiver Operating Characteristic (ROC) curves. E, The combined model involved the comprehensive analysis of EV markers TTF-1, CD151, and SFTPB and the combined model successfully distinguished NSCLC from the control group. ****P\u0026lt;0.0001 compared to NSCLC. \"ns\" indicates no significant difference in expression.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-4241602/v1/6008b3110133ec86d4b689ab.png"},{"id":55004780,"identity":"36b93d77-6733-438d-91bd-7b99772add40","added_by":"auto","created_at":"2024-04-19 18:47:30","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":94384,"visible":true,"origin":"","legend":"\u003cp\u003eThe performance of extracellular vesicle (EV) markers in peripheral blood plasma in the validation cohort is as follows: The percentage of (A) CD151\u003csup\u003e+\u003c/sup\u003e , (B) TTF-1\u003csup\u003e+\u003c/sup\u003e EVs in NSCLC were significantly lower than in the control group, (C)SFTPB\u003csup\u003e+\u003c/sup\u003eEVs shows no differential expression between NSCLC and the control group. (D) When individual markers were used to distinguish NSCLC from the control group, their performance was moderate. The use of EVs carrying TTF-1, CD151 showed separation between NSCLC and the control group, as indicated by the Receiver Operating Characteristic (ROC) curves. (E) Comprehensive analysis methods successfully distinguished NSCLC from the control group. ****P\u0026lt;0.0001 compared to NSCLC. \"ns\" indicates no significant difference in expression between the two groups.\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-4241602/v1/ad1b95faf4d895b426910c82.png"},{"id":55467594,"identity":"2f382a12-47ba-46bf-a885-f6498399b7ce","added_by":"auto","created_at":"2024-04-28 18:15:40","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3508103,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4241602/v1/6cd61a5a-e884-49f5-aeac-29074ab3b089.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Liquid Biopsy in Lung Cancer: Nano-Flow Cytometry Detection of Non-Small Cell Lung Cancer in Blood","fulltext":[{"header":"Introduction","content":"\u003cp\u003eLung cancer, a leading cause of cancer-related deaths worldwide \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e, can be classified pathologically into small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC), which accounts for approximately 85% of cases \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. NSCLC further comprises three main subtypes: adenocarcinoma (40%), squamous cell carcinoma (30%), and large cell carcinoma (10%) \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Currently, the pathogenesis of NSCLC remains elusive, with most patients being diagnosed at an advanced or late stage, often at stage 4 \u003csup\u003e4\u003c/sup\u003e. Early detection significantly enhances survival rate \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e, yet current screening methods, including sputum cytology and low-dose computed tomography (LDCT), exhibit considerable false sensitivity and specificity \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Furthermore, effective monitoring NSCLC patients undergoing surgical treatment is necessary, given that at least one-third experience recurrence within 5 years post complete surgical resection \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eTo enhance the efficacy and convenience of lung cancer screening and post-surgical monitoring, the field of liquid biopsy, particularly utilizing blood samples, is rapidly advancing. However, conventional free-form biomarkers, such as carbohydrate antigen 125 (CA125) and cytokeratin-19 fragment antigen 21\u0026thinsp;\u0026minus;\u0026thinsp;1 (CYFRA21-1), lack specificity and are susceptible to various internal environment factors. More recently, the subdomain of liquid biopsy, focusing on extracellular vesicles (EVs), including exosomes \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e, has emerged as a promising avenue for early cancer detection.\u003c/p\u003e \u003cp\u003eEVs widely distributed in bodily fluids, comprising plasma, saliva, breast milk, cerebrospinal fluid, and others \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. In terms of biomarker discovery, EVs are advantageous because they not only carry various critical biomarkers such as nucleic acids and proteins\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e but also express specific marker proteins allowing for identifying the cell types from which they originate. Indeed, several previous studies have demonstrated differences in EVs markers, including CD151\u003csup\u003e+\u003c/sup\u003e, a tetraspanin superfamily protein with four transmembrane domains, that were increased in the EVs of cancer patients, including NSCLC \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. However, most of these studies were conducted in small cohorts, and typically did not use tissue-specific methods for EVs enrichment \u003csup\u003e\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn this study, we have developed a novel strategy, i.e. nano-flow cytometry, for measuring biomarkers of NSCLC, primarily through targeted enrichment of EVs carrying lung-derived proteins, specifically thyroid transcription factor-1 (TTF-1) and surfactant protein B (SFTPB) \u003csup\u003e\u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e, along with nonspecific CD151 cancer biomarkers, for NSCLC detection. Combining TTF1\u003csup\u003e+\u003c/sup\u003e and SFTPB\u003csup\u003e+\u003c/sup\u003e with CD151\u003csup\u003e+\u003c/sup\u003e EVs effectively differentiated NSCLC in both discovery and validation cohorts.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e1. Study design\u003c/h2\u003e \u003cp\u003eThis study was a multicenter research involving a discovery cohort and a validation cohort. The discovery cohort consisted of 39 NSCLC patients and 40 age- and gender-matched healthy controls (HC). The validation cohort included 78 NSCLC patients and 77 HC with similar age and gender distributions. Samples from both cohorts were collected from the First Affiliated Hospital, Zhejiang University School of Medicine and the First Affiliated Hospital of Soochow University, with the study populations randomly distributed between the two cohorts. Only individuals clinically characterized by radiological features indicative of NSCLC and subsequently confirmed with pathological diagnosis were included in the \"NSCLC\" group, while subjects lacking clinical features of NSCLC were classified as the \"HC\" group.\u003c/p\u003e \u003cp\u003eInitially, we identified the NSCLC-associated markers TTF-1 and SFTPB, as well as the broad-spectrum tumor marker CD151. Using Western Blot, we confirmed the expression of these biomarkers on EVs. These tumor markers were further validated to be present on the surface of EVs using stochastic optical reconstruction microscopy (STORM), a microscopic technique capable of imaging individual EVs. By imaging TTF-1, CD151, and SFTPB in conjunction with general markers of EVs, we analyzed super-centrifuged plasma EVs. The localization of these markers closely aligned with the a forementioned tumor markers, providing evidence that these NSCLC-related tumor markers are indeed present on EVs.\u003c/p\u003e \u003cp\u003eAnd then, we employed Beckman coulter CytoFLEX S flow Systems (CytoFLEX S) to quantify TTF-1\u003csup\u003e+\u003c/sup\u003e, CD151\u003csup\u003e+\u003c/sup\u003e, and SFTPB\u003csup\u003e+\u003c/sup\u003e EVs in plasma samples. The quantities of EVs carrying each marker were quantified as a proportion of all EVs, and the levels between different groups were compared.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2. Methods\u003c/h2\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e2.1 Participants information and sample collection\u003c/h2\u003e \u003cp\u003eThe samples in both the discovery and validation cohorts were collected from the First Affiliated Hospital, Zhejiang University School of Medicine and the First Affiliated Hospital of Soochow University. All procedures were reviewed and approved by their respective ethics committees, and informed consent was obtained from all participants. The study population included individuals who had undergone extensive clinical imaging and pathological evaluations, ensuring that the collected patient cohorts were diagnosed with primary NSCLC.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Blood sample processing\u003c/h2\u003e \u003cp\u003eAfter obtaining informed consent from all patients, 5 ml of fasting peripheral venous blood was collected using EDTA anticoagulant tubes, and the patient's name (abbreviation), identification number, and sampling time were recorded. The collected blood samples were gently inverted 8\u0026ndash;10 times immediately after blood collection to ensure proper mixing with the anticoagulant without hemolysis. Within 60 minutes of collection, the samples were centrifuged at 1500 g for 15 minutes at room temperature. After centrifugation, the upper plasma layer was carefully transferred to a 15 ml centrifuge tube at room temperature, and the mixture was gently mixed 3\u0026ndash;4 times. The tube containing the plasma was centrifuged at 3200 g for 15 minutes at room temperature. The supernatant was aspirated and divided into 0.5 ml aliquots in low-binding centrifuge tubes. The aliquots were stored at -80\u0026deg;C until further use.\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.3 Isolation of Plasma Extracellular Vesicle\u003c/h2\u003e \u003cp\u003eThaw 0.5 ml of plasma stored at -80\u0026deg;C at room temperature (28\u0026deg;C) and centrifuge at 3000 g for 30 minutes at 4\u0026deg;C. Collect the supernatant and centrifuge at 10,000 g for 30 minutes at 4\u0026deg;C. Take 100\u0026micro;l of the supernatant and add it to an ultra-centrifuge tube containing 900\u0026micro;l of phosphate-buffered saline (PBS) (filtered through a 0.22\u0026micro;m membrane) to balance the sample. Centrifuge at 100,000 g for 60 minutes at 4\u0026deg;C. Discard 800\u0026micro;l of the supernatant and gently flick the remaining liquid 200 times. Add 800\u0026micro;l of PBS (filtered through a 0.22\u0026micro;m membrane) to the tube, balance the sample, and centrifuge at 100,000 g for 60 minutes at 4\u0026deg;C. Discard 800\u0026micro;l of the supernatant and flick the remaining liquid 200 times. The resulting 200\u0026micro;l is the processed plasma extracellular vesicle sample.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.4 Stochastic optical reconstruction microscopy\u003c/h2\u003e \u003cp\u003eAll images were acquired on the Nikon N-STORM super-resolution system (Nikon Instruments Inc.), using a Nikon Eclipse Ti inverted microscope with a 100\u0026times;TIRF objective (numerical aperture 1.49). The Alexa 561 fluorescent dye was excited with a 561 nm semiconductor laser, Alexa 488 was excited with a 488 nm semiconductor laser, and Alexa 647 was excited with a 647 nm semiconductor laser. Additionally, a lower-power 405 nm laser was employed to enhance the excitation of certain fluorescent dyes. Electron-multiplying charge-coupled device (EMCCD) cameras (Andor ixon DU897) recorded 2000 frames with an exposure time of 60 milliseconds for imaging a single cell. The capture time for a single cell typically lasted around 20 minutes. During fluorescence acquisition, the Nikon microscopy setup employed a perfect focus system (PFS) to perform real-time correction for Z-axis focus drift. Plasma-derived extracellular vesicles (EVs) were washed three times with PBS by ultracentrifugation and then immersed in 200\u0026micro;L of specialized STORM imaging buffer (7\u0026micro;L of glucose oxidase-free GLOX buffer [14 mg glucose oxidase, 50\u0026micro;L of 17 mg/mL catalase, 200\u0026micro;L of 10 mM Tris, 50 mM NaCl, pH 8.0], 70\u0026micro;L of MEA buffer [1 M]). The EVs were then combined with 620\u0026micro;L of buffer B (50 mM Tris-HCl [pH 8.0], 10 mM NaCl, 10% glucose) before data acquisition.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e2.5 Electron microscopy\u003c/h2\u003e \u003cp\u003eTake 5\u0026micro;l of the sample and apply it to an electron microscopy grid coated with a porous carbon film. Incubate for 30 minutes, remove the liquid from the back of the grid, and immerse the grid in liquid ethane using a Leica cryo plunge freezer. Observations of the sample after processing provide data on the size and morphology of extracellular vesicles using TEM.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Nanoparticle tracking analysis\u003c/h2\u003e \u003cp\u003eTake 10\u0026micro;l of the sample and dilute it 1:100 in PBS filtered through a 0.22\u0026micro;m membrane to obtain a 1ml solution. Direct light scattering measurements were performed using a purple laser (405 nm). Pre-testing was conducted to determine the ideal number of particles per frame (20\u0026ndash;100 particles/frame) for accurate concentration measurement. (Cell temperature: 25\u0026deg;C; Injection speed: 40\u0026micro;l/s). Once the sample has completed the analysis, data on the size and concentration of extracellular vesicles can be obtained.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.7 EV analysis with flow System\u003c/h2\u003e \u003cp\u003eAccording to the manufacturer's protocol, Zenon IgG Labeling Kits (Invitrogen/Life Technologies) was used to generate fluorescently conjugated antibodies. Zenon Alexa Fluor 405 Mouse IgG Labeling Kit was utilized to label monoclonal antibodies against mouse anti-CD151(10418-1-AP, Proteintech), mouse anti-TTF-1(ab227652, Abcam), mouse anti-SFTPB(13664-1-AP, Proteintech).\u003c/p\u003e \u003cp\u003eIsotype controls for the respective species were also labeled at the same final concentration as all the antibodies. Another negative control (no antibody \"blank,\" i.e., staining only) was achieved by substituting specific antibodies with the same volume of PBS in the labeling reaction.\u003c/p\u003e \u003cp\u003eThe labeled anti-CD151, TTF-1, SFTPB (Zenon Alexa Fluor 405 labeled) was added to 10\u0026micro;L of ultra-centrifuged plasma samples (equivalent antibody amount per sample: 0.1\u0026micro;g), and incubated overnight at 4\u0026deg;C. Fixation was performed using 20\u0026micro;L of 4% paraformaldehyde filtered through a 0.22\u0026micro;m filter, and left at room temperature for 20 minutes.\u003c/p\u003e \u003cp\u003eThe samples were analyzed using Beckman coulter CytoFLEX S flow Systems (CytoFLEX S), as mentioned earlier. The system was set up with a 70mW 405 nm laser for forward and side scatter as well as blue fluorescence, and a 200mW 488 nm laser for green fluorescence. The optimized microfluidic flow cytometer setup ensured stable particle counts and prevented aggregation. After dilution, the apparent concentration of the samples showed linear dilution. Reference beads and EV samples were run with high threshold settings to minimize background noise: threshold values for the 405-LALS and 405-Blue lasers were set at 17 and 25, respectively; the voltage for the 405-Blue laser was set to 1V with a voltage of 450V. The sheath fluid pressure was maintained at 150 mbar, and samples were injected at a flow rate of 1.5\u0026micro;L/min. All samples were stored at 4\u0026deg;C and analyzed within 8 hours of labeling, ensuring stable labeling under these conditions. For each batch, clinical samples were analyzed within 2 days, with samples from different diagnostic groups distributed across days. Two reference plasma samples (taken from approximately 30 HC individuals) were included in each day's measurements to help assess daily variations (\u0026lt;\u0026thinsp;5%).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e2.8 Statistical analysis\u003c/h2\u003e \u003cp\u003eNon-interventional research methods were used to compare the basic characteristics of the two sample groups. The identification of exosomes was performed using TEM, NTA, and Western blot methods, and the exosome samples from both groups were analyzed using flow cytometry. Statistical analysis and generation of corresponding charts were conducted using SPSS 23.0 software and GraphPad Prism V8. Descriptive statistics were presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (x\u0026thinsp;\u0026plusmn;\u0026thinsp;s) for continuous variables, and rates were used for categorical and ordinal data. For normally distributed quantitative data, t-tests were performed, while non-normally distributed data were analyzed using non-parametric tests (Mann-Whitney U). Chi-square test and rank sum test were employed for the comparison of qualitative data. Logistic regression analysis was conducted to assess the statistical significance of multivariate variables, and linear regression was performed to evaluate the linear relationship between single-factor variables and clinical data. ROC curve analysis was used to determine the sensitivity and specificity of diagnostic indicators and to identify the optimal cutoff values. A significance level of P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e1. Characteristics of the clinical cohorts\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e contains the pertinent clinical information, including pathological diagnoses. Additionally, characteristic radiographic demonstrations and pathological images for each cancer subtype are illustrated in their respective Figures (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe characteristics of clinic cohorts\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eDiscovery cohort\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eValidation cohort\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNSCLC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNSCLC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e78\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23 (57.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24 (61.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33(42.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e46(56.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17 (42.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15 (38.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e44(57.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e32(43.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge(SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55.03\u0026thinsp;\u0026plusmn;\u0026thinsp;16.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e63.55\u0026thinsp;\u0026plusmn;\u0026thinsp;12.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e54.13\u0026thinsp;\u0026plusmn;\u0026thinsp;14.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e60.50\u0026thinsp;\u0026plusmn;\u0026thinsp;12.92\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRange\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24\u0026ndash;85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24\u0026ndash;94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25\u0026ndash;86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26\u0026ndash;86\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdenocarcinoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33 (84.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e61 (78.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSquamous cell carcinoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (15.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14 (17.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLarge cell carcinoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 (1.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2 (2.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClinical stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage I\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage II\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (2.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3 (3.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage III\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (15.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4 (5.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage IV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33 (82.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e71 91.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e2. Define NSCLC EVs in blood\u003c/h2\u003e \u003cp\u003eTo characterize the EVs associated with NSCLC according to the recommendations of MISEV2018, EVs were enriched by ultracentrifugation. For the analysis of the size and morphological features of EVs, cryo-electron microscopy was utilized, demonstrating that the EVs were double-membrane structures with a diameter of approximately 100nm. Nanoparticle tracking analysis (NTA) further determined the size and distribution of EVs, showing a broad peak with a maximum around 100nm. We also performed Western blot (WB) analysis on EV-specific markers. As expected, WB results indicated that the post-ultracentrifugation EV samples were rich in CD151, TTF-1, SFTPB, as well as general EV markers TSG101 and Alix proteins. (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e2\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn order to characterize EVs originating from the lungs specifically, we investigated two commonly used histological markers for lung cancers, TTF1 and SFTPB, alongside CD151, a marker previously identified in EVs derived from lung cancer using STORM super-resolution single-molecule imaging microscopy \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eTo enhance the sensitivity of the experimental measurement, we have introduced a novel PE probe (Cy5 probe), which potentially enables a more precise determination of specific proteins. We confirmed that CD151, TTF-1, SFTPB and Cy5 Probe coexist with the general EV marker CD9 on the EV membrane. Nanoscale fluorescent beads with a diameter of 100nm were used as a scale standard. To confirm the identity of the detected signals in the post-ultracentrifugation plasma samples as EVs, we compared CD151, TTF-1, SFTPB and Cy5 Probe with the general EV marker CD9 on EV membranes. It was observed that CD151, TTF-1, SFTPB and Cy5 Probe along with the general EV marker (CD9), could be detected on the same plasma EVs (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3. Development of a sensitive assay for EVs quantification\u003c/h2\u003e \u003cp\u003eWe optimized the measurement of CD151\u003csup\u003e+\u003c/sup\u003e, TTF-1\u003csup\u003e+\u003c/sup\u003e, and SFTPB\u003csup\u003e+\u003c/sup\u003e EVs in plasma using the previously developed nanoscale flow cytometry technique (CytoFLEX S)(Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e4\u003c/span\u003e). We confirmed detection specificity and dilution linearity. A single reference plasma sample was run repeatedly for 7 days to demonstrate daily stability. The coefficient of variation for all markers was \u0026le;\u0026thinsp;10%. The relationship between gating positive particles and sample dilution to avoid coincidental events is illustrated.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e4. Quantification of EVs in discovery and validation cohorts\u003c/h2\u003e \u003cp\u003eThe next step involved detecting the quantity of extracellular vesicles expressing CD151, TTF-1, and SFTPB, in order to distinguish between true NSCLC and HC. For ease of comparison, we use the positivity rate for comparison. (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBiomarker levels for two cohorts\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eDiscovery cohort\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eValidation cohort\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eEV markers (percent positive\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNSCLC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNSCLC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD151\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.03\u0026thinsp;\u0026plusmn;\u0026thinsp;1.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.43\u0026thinsp;\u0026plusmn;\u0026thinsp;1.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.12\u0026thinsp;\u0026plusmn;\u0026thinsp;2.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.04\u0026thinsp;\u0026plusmn;\u0026thinsp;1.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTTF-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.40\u0026thinsp;\u0026plusmn;\u0026thinsp;2.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.26\u0026thinsp;\u0026plusmn;\u0026thinsp;2.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.71\u0026thinsp;\u0026plusmn;\u0026thinsp;3.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.86\u0026thinsp;\u0026plusmn;\u0026thinsp;1.73\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSFTPB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.12\u0026thinsp;\u0026plusmn;\u0026thinsp;2.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.02\u0026thinsp;\u0026plusmn;\u0026thinsp;4.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.72\u0026thinsp;\u0026plusmn;\u0026thinsp;3.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.76\u0026thinsp;\u0026plusmn;\u0026thinsp;4.68\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e ANOVA followed by Dunnett\u0026rsquo;s multiple comparisons test. Abbreviation: HC, healthy control; NSCLC, non-small cell lung cancer; ANOVA, analysis of variance; EV, extracellular vesicle; CD151, CD151 Protein; TTF-1, transcription termination factor 1; SFTPB, Pulmonary surfactant-associated protein B.\u003c/p\u003e \u003cp\u003eIn the discovery cohort, levels of TTF-1\u0026thinsp;+\u0026thinsp;and CD151\u0026thinsp;+\u0026thinsp;EVs in NSCLC patients were found to be lower than those in the HC group (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The marker levels were not influenced by age; however, correlations were observed between the markers. Subsequently, ROC analysis was conducted to evaluate diagnostic performance. When comparing NSCLC and HC, CD151\u0026thinsp;+\u0026thinsp;EVs exhibited a sensitivity of 70.0% and specificity of 84.6% (AUC\u0026thinsp;=\u0026thinsp;0.825, 95%CI\u0026thinsp;=\u0026thinsp;0.735\u0026ndash;0.915), while TTF-1\u0026thinsp;+\u0026thinsp;EVs showed a sensitivity of 80.0% and specificity of 87.2% (AUC\u0026thinsp;=\u0026thinsp;0.878, 95%CI\u0026thinsp;=\u0026thinsp;0.802\u0026ndash;0.954). SFTPB\u0026thinsp;+\u0026thinsp;EVs, on the other hand, demonstrated moderate performance (AUC\u0026thinsp;=\u0026thinsp;0.521, 95%CI\u0026thinsp;=\u0026thinsp;0.391\u0026ndash;0.651). Besides, the combined model of the three EV markers for differentiation between NSCLC and HC showed an AUC of 0.913 (95%CI\u0026thinsp;=\u0026thinsp;0.854\u0026ndash;0.973, sensitivity\u0026thinsp;=\u0026thinsp;84.6%, specificity\u0026thinsp;=\u0026thinsp;95.0%).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo validate the results, we collected an additional 78 confirmed NSCLC samples and confirmed the differences in total particles and all markers between NSCLC and HC groups (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e6\u003c/span\u003e). The sensitivity and specificity for CD151\u0026thinsp;+\u0026thinsp;EVs were 70.1% and 73.1%, respectively (AUC\u0026thinsp;=\u0026thinsp;0.778, 95%CI\u0026thinsp;=\u0026thinsp;0.706\u0026ndash;0.851), for TTF-1\u0026thinsp;+\u0026thinsp;EVs were 75.3% and 79.5%, respectively (AUC\u0026thinsp;=\u0026thinsp;0.841, 95%CI\u0026thinsp;=\u0026thinsp;0.780\u0026ndash;0.902). The AUC for SFTPB\u0026thinsp;+\u0026thinsp;EVs was 0.574 (95%CI\u0026thinsp;=\u0026thinsp;0.483\u0026ndash;0.666), indicating moderate discrimination between NSCLC and HC. Additionally, the composite model of TTF-1, CD151, and SFTPB combined analysis with an AUC of 0.854 (95%CI\u0026thinsp;=\u0026thinsp;0.796\u0026ndash;0.913, sensitivity\u0026thinsp;=\u0026thinsp;85.9%, specificity\u0026thinsp;=\u0026thinsp;74.0%).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe current investigations have yielded two significant discoveries. Firstly, we have identified a distinct set of markers capable of detecting blood EVs originating specifically from the lungs. Secondly, we have successfully utilized these markers to readily identify NSCLC patients using a sensitive nano-flow cytometry technology.\u003c/p\u003e \u003cp\u003eA critical aspect of EVs research in pulmonary neoplasia\u0026rsquo;s diseases revolves around delineating the targeted population, particularly in enriching EVs originating from the lungs. To accomplish this objective, we initially demonstrated the presence of TTF-1 and SFTPB in healthy lung tissue and tissues affected by NSCLC (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Subsequently, we employed various technologies to indicate that the EVs positive for TTF-1 and SFTPB were colocalized with CD151, which is a nonspecific protein marker detected in many other cancers (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e3\u003c/span\u003e) \u003csup\u003e\u003cspan additionalcitationids=\"CR19 CR20 CR21\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eTTF-1 is a transcription factor highly expressed in lung cancers, although its expression is also detected in a few other organs, e.g. the thyroid \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. SFTPB is a pulmonary surfactant protein mainly expressed in alveolar epithelial cells, contributing to the regulation of pulmonary surfactant tension and maintaining lung stability and function. In lung cancer, the expression of SFTPB is typically regulated, but the specific expression levels and its role in lung cancer development remain somewhat controversial \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e,\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eTo develop a robust assay for NSCLC, we took advantages of nano-flowcytometry to quantify the numbers of TTF-1\u003csup\u003e+\u003c/sup\u003e and SFTPB\u003csup\u003e+\u003c/sup\u003e, along with CD151\u003csup\u003e+\u003c/sup\u003e, EVs in peripheral blood of healthy individuals and patients with NSCLC. Of note, CD151 has been reported to be increased in NSCLC and other cancers \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. The results indicate that, while SFTPB \u003csup\u003e+\u003c/sup\u003e-EVs was not significant different between NSCLC patients and healthy controls, the numbers of CD151\u003csup\u003e+\u003c/sup\u003e and TTF-1\u003csup\u003e+\u003c/sup\u003e- EVs in the peripheral blood of NSCLC patients were significantly reduced compared to healthy individuals. Combined statistical analysis of the numbers of SFTPB\u003csup\u003e+\u003c/sup\u003e, TTF-1\u003csup\u003e+\u003c/sup\u003e and D151\u003csup\u003e+\u003c/sup\u003e EVs provided a more accurate assessment compared to measuring a single protein .\u003c/p\u003e \u003cp\u003ePrior studies have reported increased expression of CD151 in the EVs isolated from NSCLC \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. It's important to note that each of these previous studies quantified the concentration of CD151 from immunocaptured and lysed EV samples in plasma. In contrast, our study employs fluorescent labeling and flow cytometry to quantify the number of EVs carrying the target markers. As to the causes of a reduced number of CD151\u003csup\u003e+\u003c/sup\u003e EVs in NSCLC patients, several explanations may align with this observation; for instance, NSCLC might impact the mechanisms of EV generation, or the cellular processes involved in transferring pathological proteins from tumor cells to peripheral circulating cells. To probe these hypotheses further, a thorough examination of EV-related processes in the context of NSCLC progression is warranted to fully elucidate the underlying reasons for the observed changes in EV populations.\u003c/p\u003e \u003cp\u003eThere are a few significant shortcomings in our investigations. First, our diagnostic cohorts all utilized radiological examinations and pathological biopsies, enabling us to identify the most reliable NSCLC cases. While this is a strength of our study, allowing evaluation and analysis within the targeted NSCLC population, performance the biomarkers in other types of lung cancers remains to be determined, ideally in a large-scale, multi-site manner using robust sampling protocols in well-defined subjects. Equally important is whether these EV markers can serve as clinically meaningful indicators for early detection of NSCLC or effective indicator of recurrence of NSCLC after surgical resection of the primary neoplasm, a process involves longitudinal follow up for many years.\u003c/p\u003e \u003cp\u003eAdditionally, several technical issues must be considered regarding these detailed experimental results. The current CytoFLEX S system might not accurately detect EVs smaller than 100nm. Furthermore, the separation technique used in this study to enrich EVs from plasma, namely ultracentrifugation, is known to sometimes induce particle aggregation. As a result, imaging data showing co-localization of these NSCLC markers with common EV markers might actually represent two or more independent EV populations closely related to each other. These possibilities can be differentiated in future experiments using internal labeling of EVs in contrast to the membrane labeling used in this study.\u003c/p\u003e \u003cp\u003eLastly, the variability in total EVs quantities between subjects, coupled with the challenge of ensuring EVs specificity in plasma ion measurements, necessitates both standardization and poses challenges in marking positive EVs. In this study, we addressed this issue by attributing positive EVs to all detectable particles and interpreted changes through reporting positive ratios. A more stringent control should involve the normalization of all EVs (which are also exclusively EVs) via fluorescence labeling. However, such specificity proteins are limited. Tetraspanin proteins, such as CD9 used in our STORM study, are the most widespread EVs markers, yet they label only a subset of potential EV particles and are not suitable as markers for total EVs \u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. Therefore, the identification of these ubiquitous EV markers will provide a foundation for further EV research.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn summary, our study discovered the surface expression of SFTPB, TTF-1, and CD151 proteins on EVs of NSCLC patients. Using flow cytometry experiments, we demonstrated that through combined analysis of SFTPB\u003csup\u003e+\u003c/sup\u003e, TTF-1\u003csup\u003e+\u003c/sup\u003eand CD151\u003csup\u003e+\u003c/sup\u003e EVs, a robust differentiation between NSCLC and HC was achieved. While randomized and large-sample follow-up studies are necessary to validate these experimental findings, our results offer a proof of concept for detecting NSCLC patients through a convenient flow cytometry method in the field of liquid biopsy.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eAUC\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eArea under the curve\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eCA125\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCarbohydrate antigen 125\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eCYFRA21-1\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCytokeratin-19 fragment antigen 21\u0026thinsp;\u0026minus;\u0026thinsp;1\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eEVs\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eExtracellular vesicles\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eLDCT\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLow-dose computed tomography\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eNSCLC\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNon-small cell lung cancer\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eNTA\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNanoparticle tracking analysis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eROC\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eReceiver Operating Characteristic\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eSFTPB\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSurfactant protein B\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eSTORM\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eStochastic optical reconstruction microscopy\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eSCLC\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSmall cell lung cancer\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eTTF-1\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eThyroid transcription factor-1\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eTEM\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTransmission Electron Microscope\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eWB\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eWestern blot\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets supporting the conclusions of this article are included within the article and its additional files.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAndong Zhang: Conceptualization, Methodology, Data curation and Writing-Original draft preparation. Qiqi Gao\u0026amp; Chen Tian: Methodology, Funding acquisition, Data curation, and Writing-Original draft preparation. Wentao Chen\u0026amp; Catherine Pan: Investigation and Validation. Ling Wang, Jie Huang and Jing Zhang: Conceptualization, Supervision, Funding acquisition and Writing- Reviewing and Editing. All authors have read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by Youth Program of National Natural Science Foundation of China (82102183) to Qiqi Gao, National Natural Science Foundation of China (82201560) to Chen Tian, National Natural Science Foundation of China(NSFC), International Cooperation and Exchange Project (82020108012) to Jing Zhang.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets supporting the conclusions of this article are included within the article and available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was approved by The First Affiliated Hospital of Soochow University and The First Affiliated Hospital, Zhejiang University School of Medicine.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors have agreed to publish this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAndong Zhang , Qiqi Gao and Chen Tian have contributed equally to the work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors and Affiliations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e1\u003c/sup\u003eDepartment of General Medicine, The Affiliated Hospital of Jiaxing University, Zhejiang, Jiaxing, China\u003c/p\u003e\n\u003cp\u003eAndong Zhang\u0026amp; Jie Huang\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e2\u003c/sup\u003eDepartment of Pathology, First Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang, Hangzhou, China\u003c/p\u003e\n\u003cp\u003eQiqi Gao, Chen Tian, Wentao Chen, Catherine Pan \u0026amp; Jing Zhang\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e3\u003c/sup\u003eNational Human Brain Bank for Health and Disease, Zhejiang University, Zhejiang, Hangzhou, China\u003c/p\u003e\n\u003cp\u003eJing Zhang\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e4\u003c/sup\u003eDepartment of General Medicine, The First Affiliated Hospital of Soochow University, Jiangsu, Suzhou, China\u003c/p\u003e\n\u003cp\u003eLing Wang\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e5\u003c/sup\u003eDepartment of General Medicine, The Fourth Affiliated Hospital of Soochow University, Jiangsu, Suzhou, China\u003c/p\u003e\n\u003cp\u003eLing Wang\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorresponding authors\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCorrespondence to\u0026nbsp;Ling Wang,\u0026nbsp;Jie Huang\u0026nbsp;or Jing Zhang.\u003c/p\u003e\n\u003cp\u003eEmail:\u003c/p\u003e\n\u003cp\u003eLing Wang [email protected]\u003c/p\u003e\n\u003cp\u003eJie Huang [email protected]\u003c/p\u003e\n\u003cp\u003eJing Zhang [email protected]\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin. 2021;71(3):209\u0026ndash;49.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLasse-Opsahl E, Baliira R, Barravecchia I, McLintock E, Lee JM, Ferris SF et al. Oncogenic KRAS(G12D) extrinsically induces an immunosuppressive microenvironment in lung adenocarcinoma. bioRxiv. 2024.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRelli V, Trerotola M, Guerra E, Alberti S. Abandoning the Notion of Non-Small Cell Lung Cancer. Trends Mol Med. 2019;25(7):585\u0026ndash;94.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChansky K, Detterbeck FC, Nicholson AG, Rusch VW, Valli\u0026egrave;res E, Groome P, et al. The IASLC Lung Cancer Staging Project: External Validation of the Revision of the TNM Stage Groupings in the Eighth Edition of the TNM Classification of Lung Cancer. J Thorac oncology: official publication Int Association Study Lung Cancer. 2017;12(7):1109\u0026ndash;21.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMiller KD, Nogueira L, Devasia T, Mariotto AB, Yabroff KR, Jemal A, et al. Cancer treatment and survivorship statistics, 2022. CA Cancer J Clin. 2022;72(5):409\u0026ndash;36.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKris MG, Gaspar LE, Chaft JE, Kennedy EB, Azzoli CG, Ellis PM, et al. Adjuvant Systemic Therapy and Adjuvant Radiation Therapy for Stage I to IIIA Completely Resected Non-Small-Cell Lung Cancers: American Society of Clinical Oncology/Cancer Care Ontario Clinical Practice Guideline Update. J Clin Oncol. 2017;35(25):2960\u0026ndash;74.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAberle DR, Abtin F, Brown K. Computed tomography screening for lung cancer: has it finally arrived? Implications of the national lung screening trial. J Clin Oncol. 2013;31(8):1002\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePatz EF Jr., Pinsky P, Gatsonis C, Sicks JD, Kramer BS, Tammem\u0026auml;gi MC, et al. Overdiagnosis in low-dose computed tomography screening for lung cancer. JAMA Intern Med. 2014;174(2):269\u0026ndash;74.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAberle DR, Adams AM, Berg CD, Black WC, Clapp JD, Fagerstrom RM, et al. Reduced lung-cancer mortality with low-dose computed tomographic screening. N Engl J Med. 2011;365(5):395\u0026ndash;409.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi W, Liu JB, Hou LK, Yu F, Zhang J, Wu W, et al. Liquid biopsy in lung cancer: significance in diagnostics, prediction, and treatment monitoring. Mol Cancer. 2022;21(1):25.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi MY, Liu LZ, Dong M. Progress on pivotal role and application of exosome in lung cancer carcinogenesis, diagnosis, therapy and prognosis. Mol Cancer. 2021;20(1):22.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen R, Xu X, Qian Z, Zhang C, Niu Y, Wang Z, et al. The biological functions and clinical applications of exosomes in lung cancer. Cell Mol Life Sci. 2019;76(23):4613\u0026ndash;33.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSadej R, Grudowska A, Turczyk L, Kordek R, Romanska HM. CD151 in cancer progression and metastasis: a complex scenario. Lab Invest. 2014;94(1):41\u0026ndash;51.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWong AH, Tran T. CD151 in Respiratory Diseases. Front Cell Dev Biol. 2020;8:64.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKemper M, Krekeler C, Menck K, Lenz G, Evers G, Schulze AB et al. Liquid Biopsies in Lung Cancer. Cancers (Basel). 2023;15(5).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSandfeld-Paulsen B, Jakobsen KR, Baek R, Folkersen BH, Rasmussen TR, Meldgaard P, et al. Exosomal Proteins as Diagnostic Biomarkers in Lung Cancer. J Thorac oncology: official publication Int Association Study Lung Cancer. 2016;11(10):1701\u0026ndash;10.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSingh S, Pathak A, Kumar S, Malik PS, Elangovan R. Rapid immunomagnetic co-capture assay for quantification of lung cancer associated exosomes. J Immunol Methods. 2022;508:113324.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTurner BM, Cagle PT, Sainz IM, Fukuoka J, Shen SS, Jagirdar J, Napsin A. a new marker for lung adenocarcinoma, is complementary and more sensitive and specific than thyroid transcription factor 1 in the differential diagnosis of primary pulmonary carcinoma: evaluation of 1674 cases by tissue microarray. Arch Pathol Lab Med. 2012;136(2):163\u0026ndash;71.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBishop JA, Sharma R, Illei PB. Napsin A and thyroid transcription factor-1 expression in carcinomas of the lung, breast, pancreas, colon, kidney, thyroid, and malignant mesothelioma. Hum Pathol. 2010;41(1):20\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTaguchi A, Hanash S, Rundle A, McKeague IW, Tang D, Darakjy S, et al. Circulating pro-surfactant protein B as a risk biomarker for lung cancer. Cancer Epidemiol Biomarkers Prev. 2013;22(10):1756\u0026ndash;61.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhu J, Cai T, Zhou J, Du W, Zeng Y, Liu T, et al. CD151 drives cancer progression depending on integrin alpha3beta1 through EGFR signaling in non-small cell lung cancer. J Exp Clin Cancer Res. 2021;40(1):192.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSandfeld-Paulsen B, Aggerholm-Pedersen N, Baek R, Jakobsen KR, Meldgaard P, Folkersen BH, et al. Exosomal proteins as prognostic biomarkers in non-small cell lung cancer. Mol Oncol. 2016;10(10):1595\u0026ndash;602.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiao Y, Yang F, Li X, Chen K, Wang J. The Prognostic Value of TTF-1/NKX2-1 in Lung Squamous Cell Carcinoma. Appl Immunohistochem Mol morphology: AIMM. 2023;31(6):414\u0026ndash;20.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKong R, Patel AS, Sato T, Jiang F, Yoo S, Bao L, et al. Transcriptional Circuitry of NKX2-1 and SOX1 Defines an Unrecognized Lineage Subtype of Small-Cell Lung Cancer. Am J Respir Crit Care Med. 2022;206(12):1480\u0026ndash;94.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSin DD, Tammemagi CM, Lam S, Barnett MJ, Duan X, Tam A, et al. Pro-surfactant protein B as a biomarker for lung cancer prediction. J Clin Oncol. 2013;31(36):4536\u0026ndash;43.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKarimi N, Dalirfardouei R, Dias T, Lotvall J, Lasser C. Tetraspanins distinguish separate extracellular vesicle subpopulations in human serum and plasma - Contributions of platelet extracellular vesicles in plasma samples. J Extracell Vesicles. 2022;11(5):e12213.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Nano-Flow Cytometry Detection, Liquid biopsy, Biomarker, NSCLC, SFTPB, TTF-1, CD151","lastPublishedDoi":"10.21203/rs.3.rs-4241602/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4241602/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eNon-small cell lung cancer (NSCLC) remains a leading cause of global mortality, with current screening and diagnostic methods often lacking in sensitivity and specificity. In our endeavor to develop precise, objective, and easily accessible diagnostic biomarkers for NSCLC, this study aimed to leverage rapidly evolving liquid biopsy techniques to differentiate NSCLC patients from healthy controls by isolating peripheral blood samples and enriching extracellular vesicles (EVs) containing lung-derived proteins (TTF-1 and SFTPB), along with the cancer-associated protein CD151\u003csup\u003e+\u003c/sup\u003eEVs. Additionally, we established a nano-flow cytometry assay for plasma EVs detection. NSCLC patients demonstrated significantly reduced counts of TTF-1\u003csup\u003e+\u003c/sup\u003e EVs and CD151\u003csup\u003e+\u003c/sup\u003e EVs in plasma compared to healthy controls (P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), while SFTPB\u0026thinsp;+\u0026thinsp;EVs showed no significant difference (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05). However, integrated analysis of TTF-1\u003csup\u003e+\u003c/sup\u003e, CD151\u003csup\u003e+\u003c/sup\u003e, and SFTPB\u003csup\u003e+\u003c/sup\u003e EVs yielded area under the curve (AUC) values of 0.917 and 0.845 in the discovery and validation cohorts, respectively. Thus, while further validation is essential, the advanced technologies mentioned above are of great significance for the detection of NSCLC biomarkers.\u003c/p\u003e","manuscriptTitle":"Liquid Biopsy in Lung Cancer: Nano-Flow Cytometry Detection of Non-Small Cell Lung Cancer in Blood","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-19 18:47:24","doi":"10.21203/rs.3.rs-4241602/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"64cd782b-d9ac-4009-b5f4-879cf30e20a6","owner":[],"postedDate":"April 19th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-04-28T18:13:59+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-19 18:47:24","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4241602","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4241602","identity":"rs-4241602","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}