{"paper_id":"4b13445d-cb48-4ac2-948f-820caa607282","body_text":"Identification and validation of plasma exosomal FGL1 as an early diagnostic biomarker for non-small cell lung cancer | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Identification and validation of plasma exosomal FGL1 as an early diagnostic biomarker for non-small cell lung cancer Wentao Wang, Chenglong Guo, Xin Liu, Jindong Li, Song Zhao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7447669/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 Objective Non-small cell lung carcinoma (NSCLC) is the leading cause of cancer-related death worldwide. Nevertheless, reliable and effective biomarkers for early diagnosis of NSCLC are currently unavailable. In recent years, increasing studies suggest that exosomes have a great promise to serve as novel biomarkers in liquid biopsy. This study aimed to identify the plasma exosomal biomarkers for NSCLC early detection. Methods We utilized label-free quantification to conduct differential proteomic analysis of plasma exosomes between patients with early stage NSCLC and healthy control subjects. NSCLC samples were divided into lung squamous carcinoma (LUSC) group and lung adenocarcinoma (LUAD) group. GO and KEGG pathway analysis of differentially expressed proteins (DEPs) were performed for every module by DAVID. Furthermore, the protein with the most significant difference was validated using Enzyme-linked immunosorbent assay (ELISA) at levels of plasma exosomes and plasma respectively. Finally, the receiver operating characteristic (ROC) analysis was used to evaluate the efficiency of plasma exosomal FGL1 for early diagnosis of NSCLC. Results Compared with Control group, 65 and 53 DEPs were identified in LUSC group and LUAD group respectively. Bioinformatics analysis indicated that the DEPs were mainly involved in multiple biological functions and cancer-related pathways. Furthermore, we identified 34 proteins with similar expression trends between the LUSC and LUAD groups. Among these proteins, Fibrinogen like protein 1 (FGL1) was selected as a candidate plasma exosomal biomarker for subsequent validation since it was upregulated by more than 5-fold in NSCLC group. ELISA results showed that the plasma exosomal FGL1 concentration were significantly higher in NSCLC patients than in Control samples, which were consistent with the trend of proteomics results. Moreover, receiver operating characteristic (ROC) analysis of plasma exosomal FGL1 demonstrated that the diagnostic AUC, sensitivity, and specificity were 0.866, 82.50%, and 76.25% respectively. However, ROC analysis of plasma FGL1 revealed that the diagnostic AUC, sensitivity, and specificity were 0.757, 56.88%, and 83.75% individually. The diagnostic efficiency of plasma exosomal FGL1 was higher than plasma FGL1 in diagnosing early stage NSCLC patients. Conclusion This study provided a reference proteome map of plasma exosomes in LUSC and LUAD patients. Plasma exosomal FGL1 has the potential to become a promising biomarker for early diagnosis of NSCLC. Non-small cell lung cancer Diagnosis Biomarker Plasma Exosomes FGL1 Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Lung cancer was the most commonly diagnosed cancer, responsible for almost 2.5 million new cases and was the leading cause of cancer-related death worldwide ( 1 , 2 ). Lung cancer is broadly classified into small cell lung carcinoma (SCLC) and non-small cell lung carcinoma (NSCLC), with NSCLC accounting for approximately 85% of the cases ( 3 ). According to the histopathologic subtyping, NSCLC can be further divided into lung adenocarcinoma (LUAD), lung squamous carcinoma(LUSC), and large-cell lung cancer ( 4 ). Substantial improvements in general understanding of disease biology, application of predictive biomarkers, and refinements in treatment have led to remarkable progress and transformed outcomes for many NSCLC patients ( 5 ). However, 5 year survival for patients with stage I NSCLC is roughly 80%, and patients with stage II to stage III disease have a 5 year survival of 13–60% ( 6 ). Implementing screening programmes to diagnose patients at an earlier stage is one of the major steps needed to decrease lung cancer-related deaths and improve survivals ( 7 ). Therefore, there exists a pressing need to explore reliable and effective methods for the early diagnosis of NSCLC. Tissue biopsy has been considered as the standard method for cancer diagnosis. However, this invasive method may cause potential risks such as cancer metastasis and infection ( 8 ). Liquid biopsy refers to a technique which diagnoses diseases by detecting circulating tumor cells (CTCs), circulating tumor DNA (ctDNA) and exosomes from cerebrospinal fluid, saliva, pleural fluid, blood, ascites, urine and other body fluids ( 9 ). Compared with tissue biopsy, liquid biopsy mainly has the advantages of minimal invasion, early diagnosis and dynamic monitoring. At present, the detection of CTCs and ctDNA is of potential significance in the dynamic monitoring of tumor progression, metastasis and drug resistance ( 10 , 11 ). However, their clinical application still faces many challenges. Numerous researches have shown that exosome-mediated cellular communication is critical for tumorigenesis and cancer progression ( 12 ). In recent years, increasing studies suggest that exosomes have a great promise to serve as novel biomarkers in liquid biopsy ( 13 ). Exosomes are extracellular vesicles that possess biomolecules including proteins, lipids, DNA fragments and various RNA species reflecting a speculum of their parent cells ( 14 ). Exosomal cargo can mirror cellular alterations at early stages of the disease, even before they become detectable in systemic circulation or serum ( 15 ). The involvement of exosomes in bidirectional communication and their biological constituents substantiate its role in regulating both physiology and pathology, including multiple cancers ( 16 ). In addition, exosomal proteins are protected from proteinase dependent degradation and thus can be stably detected in the circulating plasma and serum, making them ideal biomarkers for a number of clinical applications ( 17 ). Previous studies have identified plasma exosomes as potential diagnostic markers for metastatic NSCLC ( 18 – 20 ). However, there are few studies on the plasma exosomal biomarkers for the early diagnosis of NSCLC. The use of quantitative proteomic techniques using mass spectrometry for the identification of potential candidate biomarkers is a fast-gaining field ( 21 ). Among all the proteomic methods, label-free quantification (LFQ) has emerged as an exceptional technique in proteomics owing to its broad proteome coverage, great dynamic ranges and enhanced analytical reproducibility ( 22 ). Additionally, LFQ technology has the advantages of high sensitivity and no need to label experimental samples. In the present study, we utilized LFQ technology to conduct differential proteomic analysis of plasma exosomes between patients with early stage NSCLC and healthy control subjects. Some differentially expressed proteins (DEPs) with significant changes were selected for validating. Finally, the diagnostic efficacy of candidate tumor biomarkers were evaluated with the ROC curve at the large-scale of plasma exosomes and plasma samples. Materials and methods Patients and clinical samples A total of 194 patients with early stage of NSCLC (103 males and 91 females; mean age, 59.2 years; range from 36 to 84) were enrolled in the First Affiliated Hospital of Zhengzhou University. According to pathological classification, 98 of LUSC patients and 96 of LUAD patients were included. None of the patients had received any treatment. Controls were recruited from 186 outpatients who had undergone a general medical examination. 2 mL of whole blood sample was collected in an EDTA anticoagulant tube and centrifuged at 1,500×g for 20 min to remove cells and debris. The supernatant was collected and centrifuged at 3000×g for 15 min to collect the plasma. The plasma was stored at -80°C. Isolation of plasma exosomes 400 µL of the above plasma sample was transferred into a 2 mL Eppendorf tube, diluted with 1.6 mL PBS, and centrifuged at 10,000×g for 30 min. The resultant supernatant was collected and ultracentrifuged (Beckman Coulter Class H, R, and S preparative ultracentrifuges, Type 50.4 Ti Rotor; Beckman Coulter, USA) at 150,000×g for 2 h, and the supernatant was then gently aspirated. The pellet was resuspended in PBS, which was filtered through a 0.22 µm pore filter, followed by a second step of ultracentrifugation at 150,000×g for 2h. The supernatant was gently aspirated, and the pellet was resuspended in 100 µL PBS to obtain exosomes. The isolated exosomes were stored at -80°C for later applications. All centrifugations were performed at 4°C. Identification of plasma exosomes Plasma exosomes were selected respectively from each group and characterized with nanoparticle tracking analysis (NTA) to assess the quality and efficacy of exosomes isolation. Size distribution and concentration of the isolated exosomes were determined on a NanoSight LM 10 instrument equipped with an LM14 laser module, syringe pump system and a CCD camera (Malvern Instruments, Malvern, UK). 2 µL of plasma exosomes was diluted in 1 mL of PBS (1:500, v/v). The following settings were used: camera level 11, detection threshold 2 and acquisition time 30 s. Data analysis was performed with NTA v3.1 software. Samples were analyzed in triplicate, and the final size distribution and particle concentration was the average of the three measures. Plasma exosomes and plasma to be analyzed for Western blot were lysed with 100 µL of RIPA lysis buffer (150 mM NaCl, 1% (v/v) Triton X-100, 0.5% (w/v) sodium deoxycholate, 0.1% (w/v) SDS, 50 mM Tris-HCl), containing protease inhibitors (Complete Mini, EDTA-free, Roche, Basel, Switzerland). The procedure consisted of two incubations, at 95ºC for 10 min and 5 min on ice, followed by sonication (4×10 s/cycle), and incubation on ice for 5 min. Subsequently, a centrifugation step at 12,000×g and 4°C for 10 min was performed, and the supernatant was saved. The exosomal proteins and plasma proteins were quantified with a Pierce BCA protein quantification kit (Thermo Fisher Scientific, Inc). The exosomal proteins and plasma proteins were separated using 10% SDS-PAGE and transferred to a PVDF membrane (Millipore). The PVDF membrane was blocked with 5% skim milk and incubated overnight with anti-CD9 and anti-CD63 antibody (Abcam, UK) at 4°C. The membrane was then incubated with a secondary antibody (1:5000) for 1 hr. The protein band was visualized using a fluorescent kit (P0018S, Beyotime) and a chemiluminescence imaging system (T-4600,Tanon). Protein bands were quantified with ImageJ. Lysis of exosomes and protein quantification Label free quantitative proteomics analysis was carried out following a bottom-up strategy with 4 biological replicates in each group. Plasma exosomes to be analyzed for proteomics experiment were lysed with 100 µL lysis buffer (8 mol/L urea, 1% protease inhibitor,3 µmol/L Trichostatin A (TSA), 50 mmol/L nicotinamide (NAM), and 2 mmol/L EDTA) for proteomic analysis. Protein concentration in the supernatant was determined using a 2D Quant kit (GE Healthcare, Little Chalfont, UK) according to the manufacturer’s instructions. In-solution digestion 150 µg of exosomal protein sample were received 3 µL of 1 µg/µL of trypsin and 500 µL of 100 mM triethylammonium bicarbonate (TEAB), followed by overnight digestion at 37°C. The digested sample was combined with an equal volume of 1% formic acid and centrifuged at 12,000×g for 5 min at room temperature. The supernatant was gradually loaded onto a C18 desalting column, washed three times with 1 mL of washing solution (0.1% formic acid and 4% acetonitrile), and eluted twice with 0.4 mL of elution buffer (0.1% formic acid and 75% acetonitrile). The eluents were combined and lyophilized. Identification of proteins by label-free nLC-MS/MS technique The lyophilized protein powder was dissolved in 10 µL of 2% acetonitrile and 0.1% formic acid (solvent A). The peptides were then analyzed using an Easy-nLC 1200 UHPLC system. Peptides were separated in a C18 reverse phase chromatographic column (75 µm×25 cm) with a mobile phrase of 0.1% formic in 80% acetonitrile (solvent B). The sample was eluted at 300 nL/min flow rate, with the concentration of solvent B increasing from 5% to 100% in 150 min. The peptides were analyzed using a Q Exactive HF-X mass spectrometer (Thermo Fisher Scientific, USA) equipped with a Nanospray Flex (ESI) ion source and a spray voltage of 2.3 kV. The MS was run under data dependent acquisition mode, and automatically switched between MS and MS/MS mode. The MS data from the Q Exactive were processed in MaxQuant (version 1.5.5.1 with built-in Andromeda search engine), Label-free quantifcation (LFQ) and statistical analysis were performed in MaxQuant (version 1.5.5.1) and Perseus (version 1.5.8.5) respectively. Bioinformatics analysis The volcano plot is drawn by using GraphPad Prism. The gene ontologies (GO) and KEGG pathway analysis were annotated using the DAVID bioinformatics resource tool ( https://david.ncifcrf.gov/ ). Enzyme-linked immunosorbent assay Plasma exosomal proteins and plasma proteins of NSCLC, and healthy Controls were analyzed for expression of FGL1 using a Human FGL1 ELISA kit (ab284622) (Abcam, UK). For ELISA testing, all the samples were diluted with reagent diluent (1:1000) before ELISA analysis. The samples were analyzed as recommended by the manufacturer. Statistical analysis The data between two groups were compared using the Mann-Whitney U -test and Student's t -test (independent, 2-tailed, unequal variance) with SPSS 22.0 software. The ROC curve were plotted using GraphPad Prism. p < 0.05 were considered to be statistically significant. Results Isolation and identification of plasma exosomes The NTA results showed that the average diameter of purified plasma exosomes was 107 nm (mode, 80 nm) (Fig. 1 A). Furthermore, Western blot analysis revealed that the exosomal protein markers, CD9 and CD63, were enriched in purified plasma exosomes compared with plasma (Fig. 1 B). These results were consistent with the definition of exosomes by the International Extracellular Vesicle Society. Differentially expressed proteins of plasma exosomes in NSCLC In the present study, the screening criterion to analyze the differentially expressed proteins was as follows: |log2(FC)|≥1.0; p ≤ 0.05; fold change (FC) denoted the ratio of expression between two groups. Compared with Control group, LUSC induced 65 differentially expressed proteins: 36 up-regulated proteins and 29 down-regulated proteins (Fig. 2 A). LUAD induced 53 differentially expressed proteins: 26 up-regulated proteins and 27 down-regulated proteins (Fig. 2 B). Bioinformatics analysis of DEPs GO analysis was performed to reveal the general biological functions. Figure 3 A sequentially displays the detailed items in biological process (BP), cellular component (CC), and molecular function (MF). These differentially expressed proteins in BP were mainly related to cellular process, biological regulation, and response to stimulus. NSCLC related plasma exosomal proteins in MFs were mainly associated with binding, catalytic activity, and molecular function regulator. The differentially expressed proteins in CC were mainly enriched in cellular anatomical entity and protein-containing complex. The KEGG analysis revealed that these proteins play a role in immune system, signal transduction, infectious disease, cancer, transport and catabolism, and amino acid metabolism (Fig. 3 B). Screening of the changed plasma exosomal proteins with common expression trends between LUSC and LUAD groups In order to screen for plasma exosome biomarkers for early diagnosis of NSCLC, we identified 34 proteins with similar expression trends between the LUSC and LUAD groups through comparison (Table 1 ). In addition to the inflammation related biomarkers, such as C-reactive protein (CRP) and serum amyloid A-1 protein (SAA1), among these proteins, fibrinogen like protein 1 (FGL1) was upregulated by more than 5-fold in the plasma exosomes of the NSCLC group compared to the control group. Therefore, FGL1 was selected as a candidate plasma exosomal biomarker for subsequent validation. Table 1 DEPs with similar expression trends between LUSC and LUAD No. Accession No. Gene name LUSC/Control LUAD/Control 1 P0DJI8 SAA1 18.16 44.92 2 P02741 CRP 5.42 23.74 3 Q08830 FGL1 5.27 7.56 4 P55058 PLTP 5.14 5.52 5 A0A5H1ZRS9 IGKV2D-29 5.02 6.89 6 A0A5H1ZRQ7 IGLC7 4.74 6.81 7 A6NFK2 GRXCR2 4.17 3.64 8 Q92547 TOPBP1 3.64 2.08 9 A0A0C4DGZ8 GP1BA 3.71 3.39 10 B7WNR7 TMEM196 3.29 3.38 11 P01764 IGHV3-23 2.92 3.50 12 A6NIW5 PRDX2 2.84 3.59 13 Q07954 LRP1 2.81 2.58 14 P04430 IGKV1-16 2.72 2.48 15 Q6ZRK6 CCDC73 2.49 3.04 16 P01877 IGHA2 2.32 2.82 17 A0A0C4DH32 IGHV3-20 2.20 2.15 18 P01880 IGHD -30.77 -7.32 19 P04264 KRT1 -2.94 -3.21 20 P13645 KRT10 -2.38 -2.23 21 A0A075B6H9 IGLV4-69 -8.35 -5.87 22 P35908 KRT2 -3.45 -3.46 23 A0A075B6K5 IGLV3-9 -2.95 -2.49 24 A0A0B4J2D9 IGKV1D-13 -5.71 -13.91 25 Q9UGM5 FETUB -2.27 -2.60 26 P80748 IGLV3-21 -6.43 -3.65 27 Q8NGK2 OR52B4 -3.25 -4.31 28 P01763 IGHV3-48 -4.11 -2.73 29 Q9NQ79 CRTAC1 -4.77 -2.21 30 A0A075B6K6 IGLV4-3 -3.41 -4.87 31 I3L145 SHBG -2.02 -2.03 32 P62937 PPIA -6.46 -7.56 33 B1B0D4 ADAMTSL2 -2.13 -2.16 34 P05121 SERPINE1 -2.07 -2.43 Validation the expression levels of FGL1 in plasma exosomes and plasma To determine whether plasma exosomal FGL1 could be used as a cancer biomarker for NSCLC, the ELISA results showed that the concentration of plasma exosomal FGL1 in the Control group (n = 40) was 0.84 ± 0.36 ng/ml, 1.71 ± 0.47 ng/ml in the LUSC group (n = 40), and 2.32 ± 0.48 ng/ml in the LUAD group (n = 40) (Fig. 4 A). The ELISA results were consistent with the trend of proteomics data. Plasma exosomal FGL1 were all significantly different between each other ( p < 0.0001). Meanwhile, plasma FGL1 in the Control group (n = 40) was 10.76 ± 4.14 ng/ml, 17.14 ± 3.88 ng/ml in the LUSC group (n = 40), and 18.81 ± 4.17 ng/ml in the LUAD group (n = 40) (Fig. 4 B). Plasma FGL1 levels were significantly higher in NSCLC patients than in Control samples ( p < 0.0001), but with no significant change between LUSC and LUAD patients ( p > 0.05). The efficiency evaluation of plasma exosomal FGL1 for diagnosing early stage NSCLC patients In order to test the efficiency of early diagnosis of NSCLC using plasma exosomal FGL1, in this study, we conducted ELISA detection at a larger sample level and analyzed using the ROC curve. 160 patients with early stage NSCLC (80 LUSC patients and 80 LUAD patients) and 160 healthy controls were included. ROC curve analysis of plasma exosomal FGL1 revealed that the diagnostic AUC, 95% CI, sensitivity, and specificity were 0.866, 0.827–0.904, 82.50%, and 76.25% respectively (Fig. 5 A). In addition, ROC curve analysis of plasma FGL1 revealed that the diagnostic AUC, 95% CI, sensitivity, and specificity were 0.757, 0.705–0.810, 56.88%, and 83.75% individually (Fig. 5 B). Therefore, the diagnostic efficiency of plasma exosomal FGL1 was higher than plasma FGL1 in diagnosing early stage NSCLC patients. Discussion NSCLC is one of the most deadly tumors characterized by poor survival rates worldwide ( 23 ). This is mostly due to the delayed onset of clinical symptoms and the absence of early biomarkers for NSCLC ( 24 ). Therefore, it is imperative to screen biomarkers that facilitate early detection and diagnosis to mitigate the death rate among NSCLC patients. In recent years, blood-based liquid biopsy biomarkers, including circulating tumor cells (CTCs), cell-free DNA (cfDNA), circulating tumor DNA (ctDNA), circulating-free RNAs (cfRNAs), and extracellular vesicles (EVs)/exosomes, have offered promises for non-invasive monitoring ( 25 ). As LFQ has become a widely used high-throughput detection method in the field of tumor biomarker research ( 26 ). Thus, this study aimed to explore plasma exosomal biomarkes between early stage NSCLC patients and healthy control subjects. In the current study, the isolated plasma exosomes were identified by NTA and Western blot. These results are consistent with the definition of exosomes. In order to avoid heterogeneity between the two different pathological types of NSCLC, the samples for quantitative proteomics experiments were divided into LUSC group and LUAD group. After LFQ analysis, we discovered 65 and 53 DEPs related to LUSC and LUAD respectively. GO analysis indicated that the DEPs were mainly located in cellular anatomical entity and protein-containing complex, and involved in cellular process, biological regulation, and response to stimulus. Additionally, they had the molecular functions, such as binding, catalytic activity, and molecular function regulator. KEGG analysis revealed that these DEPs played important roles in immune system, signal transduction, infectious disease, cancer, transport and catabolism, and amino acid metabolism. The bioinformatics analysis results indicated that these DEPs might be very important to the regulation of the tumor microenvironment (TME), which comprises diverse immune, stromal and endothelial cells whose spatial and functional interactions drive tumor progression, metastatic dissemination and response to treatment ( 27 ). Given that the differential protein profiles between LUSC group and LUAD group were not the same, we identified 34 proteins with similar expression trends between both groups. Among these proteins, FGL1 was selected as a candidate plasma exosomal biomarker for subsequent validation, since it was upregulated by more than 5-fold in the plasma exosomes of NSCLC patients. FGL1 is a newly emerging immune checkpoint for diagnosing and treating malignant tumors ( 28 ). FGL1 is significantly overexpressed in solid tumors, including lung, prostate, melanoma, and colorectal cancer, than those of normal tissues ( 29 ). The interaction between FGL1 in tumors and lymphocyte-activation gene 3 (LAG-3) located at the surface of T cells contributes to immune escape and promotes the growth of the tumors ( 30 ). Patients with high FGL1 expression in tumors exhibit poor overall survival and progression-free survival ( 31 ). FGL1 is a novel biomarker and target for NSCLC, and promotes tumor progression and metastasis through KDM4A/STAT3 transcription mechanism ( 32 ). FGL1 may be as a diagnostic biomarker of KRAS-mutated lung cancer, and targeting the Yap-FGL1 axis could increase the efficacy of anti-PD-1 immunotherapy ( 33 ). Thus, the accurate monitoring of FGL1 levels in tumors may be valuable for cancer theranostics. ELISA results showed that the concentration of plasma exosomal FGL1 in NSCLC was significantly upregulated compared with the Control group. They were consistent with the trend of proteomics data. In addition, Plasma FGL1 was tested by ELISA as well, and the similar results were found. In order to validate the efficiency of early diagnosis of NSCLC using plasma exosomal FGL1, we conducted ELISA detection at a larger sample level and analyzed using the ROC curve. The AUC value of plasma exosomal FGL1 was higher than plasma FGL1. Therefore, the diagnostic efficiency of plasma exosomal FGL1 was superior to plasma FGL1 in diagnosing early stage NSCLC patients. Altogether, the present study provided a differential proteome map of plasma exosomes between NSCLC and healthy controls; plasma exosomal FGL1 has the potential to become a promising biomarker for early diagnosis of NSCLC. However, the sample size in this study was limited, and further investigations with a larger number of samples are still needed. Declarations Ethics approval This study was reviewed and approved by the Ethics Committee of the First Affiliated Hospital of Zhengzhou University (No.2022-KY-0640-002) and conducted in accordance with the Declaration of Helsinki (as revised in 2013). Informed consent was obtained from all participants prior to sample collection. Consent to participate Informed consent was obtained from all individual participants included in the study. Competing interests The authors declare no competing interests. Funding This work was supported by the National Natural Science Foundation of China (Grant No. 81972181). Author Contribution WW and CG designed the study. WW and XL collected the samples and conducted the experiments. WW and JL performed the statistical analysis. WW and CG drafted the manuscript. SZ revised the manuscript. All authors read and approved the submitted version. Data Availability The data that support the findings of this study are available from the corresponding author upon reasonable request. References Hendriks LEL, Remon J, Faivre-Finn C, Garassino MC, Heymach JV, Kerr KM, et al. Non-small-cell lung cancer. Nat reviews Disease primers. 2024;10(1):71. 10.1038/s41572-024-00551-9 . PubMed PMID: 39327441. Meyer ML, Fitzgerald BG, Paz-Ares L, Cappuzzo F, Janne PA, Peters S, et al. New promises and challenges in the treatment of advanced non-small-cell lung cancer. Lancet. 2024;404(10454):803–22. 10.1016/S0140-6736 . (24)01029-8. PubMed PMID: 39121882. Xu J, Tian L, Qi W, Lv Q, Wang T. Advancements in NSCLC: From Pathophysiological Insights to Targeted Treatments. Am J Clin Oncol. 2024;47(6):291–303. .0000000000001088. PubMed PMID: 38375734; PubMed Central PMCID: PMC11107893. Li Y, Yan B, He S. Advances and challenges in the treatment of lung cancer. Biomed pharmacotherapy = Biomedecine pharmacotherapie. 2023;169:115891. 10.1016/j.biopha.2023.115891 . PubMed PMID: 37979378. Howlader N, Forjaz G, Mooradian MJ, Meza R, Kong CY, Cronin KA, et al. The Effect of Advances in Lung-Cancer Treatment on Population Mortality. New Engl J Med. 2020;383(7):640–9. 10.1056/NEJMoa1916623 . PubMed PMID: WOS:000562771200011. Goldstraw P, Chansky K, Crowley J, Rami-Porta R, Asamura H, Eberhardt WE, et al. The IASLC Lung Cancer Staging Project: Proposals for Revision of the TNM Stage Groupings in the Forthcoming (Eighth) Edition of the TNM Classification for Lung Cancer. J Thorac oncology: official publication Int Association Study Lung Cancer. 2016;11(1):39–51. 10.1016/j.jtho.2015.09.009 . PubMed PMID: 26762738. Thai AA, Solomon BJ, Sequist LV, Gainor JF, Heist RS. Lung cancer. Lancet. 2021;398(10299):535–54. 10.1016/S0140-6736(21)00312-3 . PubMed PMID: 34273294. Breadner D, Hwang DM, Husereau D, Cheema P, Doucette S, Ellis PM, et al. Implementation of Liquid Biopsy in Non-Small-Cell Lung Cancer: An Ontario Perspective. Curr Oncol. 2024;31(10):6017–31. 10.3390/curroncol31100449 . PubMed PMID: 39451753; PubMed Central PMCID: PMC11505603. Tang H, Yu D, Zhang J, Wang M, Fu M, Qian Y, et al. The new advance of exosome-based liquid biopsy for cancer diagnosis. J Nanobiotechnol. 2024;22(1):610. 10.1186/s12951-024-02863-0 . PubMed PMID: 39380060; PubMed Central PMCID: PMC11463159. Liu C, Cai Y, Mou S, Biomedicine. pharmacotherapy = Biomedecine pharmacotherapie. 2024;181:117726. 10.1016/j.biopha.2024.117726 . PubMed PMID: 39612860. Abbosh C, Hodgson D, Doherty GJ, Gale D, Black JRM, Horn L, et al. Implementing circulating tumor DNA as a prognostic biomarker in resectable non-small cell lung cancer. Trends cancer. 2024;10(7):643–54. PubMed PMID: 38839544. Orooji N, Fadaee M, Kazemi T, Yousefi B. Exosome therapeutics for non-small cell lung cancer tumorigenesis. Cancer Cell Int. 2024;24(1):360. 10.1186/s12935-024-03544-6 . PubMed PMID: 39478574; PubMed Central PMCID: PMC11523890. Yu D, Li Y, Wang M, Gu J, Xu W, Cai H, et al. Exosomes as a new frontier of cancer liquid biopsy. Mol Cancer. 2022;21(1):56. 10.1186/s12943-022-01509-9 . PubMed PMID: 35180868; PubMed Central PMCID: PMC8855550. Al-Madhagi H. The Landscape of Exosomes Biogenesis to Clinical Applications. Int J Nanomed. 2024;19:3657–75. 10.2147. /IJN.S463296. PubMed PMID: 38681093; PubMed Central PMCID: PMC11048319. Luo B, Que Z, Lu X, Qi D, Qiao Z, Yang Y, et al. Identification of exosome protein panels as predictive biomarkers for non-small cell lung cancer. Biol procedures online. 2023;25(1):29. 10.1186/s12575-023-00223-0 . PubMed PMID: 37953280; PubMed Central PMCID: PMC10641949. Padinharayil H, George A. Small extracellular vesicles: Multi-functional aspects in non-small cell lung carcinoma. Crit Rev Oncol/Hematol. 2024;198:104341. 10.1016/j.critrevonc.2024.104341 . PubMed PMID: 38575042. Wang N, Song X, Liu L, Niu L, Wang X, Song X, et al. Circulating exosomes contain protein biomarkers of metastatic non-small-cell lung cancer. Cancer Sci. 2018;109(5):1701–9. 10.1111/cas.13581 . PubMed PMID: 29573061; PubMed Central PMCID: PMC5980308. Thuya WL, Kong LR, Syn NL, Ding LW, Cheow ESH, Wong RTX, et al. FAM3C in circulating tumor-derived extracellular vesicles promotes non-small cell lung cancer growth in secondary sites. Theranostics. 2023;13(2):621–38. PubMed PMID: 36632230; PubMed Central PMCID: PMC9830426. Chang W, Zhu J, Yang D, Shang A, Sun Z, Quan W, et al. Plasma versican and plasma exosomal versican as potential diagnostic markers for non-small cell lung cancer. Respir Res. 2023;24(1):140. 10.1186/s12931-023-02423-4 . PubMed PMID: 37259101; PubMed Central PMCID: PMC10230736. Gao Y, Xie J, Yang Z, Li M, Yuan H, Li R. Functional tumor-derived exosomes in NSCLC progression and clinical implications. Front Pharmacol. 2025;16:1485661. 10.3389/fphar.2025.1485661 . PubMed PMID: 40176898; PubMed Central PMCID: PMC11962733. Rao R, Gulfishan M, Kim MS, Kashyap MK. Deciphering Cancer Complexity: Integrative Proteogenomics and Proteomics Approaches for Biomarker Discovery. Methods in molecular biology. 2025;2859:211 – 37. 10.1007/978-1-0716-4152-1_12 . PubMed PMID: 39436604. Fu J, Yang Q, Luo Y, Zhang S, Tang J, Zhang Y, et al. Label-free proteome quantification and evaluation. Brief Bioinform. 2023;24(1). 10.1093/bib/bbac477 . PubMed PMID: 36403090. Jeon H, Wang S, Song J, Gill H, Cheng H, Update. 2025: Management of Non–Small-Cell Lung Cancer. Lung. 2025;203(1):53. 10.1007/s00408-025-00801-x . PubMed PMID: 40133478; PubMed Central PMCID: PMC11937135. Sultana A, Alam MS, Khanam A, Lin Y, Ren S, Singla RK et al. An integrated bioinformatics approach to early diagnosis, prognosis and therapeutics of non-small-cell lung cancer. J Biomol Struct Dyn. 2024:1–15. doi: 10.1080/07391102.2024.2425840. PubMed PMID: 39535278. Bafiti V, Thanou E, Ouzounis S, Kotsakis A, Georgoulias V, Lianidou E, et al. Profiling Plasma Extracellular Vesicle Metabotypes and miRNAs: An Unobserved Clue for Predicting Relapse in Patients with Early-Stage NSCLC. Cancers. 2024;16(22). 10.3390/cancers16223729 . PubMed PMID: 39594687; PubMed Central PMCID: PMC11592109. Zhang H, Wu J, Gan J, Wang W, Liu Y, Song T, et al. Proteomic Analysis of Plasma Exosomes Enables the Identification of Lung Cancer in Patients With Chronic Obstructive Pulmonary Disease. Thorac cancer. 2025;16(1):e15517. PubMed PMID: 39778061; PubMed Central PMCID: PMC11717053. Rahal Z, El Darzi R, Moghaddam SJ, Cascone T, Kadara H. Tumour and microenvironment crosstalk in NSCLC progression and response to therapy. Nat reviews Clin Oncol. 2025. 10.1038/s41571-025-01021-1 . PubMed PMID: 40379986. Qian W, Zhao M, Wang R, Li H. Fibrinogen-like protein 1 (FGL1): the next immune checkpoint target. J Hematol Oncol. 2021;14(1):147. 10.1186/s13045-021-01161-8 . PubMed PMID: 34526102; PubMed Central PMCID: PMC8444356. Xu Y, Zhang J, Pan D, Yan J, Chen C, Wang L, et al. Development of Novel Peptide-Based Radiotracers for Detecting FGL1 Expression in Tumors. Mol Pharm. 2025;22(3):1605–14. 10.1021/acs.molpharmaceut.4c01293 . PubMed PMID: 39893698. Zhu S, Kou Z, Xiao C, Wang L, Zhu J, Zheng Y, et al. Silencing FGL1 promotes prostate cancer cell apoptosis and inhibits EMT progression. Sci Rep. 2025;15(1):19886. 10.1038/s41598-025-04717-7 . PubMed PMID: 40481127; PubMed Central PMCID: PMC12144232. Lv Z, Cui B, Huang X, Feng HY, Wang T, Wang HF, et al. FGL1 as a Novel Mediator and Biomarker of Malignant Progression in Clear Cell Renal Cell Carcinoma. Front Oncol. 2021;11:756843. PubMed PMID: 34956878; PubMed Central PMCID: PMC8695555. Liu TY, Yan JS, Li X, Xu L, Hao JL, Zhao SY, et al. FGL1: a novel biomarker and target for non-small cell lung cancer, promoting tumor progression and metastasis through KDM4A/STAT3 transcription mechanism. J experimental Clin cancer research: CR. 2024;43(1):213. 10.1186/s13046-024-03140-6 . PubMed PMID: 39085849; PubMed Central PMCID: PMC11293164. Jiang J, Ye P, Sun N, Zhu W, Yang M, Yu M, et al. Yap methylation-induced FGL1 expression suppresses anti-tumor immunity and promotes tumor progression in KRAS-driven lung adenocarcinoma. Cancer Commun. 2024;44(11):1350–73. PubMed PMID: 39340215; PubMed Central PMCID: PMC12015977. Additional Declarations No competing interests reported. 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14:16:58\",\"extension\":\"html\",\"order_by\":24,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":115502,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"earlyproof.html\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7447669/v1/bce1e5e6e53f25bbb896f9f3.html\"},{\"id\":93339222,\"identity\":\"a9cf5a48-5d7f-4e7a-b125-0b7828071656\",\"added_by\":\"auto\",\"created_at\":\"2025-10-12 14:24:58\",\"extension\":\"jpeg\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":17093,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eExosomes identification (\\u003cstrong\\u003eA\\u003c/strong\\u003e) NTA results showed that the isolated plasma exosomal diameters ranging from 53 to 160 nm. (\\u003cstrong\\u003eB\\u003c/strong\\u003e) Western blot analysis indicated that the exosomal biomarkers, CD9 and CD63, were enriched in purified plasma exosomes compared with plasma\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"groupimage1.jpeg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7447669/v1/f6d0d3fae79f6cd0fb5e2339.jpeg\"},{\"id\":93337414,\"identity\":\"b8d9d05d-0ffb-4bf6-b8cb-464509cc11a4\",\"added_by\":\"auto\",\"created_at\":\"2025-10-12 14:08:58\",\"extension\":\"jpeg\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":30607,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eLabel-free quantitative proteomics profiling of discovery of differentially expressed plasma exosomal proteins (\\u003cstrong\\u003eA\\u003c/strong\\u003e) The volcano plot was drawn using two factors, the fold change (log2) between LUSC group and Control group, and the \\u003cem\\u003ep\\u003c/em\\u003e-value (-log10) obtained from the \\u003cem\\u003et\\u003c/em\\u003e-test, to show the significance of differences in the data between LUSC group and Control group. The red and blue dots in the figure denote significantly up-regulated and down-regulated proteins, respectively; the black dots denote proteins with insignificant differences. (\\u003cstrong\\u003eB\\u003c/strong\\u003e) The volcano plot was drawn using two factors between LUAD group and Control group\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"groupimage2.jpeg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7447669/v1/2e6e0b49f4286ab8d26ab648.jpeg\"},{\"id\":93338096,\"identity\":\"ff522f44-2383-43f3-bf29-18b0a55e9e53\",\"added_by\":\"auto\",\"created_at\":\"2025-10-12 14:16:58\",\"extension\":\"jpeg\",\"order_by\":3,\"title\":\"Figure 3\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":61250,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eGO and KEGG pathway analyses of plasma exosomal proteins related to NSCLC (\\u003cstrong\\u003eA\\u003c/strong\\u003e) GO classification of the DEPs based on biological processes, cellular components, and molecular functions. The horizontal axis is the number of proteins, the vertical axis is the GO classification. (\\u003cstrong\\u003eB\\u003c/strong\\u003e) KEGG pathway analysis of the DEPs based on cellular processes, human diseases, environmental information processing, organismal systems and metabolism\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"groupimage3.jpeg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7447669/v1/7bbd1b1064ebe24d6b19538c.jpeg\"},{\"id\":93337426,\"identity\":\"31b3e93a-214c-4ff5-879e-e46d390a92c6\",\"added_by\":\"auto\",\"created_at\":\"2025-10-12 14:08:58\",\"extension\":\"jpeg\",\"order_by\":4,\"title\":\"Figure 4\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":28996,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eFGL1 validation by ELISA in plasma-derived exosomes and plasma between NSCLC and Control samples (\\u003cstrong\\u003eA\\u003c/strong\\u003e) Plasma exosomal FGL1 levels were significantly different between each other of LUSC, LUAD and Control group. (\\u003cstrong\\u003eB\\u003c/strong\\u003e) Plasma FGL1 levels were significantly higher in NSCLC patients than in Control samples, but with no significant change between LUSC and LUAD patients. \\u0026nbsp;\\u0026nbsp;****\\u003cem\\u003ep\\u003c/em\\u003e\\u0026lt;0.0001. ns, not significant\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"groupimage4.jpeg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7447669/v1/fb41cdeccd72124a855ac959.jpeg\"},{\"id\":93338101,\"identity\":\"2e080dd9-f8dd-4adf-9abd-2fa67fa0572d\",\"added_by\":\"auto\",\"created_at\":\"2025-10-12 14:16:58\",\"extension\":\"jpeg\",\"order_by\":5,\"title\":\"Figure 5\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":35211,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eThe diagnostic efficiency of plasma exosomal FGL1 was higher than plasma FGL1 in diagnosing early stage NSCLC patients using ROC curve analysis (\\u003cstrong\\u003eA\\u003c/strong\\u003e) The ROC curve analysis of plasma exosomal FGL1 in diagnosing early stage NSCLC patients. (\\u003cstrong\\u003eB\\u003c/strong\\u003e) The ROC curve analysis of plasma FGL1 in diagnosing early stage NSCLC patients\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"groupimage5.jpeg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7447669/v1/874366f55660f61aa3e14954.jpeg\"},{\"id\":107293566,\"identity\":\"691a8579-40d6-41d5-8859-602d2039ea93\",\"added_by\":\"auto\",\"created_at\":\"2026-04-20 06:12:06\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":554533,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7447669/v1/147bb3b2-5f11-43e5-b6bb-d679f2daee47.pdf\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"Identification and validation of plasma exosomal FGL1 as an early diagnostic biomarker for non-small cell lung cancer\",\"fulltext\":[{\"header\":\"Introduction\",\"content\":\"\\u003cp\\u003eLung cancer was the most commonly diagnosed cancer, responsible for almost 2.5\\u0026nbsp;million new cases and was the leading cause of cancer-related death worldwide (\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e). Lung cancer is broadly classified into small cell lung carcinoma (SCLC) and non-small cell lung carcinoma (NSCLC), with NSCLC accounting for approximately 85% of the cases (\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e). According to the histopathologic subtyping, NSCLC can be further divided into lung adenocarcinoma (LUAD), lung squamous carcinoma(LUSC), and large-cell lung cancer (\\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e). Substantial improvements in general understanding of disease biology, application of predictive biomarkers, and refinements in treatment have led to remarkable progress and transformed outcomes for many NSCLC patients (\\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e5\\u003c/span\\u003e). However, 5 year survival for patients with stage I NSCLC is roughly 80%, and patients with stage II to stage III disease have a 5 year survival of 13\\u0026ndash;60% (\\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e). Implementing screening programmes to diagnose patients at an earlier stage is one of the major steps needed to decrease lung cancer-related deaths and improve survivals (\\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e). Therefore, there exists a pressing need to explore reliable and effective methods for the early diagnosis of NSCLC.\\u003c/p\\u003e\\u003cp\\u003eTissue biopsy has been considered as the standard method for cancer diagnosis. However, this invasive method may cause potential risks such as cancer metastasis and infection (\\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e). Liquid biopsy refers to a technique which diagnoses diseases by detecting circulating tumor cells (CTCs), circulating tumor DNA (ctDNA) and exosomes from cerebrospinal fluid, saliva, pleural fluid, blood, ascites, urine and other body fluids (\\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e). Compared with tissue biopsy, liquid biopsy mainly has the advantages of minimal invasion, early diagnosis and dynamic monitoring. At present, the detection of CTCs and ctDNA is of potential significance in the dynamic monitoring of tumor progression, metastasis and drug resistance (\\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e). However, their clinical application still faces many challenges. Numerous researches have shown that exosome-mediated cellular communication is critical for tumorigenesis and cancer progression (\\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e). In recent years, increasing studies suggest that exosomes have a great promise to serve as novel biomarkers in liquid biopsy (\\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e13\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003eExosomes are extracellular vesicles that possess biomolecules including proteins, lipids, DNA fragments and various RNA species reflecting a speculum of their parent cells (\\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e). Exosomal cargo can mirror cellular alterations at early stages of the disease, even before they become detectable in systemic circulation or serum (\\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e). The involvement of exosomes in bidirectional communication and their biological constituents substantiate its role in regulating both physiology and pathology, including multiple cancers (\\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e16\\u003c/span\\u003e). In addition, exosomal proteins are protected from proteinase dependent degradation and thus can be stably detected in the circulating plasma and serum, making them ideal biomarkers for a number of clinical applications (\\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e17\\u003c/span\\u003e). Previous studies have identified plasma exosomes as potential diagnostic markers for metastatic NSCLC (\\u003cspan additionalcitationids=\\\"CR19\\\" citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e20\\u003c/span\\u003e). However, there are few studies on the plasma exosomal biomarkers for the early diagnosis of NSCLC.\\u003c/p\\u003e\\u003cp\\u003eThe use of quantitative proteomic techniques using mass spectrometry for the identification of potential candidate biomarkers is a fast-gaining field (\\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e21\\u003c/span\\u003e). Among all the proteomic methods, label-free quantification (LFQ) has emerged as an exceptional technique in proteomics owing to its broad proteome coverage, great dynamic ranges and enhanced analytical reproducibility (\\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e22\\u003c/span\\u003e). Additionally, LFQ technology has the advantages of high sensitivity and no need to label experimental samples.\\u003c/p\\u003e\\u003cp\\u003eIn the present study, we utilized LFQ technology to conduct differential proteomic analysis of plasma exosomes between patients with early stage NSCLC and healthy control subjects. Some differentially expressed proteins (DEPs) with significant changes were selected for validating. Finally, the diagnostic efficacy of candidate tumor biomarkers were evaluated with the ROC curve at the large-scale of plasma exosomes and plasma samples.\\u003c/p\\u003e\"},{\"header\":\"Materials and methods\",\"content\":\"\\u003cdiv id=\\\"Sec3\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003ePatients and clinical samples\\u003c/h2\\u003e\\u003cp\\u003eA total of 194 patients with early stage of NSCLC (103 males and 91 females; mean age, 59.2 years; range from 36 to 84) were enrolled in the First Affiliated Hospital of Zhengzhou University. According to pathological classification, 98 of LUSC patients and 96 of LUAD patients were included. None of the patients had received any treatment. Controls were recruited from 186 outpatients who had undergone a general medical examination. 2 mL of whole blood sample was collected in an EDTA anticoagulant tube and centrifuged at 1,500\\u0026times;g for 20 min to remove cells and debris. The supernatant was collected and centrifuged at 3000\\u0026times;g for 15 min to collect the plasma. The plasma was stored at -80\\u0026deg;C.\\u003c/p\\u003e\\u003c/div\\u003e\\n\\u003ch3\\u003eIsolation of plasma exosomes\\u003c/h3\\u003e\\n\\u003cp\\u003e400 \\u0026micro;L of the above plasma sample was transferred into a 2 mL Eppendorf tube, diluted with 1.6 mL PBS, and centrifuged at 10,000\\u0026times;g for 30 min. The resultant supernatant was collected and ultracentrifuged (Beckman Coulter Class H, R, and S preparative ultracentrifuges, Type 50.4 Ti Rotor; Beckman Coulter, USA) at 150,000\\u0026times;g for 2 h, and the supernatant was then gently aspirated. The pellet was resuspended in PBS, which was filtered through a 0.22 \\u0026micro;m pore filter, followed by a second step of ultracentrifugation at 150,000\\u0026times;g for 2h. The supernatant was gently aspirated, and the pellet was resuspended in 100 \\u0026micro;L PBS to obtain exosomes. The isolated exosomes were stored at -80\\u0026deg;C for later applications. All centrifugations were performed at 4\\u0026deg;C.\\u003c/p\\u003e\\n\\u003ch3\\u003eIdentification of plasma exosomes\\u003c/h3\\u003e\\n\\u003cp\\u003ePlasma exosomes were selected respectively from each group and characterized with nanoparticle tracking analysis (NTA) to assess the quality and efficacy of exosomes isolation. Size distribution and concentration of the isolated exosomes were determined on a NanoSight LM 10 instrument equipped with an LM14 laser module, syringe pump system and a CCD camera (Malvern Instruments, Malvern, UK). 2 \\u0026micro;L of plasma exosomes was diluted in 1 mL of PBS (1:500, v/v). The following settings were used: camera level 11, detection threshold 2 and acquisition time 30 s. Data analysis was performed with NTA v3.1 software. Samples were analyzed in triplicate, and the final size distribution and particle concentration was the average of the three measures.\\u003c/p\\u003e\\u003cp\\u003ePlasma exosomes and plasma to be analyzed for Western blot were lysed with 100 \\u0026micro;L of RIPA lysis buffer (150 mM NaCl, 1% (v/v) Triton X-100, 0.5% (w/v) sodium deoxycholate, 0.1% (w/v) SDS, 50 mM Tris-HCl), containing protease inhibitors (Complete Mini, EDTA-free, Roche, Basel, Switzerland). The procedure consisted of two incubations, at 95\\u0026ordm;C for 10 min and 5 min on ice, followed by sonication (4\\u0026times;10 s/cycle), and incubation on ice for 5 min. Subsequently, a centrifugation step at 12,000\\u0026times;g and 4\\u0026deg;C for 10 min was performed, and the supernatant was saved. The exosomal proteins and plasma proteins were quantified with a Pierce BCA protein quantification kit (Thermo Fisher Scientific, Inc). The exosomal proteins and plasma proteins were separated using 10% SDS-PAGE and transferred to a PVDF membrane (Millipore). The PVDF membrane was blocked with 5% skim milk and incubated overnight with anti-CD9 and anti-CD63 antibody (Abcam, UK) at 4\\u0026deg;C. The membrane was then incubated with a secondary antibody (1:5000) for 1 hr. The protein band was visualized using a fluorescent kit (P0018S, Beyotime) and a chemiluminescence imaging system (T-4600,Tanon). Protein bands were quantified with ImageJ.\\u003c/p\\u003e\\n\\u003ch3\\u003eLysis of exosomes and protein quantification\\u003c/h3\\u003e\\n\\u003cp\\u003eLabel free quantitative proteomics analysis was carried out following a bottom-up strategy with 4 biological replicates in each group. Plasma exosomes to be analyzed for proteomics experiment were lysed with 100 \\u0026micro;L lysis buffer (8 mol/L urea, 1% protease inhibitor,3 \\u0026micro;mol/L Trichostatin A (TSA), 50 mmol/L nicotinamide (NAM), and 2 mmol/L EDTA) for proteomic analysis. Protein concentration in the supernatant was determined using a 2D Quant kit (GE Healthcare, Little Chalfont, UK) according to the\\u003c/p\\u003e\\u003cp\\u003emanufacturer\\u0026rsquo;s instructions.\\u003c/p\\u003e\\n\\u003ch3\\u003eIn-solution digestion\\u003c/h3\\u003e\\n\\u003cp\\u003e150 \\u0026micro;g of exosomal protein sample were received 3 \\u0026micro;L of 1 \\u0026micro;g/\\u0026micro;L of trypsin and 500 \\u0026micro;L of 100 mM triethylammonium bicarbonate (TEAB), followed by overnight digestion at 37\\u0026deg;C. The digested sample was combined with an equal volume of 1% formic acid and centrifuged at 12,000\\u0026times;g for 5 min at room temperature. The supernatant was gradually loaded onto a C18 desalting column, washed three times with 1 mL of washing solution (0.1% formic acid and 4% acetonitrile), and eluted twice with 0.4 mL of elution buffer (0.1% formic acid and 75% acetonitrile). The eluents were combined and lyophilized.\\u003c/p\\u003e\\u003cdiv id=\\\"Sec8\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003eIdentification of proteins by label-free nLC-MS/MS technique\\u003c/h2\\u003e\\u003cp\\u003eThe lyophilized protein powder was dissolved in 10 \\u0026micro;L of 2% acetonitrile and 0.1% formic acid (solvent A). The peptides were then analyzed using an Easy-nLC 1200 UHPLC system. Peptides were separated in a C18 reverse phase chromatographic column (75 \\u0026micro;m\\u0026times;25 cm) with a mobile phrase of 0.1% formic in 80% acetonitrile (solvent B). The sample was eluted at 300 nL/min flow rate, with the concentration of solvent B increasing from 5% to 100% in 150 min. The peptides were analyzed using a Q Exactive HF-X mass spectrometer (Thermo Fisher Scientific, USA) equipped with a Nanospray Flex (ESI) ion source and a spray voltage of 2.3 kV. The MS was run under data dependent acquisition mode, and automatically switched between MS and MS/MS mode. The MS data from the Q Exactive were processed in MaxQuant (version 1.5.5.1 with built-in Andromeda search engine), Label-free quantifcation (LFQ) and statistical analysis were performed in MaxQuant (version 1.5.5.1) and Perseus (version 1.5.8.5) respectively.\\u003c/p\\u003e\\u003c/div\\u003e\\n\\u003ch3\\u003eBioinformatics analysis\\u003c/h3\\u003e\\n\\u003cp\\u003eThe volcano plot is drawn by using GraphPad Prism. The gene ontologies (GO) and KEGG pathway analysis were annotated using the DAVID bioinformatics resource tool (\\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://david.ncifcrf.gov/\\u003c/span\\u003e\\u003cspan address=\\\"https://david.ncifcrf.gov/\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e).\\u003c/p\\u003e\\n\\u003ch3\\u003eEnzyme-linked immunosorbent assay\\u003c/h3\\u003e\\n\\u003cp\\u003ePlasma exosomal proteins and plasma proteins of NSCLC, and healthy Controls were analyzed for expression of FGL1 using a Human FGL1 ELISA kit (ab284622) (Abcam, UK). For ELISA testing, all the samples were diluted with reagent diluent (1:1000) before ELISA analysis. The samples were analyzed as recommended by the manufacturer.\\u003c/p\\u003e\\u003cdiv id=\\\"Sec11\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003eStatistical analysis\\u003c/h2\\u003e\\u003cp\\u003eThe data between two groups were compared using the Mann-Whitney \\u003cem\\u003eU\\u003c/em\\u003e-test and Student's \\u003cem\\u003et\\u003c/em\\u003e-test (independent, 2-tailed, unequal variance) with SPSS 22.0 software. The ROC curve were plotted using GraphPad Prism. \\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05 were considered to be statistically significant.\\u003c/p\\u003e\\u003c/div\\u003e\"},{\"header\":\"Results\",\"content\":\"\\u003cdiv id=\\\"Sec13\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003eIsolation and identification of plasma exosomes\\u003c/h2\\u003e\\u003cp\\u003eThe NTA results showed that the average diameter of purified plasma exosomes was 107 nm (mode, 80 nm) (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003eA). Furthermore, Western blot analysis revealed that the exosomal protein markers, CD9 and CD63, were enriched in purified plasma exosomes compared with plasma (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003eB). These results were consistent with the definition of exosomes by the International Extracellular Vesicle Society.\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec14\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003eDifferentially expressed proteins of plasma exosomes in NSCLC\\u003c/h2\\u003e\\u003cp\\u003eIn the present study, the screening criterion to analyze the differentially expressed proteins was as follows: |log2(FC)|\\u0026ge;1.0; \\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;\\u0026le;\\u0026thinsp;0.05; fold change (FC) denoted the ratio of expression between two groups. Compared with Control group, LUSC induced 65 differentially expressed proteins: 36 up-regulated proteins and 29 down-regulated proteins (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eA). LUAD induced 53 differentially expressed proteins: 26 up-regulated proteins and 27 down-regulated proteins (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eB).\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec15\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003eBioinformatics analysis of DEPs\\u003c/h2\\u003e\\u003cp\\u003eGO analysis was performed to reveal the general biological functions. Figure\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eA sequentially displays the detailed items in biological process (BP), cellular component (CC), and molecular function (MF). These differentially expressed proteins in BP were mainly related to cellular process, biological regulation, and response to stimulus. NSCLC related plasma exosomal proteins in MFs were mainly associated with binding, catalytic activity, and molecular function regulator. The differentially expressed proteins in CC were mainly enriched in cellular anatomical entity and protein-containing complex. The KEGG analysis revealed that these proteins play a role in immune system, signal transduction, infectious disease, cancer, transport and catabolism, and amino acid metabolism (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eB).\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003cp\\u003e\\u003cb\\u003eScreening of the changed plasma exosomal proteins with common expression trends between LUSC and LUAD groups\\u003c/b\\u003e\\u003c/p\\u003e\\u003cp\\u003eIn order to screen for plasma exosome biomarkers for early diagnosis of NSCLC, we identified 34 proteins with similar expression trends between the LUSC and LUAD groups through comparison (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e). In addition to the inflammation related biomarkers, such as C-reactive protein (CRP) and serum amyloid A-1 protein (SAA1), among these proteins, fibrinogen like protein 1 (FGL1) was upregulated by more than 5-fold in the plasma exosomes of the NSCLC group compared to the control group. Therefore, FGL1 was selected as a candidate plasma exosomal biomarker for subsequent validation.\\u003c/p\\u003e\\u003cp\\u003e\\u003cstrong\\u003eTable 1\\u003c/strong\\u003e\\u0026nbsp; DEPs with similar expression trends between LUSC and LUAD\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cdiv align=\\\"center\\\"\\u003e\\n \\u003ctable border=\\\"0\\\" cellspacing=\\\"0\\\" cellpadding=\\\"0\\\" width=\\\"100%\\\" class=\\\"fr-table-selection-hover\\\"\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 7.21649%;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eNo.\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 23.7113%;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eAccession No.\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 20.6186%;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eGene name\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 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\\u003ctd style=\\\"width: 24.7423%;\\\"\\u003e\\n \\u003cp\\u003e-3.65\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 7.21649%;\\\"\\u003e\\n \\u003cp\\u003e27\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 23.7113%;\\\"\\u003e\\n \\u003cp\\u003eQ8NGK2\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 20.6186%;\\\"\\u003e\\n \\u003cp\\u003eOR52B4\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 23.7113%;\\\"\\u003e\\n \\u003cp\\u003e-3.25\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 24.7423%;\\\"\\u003e\\n \\u003cp\\u003e-4.31\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 7.21649%;\\\"\\u003e\\n \\u003cp\\u003e28\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 23.7113%;\\\"\\u003e\\n \\u003cp\\u003eP01763\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 20.6186%;\\\"\\u003e\\n \\u003cp\\u003eIGHV3-48\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 23.7113%;\\\"\\u003e\\n \\u003cp\\u003e-4.11\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 24.7423%;\\\"\\u003e\\n \\u003cp\\u003e-2.73\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 7.21649%;\\\"\\u003e\\n \\u003cp\\u003e29\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 23.7113%;\\\"\\u003e\\n \\u003cp\\u003eQ9NQ79\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 20.6186%;\\\"\\u003e\\n \\u003cp\\u003eCRTAC1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 23.7113%;\\\"\\u003e\\n \\u003cp\\u003e-4.77\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 24.7423%;\\\"\\u003e\\n \\u003cp\\u003e-2.21\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 7.21649%;\\\"\\u003e\\n \\u003cp\\u003e30\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 23.7113%;\\\"\\u003e\\n \\u003cp\\u003eA0A075B6K6\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 20.6186%;\\\"\\u003e\\n \\u003cp\\u003eIGLV4-3\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 23.7113%;\\\"\\u003e\\n \\u003cp\\u003e-3.41\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 24.7423%;\\\"\\u003e\\n \\u003cp\\u003e-4.87\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 7.21649%;\\\"\\u003e\\n \\u003cp\\u003e31\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 23.7113%;\\\"\\u003e\\n \\u003cp\\u003eI3L145\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 20.6186%;\\\"\\u003e\\n \\u003cp\\u003eSHBG\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 23.7113%;\\\"\\u003e\\n \\u003cp\\u003e-2.02\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 24.7423%;\\\"\\u003e\\n \\u003cp\\u003e-2.03\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 7.21649%;\\\"\\u003e\\n \\u003cp\\u003e32\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 23.7113%;\\\"\\u003e\\n \\u003cp\\u003eP62937\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 20.6186%;\\\"\\u003e\\n \\u003cp\\u003ePPIA\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 23.7113%;\\\"\\u003e\\n \\u003cp\\u003e-6.46\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 24.7423%;\\\"\\u003e\\n \\u003cp\\u003e-7.56\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 7.21649%;\\\"\\u003e\\n \\u003cp\\u003e33\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 23.7113%;\\\"\\u003e\\n \\u003cp\\u003eB1B0D4\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 20.6186%;\\\"\\u003e\\n \\u003cp\\u003eADAMTSL2\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 23.7113%;\\\"\\u003e\\n \\u003cp\\u003e-2.13\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 24.7423%;\\\"\\u003e\\n \\u003cp\\u003e-2.16\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 7.21649%;\\\"\\u003e\\n \\u003cp\\u003e34\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 23.7113%;\\\"\\u003e\\n \\u003cp\\u003eP05121\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 20.6186%;\\\"\\u003e\\n \\u003cp\\u003eSERPINE1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 23.7113%;\\\"\\u003e\\n \\u003cp\\u003e-2.07\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 24.7423%;\\\"\\u003e\\n \\u003cp\\u003e-2.43\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n \\u003c/table\\u003e\\n\\u003c/div\\u003e\\u003c/div\\u003e\\u003cbr\\u003e\\u003cdiv id=\\\"Sec16\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003eValidation the expression levels of FGL1 in plasma exosomes and plasma\\u003c/h2\\u003e\\u003cp\\u003eTo determine whether plasma exosomal FGL1 could be used as a cancer biomarker for NSCLC, the ELISA results showed that the concentration of plasma exosomal FGL1 in the Control group (n\\u0026thinsp;=\\u0026thinsp;40) was 0.84\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.36 ng/ml, 1.71\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.47 ng/ml in the LUSC group (n\\u0026thinsp;=\\u0026thinsp;40), and 2.32\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.48 ng/ml in the LUAD group (n\\u0026thinsp;=\\u0026thinsp;40) (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003eA). The ELISA results were consistent with the trend of proteomics data. Plasma exosomal FGL1 were all significantly different between each other (\\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.0001). Meanwhile, plasma FGL1 in the Control group (n\\u0026thinsp;=\\u0026thinsp;40) was 10.76\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;4.14 ng/ml, 17.14\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;3.88 ng/ml in the LUSC group (n\\u0026thinsp;=\\u0026thinsp;40), and 18.81\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;4.17 ng/ml in the LUAD group (n\\u0026thinsp;=\\u0026thinsp;40) (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003eB). Plasma FGL1 levels were significantly higher in NSCLC patients than in Control samples (\\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.0001), but with no significant change between LUSC and LUAD patients (\\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;\\u0026gt;\\u0026thinsp;0.05).\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec17\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003eThe efficiency evaluation of plasma exosomal FGL1 for diagnosing early stage NSCLC patients\\u003c/h2\\u003e\\u003cp\\u003eIn order to test the efficiency of early diagnosis of NSCLC using plasma exosomal FGL1, in this study, we conducted ELISA detection at a larger sample level and analyzed using the ROC curve. 160 patients with early stage NSCLC (80 LUSC patients and 80 LUAD patients) and 160 healthy controls were included. ROC curve analysis of plasma exosomal FGL1 revealed that the diagnostic AUC, 95% CI, sensitivity, and specificity were 0.866, 0.827\\u0026ndash;0.904, 82.50%, and 76.25% respectively (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig5\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003eA). In addition, ROC curve analysis of plasma FGL1 revealed that the diagnostic AUC, 95% CI, sensitivity, and specificity were 0.757, 0.705\\u0026ndash;0.810, 56.88%, and 83.75% individually (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig5\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003eB). Therefore, the diagnostic efficiency of plasma exosomal FGL1 was higher than plasma FGL1 in diagnosing early stage NSCLC patients.\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003c/div\\u003e\"},{\"header\":\"Discussion\",\"content\":\"\\u003cp\\u003eNSCLC is one of the most deadly tumors characterized by poor survival rates worldwide (\\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e23\\u003c/span\\u003e). This is mostly due to the delayed onset of clinical symptoms and the absence of early biomarkers for NSCLC (\\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e24\\u003c/span\\u003e). Therefore, it is imperative to screen biomarkers that facilitate early detection and diagnosis to mitigate the death rate among NSCLC patients. In recent years, blood-based liquid biopsy biomarkers, including circulating tumor cells (CTCs), cell-free DNA (cfDNA), circulating tumor DNA (ctDNA), circulating-free RNAs (cfRNAs), and extracellular vesicles (EVs)/exosomes, have offered promises for non-invasive monitoring (\\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e25\\u003c/span\\u003e). As LFQ has become a widely used high-throughput detection method in the field of tumor biomarker research (\\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e26\\u003c/span\\u003e). Thus, this study aimed to explore plasma exosomal biomarkes between early stage NSCLC patients and healthy control subjects.\\u003c/p\\u003e\\u003cp\\u003eIn the current study, the isolated plasma exosomes were identified by NTA and Western blot. These results are consistent with the definition of exosomes. In order to avoid heterogeneity between the two different pathological types of NSCLC, the samples for quantitative proteomics experiments were divided into LUSC group and LUAD group. After LFQ analysis, we discovered 65 and 53 DEPs related to LUSC and LUAD respectively. GO analysis indicated that the DEPs were mainly located in cellular anatomical entity and protein-containing complex, and involved in cellular process, biological regulation, and response to stimulus. Additionally, they had the molecular functions, such as binding, catalytic activity, and molecular function regulator. KEGG analysis revealed that these DEPs played important roles in immune system, signal transduction, infectious disease, cancer, transport and catabolism, and amino acid metabolism. The bioinformatics analysis results indicated that these DEPs might be very important to the regulation of the tumor microenvironment (TME), which comprises diverse immune, stromal and endothelial cells whose spatial and functional interactions drive tumor progression, metastatic dissemination and response to treatment (\\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e27\\u003c/span\\u003e). Given that the differential protein profiles between LUSC group and LUAD group were not the same, we identified 34 proteins with similar expression trends between both groups. Among these proteins, FGL1 was selected as a candidate plasma exosomal biomarker for subsequent validation, since it was upregulated by more than 5-fold in the plasma exosomes of NSCLC patients.\\u003c/p\\u003e\\u003cp\\u003eFGL1 is a newly emerging immune checkpoint for diagnosing and treating malignant tumors (\\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e28\\u003c/span\\u003e). FGL1 is significantly overexpressed in solid tumors, including lung, prostate, melanoma, and colorectal cancer, than those of normal tissues (\\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e29\\u003c/span\\u003e). The interaction between FGL1 in tumors and lymphocyte-activation gene 3 (LAG-3) located at the surface of T cells contributes to immune escape and promotes the growth of the tumors (\\u003cspan citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e30\\u003c/span\\u003e). Patients with high FGL1 expression in tumors exhibit poor overall survival and progression-free survival (\\u003cspan citationid=\\\"CR31\\\" class=\\\"CitationRef\\\"\\u003e31\\u003c/span\\u003e). FGL1 is a novel biomarker and target for NSCLC, and promotes tumor progression and metastasis through KDM4A/STAT3 transcription mechanism (\\u003cspan citationid=\\\"CR32\\\" class=\\\"CitationRef\\\"\\u003e32\\u003c/span\\u003e). FGL1 may be as a diagnostic biomarker of KRAS-mutated lung cancer, and targeting the Yap-FGL1 axis could increase the efficacy of anti-PD-1 immunotherapy (\\u003cspan citationid=\\\"CR33\\\" class=\\\"CitationRef\\\"\\u003e33\\u003c/span\\u003e). Thus, the accurate monitoring of FGL1 levels in tumors may be valuable for cancer theranostics.\\u003c/p\\u003e\\u003cp\\u003eELISA results showed that the concentration of plasma exosomal FGL1 in NSCLC was significantly upregulated compared with the Control group. They were consistent with the trend of proteomics data. In addition, Plasma FGL1 was tested by ELISA as well, and the similar results were found. In order to validate the efficiency of early diagnosis of NSCLC using plasma exosomal FGL1, we conducted ELISA detection at a larger sample level and analyzed using the ROC curve. The AUC value of plasma exosomal FGL1 was higher than plasma FGL1. Therefore, the diagnostic efficiency of plasma exosomal FGL1 was superior to plasma FGL1 in diagnosing early stage NSCLC patients.\\u003c/p\\u003e\\u003cp\\u003eAltogether, the present study provided a differential proteome map of plasma exosomes between NSCLC and healthy controls; plasma exosomal FGL1 has the potential to become a promising biomarker for early diagnosis of NSCLC. However, the sample size in this study was limited, and further investigations with a larger number of samples are still needed.\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eEthics approval\\u003c/strong\\u003e\\u003cp\\u003e This study was reviewed and approved by the Ethics Committee of the First Affiliated Hospital of Zhengzhou University (No.2022-KY-0640-002) and conducted in accordance with the Declaration of Helsinki (as revised in 2013). Informed consent was obtained from all participants prior to sample collection.\\u003c/p\\u003e\\u003c/p\\u003e\\u003cp\\u003e\\u003cstrong\\u003eConsent to participate\\u003c/strong\\u003e\\u003cp\\u003e Informed consent was obtained from all individual participants included in the study.\\u003c/p\\u003e\\u003c/p\\u003e\\u003cp\\u003e\\u003cstrong\\u003eCompeting interests\\u003c/strong\\u003e\\u003cp\\u003eThe authors declare no competing interests.\\u003c/p\\u003e\\u003ch2\\u003eFunding\\u003c/h2\\u003e\\u003cp\\u003eThis work was supported by the National Natural Science Foundation of China (Grant No. 81972181).\\u003c/p\\u003e\\u003ch2\\u003eAuthor Contribution\\u003c/h2\\u003e\\u003cp\\u003eWW and CG designed the study. WW and XL collected the samples and conducted the experiments. WW and JL performed the statistical analysis. WW and CG drafted the manuscript. SZ revised the manuscript. All authors read and approved the submitted version.\\u003c/p\\u003e\\u003ch2\\u003eData Availability\\u003c/h2\\u003e\\u003cp\\u003eThe data that support the findings of this study are available from the corresponding author upon reasonable request.\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\u003cli\\u003e\\u003cspan\\u003eHendriks LEL, Remon J, Faivre-Finn C, Garassino MC, Heymach JV, Kerr KM, et al. Non-small-cell lung cancer. Nat reviews Disease primers. 2024;10(1):71. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.1038/s41572-024-00551-9\\u003c/span\\u003e\\u003cspan address=\\\"10.1038/s41572-024-00551-9\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e. PubMed PMID: 39327441.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eMeyer ML, Fitzgerald BG, Paz-Ares L, Cappuzzo F, Janne PA, Peters S, et al. New promises and challenges in the treatment of advanced non-small-cell lung cancer. Lancet. 2024;404(10454):803\\u0026ndash;22. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.1016/S0140-6736\\u003c/span\\u003e\\u003cspan address=\\\"10.1016/S0140-6736\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e. (24)01029-8. PubMed PMID: 39121882.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eXu J, Tian L, Qi W, Lv Q, Wang T. Advancements in NSCLC: From Pathophysiological Insights to Targeted Treatments. Am J Clin Oncol. 2024;47(6):291\\u0026ndash;303. .0000000000001088. PubMed PMID: 38375734; PubMed Central PMCID: PMC11107893.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eLi Y, Yan B, He S. Advances and challenges in the treatment of lung cancer. Biomed pharmacotherapy = Biomedecine pharmacotherapie. 2023;169:115891. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.1016/j.biopha.2023.115891\\u003c/span\\u003e\\u003cspan address=\\\"10.1016/j.biopha.2023.115891\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e. PubMed PMID: 37979378.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eHowlader N, Forjaz G, Mooradian MJ, Meza R, Kong CY, Cronin KA, et al. The Effect of Advances in Lung-Cancer Treatment on Population Mortality. New Engl J Med. 2020;383(7):640\\u0026ndash;9. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.1056/NEJMoa1916623\\u003c/span\\u003e\\u003cspan address=\\\"10.1056/NEJMoa1916623\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e. PubMed PMID: WOS:000562771200011.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eGoldstraw P, Chansky K, Crowley J, Rami-Porta R, Asamura H, Eberhardt WE, et al. The IASLC Lung Cancer Staging Project: Proposals for Revision of the TNM Stage Groupings in the Forthcoming (Eighth) Edition of the TNM Classification for Lung Cancer. J Thorac oncology: official publication Int Association Study Lung Cancer. 2016;11(1):39\\u0026ndash;51. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.1016/j.jtho.2015.09.009\\u003c/span\\u003e\\u003cspan address=\\\"10.1016/j.jtho.2015.09.009\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e. PubMed PMID: 26762738.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eThai AA, Solomon BJ, Sequist LV, Gainor JF, Heist RS. Lung cancer. Lancet. 2021;398(10299):535\\u0026ndash;54. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.1016/S0140-6736(21)00312-3\\u003c/span\\u003e\\u003cspan address=\\\"10.1016/S0140-6736(21)00312-3\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e. PubMed PMID: 34273294.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eBreadner D, Hwang DM, Husereau D, Cheema P, Doucette S, Ellis PM, et al. Implementation of Liquid Biopsy in Non-Small-Cell Lung Cancer: An Ontario Perspective. Curr Oncol. 2024;31(10):6017\\u0026ndash;31. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.3390/curroncol31100449\\u003c/span\\u003e\\u003cspan address=\\\"10.3390/curroncol31100449\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e. PubMed PMID: 39451753; PubMed Central PMCID: PMC11505603.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eTang H, Yu D, Zhang J, Wang M, Fu M, Qian Y, et al. The new advance of exosome-based liquid biopsy for cancer diagnosis. J Nanobiotechnol. 2024;22(1):610. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.1186/s12951-024-02863-0\\u003c/span\\u003e\\u003cspan address=\\\"10.1186/s12951-024-02863-0\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e. PubMed PMID: 39380060; PubMed Central PMCID: PMC11463159.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eLiu C, Cai Y, Mou S, Biomedicine. pharmacotherapy = Biomedecine pharmacotherapie. 2024;181:117726. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.1016/j.biopha.2024.117726\\u003c/span\\u003e\\u003cspan address=\\\"10.1016/j.biopha.2024.117726\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e. PubMed PMID: 39612860.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eAbbosh C, Hodgson D, Doherty GJ, Gale D, Black JRM, Horn L, et al. Implementing circulating tumor DNA as a prognostic biomarker in resectable non-small cell lung cancer. Trends cancer. 2024;10(7):643\\u0026ndash;54. PubMed PMID: 38839544.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eOrooji N, Fadaee M, Kazemi T, Yousefi B. Exosome therapeutics for non-small cell lung cancer tumorigenesis. Cancer Cell Int. 2024;24(1):360. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.1186/s12935-024-03544-6\\u003c/span\\u003e\\u003cspan address=\\\"10.1186/s12935-024-03544-6\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e. PubMed PMID: 39478574; PubMed Central PMCID: PMC11523890.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eYu D, Li Y, Wang M, Gu J, Xu W, Cai H, et al. Exosomes as a new frontier of cancer liquid biopsy. Mol Cancer. 2022;21(1):56. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.1186/s12943-022-01509-9\\u003c/span\\u003e\\u003cspan address=\\\"10.1186/s12943-022-01509-9\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e. PubMed PMID: 35180868; PubMed Central PMCID: PMC8855550.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eAl-Madhagi H. The Landscape of Exosomes Biogenesis to Clinical Applications. Int J Nanomed. 2024;19:3657\\u0026ndash;75. 10.2147. /IJN.S463296. PubMed PMID: 38681093; PubMed Central PMCID: PMC11048319.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eLuo B, Que Z, Lu X, Qi D, Qiao Z, Yang Y, et al. Identification of exosome protein panels as predictive biomarkers for non-small cell lung cancer. Biol procedures online. 2023;25(1):29. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.1186/s12575-023-00223-0\\u003c/span\\u003e\\u003cspan address=\\\"10.1186/s12575-023-00223-0\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e. PubMed PMID: 37953280; PubMed Central PMCID: PMC10641949.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003ePadinharayil H, George A. Small extracellular vesicles: Multi-functional aspects in non-small cell lung carcinoma. Crit Rev Oncol/Hematol. 2024;198:104341. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.1016/j.critrevonc.2024.104341\\u003c/span\\u003e\\u003cspan address=\\\"10.1016/j.critrevonc.2024.104341\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e. PubMed PMID: 38575042.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eWang N, Song X, Liu L, Niu L, Wang X, Song X, et al. Circulating exosomes contain protein biomarkers of metastatic non-small-cell lung cancer. Cancer Sci. 2018;109(5):1701\\u0026ndash;9. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.1111/cas.13581\\u003c/span\\u003e\\u003cspan address=\\\"10.1111/cas.13581\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e. PubMed PMID: 29573061; PubMed Central PMCID: PMC5980308.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eThuya WL, Kong LR, Syn NL, Ding LW, Cheow ESH, Wong RTX, et al. FAM3C in circulating tumor-derived extracellular vesicles promotes non-small cell lung cancer growth in secondary sites. Theranostics. 2023;13(2):621\\u0026ndash;38. PubMed PMID: 36632230; PubMed Central PMCID: PMC9830426.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eChang W, Zhu J, Yang D, Shang A, Sun Z, Quan W, et al. Plasma versican and plasma exosomal versican as potential diagnostic markers for non-small cell lung cancer. Respir Res. 2023;24(1):140. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.1186/s12931-023-02423-4\\u003c/span\\u003e\\u003cspan address=\\\"10.1186/s12931-023-02423-4\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e. PubMed PMID: 37259101; PubMed Central PMCID: PMC10230736.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eGao Y, Xie J, Yang Z, Li M, Yuan H, Li R. Functional tumor-derived exosomes in NSCLC progression and clinical implications. Front Pharmacol. 2025;16:1485661. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.3389/fphar.2025.1485661\\u003c/span\\u003e\\u003cspan address=\\\"10.3389/fphar.2025.1485661\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e. PubMed PMID: 40176898; PubMed Central PMCID: PMC11962733.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eRao R, Gulfishan M, Kim MS, Kashyap MK. Deciphering Cancer Complexity: Integrative Proteogenomics and Proteomics Approaches for Biomarker Discovery. Methods in molecular biology. 2025;2859:211\\u0026thinsp;\\u0026ndash;\\u0026thinsp;37. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.1007/978-1-0716-4152-1_12\\u003c/span\\u003e\\u003cspan address=\\\"10.1007/978-1-0716-4152-1_12\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e. PubMed PMID: 39436604.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eFu J, Yang Q, Luo Y, Zhang S, Tang J, Zhang Y, et al. Label-free proteome quantification and evaluation. Brief Bioinform. 2023;24(1). \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.1093/bib/bbac477\\u003c/span\\u003e\\u003cspan address=\\\"10.1093/bib/bbac477\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e. PubMed PMID: 36403090.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eJeon H, Wang S, Song J, Gill H, Cheng H, Update. 2025: Management of Non\\u0026ndash;Small-Cell Lung Cancer. Lung. 2025;203(1):53. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.1007/s00408-025-00801-x\\u003c/span\\u003e\\u003cspan address=\\\"10.1007/s00408-025-00801-x\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e. PubMed PMID: 40133478; PubMed Central PMCID: PMC11937135.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eSultana A, Alam MS, Khanam A, Lin Y, Ren S, Singla RK et al. An integrated bioinformatics approach to early diagnosis, prognosis and therapeutics of non-small-cell lung cancer. J Biomol Struct Dyn. 2024:1\\u0026ndash;15. doi: 10.1080/07391102.2024.2425840. PubMed PMID: 39535278.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eBafiti V, Thanou E, Ouzounis S, Kotsakis A, Georgoulias V, Lianidou E, et al. Profiling Plasma Extracellular Vesicle Metabotypes and miRNAs: An Unobserved Clue for Predicting Relapse in Patients with Early-Stage NSCLC. Cancers. 2024;16(22). \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.3390/cancers16223729\\u003c/span\\u003e\\u003cspan address=\\\"10.3390/cancers16223729\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e. PubMed PMID: 39594687; PubMed Central PMCID: PMC11592109.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eZhang H, Wu J, Gan J, Wang W, Liu Y, Song T, et al. Proteomic Analysis of Plasma Exosomes Enables the Identification of Lung Cancer in Patients With Chronic Obstructive Pulmonary Disease. Thorac cancer. 2025;16(1):e15517. PubMed PMID: 39778061; PubMed Central PMCID: PMC11717053.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eRahal Z, El Darzi R, Moghaddam SJ, Cascone T, Kadara H. Tumour and microenvironment crosstalk in NSCLC progression and response to therapy. Nat reviews Clin Oncol. 2025. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.1038/s41571-025-01021-1\\u003c/span\\u003e\\u003cspan address=\\\"10.1038/s41571-025-01021-1\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e. PubMed PMID: 40379986.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eQian W, Zhao M, Wang R, Li H. Fibrinogen-like protein 1 (FGL1): the next immune checkpoint target. J Hematol Oncol. 2021;14(1):147. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.1186/s13045-021-01161-8\\u003c/span\\u003e\\u003cspan address=\\\"10.1186/s13045-021-01161-8\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e. PubMed PMID: 34526102; PubMed Central PMCID: PMC8444356.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eXu Y, Zhang J, Pan D, Yan J, Chen C, Wang L, et al. Development of Novel Peptide-Based Radiotracers for Detecting FGL1 Expression in Tumors. Mol Pharm. 2025;22(3):1605\\u0026ndash;14. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.1021/acs.molpharmaceut.4c01293\\u003c/span\\u003e\\u003cspan address=\\\"10.1021/acs.molpharmaceut.4c01293\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e. PubMed PMID: 39893698.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eZhu S, Kou Z, Xiao C, Wang L, Zhu J, Zheng Y, et al. Silencing FGL1 promotes prostate cancer cell apoptosis and inhibits EMT progression. Sci Rep. 2025;15(1):19886. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.1038/s41598-025-04717-7\\u003c/span\\u003e\\u003cspan address=\\\"10.1038/s41598-025-04717-7\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e. PubMed PMID: 40481127; PubMed Central PMCID: PMC12144232.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eLv Z, Cui B, Huang X, Feng HY, Wang T, Wang HF, et al. FGL1 as a Novel Mediator and Biomarker of Malignant Progression in Clear Cell Renal Cell Carcinoma. Front Oncol. 2021;11:756843. PubMed PMID: 34956878; PubMed Central PMCID: PMC8695555.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eLiu TY, Yan JS, Li X, Xu L, Hao JL, Zhao SY, et al. FGL1: a novel biomarker and target for non-small cell lung cancer, promoting tumor progression and metastasis through KDM4A/STAT3 transcription mechanism. J experimental Clin cancer research: CR. 2024;43(1):213. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.1186/s13046-024-03140-6\\u003c/span\\u003e\\u003cspan address=\\\"10.1186/s13046-024-03140-6\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e. PubMed PMID: 39085849; PubMed Central PMCID: PMC11293164.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eJiang J, Ye P, Sun N, Zhu W, Yang M, Yu M, et al. Yap methylation-induced FGL1 expression suppresses anti-tumor immunity and promotes tumor progression in KRAS-driven lung adenocarcinoma. Cancer Commun. 2024;44(11):1350\\u0026ndash;73. PubMed PMID: 39340215; PubMed Central PMCID: PMC12015977.\\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\":\"info@researchsquare.com\",\"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\":\"Non-small cell lung cancer, Diagnosis, Biomarker, Plasma, Exosomes, FGL1\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-7447669/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-7447669/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003ch2\\u003eObjective\\u003c/h2\\u003e\\u003cp\\u003eNon-small cell lung carcinoma (NSCLC) is the leading cause of cancer-related death worldwide. Nevertheless, reliable and effective biomarkers for early diagnosis of NSCLC are currently unavailable. In recent years, increasing studies suggest that exosomes have a great promise to serve as novel biomarkers in liquid biopsy. This study aimed to identify the plasma exosomal biomarkers for NSCLC early detection.\\u003c/p\\u003e\\u003ch2\\u003eMethods\\u003c/h2\\u003e\\u003cp\\u003eWe utilized label-free quantification to conduct differential proteomic analysis of plasma exosomes between patients with early stage NSCLC and healthy control subjects. NSCLC samples were divided into lung squamous carcinoma (LUSC) group and lung adenocarcinoma (LUAD) group. GO and KEGG pathway analysis of differentially expressed proteins (DEPs) were performed for every module by DAVID. Furthermore, the protein with the most significant difference was validated using Enzyme-linked immunosorbent assay (ELISA) at levels of plasma exosomes and plasma respectively. Finally, the receiver operating characteristic (ROC) analysis was used to evaluate the efficiency of plasma exosomal FGL1 for early diagnosis of NSCLC.\\u003c/p\\u003e\\u003ch2\\u003eResults\\u003c/h2\\u003e\\u003cp\\u003eCompared with Control group, 65 and 53 DEPs were identified in LUSC group and LUAD group respectively. Bioinformatics analysis indicated that the DEPs were mainly involved in multiple biological functions and cancer-related pathways. Furthermore, we identified 34 proteins with similar expression trends between the LUSC and LUAD groups. Among these proteins, Fibrinogen like protein 1 (FGL1) was selected as a candidate plasma exosomal biomarker for subsequent validation since it was upregulated by more than 5-fold in NSCLC group. ELISA results showed that the plasma exosomal FGL1 concentration were significantly higher in NSCLC patients than in Control samples, which were consistent with the trend of proteomics results. Moreover, receiver operating characteristic (ROC) analysis of plasma exosomal FGL1 demonstrated that the diagnostic AUC, sensitivity, and specificity were 0.866, 82.50%, and 76.25% respectively. However, ROC analysis of plasma FGL1 revealed that the diagnostic AUC, sensitivity, and specificity were 0.757, 56.88%, and 83.75% individually. The diagnostic efficiency of plasma exosomal FGL1 was higher than plasma FGL1 in diagnosing early stage NSCLC patients.\\u003c/p\\u003e\\u003ch2\\u003eConclusion\\u003c/h2\\u003e\\u003cp\\u003eThis study provided a reference proteome map of plasma exosomes in LUSC and LUAD patients. Plasma exosomal FGL1 has the potential to become a promising biomarker for early diagnosis of NSCLC.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Identification and validation of plasma exosomal FGL1 as an early diagnostic biomarker for non-small cell lung cancer\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2025-10-12 14:08:53\",\"doi\":\"10.21203/rs.3.rs-7447669/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"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\":\"d7c16545-770c-4b35-be42-293180ca0ea4\",\"owner\":[],\"postedDate\":\"October 12th, 2025\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"posted\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2026-04-20T06:11:52+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2025-10-12 14:08:53\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-7447669\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-7447669\",\"identity\":\"rs-7447669\",\"version\":[\"v1\"]},\"buildId\":\"8U1c8b4HqxoKbykW_rLl7\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}