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. 2024 Nov 14;14:27953. doi: 10.1038/s41598-024-78955-6

Proteomic profile of the antibody diversity in circulating extracellular vesicles of lung adenocarcinoma

Xinfu Huang 1,#, Lijuan Xiong 1,#, Yang Zhang 1, Xin Peng 1, Hongping Ba 2,✉, Peng Yang 1,✉
PMCID: PMC11564652  PMID: 39543163

Abstract

Immunoglobulin diversity encompasses B-cell receptor, T-cell receptor, and antibody diversity. Existing studies have focused more on the role of B-cell and T-cell receptor diversity in tumor immunity, while the role of antibody diversity is less understood. This study examined and compared the blood extracellular vesicles (EVs) of lung cancer patients and healthy individuals using proteomics and bioinformatics analyses. The results revealed that among the 270 identified proteins, those involved in defense mechanisms were the most abundant. Most of these were antibody subtypes, accounting for 50.00%. Similarly, of the 40 identified EVs differentially expressed proteins (DEPs), 29 were involved in defense mechanisms (72.50%), with a higher proportion being antibody subtypes (82.76%). Furthermore, 24 DEP antibody subtypes were implicated in 18 immune reaction-related signaling pathways. These findings suggest that human serum EVs contain a significant number of antibody subtypes, and the antibody subtypes from lung cancer serum EVs differ from those of healthy controls (HCs). The variations in antibody diversity may be closely associated with lung cancer tumor immunity.

Supplementary Information

The online version contains supplementary material available at 10.1038/s41598-024-78955-6.

Keywords: Lung adenocarcinoma, Plasma extracellular vesicles, Proteomics, Antibody diversity, Tumor immune

Subject terms: Cancer, Immunology, Biomarkers, Oncology

Introduction

Lung adenocarcinoma (LA) is a common malignant tumor. The invasion and metastasis of cancer cells in patients are complex and dynamic processes, not only related to the genetic abnormalities inherent in tumor tissues but also to the interactions of immune cells within the local tumor microenvironment1. Studies have shown that approximately two-thirds of the immune cells infiltrating tumor tissues are composed of T and B cells, with the remainder including NK cells, macrophages, and dendritic cells2. B cells play a crucial role in achieving an anti-tumor effect, with some studies indicating B cell proliferation in about 35% of lung cancers3. Additionally, tumor-infiltrating B lymphocytes can be observed at various stages of lung cancer development4 and exhibit different histological subtypes at different stages5,6. However, the contribution of antibody diversity to the anti-tumor effect of B cells remains unclear.

Immunoglobulin diversity encompasses T cell receptor (TCR), B cell receptor (BCR), and antibody diversity. Most existing studies have focused on changes in TCR and BCR diversity in lung cancer7–9, with few reports on changes in antibody diversity. B cells can produce a variety of antibodies in response to different antigenic stimuli in the environment. Antibody diversity is essential to the immune system, enabling resistance to a range of potential pathogens and malignant cells, and plays a vital role in maintaining health10. The greater the diversity of antibody subtypes, the stronger the immune system’s ability to combat diseases. Serum antibody subtypes, numbering up to 109 to 1012 molecules, have the potential to recognize a variety of antigens11. The diversity of antibodies is manifested in the variable immunoglobulin region, which is controlled by the embryonic genes of B cells and is related to the mechanisms of V, D, and J gene segment rearrangement, V-D-J junctional diversity, somatic mutation, and N region addition12. In the progression of lung cancer, B cells can produce different antibody subtypes in response to tumor antigen mutations13,14. During lung cancer progression, B cells produce different antibody subtypes stimulated by mutant tumor antigens, which are closely associated with the occurrence and development of lung cancer15,16. Analyzing changes in antibody diversity can help elucidate the anti-tumor mechanisms of lung cancer and facilitate the development of more effective immunotherapy methods or more sensitive diagnostic and prognostic markers.

Extracellular Vesicles (EVs) are a class of membrane-bound vesicles released by cells that play a significant role in intercellular communication and the transport of substances. EVs vary in size and composition, and they can encapsulate a variety of biomolecules, including proteins, lipids, nucleic acids (such as mRNA and miRNA), and membrane receptors. The size of extracellular vesicles typically ranges from 30 nanometers to 1000 nanometers17,18. B cells, stimulated by new tumor antigens, may release EVs into the bloodstream containing a large number of antibody subtypes capable of responding to tumor lesions. Protected by the EVs lipid bilayer, these antibody subtypes can evade protease digestion and persist for extended periods19. Compared the concentration of antibody subtypes involved in the response to tumor antigens between plasma and extracellular vesicles (EVs), studies have indicated that EVs contain a higher concentration of these antibody subtypes. This could be attributed to the fact that EVs avoid the high protein abundance typically found in body fluids, which makes them more amenable to analysis. For instance, a study mentioned that tumor-derived EVs, by carrying various proteins and nucleic acids, can comprehensively affect the proliferation, migration, and permeability of endothelial cells, thereby promoting the distant metastasis of tumors. This suggests that EVs act as important mediators of intercellular communication within the tumor microenvironment and may carry specific biomarkers related to tumor immune responses that might be difficult to detect in plasma due to the high protein background20. This study aims to examine LA plasma EVss and identify exosomal antibody subtypes using label-free proteomics, thereby providing a clinical basis for understanding the role of changes in antibody diversity in lung cancer tumor immunity.

Materials and methods

Patients and healthy individuals

In this study, conducted at the Second Affiliated Hospital of Guizhou University of Traditional Chinese Medicine (Guiyang, China) from January 2018 to December 2020, a cohort of 30 non-small cell lung cancer (NSCLC) patients was enrolled. The group consisted of 18 male and 12 female patients with an average age of 60.9 years, spanning an age range of 43 to 73 years. All enrolled patients had received a pathological confirmation of NSCLC and had not undergone any antitumor treatments prior to the study. Patients were excluded based on specific criteria, including having undergone preoperative chemotherapy or radiotherapy, or having a history of or concurrent with tuberculosis or other malignancies. Classification of the enrolled NSCLC patients according to the tumor and metastasis staging system21 revealed that 7 of these were metastatic patients, and 23 were non-metastatic. Concurrently, 23 healthy individuals, matching the patient group in gender distribution and with an average age of 56.6 years (ranging from 48 to 71 years), was recruited from outpatient departments. These individuals were screened and confirmed to be free of any immune disorders or tumors through a general medical examination. For the purposes of proteomics research, the study selected 9 non-metastatic patients and an equal number of healthy individuals. For the validation of the target protein identified through screening, 21 patients and an 10 healthy controls were utilized. The study was approved by the Ethics Committee of the Second Affiliated Hospital of Guizhou University of Traditional Chinese Medicine (KYW2019013), Informed consents were obtained from all subjects. All the research involving human research participants have been performed in accordance with the Declaration of Helsinki.

Plasma EVs isolation

Venous blood was collected, and EVs dervied from plasma was isolated through ultracentrifugation with some modification22. Briefly, the venous blood was centrifuged at 2,000 g for 20 min at 4 °C to eliminate the debris, after which UC was applied at 10,000 g at 4 °C for 45 min to remove larger vesicles. Next, the supernatant was filtered (0.45-µm pore filter) and centrifuged at 100,000 g at 4 °C for 75 min. The supernatant was discarded and EVs were resuspended in 400µL PBS. Then, 20µL was used for transmission electron microscopy (TEM) and 10µL for Nanoparticle tracking analysis (NTA), while the remaining EVs samples was stored at -80℃.

Transmission electron microscopy(TEM)

The EVs samples (10 µL) were added dropwise onto 100-mesh formvar-coated copper grids for 1 min while absorbing the floating liquid using filter paper. Then, uranyl acetate (10 µL) was added to the copper net for precipitation for 1 min, while the floating liquid was absorbed using filter paper. After drying at room temperature for 5–10 min, the TEM images were obtained at 80 kV.

Nanoparticle tracking analysis (NTA)

The frozen EVs samples were thawed in 25℃ water and diluted with 1×PBS using a Nano Gold system (Izon Science Ltd, Christchurch, New Zealand).

Detection of surface markers of EVs

The exosomal marker CD63 was detected using Flow Cytometry (FCM) with EVs pre-absorbed on latex beads (ThermoFisher, USA). The bead-EVs was labeled with 1:100 the FITC-labeled anti-CD63 (eBioscience, USA).

Label-free quantitative (LFQ) proteomic analysis

Plasma sample grouping

Nine patients with LA were randomly divided into three groups, each containing the mixed plasma of three patients, labeled LA 1–3, LA 4–6, and LA 7–9, respectively. Moreover, nine healthy individuals were randomly divided into three groups via the same method and labeled HC 1–3, HC 4–6, and HC 7–9.

Exosomal protein preparation

The EVs suspension were centrifuged at 17,000 g for 15 min at 4℃, followed by UC at 4℃ and 200,000 g for 1 h.The supernatant was discarded and then precipitated with 1×PBS, urea particles were added to reach a concentration of 8 M, and then shaken until fully dissolved. Next, Ultrasonic crushing on ice, based on 30% energy, ultrasonic for 1 s, stop for 1 s, cumulative 2 min. The supernatant was collected after centrifugation at 14,000 g for 20 min23.

Protein digestion and desalination

The EVs samples were solubilized in lysis buffer. Equal amounts of protein were reduced with dithiothreitol and alkylated with iodoacetamide. Next, the proteins’ suspension was diluted though adding triethylammonium bicarbonate buffer (100 mM). Subsequently, trypsin was added to the sample in a 1:50 trypsin-to-protein ratio for the first overnight digestion, and the trypsin to protein ratio of 1:100 was added to the second 4 h digestion23.

LC-MS/MS analysis

Solution A was prepared with 100% water and 0.1% formic acid, and solution B was prepared with 100% acetonitrile and 0.1% formic acid. The freeze-dried powder was dissolved in 10 µL A solution and centrifuged at 4℃ and 14,000 g for 20 min. The peptides was then isolated by linear gradient elution in the C18 column. The isolated peptide™ ion source was analyzed using a QExactive HF-X mass spectrometer and Nanospray Flex at a spray voltage of 2.4 kV and capillary temperature of 275 °C. Full-scan mass spectrometry (MS) is performed at a resolution of 120,000 in the range of m/z 350 to 1500 with a maximum ion injection time of 80 ms. The samples were analyzed by MS/MS with a resolution of 15,000 (m/z 200), a maximum ion implantation time of 45 ms, and a collision energy of 27%24.

Protein quantification and data analysis

The original MS data was analyzed using Proteome Discoverer 2.4 software and retrieved in the UniProtKB database. In the database search, carbamylation of cysteine was considered the fixed modification, while N-terminal acetylation and methionine oxidation were considered variable modifications. The MaxLFQ algorithm was used to quantify the identified protein abundance. The maximum missed cleavage for trypsin was set to 2. The MS tolerances were set to 10 ppm, and 0.02 Da were allowed for MS/MS tolerances. The cutoff value of false discovery rate (FDR) was set to < 0.01 in the peptide-spectrum match (PSM) and protein identification. Only proteins identified by at least two different peptides and in at least two sample replicates were regarded as a reliable result25. A Student’s t-test was used for protein quantitative statistical analysis.The proteins quantified with a fold change (FC) > 1.5 and a significance value of P < 0.05 were designated as DEPs, while log2 (FC) was used for further analysis.

ELISA

After extracting the plasma EVs from each individual, they are detected using an Elisa assay kit, respectively. The immunoglobulin heavy variable 2–26 (IGHV2-26), immunoglobulin lambda variable2-23 (IGLV2-23), immunoglobulin lambda variable 1–40 (IGLV1-40), and immunoglobulin lambda variable 1–47 (IGLV1-47) ELISA kits were personalized by EIAabScience Inc. (Wuhan, China), while the respective concentrations were determined using these kits according to the instructions of the manufacturer.

Bioinformatic analysis

The subcellular localization of all the identified proteins was predicted using WoLF PSORT (http://www.genscript.com/psort/wolf_psort.html), a protein subcellular localization prediction tool.

The database of clusters of Orthologous Groups (COG; http://www.ncbi.nlm.nih.gov/COG/) was used to assign possible functions to the identified proteins or DEPs in the serum EVs. Briefly, the BLASTDB program of NCBI-BLASTP (http://blast.ncbi.nlm.nih.gov/Blast.cgi) was used to create a protein database with the prefix COG. The protein sequences requiring COG annotation were compared with the COG database to obtain the corresponding COG number, function description, and function classification of the protein.

Reactome pathway analysis was performed using the Cluster Profiler package in R project [http://www.r-project.org]. Briefly, the “cluster Profiler"“ReactomePA”and “String and GGplot2” packages were installed, after which their codes were saved in a “reactome.r” file. Then, the command “rscript react rhs gene. glist./20"was used to obtain the pathway analysis results.

Statistical analysis

The data were presented as mean ± standard deviation (SD). The differences between the two groups were analyzed via Mann-Whitney tests (unpaired samples). A value of P < 0.05 was considered statistically significant. All statistical analyses were performed using GraphPad Prism V9 software (GraphPad Software, San Diego, CA, USA).

Results

Experimental design

Immunoglobulin diversity includes BCR, TCR, and antibody diversity. At present, the role of immunoglobulin diversity in the development of lung cancer is mainly focused on the analysis of B cell and TCR diversity, while little is known about antibody diversity. This study examined LA patients to determine the clinical value of antibody diversity. The serum exosomal proteins were identified using LFQ, while the antibody diversity for the identified exosomal proteins and exosomal DEPs of the LA patients were analyzed. Plasma samples were collected from the LA patients (n = 9) and HCs (n = 9) to screen the exosomal DEPs. The plasma EVs were isolated using UC and characterized via TEM and NTA. Next, label-free proteomic analysis was performed to identify the proteins and the DEPs of the LA patients and HCs. This was followed by bioinformatics analysis of the identified proteins and DEPs, including the subcellular locations and COG function analysis. Then, the antibody subtype pathways involved in the defense mechanism were analyzed via Reactome enrichment. The plasma of 21 patients and 10 HCs were collected during the validation stage. The EVs were isolated and identified via the same method, after which the levels of four candidate exosomal proteins were detected using ELISA (Supplementary Fig. 1).

Exosomal isolation and extraction and proteomic analysis

The EVs were successfully isolated from the plasma using ultracentrifugation and characterized by transmission electron microscopy (TEM), nanoparticle tracking analysis (NTA), and flow cytometry (FCM). This comprehensive approach ensures a thorough validation of the exosomal isolation, confirming both their physical characteristics and biological presence. (Fig. 1A and C). The HCs group exhibited a mean EVs size of 139.3 nm at a concentration of 3.7 × 1011 particles/mL; whereas the LA group had a mean EVs size of 146.3 nm at a concentration of 5.9 × 1011 particles/mL. Statistical analysis revealed no significant difference in EVs size between the LA and HCs groups. However, the concentration of EVs in the LA group was significantly higher compared to the HCs group (Fig. 1D and E).

Fig. 1.

Fig. 1

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Identification of the plasma EVs in the LA and HC groups. (A) The TEM images of the mixed EVs are shown at 110,000 × magnification. Scale bar = 152.8 nm. (B) The mixed exosomal size dispersion was obtained using NTA. (C) The FCM assessment of the CD63 exosomal markers. (D), The mean size of the EVs from HCs and LA plasma. (E) The mean concentrations the EVs from HCs and LA plasma. *indicates P < 0.05.

A label-free integrated approach was applied to analyze the proteomic changes between the LA patients and the HCs (Fig. 2A). The Pearson’s correlation coefficients displayed sufficient reproducibility of this experiment (Fig. 2B). A total of 3,587 peptides were detected. The number of PSMs was concentrated between 1 and 33 (Fig. 2C), while the lengths of most identified peptides involved15 to 32 amino acid residued (Fig. 2D), suggesting that the peptide sampling met the required standard.

Fig. 2.

Fig. 2

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The proteomics experiment strategy and quality control of the MS data. (A) A flow chart of the exosomal proteins analyzed using LFQ. (B) The Pearson’s correlation of the protein quantitation. Control means HCs, Infected means LA patients.(C) The number of peptides with special PSM numbers. (D) The length distribution of all the identified peptides.

The antibody diversity analysis of the quantified proteins in the plasma EVs

A total of 270 proteins were identified, 190 of which were quantified (Fig. 3A). All the identified proteins were annotated according to different categories, including GO terms, Reactome pathways, and subcellular localization, to further understand their functions.

Fig. 3.

Fig. 3

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The classification and antibody diversity of all the identified exosomal proteins. (A) The total number of identified total proteins (TP) and the quantifiable proteins (QP) in the serum EVs. The (B) subcellular locations and (C) COG function classes of the identified proteins. (D) Proportion of antibody subtypes(n = 36) in the exosomal DEPs enriched in defense mechanism(Total = 72) ,10 × 10 dot plot.

The quantified proteins were grouped based on their subcellular localizations with GO annotations, for which 12 subcellular components were identified. The top three subcellular components related to the derived proteins included the extracellular region (48.54%), plasma membrane (16.67%), and cytosol (6.14%), while the components associated with exosomal formation include lysosomes (0.29%) and the plasma membrane (16.67%) (Fig. 3B).

COG analysis of all quantified proteins showed that 72 proteins were enriched in the “defense mechanism” term (Fig. 3C), of which 36 were antibody subtypes, accounting for 50% of all proteins enriched in this term (Fig. 3D).

The antibody diversity analysis of the exosomal DEPs of the LA patients and HCs

Here, 40 quantified proteins were identified as DEPs in the LA patients and HCs, of which six were down-regulated over 1.5-fold in the LA patients while 34 were up-regulated over 1.5-fold (Fig. 4A, Supplementary Table 1, Supplementary Table 2). Only five subcellular components were recognized, including 40 extracellular region-localized DEPs, 22 plasma membrane-localized DEPs, and two cytoplasm and cytosol-localized DEPs (Fig. 4B).

Fig. 4.

Fig. 4

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The classification and antibody diversity of the exosomal DEPs. (A) A volcano map of the exosomal DEPs. The (B) subcellular locations and (C) COG function classes of the exosomal DEPs. (D) Proportion of antibody subtypes(n = 24) in the exosomal DEPs enriched in defense mechanism(Total = 29), 10 × 10 dot plot.

COG analysis of the 40 DEPs showed that 29 proteins were enriched in the “defense mechanism” term (Fig. 4C), of which 24 were antibody subtypes, accounting for 82.76% of all DEPs enriched in this term (Fig. 4D), implying that the antibody diversity changes were strongly related to the LA defense mechanism.

Reactome pathway analysis of the antibody subtypes in the exosomal DEPs

Reactome analysis was performed to gain further insight into the role of these antibody subtypes in circulating lung cancer EVs. The results showed that these antibody subtypes were involved in 18 pathways, of which the top two significantly enriched items were “scavenging of heme from plasma” and “cell surface interactions at the vascular wall”. In the “scavenging of heme from plasma” pathway, 12 antibody subtypes were significantly up-regulated in the LA patients, and 11 in the “cell surface interactions at the vascular wall” pathway (Fig. 5).

Fig. 5.

Fig. 5

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The Reactome pathway analysis of the exosomal DEPs involved in the immune defense mechanism. The gene ratio on the x-axis refers to the ratio of the number of DEP genes in a pathway to the number of all DEPs. The vertical axis represents different signaling pathways. Different colors represent different P-values. A redder color indicates a smaller P-value and a more significant degree of enrichment. The dot size represents the number of genes enriched in a pathway.

Validation of the expression of four immunoglobulins

The expression levels of these antibody subtypes were verified via ELISA, using independent samples collected from the HCs and LA patients. In total, four immunoglobulin molecules were randomly selected, including IGHV 2–26, IGLV1-40, IGLV1-47 and IGLV2-23. Compared with the HCs, the expression levels of IGLV1-47, IGLV2-23, and IGLV1-40 of non-metastatic LA were significantly increased (with P < 0.05, P < 0.05, and P < 0.01 respectively), while there was no significant difference in the expression level of the IGHV2-26 (Fig. 6A-D). Furthermore, the concentrations of IGHV2-26, IGLV1-47, IGLV2-23, and IGLV1-40 of LA were significantly increased compared to HCs (P < 0.05). It is particularly noteworthy that there is a significant difference in the expression level of the IGLV1-40 antibody molecule between the lung cancer metastatic and non-metastatic groups (Fig. 6B), suggesting that IGLV1-40 may be associated with the metastatic potential of lung cancer.

Fig. 6.

Fig. 6

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ELISA validation of differential protein expression in plasma EVs. The observation objects include HCs, LA patients, non-metastatic patients (Non-LA), and metastatic patients (Metastasis-LA). A Mann-Whitney U-test is used to compare the disease groups with the HCs. The concentrations of (A) IGHV2-26, (B) IGLV1-40, (C) IGLV1-47, and (D) IGLV2-23 in the plasma EVs. ns indicats P > 0.05, * indicates P < 0.05, ** indicates P < 0.01, *** indicates P < 0.001, all the groups are compared to HCs. ## indicates P < 0.01.

Discussion

Immunoglobulin diversity has been the focus of tumor immunity for many years. Recent immune-sequencing studies have shown that TCR diversity is closely related to the occurrence and progression of lung cancer26–30, and there are even reports that BCR and TCR diversity can be used for evaluating patient immune efficacy8,31. However, existing studies focus on the role of BCR and TCR diversity in lung cancer from the perspective of genome sequencing, while minimal research is available regarding antibody diversity. However, the gene diversity changes in the immune repertoire cannot fully represent those of post-translational (antibody) diversity. Therefore, proteomic analysis of antibody diversity is helpful in understanding the role of the immune repertoire in lung cancer.

Although blood presents an abundance of antibody subtypes, they are unsuitable for antibody diversity analysis. EVs contain many information materials from originating cells32. Theoretically, EVs released into the blood by B cells should contain a large number of antibody subtypes. However, until now, no studies are available involving the analysis of the antibody diversity in blood EVs. This study successfully isolated the plasma EVs of LA patients and HCs via UC, which were characterized using TEM and NTA analyses for quality verification. Proteomic analysis via LC-MS/MS identified 270 proteins, of which 190 were quantified. Subcellular protein localization analysis showed that two proteins were related to lysosomes involved in the formation of EVs, while 57 were associated with the plasma membrane. These results further suggested that the proteins identified in this study were derived from EVs. The COG analysis indicated that 72 identified proteins were enriched in the number of defense mechanism entries, accounting for the highest proportion of annotated COG entries of up to 37.89%. Moreover, of the protein molecules in the enriched defense mechanism items, 36 were antibody subtypes, accounting for 50.00%. These results suggest that blood EVs contain many antibody subtypes like blood, which may be the ideal quality resources for antibody diversity analysis.

B cells are vital immune cells that play an anti-tumor role in lung cancer33,34. Stimulated by the constantly mutating new tumor antigen, the EVs released by B cells into the blood should contain a large number of antibody subtypes demarcating the tumor35,36. Analyzing the exosomal antibody diversityis significant for revealing the lung cancer mechanism, finding new lung cancer markers, and even developing new immunotherapy strategies. The results of this study revealed 40 DEPs in the lung cancer serum EVs compared to the HCs, with almost all of them located in the extracellular domain. COG enrichment showed that these DEPs, like the identified exosomal proteins, were enriched in the defense mechanism entries by 29 proteins, accounting for 72.50%. Furthermore, 24 of these molecules belonged to antibody subtypes, accounting for 82.76% of the proteins enriched in the defense mechanism, suggesting that the changes in the antibody diversity of the lung cancer blood EVs were closely related to tumor immunity. Previous immune repertoire sequencing showed that BCR diversity was closely related to the occurrence and metastasis of lung cancer8,15,16, while antibody diversity analysis of lung cancer EVs supported this conclusion. To gain further insight into the function of blood EVs antibody subtypes, the Reactome pathway was enriched with the protein molecules involved in antibody diversity, revealing 18 participating pathways. Of these, the entries involving the “scavenging of heme from plasma” and “cell surface interactions at the vascular wall” were the most significant and contained the most antibody subtypes. But it is worth noting that the increase in the abundance of vesicle antibody subtypes in lung cancer patients cannot completely rule out the impact of plasma contamination. It may also be due to changes in the biological activity of the patients’ EVs, making them more likely to adsorb plasma immunoglobulins, thereby leading to an increase in the abundance of EVs antibody subtypes. We call for future research to further explore this area to gain a more comprehensive understanding of the functions of antibody subtypes in EVs and their potential clinical applications.

To validate the accuracy and reliability of the Label-Free proteomics technique in the detection of exosomal antibody subtypes, this study selected IGHV2-26, IGLV1-40, IGLV1-47, and IGLV2-23 antibody subtypes for independent verification using the ELISA method. The validation results indicated that the expression patterns of these antibody subtypes in EVs from non-metastatic lung cancer patients were largely consistent with the proteomics analysis results, further confirming the feasibility and credibility of the Label-Free technique. Notably, compared to the healthy control group, the content of exosomal antibody subtypes in the serum of lung cancer patients was significantly increased. Additionally, we found that there was a significant difference in the expression levels of the IGLV1-40 antibody molecule between the metastatic and non-metastatic lung cancer groups, suggesting that it may be associated with the metastatic potential of lung cancer. Currently, little is known about the role of serum exosomal subtypes in the progression of lung cancer. Our research results indicate that changes in serum exosomal antibody subtypes may play an important role in the progression of lung cancer, especially in the process of lung cancer metastasis. These findings emphasize the importance of further research on exosomal antibody subtypes to explore their value as potential biomarkers. Future studies should focus on the subtype analysis of serum EVs and their application in lung cancer surveillance, with the expectation of providing new insights for the early diagnosis of lung cancer.

In summary, this study used proteomics to reveal that plasma EVs contain several antibody subtypes while indicating that the exosomal antibody diversity in the plasma of the LA patients differs significantly from the HCs. These results provide a new perspective for applying exosomal antibody diversity analysis in tumor immunity, tumor diagnosis, and immunotherapy. However, the role of the circulating exosomal antibody diversity in tumor immunity and its potential clinical application requires additional research.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Material 1 (15.8KB, docx)
Supplementary Material 2 (13.5KB, docx)
Supplementary Material 3 (21.9MB, tif)

Author contributions

Conceptualization: XFH, HPB, LJX, YZ, XP and PY. Methodology: XFH, HPB and LJX. Data curation: YZ and XP. Writing, original draft preparation: HPB and LJX. Writing, review & editing: YZ, XP and PY. Supervision: PY. Visualization: XFH, HPB and LJX. All authors contributed to the article, and approved the final version of the manuscript.

Funding

This study was supported by The funding for Scientific Research Projects from Wuhan Municipal Health Commission (WX23A37), Guizhou Provincial Science and Technology Projects ([2020]4Y178), Science and Technology planning Foundation of Guiyang ([2020]-16-18), Technology Fund of the Guizhou Provincial Health Committee (gzwkg 2019-1-198) and Scientific research project of the Second Affiliated Hospital of Guizhou University of Traditional Chinese Medicine (GZEYK[2020]26).

Data availability

The datasets generated and analysed during the current study are available from the corresponding author on reasonable request. The Supplementary Material for this article can be found online. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD054628.

Declarations

Ethics approval and consent to participate

This study was authorized by the Ethics Committee of the Second Affiliated Hospital of Guizhou University of Traditional Chinese Medicine (KYW2019013).

Consent for publication

All authors agree to publish this paper.

Competing interests

The authors declare no competing interests.

Footnotes

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

These authors contributed equally: Xinfu Huang and Lijuan Xiong.

Contributor Information

Hongping Ba, Email: bhpsky@163.com.

Peng Yang, Email: 1874660968@qq.com.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary Material 1 (15.8KB, docx)
Supplementary Material 2 (13.5KB, docx)
Supplementary Material 3 (21.9MB, tif)

Data Availability Statement

The datasets generated and analysed during the current study are available from the corresponding author on reasonable request. The Supplementary Material for this article can be found online. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD054628.

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