Expression Characteristics and Biological Significance of Exosome- Related Genes in Lung Cancer

Expression Characteristics and Biological Significance of Exosome- Related Genes in 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 Expression Characteristics and Biological Significance of Exosome- Related Genes in Lung Cancer Qixiang Zhong, Yujie Zhao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6446002/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 12 You are reading this latest preprint version Abstract Background Lung cancer stands as one of the most prevalent malignancies globally, with its high morbidity and mortality intimately associated with its inconspicuous early symptoms, the advanced stages at diagnosis, and resistance to conventional therapeutic interventions. Exosomes, serving as pivotal mediators of intercellular communication, carry biomolecules that play crucial roles in tumorigenesis, progression, and metastasis, holding promise as novel targets for early diagnosis, prognostic evaluation, and treatment of lung cancer. Methods In this study, lung cancer-related datasets were obtained from the GEO database and TCGA. Through differential gene analysis, enrichment analysis, immune infiltration analysis, and drug regulatory analysis, exosome-associated genes pertinent to lung cancer were screened and identified. Results The research revealed significant downregulation of CRYAB, CAV1, HYAL1, and TUBB6 genes in lung cancer tissues, whereas SERINC2, PAICS, SLC2A1, and BIRC5 genes were markedly upregulated. These genes were predominantly enriched in biological processes such as cell migration, oxidative stress response, and cell cycle regulation, as well as in KEGG pathways like the IL-17 signaling pathway. Immune infiltration analysis demonstrated a high correlation between these genes and the infiltration levels of various immune cells. Furthermore, through drug-gene enrichment analysis and molecular docking experiments, significant correlations were found between drugs such as celecoxib and some exosome-related genes, with interaction targets existing between these drugs and CAV1, SLC2A1, and BIRC5 genes. Conclusion This study unveils the expression characteristics and biological significance of exosome-associated genes in lung cancer. The differential expression of these genes not only offers potential biomarkers for early diagnosis of lung cancer but also lays a foundation for further research into their biological functions in this disease. Lung Cancer Exosomes Gene Expression Biological Functions Immune Infiltration Drug Regulation Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1. Introduction Background Lung cancer stands as one of the most prevalent malignancies worldwide, with persistently high morbidity and mortality rates, posing a severe threat to human health. According to statistics from the World Health Organization (WHO), lung cancer accounts for over 18% of all cancer-related deaths globally, ranking first among all cancers [1; 2]. The high incidence and mortality of lung cancer are closely associated with its inconspicuous early symptoms, advanced stages at diagnosis, and resistance to conventional therapeutic interventions. Therefore, conducting in-depth research into the pathogenesis of lung cancer and identifying effective early diagnostic biomarkers and therapeutic targets are of great significance for improving the prognosis of lung cancer patients. In recent years, exosomes, as crucial mediators of intercellular communication[3], have gradually garnered attention for their roles in tumorigenesis, progression, and metastasis. Exosomes are membrane-bound vesicles with a diameter of approximately 30-150 nm, secreted by cells and widely distributed in various bodily fluids such as blood, saliva, and urine [4]. Exosomes carry a multitude of biomolecules, including proteins, mRNAs, miRNAs, etc., reflecting the physiological and pathological states of cells and transmitting information between them to regulate the biological behaviors of recipient cells. Exosomes and their cargo hold promise as novel targets for early diagnosis, prognostic evaluation, and treatment of lung cancer. For instance, specific miRNAs found in exosomes can serve as biomarkers for early diagnosis of lung cancer [5]. Furthermore, proteins and lipids within exosomes also exhibit potential as biomarkers for lung cancer, applicable in clinical diagnosis and prognosis [6]. Notably, long non-coding RNAs (lncRNAs) and circular RNAs in exosomes play pivotal roles in the progression of lung cancer. For example, aberrant expression of HOTAIR, an lncRNA in exosomes, is intimately linked to the progression of lung cancer [7; 8]. Studies have shown that tumor cell-derived exosomes can promote tumor proliferation, invasion, metastasis, and immune evasion [9; 10], while also influencing immune cells and fibroblasts within the tumor microenvironment, thereby facilitating tumor progression [5]. Hence, exosomes and their cargo present promising avenues for early diagnosis, prognostic evaluation, and treatment of lung cancer. With the advent of high-throughput sequencing technologies, transcriptomics and single-cell sequencing have emerged as powerful tools for investigating the expression profiles and functions of exosome-related genes in lung cancer [11; 12]. By analyzing transcriptomic data from lung cancer patients, differential genes associated with lung cancer can be screened, and further identification of exosome-related genes can be conducted [7]. Additionally, single-cell sequencing technology reveals the gene expression profiles of different cell types within lung cancer tissues, aiding in a deeper understanding of the mechanisms through which exosome-related genes function in lung cancer cells and the tumor microenvironment [13; 14; 15]. Concurrently, drug-gene enrichment analysis and molecular docking techniques offer possibilities for exploring the potential drug regulatory mechanisms of exosome-related genes. For example, by analyzing miRNAs and proteins in exosomes, their interactions with specific drugs can be discovered, providing new avenues for targeted therapy in lung cancer [6; 7; 16; 17]. The present study aims to systematically analyze lung cancer-related transcriptomic data, single-cell data, exosome-related genes, and drug-related data, delving into the expression profiles, functional enrichment, interactions with immune cells, and potential drug regulatory mechanisms of exosome-related genes in lung cancer. The ultimate goal is to provide new insights and evidence for early diagnosis, prognostic evaluation, and targeted therapy of lung cancer. 2. Methods 2.1 Data Acquisition and Processing of Transcriptome Data Eight lung cancer-related datasets, comprising a total of 846 samples, were obtained from the GEO database: GSE32665 (n=179), GSE32863 (n=116), GSE33532 (n=100), GSE43458 (n=110), GSE63459 (n=65), GSE74706 (n=36), GSE75037 (n=166), and GSE13481 (n=74). Each dataset was standardized using log2 transformation. The Combat method was employed to eliminate batch differences. Additionally, information on whether each sample was normal or cancerous was obtained for subsequent use. Furthermore, expression data and clinical survival data for LUAD and LUSC were downloaded from TCGA for subsequent prognostic analysis. 2.2 Acquisition and Analysis of Single-Cell Data The GSE164798 dataset was obtained from the GEO database and analyzed using scanpy. Cells were filtered based on the criteria that each cell needed to express at least 200 genes, each gene needed to be expressed in at least 3 cells, cells with a mitochondrial gene proportion less than 15% were retained, and cells with a total count less than 20,000 were retained. Subsequently, 3,000 highly variable genes were selected for PCA. The bbknn method was used for sample integration. Cell types expressing the target genes were identified, and differential gene expression analysis was conducted between high-expression and low-expression gene groups for enrichment analysis and single-cell prognostic analysis. 2.3 Acquisition of Exosome-Related Genes By searching the GeneCards database with the keyword 'exosome,' genes capable of expressing proteins with a relevance score of 2 or higher were extracted as exosome-related genes. Additionally, some exosome-related genes were collected from the literature as supplements. 2.4 Acquisition and Analysis of Drug-Related Data Drug-gene relationship data were downloaded from the DSigDB database for drug-gene enrichment analysis. For significantly enriched drugs, drug structures were downloaded from the PubChem database, and protein structures were downloaded from the RCSB database for molecular docking between drugs and proteins. 2.5 Identification of Cancer-Related Exosome Genes For the GEO datasets, samples were divided into a control group (n=370) and an experimental group (n=476) based on whether they were cancerous. Differential genes between the two groups were identified using the limma package. The obtained differential genes were intersected with exosome genes, resulting in 47 exosome-related differential genes. Subsequently, significantly related genes were screened using univariate logistic regression, yielding 47 genes. Then, these 47 genes were used as features to construct random forest and LASSO classification models for feature selection. The genes selected by both methods were intersected, ultimately yielding 8 genes as the final cancer-related exosome genes. 2.6 Enrichment Analysis KEGG and GO enrichment analyses were conducted using clusterProfiler, and GSEA analysis was also performed using clusterProfiler, with corrected p-values ≤ 0.05 considered significant. 2.7 Prognostic Analysis A prognostic model was constructed using Cox regression, and survival curves were plotted. 2.8 Immunohistochemical Images Immunohistochemical staining images of genes in cancerous and normal tissues were downloaded from the HPA database to observe expression differences. 2.9 Immune Infiltration Analysis ssGSEA was used to score immune infiltration in samples. For feature genes, the Spearman correlation coefficient between gene expression and immune infiltration scores was calculated as the correlation between them. 2.10 Quantitative real-time PCR Total RNA was extracted from five lung cell lines, including the normal bronchial epithelial cell line BEAS-2B and four lung cancer cell lines (A549, PC9, NCI-H226, SK-MES-1) using TRIzol reagent (Invitrogen, USA) following the manufacturer’s protocol. The RNA concentration and purity were assessed using a NanoDrop spectrophotometer (Thermo Fisher Scientific, USA). Reverse transcription was performed using the PrimeScript RT reagent kit (Takara, Japan) to synthesize complementary DNA (cDNA) from 1 μg of total RNA. Quantitative real-time PCR (qRT-PCR) was carried out using the SYBR Green PCR Master Mix (Takara, Japan) on a QuantStudio 6 Flex Real-Time PCR System (Applied Biosystems, USA). Gene-specific primers for BIRC5, CAV1, CRYAB, HYAL1, PAICS, SERINC2, SLC2A1, and TUBB6 were designed using the Primer-BLAST tool (NCBI) and synthesized by Sangon Biotech (Shanghai, China). The housekeeping gene GAPDH was used as an internal control.The qRT-PCR reaction conditions were as follows:Initial denaturation at 95°C for 30 seconds; Followed by 40 cycles of 95°C for 5 seconds and 60°C for 30 seconds; Melting curve analysis was performed to verify product specificity. 2.11 Statistical Analysis The Wilcoxon rank-sum test was used to determine whether there were differences between two groups. The Spearman correlation coefficient was used to calculate the correlation between two groups. Unless otherwise specified, an adjusted p-value ≤ 0.05 was considered significant. The log-rank test was used to calculate p-values for survival curves. 3. Results 3.1 Differential Gene Results The collected datasets were grouped into cancer and normal samples. Differential genes between cancer and normal samples were identified using the criteria |logFC| ≥ 2 and adjusted p-value ≤ 0.05. Figure 1A showcases the top 50 upregulated and downregulated genes, respectively, revealing significant differences in gene expression between lung cancer tissues and normal tissues. Figure 1B presents the corresponding volcano plot. The two principal components from each dataset are shown in Supplementary Figure 1. A total of 661 differential genes were identified, and intersection analysis with the collected 878 exosome-related genes yielded 47 exosome-associated differential genes (Figure 1C). Subsequently, univariate logistic regression was used to further screen the genes, and no genes were excluded. Lasso regression and random forest models were then constructed, with the optimal λ for Lasso cross-validation shown in Supplementary Figure 2A and the importance of genes screened by random forest shown in Supplementary Figure 2B. Feature selection of these 47 genes resulted in 8 intersecting genes (Figure 1D). Figure 1E displays the gene expression of these 8 genes in normal and cancer groups. As shown, CRYAB, CAV1, HYAL1, and TUBB6 genes were significantly downregulated in lung cancer tissues, whereas SERINC2, PAICS, SLC2A1, and BIRC5 genes were significantly upregulated. Figure 1F presents the expression correlation of these 8 genes in cancer. The chromosomal locations of the 8 feature genes are shown in Supplementary Figure 2C. Additionally, immunohistochemical staining images of genes in cancer and normal tissues were downloaded from the HPA database to observe the expression differences of the 8 differential genes in various tissue samples. As shown in Figure 2, there were significant expression differences for all 8 differential genes between normal and lung cancer tissue samples. 3.2 Enrichment Analysis Results GO enrichment analysis was performed on the 47 exosome-associated differential genes. As shown in Figure 3A-B, these genes were enriched in biological processes such as leukocyte migration, response to reactive oxygen species, leukocyte chemotaxis, and myeloid leukocyte migration. The most relevant cellular components were membrane rafts and membrane microdomains, while the most relevant molecular functions were enzyme inhibitor activity and cytokine receptor binding. Figure 3C illustrates the network relationships between genes and pathways among the top ten results of the GO enrichment analysis. KEGG pathway enrichment analysis revealed enrichment only in the IL-17 signaling pathway. Figure 3D shows the top 5 GSEA enrichment analysis results, indicating that the differential genes were highly enriched in processes such as Cell Cycle, Biosynthesis of Amino Acids, Ribosome, and DNA Replication, while Vascular Smooth Muscle Contraction was significantly suppressed. 3.3 Immune Cell Analysis Results Immune infiltration scores for all samples were calculated using the ssGSEA method, revealing significant differences in infiltration levels of most immune cells between normal and cancer groups (Figure 3A). Subsequently, the correlation between immune infiltration scores and feature genes was assessed, showing high correlations between the 8 exosome-associated differential genes (TUBB6, SLC2A1, SERINC2, etc.) and immune cells (Figure 3B). Figure 3C displays the UMAP plot of single-cell analysis results for lung cancer. As shown in Figure 4D, the expression of exosome target genes in immune cell types was analyzed, revealing high expression of TUBB6 in Plasma Cells, Myeloid-DC cells, and Myeloid-Mono/Macro cells, and high expression of SLC2A1 in Plasma Cells, Naive B Cells, Memory B Cells, and NKT Cells. As shown in Figure 4E, we used IGHA1, IGHA2, and IGHG1 to mark Memory B Cells, IGHD, IGHM, and CD19 to mark Naive B Cells, and XBP1, MZB1, and JCHAIN to mark Plasma Cells. Subsequently, based on the correlation between exosome target genes and immune cells shown in Figure 4B, we performed a grouped analysis of SLC2A1 and TUBB6 expression in bone marrow-derived monocytes/macrophages. As shown in Figure 4F, lung adenocarcinoma patients with high SLC2A1 expression had higher survival rates than those with low SLC2A1 expression (P=0.04). Conversely, as shown in Figure 4G, lung adenocarcinoma patients with high TUBB6 expression had lower survival rates than those with low TUBB6 expression (P=0.01). We then analyzed these genes in the LUSC dataset. As shown in Figure 5A, lung squamous cell carcinoma patients with high SLC2A1 expression in DC cells had lower survival rates than those with low expression. Figure 5B shows that lung squamous cell carcinoma patients with high SLC2A1 expression in monocytes/macrophages had lower survival rates. Figure 5C indicates that lung squamous cell carcinoma patients with high expression in Mast-cells had lower survival rates. Figure 5D shows that lung squamous cell carcinoma patients with high TUBB6 expression in monocytes/macrophages had lower survival rates. Figure 5E demonstrates that lung squamous cell carcinoma patients with high TUBB6 expression in DC cells had lower survival rates. 3.4 Drug Analysis Results Drug regulatory enrichment analysis was performed on the 8 feature genes, revealing significant correlations between drugs such as celecoxib, dacarbazine, 1h-pyrazolo[3,4-d]pyrimidine, and Nitroprusside and these genes (Figure 4A-B). Among them, celecoxib was found to be associated with CAV1, SLC2A1, and BIRC5 genes. We then downloaded the 3D structural proteins of CAV1, SLC2A1, and BIRC5 genes and the structural information of celecoxib to perform molecular docking. The results showed that celecoxib has interaction targets with CAV1, SLC2A1, and BIRC5 (Figure 4D). 3.5 Classification Model We analyzed whether the expression of a single gene among the 8 exosome-associated differential genes could predict whether a sample was lung cancer. As shown in Figure 7A, all 8 differential genes could predict canceration with good performance, with AUC values above 0.85, suggesting their high predictive value. Additionally, we fitted the 8 genes into a logistic regression model to predict canceration, achieving an ROC of 0.96 (Figure 7B). As shown in Figure 7C, for the 8 feature genes, we downloaded their RNA-binding proteins from the ENCORI database and constructed an RBP network. Figure 7D presents the transcriptional regulatory network constructed using transcription factor data related to these 8 feature genes from the TRRUST database. Finally, based on the above results, patients were divided into high-risk and low-risk groups. As shown in Figure 7E, the survival rate of high-risk lung adenocarcinoma patients was significantly lower than that of low-risk patients (P=0.0048). Similarly, Figure 7F demonstrates that the survival rate of high-risk lung squamous cell carcinoma patients was significantly lower than that of low-risk patients (P=0.0045). 3.6 Validation of Analytical Results in Cell Lines The qRT-PCR results revealed significant differences in the mRNA expression levels of BIRC5, CAV1, CRYAB, HYAL1, PAICS, SERINC2, SLC2A1, and TUBB6 between normal lung epithelial cells (BEAS-2B) and lung cancer cell lines (A549, PC9, NCI-H226, SK-MES-1). The specific findings are summarized as follows:BIRC5, HYAL1, PAICS, SERINC2, SLC2A1, and TUBB6 were significantly upregulated in all lung cancer cell lines compared to normal BEAS-2B cells (P < 0.0001).The highest expression levels were observed in PC9 and A549, suggesting that these genes are involved in lung cancer progression, particularly in lung adenocarcinoma and EGFR-mutant lung cancer.CAV1 expression showed no significant difference between normal and cancer cells, except for a significant downregulation in PC9 cells (P < 0.01), suggesting that CAV1 may play a context-dependent role in lung cancer.CRYAB showed a modest but significant increase in PC9 cells compared to normal cells (P < 0.05), while the expression levels in other lung cancer cell lines remained relatively unchanged, suggesting that CRYAB might be involved in the specific oncogenic signaling in EGFR-mutant lung cancer.The highest expression of BIRC5, HYAL1, PAICS, and SERINC2 in PC9 cells indicates that these genes might be closely related to EGFR signaling or drug resistance in lung cancer.HYAL1 and SLC2A1 showed the highest expression levels in SK-MES-1, suggesting their possible involvement in lung squamous carcinoma progression.The overall upregulation of BIRC5, HYAL1, PAICS, SERINC2, SLC2A1, and TUBB6 in lung cancer cell lines suggests that these genes may serve as potential oncogenes or biomarkers for lung cancer.CAV1 and CRYAB showed cell line–specific changes, indicating their potential roles in the molecular heterogeneity of lung cancer.The particularly high expression of BIRC5, PAICS, and SERINC2 in PC9 cells suggests that they may be involved in EGFR-mediated oncogenic pathways and could serve as targets for personalized therapy in lung adenocarcinoma with EGFR mutations. These findings provide a foundation for further investigation into the roles of these genes in lung cancer development, progression, and therapeutic resistance. 4. Discussion By integrating multiple lung cancer-related datasets, this study systematically analyzed the expression differences, functional enrichment, immune infiltration characteristics, and drug regulatory potential of exosome-related genes in lung cancer, yielding the following significant findings: Firstly, we screened differential genes related to lung cancer from multiple GEO datasets and intersected them with exosome-related genes [18; 19], ultimately identifying 8 cancer-associated exosome genes. In this study, we observed that the expressions of CRYAB, CAV1, HYAL1, and TUBB6 genes were significantly reduced in lung cancer tissues, whereas the expressions of SERINC2, PAICS, SLC2A1, and BIRC5 genes were significantly increased. These expression differences may reflect the distinct biological functions of these genes in the initiation and progression of lung cancer [20; 21], and they also exhibited pronounced expression disparities in immunohistochemical staining images, suggesting their potential crucial roles in lung cancer development. CRYAB, a small heat shock protein, primarily participates in cellular stress responses and protein folding [22; 23]. In normal tissues, CRYAB aids in maintaining cellular stability and function [ 24 ]. Its decreased expression in lung cancer tissues may indicate weakened cellular stress responses, leading to reduced cellular tolerance to damage and promoting tumorigenesis and progression [25; 26]. CAV1, an essential protein on the cell membrane, is involved in cell signaling, cytoskeleton regulation, and intercellular communication. The downregulation of CAV1 may be associated with disrupted intercellular communication and enhanced cell proliferation. HYAL1, an enzyme that degrades hyaluronic acid, a vital component of the extracellular matrix [27; 28; 29], may accumulate the extracellular matrix when its expression is reduced, thereby promoting tumor cell invasion and metastasis [ 30 ]. TUBB6, a member of the tubulin family, participates in cytoskeleton formation and cell division. Reduced TUBB6 expression may affect normal cell division and morphological maintenance, thus promoting abnormal proliferation of tumor cells [ 31 ]. SERINC2 is involved in intracellular material transport and metabolic regulation. Its increased expression may be related to metabolic reprogramming in tumor cells, supporting their rapid proliferation [ 32 ]. PAICS, a critical enzyme in the purine metabolism pathway, participates in cellular energy metabolism [ 33 ]. Its increased expression may provide more energy for tumor cells, promoting their growth and proliferation [34; 35]. SLC2A1, a glucose transporter on the cell membrane, is responsible for transporting glucose from the extracellular space into the cell. Its increased expression may enhance the glycolytic capacity of tumor cells, providing energy for their rapid growth [36; 37]. BIRC5, an apoptosis inhibitor protein, inhibits cell apoptosis. Its increased expression may allow tumor cells to evade apoptosis, thereby promoting tumor progression. The expression differences of these genes may be closely related to the biological processes of lung cancer [ 38 ]. These processes collectively drive the initiation, progression, and metastasis of lung cancer. Further analysis of the correlation between the expression levels of these genes and the clinical prognosis of lung cancer patients may provide novel biomarkers for lung cancer prognosis assessment. Enrichment analysis revealed that these exosome-related differential genes were primarily enriched in biological processes such as cell migration, oxidative stress response, and cell cycle regulation, as well as KEGG pathways such as the IL-17 signaling pathway. These biological processes and pathways are closely associated with the invasion, metastasis, and immune escape of lung cancer [ 38 , 39 ], further confirming the biological significance of exosome-related genes in lung cancer. For instance, cell migration and oxidative stress response play pivotal roles in tumor invasion and metastasis, while the IL-17 signaling pathway is closely related to the regulation of immune cells in the tumor microenvironment [39; 40; 41]. These results suggest that exosome-related genes may promote lung cancer progression by modulating these biological processes and signaling pathways. In terms of immune cell analysis, we found that these exosome-related genes were highly correlated with the infiltration levels of various immune cells. For instance, TUBB6 was highly expressed in plasma cells, myeloid dendritic cells, and myeloid monocytes/macrophages, whereas SLC2A1 was highly expressed in plasma cells, naive B cells, memory B cells, and NKT cells. Furthermore, based on the expression levels of these genes, we analyzed the survival prognosis of patients with lung adenocarcinoma and lung squamous cell carcinoma and found that the expression levels of SLC2A1 and TUBB6 were closely related to patient survival rates. These results suggest that these genes may influence the prognosis of lung cancer by affecting the functions and infiltration states of immune cells [42; 43]. This discovery provides new targets and ideas for immunotherapy in lung cancer. Future studies can further investigate the specific mechanisms of these genes in immune cells and how to improve the prognosis of lung cancer patients by modulating immune cell infiltration. The expression levels of TUBB6 and SLC2A1 are closely related to patient survival rates, suggesting that these genes may affect the prognosis of lung cancer by influencing the functions and infiltration states of immune cells[44; 45; 46]. The expression differences of these genes not only provide potential biomarkers for early diagnosis of lung cancer but also lay the foundation for further research on their biological functions in lung cancer. In terms of drug regulation, through drug-gene enrichment analysis and molecular docking experiments, we found significant correlations between drugs such as celecoxib and some exosome-related genes [47; 48; 49], and identified interaction targets with CAV1, SLC2A1, and BIRC5 genes [ 50 ]. This indicates that these drugs may affect the biological behavior of lung cancer by regulating the expression of exosome-related genes, providing potential drug candidates for targeted therapy of lung cancer. For example, celecoxib, a nonsteroidal anti-inflammatory drug, is widely used in the treatment of various inflammation-related diseases. Our study found that celecoxib may inhibit the proliferation and metastasis of lung cancer cells through interactions with CAV1, SLC2A1, and BIRC5 genes. This discovery provides a theoretical basis for the potential application of celecoxib in lung cancer treatment, and its efficacy and mechanism can be further validated through in vitro cell experiments and in vivo animal model experiments in the future. However, this study also has some limitations. Firstly, our research is primarily based on bioinformatics analysis and lacks experimental validation. Although bioinformatics analysis provides us with valuable clues and hypotheses, these results need to be further validated through in vitro cell experiments and in vivo animal model experiments [51; 52]. For instance, CRISPR/Cas9 gene editing technology can be utilized to knock out or overexpress these exosome-related genes, observing their effects on lung cancer cell proliferation, migration, and invasion, as well as their regulatory roles in immune cell infiltration. Secondly, our study only focused on the expression differences and functional enrichment of exosome-related genes in lung cancer, and the biological characteristics of exosomes themselves (such as size, morphology, content, etc.) and their mechanisms in lung cancer have not been thoroughly explored. Future studies can combine exosome isolation and identification technologies to delve into the mechanisms of exosomes in the initiation, progression, and treatment of lung cancer [ 53 ]. For example, exosomes secreted by lung cancer cells can be isolated and identified through ultracentrifugation and nanoparticle tracking analysis (NTA) technologies[54; 55], and their protein and RNA components can be analyzed to further reveal the biological functions of exosomes in lung cancer. In addition, clinical samples can be combined to study the expression differences of exosome-related genes in different lung cancer subtypes and their correlation with clinical prognosis, providing a basis for personalized treatment. 5. Conclusion This study systematically analyzed lung cancer-related transcriptome data, single-cell data, exosome-related genes, and drug-related data, screening out 8 cancer-associated exosome genes and conducting in-depth discussions on their functional enrichment, immune infiltration characteristics, and drug regulatory potential. These genes exhibited significant expression differences between lung cancer tissues and normal tissues, were highly correlated with the infiltration levels of various immune cells, and had certain predictive value for the survival prognosis of lung cancer patients. Furthermore, we found significant correlations between drugs such as celecoxib and some exosome-related genes, and identified interaction targets with CAV1, SLC2A1, and BIRC5 genes, providing potential drug candidates for targeted therapy of lung cancer. In the future, it is necessary to experimentally validate the functions of these exosome-related genes and drug regulatory mechanisms to provide new bases for early diagnosis, prognosis assessment, and targeted therapy of lung cancer. Declarations Conflict of Interest The authors declare that there are no conflicts of interest related to this work. Author Contributions QZ and YZ contributed to the study conception and design. QZ performed the data collection and analysis. YZ supervised the project and provided critical revisions. QZ drafted the manuscript, and YZ reviewed and edited the final version. Both authors have read and approved the final manuscript and agree to be accountable for the content of the work. Funding This research did not receive any funding support. Acknowledgments This is a short text to acknowledge the contributions of specific colleagues, institutions, or agencies that aided the efforts of the authors. Ethics declaration Ethics declaration: not applicable. Consent to Participate declaration All authors consent to participate in this research. Consent to Publish declaration All authors consent to publish this work. Clinical trial registration Clinical trial number: not applicable. References Bray F, Laversanne M, Sung H, Ferlay J, Siegel RL, Soerjomataram I, Jemal A. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. Cancer J Clin. 2024;74:229–63. Ma SC, Bai X, Guo XJ, Liu L, Xiao LS, Lin Y, Tan JL, Cai XT, Wen YX, Ma H, Fu QJ, Leng MX, Zhang YP, Long LL, Guo ZQ, Wu DH, Zhou JG, Dong ZY. Organ-specific metastatic landscape dissects PD-(L)1 blockade efficacy in advanced non-small cell lung cancer: applicability from clinical trials to real-world practice. BMC Med. 2022;20:120. Simons M, Raposo G. Exosomes–vesicular carriers for intercellular communication. Curr Opin Cell Biol. 2009;21:575–81. Théry C, Zitvogel L, Amigorena S. Exosomes: composition, biogenesis and function. Nat Rev Immunol. 2002;2:569–79. Zhao J, Li X, Liu L, Zhu Z, He C. Exosomes in lung cancer metastasis, diagnosis, and immunologically relevant advances. Front Immunol 14 (2023). Hsu M-T, Wang Y-K, Tseng YJ. Exosomal Proteins and Lipids as Potential Biomarkers for Lung Cancer Diagnosis, Prognosis, and Treatment. Cancers. 2022;14:732. Cao Y, Liu X, Liu J, Su Z, Liu W, Yang L, Zhang L. Diagnostic value of exosomal noncoding RNA in lung cancer: a meta-analysis. Front Oncol 14 (2024). Hussen BM, Abdullah SR, Hama Faraj GS, Rasul MF, Salihi A, Ghafouri-Fard S, Taheri M, Mokhtari M. Exosomal circular RNA: a signature for lung cancer progression. Cancer Cell Int. 2022;22:378. Peinado H, Alečković M, Lavotshkin S, Matei I, Costa-Silva B, Moreno-Bueno G, Hergueta-Redondo M, Williams C, García-Santos G, Ghajar C, Nitadori-Hoshino A, Hoffman C, Badal K, Garcia BA, Callahan MK, Yuan J, Martins VR, Skog J, Kaplan RN, Brady MS, Wolchok JD, Chapman PB, Kang Y, Bromberg J, Lyden D. Melanoma exosomes educate bone marrow progenitor cells toward a pro-metastatic phenotype through MET. Nat Med. 2012;18:883–91. Costa-Silva B, Aiello NM, Ocean AJ, Singh S, Zhang H, Thakur BK, Becker A, Hoshino A, Mark MT, Molina H, Xiang J, Zhang T, Theilen TM, García-Santos G, Williams C, Ararso Y, Huang Y, Rodrigues G, Shen TL, Labori KJ, Lothe IM, Kure EH, Hernandez J, Doussot A, Ebbesen SH, Grandgenett PM, Hollingsworth MA, Jain M, Mallya K, Batra SK, Jarnagin WR, Schwartz RE, Matei I, Peinado H, Stanger BZ, Bromberg J, Lyden D. Pancreatic cancer exosomes initiate pre-metastatic niche formation in the liver. Nat Cell Biol. 2015;17:816–26. Comprehensive molecular profiling of lung adenocarcinoma. Nature. 2014;511:543–50. Tirosh I, Izar B, Prakadan SM, Wadsworth MH 2nd, Treacy D, Trombetta JJ, Rotem A, Rodman C, Lian C, Murphy G, Fallahi-Sichani M, Dutton-Regester K, Lin JR, Cohen O, Shah P, Lu D, Genshaft AS, Hughes TK, Ziegler CG, Kazer SW, Gaillard A, Kolb KE, Villani AC, Johannessen CM, Andreev AY, Van Allen EM, Bertagnolli M, Sorger PK, Sullivan RJ, Flaherty KT, Frederick DT, Jané-Valbuena J, Yoon CH, Rozenblatt-Rosen O, Shalek AK, Regev A, Garraway LA. Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq. Science (New York, N.Y.) 352 (2016) 189–96. Chen W-W, Liu W, Li Y, Wang J, Ren Y, Wang G, Chen C, Li H. Deciphering the Immune–Tumor Interplay During Early-Stage Lung Cancer Development via Single-Cell Technology. Front Oncol 11 (2022). Li Q, Wang R, Yang Z, Li W, Yang J, Wang Z, Bai H, Cui Y, Tian Y, Wu Z, Guo Y, Xu J, Wen L, He J, Tang F, Wang J. Molecular profiling of human non-small cell lung cancer by single-cell RNA-seq. Genome Med. 2022;14:87. Wang Z, Yang L, Wang W, Zhou H, Chen J, Ma Z, Wang X, Zhang Q, Liu H, Zhou C, Guo Z, Zhang X. Comparative immunological landscape between pre- and early-stage LUAD manifested as ground-glass nodules revealed by scRNA and scTCR integrated analysis. Cell Communication Signal. 2023;21:325. Kalluri R. The biology and function of exosomes in cancer. J Clin Investig. 2016;126:1208–15. Tkach M, Théry C. Communication by Extracellular Vesicles: Where We Are and Where We Need to Go. Cell. 2016;164:1226–32. Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W. Smyth, limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 2015;43:e47. Edgar R, Domrachev M, Lash AE. Gene Expression Omnibus: NCBI gene expression and hybridization array data repository. Nucleic Acids Res. 2002;30:207–10. Kahlert C, Melo SA, Protopopov A, Tang J, Seth S, Koch M, Zhang J, Weitz J, Chin L, Futreal A, Kalluri R. Identification of double-stranded genomic DNA spanning all chromosomes with mutated KRAS and p53 DNA in the serum exosomes of patients with pancreatic cancer. J Biol Chem. 2014;289:3869–75. Melo SA, Luecke LB, Kahlert C, Fernandez AF, Gammon ST, Kaye J, LeBleu VS, Mittendorf EA, Weitz J, Rahbari N, Reissfelder C, Pilarsky C, Fraga MF, Piwnica-Worms D, Kalluri R. Glypican-1 identifies cancer exosomes and detects early pancreatic cancer. Nature. 2015;523:177–82. Taylor RP, Benjamin IJ. Small heat shock proteins: a new classification scheme in mammals. J Mol Cell Cardiol. 2005;38:433–44. Cai HB, Zhao MY, Li XH, Li YQ, Yu TH, Wang CZ, Wang LN, Xu WY, Liang B, Cai YP, Zhang F, Hong WM. Single cell sequencing revealed the mechanism of CRYAB in glioma and its diagnostic and prognostic value. Front Immunol. 2023;14:1336187. Tian B, Li X, Li W, Shi Z, He X, Wang S, Zhu X, Shi N, Li Y, Wan P, Zhu C. CRYAB suppresses ferroptosis and promotes osteogenic differentiation of human bone marrow stem cells via binding and stabilizing FTH1. Aging. 2024;16:8965–79. Qin H, Ni Y, Tong J, Zhao J, Zhou X, Cai W, Liang J, Yao X. Elevated expression of CRYAB predicts unfavorable prognosis in non-small cell lung cancer. Med Oncol (Northwood Lond Engl). 2014;31:142. Zhang J, Liu J, Wu J, Li W, Chen Z, Yang L. Progression of the role of CRYAB in signaling pathways and cancers. OncoTargets therapy. 2019;12:4129–39. Kalluri R, LeBleu VS. The biology, function, and biomedical applications of exosomes. Sci (New York N Y). 2020;367:eaau6977. Raposo G, Stoorvogel W. Extracellular vesicles: Exosomes, microvesicles, and friends. J Cell Biol. 2013;200:373–83. Li Y, Yin Z, Fan J, Zhang S, Yang W. The roles of exosomal miRNAs and lncRNAs in lung diseases. Signal Transduct Target Therapy. 2019;4:47. Witzel I, Marx AK, Müller V, Wikman H, Matschke J, Schumacher U, Stürken C, Prehm P, Laakmann E, Schmalfeldt B, Milde-Langosch K, Oliveira-Ferrer L. Role of HYAL1 expression in primary breast cancer in the formation of brain metastases. Breast Cancer Res Treat. 2017;162:427–38. Wen S, Zou R, Du X, Pan R, Li R, Xia J, Xu C, Wang R, Jiang F, Zhou G, Feng J, Zhu M, Wang X, Shen B. Identification of macrophage-related genes correlated with prognosis and immunotherapy efficacy in non-small cell lung cancer. Heliyon. 2024;10:e27170. Zeng Y, Xiao D, He H, He J, Pan H, Yang W, Chen Y, He J. SERINC2-knockdown inhibits proliferation, migration and invasion in lung adenocarcinoma. Oncol Lett. 2018;16:5916–22. Pegg AE. Mammalian polyamine metabolism and function. IUBMB Life. 2009;61:880–94. Li Y, Zhu L, Mao J, Zheng H, Hu Z, Yang S, Mao T, Zhou T, Cao P, Wu H, Wang X, Wang J, Lin F, Shen H. Genome-scale CRISPR-Cas9 screen identifies PAICS as a therapeutic target for EGFR wild-type non-small cell lung cancer. MedComm. 2024;5:e483. Tang X, Wu Y, Yang J, Zhu W. Regulating COX10-AS1 / miR-142-5p / PAICS axis inhibits the proliferation of non-small cell lung cancer. Bioengineered. 2021;12:4643–53. Xu Y, Chai B, Wang X, Wu Z, Gu Z, Liu X, Zhao Y, Chen T, Ma Z, Sun Q. miRNA-199a-5p/SLC2A1 axis regulates glucose metabolism in non-small cell lung cancer. J Cancer. 2022;13:2352–61. Ancey PB, Contat C, Boivin G, Sabatino S, Pascual J, Zangger N, Perentes JY, Peters S, Abel ED, Kirsch DG, Rathmell JC, Vozenin MC, Meylan E. GLUT1 Expression in Tumor-Associated Neutrophils Promotes Lung Cancer Growth and Resistance to Radiotherapy. Cancer Res. 2021;81:2345–57. Kahm YJ, Kim RK. BIRC5: A novel therapeutic target for lung cancer stem cells and glioma stem cells. Biochem Biophys Res Commun. 2023;682:141–7. Gorczynski RM. IL-17 Signaling in the Tumor Microenvironment. Adv Exp Med Biol. 2020;1240:47–58. Seif F, Torki Z, Zalpoor H, Habibi M, Pornour M. Breast cancer tumor microenvironment affects Treg/IL-17-producing Treg/Th17 cell axis: Molecular and therapeutic perspectives. Mol therapy oncolytics. 2023;28:132–57. Coffelt SB, Kersten K, Doornebal CW, Weiden J, Vrijland K, Hau CS, Verstegen NJM, Ciampricotti M, Hawinkels L, Jonkers J, de Visser KE. IL-17-producing γδ T cells and neutrophils conspire to promote breast cancer metastasis. Nature. 2015;522:345–8. Bindea G, Mlecnik B, Tosolini M, Kirilovsky A, Waldner M, Obenauf AC, Angell H, Fredriksen T, Lafontaine L, Berger A, Bruneval P, Fridman WH, Becker C, Pagès F, Speicher MR, Trajanoski Z, Galon J. Spatiotemporal dynamics of intratumoral immune cells reveal the immune landscape in human cancer. Immunity. 2013;39:782–95. Gentles AJ, Newman AM, Liu CL, Bratman SV, Feng W, Kim D, Nair VS, Xu Y, Khuong A, Hoang CD, Diehn M, West RB, Plevritis SK, Alizadeh AA. The prognostic landscape of genes and infiltrating immune cells across human cancers. Nat Med. 2015;21:938–45. Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, Paulovich A, Pomeroy SL, Golub TR, Lander ES, Mesirov JP. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci USA. 2005;102:15545–50. Yang TY. A SATS algorithm for jointly identifying multiple differentially expressed gene sets. Stat Med. 2011;30:2028–39. Peng J, Xiao L-S, Dong Z-Y, Li W-W, Wang K-Y, Wu D-H, Liu L. Potential predictive value of JAK2 expression for Pan-cancer response to PD-1 blockade immunotherapy. Translational Cancer Res. 2018;7:462–71. Lamb J, Crawford ED, Peck D, Modell JW, Blat IC, Wrobel MJ, Lerner J, Brunet JP, Subramanian A, Ross KN, Reich M, Hieronymus H, Wei G, Armstrong SA, Haggarty SJ, Clemons PA, Wei R, Carr SA, Lander ES, Golub TR. The Connectivity Map: using gene-expression signatures to connect small molecules, genes, and disease. Volume 313. Science; 2006. pp. 1929–35. (New York, N.Y.). Gullans SR. Connecting the dots using gene-expression profiles. N Engl J Med. 2006;355:2042–4. Morris GM, Huey R, Lindstrom W, Sanner MF, Belew RK, Goodsell DS. Olson, AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility. J Comput Chem. 2009;30:2785–91. Hashemi Goradel N, Najafi M, Salehi E, Farhood B, Mortezaee K. Cyclooxygenase-2 in cancer: A review. J Cell Physiol. 2019;234:5683–99. Huang da W, Sherman BT, Lempicki RA. Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Res. 2009;37:1–13. Kaelin WG Jr.. The concept of synthetic lethality in the context of anticancer therapy. Nat Rev Cancer. 2005;5:689–98. Xu Z, Chen Y, Ma L, Chen Y, Liu J, Guo Y, Yu T, Zhang L, Zhu L, Shu Y. Role of exosomal non-coding RNAs from tumor cells and tumor-associated macrophages in the tumor microenvironment. Mol therapy: J Am Soc Gene Therapy. 2022;30:3133–54. Zhang Y, Kim MS, Jia B, Yan J, Zuniga-Hertz JP, Han C, Cai D. Hypothalamic stem cells control ageing speed partly through exosomal miRNAs. Nature. 2017;548:52–7. Chen X, Yu L, Hao K, Yin X, Tu M, Cai L, Zhang L, Pan X, Gao Q, Huang Y. Fucosylated exosomal miRNAs as promising biomarkers for the diagnosis of early lung adenocarcinoma. Front Oncol 12 (2022). Additional Declarations No competing interests reported. Supplementary Files SupFig1.pdf SupFig.1 Demonstration of Batch Correction Effects on Lung Cancer Data A. Principal Component Analysis of Data Before Batch Correction. B. Principal Component Analysis of Data After Batch Correction. SupFig2.pdf SupFig.2 Supplementary Analysis of Lung Cancer Feature Gene Selection and Chromosomal Location A. LASSO Cross-Validation Plot for Optimal Lambda. B. Random Forest Feature Importance Plot. C. Chromosomal Locations of 8 Feature Genes. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 26 May, 2025 Reviews received at journal 25 May, 2025 Reviews received at journal 24 May, 2025 Reviewers agreed at journal 16 May, 2025 Reviews received at journal 16 May, 2025 Reviewers agreed at journal 16 May, 2025 Reviewers agreed at journal 14 May, 2025 Reviewers agreed at journal 14 May, 2025 Reviewers invited by journal 14 May, 2025 Editor assigned by journal 05 May, 2025 Submission checks completed at journal 05 May, 2025 First submitted to journal 14 Apr, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6446002","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":456579698,"identity":"c87656a8-2eac-4f79-9b3a-83502495a89c","order_by":0,"name":"Qixiang Zhong","email":"","orcid":"","institution":"First Affiliated Hospital of China Medical University","correspondingAuthor":false,"prefix":"","firstName":"Qixiang","middleName":"","lastName":"Zhong","suffix":""},{"id":456579699,"identity":"5ce890ea-9d2f-4d68-86ce-494f712b1d25","order_by":1,"name":"Yujie Zhao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAwklEQVRIiWNgGAWjYDACCRjN3tj48ANpWngONxtL4FWKoUUivU2Ahxgd8rObHz5g3HM4cebMh21A/XZyug0EtDDOOWZswPAsLXG2dGLbgwKGZGOzAwS0MEskmEkwHLDJnSed2G4AZCVuI6SFTSL9G1ChRO48yYNtEjzEaOGRyIHYMluCkUgtEhI5xQYMB9LqZ/YkAgPZgAi/yM9I3/iA4cBhY4njxx8+/FBhJ0dQCwgw/4EzDYhQPgpGwSgYBaOAMAAA8Uc91zQTtQgAAAAASUVORK5CYII=","orcid":"","institution":"Liaoning Maternal and Child Health Hospital","correspondingAuthor":true,"prefix":"","firstName":"Yujie","middleName":"","lastName":"Zhao","suffix":""}],"badges":[],"createdAt":"2025-04-14 12:23:24","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6446002/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6446002/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":82873030,"identity":"d2607785-96f2-4d09-8512-7b568a619feb","added_by":"auto","created_at":"2025-05-16 09:11:44","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":689508,"visible":true,"origin":"","legend":"\u003cp\u003eMultifaceted Analysis and Selection of Lung Cancer-Related Genes\u003c/p\u003e\n\u003cp\u003eA. Heatmap of Expression Profiles of Top 50 Upregulated and Downregulated Genes.\u003c/p\u003e\n\u003cp\u003eB. Volcano Plot Delineating Cancerous and Normal Groups.\u003c/p\u003e\n\u003cp\u003eC. Intersection of Differentially Expressed Genes and Exosome-Associated Genes.\u003c/p\u003e\n\u003cp\u003eD. Comparative Analysis of Feature Genes Selected by LASSO and Random Forest Models.\u003c/p\u003e\n\u003cp\u003eE. Expression Patterns of Ultimately Selected Genes in Cancerous and Normal Groups.\u003c/p\u003e\n\u003cp\u003eF. Correlation Analysis of Expression of Ultimately Selected Genes in Cancerous Context.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-6446002/v1/376e4a000621c2d485d840e9.png"},{"id":82873027,"identity":"3b96da23-1e7c-49d7-a953-2ebfdb6f54d5","added_by":"auto","created_at":"2025-05-16 09:11:44","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":615731,"visible":true,"origin":"","legend":"\u003cp\u003eImmunohistochemical Analysis of Exosome-Related Genes in Lung Cancer.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-6446002/v1/84760149eeec833f303e5103.png"},{"id":82873022,"identity":"abaaecf0-a560-4e9a-ba03-16dc23370d8e","added_by":"auto","created_at":"2025-05-16 09:11:43","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":577857,"visible":true,"origin":"","legend":"\u003cp\u003ePresentation of GO Enrichment and GSEA Analysis Results for Lung Cancer-Related Genes\u003c/p\u003e\n\u003cp\u003eA-B. GO Enrichment Analysis Results, with Top 10 Terms for Each Branch.\u003c/p\u003e\n\u003cp\u003eC. Illustration of the Relationship Between Genes and Functional Annotations in GO Enrichment Analysis.\u003c/p\u003e\n\u003cp\u003eD. Display of Top 5 GSEA Analysis Results.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-6446002/v1/f45a2346e5bc6ab208e46f4c.png"},{"id":82873024,"identity":"0eac7063-f281-42d0-85b2-c69c91ad6c28","added_by":"auto","created_at":"2025-05-16 09:11:43","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":777954,"visible":true,"origin":"","legend":"\u003cp\u003eAnalysis of Immune Cell Profiles in Lung Cancer Samples and Related Gene Expression\u003c/p\u003e\n\u003cp\u003eA. Boxplot of Immune Cell Scores Across Samples.\u003c/p\u003e\n\u003cp\u003eB. Correlation Between Feature Gene Expression and Immune Cell Scores.\u003c/p\u003e\n\u003cp\u003eC. UMAP Analysis Results of Lung Cancer Single-Cell Data.\u003c/p\u003e\n\u003cp\u003eD. Expression of Exosome-Targeted Genes in Immune Cells.\u003c/p\u003e\n\u003cp\u003eE. Annotation of Cell Types Using Marker Genes.\u003c/p\u003e\n\u003cp\u003eF-G. Analysis of Immune Cell Expression of Target Genes in Lung Adenocarcinoma, Based on Correlation with Exosome-Targeted Genes and Immune Cells.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-6446002/v1/29a924d0bbf12f63130200df.png"},{"id":82873019,"identity":"ad5a2aaf-b6d8-4b96-9fee-8c2ff8320b47","added_by":"auto","created_at":"2025-05-16 09:11:43","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":564278,"visible":true,"origin":"","legend":"\u003cp\u003eAnalysis of Immune Cell Expression of Exosome-Targeted Genes in Lung Squamous Cell Carcinoma\u003c/p\u003e\n\u003cp\u003eA-E. Analysis of Immune Cell Expression of Target Genes in Lung Squamous Cell Carcinoma, Based on Correlation with Exosome-Targeted Genes and Immune Cells.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-6446002/v1/eca1b8d2163762b208b03f97.png"},{"id":82873034,"identity":"9e80e3ec-c3e8-4dc3-85a3-88413424ff5f","added_by":"auto","created_at":"2025-05-16 09:11:44","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":394205,"visible":true,"origin":"","legend":"\u003cp\u003eDisplay of Drug Enrichment Analysis and Molecular Docking Results for Lung Cancer-Related Genes\u003c/p\u003e\n\u003cp\u003eA-B. Drug Enrichment Bar Chart (A) and Bubble Chart (B).\u003c/p\u003e\n\u003cp\u003eC. Drug-Gene Interaction Network.\u003c/p\u003e\n\u003cp\u003eD. Molecular Docking Results of Celecoxib with CAV1 (Left), BIRC5 (Center), and SLC2A1 (Right).\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-6446002/v1/6f745ac3d8c69ef71426b11d.png"},{"id":82873399,"identity":"155b1d7d-3438-4466-9d2c-828d5ab82a95","added_by":"auto","created_at":"2025-05-16 09:19:44","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":531641,"visible":true,"origin":"","legend":"\u003cp\u003eROC Analysis and Regulatory Network Construction for Lung Cancer Prognosis-Related Genes\u003c/p\u003e\n\u003cp\u003eA. ROC Curve for Single Gene.\u003c/p\u003e\n\u003cp\u003eB. ROC Curve for Logistic Regression Model Constructed with 8 Genes.\u003c/p\u003e\n\u003cp\u003eC. RNA-Binding Protein Network Constructed with 8 Genes.\u003c/p\u003e\n\u003cp\u003eD. Transcriptional Regulatory Network of 8 Feature Genes from TRRUST Database.\u003c/p\u003e\n\u003cp\u003eE. Prognosis of Lung Adenocarcinoma.\u003c/p\u003e\n\u003cp\u003eF. Prognosis of Lung Squamous Cell Carcinoma.\u003c/p\u003e","description":"","filename":"image7.png","url":"https://assets-eu.researchsquare.com/files/rs-6446002/v1/558883775158ccda58dac59e.png"},{"id":82873023,"identity":"8606ce6a-b73f-4a91-ba03-2128a1053f6c","added_by":"auto","created_at":"2025-05-16 09:11:43","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":303140,"visible":true,"origin":"","legend":"\u003cp\u003eThe mRNA expression levels of target genes in normal and lung cancer cell lines.\u003c/p\u003e\n\u003cp\u003eQuantitative real-time PCR (qRT-PCR) was used to evaluate the mRNA expression levels of (A) BIRC5, (B) CAV1, (C) CRYAB, (D) HYAL1, (E) PAICS, (F) SERINC2, (G) SLC2A1, and (H) TUBB6in normal human bronchial epithelial cells (BEAS-2B) and four lung cancer cell lines: A549(lung adenocarcinoma), PC9(EGFR-mutant lung adenocarcinoma), NCI-H226(lung squamous carcinoma), and SK-MES-1(lung squamous carcinoma). The data are presented as the mean ± standard deviation (SD) of at least three independent experiments. Statistical significance was assessed using one-way ANOVA followed by Tukey's post hoc test. Significance: P\u0026lt; 0.05 (), P\u0026lt; 0.01 (), P\u0026lt; 0.001 (), P\u0026lt; 0.0001 (), and ns = not significant.\u003c/p\u003e","description":"","filename":"image8.png","url":"https://assets-eu.researchsquare.com/files/rs-6446002/v1/afda34a63c55b37ff9505938.png"},{"id":82874629,"identity":"0a3a0e52-71e9-4e7a-ac7d-1e145df9da5a","added_by":"auto","created_at":"2025-05-16 09:27:51","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5204653,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6446002/v1/77e2a932-faf3-424b-981b-10a270bea1ce.pdf"},{"id":82873032,"identity":"bf527444-8b15-4d55-9cf0-04938cd6555b","added_by":"auto","created_at":"2025-05-16 09:11:44","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":622900,"visible":true,"origin":"","legend":"\u003cp\u003eSupFig.1 Demonstration of Batch Correction Effects on Lung Cancer Data\u003c/p\u003e\n\u003cp\u003eA. Principal Component Analysis of Data Before Batch Correction.\u003c/p\u003e\n\u003cp\u003eB. Principal Component Analysis of Data After Batch Correction.\u003c/p\u003e","description":"","filename":"SupFig1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6446002/v1/7da92ab04662579352cc4cfc.pdf"},{"id":82873021,"identity":"e794b997-393b-4070-b9b7-025cea6a631c","added_by":"auto","created_at":"2025-05-16 09:11:43","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":837607,"visible":true,"origin":"","legend":"\u003cp\u003eSupFig.2 Supplementary Analysis of Lung Cancer Feature Gene Selection and Chromosomal Location\u003c/p\u003e\n\u003cp\u003eA. LASSO Cross-Validation Plot for Optimal Lambda.\u003c/p\u003e\n\u003cp\u003eB. Random Forest Feature Importance Plot.\u003c/p\u003e\n\u003cp\u003eC. Chromosomal Locations of 8 Feature Genes.\u003c/p\u003e","description":"","filename":"SupFig2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6446002/v1/9e6f2190340ffd8fe3b0f228.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Expression Characteristics and Biological Significance of Exosome- Related Genes in Lung Cancer","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eBackground\u003c/p\u003e\n\u003cp\u003eLung cancer stands as one of the most prevalent malignancies worldwide, with persistently high morbidity and mortality rates, posing a severe threat to human health. According to statistics from the World Health Organization (WHO), lung cancer accounts for over 18% of all cancer-related deaths globally, ranking first among all cancers [1; 2]. The high incidence and mortality of lung cancer are closely associated with its inconspicuous early symptoms, advanced stages at diagnosis, and resistance to conventional therapeutic interventions. Therefore, conducting in-depth research into the pathogenesis of lung cancer and identifying effective early diagnostic biomarkers and therapeutic targets are of great significance for improving the prognosis of lung cancer patients.\u003c/p\u003e\n\u003cp\u003eIn recent years, exosomes, as crucial mediators of intercellular communication[3], have gradually garnered attention for their roles in tumorigenesis, progression, and metastasis. Exosomes are membrane-bound vesicles with a diameter of approximately 30-150 nm, secreted by cells and widely distributed in various bodily fluids such as blood, saliva, and urine [4]. Exosomes carry a multitude of biomolecules, including proteins, mRNAs, miRNAs, etc., reflecting the physiological and pathological states of cells and transmitting information between them to regulate the biological behaviors of recipient cells. Exosomes and their cargo hold promise as novel targets for early diagnosis, prognostic evaluation, and treatment of lung cancer. For instance, specific miRNAs found in exosomes can serve as biomarkers for early diagnosis of lung cancer\u0026nbsp;[5]. Furthermore, proteins and lipids within exosomes also exhibit potential as biomarkers for lung cancer, applicable in clinical diagnosis and prognosis\u0026nbsp;[6]. Notably, long non-coding RNAs (lncRNAs) and circular RNAs in exosomes play pivotal roles in the progression of lung cancer. For example, aberrant expression of HOTAIR, an lncRNA in exosomes, is intimately linked to the progression of lung cancer\u0026nbsp;[7; 8]. Studies have shown that tumor cell-derived exosomes can promote tumor proliferation, invasion, metastasis, and immune evasion\u0026nbsp;[9; 10], while also influencing immune cells and fibroblasts within the tumor microenvironment, thereby facilitating tumor progression\u0026nbsp;[5]. Hence, exosomes and their cargo present promising avenues for early diagnosis, prognostic evaluation, and treatment of lung cancer.\u003c/p\u003e\n\u003cp\u003eWith the advent of high-throughput sequencing technologies, transcriptomics and single-cell sequencing have emerged as powerful tools for investigating the expression profiles and functions of exosome-related genes in lung cancer [11; 12]. By analyzing transcriptomic data from lung cancer patients, differential genes associated with lung cancer can be screened, and further identification of exosome-related genes can be conducted [7]. Additionally, single-cell sequencing technology reveals the gene expression profiles of different cell types within lung cancer tissues, aiding in a deeper understanding of the mechanisms through which exosome-related genes function in lung cancer cells and the tumor microenvironment [13; 14; 15]. Concurrently, drug-gene enrichment analysis and molecular docking techniques offer possibilities for exploring the potential drug regulatory mechanisms of exosome-related genes. For example, by analyzing miRNAs and proteins in exosomes, their interactions with specific drugs can be discovered, providing new avenues for targeted therapy in lung cancer [6; 7; 16; 17].\u003c/p\u003e\n\u003cp\u003eThe present study aims to systematically analyze lung cancer-related transcriptomic data, single-cell data, exosome-related genes, and drug-related data, delving into the expression profiles, functional enrichment, interactions with immune cells, and potential drug regulatory mechanisms of exosome-related genes in lung cancer. The ultimate goal is to provide new insights and evidence for early diagnosis, prognostic evaluation, and targeted therapy of lung cancer.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003ch2\u003e2.1 Data Acquisition and Processing of Transcriptome Data\u003c/h2\u003e\n\u003cp\u003eEight lung cancer-related datasets, comprising a total of 846 samples, were obtained from the GEO database: GSE32665 (n=179), GSE32863 (n=116), GSE33532 (n=100), GSE43458 (n=110), GSE63459 (n=65), GSE74706 (n=36), GSE75037 (n=166), and GSE13481 (n=74). Each dataset was standardized using log2 transformation. The Combat method was employed to eliminate batch differences. Additionally, information on whether each sample was normal or cancerous was obtained for subsequent use. Furthermore, expression data and clinical survival data for LUAD and LUSC were downloaded from TCGA for subsequent prognostic analysis.\u003c/p\u003e\n\u003ch2\u003e2.2 Acquisition and Analysis of Single-Cell Data\u003c/h2\u003e\n\u003cp\u003eThe GSE164798 dataset was obtained from the GEO database and analyzed using scanpy. Cells were filtered based on the criteria that each cell needed to express at least 200 genes, each gene needed to be expressed in at least 3 cells, cells with a mitochondrial gene proportion less than 15% were retained, and cells with a total count less than 20,000 were retained. Subsequently, 3,000 highly variable genes were selected for PCA. The bbknn method was used for sample integration. Cell types expressing the target genes were identified, and differential gene expression analysis was conducted between high-expression and low-expression gene groups for enrichment analysis and single-cell prognostic analysis.\u003c/p\u003e\n\u003ch2\u003e2.3 Acquisition of Exosome-Related Genes\u003c/h2\u003e\n\u003cp\u003eBy searching the GeneCards database with the keyword \u0026apos;exosome,\u0026apos; genes capable of expressing proteins with a relevance score of 2 or higher were extracted as exosome-related genes. Additionally, some exosome-related genes were collected from the literature as supplements.\u003c/p\u003e\n\u003ch2\u003e2.4 Acquisition and Analysis of Drug-Related Data\u003c/h2\u003e\n\u003cp\u003eDrug-gene relationship data were downloaded from the DSigDB database for drug-gene enrichment analysis. For significantly enriched drugs, drug structures were downloaded from the PubChem database, and protein structures were downloaded from the RCSB database for molecular docking between drugs and proteins.\u003c/p\u003e\n\u003ch2\u003e2.5 Identification of Cancer-Related Exosome Genes\u003c/h2\u003e\n\u003cp\u003eFor the GEO datasets, samples were divided into a control group (n=370) and an experimental group (n=476) based on whether they were cancerous. Differential genes between the two groups were identified using the limma package. The obtained differential genes were intersected with exosome genes, resulting in 47 exosome-related differential genes. Subsequently, significantly related genes were screened using univariate logistic regression, yielding 47 genes. Then, these 47 genes were used as features to construct random forest and LASSO classification models for feature selection. The genes selected by both methods were intersected, ultimately yielding 8 genes as the final cancer-related exosome genes.\u003c/p\u003e\n\u003ch2\u003e2.6 Enrichment Analysis\u003c/h2\u003e\n\u003cp\u003eKEGG and GO enrichment analyses were conducted using clusterProfiler, and GSEA analysis was also performed using clusterProfiler, with corrected p-values \u0026le; 0.05 considered significant.\u003c/p\u003e\n\u003ch2\u003e2.7 Prognostic Analysis\u003c/h2\u003e\n\u003cp\u003eA prognostic model was constructed using Cox regression, and survival curves were plotted.\u003c/p\u003e\n\u003ch2\u003e2.8 Immunohistochemical Images\u003c/h2\u003e\n\u003cp\u003eImmunohistochemical staining images of genes in cancerous and normal tissues were downloaded from the HPA database to observe expression differences.\u003c/p\u003e\n\u003ch2\u003e2.9 Immune Infiltration Analysis\u003c/h2\u003e\n\u003cp\u003essGSEA was used to score immune infiltration in samples. For feature genes, the Spearman correlation coefficient between gene expression and immune infiltration scores was calculated as the correlation between them.\u003c/p\u003e\n\u003ch2\u003e2.10 Quantitative real-time PCR\u003c/h2\u003e\n\u003cp\u003eTotal RNA was extracted from five lung cell lines, including the normal bronchial epithelial cell line BEAS-2B and four lung cancer cell lines (A549, PC9, NCI-H226, SK-MES-1) using TRIzol reagent (Invitrogen, USA) following the manufacturer\u0026rsquo;s protocol. The RNA concentration and purity were assessed using a NanoDrop spectrophotometer (Thermo Fisher Scientific, USA). Reverse transcription was performed using the PrimeScript RT reagent kit (Takara, Japan) to synthesize complementary DNA (cDNA) from 1 \u0026mu;g of total RNA. Quantitative real-time PCR (qRT-PCR) was carried out using the SYBR Green PCR Master Mix (Takara, Japan) on a QuantStudio 6 Flex Real-Time PCR System (Applied Biosystems, USA). Gene-specific primers for BIRC5, CAV1, CRYAB, HYAL1, PAICS, SERINC2, SLC2A1, and TUBB6 were designed using the Primer-BLAST tool (NCBI) and synthesized by Sangon Biotech (Shanghai, China). The housekeeping gene GAPDH was used as an internal control.The qRT-PCR reaction conditions were as follows:Initial denaturation at 95\u0026deg;C for 30 seconds; Followed by 40 cycles of 95\u0026deg;C for 5 seconds and 60\u0026deg;C for 30 seconds; Melting curve analysis was performed to verify product specificity.\u003c/p\u003e\n\u003ch2\u003e2.11 Statistical Analysis\u003c/h2\u003e\n\u003cp\u003eThe Wilcoxon rank-sum test was used to determine whether there were differences between two groups. The Spearman correlation coefficient was used to calculate the correlation between two groups. Unless otherwise specified, an adjusted p-value \u0026le; 0.05 was considered significant. The log-rank test was used to calculate p-values for survival curves.\u003c/p\u003e"},{"header":"3. Results","content":"\u003ch2\u003e3.1 Differential Gene Results\u003c/h2\u003e\n\u003cp\u003eThe collected datasets were grouped into cancer and normal samples. Differential genes between cancer and normal samples were identified using the criteria |logFC| \u0026ge; 2 and adjusted p-value \u0026le; 0.05. Figure 1A showcases the top 50 upregulated and downregulated genes, respectively, revealing significant differences in gene expression between lung cancer tissues and normal tissues. Figure 1B presents the corresponding volcano plot. The two principal components from each dataset are shown in Supplementary Figure 1. A total of 661 differential genes were identified, and intersection analysis with the collected 878 exosome-related genes yielded 47 exosome-associated differential genes (Figure 1C). Subsequently, univariate logistic regression was used to further screen the genes, and no genes were excluded. Lasso regression and random forest models were then constructed, with the optimal \u0026lambda; for Lasso cross-validation shown in Supplementary Figure 2A and the importance of genes screened by random forest shown in Supplementary Figure 2B. Feature selection of these 47 genes resulted in 8 intersecting genes (Figure 1D). Figure 1E displays the gene expression of these 8 genes in normal and cancer groups. As shown, CRYAB, CAV1, HYAL1, and TUBB6 genes were significantly downregulated in lung cancer tissues, whereas SERINC2, PAICS, SLC2A1, and BIRC5 genes were significantly upregulated. Figure 1F presents the expression correlation of these 8 genes in cancer. The chromosomal locations of the 8 feature genes are shown in Supplementary Figure 2C.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Additionally, immunohistochemical staining images of genes in cancer and normal tissues were downloaded from the HPA database to observe the expression differences of the 8 differential genes in various tissue samples. As shown in Figure 2, there were significant expression differences for all 8 differential genes between normal and lung cancer tissue samples.\u003c/p\u003e\n\u003ch2\u003e3.2 Enrichment Analysis Results\u003c/h2\u003e\n\u003cp\u003eGO enrichment analysis was performed on the 47 exosome-associated differential genes. As shown in Figure 3A-B, these genes were enriched in biological processes such as leukocyte migration, response to reactive oxygen species, leukocyte chemotaxis, and myeloid leukocyte migration. The most relevant cellular components were membrane rafts and membrane microdomains, while the most relevant molecular functions were enzyme inhibitor activity and cytokine receptor binding.\u003c/p\u003e\n\u003cp\u003eFigure 3C illustrates the network relationships between genes and pathways among the top ten results of the GO enrichment analysis. KEGG pathway enrichment analysis revealed enrichment only in the IL-17 signaling pathway. Figure 3D shows the top 5 GSEA enrichment analysis results, indicating that the differential genes were highly enriched in processes such as Cell Cycle, Biosynthesis of Amino Acids, Ribosome, and DNA Replication, while Vascular Smooth Muscle Contraction was significantly suppressed.\u003c/p\u003e\n\u003ch2\u003e3.3 Immune Cell Analysis Results\u003c/h2\u003e\n\u003cp\u003eImmune infiltration scores for all samples were calculated using the ssGSEA method, revealing significant differences in infiltration levels of most immune cells between normal and cancer groups (Figure 3A). Subsequently, the correlation between immune infiltration scores and feature genes was assessed, showing high correlations between the 8 exosome-associated differential genes (TUBB6, SLC2A1, SERINC2, etc.) and immune cells (Figure 3B). Figure 3C displays the UMAP plot of single-cell analysis results for lung cancer. As shown in Figure 4D, the expression of exosome target genes in immune cell types was analyzed, revealing high expression of TUBB6 in Plasma Cells, Myeloid-DC cells, and Myeloid-Mono/Macro cells, and high expression of SLC2A1 in Plasma Cells, Naive B Cells, Memory B Cells, and NKT Cells.\u003c/p\u003e\n\u003cp\u003eAs shown in Figure 4E, we used IGHA1, IGHA2, and IGHG1 to mark Memory B Cells, IGHD, IGHM, and CD19 to mark Naive B Cells, and XBP1, MZB1, and JCHAIN to mark Plasma Cells. Subsequently, based on the correlation between exosome target genes and immune cells shown in Figure 4B, we performed a grouped analysis of SLC2A1 and TUBB6 expression in bone marrow-derived monocytes/macrophages. As shown in Figure 4F, lung adenocarcinoma patients with high SLC2A1 expression had higher survival rates than those with low SLC2A1 expression (P=0.04). Conversely, as shown in Figure 4G, lung adenocarcinoma patients with high TUBB6 expression had lower survival rates than those with low TUBB6 expression (P=0.01).\u003c/p\u003e\n\u003cp\u003eWe then analyzed these genes in the LUSC dataset. As shown in Figure 5A, lung squamous cell carcinoma patients with high SLC2A1 expression in DC cells had lower survival rates than those with low expression. Figure 5B shows that lung squamous cell carcinoma patients with high SLC2A1 expression in monocytes/macrophages had lower survival rates. Figure 5C indicates that lung squamous cell carcinoma patients with high expression in Mast-cells had lower survival rates. Figure 5D shows that lung squamous cell carcinoma patients with high TUBB6 expression in monocytes/macrophages had lower survival rates. Figure 5E demonstrates that lung squamous cell carcinoma patients with high TUBB6 expression in DC cells had lower survival rates.\u003c/p\u003e\n\u003ch2\u003e3.4 Drug Analysis Results\u003c/h2\u003e\n\u003cp\u003eDrug regulatory enrichment analysis was performed on the 8 feature genes, revealing significant correlations between drugs such as celecoxib, dacarbazine, 1h-pyrazolo[3,4-d]pyrimidine, and Nitroprusside and these genes (Figure 4A-B). Among them, celecoxib was found to be associated with CAV1, SLC2A1, and BIRC5 genes. We then downloaded the 3D structural proteins of CAV1, SLC2A1, and BIRC5 genes and the structural information of celecoxib to perform molecular docking. The results showed that celecoxib has interaction targets with CAV1, SLC2A1, and BIRC5 (Figure 4D).\u003c/p\u003e\n\u003ch2\u003e3.5 Classification Model\u003c/h2\u003e\n\u003cp\u003eWe analyzed whether the expression of a single gene among the 8 exosome-associated differential genes could predict whether a sample was lung cancer. As shown in Figure 7A, all 8 differential genes could predict canceration with good performance, with AUC values above 0.85, suggesting their high predictive value. Additionally, we fitted the 8 genes into a logistic regression model to predict canceration, achieving an ROC of 0.96 (Figure 7B). As shown in Figure 7C, for the 8 feature genes, we downloaded their RNA-binding proteins from the ENCORI database and constructed an RBP network. Figure 7D presents the transcriptional regulatory network constructed using transcription factor data related to these 8 feature genes from the TRRUST database. Finally, based on the above results, patients were divided into high-risk and low-risk groups. As shown in Figure 7E, the survival rate of high-risk lung adenocarcinoma patients was significantly lower than that of low-risk patients (P=0.0048). Similarly, Figure 7F demonstrates that the survival rate of high-risk lung squamous cell carcinoma patients was significantly lower than that of low-risk patients (P=0.0045).\u003c/p\u003e\n\u003ch2\u003e3.6 Validation of Analytical Results in Cell Lines\u003c/h2\u003e\n\u003cp\u003eThe qRT-PCR results revealed significant differences in the mRNA expression levels of BIRC5, CAV1, CRYAB, HYAL1, PAICS, SERINC2, SLC2A1, and TUBB6 between normal lung epithelial cells (BEAS-2B) and lung cancer cell lines (A549, PC9, NCI-H226, SK-MES-1). The specific findings are summarized as follows:BIRC5, HYAL1, PAICS, SERINC2, SLC2A1, and TUBB6 were significantly upregulated in all lung cancer cell lines compared to normal BEAS-2B cells (P \u0026lt; 0.0001).The highest expression levels were observed in PC9 and A549, suggesting that these genes are involved in lung cancer progression, particularly in lung adenocarcinoma and EGFR-mutant lung cancer.CAV1 expression showed no significant difference between normal and cancer cells, except for a significant downregulation in PC9 cells (P \u0026lt; 0.01), suggesting that CAV1 may play a context-dependent role in lung cancer.CRYAB showed a modest but significant increase in PC9 cells compared to normal cells (P \u0026lt; 0.05), while the expression levels in other lung cancer cell lines remained relatively unchanged, suggesting that CRYAB might be involved in the specific oncogenic signaling in EGFR-mutant lung cancer.The highest expression of BIRC5, HYAL1, PAICS, and SERINC2 in PC9 cells indicates that these genes might be closely related to EGFR signaling or drug resistance in lung cancer.HYAL1 and SLC2A1 showed the highest expression levels in SK-MES-1, suggesting their possible involvement in lung squamous carcinoma progression.The overall upregulation of BIRC5, HYAL1, PAICS, SERINC2, SLC2A1, and TUBB6 in lung cancer cell lines suggests that these genes may serve as potential oncogenes or biomarkers for lung cancer.CAV1 and CRYAB showed cell line\u0026ndash;specific changes, indicating their potential roles in the molecular heterogeneity of lung cancer.The particularly high expression of BIRC5, PAICS, and SERINC2 in PC9 cells suggests that they may be involved in EGFR-mediated oncogenic pathways and could serve as targets for personalized therapy in lung adenocarcinoma with EGFR mutations. These findings provide a foundation for further investigation into the roles of these genes in lung cancer development, progression, and therapeutic resistance.\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eBy integrating multiple lung cancer-related datasets, this study systematically analyzed the expression differences, functional enrichment, immune infiltration characteristics, and drug regulatory potential of exosome-related genes in lung cancer, yielding the following significant findings:\u003c/p\u003e \u003cp\u003eFirstly, we screened differential genes related to lung cancer from multiple GEO datasets and intersected them with exosome-related genes [18; 19], ultimately identifying 8 cancer-associated exosome genes. In this study, we observed that the expressions of CRYAB, CAV1, HYAL1, and TUBB6 genes were significantly reduced in lung cancer tissues, whereas the expressions of SERINC2, PAICS, SLC2A1, and BIRC5 genes were significantly increased. These expression differences may reflect the distinct biological functions of these genes in the initiation and progression of lung cancer [20; 21], and they also exhibited pronounced expression disparities in immunohistochemical staining images, suggesting their potential crucial roles in lung cancer development.\u003c/p\u003e \u003cp\u003eCRYAB, a small heat shock protein, primarily participates in cellular stress responses and protein folding [22; 23]. In normal tissues, CRYAB aids in maintaining cellular stability and function [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Its decreased expression in lung cancer tissues may indicate weakened cellular stress responses, leading to reduced cellular tolerance to damage and promoting tumorigenesis and progression [25; 26]. CAV1, an essential protein on the cell membrane, is involved in cell signaling, cytoskeleton regulation, and intercellular communication. The downregulation of CAV1 may be associated with disrupted intercellular communication and enhanced cell proliferation. HYAL1, an enzyme that degrades hyaluronic acid, a vital component of the extracellular matrix [27; 28; 29], may accumulate the extracellular matrix when its expression is reduced, thereby promoting tumor cell invasion and metastasis [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. TUBB6, a member of the tubulin family, participates in cytoskeleton formation and cell division. Reduced TUBB6 expression may affect normal cell division and morphological maintenance, thus promoting abnormal proliferation of tumor cells [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSERINC2 is involved in intracellular material transport and metabolic regulation. Its increased expression may be related to metabolic reprogramming in tumor cells, supporting their rapid proliferation [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. PAICS, a critical enzyme in the purine metabolism pathway, participates in cellular energy metabolism [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Its increased expression may provide more energy for tumor cells, promoting their growth and proliferation [34; 35]. SLC2A1, a glucose transporter on the cell membrane, is responsible for transporting glucose from the extracellular space into the cell. Its increased expression may enhance the glycolytic capacity of tumor cells, providing energy for their rapid growth [36; 37]. BIRC5, an apoptosis inhibitor protein, inhibits cell apoptosis. Its increased expression may allow tumor cells to evade apoptosis, thereby promoting tumor progression. The expression differences of these genes may be closely related to the biological processes of lung cancer [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. These processes collectively drive the initiation, progression, and metastasis of lung cancer. Further analysis of the correlation between the expression levels of these genes and the clinical prognosis of lung cancer patients may provide novel biomarkers for lung cancer prognosis assessment.\u003c/p\u003e \u003cp\u003eEnrichment analysis revealed that these exosome-related differential genes were primarily enriched in biological processes such as cell migration, oxidative stress response, and cell cycle regulation, as well as KEGG pathways such as the IL-17 signaling pathway. These biological processes and pathways are closely associated with the invasion, metastasis, and immune escape of lung cancer [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e], further confirming the biological significance of exosome-related genes in lung cancer. For instance, cell migration and oxidative stress response play pivotal roles in tumor invasion and metastasis, while the IL-17 signaling pathway is closely related to the regulation of immune cells in the tumor microenvironment [39; 40; 41]. These results suggest that exosome-related genes may promote lung cancer progression by modulating these biological processes and signaling pathways.\u003c/p\u003e \u003cp\u003eIn terms of immune cell analysis, we found that these exosome-related genes were highly correlated with the infiltration levels of various immune cells. For instance, TUBB6 was highly expressed in plasma cells, myeloid dendritic cells, and myeloid monocytes/macrophages, whereas SLC2A1 was highly expressed in plasma cells, naive B cells, memory B cells, and NKT cells. Furthermore, based on the expression levels of these genes, we analyzed the survival prognosis of patients with lung adenocarcinoma and lung squamous cell carcinoma and found that the expression levels of SLC2A1 and TUBB6 were closely related to patient survival rates. These results suggest that these genes may influence the prognosis of lung cancer by affecting the functions and infiltration states of immune cells [42; 43]. This discovery provides new targets and ideas for immunotherapy in lung cancer. Future studies can further investigate the specific mechanisms of these genes in immune cells and how to improve the prognosis of lung cancer patients by modulating immune cell infiltration. The expression levels of TUBB6 and SLC2A1 are closely related to patient survival rates, suggesting that these genes may affect the prognosis of lung cancer by influencing the functions and infiltration states of immune cells[44; 45; 46]. The expression differences of these genes not only provide potential biomarkers for early diagnosis of lung cancer but also lay the foundation for further research on their biological functions in lung cancer.\u003c/p\u003e \u003cp\u003eIn terms of drug regulation, through drug-gene enrichment analysis and molecular docking experiments, we found significant correlations between drugs such as celecoxib and some exosome-related genes [47; 48; 49], and identified interaction targets with CAV1, SLC2A1, and BIRC5 genes [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. This indicates that these drugs may affect the biological behavior of lung cancer by regulating the expression of exosome-related genes, providing potential drug candidates for targeted therapy of lung cancer. For example, celecoxib, a nonsteroidal anti-inflammatory drug, is widely used in the treatment of various inflammation-related diseases. Our study found that celecoxib may inhibit the proliferation and metastasis of lung cancer cells through interactions with CAV1, SLC2A1, and BIRC5 genes. This discovery provides a theoretical basis for the potential application of celecoxib in lung cancer treatment, and its efficacy and mechanism can be further validated through in vitro cell experiments and in vivo animal model experiments in the future.\u003c/p\u003e \u003cp\u003eHowever, this study also has some limitations. Firstly, our research is primarily based on bioinformatics analysis and lacks experimental validation. Although bioinformatics analysis provides us with valuable clues and hypotheses, these results need to be further validated through in vitro cell experiments and in vivo animal model experiments [51; 52]. For instance, CRISPR/Cas9 gene editing technology can be utilized to knock out or overexpress these exosome-related genes, observing their effects on lung cancer cell proliferation, migration, and invasion, as well as their regulatory roles in immune cell infiltration. Secondly, our study only focused on the expression differences and functional enrichment of exosome-related genes in lung cancer, and the biological characteristics of exosomes themselves (such as size, morphology, content, etc.) and their mechanisms in lung cancer have not been thoroughly explored. Future studies can combine exosome isolation and identification technologies to delve into the mechanisms of exosomes in the initiation, progression, and treatment of lung cancer [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. For example, exosomes secreted by lung cancer cells can be isolated and identified through ultracentrifugation and nanoparticle tracking analysis (NTA) technologies[54; 55], and their protein and RNA components can be analyzed to further reveal the biological functions of exosomes in lung cancer. In addition, clinical samples can be combined to study the expression differences of exosome-related genes in different lung cancer subtypes and their correlation with clinical prognosis, providing a basis for personalized treatment.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis study systematically analyzed lung cancer-related transcriptome data, single-cell data, exosome-related genes, and drug-related data, screening out 8 cancer-associated exosome genes and conducting in-depth discussions on their functional enrichment, immune infiltration characteristics, and drug regulatory potential. These genes exhibited significant expression differences between lung cancer tissues and normal tissues, were highly correlated with the infiltration levels of various immune cells, and had certain predictive value for the survival prognosis of lung cancer patients. Furthermore, we found significant correlations between drugs such as celecoxib and some exosome-related genes, and identified interaction targets with CAV1, SLC2A1, and BIRC5 genes, providing potential drug candidates for targeted therapy of lung cancer. In the future, it is necessary to experimentally validate the functions of these exosome-related genes and drug regulatory mechanisms to provide new bases for early diagnosis, prognosis assessment, and targeted therapy of lung cancer.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eConflict of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that there are no conflicts of interest related to this work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eQZ and YZ contributed to the study conception and design. QZ performed the data collection and analysis. YZ supervised the project and provided critical revisions. QZ drafted the manuscript, and YZ reviewed and edited the final version. Both authors have read and approved the final manuscript and agree to be accountable for the content of the work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research did not receive any funding support.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis is a short text to acknowledge the contributions of specific colleagues, institutions, or agencies that aided the efforts of the authors.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthics declaration: not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Participate declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors consent to participate in this research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Publish declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors consent to publish this work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial registration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eClinical trial number: not applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBray F, Laversanne M, Sung H, Ferlay J, Siegel RL, Soerjomataram I, Jemal A. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. Cancer J Clin. 2024;74:229\u0026ndash;63.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMa SC, Bai X, Guo XJ, Liu L, Xiao LS, Lin Y, Tan JL, Cai XT, Wen YX, Ma H, Fu QJ, Leng MX, Zhang YP, Long LL, Guo ZQ, Wu DH, Zhou JG, Dong ZY. Organ-specific metastatic landscape dissects PD-(L)1 blockade efficacy in advanced non-small cell lung cancer: applicability from clinical trials to real-world practice. BMC Med. 2022;20:120.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSimons M, Raposo G. Exosomes\u0026ndash;vesicular carriers for intercellular communication. Curr Opin Cell Biol. 2009;21:575\u0026ndash;81.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTh\u0026eacute;ry C, Zitvogel L, Amigorena S. Exosomes: composition, biogenesis and function. Nat Rev Immunol. 2002;2:569\u0026ndash;79.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhao J, Li X, Liu L, Zhu Z, He C. Exosomes in lung cancer metastasis, diagnosis, and immunologically relevant advances. Front Immunol 14 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHsu M-T, Wang Y-K, Tseng YJ. Exosomal Proteins and Lipids as Potential Biomarkers for Lung Cancer Diagnosis, Prognosis, and Treatment. Cancers. 2022;14:732.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCao Y, Liu X, Liu J, Su Z, Liu W, Yang L, Zhang L. Diagnostic value of exosomal noncoding RNA in lung cancer: a meta-analysis. Front Oncol 14 (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHussen BM, Abdullah SR, Hama Faraj GS, Rasul MF, Salihi A, Ghafouri-Fard S, Taheri M, Mokhtari M. Exosomal circular RNA: a signature for lung cancer progression. Cancer Cell Int. 2022;22:378.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePeinado H, Alečković M, Lavotshkin S, Matei I, Costa-Silva B, Moreno-Bueno G, Hergueta-Redondo M, Williams C, Garc\u0026iacute;a-Santos G, Ghajar C, Nitadori-Hoshino A, Hoffman C, Badal K, Garcia BA, Callahan MK, Yuan J, Martins VR, Skog J, Kaplan RN, Brady MS, Wolchok JD, Chapman PB, Kang Y, Bromberg J, Lyden D. Melanoma exosomes educate bone marrow progenitor cells toward a pro-metastatic phenotype through MET. Nat Med. 2012;18:883\u0026ndash;91.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCosta-Silva B, Aiello NM, Ocean AJ, Singh S, Zhang H, Thakur BK, Becker A, Hoshino A, Mark MT, Molina H, Xiang J, Zhang T, Theilen TM, Garc\u0026iacute;a-Santos G, Williams C, Ararso Y, Huang Y, Rodrigues G, Shen TL, Labori KJ, Lothe IM, Kure EH, Hernandez J, Doussot A, Ebbesen SH, Grandgenett PM, Hollingsworth MA, Jain M, Mallya K, Batra SK, Jarnagin WR, Schwartz RE, Matei I, Peinado H, Stanger BZ, Bromberg J, Lyden D. Pancreatic cancer exosomes initiate pre-metastatic niche formation in the liver. Nat Cell Biol. 2015;17:816\u0026ndash;26.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eComprehensive molecular profiling of lung adenocarcinoma. Nature. 2014;511:543\u0026ndash;50.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTirosh I, Izar B, Prakadan SM, Wadsworth MH 2nd, Treacy D, Trombetta JJ, Rotem A, Rodman C, Lian C, Murphy G, Fallahi-Sichani M, Dutton-Regester K, Lin JR, Cohen O, Shah P, Lu D, Genshaft AS, Hughes TK, Ziegler CG, Kazer SW, Gaillard A, Kolb KE, Villani AC, Johannessen CM, Andreev AY, Van Allen EM, Bertagnolli M, Sorger PK, Sullivan RJ, Flaherty KT, Frederick DT, Jan\u0026eacute;-Valbuena J, Yoon CH, Rozenblatt-Rosen O, Shalek AK, Regev A, Garraway LA. Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq. Science (New York, N.Y.) 352 (2016) 189\u0026ndash;96.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen W-W, Liu W, Li Y, Wang J, Ren Y, Wang G, Chen C, Li H. Deciphering the Immune\u0026ndash;Tumor Interplay During Early-Stage Lung Cancer Development via Single-Cell Technology. Front Oncol 11 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi Q, Wang R, Yang Z, Li W, Yang J, Wang Z, Bai H, Cui Y, Tian Y, Wu Z, Guo Y, Xu J, Wen L, He J, Tang F, Wang J. Molecular profiling of human non-small cell lung cancer by single-cell RNA-seq. Genome Med. 2022;14:87.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang Z, Yang L, Wang W, Zhou H, Chen J, Ma Z, Wang X, Zhang Q, Liu H, Zhou C, Guo Z, Zhang X. Comparative immunological landscape between pre- and early-stage LUAD manifested as ground-glass nodules revealed by scRNA and scTCR integrated analysis. Cell Communication Signal. 2023;21:325.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKalluri R. The biology and function of exosomes in cancer. J Clin Investig. 2016;126:1208\u0026ndash;15.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTkach M, Th\u0026eacute;ry C. Communication by Extracellular Vesicles: Where We Are and Where We Need to Go. Cell. 2016;164:1226\u0026ndash;32.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRitchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W. Smyth, limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 2015;43:e47.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEdgar R, Domrachev M, Lash AE. Gene Expression Omnibus: NCBI gene expression and hybridization array data repository. Nucleic Acids Res. 2002;30:207\u0026ndash;10.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKahlert C, Melo SA, Protopopov A, Tang J, Seth S, Koch M, Zhang J, Weitz J, Chin L, Futreal A, Kalluri R. Identification of double-stranded genomic DNA spanning all chromosomes with mutated KRAS and p53 DNA in the serum exosomes of patients with pancreatic cancer. J Biol Chem. 2014;289:3869\u0026ndash;75.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMelo SA, Luecke LB, Kahlert C, Fernandez AF, Gammon ST, Kaye J, LeBleu VS, Mittendorf EA, Weitz J, Rahbari N, Reissfelder C, Pilarsky C, Fraga MF, Piwnica-Worms D, Kalluri R. Glypican-1 identifies cancer exosomes and detects early pancreatic cancer. Nature. 2015;523:177\u0026ndash;82.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTaylor RP, Benjamin IJ. Small heat shock proteins: a new classification scheme in mammals. J Mol Cell Cardiol. 2005;38:433\u0026ndash;44.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCai HB, Zhao MY, Li XH, Li YQ, Yu TH, Wang CZ, Wang LN, Xu WY, Liang B, Cai YP, Zhang F, Hong WM. Single cell sequencing revealed the mechanism of CRYAB in glioma and its diagnostic and prognostic value. Front Immunol. 2023;14:1336187.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTian B, Li X, Li W, Shi Z, He X, Wang S, Zhu X, Shi N, Li Y, Wan P, Zhu C. CRYAB suppresses ferroptosis and promotes osteogenic differentiation of human bone marrow stem cells via binding and stabilizing FTH1. Aging. 2024;16:8965\u0026ndash;79.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eQin H, Ni Y, Tong J, Zhao J, Zhou X, Cai W, Liang J, Yao X. Elevated expression of CRYAB predicts unfavorable prognosis in non-small cell lung cancer. Med Oncol (Northwood Lond Engl). 2014;31:142.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang J, Liu J, Wu J, Li W, Chen Z, Yang L. Progression of the role of CRYAB in signaling pathways and cancers. OncoTargets therapy. 2019;12:4129\u0026ndash;39.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKalluri R, LeBleu VS. The biology, function, and biomedical applications of exosomes. Sci (New York N Y). 2020;367:eaau6977.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRaposo G, Stoorvogel W. Extracellular vesicles: Exosomes, microvesicles, and friends. J Cell Biol. 2013;200:373\u0026ndash;83.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi Y, Yin Z, Fan J, Zhang S, Yang W. The roles of exosomal miRNAs and lncRNAs in lung diseases. Signal Transduct Target Therapy. 2019;4:47.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWitzel I, Marx AK, M\u0026uuml;ller V, Wikman H, Matschke J, Schumacher U, St\u0026uuml;rken C, Prehm P, Laakmann E, Schmalfeldt B, Milde-Langosch K, Oliveira-Ferrer L. Role of HYAL1 expression in primary breast cancer in the formation of brain metastases. Breast Cancer Res Treat. 2017;162:427\u0026ndash;38.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWen S, Zou R, Du X, Pan R, Li R, Xia J, Xu C, Wang R, Jiang F, Zhou G, Feng J, Zhu M, Wang X, Shen B. Identification of macrophage-related genes correlated with prognosis and immunotherapy efficacy in non-small cell lung cancer. Heliyon. 2024;10:e27170.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZeng Y, Xiao D, He H, He J, Pan H, Yang W, Chen Y, He J. SERINC2-knockdown inhibits proliferation, migration and invasion in lung adenocarcinoma. Oncol Lett. 2018;16:5916\u0026ndash;22.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePegg AE. Mammalian polyamine metabolism and function. IUBMB Life. 2009;61:880\u0026ndash;94.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi Y, Zhu L, Mao J, Zheng H, Hu Z, Yang S, Mao T, Zhou T, Cao P, Wu H, Wang X, Wang J, Lin F, Shen H. Genome-scale CRISPR-Cas9 screen identifies PAICS as a therapeutic target for EGFR wild-type non-small cell lung cancer. MedComm. 2024;5:e483.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTang X, Wu Y, Yang J, Zhu W. Regulating COX10-AS1 / miR-142-5p / PAICS axis inhibits the proliferation of non-small cell lung cancer. Bioengineered. 2021;12:4643\u0026ndash;53.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXu Y, Chai B, Wang X, Wu Z, Gu Z, Liu X, Zhao Y, Chen T, Ma Z, Sun Q. miRNA-199a-5p/SLC2A1 axis regulates glucose metabolism in non-small cell lung cancer. J Cancer. 2022;13:2352\u0026ndash;61.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAncey PB, Contat C, Boivin G, Sabatino S, Pascual J, Zangger N, Perentes JY, Peters S, Abel ED, Kirsch DG, Rathmell JC, Vozenin MC, Meylan E. GLUT1 Expression in Tumor-Associated Neutrophils Promotes Lung Cancer Growth and Resistance to Radiotherapy. Cancer Res. 2021;81:2345\u0026ndash;57.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKahm YJ, Kim RK. BIRC5: A novel therapeutic target for lung cancer stem cells and glioma stem cells. Biochem Biophys Res Commun. 2023;682:141\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGorczynski RM. IL-17 Signaling in the Tumor Microenvironment. Adv Exp Med Biol. 2020;1240:47\u0026ndash;58.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSeif F, Torki Z, Zalpoor H, Habibi M, Pornour M. Breast cancer tumor microenvironment affects Treg/IL-17-producing Treg/Th17 cell axis: Molecular and therapeutic perspectives. Mol therapy oncolytics. 2023;28:132\u0026ndash;57.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCoffelt SB, Kersten K, Doornebal CW, Weiden J, Vrijland K, Hau CS, Verstegen NJM, Ciampricotti M, Hawinkels L, Jonkers J, de Visser KE. IL-17-producing γδ T cells and neutrophils conspire to promote breast cancer metastasis. Nature. 2015;522:345\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBindea G, Mlecnik B, Tosolini M, Kirilovsky A, Waldner M, Obenauf AC, Angell H, Fredriksen T, Lafontaine L, Berger A, Bruneval P, Fridman WH, Becker C, Pag\u0026egrave;s F, Speicher MR, Trajanoski Z, Galon J. Spatiotemporal dynamics of intratumoral immune cells reveal the immune landscape in human cancer. Immunity. 2013;39:782\u0026ndash;95.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGentles AJ, Newman AM, Liu CL, Bratman SV, Feng W, Kim D, Nair VS, Xu Y, Khuong A, Hoang CD, Diehn M, West RB, Plevritis SK, Alizadeh AA. The prognostic landscape of genes and infiltrating immune cells across human cancers. Nat Med. 2015;21:938\u0026ndash;45.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSubramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, Paulovich A, Pomeroy SL, Golub TR, Lander ES, Mesirov JP. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci USA. 2005;102:15545\u0026ndash;50.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang TY. A SATS algorithm for jointly identifying multiple differentially expressed gene sets. Stat Med. 2011;30:2028\u0026ndash;39.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePeng J, Xiao L-S, Dong Z-Y, Li W-W, Wang K-Y, Wu D-H, Liu L. Potential predictive value of JAK2 expression for Pan-cancer response to PD-1 blockade immunotherapy. Translational Cancer Res. 2018;7:462\u0026ndash;71.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLamb J, Crawford ED, Peck D, Modell JW, Blat IC, Wrobel MJ, Lerner J, Brunet JP, Subramanian A, Ross KN, Reich M, Hieronymus H, Wei G, Armstrong SA, Haggarty SJ, Clemons PA, Wei R, Carr SA, Lander ES, Golub TR. The Connectivity Map: using gene-expression signatures to connect small molecules, genes, and disease. Volume 313. Science; 2006. pp. 1929\u0026ndash;35. (New York, N.Y.).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGullans SR. Connecting the dots using gene-expression profiles. N Engl J Med. 2006;355:2042\u0026ndash;4.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMorris GM, Huey R, Lindstrom W, Sanner MF, Belew RK, Goodsell DS. Olson, AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility. J Comput Chem. 2009;30:2785\u0026ndash;91.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHashemi Goradel N, Najafi M, Salehi E, Farhood B, Mortezaee K. Cyclooxygenase-2 in cancer: A review. J Cell Physiol. 2019;234:5683\u0026ndash;99.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuang da W, Sherman BT, Lempicki RA. Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Res. 2009;37:1\u0026ndash;13.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKaelin WG Jr.. The concept of synthetic lethality in the context of anticancer therapy. Nat Rev Cancer. 2005;5:689\u0026ndash;98.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXu Z, Chen Y, Ma L, Chen Y, Liu J, Guo Y, Yu T, Zhang L, Zhu L, Shu Y. Role of exosomal non-coding RNAs from tumor cells and tumor-associated macrophages in the tumor microenvironment. Mol therapy: J Am Soc Gene Therapy. 2022;30:3133\u0026ndash;54.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang Y, Kim MS, Jia B, Yan J, Zuniga-Hertz JP, Han C, Cai D. Hypothalamic stem cells control ageing speed partly through exosomal miRNAs. Nature. 2017;548:52\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen X, Yu L, Hao K, Yin X, Tu M, Cai L, Zhang L, Pan X, Gao Q, Huang Y. Fucosylated exosomal miRNAs as promising biomarkers for the diagnosis of early lung adenocarcinoma. Front Oncol 12 (2022).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"dion","sideBox":"Learn more about [Discover Oncology](https://www.springer.com/12672)","snPcode":"","submissionUrl":"","title":"Discover Oncology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Lung Cancer, Exosomes, Gene Expression, Biological Functions, Immune Infiltration, Drug Regulation","lastPublishedDoi":"10.21203/rs.3.rs-6446002/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6446002/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eBackground\u003c/b\u003e\u003c/p\u003e \u003cp\u003eLung cancer stands as one of the most prevalent malignancies globally, with its high morbidity and mortality intimately associated with its inconspicuous early symptoms, the advanced stages at diagnosis, and resistance to conventional therapeutic interventions. Exosomes, serving as pivotal mediators of intercellular communication, carry biomolecules that play crucial roles in tumorigenesis, progression, and metastasis, holding promise as novel targets for early diagnosis, prognostic evaluation, and treatment of lung cancer.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods\u003c/b\u003e\u003c/p\u003e \u003cp\u003eIn this study, lung cancer-related datasets were obtained from the GEO database and TCGA. Through differential gene analysis, enrichment analysis, immune infiltration analysis, and drug regulatory analysis, exosome-associated genes pertinent to lung cancer were screened and identified.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe research revealed significant downregulation of CRYAB, CAV1, HYAL1, and TUBB6 genes in lung cancer tissues, whereas SERINC2, PAICS, SLC2A1, and BIRC5 genes were markedly upregulated. These genes were predominantly enriched in biological processes such as cell migration, oxidative stress response, and cell cycle regulation, as well as in KEGG pathways like the IL-17 signaling pathway. Immune infiltration analysis demonstrated a high correlation between these genes and the infiltration levels of various immune cells. Furthermore, through drug-gene enrichment analysis and molecular docking experiments, significant correlations were found between drugs such as celecoxib and some exosome-related genes, with interaction targets existing between these drugs and CAV1, SLC2A1, and BIRC5 genes.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusion\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThis study unveils the expression characteristics and biological significance of exosome-associated genes in lung cancer. The differential expression of these genes not only offers potential biomarkers for early diagnosis of lung cancer but also lays a foundation for further research into their biological functions in this disease.\u003c/p\u003e","manuscriptTitle":"Expression Characteristics and Biological Significance of Exosome- Related Genes in Lung Cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-16 09:11:38","doi":"10.21203/rs.3.rs-6446002/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-05-26T08:10:39+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-26T02:12:53+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-24T14:02:40+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"204269632231418460543656399546781343450","date":"2025-05-16T14:18:12+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-16T12:45:33+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"232613047799515061306163541267706321824","date":"2025-05-16T06:31:21+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"150434137711400300589610220792819301453","date":"2025-05-14T13:39:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"159224242479944355344724366763110761506","date":"2025-05-14T09:29:07+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-05-14T04:26:10+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-05-05T10:57:39+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-05-05T10:52:16+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Oncology","date":"2025-04-14T12:14:05+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"dion","sideBox":"Learn more about [Discover Oncology](https://www.springer.com/12672)","snPcode":"","submissionUrl":"","title":"Discover Oncology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"666f6b96-28aa-425f-ab8b-677a93e7b2e0","owner":[],"postedDate":"May 16th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-07-08T06:38:56+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-16 09:11:38","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6446002","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6446002","identity":"rs-6446002","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}