Identification of exosome-related features for prediction prognostic tumor microenvironment in lung adenocarcinoma

Identification of exosome-related features for prediction prognostic tumor microenvironment in lung adenocarcinoma | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Identification of exosome-related features for prediction prognostic tumor microenvironment in lung adenocarcinoma Yusong Chen, Siming Wang, JiaShun Xu, Zhixiong Luo This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4375278/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 3 You are reading this latest preprint version Abstract Background Lung cancer has a high morbidity and mortality rate with currently limited treatment options. There is an urgent need for prognostic markers to facilitate early diagnosis and improve survival rates. This study proposes lysosome-related genes as potential prognostic markers, as they play a significant role in the pathogenesis of lung cancer. Methods The study established a prognostic model using lysosome-related genes from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) database. Univariate Cox regression and LASSO Cox regression analyses were utilized to identify and select relevant genes, and the model was then validated in an independent cohort of lung cancer patients. Further, immune cell infiltration scores, drug susceptibility, functional and pathway enrichment analyses were conducted to evaluate the model's predictive ability. Results The study identified 26 key lysosome-related genes and found that the high-risk group, as identified by the model, had a poorer overall survival rate. Additionally, the model demonstrated a good prediction accuracy for 1-, 3-, and 5- year prognosis in the training and validation cohorts. The model's risk score was identified as an independent prognostic factor, demonstrating its potential clinical relevance. Immune cell infiltration, tumor microenvironment analyses, and drug susceptibility predictions also provided significant insights. Conclusion The proposed model based on lysosome-related genes could be a potential tool for predicting the prognosis of lung cancer patients. It may facilitate early diagnosis, inform treatment plans, and improve overall survival rates. However, further research is required to establish its practical application in clinical settings. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction In recent years, lung cancer has become one of the more common types of cancer of the respiratory system, with increasing morbidity and mortality rates [ 1 ]. Currently, lung cancer is primarily treated with surgery and chemotherapy, but the effectiveness of these treatments has declined over the years. It is estimated that 12 percent of lung cancer patients who are diagnosed with metastatic cancer will survive for five years. [ 2 ] Thus, it is essential to discover more prognostic markers for lung cancer, which are extremely important for early diagnosis and for improving survival rates. The main factors influencing the prognosis of lung cancer are the stage of lung cancer, surgical factors, living conditions and mental state. Risk factors for lung cancer are diet, age, obesity, smoking and lack of exercise.[ 3 ] Current treatments do not improve outcomes for patients with advanced lung cancer. Therefore, it is necessary to further explore the pathogenesis of lung cancer in order to improve the prognosis of lung cancer. Studies have shown that lysosomes are dynamic organelles in eukaryotic cells.[ 4 ] The monolayer contains a variety of hydrolases that receive and degrade macromolecules through secretory, endocytosis, autophagy and phagocytic membrane transport. Lysosomes are divided into primary lysosomes and secondary lysosomes. The primary lysosomes form buds on the antiplane of the Golgi complex, which are regulated by transcription factors. The abnormal function of lysosomes in some tumor cells leads to the change of enzyme level, which may be the cause of tumor occurrence [ 5 ]. Therefore, lysosome function is closely related to tumor. According to previous studies, translocation and abnormal secretion of lysosomes facilitate the invasion and metastasis of cancer cells [ 6 – 8 ]. Major histocompatibility complex (MHC) molecules and immune checkpoint lysosomal degradation in tumor cells are abnormal, and selective autophagy defects in tumor-infiltrating T lymphocytes lead to tumor metastasis. An abnormal lysosomal function and changes in the expression of some acid hydrolases are present in tumor cells. Lysosome function of tumor cells was abnormal and some acid hydrolytic enzyme expression was changed. Inhibition of lysosomal exocytosis can inhibit tumor invasion and metastasis because it does not affect acid hydrolase activity, but can also lead to instability of the lysosomal membrane and increase the sensitivity of tumor cells to drugs [ 9 – 10 ]. Therefore, lysosomes, as important biomarkers of tumor progression, however, whether lysosomal genes are effective prognostic biomarkers for lung cancer remains unclear. In this study, we constructed a model of the relationship between lysosomes- related genes and lung cancer patients and validated it in an independent cohort of lung cancer patients. We demonstrate that lysosomes play a role in the pathogenesis of lung cancer and can predict the prognosis of lung cancer. Methods Data Acquisition Transcriptome data of 598 LUAD patients from The Cancer Genome Atlas(TCGA) database( https://portal.gdc.cancer.gov ), including 59 normal samples and 539 tumor samples. After excluding normal samples and samples with no survival data, 507 tumor samples were obtained. There is also GSE68465 from the Gene Expression Omnibus (GEO) database( https://www.ncbi.nlm.nih.gov/gds ), and samples lacking survival data will be removed. Establishment and validation of prognostic model The "sva" R package is used to merge the TCGA queue and the GSE68465 cohort, and the "combat" function is used to remove the batch effect between the two. Lysosome related genes were obtained by screening. Based on lysosome related genes, the TCGA-LUAD cohort was used as the training set and the GSE68465 cohort was used as the validation set. Univariate cox regression was performed to screen genes (P < 0.05), and then least absolute shrinkage and selection operator (LASSO) Cox regression analysis was used to select genes to reduce the overfitting risk and construct a risk score formula via the multivariable Cox regression. The "survminer" R package was used to calculate the best cutoff value of risk score, and the samples were divided into high-risk group and low-risk group according to the best cutoff value. The "survminer" and "survival" packages of R software were used to plot survival curves between the low and high risk groups. The stability of the risk score was analyzed using the validation group, and the performance of the prognostic formula was evaluated by time-dependent ROC analysis using R package "survivalROC". Independent prognostic analysis and Nomogram establishment and Calibration Clinical information (including age, gender, and stage) of TCGA-LUAD patients was extracted, and univariate and multivariate Cox regression analysis was performed combined with risk score to evaluate whether risk score and clinical information were independent prognostic factors for overall survival. Based on the model risk score and independent prognostic factors, nomograms were constructed to predict 1-, 3-, and 5-OS. The Calibration curve was used to distinguish the nomogram predicted state from the real survival rate. Functional and pathway enrichment analysis In the TCGA - LUAD cohort using R package "limma" package carries on the differences in gene analysis, filter conditions for P 1. Gene Ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) were used to explore potential mechanisms and pathways in high- and low-risk groups, using the R-package "clusterProfiler" and setting a P < 0.05 significance threshold. Tumor immune microenvironment analysis In the TCGA-LUAD cohort, 22 immune cell infiltration scores were obtained using the CIBERSORT method using the "e1071", "preprocessCor", "limma" R package. Combined with the grouping information, it is visualized using the "ggplot2" and "tidyr" R packages. Combined with the risk score and immune cell infiltration scores, the relationship between the model and each immune cell was demonstrated and visualized using the "corrplot" R package. In addition, the differential analyses of stromal score, immune score and ESTIMATE score were performed based on the results of ESTIMATE using the R software package“estimate”. Subsequently, used the "ggpubr" and "ggplot2" packages to compare and visualize the immune checkpoints and tumor mutational burden(TMB) score between low- and high-risk groups. Prediction of Drug Susceptibility The "pRRophetic" R package was used to predict the half-maximal inhibitory concentration (IC50) value of an anticancer drug in different risk subgroups. The IC50 value represents the effectiveness of the substance in inhibiting a specific biological or biochemical process. Statistical Analysis All statistical analyses were performed using R software (version 4.2.2). The Wilcoxon signed-rank test was used to investigate differences in the composition of immune infiltrating cells. The correlation between risk score and immune cell was investigated using Spearman correlation analysis. Kaplan-Meier analysis was used to estimate survival curves. P values < 0.05(*),0.01 (**), and 0.001 (***) were considered statistically significant. Result In total, 949 patients were included. 507 LUAD patients from the TCGA cohort (235 [46.3%] male, mean [SD] age, 65.30 [10.03]), 442 patients from the validation cohort (223 [50.4%] male, mean [SD] age, 64.39 [10.09]) The construction and validation of novel prognostic model After screening, 133 lysosome-related genes were obtained in TCGA and GSE68465 cohorts. Then, the univariate Cox analysis was used to explore 133 lysosome-related genes. To prevent model overfitting, LASSO penalized Cox regression modeling was conducted to screen the key lysosome-related genes associated with survival. With this method, a novel prognostic gene model with 26 genes was constructed (Figure1 A-B). And then, risk scores per sample were calculated using the following model formula: In this formula, β is coefficient and X is the expression level of each prognostic gene i. The samples were divided into a high-risk group and a low-risk group according to the best cutoff value of the training cohort from TCGA-LUAD. As shown by the Kaplan–Meier analyses, patients in the high-risk group had worse OS than those in the low-risk group (P <0.0001) (Figure1 C-D). In the training cohort, the AUC values of the present risk model were 0.71, 0.71, and 0.71 for the 1-, 3-, and 5- year prognoses, respectively. We used GSE68465-cohort to validate this model. In the validation cohort, the AUC values of the present risk model were 0.70, 0.66, and 0.61 for the 1-, 3-, and 5- year prognoses, respectively (Figure1 E-F). The distribution plot of risk score and survival status revealed that the number of TCGA-LUAD patients with a status of deceased increased as the risk score in the training set rose. In GSE68465-cohort, the low-risk group maintained its superior survival status and longer survival time from the training set (Figure2 A-D). In addition, a heatmap demonstrated that the expression of 26 prognostic genes varied significantly among TCGA-LUAD patients with varying risk scores (Figure2 E-F). Figure 1: Construction and validation of the prognostic model based on the lysosome-related gene signatures in lung adenocarcinoma (LUAD). (A, B) LASSO analysis with minimal lambda value. (C)The Kaplan–Meier survival analysis showing the difference in overall survival (OS) between the high- and low-risk groups in the training, (D)and validation cohorts. (E)Time-dependent ROC curve analysis in the training, (F)Time-dependent ROC curve analysis in the validation cohorts. Figure 2: Evaluation and validation of the utility of prognostic signature in the training set and validation set (A, C) The distribution of risk score and survival status of LUAD patients with different risk scores in the training, (B, D) and validation cohorts. (E, F) Heatmap of the prognostic signatures expression profiles in the high- and low-risk groups in the training and validation cohorts, separately. Independent Prognostic Factor Analysis and construction of Nomogram Univariate and multivariate Cox regression analyses were performed by introducing age, gender, stage, TNM stage, and risk scores to assess the independence of risk scores in the survival prediction of LUAD patients. Among the samples in the training cohort, the results showed that Clinicopathologic stage, T stage, M stage, N stage, and risk score were identified as independent negative prognostic factors for patients with LUAD (Figure3 A-B). In addition, risk scores also showed significant differences in age, gender, Clinicopathologic stage, T stage, M stage and N stage (Figure4 A-P). Based on the training cohort, risk scores and clinical factors that were identified as independent negative prognostic factors were integrated to create a nomogram to improve the predictive power of survival in LUAD patients. Calibration plots for 1-, 3- and 5-years OS revealed good agreement between nomogram prediction and actual observations (Figure3 C-D). Figure 3: Construction and evaluation of the novel nomogram The univariate Cox regression analysis of the risk score and other clinical features in the training cohort, (B) The multivariate Cox regression analysis of the risk score and other clinical features in the training cohort. (C) A nomogram using risk scores combined with clinical characteristics. (D)The calibration plots of the nomogram for predicting OS probability for 1-, 3-, and 5- year in the training. Figure 4: The overall survival analysis of risk score in each clinical subtype. Risk Signature-Based Immune Cell Infiltration, Tumor Microenvironment Analyses We used CIBERSORT to quantify immune cells, it was found that the expression of Plasma cells, T cells CD4 memory resting, T cells regulatory (Tregs), Dendritic cells resting, Mast cells resting was significantly higher in the low-risk group than in the high-risk group (Figure5 A). Immune-checkpoint related genes like CD44, CD276, TNFRSF9, TNFSF4, TNFSF9, CD70, DCD1LG2 and TMIGD2, were more lowly expressed in the low-risk group (Figure5 B). What’s more, with the increase of risk score, the high-risk group had a lower estimate score, immune score, and stromal score (Figure5 C). In addition, the TMB score of LUAD was higher in high risk group (Figure5 D). Figure 5: Risk Signature-Based Immune Cell Infiltration, Tumor Microenvironment Analyses (A) The differences in the scores of immune cells between high- and low-risk groups in the training. (B)The differentially expressed immune checkpoint-related genes between the high- and low-risk groups. (C)ESTIMATE, immune, and stromal scores between the high- and low-risk groups in the training. (D)The difference in tumor mutation burden (TMB) between the high- and low-risk groups in the training. *p < 0.05, **p < 0.01, ***p < 0.001. Functional and Pathway Enrichment Analyses 502 differential expression genes (DEGs) included 264 up-regulated genes and 238 regulated genes were screened between high-risk and low-risk group in training cohort (Figure6 A-B). By considering the DEGs between the high- and low-risk groups from TCGA-LUAD, we conducted GO enrichment analysis, KEGG pathway analysis to explore the potential biological functions of these DEGs. “mitotic nuclear division”, “mitotic sister chromatid segregation”, “nuclear division”, “chromosome segregation” and “organelle fission” were the most enriched terms among the biological process categories. “condensed chromosome, centromeric region”, “condensed chromosome kinetochore”, “kinetochore”, “chromosome, centromeric region” and “spindle” were the most enriched terms among the cellular component categories, and “microtubule binding”, “tubulin binding”, “microtubule motor activity”, “peptidase inhibitor activity” and “enzyme inhibitor activity” ere the most enriched terms among the molecular function categories. “Cell cycle”, “p53 signaling pathway”, “Oocyte meiosis”, “ECM−receptor interaction” and “Progesterone−mediated oocyte maturation” were identified to be the most enriched among the KEGG pathways of the DEGs (Figure6 C-D). Figure 6: Analysis of differences between high- and low-risk groups and f unctional and pathway enrichment analyses Differential expression of ERG expression between high- and low-risk groups in the training. (B)The volcano plot exhibited both down- and up-regulated ERGs. (C)GO enrichment analysis and (D)KEGG pathway analysis based on the DEGs between the high- and low-risk groups in the training. Drug sensitive Differences in drug sensitivity of different risk subgroups were analyzed to investigate the clinical application value of the risk model. Results showed that Camptothecin, Cisplatin, Docetaxel, Doxorubicin, Etoposide, Gemcitabine, Paclitaxel，Vinorelbine and Vinblastine had good effects on patients in low-risk groups(Figure7 A-I). Figure 7: Prediction of drug susceptibility in different risk groups. (A–I) Sensitive drugs in low-risk groups. Discussion Lung adenocarcinoma, a prevalent subtype of non-small cell lung cancer (NSCLC), has a poor prognosis with a 5-year survival rate of approximately 15%. Standard treatments include surgery, chemotherapy, radiation, and targeted therapies such as tyrosine kinase inhibitors (TKIs) and immune checkpoint inhibitors (ICIs) [ 11 – 13 ]. Despite advancements, the overall prognosis remains unsatisfactory for many patients. The urgent need for novel biomarkers in early detection and prognostic prediction is evident. Identifying such biomarkers could lead to personalized treatment strategies and improved clinical outcomes. Researchers are exploring molecular signatures, gene expression profiles, and circulating tumor DNA in search of reliable biomarkers to enhance early detection, refine treatment strategies, and ultimately improve the prognosis for lung adenocarcinoma patients [ 14 – 16 ]. The relationship between lysosomes and cancer has increasingly attracted attention in recent years, as researchers strive to understand the underlying mechanisms of tumorigenesis. Lysosomes are membrane-bound organelles that contain hydrolytic enzymes responsible for the breakdown of various biomolecules, playing a crucial role in cellular metabolism and homeostasis. Several hypotheses have been proposed to explain the possible association between lysosomes and cancer development. Carcinogenic substances have been found to potentially disrupt cell division regulation and cause chromosomal abnormalities, which may be linked to the release of hydrolytic enzymes by lysosomes [ 4 , 17 , 18 ]. Moreover, certain substances that affect lysosomal membrane permeability, such as croton oil, some detergents, and hyperbaric oxygen, can act as auxiliary factors in promoting carcinogenesis, leading to abnormal cell division. Additionally, when the nuclear membrane is defective, its protective function is compromised, allowing lysosomes to dissolve chromatin and induce cellular mutations. Furthermore, some by-products of lysosomal metabolism could serve as the material basis for cancer cell proliferation, providing essential nutrients and growth factors for their survival and expansion. Lastly, carcinogenic substances entering cells are often stored in lysosomes before integrating with chromosomes, a phenomenon confirmed by radiographic autoradiography studies [ 19 ]. In our recent study, we successfully integrated lysosomal gene signatures to construct a prognostic model for lung adenocarcinoma. This model holds significant potential in guiding personalized treatment strategies and improving clinical outcomes for patients. By utilizing a lysosomal signature-based scoring system, we were able to stratify lung adenocarcinoma patients into high and low-risk groups, which allowed for a more accurate prediction of patient survival outcomes. Our research involved the systematic analysis of lysosomal gene expression profiles in lung adenocarcinoma patients, followed by the development of a prognostic signature using a combination of these genes. The model was then tested and validated in independent patient cohorts to ensure its robustness and reliability. The performance of our lysosomal signature-based model was assessed by measuring the area under the receiver operating characteristic (ROC) curve. Impressively, the ROC value exceeded 0.70, indicating a strong ability to distinguish between high and low-risk patients in terms of survival outcomes. This achievement underscores the potential clinical utility of our model in predicting prognosis and guiding treatment decisions for lung adenocarcinoma patients. In addition, Lysosomes play a crucial role in immune function, as these membrane-bound organelles are responsible for the degradation and recycling of various biomolecules within the cell. Lysosomes contribute to immune processes through several mechanisms, including phagocytosis, autophagy, and antigen presentation [ 9 , 20 ]. In phagocytosis, immune cells such as macrophages engulf and destroy pathogens, foreign particles, and cellular debris. Once engulfed, these materials are sequestered within phagosomes, which then fuse with lysosomes. The hydrolytic enzymes within lysosomes break down the contents of the phagosome, effectively neutralizing the threat. Autophagy is a cellular process that involves the degradation and recycling of damaged organelles and misfolded proteins. This process not only maintains cellular homeostasis but also serves as a defense mechanism against intracellular pathogens [ 21 ]. Lysosomes play a key role in autophagy by fusing with autophagosomes to degrade their contents, thereby eliminating potential threats to the cell. Additionally, lysosomes contribute to antigen presentation, a crucial step in activating the adaptive immune response. Antigen-presenting cells, such as dendritic cells and macrophages, internalize pathogens and process them within lysosomes. The resulting peptide fragments are then loaded onto major histocompatibility complex (MHC) molecules and displayed on the cell surface, which ultimately triggers the activation of T cells and the adaptive immune response [ 22 – 23 ]. Our study revealed notable differences in immune characteristics between high and low lysosomal signature score groups in lung adenocarcinoma patients. Using CIBERSORT to quantify immune cells, we found that the low-risk group exhibited significantly higher expression of plasma cells, resting CD4 memory T cells, regulatory T cells (Tregs), resting dendritic cells, and resting mast cells compared to the high-risk group. Moreover, immune checkpoint-related genes such as CD44, CD276, TNFRSF9, TNFSF4, TNFSF9, CD70, CD1LG2, and TMIGD2 were expressed at lower levels in the low-risk group.As the risk score increased, the high-risk group demonstrated lower estimate, immune, and stromal scores, suggesting a less favorable tumor microenvironment. Additionally, the tumor mutational burden (TMB) score was higher in the high-risk group, indicating a greater likelihood of genomic instability and potential resistance to immunotherapy. These findings highlight the significant immunological differences between high and low lysosomal signature score groups and emphasize the potential clinical implications of these disparities in predicting prognosis and guiding treatment decisions for lung adenocarcinoma patients. In summary, our study, including 949 patients, developed a 26-gene prognostic model based on lysosome-related genes for lung adenocarcinoma. This model stratified patients into high and low-risk groups, with the low-risk group having better overall survival. Immune cell infiltration and tumor microenvironment analyses showed significant differences between groups. The model also revealed differences in drug sensitivity, with low-risk patients responding better to common cancer drugs. This novel prognostic model may help guide personalized treatment strategies and improve clinical outcomes for lung adenocarcinoma patients. Conclusion The proposed model based on lysosome-related genes could be a potential tool for predicting the prognosis of lung cancer patients. It may facilitate early diagnosis, inform treatment plans, and improve overall survival rates. However, further research is required to establish its practical application in clinical settings. Abbreviations TCGA The Cancer Genome Atlas GEO The Gene Expression Omnibus MHC Major histocompatibility complex GO Gene Ontology LASSO least absolute shrinkage and selection operator KEGG Kyoto encyclopedia of genes and genomes LUAD Lung adenocarcinoma TMB tumor mutational burden IC50 the half-maximal inhibitory concentration TKIs tyrosine kinase inhibitors ICIs immune checkpoint inhibitors ROC receiver operating characteristic MHC major histocompatibility complex Declarations Ethics approval and consent to participate This article does not contain any studies with human participants or animals performed by any of the authors. Consent for publication Not applicable. Availability of data and materials The data of this study are available in the The Cancer Genome Atlas (TCGA, https://portal.gdc.cancer.gov), the Gene Expression Comprehensive Database (GEO, http://www.ncbi.nlm.nih.gov/geo). Competing Interests The authors have declared that no competing interest exists. Funding No funding. Author contributions Luo and Wang conceived the study design. Data acquisition was carried out by Xu. Chen and Wang conducted the data analysis. Chen designed data visualization. Chen, Wang and Xu wrote the original draft. Revision of the manuscript was done by Luo and Chen. All the authors read and approved the final manuscript. Acknowledgements We would like to thank for support and design the research. References Allemani C, Matsuda T, Di Carlo V, et al. 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Chung C, Seo W, Silwal P, Jo EK. Crosstalks between inflammasome and autophagy in cancer. J Hematol Oncol. 2020;13(1):100. 10.1186/s13045-020-00936-9 . Published 2020 Jul 23. Cao M, Luo X, Wu K, He X. Targeting lysosomes in human disease: from basic research to clinical applications. Signal Transduct Target Ther. 2021;6(1):379. 10.1038/s41392-021-00778-y . Published 2021 Nov 8. Button RW, Luo S. The formation of autophagosomes during lysosomal defect: A new source of cytotoxicity. Autophagy. 2017;13(10):1797–8. 10.1080/15548627.2017.1358850 . Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4375278","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":300137606,"identity":"d7098bd0-8329-4d3b-aab9-42d9b6954ed6","order_by":0,"name":"Yusong Chen","email":"","orcid":"","institution":"Sun Yat-sen Memorial Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yusong","middleName":"","lastName":"Chen","suffix":""},{"id":300137609,"identity":"b1cd653f-f7a4-455f-ac38-15f66ecb05a7","order_by":1,"name":"Siming Wang","email":"","orcid":"","institution":"Sun Yat-sen Memorial Hospital","correspondingAuthor":false,"prefix":"","firstName":"Siming","middleName":"","lastName":"Wang","suffix":""},{"id":300137611,"identity":"7fd4c5ea-442c-4335-836c-094f01121b4e","order_by":2,"name":"JiaShun Xu","email":"","orcid":"","institution":"Dongguan Qingxi Hospital","correspondingAuthor":false,"prefix":"","firstName":"JiaShun","middleName":"","lastName":"Xu","suffix":""},{"id":300137613,"identity":"67aa77e6-c087-4707-b3a6-eabf146d89e1","order_by":3,"name":"Zhixiong Luo","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAz0lEQVRIiWNgGAWjYFCCBMYDDAwScvzyhw8c+PCDOC0MQC0WxpIz2BIPzuwhXktF4oYbPMaHOdiI0KDbnvzg4M82icSZs3s+HGbgYZDnFzuAX4vZmWcGByTOSBj3y5zdcLjAgsFw5uwEAlpu5DAcMKiQkJ3ZkLvh8AwehgSD28RoSTCQYNxwIOfBYR42YrUcqJBQ3ABkEKkF6JeDDUC/SPYcMwAGsgQRfjme/PDhz7Y6OX725scfPvywkeeXJqAFHUiQpnwUjIJRMApGAXYAALXCTrZzK9+LAAAAAElFTkSuQmCC","orcid":"","institution":"Dongguan Qingxi Hospital","correspondingAuthor":true,"prefix":"","firstName":"Zhixiong","middleName":"","lastName":"Luo","suffix":""}],"badges":[],"createdAt":"2024-05-06 08:29:24","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4375278/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4375278/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":56620600,"identity":"4bb5a6b9-48e4-4bd2-ad60-5f43224056ec","added_by":"auto","created_at":"2024-05-16 18:00:08","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":83418,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eConstruction and validation of the prognostic model based on the lysosome-related gene signatures in lung adenocarcinoma (LUAD).\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A, B) LASSO analysis with minimal lambda value. (C)The Kaplan–Meier survival analysis showing the difference in overall survival (OS) between the high- and low-risk groups in the training, (D)and validation cohorts. (E)Time-dependent ROC curve analysis in the training, (F)Time-dependent ROC curve analysis in the validation cohorts.\u003c/p\u003e","description":"","filename":"figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4375278/v1/9c6e742d0321abb43ac23e91.png"},{"id":56621419,"identity":"afd6a9ab-70f9-49a4-8525-cede4ed98561","added_by":"auto","created_at":"2024-05-16 18:08:08","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":100765,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEvaluation and validation of the utility of prognostic signature in the training set and validation set\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A, C) The distribution of risk score and survival status of LUAD patients with different risk scores in the training, (B, D) and validation cohorts. (E, F) Heatmap of the prognostic signatures expression profiles in the high- and low-risk groups in the training and validation cohorts, separately.\u003c/p\u003e","description":"","filename":"figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4375278/v1/d6e8ba691a2fab444bb77c1a.png"},{"id":56620602,"identity":"52104039-89b9-4110-a6f3-3de0fc62ffae","added_by":"auto","created_at":"2024-05-16 18:00:08","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":37063,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eConstruction and evaluation of the novel nomogram\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) The univariateCox regression analysis of the risk score and other clinical features in the training cohort, (B) The multivariate Cox regression analysis of the risk score and other clinical features in the training cohort. (C) A nomogram using risk scores combined with clinical characteristics. (D)The calibration plots of the nomogram for predicting OS probability for 1-, 3-, and 5- year in the training.\u003c/p\u003e","description":"","filename":"figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-4375278/v1/e54895348be2bcedd2936d8d.png"},{"id":56620605,"identity":"5678df31-8514-4f97-87d4-90ccf79e2502","added_by":"auto","created_at":"2024-05-16 18:00:08","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":54057,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe overall survival analysis of risk score in each clinical subtype.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-4375278/v1/990b75b9519fc406ba765059.png"},{"id":56620604,"identity":"9fb2832c-c11b-46db-bb0a-85fa80509161","added_by":"auto","created_at":"2024-05-16 18:00:08","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":44302,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRisk Signature-Based Immune Cell Infiltration, Tumor Microenvironment Analyses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) The differences in the scores of immune cells between high- and low-risk groups in the training. (B)The differentially expressed immune checkpoint-related genes between the high- and low-risk groups. (C)ESTIMATE, immune, and stromal scores between the high- and low-risk groups in the training. (D)The difference in tumor mutation burden (TMB) between the high- and low-risk groups in the training. *p \u0026lt; 0.05, **p \u0026lt; 0.01, ***p \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-4375278/v1/181c93c98f79b9a6e51781fe.png"},{"id":56620606,"identity":"eeaa23b4-1c77-4955-b01b-fc2e029d99f4","added_by":"auto","created_at":"2024-05-16 18:00:09","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":393952,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAnalysis of differences between high- and low-risk groups and functional and pathway enrichment analyses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Differential expression of ERG expression between high- and low-risk groups in the training. (B)The volcano plot exhibited both down- and up-regulated ERGs. (C)GO enrichment analysis and (D)KEGG pathway analysis based on the DEGs between the high- and low-risk groups in the training.\u003c/p\u003e","description":"","filename":"figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-4375278/v1/79a20f4f664f83dcddf8589b.png"},{"id":56620603,"identity":"d34a4fe4-50bf-4604-9741-43c5cf3ff1c6","added_by":"auto","created_at":"2024-05-16 18:00:08","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":30620,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePrediction of drug susceptibility in different risk groups.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A–I) Sensitive drugs in low-risk groups.\u003c/p\u003e","description":"","filename":"figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-4375278/v1/71320af24e3a86c00678ab20.png"},{"id":56621420,"identity":"0bfc8107-e25d-403f-aaee-48e0f5102803","added_by":"auto","created_at":"2024-05-16 18:08:14","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1367993,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4375278/v1/17d256fd-cf88-42bf-b1f0-6a6c0b5502ef.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Identification of exosome-related features for prediction prognostic tumor microenvironment in lung adenocarcinoma","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIn recent years, lung cancer has become one of the more common types of cancer of the respiratory system, with increasing morbidity and mortality rates [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Currently, lung cancer is primarily treated with surgery and chemotherapy, but the effectiveness of these treatments has declined over the years. It is estimated that 12 percent of lung cancer patients who are diagnosed with metastatic cancer will survive for five years. [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e] Thus, it is essential to discover more prognostic markers for lung cancer, which are extremely important for early diagnosis and for improving survival rates.\u003c/p\u003e \u003cp\u003eThe main factors influencing the prognosis of lung cancer are the stage of lung cancer, surgical factors, living conditions and mental state. Risk factors for lung cancer are diet, age, obesity, smoking and lack of exercise.[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] Current treatments do not improve outcomes for patients with advanced lung cancer. Therefore, it is necessary to further explore the pathogenesis of lung cancer in order to improve the prognosis of lung cancer. Studies have shown that lysosomes are dynamic organelles in eukaryotic cells.[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] The monolayer contains a variety of hydrolases that receive and degrade macromolecules through secretory, endocytosis, autophagy and phagocytic membrane transport. Lysosomes are divided into primary lysosomes and secondary lysosomes. The primary lysosomes form buds on the antiplane of the Golgi complex, which are regulated by transcription factors. The abnormal function of lysosomes in some tumor cells leads to the change of enzyme level, which may be the cause of tumor occurrence [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Therefore, lysosome function is closely related to tumor.\u003c/p\u003e \u003cp\u003eAccording to previous studies, translocation and abnormal secretion of lysosomes facilitate the invasion and metastasis of cancer cells [\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Major histocompatibility complex (MHC) molecules and immune checkpoint lysosomal degradation in tumor cells are abnormal, and selective autophagy defects in tumor-infiltrating T lymphocytes lead to tumor metastasis. An abnormal lysosomal function and changes in the expression of some acid hydrolases are present in tumor cells. Lysosome function of tumor cells was abnormal and some acid hydrolytic enzyme expression was changed. Inhibition of lysosomal exocytosis can inhibit tumor invasion and metastasis because it does not affect acid hydrolase activity, but can also lead to instability of the lysosomal membrane and increase the sensitivity of tumor cells to drugs [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Therefore, lysosomes, as important biomarkers of tumor progression, however, whether lysosomal genes are effective prognostic biomarkers for lung cancer remains unclear.\u003c/p\u003e \u003cp\u003eIn this study, we constructed a model of the relationship between lysosomes- related genes and lung cancer patients and validated it in an independent cohort of lung cancer patients. We demonstrate that lysosomes play a role in the pathogenesis of lung cancer and can predict the prognosis of lung cancer.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData Acquisition\u003c/h2\u003e \u003cp\u003eTranscriptome data of 598 LUAD patients from The Cancer Genome Atlas(TCGA) database(\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://portal.gdc.cancer.gov\u003c/span\u003e\u003cspan address=\"https://portal.gdc.cancer.gov\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), including 59 normal samples and 539 tumor samples. After excluding normal samples and samples with no survival data, 507 tumor samples were obtained. There is also GSE68465 from the Gene Expression Omnibus (GEO) database(\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/gds\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/gds\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), and samples lacking survival data will be removed.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eEstablishment and validation of prognostic model\u003c/h2\u003e \u003cp\u003eThe \"sva\" R package is used to merge the TCGA queue and the GSE68465 cohort, and the \"combat\" function is used to remove the batch effect between the two. Lysosome related genes were obtained by screening. Based on lysosome related genes, the TCGA-LUAD cohort was used as the training set and the GSE68465 cohort was used as the validation set. Univariate cox regression was performed to screen genes (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and then least absolute shrinkage and selection operator (LASSO) Cox regression analysis was used to select genes to reduce the overfitting risk and construct a risk score formula via the multivariable Cox regression. The \"survminer\" R package was used to calculate the best cutoff value of risk score, and the samples were divided into high-risk group and low-risk group according to the best cutoff value. The \"survminer\" and \"survival\" packages of R software were used to plot survival curves between the low and high risk groups. The stability of the risk score was analyzed using the validation group, and the performance of the prognostic formula was evaluated by time-dependent ROC analysis using R package \"survivalROC\".\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eIndependent prognostic analysis and Nomogram establishment and Calibration\u003c/h2\u003e \u003cp\u003eClinical information (including age, gender, and stage) of TCGA-LUAD patients was extracted, and univariate and multivariate Cox regression analysis was performed combined with risk score to evaluate whether risk score and clinical information were independent prognostic factors for overall survival. Based on the model risk score and independent prognostic factors, nomograms were constructed to predict 1-, 3-, and 5-OS. The Calibration curve was used to distinguish the nomogram predicted state from the real survival rate.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eFunctional and pathway enrichment analysis\u003c/h2\u003e \u003cp\u003eIn the TCGA - LUAD cohort using R package \"limma\" package carries on the differences in gene analysis, filter conditions for P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, |LogFC| \u0026gt; 1. Gene Ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) were used to explore potential mechanisms and pathways in high- and low-risk groups, using the R-package \"clusterProfiler\" and setting a P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 significance threshold.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eTumor immune microenvironment analysis\u003c/h2\u003e \u003cp\u003eIn the TCGA-LUAD cohort, 22 immune cell infiltration scores were obtained using the CIBERSORT method using the \"e1071\", \"preprocessCor\", \"limma\" R package. Combined with the grouping information, it is visualized using the \"ggplot2\" and \"tidyr\" R packages. Combined with the risk score and immune cell infiltration scores, the relationship between the model and each immune cell was demonstrated and visualized using the \"corrplot\" R package. In addition, the differential analyses of stromal score, immune score and ESTIMATE score were performed based on the results of ESTIMATE using the R software package\u0026ldquo;estimate\u0026rdquo;. Subsequently, used the \"ggpubr\" and \"ggplot2\" packages to compare and visualize the immune checkpoints and tumor mutational burden(TMB) score between low- and high-risk groups.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003ePrediction of Drug Susceptibility\u003c/h2\u003e \u003cp\u003eThe \"pRRophetic\" R package was used to predict the half-maximal inhibitory concentration (IC50) value of an anticancer drug in different risk subgroups. The IC50 value represents the effectiveness of the substance in inhibiting a specific biological or biochemical process.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eAll statistical analyses were performed using R software (version 4.2.2). The Wilcoxon signed-rank test was used to investigate differences in the composition of immune infiltrating cells. The correlation between risk score and immune cell was investigated using Spearman correlation analysis. Kaplan-Meier analysis was used to estimate survival curves. P values\u0026thinsp;\u0026lt;\u0026thinsp;0.05(*),0.01 (**), and 0.001 (***) were considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Result","content":"\u003cp\u003eIn total, 949 patients were included. 507 LUAD patients from the TCGA cohort (235 [46.3%] male, mean [SD] age, 65.30 [10.03]), 442 patients from the validation cohort (223 [50.4%] male, mean [SD] age, 64.39 [10.09])\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe construction and validation of novel prognostic model\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAfter screening, 133 lysosome-related genes were obtained in TCGA and GSE68465 cohorts. Then, the univariate Cox analysis was used to explore 133 lysosome-related genes. To prevent model overfitting, LASSO penalized Cox regression modeling was conducted to screen the key lysosome-related genes associated with survival. With this method, a novel prognostic gene model with 26 genes was constructed (Figure1 A-B). And then, risk scores per sample were calculated using the following model formula:\u003c/p\u003e\n\u003cp\u003e\u003cimg src=\"data:image/png;base64,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\"\u003e\u003c/p\u003e\n\u003cp\u003eIn this formula, \u0026beta; is coefficient and X is the expression level of each prognostic gene i. The samples were divided into a high-risk group and a low-risk group according to the best cutoff value of the training cohort from TCGA-LUAD. As shown by the Kaplan\u0026ndash;Meier analyses, patients in the high-risk group had worse OS than those in the low-risk group (P \u0026lt;0.0001) (Figure1 C-D). In the training cohort, the AUC values of the present risk model were 0.71, 0.71, and 0.71 for the 1-, 3-, and 5- year prognoses, respectively. We used GSE68465-cohort to validate this model. In the validation cohort, the AUC values of the present risk model were 0.70, 0.66, and 0.61 for the 1-, 3-, and 5- year prognoses, respectively (Figure1 E-F). The distribution plot of risk score and survival status revealed that the number of TCGA-LUAD patients with a status of deceased increased as the risk score in the training set rose. In GSE68465-cohort, the low-risk group maintained its superior survival status and longer survival time from the training set (Figure2 A-D). In addition, a heatmap demonstrated that the expression of 26 prognostic genes varied significantly among TCGA-LUAD patients with varying risk scores (Figure2 E-F).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 1: Construction and validation of the prognostic model based on the lysosome-related gene signatures in lung adenocarcinoma (LUAD).\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A, B) LASSO analysis with minimal lambda value. (C)The Kaplan\u0026ndash;Meier survival analysis showing the difference in overall survival (OS) between the high- and low-risk groups in the training, (D)and validation cohorts. (E)Time-dependent ROC curve analysis in the training, (F)Time-dependent ROC curve analysis in the validation cohorts.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 2: Evaluation and validation of the utility of prognostic signature in the training set and validation set\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A, C) The distribution of risk score and survival status of LUAD patients with different risk scores in the training, (B, D) and validation cohorts. (E, F) Heatmap of the prognostic signatures expression profiles in the high- and low-risk groups in the training and validation cohorts, separately.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIndependent Prognostic Factor Analysis and construction of Nomogram\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUnivariate and multivariate Cox regression analyses were performed by introducing age, gender, stage, TNM stage, and risk scores to assess the independence of risk scores in the survival prediction of LUAD patients. Among the samples in the training cohort, the results showed that Clinicopathologic stage, T stage, M stage, N stage, and risk score were identified as independent negative prognostic factors for patients with LUAD (Figure3 A-B). In addition, risk scores also showed significant differences in age, gender, Clinicopathologic stage, T stage, M stage and N stage (Figure4 A-P). Based on the training cohort, risk scores and clinical factors that were identified as independent negative prognostic factors were integrated to create a nomogram to improve the predictive power of survival in LUAD patients. Calibration plots for 1-, 3- and 5-years OS revealed good agreement between nomogram prediction and actual observations (Figure3 C-D).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 3: Construction and evaluation of the novel nomogram\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe univariate Cox regression analysis of the risk score and other clinical features in the training cohort, (B) The multivariate Cox regression analysis of the risk score and other clinical features in the training cohort. (C) A nomogram using risk scores combined with clinical characteristics. (D)The calibration plots of the nomogram for predicting OS probability for 1-, 3-, and 5- year in the training.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 4: The overall survival analysis of risk score in each clinical subtype.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRisk Signature-Based Immune Cell Infiltration, Tumor Microenvironment Analyses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe used CIBERSORT to quantify immune cells, it was found that the expression of Plasma cells, T cells CD4 memory resting, T cells regulatory (Tregs), Dendritic cells resting, Mast cells resting was significantly higher in the low-risk group than in the high-risk group (Figure5 A). Immune-checkpoint related genes like CD44, CD276, TNFRSF9, TNFSF4, TNFSF9, CD70, DCD1LG2 and TMIGD2, were more lowly expressed in the low-risk group (Figure5 B). What\u0026rsquo;s more, with the increase of risk score, the high-risk group had a lower estimate score, immune score, and stromal score (Figure5 C). In addition, the TMB score of LUAD was higher in high risk group (Figure5 D).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 5:\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eRisk Signature-Based Immune Cell Infiltration, Tumor Microenvironment Analyses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) The differences in the scores of immune cells between high- and low-risk groups in the training. (B)The differentially expressed immune checkpoint-related genes between the high- and low-risk groups. (C)ESTIMATE, immune, and stromal scores between the high- and low-risk groups in the training. (D)The difference in tumor mutation burden (TMB) between the high- and low-risk groups in the training. *p \u0026lt; 0.05, **p \u0026lt; 0.01, ***p \u0026lt; 0.001.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunctional and Pathway Enrichment Analyses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e502 differential expression genes (DEGs) included 264 up-regulated genes and 238 regulated genes were screened between high-risk and low-risk group in training cohort (Figure6 A-B). By considering the DEGs between the high- and low-risk groups from TCGA-LUAD, we conducted GO enrichment analysis, KEGG pathway analysis to explore the potential biological functions of these DEGs. \u0026ldquo;mitotic nuclear division\u0026rdquo;, \u0026ldquo;mitotic sister chromatid segregation\u0026rdquo;, \u0026ldquo;nuclear division\u0026rdquo;, \u0026ldquo;chromosome segregation\u0026rdquo; and \u0026ldquo;organelle fission\u0026rdquo; were the most enriched terms among the biological process categories. \u0026ldquo;condensed chromosome, centromeric region\u0026rdquo;, \u0026ldquo;condensed chromosome kinetochore\u0026rdquo;, \u0026ldquo;kinetochore\u0026rdquo;, \u0026ldquo;chromosome, centromeric region\u0026rdquo; and \u0026ldquo;spindle\u0026rdquo; were the most enriched terms among the cellular component categories, and \u0026ldquo;microtubule binding\u0026rdquo;, \u0026ldquo;tubulin binding\u0026rdquo;, \u0026ldquo;microtubule motor activity\u0026rdquo;, \u0026ldquo;peptidase inhibitor activity\u0026rdquo; and \u0026ldquo;enzyme inhibitor activity\u0026rdquo; ere the most enriched terms among the molecular function categories. \u0026ldquo;Cell cycle\u0026rdquo;, \u0026ldquo;p53 signaling pathway\u0026rdquo;, \u0026ldquo;Oocyte meiosis\u0026rdquo;, \u0026ldquo;ECM\u0026minus;receptor interaction\u0026rdquo; and \u0026ldquo;Progesterone\u0026minus;mediated oocyte maturation\u0026rdquo; were identified to be the most enriched among the KEGG pathways of the DEGs (Figure6 C-D).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 6: Analysis of differences between high- and low-risk groups and f\u003c/strong\u003e\u003cstrong\u003eunctional and pathway enrichment analyses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDifferential expression of ERG expression between high- and low-risk groups in the training. (B)The volcano plot exhibited both down- and up-regulated ERGs. (C)GO enrichment analysis and (D)KEGG pathway analysis based on the DEGs between the high- and low-risk groups in the training.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDrug sensitive\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDifferences in drug sensitivity of different risk subgroups were analyzed to investigate the clinical application value of the risk model. Results showed that Camptothecin, Cisplatin, Docetaxel, Doxorubicin, Etoposide, Gemcitabine, Paclitaxel，Vinorelbine and Vinblastine had good effects on patients in low-risk groups(Figure7 A-I).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 7: Prediction of drug susceptibility in different risk groups.\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A\u0026ndash;I) Sensitive drugs in low-risk groups.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eLung adenocarcinoma, a prevalent subtype of non-small cell lung cancer (NSCLC), has a poor prognosis with a 5-year survival rate of approximately 15%. Standard treatments include surgery, chemotherapy, radiation, and targeted therapies such as tyrosine kinase inhibitors (TKIs) and immune checkpoint inhibitors (ICIs) [\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Despite advancements, the overall prognosis remains unsatisfactory for many patients. The urgent need for novel biomarkers in early detection and prognostic prediction is evident. Identifying such biomarkers could lead to personalized treatment strategies and improved clinical outcomes. Researchers are exploring molecular signatures, gene expression profiles, and circulating tumor DNA in search of reliable biomarkers to enhance early detection, refine treatment strategies, and ultimately improve the prognosis for lung adenocarcinoma patients [\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe relationship between lysosomes and cancer has increasingly attracted attention in recent years, as researchers strive to understand the underlying mechanisms of tumorigenesis. Lysosomes are membrane-bound organelles that contain hydrolytic enzymes responsible for the breakdown of various biomolecules, playing a crucial role in cellular metabolism and homeostasis. Several hypotheses have been proposed to explain the possible association between lysosomes and cancer development. Carcinogenic substances have been found to potentially disrupt cell division regulation and cause chromosomal abnormalities, which may be linked to the release of hydrolytic enzymes by lysosomes [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Moreover, certain substances that affect lysosomal membrane permeability, such as croton oil, some detergents, and hyperbaric oxygen, can act as auxiliary factors in promoting carcinogenesis, leading to abnormal cell division. Additionally, when the nuclear membrane is defective, its protective function is compromised, allowing lysosomes to dissolve chromatin and induce cellular mutations. Furthermore, some by-products of lysosomal metabolism could serve as the material basis for cancer cell proliferation, providing essential nutrients and growth factors for their survival and expansion. Lastly, carcinogenic substances entering cells are often stored in lysosomes before integrating with chromosomes, a phenomenon confirmed by radiographic autoradiography studies [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn our recent study, we successfully integrated lysosomal gene signatures to construct a prognostic model for lung adenocarcinoma. This model holds significant potential in guiding personalized treatment strategies and improving clinical outcomes for patients. By utilizing a lysosomal signature-based scoring system, we were able to stratify lung adenocarcinoma patients into high and low-risk groups, which allowed for a more accurate prediction of patient survival outcomes. Our research involved the systematic analysis of lysosomal gene expression profiles in lung adenocarcinoma patients, followed by the development of a prognostic signature using a combination of these genes. The model was then tested and validated in independent patient cohorts to ensure its robustness and reliability. The performance of our lysosomal signature-based model was assessed by measuring the area under the receiver operating characteristic (ROC) curve. Impressively, the ROC value exceeded 0.70, indicating a strong ability to distinguish between high and low-risk patients in terms of survival outcomes. This achievement underscores the potential clinical utility of our model in predicting prognosis and guiding treatment decisions for lung adenocarcinoma patients.\u003c/p\u003e \u003cp\u003eIn addition, Lysosomes play a crucial role in immune function, as these membrane-bound organelles are responsible for the degradation and recycling of various biomolecules within the cell. Lysosomes contribute to immune processes through several mechanisms, including phagocytosis, autophagy, and antigen presentation [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. In phagocytosis, immune cells such as macrophages engulf and destroy pathogens, foreign particles, and cellular debris. Once engulfed, these materials are sequestered within phagosomes, which then fuse with lysosomes. The hydrolytic enzymes within lysosomes break down the contents of the phagosome, effectively neutralizing the threat. Autophagy is a cellular process that involves the degradation and recycling of damaged organelles and misfolded proteins. This process not only maintains cellular homeostasis but also serves as a defense mechanism against intracellular pathogens [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Lysosomes play a key role in autophagy by fusing with autophagosomes to degrade their contents, thereby eliminating potential threats to the cell.\u003c/p\u003e \u003cp\u003eAdditionally, lysosomes contribute to antigen presentation, a crucial step in activating the adaptive immune response. Antigen-presenting cells, such as dendritic cells and macrophages, internalize pathogens and process them within lysosomes. The resulting peptide fragments are then loaded onto major histocompatibility complex (MHC) molecules and displayed on the cell surface, which ultimately triggers the activation of T cells and the adaptive immune response [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Our study revealed notable differences in immune characteristics between high and low lysosomal signature score groups in lung adenocarcinoma patients. Using CIBERSORT to quantify immune cells, we found that the low-risk group exhibited significantly higher expression of plasma cells, resting CD4 memory T cells, regulatory T cells (Tregs), resting dendritic cells, and resting mast cells compared to the high-risk group. Moreover, immune checkpoint-related genes such as CD44, CD276, TNFRSF9, TNFSF4, TNFSF9, CD70, CD1LG2, and TMIGD2 were expressed at lower levels in the low-risk group.As the risk score increased, the high-risk group demonstrated lower estimate, immune, and stromal scores, suggesting a less favorable tumor microenvironment. Additionally, the tumor mutational burden (TMB) score was higher in the high-risk group, indicating a greater likelihood of genomic instability and potential resistance to immunotherapy. These findings highlight the significant immunological differences between high and low lysosomal signature score groups and emphasize the potential clinical implications of these disparities in predicting prognosis and guiding treatment decisions for lung adenocarcinoma patients.\u003c/p\u003e \u003cp\u003eIn summary, our study, including 949 patients, developed a 26-gene prognostic model based on lysosome-related genes for lung adenocarcinoma. This model stratified patients into high and low-risk groups, with the low-risk group having better overall survival. Immune cell infiltration and tumor microenvironment analyses showed significant differences between groups. The model also revealed differences in drug sensitivity, with low-risk patients responding better to common cancer drugs. This novel prognostic model may help guide personalized treatment strategies and improve clinical outcomes for lung adenocarcinoma patients.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe proposed model based on lysosome-related genes could be a potential tool for predicting the prognosis of lung cancer patients. It may facilitate early diagnosis, inform treatment plans, and improve overall survival rates. However, further research is required to establish its practical application in clinical settings.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eTCGA \u0026nbsp; The Cancer Genome Atlas\u003c/p\u003e\n\u003cp\u003eGEO \u0026nbsp; \u0026nbsp;The Gene Expression Omnibus\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMHC \u0026nbsp; \u0026nbsp;Major histocompatibility complex\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGO \u0026nbsp; \u0026nbsp; Gene Ontology\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eLASSO \u0026nbsp;least absolute shrinkage and selection operator\u003c/p\u003e\n\u003cp\u003eKEGG \u0026nbsp; Kyoto encyclopedia of genes and genomes\u003c/p\u003e\n\u003cp\u003eLUAD \u0026nbsp; Lung adenocarcinoma\u003c/p\u003e\n\u003cp\u003eTMB \u0026nbsp; \u0026nbsp;tumor mutational burden\u003c/p\u003e\n\u003cp\u003eIC50 \u0026nbsp; \u0026nbsp;the half-maximal inhibitory concentration\u003c/p\u003e\n\u003cp\u003eTKIs \u0026nbsp; \u0026nbsp;tyrosine kinase inhibitors\u003c/p\u003e\n\u003cp\u003eICIs \u0026nbsp; \u0026nbsp; immune checkpoint inhibitors\u003c/p\u003e\n\u003cp\u003eROC \u0026nbsp; \u0026nbsp;receiver operating characteristic\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMHC \u0026nbsp; \u0026nbsp;major histocompatibility complex\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis article does not contain any studies with human participants or animals performed by any of the authors.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data of this study are available in the The Cancer Genome Atlas (TCGA, https://portal.gdc.cancer.gov), the Gene Expression Comprehensive Database (GEO, http://www.ncbi.nlm.nih.gov/geo).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have declared that no competing interest exists.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLuo and Wang conceived the study design. Data acquisition was carried out by Xu. Chen and Wang conducted the data analysis. Chen designed data visualization. Chen, Wang and Xu wrote the original draft. Revision of the manuscript was done by Luo and Chen. All the authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to thank for support and design the research.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAllemani C, Matsuda T, Di Carlo V, et al. Global surveillance of trends in cancer survival 2000-14 (CONCORD-3): analysis of individual records for 37 513 025 patients diagnosed with one of 18 cancers from 322 population-based registries in 71 countries. 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Autophagy. 2017;13(10):1797\u0026ndash;8. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1080/15548627.2017.1358850\u003c/span\u003e\u003cspan address=\"10.1080/15548627.2017.1358850\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\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":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-bioinformatics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"binf","sideBox":"Learn more about [BMC Bioinformatics](http://bmcbioinformatics.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/binf","title":"BMC Bioinformatics","twitterHandle":"@BMC_Bioinformatics","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-4375278/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4375278/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eLung cancer has a high morbidity and mortality rate with currently limited treatment options. There is an urgent need for prognostic markers to facilitate early diagnosis and improve survival rates. This study proposes lysosome-related genes as potential prognostic markers, as they play a significant role in the pathogenesis of lung cancer.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThe study established a prognostic model using lysosome-related genes from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) database. Univariate Cox regression and LASSO Cox regression analyses were utilized to identify and select relevant genes, and the model was then validated in an independent cohort of lung cancer patients. Further, immune cell infiltration scores, drug susceptibility, functional and pathway enrichment analyses were conducted to evaluate the model's predictive ability.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe study identified 26 key lysosome-related genes and found that the high-risk group, as identified by the model, had a poorer overall survival rate. Additionally, the model demonstrated a good prediction accuracy for 1-, 3-, and 5- year prognosis in the training and validation cohorts. The model's risk score was identified as an independent prognostic factor, demonstrating its potential clinical relevance. Immune cell infiltration, tumor microenvironment analyses, and drug susceptibility predictions also provided significant insights.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe proposed model based on lysosome-related genes could be a potential tool for predicting the prognosis of lung cancer patients. It may facilitate early diagnosis, inform treatment plans, and improve overall survival rates. However, further research is required to establish its practical application in clinical settings.\u003c/p\u003e","manuscriptTitle":"Identification of exosome-related features for prediction prognostic tumor microenvironment in lung adenocarcinoma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-05-16 18:00:04","doi":"10.21203/rs.3.rs-4375278/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorAssigned","content":"","date":"2024-05-08T10:41:36+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-05-08T09:55:35+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Bioinformatics","date":"2024-05-06T08:27:26+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-bioinformatics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"binf","sideBox":"Learn more about [BMC Bioinformatics](http://bmcbioinformatics.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/binf","title":"BMC Bioinformatics","twitterHandle":"@BMC_Bioinformatics","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"bf81327b-0b72-4f56-a02e-f445fdeefc1b","owner":[],"postedDate":"May 16th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2024-05-16T18:00:04+00:00","versionOfRecord":[],"versionCreatedAt":"2024-05-16 18:00:04","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4375278","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4375278","identity":"rs-4375278","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}