{"paper_id":"cec4b9e3-5786-4cf2-b9e2-224cae19a1d5","body_text":"Identification of Lysosome-Related Features for Predicting 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 Lysosome-Related Features for Predicting Prognostic Tumor Microenvironment in Lung Adenocarcinoma Hongwei Chen, Huixin Xu, Jiashun Xu, Qingjian Li, Zhixiong Luo This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9363540/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Background Lung cancer, associated with high morbidity and mortality, currently has limited treatment options. Identifying prognostic markers is essential to enable early diagnosis and improve survival rates. This study investigates lysosome-related genes as potential prognostic markers, given their critical role in lung cancer pathogenesis. Methods Using data from TCGA and GEO database, a prognostic model based on lysosome-related genes was developed. Univariate Cox regression and LASSO Cox regression analyses were performed to identify relevant genes, and the model was validated with an independent lung cancer patient cohort. Additionally, immune cell infiltration scores, drug susceptibility, and pathway enrichment analyses were conducted to assess the model's predictive performance. Results A prognostic signature composed of 26 lysosome-related genes effectively separated patients into high- and low-risk subgroups, which showed distinct overall survival outcomes and consistent predictive ability across both cohorts. Random forest prioritization highlighted CTSV as a top contributor; high CTSV expression was associated with worse survival and remained significant in multivariate Cox analysis. Functionally, CTSV overexpression promoted LUAD cell viability, clonogenic growth, and migration, whereas CTSV knockdown produced opposite effects. Conclusion This lysosome-related signature provides a robust tool for prognostic stratification in LUAD, and CTSV represents a clinically relevant and functionally validated candidate that may link lysosome biology to tumor aggressiveness, warranting further mechanistic and translational investigation. lysosome-related genes lung adenocarcinoma prognostic model CTSV tumor microenvironment bioinformatics analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1 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, further investigation into the mechanisms underlying lung cancer is essential for improving patient prognosis. 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 anti-plane 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. In recent years, multiple studies have explored gene and pathomics signatures for predicting lung cancer prognosis, yielding promising results. For instance, previous studies have identified genetic markers and imaging features associated with survival outcomes in lung cancer patients, underscoring the value of multi-omics approaches in risk stratification. Similarly, other studies have employed comprehensive bioinformatics and machine learning techniques to develop prognostic models based on gene expression profiles, highlighting both the potential and challenges of these methods) [ 6 – 9 ]. Our study builds on this foundation by focusing specifically on lysosome-related genes as prognostic markers, adding a unique perspective to the existing body of research. According to previous studies, translocation and abnormal secretion of lysosomes facilitate the invasion and metastasis of cancer cells[ 10 – 12 ]. 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[ 13 ]. 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.[ 14 , 15 ] 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. 2 METHODS Data Acquisition Transcriptome data of 598 lung adenocarcinoma (LUAD) patients were obtained 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 missing survival data, 507 tumor samples were retained for analysis. Additional gene expression data were sourced from the GSE68465 dataset in the Gene Expression Omnibus (GEO) database ( https://www.ncbi.nlm.nih.gov/gds ), with samples lacking survival data excluded to maintain consistency. Data Preprocessing and Normalization Data preprocessing involved log transformation and normalization steps to minimize variability across datasets, enhancing comparability. Batch effect correction, as noted above, was performed to ensure consistency between TCGA and GEO datasets. Selection of Lysosome-Related Genes Lysosome-related genes were identified based on annotations from the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases, focusing on genes involved in lysosomal pathways. Genes selected demonstrated biological relevance in lysosome-related processes and were previously associated with cancer progression. The initial screening of these genes in the TCGA-LUAD dataset used univariate Cox regression analysis (P < 0.05) to identify genes significantly associated with survival, ensuring their potential as prognostic markers. Establishment and validation of prognostic model To merge the TCGA and GEO (GSE68465) datasets, the “sva” R package was used, with the “combat” function applied to remove batch effects, enhancing the consistency of the integrated dataset. The TCGA-LUAD cohort was used as the training set, while the GSE68465 cohort served as the validation set to allow for independent assessment of the model’s robustness. Following initial screening, least absolute shrinkage and selection operator (LASSO) Cox regression analysis was applied to refine the gene set, reducing the risk of overfitting. This process yielded 26 lysosome-related genes used to construct a risk score formula through multivariable Cox regression analysis. The “survminer” R package was employed to calculate the optimal cutoff risk score, dividing samples into high-risk and low-risk groups based on this threshold. Validation Process and Performance Assessment The model’s risk score stability was tested using the validation cohort (GSE68465). The validation cohort was selected independently of the training set, with criteria including adequate sample size and data completeness. Time-dependent receiver operating characteristic (ROC) analysis using the “survivalROC” R package assessed the predictive accuracy of the model for 1-, 3-, and 5-year survival outcomes, validating its clinical utility. Random forest–based gene prioritization and CTSV survival analyses To prioritize individual genes within the 26-gene prognostic signature, a random forest (RF) classifier was trained using the expression levels of the 26 genes as input features and the model-derived risk group (high vs low risk) as the outcome. Feature importance was assessed by permutation and reported as MeanDecreaseAccuracy. For clinical evaluation of CTSV, TCGA-LUAD patients were stratified into CTSV-high and CTSV-low groups using the median CTSV expression. Overall survival differences were assessed by Kaplan–Meier analysis with log-rank testing, and Cox proportional hazards regression was performed to estimate hazard ratios with 95% confidence intervals; multivariate models adjusted for available clinical covariates. Cell culture, transfection, and functional assays A549 cells were cultured under standard conditions (37°C, 5% CO2) and transfected with CTSV expression plasmids (Mock/Vector/CTSV) or two independent siRNAs targeting CTSV (siCTSV-1 and siCTSV-2) using lipofection according to the manufacturer’s instructions. CTSV modulation efficiency was verified at the mRNA and/or protein level prior to phenotypic assays. Cell viability was assessed using CCK-8 at the indicated time points. Clonogenic capacity was evaluated by plate colony formation assays. Cell migration was examined by transwell migration assays and wound-healing assays with images acquired at 0, 24, and 48 h. For chemotherapy stress experiments, cells were treated with cisplatin at the indicated concentration and viability was assessed by live/dead staining when applicable. 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 < 0.05, |LogFC| > 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[ 16 ]. Combined with the grouping information, it is visualized using the \"ggplot2\" and \"tidyr\" R packages. Based on the risk score and immune cell infiltration levels, the associations between the prognostic model and individual immune cells were analyzed and visualized using the “corrplot” R package. Moreover, differences in stromal score, immune score, and ESTIMATE score were analyzed based on the ESTIMATE results using the “estimate” R package. 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 applied to estimate the half-maximal inhibitory concentration (IC50) of anticancer agents in different risk groups. IC50 is a commonly used indicator of a drug’s potency in suppressing a given biological or biochemical activity.[ 17 ]. Statistical Analysis All statistical analyses were conducted in R software (version 4.2.2). Differences in the proportions of infiltrating immune cells were assessed using the Wilcoxon signed-rank test, and the association between risk score and immune cell infiltration was evaluated by 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. 3 RESULTS 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]) 3.1 The construction and validation of novel prognostic model After filtering, 133 lysosome-related genes were identified in the TCGA and GSE68465 cohorts. Univariate Cox regression analysis was subsequently performed to assess their associations with survival. To reduce the risk of overfitting, LASSO Cox regression was further applied to select the most informative survival-related lysosome-associated genes. Based on this approach, a prognostic signature consisting of 26 genes was established (Fig. 1 A-B). The risk score for each sample was then calculated according to the following formula: $$\\:\\:Risk\\:Score=\\sum\\:_{i=1}^{n}{\\beta\\:}_{i}\\times\\:{X}_{i}$$ In this formula, β represents the regression coefficient, and X denotes the expression level of each prognostic gene. Based on the optimal cutoff value derived from the TCGA-LUAD training cohort, patients were classified into high- and low-risk groups. Kaplan–Meier survival analysis showed that overall survival was significantly poorer in the high-risk group than in the low-risk group (P < 0.0001; Fig. 1 C-D). In the training cohort, the AUC values of the prognostic model for predicting 1-, 3-, and 5-year survival were 0.71, 0.71, and 0.71, respectively. The robustness of this model was further assessed in the GSE68465 cohort. In the validation cohort, the corresponding AUC values for 1-, 3-, and 5-year survival were 0.70, 0.66, and 0.61, respectively (Fig. 1 E-F). Risk score distribution and survival status analyses in the TCGA-LUAD training set indicated that the number of deceased patients increased with rising risk scores. Consistent with the training cohort, patients in the low-risk group of the GSE68465 cohort exhibited more favorable survival status and longer survival time (Fig. 2 A-D). Moreover, the heatmap revealed distinct expression patterns of the 26 prognostic genes among TCGA-LUAD patients with different risk scores (Fig. 2 E-F). 3.2 CTSV is prioritized by machine learning and validated as a prognostic and pro-tumorigenic factor in LUAD Given that multigene prognostic signatures often contain correlated features, we used a random forest (RF) approach to assess the relative contribution of each gene and facilitate candidate prioritization. Using the 26 genes as input variables, the RF model was trained to classify high- vs low-risk patients, and feature importance was evaluated by permutation (MeanDecreaseAccuracy). CTSV ranked among the top contributors, suggesting that it provides non-redundant predictive information beyond other genes in the signature (Fig. 3 A). Motivated by its high RF importance, we next evaluated the clinical relevance of CTSV in the TCGA-LUAD cohort. Kaplan–Meier analysis showed that patients with high CTSV expression had significantly worse overall survival than those with low expression (Fig. 3 B). Moreover, multivariate Cox regression adjusting for available clinical covariates confirmed CTSV as an independent prognostic factor (Fig. 3 C). While feature importance does not imply causality, the convergent evidence from RF prioritization and clinical survival analyses, together with CTSV’s lysosomal localization and enzymatic activity, supports CTSV as an experimentally tractable and mechanistically plausible candidate. We next assessed whether CTSV directly contributes to malignant phenotypes in LUAD cells using complementary gain- and loss-of-function approaches. In A549 cells, ectopic CTSV expression significantly enhanced cell viability and growth kinetics in CCK-8 assays, whereas CTSV silencing with two independent siRNAs consistently suppressed proliferative capacity (Fig. 3 D). Consistently, plate colony formation assays demonstrated that CTSV overexpression increased clonogenic outgrowth, while CTSV depletion markedly reduced colony number (Fig. 3 E). Moreover, transwell migration assays showed that CTSV promoted LUAD cell motility, with reduced migratory activity upon CTSV knockdown and increased migration following CTSV overexpression (Fig. 3 F). Wound-healing assays yielded concordant results, showing delayed scratch closure in CTSV-silenced cells and accelerated wound closure in CTSV-overexpressing cells over time (Fig. 3 G). Collectively, these bidirectional functional assays establish CTSV as a pro-tumorigenic regulator that promotes LUAD cell growth, clonogenicity, survival under chemotherapy stress, and migratory capacity in vitro, thereby providing a functional basis for subsequent mechanistic interrogation. 3.3 Independent Prognostic Factor Analysis and construction of Nomogram Univariate and multivariate Cox regression analyses incorporating age, sex, clinical stage, TNM stage, and risk score were conducted to determine whether the risk score could serve as an independent predictor of survival in patients with LUAD. In the training cohort, clinicopathological stage, T stage, M stage, N stage, and risk score were all identified as independent adverse prognostic factors (Fig. 4 A-B). Moreover, the risk score was significantly associated with age, sex, clinicopathological stage, T stage, M stage, and N stage (Fig. 5 A-P). Based on the training cohort, a nomogram integrating the risk score and independent prognostic clinical variables was constructed to enhance survival prediction in LUAD patients. The calibration curves for 1-, 3-, and 5-year overall survival showed good concordance between the predicted and observed outcomes (Fig. 4 C-D). 3.4 Risk Signature-Based Immune Cell Infiltration, Tumor Microenvironment Analyses CIBERSORT analysis showed that the low-risk group had significantly higher proportions of plasma cells, resting CD4 memory T cells, regulatory T cells (Tregs), resting dendritic cells, and resting mast cells than the high-risk group (Fig. 6 A). Immune-checkpoint related genes like CD44, CD276, TNFRSF9, TNFSF4, TNFSF9, CD70, DCD1LG2 and TMIGD2, were more lowly expressed in the low-risk group (Fig. 6 B). What’s more, with the increase of risk score, the high-risk group had a lower estimate score, immune score, and stromal score (Fig. 6 C). In addition, the TMB score of LUAD was higher in high risk group (Fig. 6 D). 3.5 Functional and Pathway Enrichment Analyses In the training cohort, 502 DEGs were identified between the high- and low-risk groups, including 264 upregulated and 238 downregulated genes (Fig. 7 A-B). To explore the potential biological roles of these genes, GO and KEGG enrichment analyses were conducted based on the DEGs derived from TCGA-LUAD. GO analysis showed that the DEGs were mainly enriched in mitotic nuclear division, mitotic sister chromatid segregation, nuclear division, chromosome segregation, and organelle fission in the biological process category; condensed chromosome, centromeric region, condensed chromosome kinetochore, kinetochore, chromosome, centromeric region, and spindle in the cellular component category; and microtubule binding, tubulin binding, microtubule motor activity, peptidase inhibitor activity, and enzyme inhibitor activity in the molecular function category. KEGG analysis further indicated significant enrichment in the cell cycle, p53 signaling pathway, oocyte meiosis, ECM-receptor interaction, and progesterone-mediated oocyte maturation pathways (Fig. 7 C-D). 3.6 Drug sensitive To further evaluate the potential clinical utility of the risk model, differences in drug sensitivity between the two risk subgroups were compared. The results indicated that patients in the low-risk group were more sensitive to Camptothecin, Cisplatin, Docetaxel, Doxorubicin, Etoposide, Gemcitabine, Paclitaxel, Vinorelbine, and Vinblastine (Fig. 8A-I). 4 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) [ 18 – 20 ]. 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 [ 21 – 23 ]. 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 , 24 , 25 ]. 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[ 26 ]. 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. While our study demonstrates that the proposed risk model can effectively stratify patients and predict drug sensitivity, it is essential to consider potential confounding factors. Specifically, variations in treatment regimens among patients included in the study could impact the observed drug sensitivity differences between risk groups. These variations may introduce biases that affect the robustness of our findings. Future studies should consider stratifying patients by specific treatment types or performing subgroup analyses to account for these differences. Additionally, incorporating detailed treatment information could enhance the predictive accuracy of the model and provide more reliable insights into its clinical applicability in diverse therapeutic contexts.Importantly, multigene signatures often contain correlated features, which can complicate interpretation and downstream translation. To address this, we applied a random forest–based prioritization framework and nominated CTSV as a top informative gene within the signature. CTSV expression was associated with inferior survival and retained significance in multivariate analyses, highlighting its clinical relevance. Furthermore, complementary gain- and loss-of-function experiments in LUAD cells demonstrated that CTSV promotes malignant phenotypes, including increased cell growth, clonogenicity, and migratory capacity, providing functional support that CTSV is not merely a passive marker but may contribute to tumor aggressiveness. CTSV encodes cathepsin V, a lysosomal cysteine protease, and several non-mutually exclusive mechanisms may link CTSV to LUAD progression. Dysregulated lysosomal protease activity can facilitate tumor invasion through extracellular matrix remodeling and lysosomal exocytosis, support survival under stress by tuning autophagy–lysosome flux, and influence antigen processing and immune signaling, thereby shaping tumor–immune interactions. The lysosome-centered nutrient-sensing axis (e.g., mTORC1) offers an additional framework to connect lysosomal states to proliferation programs; therefore, mechanistic studies are warranted to define the CTSV-dependent pathways that drive the observed phenotypes. 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[ 14 , 27 ]. 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[ 28 ]. 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[ 29 – 31 ]. Our study revealed notable differences in immune characteristics between high and low lysosomal signature score groups in lung adenocarcinoma patients. Immune cell profiling by CIBERSORT showed that the low-risk group had significantly higher infiltration levels of plasma cells, resting CD4 memory T cells, regulatory T cells (Tregs), resting dendritic cells, and resting mast cells than 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. This study is based on publicly available data from the TCGA and GEO databases, which, while providing extensive gene expression and clinical information, come with certain limitations. First, the TCGA and GEO datasets predominantly represent specific geographic and ethnic groups, which may introduce selection bias and limit the generalizability of our findings. Additionally, variations in sample quality and processing methods across these datasets may introduce technical biases that could impact the analysis results. Furthermore, the retrospective nature of the data restricts causal inference, allowing for only associative findings. To address these limitations, future studies should validate our model in independent clinical cohorts from more diverse populations and explore its efficacy in practical clinical applications. Our findings align with previous studies[ 6 – 9 , 32 ], which demonstrate the utility of gene signatures and multi-omics approaches in predicting lung cancer outcomes. Unlike prior research, which largely centers on broader gene panels or imaging features, our study focuses specifically on lysosome-related genes. This distinction allows us to address the unique role of lysosomal pathways in lung cancer progression. However, we acknowledge that the incorporation of other omics data, as explored in these studies, could further enhance the predictive power and clinical relevance of our model. Future research could benefit from integrating lysosome-related markers with other established gene and imaging signatures to improve the robustness of lung cancer prognostic models. 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. 5 CONCLUSIONS We developed and validated a lysosome-related 26-gene prognostic signature that stratifies LUAD patients into distinct risk groups and reflects differences in the tumor microenvironment and predicted therapeutic sensitivities. Within this signature, random forest–based prioritization and clinical survival analyses highlighted CTSV as a key prognostic gene, and in vitro assays further demonstrated that CTSV promotes LUAD malignant behaviors. Together, these findings provide a prognostic framework and nominate CTSV as a clinically relevant and functionally supported candidate biomarker and potential therapeutic target, warranting further mechanistic and translational validation. Declarations Author Contribution Hongwei Chen: Software,Validation, Formal analysis, Writing - Original Draft, Visualization Huixin Xu: Methodology,Software,Formal analysis, Writing - Original Draft Jiashun Xu: Data Curation, Writing - Original Draft Qingjian Li: Methodology, Supervision, Writing - Review & Editing Zhixiong Luo: Conceptualization, Methodology, Writing - Review & Editing Acknowledgments Not applicable. Funding None. Availability of data and materials The data of this study are available in The Cancer Genome Atlas (TCGA, https://portal.gdc.cancer.gov), the Gene Expression Comprehensive Database (GEO, http://www.ncbi.nlm.nih.gov/geo). Disclosure of conflict of interset The authors have declared that no competing interest exists. Ethics statements This article does not contain any studies with human participants or animals performed by any of the authors. References Allemani C, Matsuda T, Di Carlo V, Harewood R, Matz M, Nikšić M, Bonaventure A, Valkov M, Johnson CJ, Estève J, Ogunbiyi OJ, Azevedo E, Silva G, Chen W-Q, Eser S, Engholm G, Stiller CA, Monnereau A, Woods RR, Visser O, Lim GH, Aitken J, Weir HK, Coleman MP. 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. Lancet (London England). 2018;391:1023–75. 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Ettinger DS, Wood DE, Aisner DL, Akerley W, Bauman JR, Bharat A, Bruno DS, Chang JY, Chirieac LR, D'Amico TA, DeCamp M, Dilling TJ, Dowell J, Gettinger S, Grotz TE, Gubens MA, Hegde A, Lackner RP, Lanuti M, Lin J, Loo BW, Lovly CM, Maldonado F, Massarelli E, Morgensztern D, Ng T, Otterson GA, Pacheco JM, Patel SP, Riely GJ, Riess J, Schild SE, Shapiro TA, Singh AP, Stevenson J, Tam A, Tanvetyanon T, Yanagawa J, Yang SC, Yau E. Gregory K and Hughes M. Non-Small Cell Lung Cancer, Version 3.2022, NCCN Clinical Practice Guidelines in Oncology. J Natl Compr Cancer Network: JNCCN. 2022;20:497–530. Jiang T, Wang G, Liu Y, Feng L, Wang M, Liu J, Chen Y, Ouyang L. Development of small-molecule tropomyosin receptor kinase (TRK) inhibitors for NTRK fusion cancers. Acta Pharm Sinica B. 2021;11:355–72. Hsiao SJ, Zehir A, Sireci AN, Aisner DL. Detection of Tumor NTRK Gene Fusions to Identify Patients Who May Benefit from Tyrosine Kinase (TRK) Inhibitor Therapy. 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The evolution of lung cancer and impact of subclonal selection in TRACERx. Nature. 2023;616:525–33. Abbosh C, Frankell AM, Harrison T, Kisistok J, Garnett A, Johnson L, Veeriah S, Moreau M, Chesh A, Chaunzwa TL, Weiss J, Schroeder MR, Ward S, Grigoriadis K, Shahpurwalla A, Litchfield K, Puttick C, Biswas D, Karasaki T, Black JRM, Martínez-Ruiz C, Bakir MA, Pich O, Watkins TBK, Lim EL, Huebner A, Moore DA, Godin-Heymann N, L'Hernault A, Bye H, Odell A, Roberts P, Gomes F, Patel AJ, Manzano E, Hiley CT, Carey N, Riley J, Cook DE, Hodgson D, Stetson D, Barrett JC, Kortlever RM, Evan GI, Hackshaw A, Daber RD, Shaw JA, Aerts HJWL, Licon A, Stahl J, Jamal-Hanjani M, Birkbak NJ. McGranahan N and Swanton C. Tracking early lung cancer metastatic dissemination in TRACERx using ctDNA. Nature. 2023;616:553–62. Martínez-Ruiz C, Black JRM, Puttick C, Hill MS, Demeulemeester J, Larose Cadieux E, Thol K, Jones TP, Veeriah S, Naceur-Lombardelli C, Toncheva A, Prymas P, Rowan A, Ward S, Cubitt L, Athanasopoulou F, Pich O, Karasaki T, Moore DA, Salgado R, Colliver E, Castignani C, Dietzen M, Huebner A, Al Bakir M, Tanić M, Watkins TBK, Lim EL, Al-Rashed AM, Lang D, Clements J, Cook DE, Rosenthal R, Wilson GA, Frankell AM, de Carné Trécesson S, East P, Kanu N, Litchfield K, Birkbak NJ, Hackshaw A, Beck S, Van Loo P, Jamal-Hanjani M. Swanton C and McGranahan N. Genomic-transcriptomic evolution in lung cancer and metastasis. Nature. 2023;616:543–52. Piao S, Amaravadi RK. Targeting the lysosome in cancer. Ann N Y Acad Sci. 2016;1371:45–54. Lawrence RE, Zoncu R. The lysosome as a cellular centre for signalling, metabolism and quality control. Nat Cell Biol. 2019;21:133–42. Tang T, Yang Z-Y, Wang D, Yang X-Y, Wang J, Li L, Wen Q, Gao L, Bian X-W, Yu S-C. The role of lysosomes in cancer development and progression. Cell Bioscience. 2020;10:131. Wang Y, Du J, Wu X, Abdelrehem A, Ren Y, Liu C, Zhou X, Wang S. Crosstalk between autophagy and microbiota in cancer progression. Mol Cancer. 2021;20:163. Chung C, Seo W, Silwal P, Jo E-K. Crosstalks between inflammasome and autophagy in cancer. J Hematol Oncol. 2020;13:100. Cao M, Luo X, Wu K, He X. Targeting lysosomes in human disease: from basic research to clinical applications. Signal Transduct Target Therapy. 2021;6:379. Button RW, Luo S. The formation of autophagosomes during lysosomal defect: A new source of cytotoxicity. Autophagy. 2017;13:1797–8. Huang Z, Xiao Z, Yu L, Liu J, Yang Y, Ouyang W. Tumor-associated macrophages in non-small-cell lung cancer: From treatment resistance mechanisms to therapeutic targets. Crit Rev Oncol/Hematol. 2024;196:104284. Yu L, Huang Z, Xiao Z, Tang X, Zeng Z, Tang X, Ouyang W. Unveiling the best predictive models for early–onset metastatic cancer: Insights and innovations (Review). Oncol Rep 2024; 51. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 17 May, 2026 Reviews received at journal 15 May, 2026 Reviewers agreed at journal 10 May, 2026 Reviewers agreed at journal 07 May, 2026 Reviewers invited by journal 04 May, 2026 Editor invited by journal 20 Apr, 2026 Editor assigned by journal 09 Apr, 2026 Submission checks completed at journal 09 Apr, 2026 First submitted to journal 09 Apr, 2026 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. 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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-9363540\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":false,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":637834262,\"identity\":\"505dd4e3-5970-4357-87e5-2a8d3e1c3144\",\"order_by\":0,\"name\":\"Hongwei Chen\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Sun Yat-sen Memorial Hospital\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Hongwei\",\"middleName\":\"\",\"lastName\":\"Chen\",\"suffix\":\"\"},{\"id\":637834263,\"identity\":\"63c4d1c3-27c4-406c-97ab-4d0217bf7786\",\"order_by\":1,\"name\":\"Huixin Xu\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Sun Yat-sen Memorial Hospital\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Huixin\",\"middleName\":\"\",\"lastName\":\"Xu\",\"suffix\":\"\"},{\"id\":637834264,\"identity\":\"65cd80ed-9869-4903-9a2b-d10bf90a3fba\",\"order_by\":2,\"name\":\"Jiashun Xu\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Dongguan Qingxi Hospital\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Jiashun\",\"middleName\":\"\",\"lastName\":\"Xu\",\"suffix\":\"\"},{\"id\":637834265,\"identity\":\"c6cd24ff-d1d6-4972-833a-35c26923cbbd\",\"order_by\":3,\"name\":\"Qingjian Li\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Sun Yat-sen Memorial Hospital\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Qingjian\",\"middleName\":\"\",\"lastName\":\"Li\",\"suffix\":\"\"},{\"id\":637834266,\"identity\":\"2ed97948-8ee1-4b24-a2c3-4e997aba89eb\",\"order_by\":4,\"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\":\"2026-04-09 05:38:17\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-9363540/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-9363540/v1\",\"draftVersion\":[],\"editorialEvents\":[],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":109119506,\"identity\":\"5eb0327a-7409-4eb3-a8fa-da1a5d784b52\",\"added_by\":\"auto\",\"created_at\":\"2026-05-12 16:58:34\",\"extension\":\"png\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":1334210,\"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\":\"image1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-9363540/v1/f5661c7cbb5ebcd4f142fcfe.png\"},{\"id\":109204945,\"identity\":\"06c94dfb-7616-45a0-b350-83874a202ca5\",\"added_by\":\"auto\",\"created_at\":\"2026-05-13 15:02:56\",\"extension\":\"png\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":1986932,\"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\":\"image2.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-9363540/v1/7a41d53c5cda99e776bbe77b.png\"},{\"id\":109119507,\"identity\":\"b5e2dac4-5314-47e0-ae82-03f81874c09b\",\"added_by\":\"auto\",\"created_at\":\"2026-05-12 16:58:34\",\"extension\":\"png\",\"order_by\":3,\"title\":\"Figure 3\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":6186930,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eCTSV is prioritized by machine learning, predicts poor prognosis, and promotes malignant phenotypes in LUAD cells\\u003c/strong\\u003e\\u003cbr\\u003e\\n \\u003cstrong\\u003e(A) \\u003c/strong\\u003eRandom forest–based feature importance ranking of the 26-gene lysosome-related prognostic signature for high- vs low-risk classification. Variable importance was assessed by permutation and expressed as MeanDecreaseAccuracy; the right heatmap indicates relative expression patterns across risk groups (C1/C2). \\u003cstrong\\u003e(B)\\u003c/strong\\u003eKaplan–Meier overall survival curves for TCGA-LUAD patients stratified by CTSV expression (high vs low); \\u003cem\\u003eP\\u003c/em\\u003e value was calculated using the log-rank test. \\u003cstrong\\u003e(C)\\u003c/strong\\u003e Multivariate Cox regression analysis evaluating the independent prognostic value of CTSV expression in TCGA-LUAD after adjustment for clinical covariates; hazard ratios (HRs) with 95% confidence intervals (CIs) are shown. \\u003cstrong\\u003e(D)\\u003c/strong\\u003e CCK-8 assays showing growth kinetics of A549 cells with CTSV overexpression (Mock/Vector/CTSV) or CTSV knockdown using two independent siRNAs (siCTL, siCTSV-1, siCTSV-2). \\u003cstrong\\u003e(E)\\u003c/strong\\u003eRepresentative images of plate colony formation assays in A549 cells following CTSV knockdown or overexpression. \\u003cstrong\\u003e(F)\\u003c/strong\\u003e Representative images of transwell migration assays in A549 cells with CTSV knockdown or overexpression. \\u003cstrong\\u003e(G)\\u003c/strong\\u003e Representative wound-healing images (0, 24, and 48 h) demonstrating reduced migratory capacity upon CTSV silencing in A549 cells.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"image3.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-9363540/v1/90b3fa7a95c2f30a829bd09d.png\"},{\"id\":109119510,\"identity\":\"0a3cd272-a1a5-4d20-9e9c-df61d3200564\",\"added_by\":\"auto\",\"created_at\":\"2026-05-12 16:58:34\",\"extension\":\"png\",\"order_by\":4,\"title\":\"Figure 4\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":574028,\"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 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\",\"description\":\"\",\"filename\":\"image4.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-9363540/v1/8bc8b62fb61111d927fc75b2.png\"},{\"id\":109119513,\"identity\":\"f019c752-3046-4d3d-b419-875799cc1168\",\"added_by\":\"auto\",\"created_at\":\"2026-05-12 16:58:34\",\"extension\":\"png\",\"order_by\":5,\"title\":\"Figure 5\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":955577,\"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\":\"image5.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-9363540/v1/1ea9c9dd88b663b864e81155.png\"},{\"id\":109119509,\"identity\":\"2d81f9b2-2538-40e5-9997-a9cefedf3f3a\",\"added_by\":\"auto\",\"created_at\":\"2026-05-12 16:58:34\",\"extension\":\"png\",\"order_by\":6,\"title\":\"Figure 6\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":584267,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eRisk Signature-Based Immune Cell Infiltration, Tumor Micro- environment 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\":\"image6.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-9363540/v1/09eee9d5e1c40ff5ad671310.png\"},{\"id\":109119512,\"identity\":\"7d458923-31dc-4b50-97fe-ba8681f5190b\",\"added_by\":\"auto\",\"created_at\":\"2026-05-12 16:58:34\",\"extension\":\"png\",\"order_by\":7,\"title\":\"Figure 7\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":3546449,\"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\\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\",\"description\":\"\",\"filename\":\"image7.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-9363540/v1/e7be81013da0b925a4f531c9.png\"},{\"id\":109119511,\"identity\":\"d43ad191-6965-4ccd-99af-54070b43cf57\",\"added_by\":\"auto\",\"created_at\":\"2026-05-12 16:58:34\",\"extension\":\"png\",\"order_by\":8,\"title\":\"Figure 8\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":449475,\"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\":\"image8.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-9363540/v1/40ed7b2f056b4ac1c3f91ae8.png\"},{\"id\":109207816,\"identity\":\"f416629b-1d59-4936-9b42-4dca08c82db6\",\"added_by\":\"auto\",\"created_at\":\"2026-05-13 15:21:57\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":15053869,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-9363540/v1/be77208a-96fb-4d5d-87aa-5e53b4bb24b6.pdf\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"Identification of Lysosome-Related Features for Predicting Prognostic Tumor Microenvironment in Lung Adenocarcinoma\",\"fulltext\":[{\"header\":\"1 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, further investigation into the mechanisms underlying lung cancer is essential for improving patient prognosis. 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 anti-plane 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\\u003eIn recent years, multiple studies have explored gene and pathomics signatures for predicting lung cancer prognosis, yielding promising results. For instance, previous studies have identified genetic markers and imaging features associated with survival outcomes in lung cancer patients, underscoring the value of multi-omics approaches in risk stratification. Similarly, other studies have employed comprehensive bioinformatics and machine learning techniques to develop prognostic models based on gene expression profiles, highlighting both the potential and challenges of these methods) [\\u003cspan additionalcitationids=\\\"CR7 CR8\\\" citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e]. Our study builds on this foundation by focusing specifically on lysosome-related genes as prognostic markers, adding a unique perspective to the existing body of research.\\u003c/p\\u003e \\u003cp\\u003eAccording to previous studies, translocation and abnormal secretion of lysosomes facilitate the invasion and metastasis of cancer cells[\\u003cspan additionalcitationids=\\\"CR11\\\" citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\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[\\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e13\\u003c/span\\u003e]. 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=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\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\":\"2 METHODS\",\"content\":\"\\u003cp\\u003e \\u003cb\\u003eData Acquisition\\u003c/b\\u003e \\u003c/p\\u003e \\u003cp\\u003eTranscriptome data of 598 lung adenocarcinoma (LUAD) patients were obtained 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 missing survival data, 507 tumor samples were retained for analysis. Additional gene expression data were sourced from the GSE68465 dataset in 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), with samples lacking survival data excluded to maintain consistency.\\u003c/p\\u003e \\u003cp\\u003e \\u003cb\\u003eData Preprocessing and Normalization\\u003c/b\\u003e \\u003c/p\\u003e \\u003cp\\u003eData preprocessing involved log transformation and normalization steps to minimize variability across datasets, enhancing comparability. Batch effect correction, as noted above, was performed to ensure consistency between TCGA and GEO datasets.\\u003c/p\\u003e \\u003cp\\u003e \\u003cb\\u003eSelection of Lysosome-Related Genes\\u003c/b\\u003e \\u003c/p\\u003e \\u003cp\\u003eLysosome-related genes were identified based on annotations from the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases, focusing on genes involved in lysosomal pathways. Genes selected demonstrated biological relevance in lysosome-related processes and were previously associated with cancer progression. The initial screening of these genes in the TCGA-LUAD dataset used univariate Cox regression analysis (P\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05) to identify genes significantly associated with survival, ensuring their potential as prognostic markers.\\u003c/p\\u003e \\u003cp\\u003e \\u003cb\\u003eEstablishment and validation of prognostic model\\u003c/b\\u003e \\u003c/p\\u003e \\u003cp\\u003eTo merge the TCGA and GEO (GSE68465) datasets, the \\u0026ldquo;sva\\u0026rdquo; R package was used, with the \\u0026ldquo;combat\\u0026rdquo; function applied to remove batch effects, enhancing the consistency of the integrated dataset. The TCGA-LUAD cohort was used as the training set, while the GSE68465 cohort served as the validation set to allow for independent assessment of the model\\u0026rsquo;s robustness.\\u003c/p\\u003e \\u003cp\\u003eFollowing initial screening, least absolute shrinkage and selection operator (LASSO) Cox regression analysis was applied to refine the gene set, reducing the risk of overfitting. This process yielded 26 lysosome-related genes used to construct a risk score formula through multivariable Cox regression analysis. The \\u0026ldquo;survminer\\u0026rdquo; R package was employed to calculate the optimal cutoff risk score, dividing samples into high-risk and low-risk groups based on this threshold.\\u003c/p\\u003e \\u003cp\\u003e \\u003cb\\u003eValidation Process and Performance Assessment\\u003c/b\\u003e \\u003c/p\\u003e \\u003cp\\u003eThe model\\u0026rsquo;s risk score stability was tested using the validation cohort (GSE68465). The validation cohort was selected independently of the training set, with criteria including adequate sample size and data completeness. Time-dependent receiver operating characteristic (ROC) analysis using the \\u0026ldquo;survivalROC\\u0026rdquo; R package assessed the predictive accuracy of the model for 1-, 3-, and 5-year survival outcomes, validating its clinical utility.\\u003c/p\\u003e \\u003cp\\u003e \\u003cb\\u003eRandom forest\\u0026ndash;based gene prioritization and CTSV survival analyses\\u003c/b\\u003e \\u003c/p\\u003e \\u003cp\\u003eTo prioritize individual genes within the 26-gene prognostic signature, a random forest (RF) classifier was trained using the expression levels of the 26 genes as input features and the model-derived risk group (high vs low risk) as the outcome. Feature importance was assessed by permutation and reported as MeanDecreaseAccuracy. For clinical evaluation of CTSV, TCGA-LUAD patients were stratified into CTSV-high and CTSV-low groups using the median CTSV expression. Overall survival differences were assessed by Kaplan\\u0026ndash;Meier analysis with log-rank testing, and Cox proportional hazards regression was performed to estimate hazard ratios with 95% confidence intervals; multivariate models adjusted for available clinical covariates.\\u003c/p\\u003e \\u003cp\\u003e \\u003cb\\u003eCell culture, transfection, and functional assays\\u003c/b\\u003e \\u003c/p\\u003e \\u003cp\\u003eA549 cells were cultured under standard conditions (37\\u0026deg;C, 5% CO2) and transfected with CTSV expression plasmids (Mock/Vector/CTSV) or two independent siRNAs targeting CTSV (siCTSV-1 and siCTSV-2) using lipofection according to the manufacturer\\u0026rsquo;s instructions. CTSV modulation efficiency was verified at the mRNA and/or protein level prior to phenotypic assays. Cell viability was assessed using CCK-8 at the indicated time points. Clonogenic capacity was evaluated by plate colony formation assays. Cell migration was examined by transwell migration assays and wound-healing assays with images acquired at 0, 24, and 48 h. For chemotherapy stress experiments, cells were treated with cisplatin at the indicated concentration and viability was assessed by live/dead staining when applicable.\\u003c/p\\u003e \\u003cp\\u003e \\u003cb\\u003eIndependent prognostic analysis and Nomogram establishment and Calibration\\u003c/b\\u003e \\u003c/p\\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 \\u003cp\\u003e \\u003cb\\u003eFunctional and pathway enrichment analysis\\u003c/b\\u003e \\u003c/p\\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 \\u003cp\\u003e \\u003cb\\u003eTumor immune microenvironment analysis\\u003c/b\\u003e \\u003c/p\\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[\\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e16\\u003c/span\\u003e]. Combined with the grouping information, it is visualized using the \\\"ggplot2\\\" and \\\"tidyr\\\" R packages. Based on the risk score and immune cell infiltration levels, the associations between the prognostic model and individual immune cells were analyzed and visualized using the \\u0026ldquo;corrplot\\u0026rdquo; R package. Moreover, differences in stromal score, immune score, and ESTIMATE score were analyzed based on the ESTIMATE results using the \\u0026ldquo;estimate\\u0026rdquo; R package. 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 \\u003cp\\u003e \\u003cb\\u003ePrediction of Drug Susceptibility\\u003c/b\\u003e \\u003c/p\\u003e \\u003cp\\u003eThe \\u0026ldquo;pRRophetic\\u0026rdquo; R package was applied to estimate the half-maximal inhibitory concentration (IC50) of anticancer agents in different risk groups. IC50 is a commonly used indicator of a drug\\u0026rsquo;s potency in suppressing a given biological or biochemical activity.[\\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e17\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003e \\u003cb\\u003eStatistical Analysis\\u003c/b\\u003e \\u003c/p\\u003e \\u003cp\\u003eAll statistical analyses were conducted in R software (version 4.2.2). Differences in the proportions of infiltrating immune cells were assessed using the Wilcoxon signed-rank test, and the association between risk score and immune cell infiltration was evaluated by 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\"},{\"header\":\"3 RESULTS\",\"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 \\u003cdiv id=\\\"Sec4\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e3.1 The construction and validation of novel prognostic model\\u003c/h2\\u003e \\u003cp\\u003eAfter filtering, 133 lysosome-related genes were identified in the TCGA and GSE68465 cohorts. Univariate Cox regression analysis was subsequently performed to assess their associations with survival. To reduce the risk of overfitting, LASSO Cox regression was further applied to select the most informative survival-related lysosome-associated genes. Based on this approach, a prognostic signature consisting of 26 genes was established (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003eA-B). The risk score for each sample was then calculated according to the following formula:\\u003cdiv id=\\\"Equa\\\" class=\\\"Equation\\\"\\u003e\\u003cdiv format=\\\"TEX\\\" class=\\\"mathdisplay\\\" id=\\\"FileID_Equa\\\" name=\\\"EquationSource\\\"\\u003e\\n$$\\\\:\\\\:Risk\\\\:Score=\\\\sum\\\\:_{i=1}^{n}{\\\\beta\\\\:}_{i}\\\\times\\\\:{X}_{i}$$\\u003c/div\\u003e\\u003c/div\\u003e\\u003c/p\\u003e \\u003cp\\u003eIn this formula, β represents the regression coefficient, and X denotes the expression level of each prognostic gene. Based on the optimal cutoff value derived from the TCGA-LUAD training cohort, patients were classified into high- and low-risk groups. Kaplan\\u0026ndash;Meier survival analysis showed that overall survival was significantly poorer in the high-risk group than in the low-risk group (P\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.0001; Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003eC-D). In the training cohort, the AUC values of the prognostic model for predicting 1-, 3-, and 5-year survival were 0.71, 0.71, and 0.71, respectively. The robustness of this model was further assessed in the GSE68465 cohort. In the validation cohort, the corresponding AUC values for 1-, 3-, and 5-year survival were 0.70, 0.66, and 0.61, respectively (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003eE-F). Risk score distribution and survival status analyses in the TCGA-LUAD training set indicated that the number of deceased patients increased with rising risk scores. Consistent with the training cohort, patients in the low-risk group of the GSE68465 cohort exhibited more favorable survival status and longer survival time (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eA-D). Moreover, the heatmap revealed distinct expression patterns of the 26 prognostic genes among TCGA-LUAD patients with different risk scores (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eE-F).\\u003c/p\\u003e \\u003cp\\u003e \\u003cb\\u003e3.2 CTSV is prioritized by machine learning and validated as a prognostic and pro-tumorigenic factor in LUAD\\u003c/b\\u003e \\u003c/p\\u003e \\u003cp\\u003eGiven that multigene prognostic signatures often contain correlated features, we used a random forest (RF) approach to assess the relative contribution of each gene and facilitate candidate prioritization. Using the 26 genes as input variables, the RF model was trained to classify high- vs low-risk patients, and feature importance was evaluated by permutation (MeanDecreaseAccuracy). CTSV ranked among the top contributors, suggesting that it provides non-redundant predictive information beyond other genes in the signature (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eA). Motivated by its high RF importance, we next evaluated the clinical relevance of CTSV in the TCGA-LUAD cohort. Kaplan\\u0026ndash;Meier analysis showed that patients with high CTSV expression had significantly worse overall survival than those with low expression (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eB). Moreover, multivariate Cox regression adjusting for available clinical covariates confirmed CTSV as an independent prognostic factor (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eC). While feature importance does not imply causality, the convergent evidence from RF prioritization and clinical survival analyses, together with CTSV\\u0026rsquo;s lysosomal localization and enzymatic activity, supports CTSV as an experimentally tractable and mechanistically plausible candidate. We next assessed whether CTSV directly contributes to malignant phenotypes in LUAD cells using complementary gain- and loss-of-function approaches. In A549 cells, ectopic CTSV expression significantly enhanced cell viability and growth kinetics in CCK-8 assays, whereas CTSV silencing with two independent siRNAs consistently suppressed proliferative capacity (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eD). Consistently, plate colony formation assays demonstrated that CTSV overexpression increased clonogenic outgrowth, while CTSV depletion markedly reduced colony number (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eE). Moreover, transwell migration assays showed that CTSV promoted LUAD cell motility, with reduced migratory activity upon CTSV knockdown and increased migration following CTSV overexpression (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eF). Wound-healing assays yielded concordant results, showing delayed scratch closure in CTSV-silenced cells and accelerated wound closure in CTSV-overexpressing cells over time (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eG). Collectively, these bidirectional functional assays establish CTSV as a pro-tumorigenic regulator that promotes LUAD cell growth, clonogenicity, survival under chemotherapy stress, and migratory capacity in vitro, thereby providing a functional basis for subsequent mechanistic interrogation.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec5\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e3.3 Independent Prognostic Factor Analysis and construction of Nomogram\\u003c/h2\\u003e \\u003cp\\u003eUnivariate and multivariate Cox regression analyses incorporating age, sex, clinical stage, TNM stage, and risk score were conducted to determine whether the risk score could serve as an independent predictor of survival in patients with LUAD. In the training cohort, clinicopathological stage, T stage, M stage, N stage, and risk score were all identified as independent adverse prognostic factors (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003eA-B). Moreover, the risk score was significantly associated with age, sex, clinicopathological stage, T stage, M stage, and N stage (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig5\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003eA-P). Based on the training cohort, a nomogram integrating the risk score and independent prognostic clinical variables was constructed to enhance survival prediction in LUAD patients. The calibration curves for 1-, 3-, and 5-year overall survival showed good concordance between the predicted and observed outcomes (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003eC-D).\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec6\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e3.4 Risk Signature-Based Immune Cell Infiltration, Tumor Microenvironment Analyses\\u003c/h2\\u003e \\u003cp\\u003eCIBERSORT analysis showed that the low-risk group had significantly higher proportions of plasma cells, resting CD4 memory T cells, regulatory T cells (Tregs), resting dendritic cells, and resting mast cells than the high-risk group (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig6\\\" class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003eA). Immune-checkpoint related genes like CD44, CD276, TNFRSF9, TNFSF4, TNFSF9, CD70, DCD1LG2 and TMIGD2, were more lowly expressed in the low-risk group (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig6\\\" class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003eB). What\\u0026rsquo;s more, with the increase of risk score, the high-risk group had a lower estimate score, immune score, and stromal score (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig6\\\" class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003eC). In addition, the TMB score of LUAD was higher in high risk group (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig6\\\" class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003eD).\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec7\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e3.5 Functional and Pathway Enrichment Analyses\\u003c/h2\\u003e \\u003cp\\u003eIn the training cohort, 502 DEGs were identified between the high- and low-risk groups, including 264 upregulated and 238 downregulated genes (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig7\\\" class=\\\"InternalRef\\\"\\u003e7\\u003c/span\\u003eA-B). To explore the potential biological roles of these genes, GO and KEGG enrichment analyses were conducted based on the DEGs derived from TCGA-LUAD. GO analysis showed that the DEGs were mainly enriched in mitotic nuclear division, mitotic sister chromatid segregation, nuclear division, chromosome segregation, and organelle fission in the biological process category; condensed chromosome, centromeric region, condensed chromosome kinetochore, kinetochore, chromosome, centromeric region, and spindle in the cellular component category; and microtubule binding, tubulin binding, microtubule motor activity, peptidase inhibitor activity, and enzyme inhibitor activity in the molecular function category. KEGG analysis further indicated significant enrichment in the cell cycle, p53 signaling pathway, oocyte meiosis, ECM-receptor interaction, and progesterone-mediated oocyte maturation pathways (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig7\\\" class=\\\"InternalRef\\\"\\u003e7\\u003c/span\\u003eC-D).\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec8\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e3.6 Drug sensitive\\u003c/h2\\u003e \\u003cp\\u003eTo further evaluate the potential clinical utility of the risk model, differences in drug sensitivity between the two risk subgroups were compared. The results indicated that patients in the low-risk group were more sensitive to Camptothecin, Cisplatin, Docetaxel, Doxorubicin, Etoposide, Gemcitabine, Paclitaxel, Vinorelbine, and Vinblastine (Fig.\\u0026nbsp;8A-I).\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"4 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=\\\"CR19\\\" citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e20\\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=\\\"CR22\\\" citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e21\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e23\\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=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e24\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e25\\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=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e26\\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. While our study demonstrates that the proposed risk model can effectively stratify patients and predict drug sensitivity, it is essential to consider potential confounding factors. Specifically, variations in treatment regimens among patients included in the study could impact the observed drug sensitivity differences between risk groups. These variations may introduce biases that affect the robustness of our findings. Future studies should consider stratifying patients by specific treatment types or performing subgroup analyses to account for these differences. Additionally, incorporating detailed treatment information could enhance the predictive accuracy of the model and provide more reliable insights into its clinical applicability in diverse therapeutic contexts.Importantly, multigene signatures often contain correlated features, which can complicate interpretation and downstream translation. To address this, we applied a random forest\\u0026ndash;based prioritization framework and nominated CTSV as a top informative gene within the signature. CTSV expression was associated with inferior survival and retained significance in multivariate analyses, highlighting its clinical relevance. Furthermore, complementary gain- and loss-of-function experiments in LUAD cells demonstrated that CTSV promotes malignant phenotypes, including increased cell growth, clonogenicity, and migratory capacity, providing functional support that CTSV is not merely a passive marker but may contribute to tumor aggressiveness.\\u003c/p\\u003e \\u003cp\\u003eCTSV encodes cathepsin V, a lysosomal cysteine protease, and several non-mutually exclusive mechanisms may link CTSV to LUAD progression. Dysregulated lysosomal protease activity can facilitate tumor invasion through extracellular matrix remodeling and lysosomal exocytosis, support survival under stress by tuning autophagy\\u0026ndash;lysosome flux, and influence antigen processing and immune signaling, thereby shaping tumor\\u0026ndash;immune interactions. The lysosome-centered nutrient-sensing axis (e.g., mTORC1) offers an additional framework to connect lysosomal states to proliferation programs; therefore, mechanistic studies are warranted to define the CTSV-dependent pathways that drive the observed phenotypes.\\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=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e27\\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=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e28\\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 additionalcitationids=\\\"CR30\\\" citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e29\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR31\\\" class=\\\"CitationRef\\\"\\u003e31\\u003c/span\\u003e]. Our study revealed notable differences in immune characteristics between high and low lysosomal signature score groups in lung adenocarcinoma patients. Immune cell profiling by CIBERSORT showed that the low-risk group had significantly higher infiltration levels of plasma cells, resting CD4 memory T cells, regulatory T cells (Tregs), resting dendritic cells, and resting mast cells than 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\\u003eThis study is based on publicly available data from the TCGA and GEO databases, which, while providing extensive gene expression and clinical information, come with certain limitations. First, the TCGA and GEO datasets predominantly represent specific geographic and ethnic groups, which may introduce selection bias and limit the generalizability of our findings. Additionally, variations in sample quality and processing methods across these datasets may introduce technical biases that could impact the analysis results. Furthermore, the retrospective nature of the data restricts causal inference, allowing for only associative findings. To address these limitations, future studies should validate our model in independent clinical cohorts from more diverse populations and explore its efficacy in practical clinical applications.\\u003c/p\\u003e \\u003cp\\u003eOur findings align with previous studies[\\u003cspan additionalcitationids=\\\"CR7 CR8\\\" citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR32\\\" class=\\\"CitationRef\\\"\\u003e32\\u003c/span\\u003e], which demonstrate the utility of gene signatures and multi-omics approaches in predicting lung cancer outcomes. Unlike prior research, which largely centers on broader gene panels or imaging features, our study focuses specifically on lysosome-related genes. This distinction allows us to address the unique role of lysosomal pathways in lung cancer progression. However, we acknowledge that the incorporation of other omics data, as explored in these studies, could further enhance the predictive power and clinical relevance of our model. Future research could benefit from integrating lysosome-related markers with other established gene and imaging signatures to improve the robustness of lung cancer prognostic models.\\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\":\"5 CONCLUSIONS\",\"content\":\"\\u003cp\\u003eWe developed and validated a lysosome-related 26-gene prognostic signature that stratifies LUAD patients into distinct risk groups and reflects differences in the tumor microenvironment and predicted therapeutic sensitivities. Within this signature, random forest\\u0026ndash;based prioritization and clinical survival analyses highlighted CTSV as a key prognostic gene, and in vitro assays further demonstrated that CTSV promotes LUAD malignant behaviors. Together, these findings provide a prognostic framework and nominate CTSV as a clinically relevant and functionally supported candidate biomarker and potential therapeutic target, warranting further mechanistic and translational validation.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eAuthor Contribution\\u003c/strong\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eHongwei Chen: Software,Validation, Formal analysis, Writing - Original Draft, Visualization\\u003c/p\\u003e\\n\\u003cp\\u003eHuixin Xu: Methodology,Software,Formal analysis, Writing - Original Draft\\u003c/p\\u003e\\n\\u003cp\\u003eJiashun Xu: Data Curation, Writing - Original Draft\\u003c/p\\u003e\\n\\u003cp\\u003eQingjian Li: Methodology, Supervision, Writing - Review \\u0026amp; Editing\\u003c/p\\u003e\\n\\u003cp\\u003eZhixiong Luo: Conceptualization, Methodology, Writing - Review \\u0026amp; Editing\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAcknowledgments\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eNot applicable.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eFunding\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eNone.\\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 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\\u003eDisclosure of conflict of interset\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe authors have declared that no competing interest exists.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eEthics statements\\u0026nbsp;\\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\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\u003cli\\u003e\\u003cspan\\u003eAllemani C, Matsuda T, Di Carlo V, Harewood R, Matz M, Nikšić M, Bonaventure A, Valkov M, Johnson CJ, Est\\u0026egrave;ve J, Ogunbiyi OJ, Azevedo E, Silva G, Chen W-Q, Eser S, Engholm G, Stiller CA, Monnereau A, Woods RR, Visser O, Lim GH, Aitken J, Weir HK, Coleman MP. 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Oncol Rep 2024; 51.\\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\":\"info@researchsquare.com\",\"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\":\"lysosome-related genes, lung adenocarcinoma, prognostic model, CTSV, tumor microenvironment, bioinformatics analysis\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-9363540/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-9363540/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003ch2\\u003eBackground\\u003c/h2\\u003e \\u003cp\\u003eLung cancer, associated with high morbidity and mortality, currently has limited treatment options. Identifying prognostic markers is essential to enable early diagnosis and improve survival rates. This study investigates lysosome-related genes as potential prognostic markers, given their critical role in lung cancer pathogenesis.\\u003c/p\\u003e\\u003ch2\\u003eMethods\\u003c/h2\\u003e \\u003cp\\u003eUsing data from TCGA and GEO database, a prognostic model based on lysosome-related genes was developed. Univariate Cox regression and LASSO Cox regression analyses were performed to identify relevant genes, and the model was validated with an independent lung cancer patient cohort. Additionally, immune cell infiltration scores, drug susceptibility, and pathway enrichment analyses were conducted to assess the model's predictive performance.\\u003c/p\\u003e\\u003ch2\\u003eResults\\u003c/h2\\u003e \\u003cp\\u003eA prognostic signature composed of 26 lysosome-related genes effectively separated patients into high- and low-risk subgroups, which showed distinct overall survival outcomes and consistent predictive ability across both cohorts. Random forest prioritization highlighted CTSV as a top contributor; high CTSV expression was associated with worse survival and remained significant in multivariate Cox analysis. Functionally, CTSV overexpression promoted LUAD cell viability, clonogenic growth, and migration, whereas CTSV knockdown produced opposite effects.\\u003c/p\\u003e\\u003ch2\\u003eConclusion\\u003c/h2\\u003e \\u003cp\\u003eThis lysosome-related signature provides a robust tool for prognostic stratification in LUAD, and CTSV represents a clinically relevant and functionally validated candidate that may link lysosome biology to tumor aggressiveness, warranting further mechanistic and translational investigation.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Identification of Lysosome-Related Features for Predicting Prognostic Tumor Microenvironment in Lung Adenocarcinoma\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2026-05-12 16:58:29\",\"doi\":\"10.21203/rs.3.rs-9363540/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2026-05-18T02:43:26+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2026-05-15T06:32:21+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"106999184214457698030693139287843322590\",\"date\":\"2026-05-11T02:01:03+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"315930965591054740494852376991202038595\",\"date\":\"2026-05-08T03:56:50+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewersInvited\",\"content\":\"\",\"date\":\"2026-05-04T17:24:47+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorInvited\",\"content\":\"\",\"date\":\"2026-04-20T10:00:47+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorAssigned\",\"content\":\"\",\"date\":\"2026-04-09T13:22:35+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"checksComplete\",\"content\":\"\",\"date\":\"2026-04-09T13:22:07+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"submitted\",\"content\":\"Discover Oncology\",\"date\":\"2026-04-09T05:21:16+00:00\",\"index\":\"\",\"fulltext\":\"\"}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"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\":\"bf81327b-0b72-4f56-a02e-f445fdeefc1b\",\"owner\":[],\"postedDate\":\"May 12th, 2026\",\"published\":true,\"recentEditorialEvents\":[{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2026-05-18T02:43:26+00:00\",\"index\":75,\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2026-05-15T06:32:21+00:00\",\"index\":74,\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"106999184214457698030693139287843322590\",\"date\":\"2026-05-11T02:01:03+00:00\",\"index\":72,\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"315930965591054740494852376991202038595\",\"date\":\"2026-05-08T03:56:50+00:00\",\"index\":59,\"fulltext\":\"\"},{\"type\":\"reviewersInvited\",\"content\":\"40\",\"date\":\"2026-05-04T17:24:47+00:00\",\"index\":\"\",\"fulltext\":\"\"}],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"under-review\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2026-05-12T16:58:29+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2026-05-12 16:58:29\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-9363540\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-9363540\",\"identity\":\"rs-9363540\",\"version\":[\"v1\"]},\"buildId\":\"XKTyCvWXoU3ODBz1xrDgd\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}