EXOSC5: A Novel Biomarker for Poor Prognosis in Lung Adenocarcinoma

EXOSC5: A Novel Biomarker for Poor Prognosis 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 EXOSC5: A Novel Biomarker for Poor Prognosis in Lung Adenocarcinoma Jianxun Xu, Zhengwei Zhang, Keyao Han, Zhen Yang, Fengmei Guo This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5782899/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 14 Apr, 2025 Read the published version in BMC Cancer → Version 1 posted 7 You are reading this latest preprint version Abstract Background : Exosome complex gene 5 (EXOSC5), a member of the exosome complex family, is highly tumorigenic in various cancers. However, its prognostic value and underlying mechanisms in lung adenocarcinoma (LUAD) remain unclear. Methods : We analysed LUAD data from The Cancer Genome Atlas (TCGA) via bioinformatics tools. Logistic regression was used to assess the associations between clinical information and EXOSC5 expression in LUAD patients. EXOSC5 expression was validated by immunohistochemistry (IHC) and western blotting, and its functional role was investigated through gene set enrichment analysis (GSEA) and in vitro experiments. Results : EXOSC5 was significantly upregulated in LUAD and associated with poor prognosis. The risk model based on EXOSC5 expression outperformed the traditional staging system for prognosis prediction. EXOSC5 promoted tumor progression by regulating the cell cycle, proliferation, and immune cell infiltration. High EXOSC5 expression was correlated with resistance to anti-PD1 immunotherapy. Conclusion : EXOSC5 is a novel oncogenic factor in LUAD that promotes tumor progression and immune evasion and may serve as a prognostic biomarker and therapeutic target. EXOSC5 lung adenocarcinoma prognostic biomarker immune infiltration anti-PD1 therapy tumor microenvironment Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Lung cancer, particularly lung adenocarcinoma (LUAD), remains the leading cause of cancer-related deaths worldwide. Despite significant advances in lung cancer treatment, it remains the leading cause of cancer-related deaths worldwide. [ 1 ] , with lung adenocarcinoma being its major subtype [ 2 – 3 ] . Owing to the recurrence of early-stage LUAD and the lack of effective treatment options for advanced-stage patients, the 5-year survival rate for LUAD patients ultimately falls below 15% [ 4 ] . Therefore, understanding the molecular mechanisms driving LUAD progression is crucial for developing effective diagnostic and therapeutic strategies. The exosome complex, including exosome complex gene 5 (EXOSC5), plays a crucial role in RNA processing and degradation and has been implicated in tumorigenesis [ 5 ] . EXOSC5, a noncatalytic subunit of the RNA exosome complex [ 6 ] , has been shown to promote tumor progression in various cancers through mechanisms involving cell cycle regulation and immune evasion [ 7 – 9 ] . Japanese scholars reported that increased activity of the EXOSC gene was associated with poor prognosis in certain types of cancer patients [ 10 ] . Recent studies have identified EXOSC5 as a prognostic marker in colorectal cancer, where it activates the ERK and Akt pathways [ 11 ] . However, its role in LUAD remains poorly understood. In this study, we investigated the role of EXOSC5 in LUAD progression and its potential as a prognostic biomarker and therapeutic target. We hypothesized that elevated EXOSC5 expression is associated with poor clinical outcomes in LUAD patients. Through comprehensive analysis of EXOSC5 expression and functional studies, we aimed to elucidate its role in LUAD and explore its potential as a novel therapeutic target. Materials and methods Data acquisition and preprocessing RNA sequencing and clinical data were obtained from The Cancer Genome Atlas (TCGA) database ( https://portal.gdc.cancer.gov ). The dataset included 539 LUAD samples and 59 nontumor tissue samples. The expression data for EXOSC5 were extracted from the sequencing data. A total of 500 cases with complete clinical data, including survival status, sex, age, TNM stage, and stage, were compiled. All patients had undergone surgical treatment and had complete five-year follow-up data (Supplementary Table 1). Additionally, 16 anti-PD1 treatment samples were obtained from the GEO database (GSE120644), and 77 anti-PD1 treatment samples were sourced from a published article (PMID: 37024582).To ensure minimal batch effects within TCGA data, we performed principal component analysis (PCA) on all TCGA samples .As shown in Supplementary Fig. 1A, PCA revealed that: PC1 (57.2% variance) primarily separated tumor samples (n = 539) from normal samples (n = 59). PC2 (21.8% variance) reflected additional biological heterogeneity (e.g., tumor subtypes or differentiation grades).The high variance explained by PC1 demonstrates that tumor-normal biological differences dominate the dataset. Technical batch effects were negligible after DESeq2 normalization. Therefore, additional correction was deemed unnecessary.For the GEO cohort (GSE120644), raw data were processed using the limma package with variance stabilizing transformation (VST). For the NTU cohort, immunohistochemistry (IHC) staining was performed in a single laboratory under standardized protocols, minimizing experimental batch variability. Between July 2018 and October 2019, fresh tumor and normal tissue samples were collected from 5 LUAD patients at the Affiliated Hospital of Nantong University (NTU). Paraffin sections from 194 LUAD patients treated between 2012 and 2013 were obtained from the pathology department (Supplementary Table 2). Informed consent was obtained from all patients, and the study was approved by the ethics committee. We also utilized multiple database platforms: the cBioPortal (cBioPortal for Cancer Genomics) to display the mutation status of the EXOSC5 gene and the Protein Atlas (The Human Protein Atlas) to visualize the protein structure of the gene. EXOSC5 protein expression data from 211 tissue samples (109 LUAD samples and 102 normal lung tissue samples) were obtained from the Protein Atlas. Immunohistochemistry The tissue samples were fixed, sectioned, baked, dewaxed, and then incubated with an anti-EXOSC5 polyclonal antibody. After washing, a secondary antibody and DAB solution were applied to form a brown‒yellow product. The sections were evaluated by three experienced pathologists. Three independent pathologists evaluated the slides without knowledge of patient characteristics. We implemented blinded scoring and calculated inter-observer agreement using Fleiss’ Kappa (κ = 0.731)(Supplementary table 3 ). The staining results were scored on the basis of the proportion of stained cells: 1 (1%-30%), 2 (31%-50%), 3 (51%-70%), or 4 (71%-100%). The staining intensity was scored as 0 (no staining), 1 (light yellow), 2 (dark yellow), or 3 (brown particles). EXOSC5 expression was categorized into high- and low-level groups on the basis of the product of the two scores: ≤4 for the low-level group and > 4 for the high-level group. Western blot LUAD tissues and cells were lysed, and proteins were harvested after 48 hours. The lysate was centrifuged at 12,000 rpm, and the supernatant was collected for further analysis. The protein concentration was determined via a Bio-Rad protein assay. SDS‒PAGE was performed at 120 V, and the proteins were transferred to a PVDF membrane at 200 mA. The membrane was incubated with an anti-EXOSC5 antibody (1:1000 dilution; Proteintech) and an anti-β-actin antibody (1:10,000 dilution; Proteintech) overnight at 4°C. After washing, a goat anti-mouse/rabbit secondary antibody (1:10,000; LI-COR) was added, and the membrane was incubated for 2 hours at room temperature. The protein bands were visualized via an enhanced chemiluminescence (ECL) detection system (Pierce, Rockford, IL, USA). Cell Culture and Transfection H1650, H1299, SPCA-1, and A549 cell lines were purchased from the Chinese Academy of Sciences Shanghai Cell Bank. The cells were cultured in RPMI 1640 medium (10% qualified FBS, 100 U/mL penicillin‒streptomycin) at 37°C with 5% CO2. Cells from the third to sixth generations were used. When the cell density reached 80%, the medium was replaced with RPMI 1640 without antibiotics, and the cells were cultured for an additional hour. Lentiviruses for EXOSC5 knockdown (LV-EXOSC5-RNAi: sequence 5′-GAAGGTCAGCAAAAGATT-3′) and EXOSC5 overexpression (LV-EXOSC5: serial number NM_020158.4) were designed and synthesized by GeneChem Technologies (Shanghai, China). The lentiviral vectors were mixed with the enhancer and added to a six-well plate. After 24 hours, the medium was replaced with fresh complete medium, and proteins were extracted after 48 hours for further experiments. Flow cytometry for apoptosis detection We took logarithmically growing cells, fully digested them with gentle trypsin, added complete culture medium, mixed them well, and transferred them to an EP tube. After the cells were washed twice with precooled PBS, centrifugation was performed with the parameters set to 1000 rpm for 5 minutes. Two hundred microliters of 1×binding buffer (10×binding buffer:sterile saline = 1:9) was added to suspend the cells, which were mixed well with a pipette, and then, Annexin V fluorescent dye was added and incubated at room temperature through a 10-minute interval. Centrifuge with specific parameters set to 1000 rpm for 5 minutes. After centrifugation, the mixture was mixed well, and PI fluorescent dye was added. Quantitative detection was performed within 1 hour, data were obtained through flow cytometry detection, and statistical analysis was performed. Cell Counting Kit-8 (CCK-8) assay The cells were seeded at a density of 1×103 cells per well in a 96-well plate. CCK-8 reagent was added to each well, and the plates were incubated at 37°C for 2 hours. The absorbance was measured via a microplate reader (Bio-Rad, USA). Bioinformatics and Genetic Analysis The Protein Atlas was used to visualize the EXOSC5 protein structure. Statistical analysis was performed via the stats package, and data visualization was performed via ggplot2. Differentially expressed genes (|logFC|>1) were identified via the DESeq2 package. The EXOSC5 gene mutation status was displayed via cBioPortal. Methylation site data, copy numbers, and expression levels were obtained from TCGA-LUAD samples via the Xena website. Correlation analysis between methylation sites and EXOSC5 expression was performed, and the results were visualized via histograms and lollipop plots. The CNV mutation status was displayed via pie charts, and scatter plots were generated to show the correlation between CNV and EXOSC5 expression. The TCGA cohort was divided into EXOSC5-low and EXOSC5-high groups on the basis of median EXOSC5 mRNA expression. The NTU cohort was similarly divided on the basis of a score of 4. Differences between groups were analysed via t tests. A Cox regression model was used to identify independent prognostic risk factors, and the results are displayed in a forest plot. Survival curves were generated via the Kaplan‒Meier method, and differences between groups were assessed via Cox regression analysis. The proportional hazards assumption was tested via the survival package. The rms package was used to construct a nomogram model, perform calibration analysis, and visualize the results. ROC analysis was conducted via the pROC package. We employed the ssGSEA algorithm from the GSVA package [12] and immune cell markers from a published immunology article [13] to analyse the correlations among EXOSC5 expression, immune infiltration, and immune-related genes. Spearman statistical analysis was used, and the results were visualized via lollipop and scatter plots. The proportion of each group in each classification was assessed, and survival analysis of immune infiltration was performed via the Kaplan‒Meier method. Differences between groups were determined via Cox regression analysis. Statistical analysis All the experiments were performed in triplicate. Differences between two groups were analysed via the t test, and multiple groups were compared via one-way ANOVA. Survival analysis was conducted via the Kaplan‒Meier method, and differences were assessed via the log-rank test. Hazard ratios, 95% confidence intervals, and p values are reported. Correlation analyses were performed via Spearman's or Pearson's tests. All the statistical analyses were conducted via R software (version 4.1.0). A p value < 0.05 was considered statistically significant. Results Expression and alterations of EXOSC5 in LUAD First, we searched for the three-dimensional structure of the gene in ProteinAtlas (Fig. 1 A). We examined the mRNA levels of EXOSC5 in samples from the TCGA database. Our results indicated that EXOSC5 was significantly upregulated (log2(FC) > 1, P < 0.001; Fig. 1 B), and the protein expression levels of EXOSC5 were also elevated in the LUAD of 211 tissue samples obtained from the Protein Atlas database (Fig. 1 C). IHC staining was further performed on tumor tissues from 194 patients. The analysis revealed that a total of 107 samples (55.15%) presented low EXOSC5 expression, whereas 87 samples (44.85%) presented relatively high EXOSC5 expression. We selected significantly different sample images for display (Fig. 1 D). The immunoblotting results indicated that the EXOSC5 protein content was significantly greater in 5 pairs of LUAD tumor samples than in normal lung samples (Fig. 1 E). Tumor progression is driven by genetic alterations, including DNA methylation, gene mutations, and copy number variations (CNVs). DNA methylation, an established epigenetic modification, has been implicated in the disruption of immune cell homeostasis and tumor immune surveillance. Our findings revealed that samples exhibiting high EXOSC5 expression presented reduced methylation levels at the transcription start site (TSS) (Fig. 1 F), and a negative correlation was observed between the TSS methylation level and EXOSC5 expression (Fig. 1 G). Notably, the expression level of the EXOSC5 gene was significantly associated with the methylation status of multiple CpG sites, suggesting that EXOSC5 gene expression might be modulated by the methylation of these sites, particularly at the TSS. CNVs constitute a crucial form of genomic structural variation, and in lung adenocarcinoma, the CNV profile of the EXOSC5 gene is characterized predominantly by diploid normal copies (49%), followed by single-copy deletions (29%) and amplifications (22%) (Fig. 1 H). Furthermore, a negative correlation was evident between EXOSC5 expression and its CNV value (Fig. 1 I). Using the cBioPortal platform, we undertook a comprehensive analysis of exon mutations within the RNase_PH and RNase_PH_C domains of the EXOSC5 gene. Our analysis identified a variety of mutation types within this region, with missense mutations and gene fusions being the most prevalent. Specifically, one missense mutation was located in the central region of the RNase_PH gene, and one gene fusion was identified at the initiation of the RNase_PH_C domain. Additionally, two variants of unknown significance (VUS) were observed, whereas driver mutations, truncation mutations, in-frame mutations, and splicing mutations were absent from this region. Notably, the "A131T" mutation site, highlighted in the figure, may represent a key mutation point within this region and merits further investigation and analysis (Fig. 1 J). These mutations have the potential to impact the function of the EXOSC5 gene, subsequently influencing normal cellular physiology. In the TCGA LUAD dataset, EXOSC5 gene mutations were primarily classified as amplifications, structural variations, and mutations, with frequencies of 0.2%, 0.4%, and 0.8%, respectively. The variant type with a frequency of 1% remained unspecified (Fig. 1 K). Univariate and multivariate analyses and prognostic nomograms for overall survival The univariate and multivariate analysis results, stratified by different clinical characteristics, are summarized in Figs. 2 and 3 for the TCGA and NTU cohorts. Univariate analysis revealed that EXOSC5 expression (P = 0.003) and TNM stage (P < 0.001) were significant risk factors for overall survival (OS) (Fig. 2 A). Similarly, multivariate analysis revealed that N1 and T4 stages (P < 0.031 and P < 0.015) were significant risk factors for OS (Fig. 2 A). Furthermore, within the TCGA cohort, patients with low EXOSC5 expression had longer OS (P = 0.032; Fig. 2 B). Subgroup analysis further revealed that EXOSC5 expression increased with increasing N stage (Fig. 2 C and 2 D). We developed a three-year and five-year survival prognosis model for LUAD patients (Fig. 2 E). OS was predicted on the basis of the cumulative score of risk factors for each patient. The calibration charts showed a general fit between the actual and predicted OS curves, indicating good predictive performance of our nomogram (Supplementary Fig. 1B). We plotted ROC curves to evaluate the accuracy of the nomogram model, stage, and other prognostic factors. The nomogram model had greater areas under the curve than did stage or other risk factors at both three and five years (AUC: 0.692 and 0.714) (Fig. 2 F and 2 G). Figure 3 displays the outcomes of both the univariate and multivariate analyses conducted via IHC. Univariate analysis revealed EXOSC5 expression (P < 0.001), tumor size (P < 0.001), differentiation (P = 0.009), and TNM stage (P < 0.001) as crucial risk factors influencing OS (Fig. 3 A). Moreover, the multivariate analysis highlighted differentiation and the IHC staining score for EXOSC5 (P < = 0.003 and P < 0.001) as significant risk factors for OS (Fig. 3 A). Furthermore, within the NTU cohort, patients with low EXOSC5 expression had longer OS (P < 0.032; Fig. 3 B). Subgroup analysis further revealed that EXOSC5 expression increased with increasing stage (Fig. 3 D) but was not correlated with N stage, which differed from the findings in the TCGA cohort (Fig. 3 C). Similarly, we developed a three-year and five-year survival prognosis model for LUAD patients (Fig. 3 E). The calibration charts revealed good agreement between the actual and predicted OS curves, indicating the excellent predictive performance of our nomogram (Supplementary Fig. 1C). Similarly, we plotted ROC curves to analyse the accuracy of the nomogram model, stage, and other prognostic factors for prediction. The nomogram model had greater areas under the curve than did stage or other risk factors at both three and five years (AUC: 0.861 and 0.854) (Fig. 3 F and 3 G). GSEA revealed that high expression of EXOSC5 promoted the occurrence and development of LUAD A GSEA plot was generated on the basis of the DEGs. Notably, the GSEA revealed high-scoring gene sets such as "immune receptor activity, meiotic cell cycle process, positive regulation of the cell cycle process, activation of the immune response, endothelial cell proliferation, regulation of the intrinsic apoptotic signalling pathway, regulation of T-cell differentiation in the thymus , and regulation of alpha beta T-cell activation", suggesting that EXOSC5 is involved mainly in regulating the cell cycle, apoptosis, and cell proliferation and participates in the process of cellular immunity (Fig. 4 A and 4 B). To test the bioinformatics prediction results and verify the role of EXOSC5 in the proliferation and apoptosis of LUAD tumor cells, we conducted validation through in vitro cell experiments. First, we examined the expression level of EXOSC5 in the H1650, H1299, SPCA-1 and A549 cell lines. Western blotting revealed that EXOSC5 was expressed in all LUAD tumor cell lines, with a relatively high protein expression level in the A549 cell line, intermediate expression in SPCA-1 and H1650 cells, and relatively low expression in H1299 cells (Fig. 4 C). Finally, we used an LV-EXOSC5-RNAi lentivirus to transduce A549 cells, resulting in the knockdown of EXOSC5 in the A549 cell line (Fig. 4 D). Second, the LV-EXOSC5 lentivirus was used to transduce H1299 cells, which resulted in the upregulation of EXOSC5 protein levels in the H1299 cell line (Fig. 4 E). In vitro experiments confirmed that EXOSC5 knockdown in A549 cells increased apoptosis (Fig. 4 F), whereas EXOSC5 overexpression in H1299 cells decreased apoptosis (Fig. 4 G). EXOSC5 knockdown also led to G1 phase arrest and reduced the number of S phase A549 cells (Fig. 4 H), whereas EXOSC5 overexpression in H1299 cells increased the number of S phase cells (Fig. 4 I). EDU assays revealed that EXOSC5 knockdown significantly reduced the proliferation of A549 cells (Fig. 5 A), whereas EXOSC5 overexpression increased the proliferation of H1299 cells (Fig. 5 B). High EXOSC5 expression inhibits immune infiltration in LUAD High EXOSC5 expression was associated with lower stromal and immune scores in LUAD (Fig. 6 A). Among the 24 immune cell types, most immune cells were enriched in LUAD patients with low EXOSC5 expression, whereas high EXOSC5 expression was associated with increased Th2 cells (Fig. 6 B). The numbers of Th2 cells and CD8 T cells were positively correlated with the expression of EXOSC5, whereas the scores of most immune cells were negatively correlated with the expression of EXOSC5 (Fig. 6 C). The correlation coefficient between Tem and EXOSC5 reached − 0.316 (Fig. 6 D), and the correlation coefficient between Tcm and EXOSC5 reached − 0.341 (Fig. 6 E). Survival analysis revealed that the EXOSC5-High/Immune-Low subgroup had the worst prognosis, whereas the EXOSC5-Low/Immune-High subgroup had the best prognosis (Fig. 6 F and 6 G). Therefore, changes in infiltration proportions suggest that high expression of EXOSC5 inhibits the infiltration of immune cells. Conversely, the absence of EXOSC5 promoted the infiltration of immune cells. To further explore the complex interplay between EXOSC5 and the immunosuppressive TME, we comprehensively analysed the correlations between EXOSC5 expression and a broad array of immune-related genes in lung adenocarcinoma (LUAD). Our results revealed that EXOSC5 gene expression was significantly associated with diverse immunosuppressive genes, along with chemokines, chemokine receptors, and genes encoding MHC complex components. Notably, EXOSC5 displayed robust correlations with immunosuppressive genes, including ADORA2A, PDCD1LG2, and CSF1R. The correlation between EXOSC5 and ADORA2A was exceptionally strong (P < 0.001), implying a central role for EXOSC5 in the regulation of these immunosuppressive genes, with subsequent implications for the immune microenvironment of LUAD (Fig. 6 H). With respect to chemokines, EXOSC5 was significantly correlated with CCL13, CXCL5, and CXCL12 (all P < 0.001), suggesting its potential involvement in the regulation of these chemokine genes, which could in turn affect tumor cell migration and invasion (Fig. 6 I). In the context of chemokine receptors, EXOSC5 exhibited strong correlations with receptors such as CXCR1, CCR8, CCR2, and XCR1 (all P < 0.001), indicating that EXOSC5 may modulate tumor cell chemotaxis through the regulation of these receptor genes (Fig. 6 J). However, although EXOSC5 was significantly correlated with certain MHC complex genes, such as HLA-DQA1 and HLA-DOA (P < 0.001), its overall contribution to the regulation of MHC gene expression appears to be relatively constrained (Fig. 6 K). Collectively, these findings underscore the potential importance of EXOSC5 in regulating immunosuppressive and chemokine signalling pathways, thereby contributing to the modulation of the immune microenvironment in LUAD. Relationship between EXOSC5 and the immune therapy response In recent years, substantial progress has been made in the field of cancer immunotherapy with the introduction of immune checkpoint inhibitors (ICIs), which exhibit notable therapeutic efficacy in clinical settings. Established predictive biomarkers, including tumor mutation burden (TMB) and microsatellite instability (MSI) scores, have proven invaluable in assessing the response to ICI therapy and overall patient prognosis. EXOSC5 expression was weakly correlated with MSI (R: 0.086, P = 0.041) but moderately positively correlated with TMB (R: 0.219, P < 0.001) (Fig. 7 A and 7 B). Furthermore, within the TCGA-LUAD dataset, we identified a statistically significant, albeit very weak, positive correlation between PD1 and EXOSC5 gene expression (R: 0.149, P < 0.001) (Fig. 7 C). Notably, EXOSC5 mRNA expression levels were significantly different between responders and nonresponders to anti-PD-1 therapy (Fig. 7 D). Specifically, patients who experienced disease remission following anti-PD-1 treatment tended to exhibit lower EXOSC5 expression (Fig. 7 E). Survival analysis after anti-PD-1 therapy demonstrated that EXOSC5 expression profoundly affected both overall survival (OS) and progression-free survival (PFS) (Fig. 7 F, G). The hazard ratios (HRs) for the high EXOSC5 expression group were markedly greater than those for the low EXOSC5 expression group for both OS (HR: 2.16) and PFS (HR: 1.84), with statistical significance indicated by P < 0.05. These findings suggest that EXOSC5 expression levels could serve as a crucial biomarker for predicting the efficacy of anti-PD-1 therapy. Moreover, targeted therapeutic strategies against EXOSC5 may offer novel alternative treatments for patients with high EXOSC5 expression, potentially enhancing survival outcomes. Additionally, our research included a thorough exploration of the Genomics of Drug Sensitivity in Cancer (GDSC) database. By integrating the half-maximal inhibitory concentration (IC50) values for 265 small-molecule drugs with corresponding mRNA gene expression data from 860 cell lines, we aimed to identify potential antitumour drugs associated with EXOSC5 expression. A heatmap visually depicts the correlations between 26 significantly distinct drugs and EXOSC5 mRNA expression, utilizing a color gradient from blue (indicating low estimated drug sensitivity) to red (indicating high estimated drug sensitivity) (Fig. 7 H). Complementary scatter plots further illustrated the correlation between the estimated drug sensitivity score (Z score) and EXOSC5 RNA expression (Fig. 7 I). Discussion Lung adenocarcinoma (LUAD) remains a leading cause of cancer-related mortality, with a 5-year survival rate of less than 15%. Despite advances in treatment, the prognosis for LUAD patients remains poor, particularly for those with advanced disease [ 14 ] . Our study identified EXOSC5 as a novel prognostic biomarker and therapeutic target in LUAD, with high EXOSC5 expression associated with poor overall survival (OS) and resistance to immunotherapy. Genetic alterations of EXOSC5 in LUAD The development and progression of LUAD are driven by a complex interplay of genetic alterations. Our study revealed that EXOSC5 expression is regulated by DNA methylation, with high EXOSC5 expression associated with reduced methylation levels at the transcription start site (TSS). These findings suggest that hypomethylation of the EXOSC5 promoter may contribute to its overexpression in LUAD, suggesting that increased methylation status may suppress gene expression, consequently influencing tumor advancement [ 15 ] . Additionally, we observed a negative correlation between EXOSC5 expression and CNV values, indicating that copy number alterations may also play a role in modulating EXOSC5 expression. Alterations in copy number can elicit changes in gene dosage, subsequently modulating downstream signalling pathways and the biological attributes of tumor cells [ 16 ] . Furthermore, we identified several mutations in the EXOSC5 gene, particularly in the RNase_PH and RNase_PH_C domains, which may disrupt its structural and functional integrity, potentially enhancing its oncogenic activity. EXOSC5 as a Prognostic Biomarker To date, many researchers still use lung cancer clinical phenotypes to predict patient prognosis, while the International Association for the Study of Lung Cancer (IASLC) does not recommend these clinical phenotypes for prognosis prediction [ 17 ] . Some scholars also believe that the TNM staging guidelines are not sufficient to predict OS at the individual level, as many early-stage patients may experience late recurrence [ 18 ] . With the development of genomics, new research strategies have emerged. In renal clear cell carcinoma, researchers have established a m6A risk model through genomic studies, which has a notable correlation with patient prognosis [ 19 ] . Our findings demonstrate that EXOSC5 is significantly overexpressed in LUAD tissues and that its expression level correlates with poor prognosis, particularly in patients with advanced disease and lymph node metastasis. Compared with traditional TNM staging, the nomogram model we developed, which incorporates EXOSC5 expression and other risk factors, significantly improved the accuracy of survival prediction. These findings suggest that EXOSC5 could serve as a valuable biomarker for identifying high-risk LUAD patients who may benefit from more aggressive treatment strategies. Mechanisms of EXOSC5 in LUAD Progression EXOSC5 promotes tumor progression by regulating the cell cycle, proliferation, and apoptosis. In vitro experiments revealed that EXOSC5 knockdown in A549 cells increased apoptosis and induced G1 phase arrest, whereas EXOSC5 overexpression in H1299 cells increased proliferation and reduced apoptosis. These findings are consistent with those of previous studies in colorectal cancer, where EXOSC5 was shown to activate the ERK and AKT pathways, leading to decreased expression of p21 and p27 and promoting the G1/S transition [ 20 ] . Additionally, EXOSC5 may play a role in apoptotic DNA degradation [ 21 ] , as it forms homodimers with CRN-5, which possesses both RNase and DNase activity [ 22 ] . EXOSC5 and the Immune Microenvironment Our study revealed that high EXOSC5 expression is associated with reduced immune cell infiltration in LUAD. Investigations by McNeel DG revealed that EXOSC5 has the potential to trigger specific humoral immune reactions in approximately one-third of melanoma and prostate cancer patients, and responsiveness to other overexpressed tumor antigens was detected in prostate cancer patients [ 23 – 24 ] . Patients with low EXOSC5 expression had increased proportions of most immune cells, whereas high EXOSC5 expression was associated with increased Th2 cells. Yang reported that individuals with high-titre-specific antibodies to the EXOSC5 antigen elicited specific T-cell responses [ 25 ] . Zhou's data indicated that DCs cotransfected with EXOSC5 can induce CTL, which is feasible and effective in eliciting specific antitumour cytotoxicity [ 26 ] . EXOSC5 expression was negatively correlated with Tem and Tcm cells, suggesting that high EXOSC5 expression may inhibit the infiltration of these immune cells, leading to poor prognosis. Tems are activated Tcm cells that, upon restimulation by antigens, can continue to produce many clonal effector memory T cells carrying the same antigen. In 2005, Klebanoff CA first demonstrated that Tcm cells possess superior antitumour capabilities [ 27 ] . Clinical studies by the National Institutes of Health (NIH) in 2012 revealed that Tcm cells and their clonal derivative T cells are highly effective antitumour immune T cells [ 28 ] . Conversely, low EXOSC5 expression promotes immune cell infiltration, resulting in better outcomes. Furthermore, we explored the relationships between EXOSC5 and immune-related genes, including chemokines, chemokine receptors, and immunosuppressive genes. EXOSC5 expression was strongly correlated with the expression of immunosuppressive genes such as ADORA2A, PDCD1LG2, and CSF1R, suggesting its role in modulating the immunosuppressive TME. Our study revealed significant correlations between EXOSC5 and key immunosuppressive genes, such as LAG3 and NECTIN2, which may provide insight into the paradoxical tumor growth and dissemination observed in some LUAD patients despite robust immune responses. Additionally, EXOSC5 was significantly correlated with chemokines (CCL13, CXCL5, and CXCL12, for example). and their receptors (CXCR1, CCR8, CCR2, etc.). ), which are known to orchestrate the recruitment of T central memory (Tcm) and T effector memory (Tem) cells to the tumor microenvironment [ 29 ] . While direct links between MHC complex genes and Tcm/Tem cells have not been explicitly documented, the interaction between MHC molecules and T cells during antigen presentation is indispensable for the activation and proliferation of these T-cell subsets [ 30 ] . The enrichment of Th2 cells in EXOSC5-high LUAD aligns with their pro-tumorigenic role. Th2 cytokines (e.g., IL-4, IL-13) polarize macrophages toward an M2 phenotype, suppress cytotoxic CD8 + T-cell activity, and promote angiogenesis [ 31 – 34 ] . EXOSC5 may stabilize Th2-associated transcripts (e.g., GATA3) or degrade Th1-polarizing factors (e.g., STAT1), though this requires experimental validation. Th2 polarization inhibits the Th1-driven anti-tumor response, leading to resistance to PD-1/PD-L1 [ 35 ] , which is consistent with our observation results (Fig. 7 F-G). This mechanism is that Th2 polarization recruits myeloid-derived suppressor cells (MDSCs) and M2 macrophages through CCL2/CCL17 chemokines, inhibits the function of CD8 + T cells and drives resistance to immune checkpoint inhibitors [ 36 ] .Notably, ADORA2A—a gene strongly correlated with EXOSC5—activates IL-4/IL-13 secretion in T cells [ 37 ] , potentially explaining Th2 enrichment. The paradoxical suppression of Tem/Tcm cells further suggests a Th2-dominated, immunosuppressive TME in EXOSC5-high tumors.These findings suggest that EXOSC5 may play a crucial role in regulating the immune landscape of LUAD, potentially contributing to tumor immune evasion. EXOSC5 and immunotherapy resistance In tumors with high TMB, elevated EXOSC5 expression suggests enhanced proliferative capacity and a complex immune microenvironment. High EXOSC5 expression was associated with resistance to anti-PD1 immunotherapy, as patients with lower EXOSC5 expression were more likely to respond to treatment. These findings suggest that EXOSC5 may serve as a predictive biomarker for immunotherapy response. Additionally, our analysis of the GDSC database revealed correlations between EXOSC5 expression and drug sensitivity, indicating that targeting EXOSC5 could increase the efficacy of existing therapies. Limitations Our study has several limitations. First, the NTU cohort’s sample size, though sufficient for initial validation, may underpower subgroup analyses of rare clinical or molecular subtypes. Second, in vitro experiments were limited to two cell lines (A549 and H1299), which may not fully recapitulate the heterogeneity of LUAD. Future studies should validate EXOSC5’s functional roles in additional models. Third, while our bioinformatics analysis identified correlations between EXOSC5 and immune infiltration, mechanistic studies (e.g., CRISPR-edited EXOSC5 cells co-cultured with T cells) are required to establish causality in immune suppression. Clinical Implications and Future Directions Our findings highlight the potential of EXOSC5 as a prognostic biomarker and therapeutic target in LUAD. However, further validation through multicenter clinical trials is needed to confirm the prognostic utility of EXOSC5 expression. Future studies should also explore the mechanisms underlying EXOSC5-mediated immune evasion and its potential as a target for combination therapies. Declarations Ethics approval and consent to participate: This study was officially recognized for it strictly carried out the procedures for care and use admitted by the Ethics Committee of the Affiliated Hospital of Nantong University (Ethic Number: 2018-K020). Written informed consents were obtained from all participants. We confirmed that experiments on humans and/or the use of human tissue samples were performed in accordance with relevant guidelines and regulations. Consent for publication: Manuscript is approved by all authors for publication. Availability of data and materials: RNA sequencing and clinical data were obtained from The Cancer Genome Atlas database (https://portal.gdc.cancer.gov). Anti-PD1 treatment samples were obtained from the GEO database (GSE120644). IHC results and patient clinical information are available by contacting J.X.Xu ( [email protected] ). Competing interests: We declare that we have no conflict of interest. Funding: This study was funded by grants from the Surface Project of Nantong Municipal Health Commission (Mandatory Subject), Jiangsu, China (No. MS2023105). Authors' contributions: Guo and Yang conceived the idea and designed the experiments. Xu and Zhang performed all experiments and analyzed the data. Ke provided technical assistance and helpful comments. Xu and Zhang wrote the manuscript. All authors approved the final version. References Bray, F., Ferlay, J., Soerjomataram, I., Siegel, RL., Torre, LA., Jemal, A, (2018). Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA-CANCER J CLIN, 68 (6), 394-424. https://doi.org/10.3322/caac.21492 Chen, W., Zheng, R., Baade, PD., Zhang, S., Zeng, H., Bray, F., et al (2016). Cancer statistics in China, 2015. CA-CANCER J CLIN, 66 (2), 115-32. https://doi.org/10.3322/caac.21338 Siegel, RL., Miller, KD., Jemal, A, (2018). Cancer statistics, 2018. 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Supplementary Files SupplementaryTable1.docx SupplementaryTable2.docx Supplementarytable3.docx supplementaryfile.pdf SupplementaryFigure1A.pdf SupplementaryFigure1B.pdf SupplementaryFigure1C.pdf Cite Share Download PDF Status: Published Journal Publication published 14 Apr, 2025 Read the published version in BMC Cancer → Version 1 posted Editorial decision: Accepted 01 Apr, 2025 Reviews received at journal 30 Mar, 2025 Reviewers agreed at journal 30 Mar, 2025 Reviewers agreed at journal 29 Mar, 2025 Reviewers invited by journal 20 Mar, 2025 Submission checks completed at journal 19 Mar, 2025 First submitted to journal 19 Mar, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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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-5782899","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":431668463,"identity":"8c8d132e-12a7-4781-bcf5-5ce251fcdf55","order_by":0,"name":"Jianxun Xu","email":"","orcid":"","institution":"Affiliated Hospital of Nantong University","correspondingAuthor":false,"prefix":"","firstName":"Jianxun","middleName":"","lastName":"Xu","suffix":""},{"id":431668464,"identity":"55eb3ad2-a893-424c-9f67-d1f7092902e6","order_by":1,"name":"Zhengwei Zhang","email":"","orcid":"","institution":"Affiliated Hospital of Nantong University","correspondingAuthor":false,"prefix":"","firstName":"Zhengwei","middleName":"","lastName":"Zhang","suffix":""},{"id":431668465,"identity":"2d18caf1-ee25-4be7-82e0-6a0e020e0e8a","order_by":2,"name":"Keyao Han","email":"","orcid":"","institution":"Affiliated Hospital of Nantong University","correspondingAuthor":false,"prefix":"","firstName":"Keyao","middleName":"","lastName":"Han","suffix":""},{"id":431668466,"identity":"9c64c193-511f-44c1-bbcb-5fa574ff362c","order_by":3,"name":"Zhen Yang","email":"","orcid":"","institution":"Affiliated Hospital of Nantong University","correspondingAuthor":false,"prefix":"","firstName":"Zhen","middleName":"","lastName":"Yang","suffix":""},{"id":431668467,"identity":"bbc26fc6-0a98-46ae-95fc-73189ffab569","order_by":4,"name":"Fengmei Guo","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4UlEQVRIiWNgGAWjYNACAxs5fmb+jw8SKmyI1VKQZizZ3mBs8OBMGrFaPhxO3HDmgJnkw7ZDhBXLz8g99pjH4LCxwY2EtIoEtgMM/O3dCXi1GNzISzfmMUiXk7yRcOxGAs8dBokzZzfg1yKRYybNY2BtzHcjse1GgsQzoEgufi3yM8BamBMbbiSzFSQYHCasheEGWItz4oQzx9gYEhKI0GJw5o2Z5BwDUCD3MEskHEjjIegX+fYcM4k3f0BRycP48ec/IKO9l4DDgICJB4nDg1MZMmD8QZSyUTAKRsEoGLEAAIzOSaT+dSpeAAAAAElFTkSuQmCC","orcid":"","institution":"Affiliated Hospital of Nantong University","correspondingAuthor":true,"prefix":"","firstName":"Fengmei","middleName":"","lastName":"Guo","suffix":""}],"badges":[],"createdAt":"2025-01-07 16:08:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5782899/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5782899/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12885-025-14059-2","type":"published","date":"2025-04-14T15:57:28+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":79089520,"identity":"62a92745-e6c9-4873-ae57-b5a8d958e55b","added_by":"auto","created_at":"2025-03-24 09:45:19","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":856522,"visible":true,"origin":"","legend":"\u003cp\u003eEXOSC5 expression and genetic alterations in LUAD.\u003c/p\u003e\n\u003cp\u003eA: 3D structure of EXOSC5 in ProteinAtlas. B-C: mRNA and protein expression levels of EXOSC5 in LUAD. D: EXOSC5 antibody staining of LUAD tissues at ×50 and ×200 magnification, showing high and low expression. E: Western blot showing EXOSC5 expression in five LUAD sample pairs. F: DNA methylation levels grouped by EXOSC5 expression in LUAD. G: Correlation between DNA methylation and EXOSC5 expression. H: Distribution of CNV types in the TCGA cohort. I: Correlations between EXOSC5 expression and CNV values. J-K: Mutation status and sites of EXOSC5 in TCGA-LUAD.\u003c/p\u003e","description":"","filename":"fig1.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5782899/v1/e37f2cb62df7111bf9198360.jpg"},{"id":79091221,"identity":"c0d19ce0-edfb-41a4-bb24-f655fd0f83dd","added_by":"auto","created_at":"2025-03-24 10:01:19","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":804430,"visible":true,"origin":"","legend":"\u003cp\u003eEXOSC5 is a high-risk factor in LUAD (TCGA cohort).\u003cbr\u003e\nA: Univariate and multivariate OS analyses of the TCGA cohort. B: Survival comparison between the high and low EXOSC5 groups. C-D: EXOSC5 mRNA expression across N stages and stage classifications. E: Three- and five-year OS nomogram incorporating EXOSC5 expression, N stage, T stage, and stage classification. F-G: Three- and five-year ROC curves for EXOSC5 expression, stage classification, and the risk model.\u003c/p\u003e","description":"","filename":"fig2.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5782899/v1/fc82db7f13b73263a4483e04.jpg"},{"id":79089518,"identity":"a3088444-7c8a-4e2d-9fa8-5d421ef98526","added_by":"auto","created_at":"2025-03-24 09:45:19","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":818143,"visible":true,"origin":"","legend":"\u003cp\u003eEXOSC5 is a high-risk factor in LUAD (NTU cohort).\u003c/p\u003e\n\u003cp\u003eA: Univariate and multivariate OS analyses in the NTU cohort. B: Survival comparison between the high- and low-EXOSC5 groups in the NTU cohort. C-D: EXOSC5 mRNA expression across N stages and stage classifications. E: Three- and five-year OS nomogram incorporating the EXOSC5 immunohistochemistry score, differentiation status, size, and stage classification. F-G: Three-year and five-year ROC curves based on the EXOSC5 immunohistochemistry score, size, stage classification, and risk model.\u003c/p\u003e","description":"","filename":"fig3.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5782899/v1/7f65fd104a89f503555c430e.jpg"},{"id":79091222,"identity":"c47a8d31-77ca-420d-bd67-dc12b15c0903","added_by":"auto","created_at":"2025-03-24 10:01:20","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1030468,"visible":true,"origin":"","legend":"\u003cp\u003eEXOSC5-related oncogenic pathways: prediction and experimental validation.\u003c/p\u003e\n\u003cp\u003eA-B: GSEA results visualized via mountain plots (A) \u0026amp; bar charts (B). C: EXOSC5 expression in various cell lines (H1650, H1255, SPCA1, and A549). D: Comparison of EXOSC5 expression in A549 cells (normal, lentivirus-control, and lentivirus-EXOSC5-knockdown). E: Comparison of EXOSC5 expression in H1299 cells (normal, lentivirus-control, and lentivirus-EXOSC5-overexpressing). F: EXOSC5 knockdown in A549 cells increased apoptosis. G: EXOSC5 overexpression in H1299 cells decreases apoptosis. H: EXOSC5 knockdown in A549 cells altered the cell cycle (increase in G1, decrease in S). I: EXOSC5 overexpression in H1299 cells altered the cell cycle (G1 decrease, S increase). *P\u0026lt;0.05; **P\u0026lt;0.01.\u003c/p\u003e","description":"","filename":"fig4.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5782899/v1/dca156864319a6f9ceca787c.jpg"},{"id":79089534,"identity":"b4424e5f-4a7b-4295-82d0-710917702c5f","added_by":"auto","created_at":"2025-03-24 09:45:20","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":425078,"visible":true,"origin":"","legend":"\u003cp\u003eExperimental confirmation that EXOSC5 promotes LUAD cell proliferation. A: Lentiviral knockout of EXOSC5 in A549 cells significantly inhibited cell proliferation. B: Lentiviral overexpression of EXOSC5 in H1299 cells enhances cell proliferation. *P\u0026lt;0.05; **P\u0026lt;0.01.\u003c/p\u003e","description":"","filename":"fig5.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5782899/v1/b4c315652963d484226d8be1.jpg"},{"id":79091224,"identity":"30a9df09-7bf3-4c93-823f-83d581ef7d53","added_by":"auto","created_at":"2025-03-24 10:01:20","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1020805,"visible":true,"origin":"","legend":"\u003cp\u003eHigh EXOSC5 expression suppresses immune infiltration in the TCGA-LUAD cohort.\u003c/p\u003e\n\u003cp\u003eA: Bar charts comparing the EstimateScore, StromalScore, and ImmuneScore in LUAD vs. normal tissues. B: Immune cell type scores in LUAD vs. normal samples (24 types). C: Spearman correlation analysis of EXOSC5 mRNA with 24 types of immune cells. D-E: Scatter plots showing the correlations of the Tem and Tcm cell scores with EXOSC5 expression. F-G: Survival analysis of EXOSC5/immune subgroups. H-K: Lollipop plots showing the correlations of EXOSC5 with immunosuppressive genes (H), chemokine receptors (I), chemokines (J), and MHC genes (K).\u003c/p\u003e","description":"","filename":"fig6.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5782899/v1/499b048f4a57f17dc903a6af.jpg"},{"id":79089523,"identity":"fc6481ce-6a86-4d7d-a3bd-fdda71c3580b","added_by":"auto","created_at":"2025-03-24 09:45:20","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":848973,"visible":true,"origin":"","legend":"\u003cp\u003eAssociation of EXOSC5 expression with immune therapy outcome.\u003cbr\u003e\nA-B: Scatter plots depicting the correlations between EXOSC5 expression and TMB (A) and MSI (B). C: Scatter plot showing the relationship between EXOSC5 and PD1 expression. D: EXOSC5 mRNA levels in anti-PD1-treated samples from the GSE120644 dataset were compared between responders and nonresponders. E: EXOSC5 mRNA levels in anti-PD1-treated samples compared between PR/CR patients and PD/SD patients (PMID: 37024582). F-G: EXOSC5 expression levels were correlated with overall survival (OS) and progression-free survival (PFS) in anti-PD1-treated patients. H: Heatmap showing drug sensitivity in EXOSC5-low vs. EXOSC5-high samples, with color intensity indicating drug sensitivity. I: Correlations of drug sensitivity scores with EXOSC5 RNA expression.\u003c/p\u003e","description":"","filename":"fig7.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5782899/v1/a9f6a2ba18d84975ccfa7e92.jpg"},{"id":81050820,"identity":"e03311bc-0aeb-48ad-837a-b7f2317280f9","added_by":"auto","created_at":"2025-04-21 16:05:42","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6601845,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5782899/v1/c1fc597f-769e-4ce1-9f90-7cf28421452c.pdf"},{"id":79090408,"identity":"10100c65-efbe-4116-a059-b818b6821979","added_by":"auto","created_at":"2025-03-24 09:53:20","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":17594,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable1.docx","url":"https://assets-eu.researchsquare.com/files/rs-5782899/v1/06ee3415253231ee1c682842.docx"},{"id":79090410,"identity":"03f09c31-d53b-4a54-9c92-c772d7265054","added_by":"auto","created_at":"2025-03-24 09:53:20","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":19538,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable2.docx","url":"https://assets-eu.researchsquare.com/files/rs-5782899/v1/6f936b7974ef11bca47805c6.docx"},{"id":79089514,"identity":"efe64404-ae74-4fc4-95e7-05d9ea67fb2c","added_by":"auto","created_at":"2025-03-24 09:45:19","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":11687,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarytable3.docx","url":"https://assets-eu.researchsquare.com/files/rs-5782899/v1/850c9abb8a7e5da52c05ce2c.docx"},{"id":79090415,"identity":"af45375a-091e-40c7-ada5-b7b2b7708c71","added_by":"auto","created_at":"2025-03-24 09:53:20","extension":"pdf","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":299824,"visible":true,"origin":"","legend":"","description":"","filename":"supplementaryfile.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5782899/v1/8904287c128e9ea4ccf44266.pdf"},{"id":79089529,"identity":"a510c834-5beb-4a3b-b8a9-e49c5aa2cf85","added_by":"auto","created_at":"2025-03-24 09:45:20","extension":"pdf","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":430119,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigure1A.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5782899/v1/fb1a8fe5dc548f18221f2da6.pdf"},{"id":79091223,"identity":"329729ee-c173-4fd1-bc56-f72e85ed422a","added_by":"auto","created_at":"2025-03-24 10:01:20","extension":"pdf","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":9840,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigure1B.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5782899/v1/13fd80c9d1b43b65d82b55f2.pdf"},{"id":79089515,"identity":"37f0bf3b-4a42-45a7-8b36-79c4a9eb168e","added_by":"auto","created_at":"2025-03-24 09:45:19","extension":"pdf","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":9001,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigure1C.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5782899/v1/26302b6dbe11203cb783c508.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"EXOSC5: A Novel Biomarker for Poor Prognosis in Lung Adenocarcinoma","fulltext":[{"header":"Introduction","content":"\u003cp\u003eLung cancer, particularly lung adenocarcinoma (LUAD), remains the leading cause of cancer-related deaths worldwide. Despite significant advances in lung cancer treatment, it remains the leading cause of cancer-related deaths worldwide. \u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e, with lung adenocarcinoma being its major subtype\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. Owing to the recurrence of early-stage LUAD and the lack of effective treatment options for advanced-stage patients, the 5-year survival rate for LUAD patients ultimately falls below 15%\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. Therefore, understanding the molecular mechanisms driving LUAD progression is crucial for developing effective diagnostic and therapeutic strategies.\u003c/p\u003e \u003cp\u003eThe exosome complex, including exosome complex gene 5 (EXOSC5), plays a crucial role in RNA processing and degradation and has been implicated in tumorigenesis \u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. EXOSC5, a noncatalytic subunit of the RNA exosome complex \u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e, has been shown to promote tumor progression in various cancers through mechanisms involving cell cycle regulation and immune evasion\u003csup\u003e[\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. Japanese scholars reported that increased activity of the EXOSC gene was associated with poor prognosis in certain types of cancer patients\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. Recent studies have identified EXOSC5 as a prognostic marker in colorectal cancer, where it activates the ERK and Akt pathways\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. However, its role in LUAD remains poorly understood.\u003c/p\u003e \u003cp\u003eIn this study, we investigated the role of EXOSC5 in LUAD progression and its potential as a prognostic biomarker and therapeutic target. We hypothesized that elevated EXOSC5 expression is associated with poor clinical outcomes in LUAD patients. Through comprehensive analysis of EXOSC5 expression and functional studies, we aimed to elucidate its role in LUAD and explore its potential as a novel therapeutic target.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData acquisition and preprocessing\u003c/h2\u003e \u003cp\u003eRNA sequencing and clinical data 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). The dataset included 539 LUAD samples and 59 nontumor tissue samples. The expression data for EXOSC5 were extracted from the sequencing data. A total of 500 cases with complete clinical data, including survival status, sex, age, TNM stage, and stage, were compiled. All patients had undergone surgical treatment and had complete five-year follow-up data (Supplementary Table\u0026nbsp;1). Additionally, 16 anti-PD1 treatment samples were obtained from the GEO database (GSE120644), and 77 anti-PD1 treatment samples were sourced from a published article (PMID: 37024582).To ensure minimal batch effects within TCGA data, we performed principal component analysis (PCA) on all TCGA samples .As shown in Supplementary Fig.\u0026nbsp;1A, PCA revealed that: PC1 (57.2% variance) primarily separated tumor samples (n\u0026thinsp;=\u0026thinsp;539) from normal samples (n\u0026thinsp;=\u0026thinsp;59). PC2 (21.8% variance) reflected additional biological heterogeneity (e.g., tumor subtypes or differentiation grades).The high variance explained by PC1 demonstrates that tumor-normal biological differences dominate the dataset. Technical batch effects were negligible after DESeq2 normalization. Therefore, additional correction was deemed unnecessary.For the GEO cohort (GSE120644), raw data were processed using the limma package with variance stabilizing transformation (VST). For the NTU cohort, immunohistochemistry (IHC) staining was performed in a single laboratory under standardized protocols, minimizing experimental batch variability.\u003c/p\u003e \u003cp\u003eBetween July 2018 and October 2019, fresh tumor and normal tissue samples were collected from 5 LUAD patients at the Affiliated Hospital of Nantong University (NTU). Paraffin sections from 194 LUAD patients treated between 2012 and 2013 were obtained from the pathology department (Supplementary Table\u0026nbsp;2). Informed consent was obtained from all patients, and the study was approved by the ethics committee.\u003c/p\u003e \u003cp\u003eWe also utilized multiple database platforms: the cBioPortal (cBioPortal for Cancer Genomics) to display the mutation status of the EXOSC5 gene and the Protein Atlas (The Human Protein Atlas) to visualize the protein structure of the gene. EXOSC5 protein expression data from 211 tissue samples (109 LUAD samples and 102 normal lung tissue samples) were obtained from the Protein Atlas.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eImmunohistochemistry\u003c/h3\u003e\n\u003cp\u003eThe tissue samples were fixed, sectioned, baked, dewaxed, and then incubated with an anti-EXOSC5 polyclonal antibody. After washing, a secondary antibody and DAB solution were applied to form a brown‒yellow product. The sections were evaluated by three experienced pathologists. Three independent pathologists evaluated the slides without knowledge of patient characteristics. We implemented blinded scoring and calculated inter-observer agreement using Fleiss\u0026rsquo; Kappa (κ\u0026thinsp;=\u0026thinsp;0.731)(Supplementary table \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The staining results were scored on the basis of the proportion of stained cells: 1 (1%-30%), 2 (31%-50%), 3 (51%-70%), or 4 (71%-100%). The staining intensity was scored as 0 (no staining), 1 (light yellow), 2 (dark yellow), or 3 (brown particles). EXOSC5 expression was categorized into high- and low-level groups on the basis of the product of the two scores: \u0026le;4 for the low-level group and \u0026gt;\u0026thinsp;4 for the high-level group.\u003c/p\u003e\n\u003ch3\u003eWestern blot\u003c/h3\u003e\n\u003cp\u003eLUAD tissues and cells were lysed, and proteins were harvested after 48 hours. The lysate was centrifuged at 12,000 rpm, and the supernatant was collected for further analysis. The protein concentration was determined via a Bio-Rad protein assay. SDS‒PAGE was performed at 120 V, and the proteins were transferred to a PVDF membrane at 200 mA. The membrane was incubated with an anti-EXOSC5 antibody (1:1000 dilution; Proteintech) and an anti-β-actin antibody (1:10,000 dilution; Proteintech) overnight at 4\u0026deg;C. After washing, a goat anti-mouse/rabbit secondary antibody (1:10,000; LI-COR) was added, and the membrane was incubated for 2 hours at room temperature. The protein bands were visualized via an enhanced chemiluminescence (ECL) detection system (Pierce, Rockford, IL, USA).\u003c/p\u003e\n\u003ch3\u003eCell Culture and Transfection\u003c/h3\u003e\n\u003cp\u003eH1650, H1299, SPCA-1, and A549 cell lines were purchased from the Chinese Academy of Sciences Shanghai Cell Bank. The cells were cultured in RPMI 1640 medium (10% qualified FBS, 100 U/mL penicillin‒streptomycin) at 37\u0026deg;C with 5% CO2. Cells from the third to sixth generations were used. When the cell density reached 80%, the medium was replaced with RPMI 1640 without antibiotics, and the cells were cultured for an additional hour. Lentiviruses for EXOSC5 knockdown (LV-EXOSC5-RNAi: sequence 5\u0026prime;-GAAGGTCAGCAAAAGATT-3\u0026prime;) and EXOSC5 overexpression (LV-EXOSC5: serial number NM_020158.4) were designed and synthesized by GeneChem Technologies (Shanghai, China). The lentiviral vectors were mixed with the enhancer and added to a six-well plate. After 24 hours, the medium was replaced with fresh complete medium, and proteins were extracted after 48 hours for further experiments.\u003c/p\u003e\n\u003ch3\u003eFlow cytometry for apoptosis detection\u003c/h3\u003e\n\u003cp\u003eWe took logarithmically growing cells, fully digested them with gentle trypsin, added complete culture medium, mixed them well, and transferred them to an EP tube. After the cells were washed twice with precooled PBS, centrifugation was performed with the parameters set to 1000 rpm for 5 minutes. Two hundred microliters of 1\u0026times;binding buffer (10\u0026times;binding buffer:sterile saline\u0026thinsp;=\u0026thinsp;1:9) was added to suspend the cells, which were mixed well with a pipette, and then, Annexin V fluorescent dye was added and incubated at room temperature through a 10-minute interval. Centrifuge with specific parameters set to 1000 rpm for 5 minutes. After centrifugation, the mixture was mixed well, and PI fluorescent dye was added. Quantitative detection was performed within 1 hour, data were obtained through flow cytometry detection, and statistical analysis was performed.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eCell Counting Kit-8 (CCK-8) assay\u003c/h2\u003e \u003cp\u003eThe cells were seeded at a density of 1\u0026times;103 cells per well in a 96-well plate. CCK-8 reagent was added to each well, and the plates were incubated at 37\u0026deg;C for 2 hours. The absorbance was measured via a microplate reader (Bio-Rad, USA).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eBioinformatics and Genetic Analysis\u003c/h3\u003e\n\u003cp\u003eThe Protein Atlas was used to visualize the EXOSC5 protein structure. Statistical analysis was performed via the stats package, and data visualization was performed via ggplot2. Differentially expressed genes (|logFC|\u0026gt;1) were identified via the DESeq2 package. The EXOSC5 gene mutation status was displayed via cBioPortal. Methylation site data, copy numbers, and expression levels were obtained from TCGA-LUAD samples via the Xena website. Correlation analysis between methylation sites and EXOSC5 expression was performed, and the results were visualized via histograms and lollipop plots. The CNV mutation status was displayed via pie charts, and scatter plots were generated to show the correlation between CNV and EXOSC5 expression.\u003c/p\u003e \u003cp\u003eThe TCGA cohort was divided into EXOSC5-low and EXOSC5-high groups on the basis of median EXOSC5 mRNA expression. The NTU cohort was similarly divided on the basis of a score of 4. Differences between groups were analysed via t tests. A Cox regression model was used to identify independent prognostic risk factors, and the results are displayed in a forest plot. Survival curves were generated via the Kaplan‒Meier method, and differences between groups were assessed via Cox regression analysis. The proportional hazards assumption was tested via the survival package. The rms package was used to construct a nomogram model, perform calibration analysis, and visualize the results. ROC analysis was conducted via the pROC package.\u003c/p\u003e \u003cp\u003eWe employed the ssGSEA algorithm from the GSVA package [12] and immune cell markers from a published immunology article [13] to analyse the correlations among EXOSC5 expression, immune infiltration, and immune-related genes. Spearman statistical analysis was used, and the results were visualized via lollipop and scatter plots. The proportion of each group in each classification was assessed, and survival analysis of immune infiltration was performed via the Kaplan‒Meier method. Differences between groups were determined via Cox regression analysis.\u003c/p\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eAll the experiments were performed in triplicate. Differences between two groups were analysed via the t test, and multiple groups were compared via one-way ANOVA. Survival analysis was conducted via the Kaplan‒Meier method, and differences were assessed via the log-rank test. Hazard ratios, 95% confidence intervals, and p values are reported. Correlation analyses were performed via Spearman's or Pearson's tests. All the statistical analyses were conducted via R software (version 4.1.0). A p value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eExpression and alterations of EXOSC5 in LUAD\u003c/h2\u003e \u003cp\u003eFirst, we searched for the three-dimensional structure of the gene in ProteinAtlas (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). We examined the mRNA levels of EXOSC5 in samples from the TCGA database. Our results indicated that EXOSC5 was significantly upregulated (log2(FC)\u0026thinsp;\u0026gt;\u0026thinsp;1, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB), and the protein expression levels of EXOSC5 were also elevated in the LUAD of 211 tissue samples obtained from the Protein Atlas database (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). IHC staining was further performed on tumor tissues from 194 patients. The analysis revealed that a total of 107 samples (55.15%) presented low EXOSC5 expression, whereas 87 samples (44.85%) presented relatively high EXOSC5 expression. We selected significantly different sample images for display (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD). The immunoblotting results indicated that the EXOSC5 protein content was significantly greater in 5 pairs of LUAD tumor samples than in normal lung samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTumor progression is driven by genetic alterations, including DNA methylation, gene mutations, and copy number variations (CNVs). DNA methylation, an established epigenetic modification, has been implicated in the disruption of immune cell homeostasis and tumor immune surveillance. Our findings revealed that samples exhibiting high EXOSC5 expression presented reduced methylation levels at the transcription start site (TSS) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eF), and a negative correlation was observed between the TSS methylation level and EXOSC5 expression (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eG). Notably, the expression level of the EXOSC5 gene was significantly associated with the methylation status of multiple CpG sites, suggesting that EXOSC5 gene expression might be modulated by the methylation of these sites, particularly at the TSS.\u003c/p\u003e \u003cp\u003eCNVs constitute a crucial form of genomic structural variation, and in lung adenocarcinoma, the CNV profile of the EXOSC5 gene is characterized predominantly by diploid normal copies (49%), followed by single-copy deletions (29%) and amplifications (22%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eH). Furthermore, a negative correlation was evident between EXOSC5 expression and its CNV value (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eI). Using the cBioPortal platform, we undertook a comprehensive analysis of exon mutations within the RNase_PH and RNase_PH_C domains of the EXOSC5 gene. Our analysis identified a variety of mutation types within this region, with missense mutations and gene fusions being the most prevalent. Specifically, one missense mutation was located in the central region of the RNase_PH gene, and one gene fusion was identified at the initiation of the RNase_PH_C domain. Additionally, two variants of unknown significance (VUS) were observed, whereas driver mutations, truncation mutations, in-frame mutations, and splicing mutations were absent from this region. Notably, the \"A131T\" mutation site, highlighted in the figure, may represent a key mutation point within this region and merits further investigation and analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eJ). These mutations have the potential to impact the function of the EXOSC5 gene, subsequently influencing normal cellular physiology. In the TCGA LUAD dataset, EXOSC5 gene mutations were primarily classified as amplifications, structural variations, and mutations, with frequencies of 0.2%, 0.4%, and 0.8%, respectively. The variant type with a frequency of 1% remained unspecified (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eK).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eUnivariate and multivariate analyses and prognostic nomograms for overall survival\u003c/h2\u003e \u003cp\u003eThe univariate and multivariate analysis results, stratified by different clinical characteristics, are summarized in Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e for the TCGA and NTU cohorts. Univariate analysis revealed that EXOSC5 expression (P\u0026thinsp;=\u0026thinsp;0.003) and TNM stage (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were significant risk factors for overall survival (OS) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Similarly, multivariate analysis revealed that N1 and T4 stages (P\u0026thinsp;\u0026lt;\u0026thinsp;0.031 and P\u0026thinsp;\u0026lt;\u0026thinsp;0.015) were significant risk factors for OS (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Furthermore, within the TCGA cohort, patients with low EXOSC5 expression had longer OS (P\u0026thinsp;=\u0026thinsp;0.032; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). Subgroup analysis further revealed that EXOSC5 expression increased with increasing N stage (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD). We developed a three-year and five-year survival prognosis model for LUAD patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE). OS was predicted on the basis of the cumulative score of risk factors for each patient. The calibration charts showed a general fit between the actual and predicted OS curves, indicating good predictive performance of our nomogram (Supplementary Fig.\u0026nbsp;1B). We plotted ROC curves to evaluate the accuracy of the nomogram model, stage, and other prognostic factors. The nomogram model had greater areas under the curve than did stage or other risk factors at both three and five years (AUC: 0.692 and 0.714) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eG).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e displays the outcomes of both the univariate and multivariate analyses conducted via IHC. Univariate analysis revealed EXOSC5 expression (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), tumor size (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), differentiation (P\u0026thinsp;=\u0026thinsp;0.009), and TNM stage (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) as crucial risk factors influencing OS (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Moreover, the multivariate analysis highlighted differentiation and the IHC staining score for EXOSC5 (P\u0026thinsp;\u0026lt;\u0026thinsp;=\u0026thinsp;0.003 and P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) as significant risk factors for OS (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Furthermore, within the NTU cohort, patients with low EXOSC5 expression had longer OS (P\u0026thinsp;\u0026lt;\u0026thinsp;0.032; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). Subgroup analysis further revealed that EXOSC5 expression increased with increasing stage (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD) but was not correlated with N stage, which differed from the findings in the TCGA cohort (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). Similarly, we developed a three-year and five-year survival prognosis model for LUAD patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE). The calibration charts revealed good agreement between the actual and predicted OS curves, indicating the excellent predictive performance of our nomogram (Supplementary Fig.\u0026nbsp;1C). Similarly, we plotted ROC curves to analyse the accuracy of the nomogram model, stage, and other prognostic factors for prediction. The nomogram model had greater areas under the curve than did stage or other risk factors at both three and five years (AUC: 0.861 and 0.854) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eG).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eGSEA revealed that high expression of EXOSC5 promoted the occurrence and development of LUAD\u003c/h2\u003e \u003cp\u003eA \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eGSEA plot was generated\u003c/span\u003e on the basis of the DEGs. \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eNotably, the GSEA\u003c/span\u003e revealed \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003ehigh-scoring gene sets such as \"immune receptor activity, meiotic cell cycle process, positive regulation of\u003c/span\u003e the \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003ecell cycle process, activation of\u003c/span\u003e the \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eimmune response, endothelial cell proliferation, regulation of\u003c/span\u003e the \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eintrinsic apoptotic signalling pathway, regulation of T-cell differentiation in\u003c/span\u003e the \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003ethymus\u003c/span\u003e, and \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eregulation of alpha beta T-cell\u003c/span\u003e activation\", suggesting that EXOSC5 is involved mainly in regulating the cell cycle, apoptosis, and cell proliferation and participates in the process of cellular immunity (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). To test the bioinformatics prediction results and verify the role of EXOSC5 in the proliferation and apoptosis of LUAD tumor cells, we conducted validation through in vitro cell experiments. First, we examined the expression level of EXOSC5 in the H1650, H1299, SPCA-1 and A549 cell lines. Western blotting revealed that EXOSC5 was expressed in all LUAD tumor cell lines, with a relatively high protein expression level in the A549 cell line, intermediate expression in SPCA-1 and H1650 cells, and relatively low expression in H1299 cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). Finally, we used an LV-EXOSC5-RNAi lentivirus to transduce A549 cells, resulting in the knockdown of EXOSC5 in the A549 cell line (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD). Second, the LV-EXOSC5 lentivirus was used to transduce H1299 cells, which resulted in the upregulation of EXOSC5 protein levels in the H1299 cell line (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE). In vitro experiments confirmed that EXOSC5 knockdown in A549 cells increased apoptosis (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eF), whereas EXOSC5 overexpression in H1299 cells decreased apoptosis (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eG). EXOSC5 knockdown also led to G1 phase arrest and reduced the number of S phase A549 cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eH), whereas EXOSC5 overexpression in H1299 cells increased the number of S phase cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eI). EDU assays revealed that EXOSC5 knockdown significantly reduced the proliferation of A549 cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA), whereas EXOSC5 overexpression increased the proliferation of H1299 cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eHigh EXOSC5 expression inhibits immune infiltration in LUAD\u003c/h2\u003e \u003cp\u003eHigh EXOSC5 expression was associated with lower stromal and immune scores in LUAD (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). Among the 24 immune cell types, most immune cells were enriched in LUAD patients with low EXOSC5 expression, whereas high EXOSC5 expression was associated with increased Th2 cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). The numbers of Th2 cells and CD8 T cells were positively correlated with the expression of EXOSC5, whereas the scores of most immune cells were negatively correlated with the expression of EXOSC5 (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC). The correlation coefficient between Tem and EXOSC5 reached \u0026minus;\u0026thinsp;0.316 (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD), and the correlation coefficient between Tcm and EXOSC5 reached \u0026minus;\u0026thinsp;0.341 (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eE). Survival analysis revealed that the EXOSC5-High/Immune-Low subgroup had the worst prognosis, whereas the EXOSC5-Low/Immune-High subgroup had the best prognosis (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eF and \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eG). Therefore, changes in infiltration proportions suggest that high expression of EXOSC5 inhibits the infiltration of immune cells. Conversely, the absence of EXOSC5 promoted the infiltration of immune cells.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo further explore the complex interplay between EXOSC5 and the immunosuppressive TME, we comprehensively analysed the correlations between EXOSC5 expression and a broad array of immune-related genes in lung adenocarcinoma (LUAD). Our results revealed that EXOSC5 gene expression was significantly associated with diverse immunosuppressive genes, along with chemokines, chemokine receptors, and genes encoding MHC complex components. Notably, EXOSC5 displayed robust correlations with immunosuppressive genes, including ADORA2A, PDCD1LG2, and CSF1R. The correlation between EXOSC5 and ADORA2A was exceptionally strong (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), implying a central role for EXOSC5 in the regulation of these immunosuppressive genes, with subsequent implications for the immune microenvironment of LUAD (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eH).\u003c/p\u003e \u003cp\u003eWith respect to chemokines, EXOSC5 was significantly correlated with CCL13, CXCL5, and CXCL12 (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), suggesting its potential involvement in the regulation of these chemokine genes, which could in turn affect tumor cell migration and invasion (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eI). In the context of chemokine receptors, EXOSC5 exhibited strong correlations with receptors such as CXCR1, CCR8, CCR2, and XCR1 (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), indicating that EXOSC5 may modulate tumor cell chemotaxis through the regulation of these receptor genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eJ). However, although EXOSC5 was significantly correlated with certain MHC complex genes, such as HLA-DQA1 and HLA-DOA (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), its overall contribution to the regulation of MHC gene expression appears to be relatively constrained (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eK). Collectively, these findings underscore the potential importance of EXOSC5 in regulating immunosuppressive and chemokine signalling pathways, thereby contributing to the modulation of the immune microenvironment in LUAD.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eRelationship between EXOSC5 and the immune therapy response\u003c/h2\u003e \u003cp\u003eIn recent years, substantial progress has been made in the field of cancer immunotherapy with the introduction of immune checkpoint inhibitors (ICIs), which exhibit notable therapeutic efficacy in clinical settings. Established predictive biomarkers, including tumor mutation burden (TMB) and microsatellite instability (MSI) scores, have proven invaluable in assessing the response to ICI therapy and overall patient prognosis. EXOSC5 expression was weakly correlated with MSI (R: 0.086, P\u0026thinsp;=\u0026thinsp;0.041) but moderately positively correlated with TMB (R: 0.219, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA and \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB). Furthermore, within the TCGA-LUAD dataset, we identified a statistically significant, albeit very weak, positive correlation between PD1 and EXOSC5 gene expression (R: 0.149, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC). Notably, EXOSC5 mRNA expression levels were significantly different between responders and nonresponders to anti-PD-1 therapy (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eD). Specifically, patients who experienced disease remission following anti-PD-1 treatment tended to exhibit lower EXOSC5 expression (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eE). Survival analysis after anti-PD-1 therapy demonstrated that EXOSC5 expression profoundly affected both overall survival (OS) and progression-free survival (PFS) (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eF, G). The hazard ratios (HRs) for the high EXOSC5 expression group were markedly greater than those for the low EXOSC5 expression group for both OS (HR: 2.16) and PFS (HR: 1.84), with statistical significance indicated by P\u0026thinsp;\u0026lt;\u0026thinsp;0.05. These findings suggest that EXOSC5 expression levels could serve as a crucial biomarker for predicting the efficacy of anti-PD-1 therapy. Moreover, targeted therapeutic strategies against EXOSC5 may offer novel alternative treatments for patients with high EXOSC5 expression, potentially enhancing survival outcomes.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAdditionally, our research included a thorough exploration of the Genomics of Drug Sensitivity in Cancer (GDSC) database. By integrating the half-maximal inhibitory concentration (IC50) values for 265 small-molecule drugs with corresponding mRNA gene expression data from 860 cell lines, we aimed to identify potential antitumour drugs associated with EXOSC5 expression. A heatmap visually depicts the correlations between 26 significantly distinct drugs and EXOSC5 mRNA expression, utilizing a color gradient from blue (indicating low estimated drug sensitivity) to red (indicating high estimated drug sensitivity) (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eH). Complementary scatter plots further illustrated the correlation between the estimated drug sensitivity score (Z score) and EXOSC5 RNA expression (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eI).\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eLung adenocarcinoma (LUAD) remains a leading cause of cancer-related mortality, with a 5-year survival rate of less than 15%. Despite advances in treatment, the prognosis for LUAD patients remains poor, particularly for those with advanced disease\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. Our study identified EXOSC5 as a novel prognostic biomarker and therapeutic target in LUAD, with high EXOSC5 expression associated with poor overall survival (OS) and resistance to immunotherapy.\u003c/p\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eGenetic alterations of EXOSC5 in LUAD\u003c/h2\u003e \u003cp\u003eThe development and progression of LUAD are driven by a complex interplay of genetic alterations. Our study revealed that EXOSC5 expression is regulated by DNA methylation, with high EXOSC5 expression associated with reduced methylation levels at the transcription start site (TSS). These findings suggest that hypomethylation of the EXOSC5 promoter may contribute to its overexpression in LUAD, suggesting that increased methylation status may suppress gene expression, consequently influencing tumor advancement \u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. Additionally, we observed a negative correlation between EXOSC5 expression and CNV values, indicating that copy number alterations may also play a role in modulating EXOSC5 expression. Alterations in copy number can elicit changes in gene dosage, subsequently modulating downstream signalling pathways and the biological attributes of tumor cells \u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e. Furthermore, we identified several mutations in the EXOSC5 gene, particularly in the RNase_PH and RNase_PH_C domains, which may disrupt its structural and functional integrity, potentially enhancing its oncogenic activity.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eEXOSC5 as a Prognostic Biomarker\u003c/h2\u003e \u003cp\u003eTo date, many researchers still use lung cancer clinical phenotypes to predict patient prognosis, while the International Association for the Study of Lung Cancer (IASLC) does not recommend these clinical phenotypes for prognosis prediction\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e. Some scholars also believe that the TNM staging guidelines are not sufficient to predict OS at the individual level, as many early-stage patients may experience late recurrence\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. With the development of genomics, new research strategies have emerged. In renal clear cell carcinoma, researchers have established a m6A risk model through genomic studies, which has a notable correlation with patient prognosis\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e. Our findings demonstrate that EXOSC5 is significantly overexpressed in LUAD tissues and that its expression level correlates with poor prognosis, particularly in patients with advanced disease and lymph node metastasis. Compared with traditional TNM staging, the nomogram model we developed, which incorporates EXOSC5 expression and other risk factors, significantly improved the accuracy of survival prediction. These findings suggest that EXOSC5 could serve as a valuable biomarker for identifying high-risk LUAD patients who may benefit from more aggressive treatment strategies.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eMechanisms of EXOSC5 in LUAD Progression\u003c/h2\u003e \u003cp\u003eEXOSC5 promotes tumor progression by regulating the cell cycle, proliferation, and apoptosis. In vitro experiments revealed that EXOSC5 knockdown in A549 cells increased apoptosis and induced G1 phase arrest, whereas EXOSC5 overexpression in H1299 cells increased proliferation and reduced apoptosis. These findings are consistent with those of previous studies in colorectal cancer, where EXOSC5 was shown to activate the ERK and AKT pathways, leading to decreased expression of p21 and p27 and promoting the G1/S transition\u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e. Additionally, EXOSC5 may play a role in apoptotic DNA degradation\u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e, as it forms homodimers with CRN-5, which possesses both RNase and DNase activity\u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eEXOSC5 and the Immune Microenvironment\u003c/h2\u003e \u003cp\u003eOur study revealed that high EXOSC5 expression is associated with reduced immune cell infiltration in LUAD. Investigations by McNeel DG revealed that EXOSC5 has the potential to trigger specific humoral immune reactions in approximately one-third of melanoma and prostate cancer patients, and responsiveness to other overexpressed tumor antigens was detected in prostate cancer patients\u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e. Patients with low EXOSC5 expression had increased proportions of most immune cells, whereas high EXOSC5 expression was associated with increased Th2 cells. Yang reported that individuals with high-titre-specific antibodies to the EXOSC5 antigen elicited specific T-cell responses\u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e. Zhou's data indicated that DCs cotransfected with EXOSC5 can induce CTL, which is feasible and effective in eliciting specific antitumour cytotoxicity\u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e. EXOSC5 expression was negatively correlated with Tem and Tcm cells, suggesting that high EXOSC5 expression may inhibit the infiltration of these immune cells, leading to poor prognosis. Tems are activated Tcm cells that, upon restimulation by antigens, can continue to produce many clonal effector memory T cells carrying the same antigen. In 2005, Klebanoff CA first demonstrated that Tcm cells possess superior antitumour capabilities\u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e. Clinical studies by the National Institutes of Health (NIH) in 2012 revealed that Tcm cells and their clonal derivative T cells are highly effective antitumour immune T cells\u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e. Conversely, low EXOSC5 expression promotes immune cell infiltration, resulting in better outcomes.\u003c/p\u003e \u003cp\u003eFurthermore, we explored the relationships between EXOSC5 and immune-related genes, including chemokines, chemokine receptors, and immunosuppressive genes. EXOSC5 expression was strongly correlated with the expression of immunosuppressive genes such as ADORA2A, PDCD1LG2, and CSF1R, suggesting its role in modulating the immunosuppressive TME. Our study revealed significant correlations between EXOSC5 and key immunosuppressive genes, such as LAG3 and NECTIN2, which may provide insight into the paradoxical tumor growth and dissemination observed in some LUAD patients despite robust immune responses. Additionally, EXOSC5 was significantly correlated with chemokines (CCL13, CXCL5, and CXCL12, for example). and their receptors (CXCR1, CCR8, CCR2, etc.). ), which are known to orchestrate the recruitment of T central memory (Tcm) and T effector memory (Tem) cells to the tumor microenvironment\u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e. While direct links between MHC complex genes and Tcm/Tem cells have not been explicitly documented, the interaction between MHC molecules and T cells during antigen presentation is indispensable for the activation and proliferation of these T-cell subsets \u003csup\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e. The enrichment of Th2 cells in EXOSC5-high LUAD aligns with their pro-tumorigenic role. Th2 cytokines (e.g., IL-4, IL-13) polarize macrophages toward an M2 phenotype, suppress cytotoxic CD8\u0026thinsp;+\u0026thinsp;T-cell activity, and promote angiogenesis \u003csup\u003e[\u003cspan additionalcitationids=\"CR32 CR33\" citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/sup\u003e. EXOSC5 may stabilize Th2-associated transcripts (e.g., GATA3) or degrade Th1-polarizing factors (e.g., STAT1), though this requires experimental validation. Th2 polarization inhibits the Th1-driven anti-tumor response, leading to resistance to PD-1/PD-L1 \u003csup\u003e[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]\u003c/sup\u003e, which is consistent with our observation results (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eF-G). This mechanism is that Th2 polarization recruits myeloid-derived suppressor cells (MDSCs) and M2 macrophages through CCL2/CCL17 chemokines, inhibits the function of CD8\u0026thinsp;+\u0026thinsp;T cells and drives resistance to immune checkpoint inhibitors \u003csup\u003e[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/sup\u003e.Notably, ADORA2A\u0026mdash;a gene strongly correlated with EXOSC5\u0026mdash;activates IL-4/IL-13 secretion in T cells \u003csup\u003e[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]\u003c/sup\u003e, potentially explaining Th2 enrichment. The paradoxical suppression of Tem/Tcm cells further suggests a Th2-dominated, immunosuppressive TME in EXOSC5-high tumors.These findings suggest that EXOSC5 may play a crucial role in regulating the immune landscape of LUAD, potentially contributing to tumor immune evasion.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eEXOSC5 and immunotherapy resistance\u003c/h2\u003e \u003cp\u003eIn tumors with high TMB, elevated EXOSC5 expression suggests enhanced proliferative capacity and a complex immune microenvironment. High EXOSC5 expression was associated with resistance to anti-PD1 immunotherapy, as patients with lower EXOSC5 expression were more likely to respond to treatment. These findings suggest that EXOSC5 may serve as a predictive biomarker for immunotherapy response. Additionally, our analysis of the GDSC database revealed correlations between EXOSC5 expression and drug sensitivity, indicating that targeting EXOSC5 could increase the efficacy of existing therapies.\u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eOur study has several limitations. First, the NTU cohort\u0026rsquo;s sample size, though sufficient for initial validation, may underpower subgroup analyses of rare clinical or molecular subtypes. Second, in vitro experiments were limited to two cell lines (A549 and H1299), which may not fully recapitulate the heterogeneity of LUAD. Future studies should validate EXOSC5\u0026rsquo;s functional roles in additional models. Third, while our bioinformatics analysis identified correlations between EXOSC5 and immune infiltration, mechanistic studies (e.g., CRISPR-edited EXOSC5 cells co-cultured with T cells) are required to establish causality in immune suppression.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003eClinical Implications and Future Directions\u003c/h2\u003e \u003cp\u003eOur findings highlight the potential of EXOSC5 as a prognostic biomarker and therapeutic target in LUAD. However, further validation through multicenter clinical trials is needed to confirm the prognostic utility of EXOSC5 expression. Future studies should also explore the mechanisms underlying EXOSC5-mediated immune evasion and its potential as a target for combination therapies.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003eEthics approval and consent to participate: This study was officially recognized for it strictly carried out the procedures for care and use admitted by the Ethics Committee of the Affiliated Hospital of Nantong University (Ethic Number: 2018-K020). Written informed consents were obtained from all participants. We confirmed that experiments on humans and/or the use of human tissue samples were performed in accordance with relevant guidelines and regulations.\u003c/p\u003e\n\u003cp\u003eConsent for publication: Manuscript is approved by all authors for publication.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAvailability of data and materials: RNA sequencing and clinical data were obtained from The Cancer Genome Atlas database (https://portal.gdc.cancer.gov). Anti-PD1 treatment samples were obtained from the GEO database (GSE120644). IHC results and patient clinical information are available by contacting J.X.Xu ([email protected]).\u003c/p\u003e\n\u003cp\u003eCompeting interests: We declare that we have no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Funding: This study was funded by grants from the Surface Project of Nantong Municipal Health Commission (Mandatory Subject), Jiangsu, China (No. MS2023105).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAuthors\u0026apos; contributions: Guo and Yang conceived the idea and designed the experiments. Xu and Zhang performed all experiments and analyzed the data. Ke provided technical assistance and helpful comments. Xu and Zhang wrote the manuscript. All authors approved the final version.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBray, F., Ferlay, J., Soerjomataram, I., Siegel, RL., Torre, LA., Jemal, A, (2018). 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JCI Insight, 3 (17), https://doi.org/10.1172/jci.insight.91954\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcan","sideBox":"Learn more about [BMC Cancer](http://bmccancer.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcan/default.aspx","title":"BMC Cancer","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"EXOSC5, lung adenocarcinoma, prognostic biomarker, immune infiltration, anti-PD1 therapy, tumor microenvironment","lastPublishedDoi":"10.21203/rs.3.rs-5782899/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5782899/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cem\u003eBackground\u003c/em\u003e: Exosome complex gene 5 (EXOSC5), a member of the exosome complex family, is highly tumorigenic in various cancers. However, its prognostic value and underlying mechanisms in lung adenocarcinoma (LUAD) remain unclear.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eMethods\u003c/em\u003e: We analysed LUAD data from The Cancer Genome Atlas\u003cem\u003e \u003c/em\u003e(TCGA) via bioinformatics tools. Logistic regression was used to assess the associations between clinical information and EXOSC5 expression in LUAD patients. EXOSC5 expression was validated by immunohistochemistry (IHC) and western blotting, and its functional role was investigated through gene set enrichment analysis (GSEA) and in vitro experiments.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eResults\u003c/em\u003e: EXOSC5 was significantly upregulated in LUAD and associated with poor prognosis. The risk model based on EXOSC5 expression outperformed the traditional staging system for prognosis prediction. EXOSC5 promoted tumor progression by regulating the cell cycle, proliferation, and immune cell infiltration. High EXOSC5 expression was correlated with resistance to anti-PD1 immunotherapy.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eConclusion\u003c/em\u003e: EXOSC5 is a novel oncogenic factor in LUAD that promotes tumor progression and immune evasion and may serve as a prognostic biomarker and therapeutic target.\u003c/p\u003e","manuscriptTitle":"EXOSC5: A Novel Biomarker for Poor Prognosis in Lung Adenocarcinoma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-24 09:45:14","doi":"10.21203/rs.3.rs-5782899/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Accepted","date":"2025-04-01T11:24:24+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-03-30T06:59:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"153789761540882980397276522077434797930","date":"2025-03-30T06:46:38+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"84104317007514232094439687785000788122","date":"2025-03-29T15:19:15+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-03-20T15:14:36+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-03-20T02:15:19+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Cancer","date":"2025-03-19T18:02:07+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcan","sideBox":"Learn more about [BMC Cancer](http://bmccancer.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcan/default.aspx","title":"BMC Cancer","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3f47de8f-c6ac-41bf-816f-f9658c99728e","owner":[],"postedDate":"March 24th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-04-21T16:00:31+00:00","versionOfRecord":{"articleIdentity":"rs-5782899","link":"https://doi.org/10.1186/s12885-025-14059-2","journal":{"identity":"bmc-cancer","isVorOnly":false,"title":"BMC Cancer"},"publishedOn":"2025-04-14 15:57:28","publishedOnDateReadable":"April 14th, 2025"},"versionCreatedAt":"2025-03-24 09:45:14","video":"","vorDoi":"10.1186/s12885-025-14059-2","vorDoiUrl":"https://doi.org/10.1186/s12885-025-14059-2","workflowStages":[]},"version":"v1","identity":"rs-5782899","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5782899","identity":"rs-5782899","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}