Role of USP5 in immune infiltration in non--small-cell lung cancer and its prognostic value confirmed through bioinformatics analysis and experimental validation

Role of USP5 in immune infiltration in non--small-cell lung cancer and its prognostic value confirmed through bioinformatics analysis and experimental validation | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 19 May 2025 V1 Latest version Share on Role of USP5 in immune infiltration in non--small-cell lung cancer and its prognostic value confirmed through bioinformatics analysis and experimental validation Authors : Yonghong Xu , Yifei Xie , Jian Zhao 0000-0003-0098-0283 , Jiansheng Zhang , Jie Zhao , Yongliang Du , Jianjie Zhu , Yuanyuan Zeng , Jian-An Huang , and Zeyi Liu 0000-0003-2528-6909 [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.174765458.85031684/v1 198 views 145 downloads Contents Abstract Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Background: USP5, a deubiquitinating enzyme, is linked to various cancers. However, its relationship with immune infiltration and its prognostic significance in non–small-cell lung cancer (NSCLC) remains to be determined. Methods: USP5 expression patterns in NSCLC were analyzed using data sourced from the Gene Expression Omnibus and The Cancer Genome Atlas databases. Functional enrichment analyses were performed to predict the role of USP5 in NSCLC development, and Hub genes were identified through a protein–protein interaction (PPI) network. Immune cell infiltration was assessed via single-sample gene set enrichment analysis, while the prognostic significance of USP5 was evaluated using the Kaplan–Meier method and Cox regression analysis. To facilitate survival rate predictions at different time points, a prognostic model was developed. Previous findings were validated using real-time PCR and in vitro functional assays in NSCLC cell lines. Results: USP5 expression was found to be markedly elevated in NSCLC tissues when compared to normal tissues. Functional enrichment analysis revealed the involvement of USP5 in regulating key pathways linked to lung adenocarcinoma development. PPI network analysis revealed several potential interactions contributing to NSCLC progression. A correlation was observed between higher USP5 levels and the reduced presence of immune cells (e.g., macrophages, CD8+T cells, NK cells, and iDCs) within the tumor microenvironment. ROC curves confirmed the prognostic value of USP5 for NSCLC. Functional studies in NSCLC cell lines confirmed the molecular effects of USP5 on NSCLC development. Conclusions: USP5 appears to be a reliable marker for diagnosing NSCLC and predicting its prognosis. Further investigation into the role of USP5 in immune responses may aid in the development of immunotherapies for NSCLC. Introduction Non–small-cell lung cancer (NSCLC), which accounts for about 80% of all lung cancer cases, is among the leading causes of cancer-related deaths worldwide. Lung adenocarcinoma and squamous cell carcinoma are the primary subtypes of NSCLC. Genomic research has provided key insights into the molecular changes associated with NSCLC, and large quantities of related data have been deposited in The Cancer Genome Atlas (TCGA). Ubiquitin-specific proteases (USPs), a crucial subset of deubiquitinating enzymes (DUBs), play a key role in tumorigenesis by regulating protein functions [1] . Ubiquitination and deubiquitination, which are facilitated by ubiquitin ligases and DUBs, respectively, are essential post-translational modifications for protein homeostasis [2] and have been linked to numerous diseases [3-7] . USP5 is an important member of the DUB subfamily and has been implicated in tumor development. Notably, high levels of USP5 have been detected in various cancers, including those affecting the nervous system. The upregulation of USP5 is associated with a poor prognosis in several malignancies, such as glioblastoma, digestive system tumors (including hepatocellular carcinoma, pancreatic cancer, and colorectal cancer), and breast cancer. Studies have shown that USP5 affects the activity of various proteins in cancer cells. In NSCLC, it activates the USP5-Beclin1 axis, thereby reducing p53-dependent senescence [8] . Meanwhile, the USP5-FoxM1 pathway enhances Wnt signaling and tumor cell growth by promoting β-catenin activity and suppressing ICAT, serving as a novel mechanism for the regulation of canonical Wnt signaling and cell proliferation [9,10] . However, the relationship of USP5 with immune infiltration in NSCLC and its prognostic value in this type of cancer have not been explored so far, despite its potential as a therapeutic target. Based on previous studies, we hypothesized that the immune effects of USP5 are linked to lymph node metastasis in NSCLC and influence patient prognosis. To test this hypothesis, clinical data from TCGA were analyzed in this study, and functional enrichment and prognostic analyses were performed. Finally, the results were validated through real-time PCR and in vitro functional assays in NSCLC cell lines. Materials and methods Data collection This study analyzed mRNA expression and clinical data on NSCLC patients from the Genotype-Tissue Expression (GTEx) and TCGA databases. RNA-seq data from both unpaired and paired NSCLC samples were collected from TCGA and processed. Data normalization, standardization, and visualization were conducted using the ’Limma’ package and other R packages (version 3.6.3). Additionally, multi-omics analysis using data from the Human Protein Atlas [11] (version 24.0) was performed to examine USP5 protein expression in NSCLC. Tissue samples To analyze USP5 protein levels in 48 paired samples of lung adenocarcinoma and adjacent normal tissues, tissue microarrays (ZL-LugA961) were obtained from Weiao Biotechnology Co., Ltd (Shanghai, China).The research received ethical approval under the designation ZLL-15-01 from the Shanghai Zhuoli Biotech Company. The sequences of the primers are provided in the key resources table. Cell culture The human NSCLC cell lines A549, H838, H292, HCC827, PC-9, H358, NCI-H1299, as well as HEK 293T and HBE cells, were used for this study. All cell lines were obtained from the Chinese Academy of Sciences. The cells were cultured at 37°C under 5% CO 2 in either Ham’s F-12K medium or Dulbecco’s modified eagle medium (DMEM), both enriched with 10% fetal bovine serum (FBS) and 1% antibiotics. Lentiviral constructs were utilized to stably overexpress wild-type USP5 in lung cells. Real-time PCR RNA extraction was performed using the TRIzol reagent (TaKaRa) and subsequently converted to cDNA using the PrimeScript RT Kit. Quantitative PCR was carried out using the SYBR green qRT-PCR System (Thermo Fisher), with GAPDH and ACTIN serving as reference genes. The USP 5 primer sequence is as follows:USP5-F：GCAGAAGACAGACAAGACGATGAC,USP5-R:GGCACACCTGACTCCTGGATC,Data analysis was performed based on the 2ΔΔCt method. Wound healing assay A549 and H1299 stable cell lines were maintained in 6-well plates until they reached a monolayer at approximately 70-80% confluence. A new 10-µl pipette tip was employed to scratch across the center of each well, creating a gap that matched the tip’s width.A second scratch was made at a right angle to form a cross pattern. PBS was used to wash the wells twice,Ensuring any detached cells were removed. The medium was refreshed, and the cells underwent a 24-hour incubation.Cell migration was observed and documented using a standardized CKX41 microscope (Olympus). Cell migration and invasion assays The assaysemployed 24-well plates with Transwell inserts featuring 8-µm pores, sourced from Corning, USA. For the migration assay, stable cells were placed in a serum-free medium and incubated in the upper chamber of the Transwell insert at 37 °C for 24 hours. Moreover, 800 µL of complete medium with 10% serum was added to the lower chamber. The cells that traveled to the insert’s underside were subsequently fixed in 100% methanol for 30 minutes.Following this, the cells were air-dried for 10 minutes and then stained with 0.1% crystal violet for 30 minutes.In terms of the invasion assay,Matrigel (BD Science, USA) was diluted in a medium lacking serum and used to precoat the inserts, then incubated for 2 hours at 37 °C. The other procedures conformed to the migration assay protocol. Cellular images were acquired using an Olympus CKX41microscope, with at least three fields counted at a magnification of ×100. Each assay was done in triplicate. Western blot analysis The Western blot was executed according to standard procedures. Primary antibodies were used :anti-E-cadherin(#610181),anti-N-cadherin(#610920)fromBD (Becton,Dickinson and Company USA);anti-USP5(10473-1-AP)),anti-GAPDH (10494-1-AP) from ProteinTech (Wuhan,China); Secondary antibodies used were anti-rabbit (CW0103S) and anti-mouse(CW0102S) from CWBIO (China). Differential expression analysis of USP5 Patients were divided into high and low expression groups based on the median expression of the mRNA expression of USP5. Using the DESeq2R package, differential gene expression was analyzed between these two groups [12] . Differentially expressed genes (DEGs) were identified using the following criteria: adjusted |log2-fold change (FC)|>1 and p-value < 0.05. The correlation between the expression of USP5 and that of the top 10 DEGs was examined using Spearman’s correlation analysis, a statistical method used to assess the strength and direction of the relationship between two variables. Functional enrichment analysis DEGs associated with USP5 were chosen for functional enrichment analysis using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. GO terms and KEGG pathways were employed to identify the potential biological functions of USP5. The criteria for enrichment were an FDR < 0.25 and an adjusted p-value < 0.05. Identification of protein–protein interaction (PPI) networks The STRING database was used to build a PPI network and predict the functional relationships and interactions of proteins [13] . The network model, featuring genes with a score of ≥0.4, was visualized using Cytoscape (version 3.9.1). The cytoHubba plugin [14] was employed to identify hub genes within the PPI network. Relationship between USP5 and immune infiltration Immune infiltration was evaluated by analyzing 24 distinct subtypes of immune cells, and their relative enrichment was quantified based on USP5 expression using single-sample gene set enrichment analysis (GSEA) via the R package GSVA [15] . Spearman’s correlation analyses were employed to examine the association between USP5 expression levels and the abundance of 24 distinct immune cell types. Additionally, the Wilcoxon rank-sum test was performed to assess variations in immune infiltration among groups with varying USP5 expression levels. Validation of the nomogram Overall survival probability was estimated using a multivariate Cox proportional hazards model. Additionally, a column chart was used to visually depict the correlation between independent prognostic factors and the likelihood of survival. The accuracy of the chart was validated using a calibration plot, and its predictive accuracy was evaluated based on contrasting observed and anticipated survival probabilities using a concordance index to measure discrepancies. The software facilitated the generation of time-dependent receiver operating characteristic (ROC) curves, providing a thorough analysis of predictive value over time. These curves enabled the evaluation of the model’s accuracy in predicting survival probabilities across different disease stages, thus demonstrating its reliability and applicability in clinical settings. Statistical analysis Data analysis was performed using SPSS version 22.0. Differential expression between NSCLC and normal tissues was assessed using the Wilcoxon signed-rank test, and group comparisons were performed with a one-way ANOVA. p-values < 0.05 indicated statistical significance. Results USP5 is upregulated in NSCLC and is a predictor of NSCLC Our pan-cancer analysis demonstrated that most tumors show upregulated USP5 expression. Analysis based on the TIMER database (Figure 1A) indicated that USP5 expression was upregulated in many cancer types, including bladder urothelial carcinoma (BLCA), breast invasive carcinoma (BRCA), cholangiocarcinoma (CHOL), colon adenocarcinoma (COAD), esophageal carcinoma (ESCA), head and neck squamous cell carcinoma (HNSC), kidney renal papillary cell carcinoma (KIRP), liver hepatocellular carcinoma (LIHC), lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC), and stomach adenocarcinoma (STAD) when compared to normal tissues (Figure 1B). Additionally, USP5 levels were notably higher in NSCLC tissues compared to normal tissues (Figure 1C). In 108 NSCLC samples, USP5 expression was significantly elevated in cancerous tissues versus adjacent tissues (Figure 1D). ROC curve analysis revealed that USP5 gene expression is a highly accurate predictor of NSCLC,with an area under the curve (AUC) of 0.805. This demonstrated its strong discriminatory power in distinguishing between NSCLC and healthy tissues (Figure 1E). According to univariate Cox regression analysis, pathological T stage, N stage, M stage, and USP5 were identified as risk factors in LUAD patients (table1), and multivariate Cox regression analysis also recognized USP5 as an independent prognostic factor (table2).Our findings suggest that USP5 could serve as a dependable biomarker for the diagnosis of NSCLC, assisting in its early detection and successful treatment. Elevated levels of USP5 in LUAD tissues and its role in metastasis. Further, we performed a detailed analysis to assess USP5 levels in NSCLC tissues and determine their significance using various experimental methods. We compared USP5 expressions in several NSCLC cell lines and normal lung cells. RT-qPCR analysis demonstrated a significant upregulation of USP5 in lung adenocarcinoma cell lines when compared with normal lung epithelial cells (Figure 2A). In our expanded analysis, we included NSCLC tissues and adjacent normal tissues from patients (sourced from the Weiao Biotechnology Co., Ltd) for immunohistochemistry (IHC) analysis. IHC data also revealed upregulated USP5 expression in NSCLC (Figure 2A-2B). Taken together, qRT-PCR revealed significantly elevated USP5 expression in NSCLC cells versus normal lung epithelial cells. To study the effect of USP5 on the migration and invasion abilities of non-small cell lung cancer (NSCLC) cells,To transfect A549 and H1299 cell lines with two unique siRNAs targeting USP5 and to stably overexpress USP5, lentiviral vectors were utilized.Using qRT-PCR and western blot analysis, the knockdown or overexpression of USP5 was validated at the mRNA and protein levels, respectively (Figure2C-2D).Further wound healing and Transwell experiments confirmed these findings,revealing that the reduction of USP5 expression led to decreased cell motility and invasiveness (Figure 2E-2H). whereas overexpression of USP5 led to increased migration and invasion (Figure 2C).Overall, these results suggest that USP5 enhances the migration and invasion of NSCLC cells. Association of USP5 expression with clinicopathologic variables The link between USP5 and pathological tumor characteristics was examined. Accordingly, increased USP5 expression was identified at advanced stages of NSCLC (Figure 3A). Elevated USP5 expression was significantly linked to higher T stages (Figure 3B) and lymph node metastasis (N1, N2, N3, stages compared to N0) (Figure 3C) in NSCLC. Additionally, USP5 expression was higher in cases of progressive disease (PD) than in cases of complete response (CR), partial response (PR), and stable disease (SD) (Figure 3D). Furthermore, elevated USP5 expression levels were correlated with overall survival (OS) and disease-specific survival (DSS) (Figure 3E, 3F). However, USP5 expression was not significantly correlated with PFI (Progression-Free Interval), smoking habits, or M stage (Figure 3G, 3H, 3I). Prognostic implications of USP5 expression in NSCLC The relationship between USP5 levels and NSCLC prognosis was assessed using the Kaplan–Meier method. Patients were split into high and low USP5 expression groups. Higher USP5 expression was correlated with poorer OS and shorter DSS (Figures 4A, 4B). In NSCLC patients, pathological stage, smoking status, and gender were all identified as independent factors influencing OS (Figures 4C, 4D, 4E). Patients exhibiting high USP5 expression experienced poorer outcomes across various tumor size subgroups (specifically T1 and T2, T3, and T4) (Figure 4F), conditions of lymph node metastasis (N0, N1 and N2, N3) (Figure 4G), and pathologic M stage (Figure 4H). Cox regression analysis was used to further explore prognostic indicators in NSCLC patients. (Table 1). Functional enrichment of DEGs associated with USP5 In total, 651 coding genes exhibited differential expression between groups with varying USP5 expression levels. Compared with the USP5 low-expression group, the USP5 high-expression group showed 431 (63.3%) downregulated genes and 220 (36.7%) upregulated genes (Figure 5A). We focused on the top 10 DEGs, examining their relationship with USP5 and exploring their interconnections. All DEGs were subjected to GO and KEGG enrichment analysis (Figure 5B). In GO analysis, the DEGs were primarily enriched in protein deubiquitination under the Biological Process (BP) module; nucleosome assembly, ribonucleoprotein complex assembly, and chromatin assembly under the Cellular Component (CC) module; and heterodimerization of proteins, association with nucleosomal DNA, and nucleosome binding under the Molecular Function (MF) module. KEGG pathway analysis revealed that the DEGs were most significantly enriched for Systemic lupus erythematosus, Alcoholism, and Viral carcinogenesis (Figure 5C-E). Additionally, GSEA revealed a critical enrichment of the ATR pathway, which is essential for maintaining host genomic integrity, in the group with high USP5 levels (Figure 5F). Notably, the dysregulation of the ATR pathway and mutations in ATR pathway-related genes are often detected in human cancers. Development and examination of the PPI network A PPI network was constructed using the STRING database and Cytoscape software to elucidate the interactions among the identified DEGs and highlight complex gene relationships (Figure6A). Cytoscape, together with the MCODE plug-in, was employed to detect the most important module. Functional enrichment analysis was conducted utilizing the Database for Annotation, Visualization, and Integrated Discovery (DAVID), revealing the significance of the 35 common DEGs (Figure6B). Among these, the top ten hub genes — USP5, UBA52, RPS27A, RAD23A, PSMD14, UCHL3, USP14, UBB, UBC, and ZUP1 — were identified based on their degree scores using the CytoHubba plug-in (Figure6C). This demonstrated the significance of these 10 hub genes in regulating biological processes and pathways, suggesting their potential as key targets for NSCLC treatment. Relationship between USP5 and immune infiltration A notable inverse relationship was observed between USP5 levels and the infiltration of immune cells, particularly iDCs, Mast cells, CD8+ T cells, TFH cells, and Th17 cells, in the tumor microenvironment. This indicated that higher USP5 levels are associated with notably lower enrichment scores for these immune cells (Figure7A). Further details are provided in Figures7B-7I. Additionally, a notable positive correlation was found between USP5 and FGR expression in NSCLC ( p <0.05) (Figure7J). The distribution of immune cell scores is presented through a stacked bar chart (Figure7K). The horizontal axis represents the samples with different USP5 expression, while the vertical axis indicates the percentage (by default) or the immune infiltration score for each cell in a single sample.The subplots in the figure visually compare the score distributions of highly grouped samples ( p <0.05)(Figure7K). Development and verification of a nomogram containing independent variables A detailed analysis of independent factors enabled the development of a comprehensive nomogram for predicting patient prognosis in NSCLC.In NSCLC patients, a higher number of total chart points correlated with a poorer prognosis due to an increase in adverse factors (Figure8A). Thus, a higher total score was linked to a worse prognosis.To determine the predictive accuracy and reliability of the nomogram, calibration curves were generated (Figures8B-8D). According to this analysis, USP5—a gene related to NSCLC susceptibility—appeared to be an essential independent prognostic component impacting patient outcomes in NSCLC. Discussion This research highlights the significance of identifying novel biomarkers to enhance prognostication and personalized treatment in cases of NSCLC. In this study, using TCGA data, we observed the significant upregulation of USP5 mRNA in several types of cancers [16-19] . Elevated USP5 protein levels were detected in LUSC, LUAD, breast cancer, and KIRP. Survival analysis revealed that NSCLC patients with higher USP5 expression experienced poorer outcomes. Further, our experiments demonstrated a significant difference in USP5 expression between NSCLC cells and normal lung cells. Finally, ROC curve analysis demonstrated that USP5 gene expression can be used to effectively detect NSCLC, with an AUC of 0.805, indicating its strong ability to differentiate between NSCLC patients and healthy individuals . USP5 has been identified as a potential oncogene and is highly expressed in various tumors, including gastrointestinal stromal tumors, bladder cancer, hepatocellular carcinoma, cholangiocarcinoma, and osteosarcoma. Thus, we speculated that USP5 expression may also serve as a novel biomarker for NSCLC diagnosis. Our study found a correlation between elevated USP5 levels in NSCLC and pathological indicators such as T stage, lymph node metastasis, therapy outcomes, and DSS. Moreover, high USP5 levels were found to be an independent prognostic factor for poor OS. Previous research indicates that inhibiting USP5 or ERK can destabilize PD-1 and activate CD8+Tcells, preventing immune evasion and tumor progression [20] . NSCLC patients with low USP5 expression exhibited a worse prognosis than those with high USP5 expression. CD8+Tcells, iDCs, Th2cells, and macrophages are key immune cells in the tumor microenvironment and play important roles in the anti-tumor immune response [21] . Our study found an inverse relationship between USP5 gene expression in NSCLC and the infiltration of macrophages, CD8+Tcells, T cells, and iDCs . The type, density, and location of immune cells in the tumor microenvironment can influence patient outcomes. Furthermore, infiltrating immune cells act as individual predictors for responses to targeted PD-1/PD-L1 therapies and neoadjuvant chemotherapy [22] . Enhancing CD8+Tcells infiltration has been shown to improve NSCLC prognosis. This is consistent with our findings, suggesting that elevated USP5 levels may influence NSCLC outcomes by modifying immune cell infiltration. Elevated USP5 expression can contribute to poor gastric cancer outcomes by reducing p53 levels and activity, thereby impairing the transcription of p53 target genes and affecting cancer cell regulation. Inhibiting USP5 reduces the migration of chemo-resistant gastric cancer cells by suppressing epithelial–mesenchymal transitions and RhoA activity. Inhibiting USP5 thus shows promise as a therapeutic strategy against chemoresistance in gastric cancer, indicating the potential of USP5 inhibitors in future clinical treatments [23,24] . Collectively, this study provides new insights into the correlation between USP5 levels and prognostic outcomes in NSCLC patients. However, it has several limitations. First, the study relied on a single dataset, which may limit patient diversity and introduce selection bias. Second, the majority of the data were sourced from online databases, preventing the assessment of patients’ chemotherapy regimens, which could influence their prognosis and USP5 expression. Further comprehensive experiments are required to confirm the findings of this study. Conclusion This study reveals a significant association between USP5 expression and invasive clinical characteristics in NSCLC, such as primary therapy outcomes and lymph node involvement. The findings indicate that USP5 may negatively influence immune cell infiltration, potentially impairing the body’s immune response against tumors. We propose that USP5 may act as a novel prognostic biomarker for NSCLC, although its precise role in cancer initiation and metastasis remains to be elucidated. Further research is required to better understand the biological pathways involved in USP5’s contribution to NSCLC. Declarations Data availability statement The corresponding author can offer the data backing this study’s findings if asked. All authors have reviewed and approved the manuscript’s published version. Disclosure statement The authors declared no conflicts of interest. Funding This work was supported by the Postgraduate Research & Practice Innovation Program of Jiangsu Province (5832011923). Data availability statement The study’s data is available for download from the GEO database (https://www.ncbi.nlm.nih. gov/geo/),reference number 19, 20. References 1. 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Peng, Z.M., Han, X.J., Wang, T., Li, J.J., Yang, C.X., Tou, F.F., and Zhang, Z. (2024). PFKP deubiquitination and stabilization by USP5 activate aerobic glycolysis to promote triple-negative breast cancer progression. Breast Cancer Res 26 , 10.10.1186/s13058-024-01767-z. Figure Legends Figure1 Figure1: The expression level of USP5 in tumors.(A)expression across various cancers analyzed using TCGA and GTEx data. (B) USP5 expression analyzed with TIMER. (C) Wilcoxon rank sum test for assessing differences in USP5 expression between normal lung tissues (108 cases) and adjacent NSCLC tissues (108 cases). (D) Wilcoxon signed-rank test for comparing USP5 expression in these tissues (108 cases). (E) ROC curve area showing the value of USP5 as a diagnostic biomarker for NSCLC (AUC = 0.805). (p-values were determined using a two-tailed unpaired Student’s t-test, with significance levels represented as * for p < 0.05, ** for p < 0.01, and *** for p <0.001). Table1: Forest plot illustrating overall survival (OS) in NSCLC patients with varying USP5 levels, derived from multivariate Cox regression analysis. Table 2. Cox regression analysis of USP5 in NSCLC. Characteristics Total(N) Univariate analysis Multivariate analysis Hazard ratio (95% CI) P value Hazard ratio (95% CI) P value USP5 515 Low 257 Reference Reference High 258 1.364 (1.030 - 1.806) 0.031 1.364 (1.030 - 1.806) 0.031 Figure 2 Figure2: (A) Images from immunohistochemistry depict USP5 expression in both normal and LUAD tissues (n=48) (B) In NSCLC cell lines and the normal bronchial epithelial cell 16HBE, USP5 expression was evaluated using qRT-PCR and Western blot.(C-F) The validation of USP5 knockdown and overexpression in A549 and H1299 cells was performed using qRT-PCR and Western blot at mRNA and protein levels using GAPDH as the internal control. (G-K) Wound healing and transwell assays demonstrate the mobility and invasion of NSCLC cells when USP5 is knocked down or overexpressed, with the bottom panel showing statistical data on the number of cells that migrated and invaded. All results are displayed as mean ± SD from three distinct experiments. Figure3 Figure3: Correlation between USP5 expression levels and clinical parameters of NSCLC (108 cases). (A) Pathological stage. (B) T stage. (C) N stage. (D) Primary treatment outcomes. (E-H). Correlation between USP5 expression (E) OS, (F) DSS, (G) PFI (Progression-Free Interval), (G) smoking status, and (H) M stage. (* p < 0.05, ** p < 0.01, and *** p < 0.001). Figure4 Figure4: Kaplan-Meier analysis evaluating the prognostic indicators of NSCLC (108 cases) in relation to USP5 expression levels. Comparison between high and low USP5 expression among NSCLC patients was conducted across several parameters: (A) overall survival, (B) disease-specific survival, (C) pathologic stage, (D) smoking status, (E) gender, (F) T stage, (G) pathologic N stage, and (H) pathologic M stage. Figure5 Figure5: Examination of DEGs related to USP5 using functional enrichment analysis based on GO terms, KEGG pathways, and GSEA.(A) Volcano plot highlighting DEGs, with blue dots indicating down-regulated genes (325 genes) and yellow dots indicating up-regulated genes (212 genes).(B)Top ten DEGs showing a positive correlation with USP5 levels.(C-F) GO analyses and GSEA of these DEGs. Figure6 Figure6: Interaction network and analysis of key genes.(A)Most notable module in the PPI network, which included 21 nodes and 230 edges.(B)Hub genes identified using Cytoscape, with node colors representing the functional annotation of ontologies.(C)PPI network constructed using DEGs, where blue points represent upregulated genes with a fold change greater than 1.0 and a corrected p-value below 0.05, while red points denote downregulated genes. Figure7 Figure7: The connection between USP5 levels and immune cell infiltration in NSCLC.(A) Association between USP5 expression and the presence of 20 diverse immune cell types in the TME. (B–I) Analysis of the infiltration of immune cells, including iDCs, Mast cells, CD8+Tcells, TFH cells, and Th17 cells between groups with high and low USP5 expression. (J) Heat map showing the correlation between different variables. (K) Stacked bar graphs showing the distribution of immune cell scores (p-values were calculated with a two-tailed unpaired Student’s t-test, * p < 0.05, ** p < 0.01). Figure8 Figure8: Nomogram and calibration curves for predicting overall survival rates in patients with lung cancer.(A)Nomogram chart visually representing the OS rates in LUAD patients after 1, 3, and 5 years.(B–D)Calibration curves for the prediction of survival rates in cancer patients at specific intervals. Information & Authors Information Version history V1 Version 1 19 May 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Authors Affiliations Yonghong Xu First Affiliated Hospital of Soochow University View all articles by this author Yifei Xie First Affiliated Hospital of Soochow University View all articles by this author Jian Zhao 0000-0003-0098-0283 First Affiliated Hospital of Soochow University View all articles by this author Jiansheng Zhang First Affiliated Hospital of Soochow University View all articles by this author Jie Zhao The Second Affiliated Hospital of Xuzhou Medical University View all articles by this author Yongliang Du The Second Affiliated Hospital of Xuzhou Medical University View all articles by this author Jianjie Zhu First Affiliated Hospital of Soochow University View all articles by this author Yuanyuan Zeng First Affiliated Hospital of Soochow University View all articles by this author Jian-An Huang First Affiliated Hospital of Soochow University View all articles by this author Zeyi Liu 0000-0003-2528-6909 [email protected] First Affiliated Hospital of Soochow University View all articles by this author Metrics & Citations Metrics Article Usage 198 views 145 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Yonghong Xu, Yifei Xie, Jian Zhao, et al. Role of USP5 in immune infiltration in non--small-cell lung cancer and its prognostic value confirmed through bioinformatics analysis and experimental validation. Authorea . 19 May 2025. DOI: https://doi.org/10.22541/au.174765458.85031684/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . 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