Integrative and Comprehensive Pan-cancer Analysis of Ubiquitin Specific Peptidase 11 (USP11) As a Prognostic and Immunological Biomarker

Integrative and Comprehensive Pan-cancer Analysis of Ubiquitin Specific Peptidase 11 (USP11) As a Prognostic and Immunological Biomarker | 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 Integrative and Comprehensive Pan-cancer Analysis of Ubiquitin Specific Peptidase 11 (USP11) As a Prognostic and Immunological Biomarker Lijuan Cui, Ling Yang, Boan Lai, Lingzhi Luo, Haoyue Deng, Zhongyi Chen, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3828450/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 30 Jun, 2024 Read the published version in Heliyon → Version 1 posted You are reading this latest preprint version Abstract Purpose The role of USP11 as a crucial regulator in cancer has gained significant attention due to its deubiquitinating enzyme catalytic activity. However, a comprehensive evaluation of USP11 in pan-cancer studies is currently lacking. Methods Our analysis incorporates data from multiple sources, including five immunotherapy cohorts, thirty-three cohorts from The Cancer Genome Atlas (TCGA), and sixteen cohorts from the Gene Expression Omnibus (GEO), two of which were transcriptomic at the single-cell level. Results Our findings show that the aberrant expression of USP11 was found to be predictive of survival outcomes in various cancer types. And the highest frequency of genomic alterations occurred in uterine corpus endometrial carcinoma (UCEC), and single-cell transcriptome analysis of UCEC further revealed a significantly higher expression of USP11 in plasmacytoid dendritic cells and mast cells. Notably, the expression of USP11 was related to the infiltration levels of CD8+ T cells and natural killing (NK) activated cells. Furthermore, in the skin cutaneous melanoma (SKCM) phs000452 cohort, patients who had higher levels of USP11 mRNA during immunotherapy experienced a significantly shorter median progression-free survival. Conclusion Based on our findings, USP11 emerges as a promising molecular biomarker with potential implications for predicting patient prognosis and immunoreaction in pan-cancer. USP11 prognosis immunotherapy pan-cancer immune cell Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction Ubiquitin-specific peptidase 11 ( USP11 ), a member of the largest subfamily of cysteine protease deubiquitinating enzymes, plays a crucial role in the regulation of various biological processes, including cell cycle control, DNA repair mechanisms, and tumor development. It is located in a gene cluster on chromosome Xp11 and consists of 23,963 amino acids, with an approximate molecular weight of 109,817 Da(Ideguchi et al., 2002 ). Similar to its counterparts USP4 and USP15 , USP11 possesses two ubiquitin-like (UBL) domains and a N-terminal domain specific to ubiquitin-specific proteases(X. Zhu, Ménard, & Sulea, 2007 ). Notably, the N-terminal domain harbors a critical cysteine residue at position 318, which is directly involved in the enzymatic activity of USP11 . Any mutation or deletion affecting this residue can result in the loss of deubiquitinating function(Chiang et al., 2021 ; Harper et al., 2014 ). Distinctively, the UBL domain of USP11 displays a tandem arrangement, featuring a shortened β-hairpin at the interface of the two domains and exhibiting unique surface characteristics(Harper et al., 2014 ). USP11 predominantly resides in the nucleus of cells during their non-dividing state and exhibits a widespread distribution throughout cells undergoing mitosis(Ideguchi et al., 2002 ). USP11 exhibits binding affinity towards several substrates, thereby facilitating their stabilization and deubiquitination. One such interaction involves RAN binding protein 9 ( RANBP9 ), wherein USP11 acts to correct microtubule nucleation by promoting the deubiquitination and subsequent stabilization of RANBP9 (Ideguchi et al., 2002 ). Additionally, recent investigations have unveiled the role of USP11 in regulating the function of antigen-presenting cells in conjunction with v-rel reticuloendotheliosis viral oncogene homolog b ( RELB )(Bouwmeester et al., 2004 ). Moreover, USP11 exerts a significant influence on various biological processes including inflammation, immunity, cell proliferation, and apoptosis. Notably, its impact is evident in the modulation of TNFα-mediated NF-κB activation through the stabilization of IκB kinase α ( IKKα ), thereby exerting regulatory control over this signaling pathway(Schmukle & Walczak, 2012 ). Furthermore, USP11 enhances transforming growth factor β 1 ( TGFβ1 ) signaling by deubiquitinating and stabilizing TβRII , thereby contributing to the regulation of cellular responses mediated by TGFβ1 (Jacko et al., 2016 ). Furthermore, USP11 has been implicated in the regulation of the Hippo pathway through its modulation of the VGLL4 / YAP - TEAD s regulatory loop, suggesting its involvement in the regulation of cell growth and organ size control(E. Zhang et al., 2016 ). Collectively, the body of evidence from these studies presents compelling support for the notion that USP11 exerts its biological functions through its interactions with multiple regulators, including RANBP9 , RELB , IKK α, TGFβ , and components of the Hippo pathway, among others. In addition to the aforementioned activities, USP11 is prominently involved in the regulation of DNA repair processes, which is of particular interest in physiological contexts. Recent investigations have unveiled a previously unidentified binding site within the non-catalytic UBL region of USP11 . The crystal structure analysis of the USP11 peptide complex has provided evidence that this binding site within USP11 interacts with a helical motif and exerts regulatory control over its function in DNA repair processes(Spiliotopoulos et al., 2019 ). USP11 has been recognized as a crucial regulator of the repair of double-strand breaks (DSBs), a critical DNA damage event. Its interaction with BRCA2 has been identified as a key mechanism by which USP11 regulates DSB repair(Schoenfeld, Apgar, Dolios, Wang, & Aaronson, 2004). Additionally, USP11 plays a role in the recruitment of specific DSB repair proteins, including TP53BP1 and RAD51, to the sites of DNA damage for efficient repair(Wiltshire et al., 2010 ). Furthermore, another study has presented compelling evidence demonstrating the essential role of USP11 in facilitating the efficient repair of DNA damage by the homologous recombination proteins BRCA1 and BRCA2(Wiltshire et al., 2010 ). In this study, we utilized both the TCGA project and GEO databases to conduct a pan-cancer analysis of USP11 . Our analysis included an assessment of differential expression, correlations between USP11 expression levels and patient survival, identification of linked microRNAs, assessment of genetic alterations, and investigation of potential drugs and immune infiltration. Additionally, we evaluated the potential application of USP11 as a biomarker for immunotherapy using five real-world immunotherapy cohorts and two single cell datasets. To our knowledge, this is the first comprehensive analysis of the molecular mechanisms of USP11 utilizing multi-omics data, as well as the first investigation of the association of USP11 with immune response in various types of cancer. Materials and methods Data acquisition The thirty-three cancers of interest in this study, with their full names and abbreviations, are presented in Table 1 . Transcriptomic (mRNA and microRNA), genomic and clinical data of thirty-three cancer types involving 10,251 patients from TCGA was downloaded from https://xenabrowser.net/datapages/ . Microarray datasets of GSE13507, GSE41258, GSE90604, GSE31056, GSE36895, GSE11151, GSE101728, GSE10072, GSE30219, GSE71729, GSE87211, GSE15605, GSE46517 and GSE63678, with the information of adjacent normal tissue, were downloaded from GEO ( https://www.ncbi.nlm.nih.gov/geo/)(Gulluoglu et al., 2018 ; Kabbarah et al., 2010 ; Kim et al., 2010 ; Landi et al., 2008 ; Moffitt et al., 2015 ; Pappa et al., 2015 ; Peña-Llopis et al., 2012 ; Raskin et al., 2013 ; Reis et al., 2011 ; Rousseaux et al., 2013 ; Sheffer et al., 2009 ; Yusenko et al., 2009 ; H. R. Zhu et al., 2019 ). The results of the analysis of two single-cell datasets of primary uterine corpus endometrial carcinoma, GSE154763 (n = 9) and GSE139555 (n = 3), were obtained through a online website called Tumor Immune Single-cell Hub 2 (TISCH2, http://tisch.comp-genomics.org/home/ ) to study the distribution of the USP11 gene in cell subpopulations(Camps et al., 2023 ; Cheng et al., 2021 ; Wu et al., 2020 ). Data from two immunotherapy cohorts (bladder urothelial carcinoma, BLCA, GSE176307; SKCM, GSE100797) were downloaded from GEO(Lauss et al., 2017 ; Rose et al., 2021 ). Information on two immunotherapy cohorts of KIRC patients (kidney renal clear cell carcinoma, PMID32472114 and PMID32895571) was obtained from the literature supplement and the cohorts were named with the PubMed number of the literatures(Braun et al., 2020 ; Motzer et al., 2020 ). The phs000452 cohort of SKCM was obtained from the Melanoma Genome Sequencing Project(Łuksza et al., 2017 ). Table 1 List of cancer types. Study Abbreviation Study Name ACC Adrenocortical carcinoma BLCA Bladder Urothelial Carcinoma BRCA Breast invasive carcinoma CESC Cervical squamous cell carcinoma and endocervical adenocarcinoma CHOL Cholangiocarcinoma COAD Colon adenocarcinoma DLBC Lymphoid Neoplasm Diffuse Large B-cell Lymphoma ESCA Esophageal carcinoma GBM Glioblastoma multiforme HNSC Head and Neck squamous cell carcinoma KICH Kidney Chromophobe KIRC Kidney renal clear cell carcinoma KIRP Kidney renal papillary cell carcinoma LAML Acute Myeloid Leukemia LGG Brain Lower Grade Glioma LIHC Liver hepatocellular carcinoma LUAD Lung adenocarcinoma LUSC Lung squamous cell carcinoma MESO Mesothelioma OV Ovarian serous cystadenocarcinoma PAAD Pancreatic adenocarcinoma PCPG Pheochromocytoma and Paraganglioma PRAD Prostate adenocarcinoma READ Rectum adenocarcinoma SARC Sarcoma SKCM Skin Cutaneous Melanoma STAD Stomach adenocarcinoma TGCT Testicular Germ Cell Tumors THCA Thyroid carcinoma THYM Thymoma UCEC Uterine Corpus Endometrial Carcinoma UCS Uterine Carcinosarcoma UVM Uveal Melanoma Survival analysis In each cancer patients were equally divided into three groups according to the mRNA expression level of USP11 , i.e. USP11 -High, USP11 -Midlle, USP11 -Low. Cox survival correlation analysis between USP11 -High and USP11 -Low patient groups was completed in the R package survival, and the results were forest plotted using the R package forestplot to demonstrate(Gordon, Lumley, & Gordon, 2019 ; Therneau & Lumley, 2015 ). Kaplan-Meier curves were carried out to compare the survival time differences by R package survminer(Kassambara, Kosinski, Biecek, & Fabian, 2017). USP11-linked miRNA miRNA prediction for USP11 was performed in six miRNA-mRNA link databases by using the R package multiMiR, including ElMMo, MicroCosm, miRanda, DIANA-microT, PITA and TargetScan(Ru et al., 2014 ). All the prediction results are presented in the form of an upset map using the R package UpSetR(Conway, Lex, & Gehlenborg, 2017 ). Pearson correlation between USP11 and miRNAs was calculated using the R package Hmisc, and the heatmap was made using the R package pheatmap, scatter plot and boxplot were made using the R package ggpubr(Harrell Jr & Harrell Jr, 2019 ; Kassambara & Kassambara, 2020 ; Kolde & Kolde, 2018 ). Drug sensitivity The Genomics of Drug Sensitivity in Cancer (GDSC) version 2 database contains IC50 and transcriptomic data for 167 drug-treated cell lines using the R package oncoPredict, which predicts drug IC50 for each patient of TCGA based on transcriptomic data(Maeser, Gruener, & Huang, 2021 ). Calculation of Pearson correlation between IC50 and USP11 expression, and plotting of heatmap, scatter plot and box plot, refer to USP11 -linked miRNA section of method. Immune infiltration The codes of CIBERSORT absolute were employed to estimate the infiltration levels of immune cells(Chen, Khodadoust, Liu, Newman, & Alizadeh, 2018). The calculation of the CIBERSORT absolute has been completed for the twenty-two immune cell scores that were downloaded from TIMER2.0(T. Li et al., 2020 ). Calculation of Pearson correlation between IC50 and USP11 expression, and plotting of heatmap and box plot, refer to USP11 -linked miRNA section of method. Statistical analysis All statistical analyses were performed using R language ( https://www.r-project.org/ ). Differences between the two groups and among multiple groups were analyzed using the default Wilcoxon’s test and one-way analysis of variance (ANOVA), respectively. The differences in overall survival between groups were determined by Kaplan-Meier analysis and a log-rank test. P value < 0.05 was considered to be statistically significant if not otherwise stated. Result Expression of USP11 A gene expression landscape of USP11 across cancers was conducted using data from the TCGA and GEO projects. The mRNA expression levels of USP11 were found to be relatively higher in LGG and pheochromocytoma and paraganglioma (PCPG) ( Fig. 1 A ) . Notably, significant differences in USP11 transcriptional levels were observed between tumor tissues and adjacent normal tissues in sixteen types of cancer, as depicted in Fig. 1 B. Comparative analysis revealed significantly decreased expression of USP11 in BLCA, breast invasive carcinoma (BRCA), KIRC, kidney renal papillary cell carcinoma (KIRP), lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC), prostate adenocarcinoma (PRAD), thyroid carcinoma (THCA), and UCEC compared to normal tissue samples. Conversely, increased expression of USP11 was observed in head and neck squamous cell carcinoma (HNSC), KICH, and liver hepatocellular carcinoma (LIHC) (all p-values < 0.05) compared to normal tissues. Analysis of the GEO datasets revealed significant downregulation of USP11 expression in tumor tissues of KIRP, LUAD, LUSC, PAAD and UCEC, but upregulation in normal tissues of rectum adenocarcinoma (READ) and SKCM ( Fig. 1 C ) . Prognostic biomarker To assess the impact of USP11 on cancer patient prognosis, we conducted a comprehensive analysis of the relationship between USP11 mRNA expression levels and survival outcomes. Patients within each cancer type were stratified into three groups based on USP11 expression levels, and a forest plot was generated to visualize the associations with overall survival (OS). The results demonstrated that high expression of USP11 was significantly associated with improved overall survival in CESC, KICH, LGG, PAAD and UVM ( Fig. 2 A ) . Further analysis focusing on CESC revealed that patients with higher USP11 expression exhibited longer overall survival (log-rank p for trend = 0.018; HR = 0.51, 95% CI 0.29–0.90) ( Fig. 2 B ) . Similar Kaplan-Meier survival curves were observed in KICH (p = 0.048; HR = 0.16, 95% CI 0.03–0.94), LGG (p < 0.001; HR = 0.38, 95% CI 0.25–0.58), PAAD (p = 0.005; HR = 0.49, 95% CI 0.30–0.80), and UVM (p = 0.010; HR = 0.26, 95% CI 0.10–0.67) ( Fig. 2 C, 2 D, 2 E, and 2 F ) . Genetic alteration To comprehensively investigate the mutational characteristics of USP11 in tumor progression, we conducted mutation and copy number variation (CNV) analyses using genomic data from the TCGA database across thirty-two cancer types. Among the 11,141 tumor patients included in the analysis, a total of 394 patients exhibited USP11 gene variants, with 42 patients having both USP11 mutations and CNVs ( Fig. 3 A ) . Comparative analysis with the 10,705 patients without USP11 variants ( Fig. 3 B, 3 C, and 3 D ) revealed that patients with USP11 mutations (255 patients) had a more favorable overall survival prognosis (p = 0.034; HR = 0.79, 95% CI 0.64–0.96), while patients with USP11 copy number amplification (120 patients) had a poorer overall survival prognosis (p = 0.028; HR = 1.35, 95% CI 0.99–1.83). However, no significant difference in overall survival prognosis was observed among patients with USP11 copy number deletion (61 patients). It is important to note that due to variations in the number of patients across different cancer types in the TCGA dataset, we conducted statistical analysis of the gene variation rates within each cancer type. The top three cancer types with the highest USP11 gene variation rates were UCEC, OV, and SKCM ( Fig. 3 E ) . Specifically, UCEC exhibited the highest mutation rate, OV had the highest copy number amplification rate, and esophageal carcinoma (ESCA) had the highest copy number deletion rate ( Fig. 3 E ) . Single cell localization We examined the distribution of USP11 across different cellular taxa using single-cell datasets GSE154763 and GSE139555 in UCEC. In the GSE154763 dataset, cells were categorized into dendritic cells (DC), mast cells, mono/macro cells, and plasmacytoid dendritic cells (pDCs) ( Fig. 4 A ) . Similar to the expression pattern of GZMB , USP11 also exhibited significantly higher expression levels in pDCs ( Fig. 4 B and 4 C ) . In contrast, USP11 demonstrated elevated expression in mast cells, which differed from the expression pattern of GZMB ( Fig. 4 D ) . Furthermore, in comparison to its expression in normal tissues, USP11 exhibited significantly higher expression levels in pDC cells and mono/macro cells in tumor tissues ( Fig. 4 E ) . In the GSE139555 dataset, the cellular taxa comprised conventional CD4 + T cells (CD4Tconv), CD8 + T cells (CD8T), CD8 + T exhausted cells (CD8Tex), fibroblasts, proliferating T cells (Tprolif), and T regulatory cells (Treg) ( Fig. 4 F ) . Unlike CD8A , which showed preferential high expression in CD8T, CD8Tex, and Tprolif cells, USP11 displayed broad expression across almost all cell types at a low mRNA level ( Fig. 4 G and 4 H ) . Immune infiltration To investigate the potential relationship between USP11 gene expression and immune cell infiltration in various cancer types of TCGA, we employed the CIBERSORT-ABS algorithm. The analysis revealed a statistically significant positive correlation between USP11 expression and the infiltration of CD8 + T cells and activated natural killer (NK) cells in ESCA, HNSC, LIHC, PRAD, and STAD tumors. Conversely, a statistically significant negative correlation was observed between USP11 expression and the infiltration of CD8 + T cells and activated NK cells in LGG, THCA, and UVM tumors ( Fig. 5 A ) . Notably, in SKCM, a cancer type extensively studied in the context of immunotherapy, USP11 exhibited a significant positive correlation with resting NK cells and a negative correlation with M1 macrophages and resting mast cells (Fig. 5 A). Furthermore, in an intergroup comparison, we found that the infiltration of CD8 + T cells was significantly higher in the USP11 high expression group compared to the USP11 low expression group in ESCA, HNSC, LIHC, PRAD, and STAD ( Fig. 5 B ) . Biomarker for immunotherapy To evaluate the potential of USP11 as an immunotherapy biomarker, six real-world immunotherapy cohorts were analyzed in this study. In the GSE176307 cohort of BLCA patients, those with high USP11 expression exhibited a median progression-free survival (mPFS) of 2.2 months, which was superior to the 1.4 months observed in subjects with low USP11 expression. Although not statistically significant, the trend indicated improved outcomes in patients with high USP11 expression (log-rank p = 0.075; HR = 0.52, 95% CI 0.20–1.37) ( Fig. 6 A ) . However, in the KIRC patients from the PMID32472114 and PMID32895571 cohorts, no significant difference in progression-free survival was observed between the USP11 -high and USP11 -low groups following immunotherapy ( Fig. 6 B and 6 C ) . In the SKCM phs000452 cohort, patients with higher USP11 mRNA levels had significantly shorter PFS compared to patients with lower USP11 mRNA levels (log-rank p = 0.023; mPFS: 2.8 months vs. 6.3 months; HR = 1.85, 95% CI 1.04–3.63) ( Fig. 6 D ) . A similar trend was observed in the Kaplan-Meier survival curve of the GSE100797 cohort, another immunotherapy melanoma cohort, although the log-rank p-value did not reach statistical significance ( Fig. 6 E ) . Potential drug In the database Genomics of Drug Sensitivity in Cancer (GDSC) version 2, transcriptomic data from 20 tumor cell lines were collected after 161 drug treatments. Using a ridge regression model, the drug sensitivity of 8259 patients from TCGA across 20 cancer types was predicted and correlated with USP11 expression levels ( Fig. 7 A ) . The two drugs with the strongest correlation with USP11 expression were selected for each tumor type, resulting in a heatmap of 35 drugs based on their correlation with USP11 ( Fig. 7 A ) . Among all the analyzed results, the strongest positive correlation was observed between UMI77 and USP11 in LGG ( Fig. 7 B ) . The predicted IC50 values of UMI77 in BLCA, BRCA, HNSC, LGG, LIHC, PAAD, PRAD, STAD, THCA, and UCEC patients, based on the USP11 expression grouping, showed significant differences between the groups ( Fig. 7 C ) . Conversely, the most strongly negative correlation was observed between LY2109761 and USP11 in LGG ( Fig. 7 D ) . The predicted IC50 values of LY2109761 in BRCA, CESC, DLBC, GBM, KIRC, PAAD, PRAD, SKCM, STAD, and UCEC patients, based on USP11 expression grouping, also showed significant differences between the groups ( Fig. 7 E ) . Targeted miRNA MicroRNA- USP11 interactions play a crucial role in the regulatory molecular mechanism associated with the physiological activity of USP11 . To predict microRNAs targeting USP11 , we utilized six databases and identified a total of 186 potential microRNAs ( Fig. 8 A ) . Among these, 124 microRNAs were predicted by TargetScan, 73 by PITA, 48 by DIANA-microT, 33 by MicroCosm, 21 by miRanda, and 10 by ElMMo. Interestingly, 14 microRNAs were predicted by three or more of the six databases simultaneously ( Fig. 8 A ) . However, when analyzing the TCGA pan-cancer project data, only 12 out of the 186 potential microRNAs targeting USP11 were detected. The correlation between these 12 microRNAs and USP11 expression levels is shown in the heatmap ( Fig. 8 B ) . Among them, the three microRNAs showing the strongest negative correlation with USP11 expression were hsa.miR.199a.3p in PCPG (rho=-0.49, p < 0.001), hsa.miR.330.5p in KICH (rho=-0.46, p < 0.001), and hsa.miR125a.3p in TGCT (rho=-0.40, p < 0.001) ( Fig. 8 C, 8 D, 8 E ) . Discussions USP11 has garnered significant attention as a critical regulator in numerous cancer-associated signaling pathways, offering promising prospects for targeted therapeutic interventions. Nevertheless, our understanding of its involvement in various cancer types remains limited. To address this knowledge gap, our study employed a comprehensive multiomics approach encompassing thirty-three distinct cancer types. The aim was to elucidate the molecular mechanisms underlying the observed elevated expression of USP11 in cancer. Our research outcomes not only underscore the prognostic significance of USP11 expression across diverse cancer types originating from different organ systems but also unveil its previously unreported pivotal role in immune response. We have observed a prevalent upregulation of USP11 in diverse tumor types, suggesting its critical involvement in tumor progression. Through Cox proportional hazards model analysis and Kaplan-Meier survival curves in a pan-cancer study, we have demonstrated a significant correlation between increased USP11 expression and improved overall survival in specific tumor types, namely CESC, KICH, LGG, PAAD, and UVM. These findings are further supported by the research of other scientific teams, affirming the prognostic relevance of USP11 expression in tumor patients. For instance, in colorectal cancer, the USP11 /PPP1CA complex has been implicated in driving disease progression by activating the ERK/MAPK signaling pathway(Sun et al., 2019 ). Investigations have elucidated the upregulation of USP11 in colorectal cancer, wherein it exerts its effects by stabilizing IGF2BP3 , thereby promoting proliferation and metastasis(Y.-Y. Huang et al., 2021 ). Moreover, the targeting of USP11 using mitoxantrone, a potential inhibitor of pancreatic cancer cell survival, highlights its involvement in pancreatic cancer progression and underscores its therapeutic potential(Burkhart et al., 2013 ). In the context of breast cancer, elevated levels of USP11 have been associated with increased invasive behavior in in vitro studies and enhanced metastatic potential in in vivo experiments(Garcia et al., 2018 ). Intriguingly, a cohort study involving breast cancer patients has revealed a significant correlation between high USP11 expression and lower survival rates(Dwane et al., 2020 ). Notably, in hepatocellular carcinoma, USP11 has been identified as a prognostic indicator of poor outcomes, facilitating metastasis through its deubiquitinating activity on NF90 (C. Zhang et al., 2020 ; S. Zhang et al., 2018 ). Additionally, an intriguing mechanism has been elucidated, wherein USP11 promotes melanoma proliferation by deubiquitinating NONO , a protein that is upregulated in melanoma and associated with unfavorable prognoses(Feng et al., 2021 ). Collectively, these findings indicate the therapeutic potential of targeting USP11 in various malignancies, offering novel avenues for clinical interventions. The infiltration of immune cells is a characteristic feature observed in most types of cancer. Although the specific role of USP11 in immune infiltration and immunotherapy has not been extensively studied, insights can be gleaned from research conducted on genes belonging to the homologous family of USP11 . Several members of the ubiquitin-specific peptidase family, including USP3 , USP21 , USP14 , USP25 , and USP27X , have been identified as potential modulators of immune responses by eliminating K63 -linked ubiquitin chains from retinoic acid-inducible gene-I and melanoma differentiation-associated protein 5(Cui et al., 2014 ; Fan et al., 2014 ; H. Li et al., 2019 ; Tao, Chu, Xin, Li, & Sun, 2020; Zhong et al., 2013 ). Furthermore, investigations have demonstrated that USP9 , USP9X , USP22 , and ubiquitin C-terminal hydrolase L1 can deubiquitinate and stabilize the PD-L1 protein(X. Huang et al., 2019 ; Jingjing et al., 2018 ; Mao et al., 2020 ). Deubiquitination has also been implicated in various aspects of immune regulation, such as T cell receptor signaling, T cell differentiation, and immune tolerance(Zeng, Ma, Yang, & Liu, 2017). The growing body of evidence highlights the significant role of deubiquitination in modulating immune responses. Therefore, it is reasonable to hypothesize that USP11 may hold potential applications in the field of immunotherapy. In our study, we report for the first time that elevated expression of USP11 is associated with enhanced immune infiltration of CD8 + T cells and activated NK cells in patients with ESCA, HNSC, LIHC, PRAD, and STAD. Additionally, in the cohort of patients with SKCM receiving immunotherapy (phs000452), those with high USP11 expression exhibited a potentially shorter median PFS compared to those with low USP11 expression. These findings suggest that USP11 could serve as a biomarker for guiding decisions on the suitability of immunotherapy in patients with SKCM. Despite the comprehensive integration of information from multiple databases regarding the role of USP11 in pan-cancer, there are several limitations in our study. Firstly, although increased USP11 expression is associated with improved prognosis in some tumors, the specific mechanism has not been verified. Secondly, the evaluation of immune cell infiltration relies on bioinformatic algorithms, which introduces the potential for systematic bias in immune cell marker analysis. Furthermore, the omics data and patient information all come from public databases and have not been verified experimentally in clinic. Therefore, further investigation should focus on experimental studies and real-world clinical cohort studies of USP11 to elucidate the precise role of USP11 in tumor immunity and address these limitations. Conclusion In summary, we applied bioinformatics approaches to conduct a pan-cancer analysis of USP11 by integrating transcriptomic, genomic, pharmacogenomic and clinical data. Our pan-cancer analysis revealed a significant association between USP11 and both survival prognosis and immune response. Notably, we present novel evidence suggesting that USP11 expression plays a role in mediating immune infiltration of CD8 + T cells and NK cells, and it also impacts the progression-free survival of SKCM patients undergoing immunotherapy. Moreover, we identified potential regulators of USP11 expression, including small molecule drugs such as UMI77 and LY2019761, as well as miRNAs such as miR.199a.3p and miR.330a.5p. These findings provide valuable insights into a new therapeutic approach that combines USP11 modulators with existing checkpoint inhibitors, potentially offering an effective strategy for combating tumors. Declarations Acknowledgements We sincerely acknowledge the contributions from the TCGA project. Data availability statement The datasets generated and/or analyzed during the current study are available in The Cancer Genome Atlas (TCGA) (https://xenabrowser.net/datapages/) and Gene Expression Omnibus (GEO) (https://www.ncbi.nlm.nih.gov/geo/) datasets. You could download all data included in this study by searching accession numbers. Author contributions Cui Lijuan and Yang Ling: conception and design; Cui Lijuan, Lai Boan and Luo Lingzhi: collection, assembly, analysis, and visualization of data; Deng Haoyue: data and figure interpretation. Cui Lijuan and Chen Zhongyi: writing–original draft. Cui Lijuan and Wang Zixing: writing–review and editing, supervision. All authors contributed to the writing and revision of the manuscript, knew the content of it, and approved its submission. Ethics statement All the data included in the analysis were obtained from public databases without the need of permissions from local ethical committees. 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Comprehensive analysis of long non‑coding RNA‑messenger RNA‑microRNA co‑expression network identifies cell cycle‑related lncRNA in hepatocellular carcinoma. International journal of molecular medicine , 44 (5), 1844-1854. Zhu, X., Ménard, R., & Sulea, T. (2007). High incidence of ubiquitin‐like domains in human ubiquitin‐specific proteases. Proteins: Structure, Function, and Bioinformatics , 69 (1), 1-7. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 30 Jun, 2024 Read the published version in Heliyon → Version 1 posted 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-3828450","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":264779646,"identity":"8484e7b0-319e-4f14-ac5d-9fadd36cd020","order_by":0,"name":"Lijuan Cui","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAxElEQVRIiWNgGAWjYFACxsYDDAwWzPzMzAcfEKulAahFglmynS3ZgGh7QFoYDM7zmAkQpVx+2uGGAx9qJNiNDzOYMTDU2EQT1GJwO7Hh4IxjEsxmhxnSHjAcS8ttIKhFOrHhMG8DWMtxA8aGw4S1yM8GavkL1GLczNgmQZQWBqDDDjMCtRgwM7MRpwXslx6gXyQOszEbJBDjF/nZ6Q8f/KixSebvP//xwYcaGyIcBgXJYDKBWOUgYEeK4lEwCkbBKBhhAAD9uT42JpKgkwAAAABJRU5ErkJggg==","orcid":"","institution":"Suining Central Hospital","correspondingAuthor":true,"prefix":"","firstName":"Lijuan","middleName":"","lastName":"Cui","suffix":""},{"id":264779647,"identity":"59e080eb-8dba-4c1b-8583-e24f65debff3","order_by":1,"name":"Ling Yang","email":"","orcid":"","institution":"Suining Central Hospital","correspondingAuthor":false,"prefix":"","firstName":"Ling","middleName":"","lastName":"Yang","suffix":""},{"id":264779648,"identity":"3c25d530-d50f-479b-bed1-e5b53e5c8c09","order_by":2,"name":"Boan Lai","email":"","orcid":"","institution":"Suining Central Hospital","correspondingAuthor":false,"prefix":"","firstName":"Boan","middleName":"","lastName":"Lai","suffix":""},{"id":264779649,"identity":"f1383d06-9268-40e6-bb40-4dda979538e1","order_by":3,"name":"Lingzhi Luo","email":"","orcid":"","institution":"Suining Central Hospital","correspondingAuthor":false,"prefix":"","firstName":"Lingzhi","middleName":"","lastName":"Luo","suffix":""},{"id":264779650,"identity":"92dcb4b0-e863-4b8b-8a61-4768aef88f42","order_by":4,"name":"Haoyue Deng","email":"","orcid":"","institution":"Suining Central Hospital","correspondingAuthor":false,"prefix":"","firstName":"Haoyue","middleName":"","lastName":"Deng","suffix":""},{"id":264779651,"identity":"d507b9da-d554-40ca-9042-b8a112ea8c99","order_by":5,"name":"Zhongyi Chen","email":"","orcid":"","institution":"Suining Central Hospital","correspondingAuthor":false,"prefix":"","firstName":"Zhongyi","middleName":"","lastName":"Chen","suffix":""},{"id":264779652,"identity":"90f303df-bf8b-45c1-81eb-255f6a165712","order_by":6,"name":"Zixing Wang","email":"","orcid":"","institution":"Suining Central Hospital","correspondingAuthor":false,"prefix":"","firstName":"Zixing","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2024-01-02 03:29:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3828450/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3828450/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1016/j.heliyon.2024.e34523","type":"published","date":"2024-07-01T00:38:59+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":49235435,"identity":"afcb5bc8-d5eb-4c76-9d61-b1764d21efe8","added_by":"auto","created_at":"2024-01-05 17:50:40","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1888118,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eUSP11\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e expression status in different tumors and normal tissues. (A) \u003c/strong\u003eThe mRNA expression of \u003cem\u003eUSP11\u003c/em\u003e across thirty-three cancer types from TCGA data. \u003cstrong\u003e(B)\u003c/strong\u003eThe TCGA project’s \u003cem\u003eUSP11\u003c/em\u003e gene expression difference in sixteen different tumors and adjacent normal tissues. \u003cstrong\u003e(C)\u003c/strong\u003e The GEO project’s \u003cem\u003eUSP11\u003c/em\u003egene expression difference in fourteen different tumors and adjacent normal tissues.\u003c/p\u003e","description":"","filename":"Onlinefloatimage1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3828450/v1/71acb86aebc480c83d1dec77.jpg"},{"id":49236789,"identity":"302ddc97-fd57-4fbf-b2e8-f873dc41bf03","added_by":"auto","created_at":"2024-01-05 17:58:41","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":854058,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAssociation of \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eUSP11\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e with overall survival prognosis. (A) \u003c/strong\u003eHazard ratio of \u003cem\u003eUSP11\u003c/em\u003e expression in different cancers from the TCGA dataset. \u003cstrong\u003e(B-F) \u003c/strong\u003eKaplan–Meier OS analysis of \u003cem\u003eUSP11\u003c/em\u003e in CESC, KICH, LGG, PAAD and UVM in TCGA. The tertile value of \u003cem\u003eUSP11\u003c/em\u003e in each tumor was considered as the cutoff value.\u003c/p\u003e","description":"","filename":"Onlinefloatimage2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3828450/v1/0813418eaed3ede94b33d91d.jpg"},{"id":49236788,"identity":"5aaa21a3-f2f3-4d29-8c6b-32cc0432351b","added_by":"auto","created_at":"2024-01-05 17:58:40","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1397721,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGenetic alterations of \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eUSP11\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e with implication in prognosis. (A) \u003c/strong\u003eNumber of patients with \u003cem\u003eUSP11\u003c/em\u003evariants, including mutation and copy number amplification or deep deletion, in thirty-two cancers from the TCGA dataset. \u003cstrong\u003e(B)\u003c/strong\u003eKaplan–Meier OS analysis of all patients with \u003cem\u003eUSP11\u003c/em\u003e mutation and wild. \u003cstrong\u003e(C)\u003c/strong\u003eKaplan–Meier OS analysis of all patients with \u003cem\u003eUSP11\u003c/em\u003e amplification and wild. \u003cstrong\u003e(D)\u003c/strong\u003e Kaplan–Meier OS analysis of all patients with \u003cem\u003eUSP11\u003c/em\u003edeep deletion and wild. \u003cstrong\u003e(E)\u003c/strong\u003e The percentage of USP11 mutation, copy number amplification, deep deletion and all variants in different cancers.\u003c/p\u003e","description":"","filename":"Onlinefloatimage3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3828450/v1/79859fc89ae63f8d633de217.jpg"},{"id":49235436,"identity":"c13a5007-2aef-47c9-9cd4-6c907469f279","added_by":"auto","created_at":"2024-01-05 17:50:40","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":607696,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eLocalizations of \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eUSP11\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e in single cell level of UCEC datasets. (A) \u003c/strong\u003eCellular taxa of the GSE154763, including dendritic cells, mast cells, mono/macro cells, and plasmacytoid dendritic cells. (B) Distribution of \u003cem\u003eUSP11\u003c/em\u003e. (C) Distribution of \u003cem\u003eGZMB\u003c/em\u003e. (D) Expression of \u003cem\u003eUSP11\u003c/em\u003e in tumor. (E) Comparisons of \u003cem\u003eUSP11\u003c/em\u003e in normal and tumor tissues. (F) Cellular taxa of the GSE139555, including CD4Tconv, CD8T, CD8Tex, fibroblasts, Tprolif, and Treg. (G) Distribution of \u003cem\u003eUSP11\u003c/em\u003e. (H) Distribution of \u003cem\u003eCD8A\u003c/em\u003e.\u003c/p\u003e","description":"","filename":"Onlinefloatimage4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3828450/v1/2037cddb256ebccfb3243c48.jpg"},{"id":49235440,"identity":"0601ada0-88ae-4f7a-ba69-bae0b29931b6","added_by":"auto","created_at":"2024-01-05 17:50:41","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":819253,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eUSP11 \u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003ein tumor immunity. (A)\u003c/strong\u003eCorrelation of \u003cem\u003eUSP11\u003c/em\u003e expression with twenty-two immune infiltrating cells in pan-cancer by CIBERSORT Absolute algorithm. \u003cstrong\u003e(B)\u003c/strong\u003e Comparisons of CD8+ T cell infiltration among \u003cem\u003eUSP11\u003c/em\u003ehigh, middle and low expression groups.\u003c/p\u003e","description":"","filename":"Onlinefloatimage5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3828450/v1/6f2b0a071c14f9e5f25610a9.jpg"},{"id":49235439,"identity":"9d13cc55-5a07-43c2-a3da-f48fa65252d6","added_by":"auto","created_at":"2024-01-05 17:50:40","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1137251,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eUSP11\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003eas biomarker in patients ongoing immunotherapy.\u003c/strong\u003eKaplan–Meier PFS analysis of USP11 in patients with BLCA from the GSE176307 cohort \u003cstrong\u003e(A)\u003c/strong\u003e, KIRC from the PMID32472114 cohort \u003cstrong\u003e(B)\u003c/strong\u003e, KIRC from the PMID32895571 cohort \u003cstrong\u003e(C)\u003c/strong\u003e, SKCM from the phs000452 cohort \u003cstrong\u003e(D)\u003c/strong\u003e, SKCM from the GSE100797 cohort \u003cstrong\u003e(E)\u003c/strong\u003e.\u003c/p\u003e","description":"","filename":"Onlinefloatimage6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3828450/v1/3594e6b0e1f1e2423f5317f0.jpg"},{"id":49235442,"identity":"450669d7-f705-443a-98bb-43b10b787393","added_by":"auto","created_at":"2024-01-05 17:50:41","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":2593475,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePotential related drugs. (A)\u003c/strong\u003e Correlation of \u003cem\u003eUSP11\u003c/em\u003e expression with the sensitivity of thirty-five drugs in pan-cancer by GDSC2 data and ridge regression algorithm. \u003cstrong\u003e(B)\u003c/strong\u003e The scatter plot of the top positively correlated drug UMI.77 in BLCA. \u003cstrong\u003e(C)\u003c/strong\u003e Comparisons of the sensitivity of UMI.77 among \u003cem\u003eUSP11\u003c/em\u003e high, middle and low expression groups. \u003cstrong\u003e(D)\u003c/strong\u003e The scatter plot of the top negatively correlated drug LY2109761 in GBM. \u003cstrong\u003e(E)\u003c/strong\u003e Comparisons of the sensitivity of LY2109761 among \u003cem\u003eUSP11\u003c/em\u003e high, middle and low expression groups.\u003c/p\u003e","description":"","filename":"Onlinefloatimage7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3828450/v1/a7bd4c72a6d106e07f756a4a.jpg"},{"id":49235438,"identity":"3df5bb64-b764-47bb-b1b5-0a62f36da848","added_by":"auto","created_at":"2024-01-05 17:50:40","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":1777288,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePotential related miRNAs. (A)\u003c/strong\u003eThe upset plot of USP11-targeted miRNAs predicted by six mRNA-miRNA databases, of which miRNAs overlapped in more than three databases was shown in table. \u003cstrong\u003e(B)\u003c/strong\u003eExpression correlation of \u003cem\u003eUSP11\u003c/em\u003e with miRNAs detected in the TCGA project. \u003cstrong\u003e(C-E)\u003c/strong\u003e Scatter plots of the top threenegatively correlated miRNAs, miR.119a.3p, miR.330.5p and miR.125a.3p.\u003c/p\u003e","description":"","filename":"Onlinefloatimage8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3828450/v1/4f9ce6d9514836daa02e47f8.jpg"},{"id":60109173,"identity":"19250ba1-d816-41c9-b331-f88fb823ff6c","added_by":"auto","created_at":"2024-07-12 00:39:08","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":11886418,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3828450/v1/7cb24d9d-65f3-4bc9-a1f8-0781878cd413.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Integrative and Comprehensive Pan-cancer Analysis of Ubiquitin Specific Peptidase 11 (USP11) As a Prognostic and Immunological Biomarker","fulltext":[{"header":"Introduction","content":"\u003cp\u003eUbiquitin-specific peptidase 11 (\u003cem\u003eUSP11\u003c/em\u003e), a member of the largest subfamily of cysteine protease deubiquitinating enzymes, plays a crucial role in the regulation of various biological processes, including cell cycle control, DNA repair mechanisms, and tumor development. It is located in a gene cluster on chromosome Xp11 and consists of 23,963 amino acids, with an approximate molecular weight of 109,817 Da(Ideguchi et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). Similar to its counterparts \u003cem\u003eUSP4\u003c/em\u003e and \u003cem\u003eUSP15\u003c/em\u003e, \u003cem\u003eUSP11\u003c/em\u003e possesses two ubiquitin-like (UBL) domains and a N-terminal domain specific to ubiquitin-specific proteases(X. Zhu, M\u0026eacute;nard, \u0026amp; Sulea, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Notably, the N-terminal domain harbors a critical cysteine residue at position 318, which is directly involved in the enzymatic activity of \u003cem\u003eUSP11\u003c/em\u003e. Any mutation or deletion affecting this residue can result in the loss of deubiquitinating function(Chiang et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Harper et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Distinctively, the UBL domain of \u003cem\u003eUSP11\u003c/em\u003e displays a tandem arrangement, featuring a shortened β-hairpin at the interface of the two domains and exhibiting unique surface characteristics(Harper et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). \u003cem\u003eUSP11\u003c/em\u003e predominantly resides in the nucleus of cells during their non-dividing state and exhibits a widespread distribution throughout cells undergoing mitosis(Ideguchi et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2002\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cem\u003eUSP11\u003c/em\u003e exhibits binding affinity towards several substrates, thereby facilitating their stabilization and deubiquitination. One such interaction involves RAN binding protein 9 (\u003cem\u003eRANBP9\u003c/em\u003e), wherein \u003cem\u003eUSP11\u003c/em\u003e acts to correct microtubule nucleation by promoting the deubiquitination and subsequent stabilization of \u003cem\u003eRANBP9\u003c/em\u003e(Ideguchi et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). Additionally, recent investigations have unveiled the role of \u003cem\u003eUSP11\u003c/em\u003e in regulating the function of antigen-presenting cells in conjunction with v-rel reticuloendotheliosis viral oncogene homolog b (\u003cem\u003eRELB\u003c/em\u003e)(Bouwmeester et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). Moreover, \u003cem\u003eUSP11\u003c/em\u003e exerts a significant influence on various biological processes including inflammation, immunity, cell proliferation, and apoptosis. Notably, its impact is evident in the modulation of TNFα-mediated NF-κB activation through the stabilization of IκB kinase α (\u003cem\u003eIKKα\u003c/em\u003e), thereby exerting regulatory control over this signaling pathway(Schmukle \u0026amp; Walczak, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Furthermore, \u003cem\u003eUSP11\u003c/em\u003e enhances transforming growth factor β 1 (\u003cem\u003eTGFβ1\u003c/em\u003e) signaling by deubiquitinating and stabilizing \u003cem\u003eTβRII\u003c/em\u003e, thereby contributing to the regulation of cellular responses mediated by \u003cem\u003eTGFβ1\u003c/em\u003e(Jacko et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Furthermore, \u003cem\u003eUSP11\u003c/em\u003e has been implicated in the regulation of the Hippo pathway through its modulation of the \u003cem\u003eVGLL4\u003c/em\u003e/\u003cem\u003eYAP\u003c/em\u003e-\u003cem\u003eTEAD\u003c/em\u003es regulatory loop, suggesting its involvement in the regulation of cell growth and organ size control(E. Zhang et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Collectively, the body of evidence from these studies presents compelling support for the notion that \u003cem\u003eUSP11\u003c/em\u003e exerts its biological functions through its interactions with multiple regulators, including \u003cem\u003eRANBP9\u003c/em\u003e, \u003cem\u003eRELB\u003c/em\u003e, \u003cem\u003eIKK\u003c/em\u003eα, \u003cem\u003eTGFβ\u003c/em\u003e, and components of the Hippo pathway, among others.\u003c/p\u003e \u003cp\u003eIn addition to the aforementioned activities, \u003cem\u003eUSP11\u003c/em\u003e is prominently involved in the regulation of DNA repair processes, which is of particular interest in physiological contexts. Recent investigations have unveiled a previously unidentified binding site within the non-catalytic UBL region of \u003cem\u003eUSP11\u003c/em\u003e. The crystal structure analysis of the \u003cem\u003eUSP11\u003c/em\u003e peptide complex has provided evidence that this binding site within \u003cem\u003eUSP11\u003c/em\u003e interacts with a helical motif and exerts regulatory control over its function in DNA repair processes(Spiliotopoulos et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). \u003cem\u003eUSP11\u003c/em\u003e has been recognized as a crucial regulator of the repair of double-strand breaks (DSBs), a critical DNA damage event. Its interaction with \u003cem\u003eBRCA2\u003c/em\u003e has been identified as a key mechanism by which \u003cem\u003eUSP11\u003c/em\u003e regulates DSB repair(Schoenfeld, Apgar, Dolios, Wang, \u0026amp; Aaronson, 2004). Additionally, \u003cem\u003eUSP11\u003c/em\u003e plays a role in the recruitment of specific DSB repair proteins, including TP53BP1 and RAD51, to the sites of DNA damage for efficient repair(Wiltshire et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Furthermore, another study has presented compelling evidence demonstrating the essential role of \u003cem\u003eUSP11\u003c/em\u003e in facilitating the efficient repair of DNA damage by the homologous recombination proteins BRCA1 and BRCA2(Wiltshire et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn this study, we utilized both the TCGA project and GEO databases to conduct a pan-cancer analysis of \u003cem\u003eUSP11\u003c/em\u003e. Our analysis included an assessment of differential expression, correlations between \u003cem\u003eUSP11\u003c/em\u003e expression levels and patient survival, identification of linked microRNAs, assessment of genetic alterations, and investigation of potential drugs and immune infiltration. Additionally, we evaluated the potential application of \u003cem\u003eUSP11\u003c/em\u003e as a biomarker for immunotherapy using five real-world immunotherapy cohorts and two single cell datasets. To our knowledge, this is the first comprehensive analysis of the molecular mechanisms of \u003cem\u003eUSP11\u003c/em\u003e utilizing multi-omics data, as well as the first investigation of the association of \u003cem\u003eUSP11\u003c/em\u003e with immune response in various types of cancer.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData acquisition\u003c/h2\u003e \u003cp\u003eThe thirty-three cancers of interest in this study, with their full names and abbreviations, are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Transcriptomic (mRNA and microRNA), genomic and clinical data of thirty-three cancer types involving 10,251 patients from TCGA was downloaded from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://xenabrowser.net/datapages/\u003c/span\u003e\u003cspan address=\"https://xenabrowser.net/datapages/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Microarray datasets of GSE13507, GSE41258, GSE90604, GSE31056, GSE36895, GSE11151, GSE101728, GSE10072, GSE30219, GSE71729, GSE87211, GSE15605, GSE46517 and GSE63678, with the information of adjacent normal tissue, were downloaded from GEO (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/geo/)(Gulluoglu et al., 2018\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/geo/)(Gulluoglu et al., 2018\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e; Kabbarah et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Kim et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Landi et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Moffitt et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Pappa et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Pe\u0026ntilde;a-Llopis et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Raskin et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Reis et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Rousseaux et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Sheffer et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Yusenko et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; H. R. Zhu et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe results of the analysis of two single-cell datasets of primary uterine corpus endometrial carcinoma, GSE154763 (n\u0026thinsp;=\u0026thinsp;9) and GSE139555 (n\u0026thinsp;=\u0026thinsp;3), were obtained through a online website called Tumor Immune Single-cell Hub 2 (TISCH2, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://tisch.comp-genomics.org/home/\u003c/span\u003e\u003cspan address=\"http://tisch.comp-genomics.org/home/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) to study the distribution of the \u003cem\u003eUSP11\u003c/em\u003e gene in cell subpopulations(Camps et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Cheng et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Wu et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eData from two immunotherapy cohorts (bladder urothelial carcinoma, BLCA, GSE176307; SKCM, GSE100797) were downloaded from GEO(Lauss et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Rose et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Information on two immunotherapy cohorts of KIRC patients (kidney renal clear cell carcinoma, PMID32472114 and PMID32895571) was obtained from the literature supplement and the cohorts were named with the PubMed number of the literatures(Braun et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Motzer et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The phs000452 cohort of SKCM was obtained from the Melanoma Genome Sequencing Project(Łuksza et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eList of cancer types.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStudy Abbreviation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStudy Name\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eACC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdrenocortical carcinoma\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBLCA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBladder Urothelial Carcinoma\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBRCA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBreast invasive carcinoma\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCESC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCervical squamous cell carcinoma and endocervical adenocarcinoma\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCHOL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCholangiocarcinoma\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCOAD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eColon adenocarcinoma\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDLBC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLymphoid Neoplasm Diffuse Large B-cell Lymphoma\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eESCA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEsophageal carcinoma\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGBM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGlioblastoma multiforme\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHNSC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHead and Neck squamous cell carcinoma\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKICH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKidney Chromophobe\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKIRC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKidney renal clear cell carcinoma\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKIRP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKidney renal papillary cell carcinoma\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLAML\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAcute Myeloid Leukemia\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLGG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBrain Lower Grade Glioma\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLIHC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLiver hepatocellular carcinoma\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLUAD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLung adenocarcinoma\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLUSC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLung squamous cell carcinoma\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMESO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMesothelioma\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOvarian serous cystadenocarcinoma\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePAAD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePancreatic adenocarcinoma\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePCPG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePheochromocytoma and Paraganglioma\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePRAD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProstate adenocarcinoma\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eREAD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRectum adenocarcinoma\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSARC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSarcoma\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSKCM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSkin Cutaneous Melanoma\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSTAD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStomach adenocarcinoma\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTGCT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTesticular Germ Cell Tumors\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTHCA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThyroid carcinoma\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTHYM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThymoma\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUCEC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUterine Corpus Endometrial Carcinoma\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUCS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUterine Carcinosarcoma\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUVM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUveal Melanoma\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eSurvival analysis\u003c/h2\u003e \u003cp\u003eIn each cancer patients were equally divided into three groups according to the mRNA expression level of \u003cem\u003eUSP11\u003c/em\u003e, i.e. \u003cem\u003eUSP11\u003c/em\u003e-High, \u003cem\u003eUSP11\u003c/em\u003e-Midlle, \u003cem\u003eUSP11\u003c/em\u003e-Low. Cox survival correlation analysis between \u003cem\u003eUSP11\u003c/em\u003e-High and \u003cem\u003eUSP11\u003c/em\u003e-Low patient groups was completed in the R package survival, and the results were forest plotted using the R package forestplot to demonstrate(Gordon, Lumley, \u0026amp; Gordon, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Therneau \u0026amp; Lumley, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Kaplan-Meier curves were carried out to compare the survival time differences by R package survminer(Kassambara, Kosinski, Biecek, \u0026amp; Fabian, 2017).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eUSP11-linked miRNA\u003c/h2\u003e \u003cp\u003emiRNA prediction for \u003cem\u003eUSP11\u003c/em\u003e was performed in six miRNA-mRNA link databases by using the R package multiMiR, including ElMMo, MicroCosm, miRanda, DIANA-microT, PITA and TargetScan(Ru et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). All the prediction results are presented in the form of an upset map using the R package UpSetR(Conway, Lex, \u0026amp; Gehlenborg, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Pearson correlation between \u003cem\u003eUSP11\u003c/em\u003e and miRNAs was calculated using the R package Hmisc, and the heatmap was made using the R package pheatmap, scatter plot and boxplot were made using the R package ggpubr(Harrell Jr \u0026amp; Harrell Jr, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Kassambara \u0026amp; Kassambara, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Kolde \u0026amp; Kolde, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eDrug sensitivity\u003c/h2\u003e \u003cp\u003eThe Genomics of Drug Sensitivity in Cancer (GDSC) version 2 database contains IC50 and transcriptomic data for 167 drug-treated cell lines using the R package oncoPredict, which predicts drug IC50 for each patient of TCGA based on transcriptomic data(Maeser, Gruener, \u0026amp; Huang, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Calculation of Pearson correlation between IC50 and \u003cem\u003eUSP11\u003c/em\u003e expression, and plotting of heatmap, scatter plot and box plot, refer to \u003cspan refid=\"Sec5\" class=\"InternalRef\"\u003e\u003cem\u003eUSP11\u003c/em\u003e-linked miRNA\u003c/span\u003e section of method.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eImmune infiltration\u003c/h2\u003e \u003cp\u003eThe codes of CIBERSORT absolute were employed to estimate the infiltration levels of immune cells(Chen, Khodadoust, Liu, Newman, \u0026amp; Alizadeh, 2018). The calculation of the CIBERSORT absolute has been completed for the twenty-two immune cell scores that were downloaded from TIMER2.0(T. Li et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Calculation of Pearson correlation between IC50 and \u003cem\u003eUSP11\u003c/em\u003e expression, and plotting of heatmap and box plot, refer to \u003cspan refid=\"Sec5\" class=\"InternalRef\"\u003e\u003cem\u003eUSP11\u003c/em\u003e-linked miRNA\u003c/span\u003e section of method.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eAll statistical analyses were performed using R language (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.r-project.org/\u003c/span\u003e\u003cspan address=\"https://www.r-project.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Differences between the two groups and among multiple groups were analyzed using the default Wilcoxon\u0026rsquo;s test and one-way analysis of variance (ANOVA), respectively. The differences in overall survival between groups were determined by Kaplan-Meier analysis and a log-rank test. P value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered to be statistically significant if not otherwise stated.\u003c/p\u003e \u003c/div\u003e"},{"header":"Result","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eExpression of USP11\u003c/h2\u003e \u003cp\u003eA gene expression landscape of \u003cem\u003eUSP11\u003c/em\u003e across cancers was conducted using data from the TCGA and GEO projects. The mRNA expression levels of \u003cem\u003eUSP11\u003c/em\u003e were found to be relatively higher in LGG and pheochromocytoma and paraganglioma (PCPG) \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA\u003cb\u003e)\u003c/b\u003e. Notably, significant differences in \u003cem\u003eUSP11\u003c/em\u003e transcriptional levels were observed between tumor tissues and adjacent normal tissues in sixteen types of cancer, as depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB. Comparative analysis revealed significantly decreased expression of \u003cem\u003eUSP11\u003c/em\u003e in BLCA, breast invasive carcinoma (BRCA), KIRC, kidney renal papillary cell carcinoma (KIRP), lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC), prostate adenocarcinoma (PRAD), thyroid carcinoma (THCA), and UCEC compared to normal tissue samples. Conversely, increased expression of \u003cem\u003eUSP11\u003c/em\u003e was observed in head and neck squamous cell carcinoma (HNSC), KICH, and liver hepatocellular carcinoma (LIHC) (all p-values\u0026thinsp;\u0026lt;\u0026thinsp;0.05) compared to normal tissues. Analysis of the GEO datasets revealed significant downregulation of \u003cem\u003eUSP11\u003c/em\u003e expression in tumor tissues of KIRP, LUAD, LUSC, PAAD and UCEC, but upregulation in normal tissues of rectum adenocarcinoma (READ) and SKCM \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003ePrognostic biomarker\u003c/h2\u003e \u003cp\u003eTo assess the impact of \u003cem\u003eUSP11\u003c/em\u003e on cancer patient prognosis, we conducted a comprehensive analysis of the relationship between \u003cem\u003eUSP11\u003c/em\u003e mRNA expression levels and survival outcomes. Patients within each cancer type were stratified into three groups based on \u003cem\u003eUSP11\u003c/em\u003e expression levels, and a forest plot was generated to visualize the associations with overall survival (OS). The results demonstrated that high expression of \u003cem\u003eUSP11\u003c/em\u003e was significantly associated with improved overall survival in CESC, KICH, LGG, PAAD and UVM \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA\u003cb\u003e)\u003c/b\u003e. Further analysis focusing on CESC revealed that patients with higher \u003cem\u003eUSP11\u003c/em\u003e expression exhibited longer overall survival (log-rank p for trend\u0026thinsp;=\u0026thinsp;0.018; HR\u0026thinsp;=\u0026thinsp;0.51, 95% CI 0.29\u0026ndash;0.90) \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB\u003cb\u003e)\u003c/b\u003e. Similar Kaplan-Meier survival curves were observed in KICH (p\u0026thinsp;=\u0026thinsp;0.048; HR\u0026thinsp;=\u0026thinsp;0.16, 95% CI 0.03\u0026ndash;0.94), LGG (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; HR\u0026thinsp;=\u0026thinsp;0.38, 95% CI 0.25\u0026ndash;0.58), PAAD (p\u0026thinsp;=\u0026thinsp;0.005; HR\u0026thinsp;=\u0026thinsp;0.49, 95% CI 0.30\u0026ndash;0.80), and UVM (p\u0026thinsp;=\u0026thinsp;0.010; HR\u0026thinsp;=\u0026thinsp;0.26, 95% CI 0.10\u0026ndash;0.67) \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC, \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD, \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE, and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eGenetic alteration\u003c/h2\u003e \u003cp\u003eTo comprehensively investigate the mutational characteristics of \u003cem\u003eUSP11\u003c/em\u003e in tumor progression, we conducted mutation and copy number variation (CNV) analyses using genomic data from the TCGA database across thirty-two cancer types. Among the 11,141 tumor patients included in the analysis, a total of 394 patients exhibited \u003cem\u003eUSP11\u003c/em\u003e gene variants, with 42 patients having both \u003cem\u003eUSP11\u003c/em\u003e mutations and CNVs \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA\u003cb\u003e)\u003c/b\u003e. Comparative analysis with the 10,705 patients without \u003cem\u003eUSP11\u003c/em\u003e variants \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB, \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC, and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD\u003cb\u003e)\u003c/b\u003e revealed that patients with \u003cem\u003eUSP11\u003c/em\u003e mutations (255 patients) had a more favorable overall survival prognosis (p\u0026thinsp;=\u0026thinsp;0.034; HR\u0026thinsp;=\u0026thinsp;0.79, 95% CI 0.64\u0026ndash;0.96), while patients with \u003cem\u003eUSP11\u003c/em\u003e copy number amplification (120 patients) had a poorer overall survival prognosis (p\u0026thinsp;=\u0026thinsp;0.028; HR\u0026thinsp;=\u0026thinsp;1.35, 95% CI 0.99\u0026ndash;1.83). However, no significant difference in overall survival prognosis was observed among patients with \u003cem\u003eUSP11\u003c/em\u003e copy number deletion (61 patients). It is important to note that due to variations in the number of patients across different cancer types in the TCGA dataset, we conducted statistical analysis of the gene variation rates within each cancer type. The top three cancer types with the highest \u003cem\u003eUSP11\u003c/em\u003e gene variation rates were UCEC, OV, and SKCM \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE\u003cb\u003e)\u003c/b\u003e. Specifically, UCEC exhibited the highest mutation rate, OV had the highest copy number amplification rate, and esophageal carcinoma (ESCA) had the highest copy number deletion rate \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eSingle cell localization\u003c/h2\u003e \u003cp\u003eWe examined the distribution of \u003cem\u003eUSP11\u003c/em\u003e across different cellular taxa using single-cell datasets GSE154763 and GSE139555 in UCEC. In the GSE154763 dataset, cells were categorized into dendritic cells (DC), mast cells, mono/macro cells, and plasmacytoid dendritic cells (pDCs) \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA\u003cb\u003e)\u003c/b\u003e. Similar to the expression pattern of \u003cem\u003eGZMB\u003c/em\u003e, \u003cem\u003eUSP11\u003c/em\u003e also exhibited significantly higher expression levels in pDCs \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC\u003cb\u003e)\u003c/b\u003e. In contrast, \u003cem\u003eUSP11\u003c/em\u003e demonstrated elevated expression in mast cells, which differed from the expression pattern of \u003cem\u003eGZMB\u003c/em\u003e \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD\u003cb\u003e)\u003c/b\u003e. Furthermore, in comparison to its expression in normal tissues, \u003cem\u003eUSP11\u003c/em\u003e exhibited significantly higher expression levels in pDC cells and mono/macro cells in tumor tissues \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE\u003cb\u003e)\u003c/b\u003e. In the GSE139555 dataset, the cellular taxa comprised conventional CD4\u0026thinsp;+\u0026thinsp;T cells (CD4Tconv), CD8\u0026thinsp;+\u0026thinsp;T cells (CD8T), CD8\u0026thinsp;+\u0026thinsp;T exhausted cells (CD8Tex), fibroblasts, proliferating T cells (Tprolif), and T regulatory cells (Treg) \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eF\u003cb\u003e)\u003c/b\u003e. Unlike \u003cem\u003eCD8A\u003c/em\u003e, which showed preferential high expression in CD8T, CD8Tex, and Tprolif cells, \u003cem\u003eUSP11\u003c/em\u003e displayed broad expression across almost all cell types at a low mRNA level \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eG and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eH\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eImmune infiltration\u003c/h2\u003e \u003cp\u003eTo investigate the potential relationship between \u003cem\u003eUSP11\u003c/em\u003e gene expression and immune cell infiltration in various cancer types of TCGA, we employed the CIBERSORT-ABS algorithm. The analysis revealed a statistically significant positive correlation between \u003cem\u003eUSP11\u003c/em\u003e expression and the infiltration of CD8\u0026thinsp;+\u0026thinsp;T cells and activated natural killer (NK) cells in ESCA, HNSC, LIHC, PRAD, and STAD tumors. Conversely, a statistically significant negative correlation was observed between \u003cem\u003eUSP11\u003c/em\u003e expression and the infiltration of CD8\u0026thinsp;+\u0026thinsp;T cells and activated NK cells in LGG, THCA, and UVM tumors \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003eA\u003cb\u003e)\u003c/b\u003e. Notably, in SKCM, a cancer type extensively studied in the context of immunotherapy, \u003cem\u003eUSP11\u003c/em\u003e exhibited a significant positive correlation with resting NK cells and a negative correlation with M1 macrophages and resting mast cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). Furthermore, in an intergroup comparison, we found that the infiltration of CD8\u0026thinsp;+\u0026thinsp;T cells was significantly higher in the \u003cem\u003eUSP11\u003c/em\u003e high expression group compared to the \u003cem\u003eUSP11\u003c/em\u003e low expression group in ESCA, HNSC, LIHC, PRAD, and STAD \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003eB\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eBiomarker for immunotherapy\u003c/h2\u003e \u003cp\u003eTo evaluate the potential of \u003cem\u003eUSP11\u003c/em\u003e as an immunotherapy biomarker, six real-world immunotherapy cohorts were analyzed in this study. In the GSE176307 cohort of BLCA patients, those with high \u003cem\u003eUSP11\u003c/em\u003e expression exhibited a median progression-free survival (mPFS) of 2.2 months, which was superior to the 1.4 months observed in subjects with low \u003cem\u003eUSP11\u003c/em\u003e expression. Although not statistically significant, the trend indicated improved outcomes in patients with high \u003cem\u003eUSP11\u003c/em\u003e expression (log-rank p\u0026thinsp;=\u0026thinsp;0.075; HR\u0026thinsp;=\u0026thinsp;0.52, 95% CI 0.20\u0026ndash;1.37) \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e6\u003c/span\u003eA\u003cb\u003e)\u003c/b\u003e. However, in the KIRC patients from the PMID32472114 and PMID32895571 cohorts, no significant difference in progression-free survival was observed between the \u003cem\u003eUSP11\u003c/em\u003e-high and \u003cem\u003eUSP11\u003c/em\u003e-low groups following immunotherapy \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e6\u003c/span\u003eB and \u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e6\u003c/span\u003eC\u003cb\u003e)\u003c/b\u003e. In the SKCM phs000452 cohort, patients with higher \u003cem\u003eUSP11\u003c/em\u003e mRNA levels had significantly shorter PFS compared to patients with lower \u003cem\u003eUSP11\u003c/em\u003e mRNA levels (log-rank p\u0026thinsp;=\u0026thinsp;0.023; mPFS: 2.8 months vs. 6.3 months; HR\u0026thinsp;=\u0026thinsp;1.85, 95% CI 1.04\u0026ndash;3.63) \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e6\u003c/span\u003eD\u003cb\u003e)\u003c/b\u003e. A similar trend was observed in the Kaplan-Meier survival curve of the GSE100797 cohort, another immunotherapy melanoma cohort, although the log-rank p-value did not reach statistical significance \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e6\u003c/span\u003eE\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003ePotential drug\u003c/h2\u003e \u003cp\u003eIn the database Genomics of Drug Sensitivity in Cancer (GDSC) version 2, transcriptomic data from 20 tumor cell lines were collected after 161 drug treatments. Using a ridge regression model, the drug sensitivity of 8259 patients from TCGA across 20 cancer types was predicted and correlated with \u003cem\u003eUSP11\u003c/em\u003e expression levels \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e7\u003c/span\u003eA\u003cb\u003e)\u003c/b\u003e. The two drugs with the strongest correlation with \u003cem\u003eUSP11\u003c/em\u003e expression were selected for each tumor type, resulting in a heatmap of 35 drugs based on their correlation with \u003cem\u003eUSP11\u003c/em\u003e \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e7\u003c/span\u003eA\u003cb\u003e)\u003c/b\u003e. Among all the analyzed results, the strongest positive correlation was observed between UMI77 and \u003cem\u003eUSP11\u003c/em\u003e in LGG \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e7\u003c/span\u003eB\u003cb\u003e)\u003c/b\u003e. The predicted IC50 values of UMI77 in BLCA, BRCA, HNSC, LGG, LIHC, PAAD, PRAD, STAD, THCA, and UCEC patients, based on the \u003cem\u003eUSP11\u003c/em\u003e expression grouping, showed significant differences between the groups \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e7\u003c/span\u003eC\u003cb\u003e)\u003c/b\u003e. Conversely, the most strongly negative correlation was observed between LY2109761 and \u003cem\u003eUSP11\u003c/em\u003e in LGG \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e7\u003c/span\u003eD\u003cb\u003e)\u003c/b\u003e. The predicted IC50 values of LY2109761 in BRCA, CESC, DLBC, GBM, KIRC, PAAD, PRAD, SKCM, STAD, and UCEC patients, based on \u003cem\u003eUSP11\u003c/em\u003e expression grouping, also showed significant differences between the groups \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e7\u003c/span\u003eE\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eTargeted miRNA\u003c/h2\u003e \u003cp\u003eMicroRNA-\u003cem\u003eUSP11\u003c/em\u003e interactions play a crucial role in the regulatory molecular mechanism associated with the physiological activity of \u003cem\u003eUSP11\u003c/em\u003e. To predict microRNAs targeting \u003cem\u003eUSP11\u003c/em\u003e, we utilized six databases and identified a total of 186 potential microRNAs \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e8\u003c/span\u003eA\u003cb\u003e)\u003c/b\u003e. Among these, 124 microRNAs were predicted by TargetScan, 73 by PITA, 48 by DIANA-microT, 33 by MicroCosm, 21 by miRanda, and 10 by ElMMo. Interestingly, 14 microRNAs were predicted by three or more of the six databases simultaneously \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e8\u003c/span\u003eA\u003cb\u003e)\u003c/b\u003e. However, when analyzing the TCGA pan-cancer project data, only 12 out of the 186 potential microRNAs targeting \u003cem\u003eUSP11\u003c/em\u003e were detected. The correlation between these 12 microRNAs and \u003cem\u003eUSP11\u003c/em\u003e expression levels is shown in the heatmap \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e8\u003c/span\u003eB\u003cb\u003e)\u003c/b\u003e. Among them, the three microRNAs showing the strongest negative correlation with \u003cem\u003eUSP11\u003c/em\u003e expression were hsa.miR.199a.3p in PCPG (rho=-0.49, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), hsa.miR.330.5p in KICH (rho=-0.46, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and hsa.miR125a.3p in TGCT (rho=-0.40, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e8\u003c/span\u003eC, \u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e8\u003c/span\u003eD, \u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e8\u003c/span\u003eE\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussions","content":"\u003cp\u003e \u003cem\u003eUSP11\u003c/em\u003e has garnered significant attention as a critical regulator in numerous cancer-associated signaling pathways, offering promising prospects for targeted therapeutic interventions. Nevertheless, our understanding of its involvement in various cancer types remains limited. To address this knowledge gap, our study employed a comprehensive multiomics approach encompassing thirty-three distinct cancer types. The aim was to elucidate the molecular mechanisms underlying the observed elevated expression of \u003cem\u003eUSP11\u003c/em\u003e in cancer. Our research outcomes not only underscore the prognostic significance of \u003cem\u003eUSP11\u003c/em\u003e expression across diverse cancer types originating from different organ systems but also unveil its previously unreported pivotal role in immune response.\u003c/p\u003e \u003cp\u003eWe have observed a prevalent upregulation of \u003cem\u003eUSP11\u003c/em\u003e in diverse tumor types, suggesting its critical involvement in tumor progression. Through Cox proportional hazards model analysis and Kaplan-Meier survival curves in a pan-cancer study, we have demonstrated a significant correlation between increased \u003cem\u003eUSP11\u003c/em\u003e expression and improved overall survival in specific tumor types, namely CESC, KICH, LGG, PAAD, and UVM. These findings are further supported by the research of other scientific teams, affirming the prognostic relevance of \u003cem\u003eUSP11\u003c/em\u003e expression in tumor patients. For instance, in colorectal cancer, the \u003cem\u003eUSP11\u003c/em\u003e/PPP1CA complex has been implicated in driving disease progression by activating the ERK/MAPK signaling pathway(Sun et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Investigations have elucidated the upregulation of \u003cem\u003eUSP11\u003c/em\u003e in colorectal cancer, wherein it exerts its effects by stabilizing \u003cem\u003eIGF2BP3\u003c/em\u003e, thereby promoting proliferation and metastasis(Y.-Y. Huang et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Moreover, the targeting of \u003cem\u003eUSP11\u003c/em\u003e using mitoxantrone, a potential inhibitor of pancreatic cancer cell survival, highlights its involvement in pancreatic cancer progression and underscores its therapeutic potential(Burkhart et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). In the context of breast cancer, elevated levels of \u003cem\u003eUSP11\u003c/em\u003e have been associated with increased invasive behavior in in vitro studies and enhanced metastatic potential in in vivo experiments(Garcia et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Intriguingly, a cohort study involving breast cancer patients has revealed a significant correlation between high \u003cem\u003eUSP11\u003c/em\u003e expression and lower survival rates(Dwane et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Notably, in hepatocellular carcinoma, \u003cem\u003eUSP11\u003c/em\u003e has been identified as a prognostic indicator of poor outcomes, facilitating metastasis through its deubiquitinating activity on \u003cem\u003eNF90\u003c/em\u003e(C. Zhang et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; S. Zhang et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Additionally, an intriguing mechanism has been elucidated, wherein \u003cem\u003eUSP11\u003c/em\u003e promotes melanoma proliferation by deubiquitinating \u003cem\u003eNONO\u003c/em\u003e, a protein that is upregulated in melanoma and associated with unfavorable prognoses(Feng et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Collectively, these findings indicate the therapeutic potential of targeting \u003cem\u003eUSP11\u003c/em\u003e in various malignancies, offering novel avenues for clinical interventions.\u003c/p\u003e \u003cp\u003eThe infiltration of immune cells is a characteristic feature observed in most types of cancer. Although the specific role of \u003cem\u003eUSP11\u003c/em\u003e in immune infiltration and immunotherapy has not been extensively studied, insights can be gleaned from research conducted on genes belonging to the homologous family of \u003cem\u003eUSP11\u003c/em\u003e. Several members of the ubiquitin-specific peptidase family, including \u003cem\u003eUSP3\u003c/em\u003e, \u003cem\u003eUSP21\u003c/em\u003e, \u003cem\u003eUSP14\u003c/em\u003e, \u003cem\u003eUSP25\u003c/em\u003e, and \u003cem\u003eUSP27X\u003c/em\u003e, have been identified as potential modulators of immune responses by eliminating \u003cem\u003eK63\u003c/em\u003e-linked ubiquitin chains from retinoic acid-inducible gene-I and melanoma differentiation-associated protein 5(Cui et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Fan et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; H. Li et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Tao, Chu, Xin, Li, \u0026amp; Sun, 2020; Zhong et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Furthermore, investigations have demonstrated that \u003cem\u003eUSP9\u003c/em\u003e, \u003cem\u003eUSP9X\u003c/em\u003e, \u003cem\u003eUSP22\u003c/em\u003e, and ubiquitin C-terminal hydrolase L1 can deubiquitinate and stabilize the PD-L1 protein(X. Huang et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Jingjing et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Mao et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Deubiquitination has also been implicated in various aspects of immune regulation, such as T cell receptor signaling, T cell differentiation, and immune tolerance(Zeng, Ma, Yang, \u0026amp; Liu, 2017). The growing body of evidence highlights the significant role of deubiquitination in modulating immune responses. Therefore, it is reasonable to hypothesize that \u003cem\u003eUSP11\u003c/em\u003e may hold potential applications in the field of immunotherapy. In our study, we report for the first time that elevated expression of \u003cem\u003eUSP11\u003c/em\u003e is associated with enhanced immune infiltration of CD8\u0026thinsp;+\u0026thinsp;T cells and activated NK cells in patients with ESCA, HNSC, LIHC, PRAD, and STAD. Additionally, in the cohort of patients with SKCM receiving immunotherapy (phs000452), those with high \u003cem\u003eUSP11\u003c/em\u003e expression exhibited a potentially shorter median PFS compared to those with low \u003cem\u003eUSP11\u003c/em\u003e expression. These findings suggest that \u003cem\u003eUSP11\u003c/em\u003e could serve as a biomarker for guiding decisions on the suitability of immunotherapy in patients with SKCM.\u003c/p\u003e \u003cp\u003eDespite the comprehensive integration of information from multiple databases regarding the role of \u003cem\u003eUSP11\u003c/em\u003e in pan-cancer, there are several limitations in our study. Firstly, although increased \u003cem\u003eUSP11\u003c/em\u003e expression is associated with improved prognosis in some tumors, the specific mechanism has not been verified. Secondly, the evaluation of immune cell infiltration relies on bioinformatic algorithms, which introduces the potential for systematic bias in immune cell marker analysis. Furthermore, the omics data and patient information all come from public databases and have not been verified experimentally in clinic. Therefore, further investigation should focus on experimental studies and real-world clinical cohort studies of \u003cem\u003eUSP11\u003c/em\u003e to elucidate the precise role of \u003cem\u003eUSP11\u003c/em\u003e in tumor immunity and address these limitations.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn summary, we applied bioinformatics approaches to conduct a pan-cancer analysis of \u003cem\u003eUSP11\u003c/em\u003e by integrating transcriptomic, genomic, pharmacogenomic and clinical data. Our pan-cancer analysis revealed a significant association between \u003cem\u003eUSP11\u003c/em\u003e and both survival prognosis and immune response. Notably, we present novel evidence suggesting that \u003cem\u003eUSP11\u003c/em\u003e expression plays a role in mediating immune infiltration of CD8\u0026thinsp;+\u0026thinsp;T cells and NK cells, and it also impacts the progression-free survival of SKCM patients undergoing immunotherapy. Moreover, we identified potential regulators of \u003cem\u003eUSP11\u003c/em\u003e expression, including small molecule drugs such as UMI77 and LY2019761, as well as miRNAs such as miR.199a.3p and miR.330a.5p. These findings provide valuable insights into a new therapeutic approach that combines \u003cem\u003eUSP11\u003c/em\u003e modulators with existing checkpoint inhibitors, potentially offering an effective strategy for combating tumors.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe sincerely acknowledge the contributions from the TCGA project.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analyzed during the current study are available in The Cancer Genome Atlas (TCGA) (https://xenabrowser.net/datapages/) and Gene Expression Omnibus (GEO) (https://www.ncbi.nlm.nih.gov/geo/) datasets. You could download all data included in this study by searching accession numbers.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCui Lijuan and Yang Ling: conception and design; Cui Lijuan, Lai Boan and Luo Lingzhi: collection, assembly, analysis, and visualization of data; Deng Haoyue: data and figure interpretation. Cui Lijuan and Chen Zhongyi: writing\u0026ndash;original draft. Cui Lijuan and Wang Zixing: writing\u0026ndash;review and editing, supervision. All authors contributed to the writing and revision of the manuscript, knew the content of it, and approved its submission.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll the data included in the analysis were obtained from public databases without the need of permissions from local ethical committees.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author(s) declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBouwmeester, T., Bauch, A., Ruffner, H., et al. (2004). A physical and functional map of the human TNF-\u0026alpha;/NF-\u0026kappa;B signal transduction pathway. Nature cell biology\u003cem\u003e, 6\u003c/em\u003e(2), 97-105.\u003c/li\u003e\n\u003cli\u003eBraun, D. A., Hou, Y., Bakouny, Z., et al. (2020). 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High incidence of ubiquitin‐like domains in human ubiquitin‐specific proteases. Proteins: Structure, Function, and Bioinformatics\u003cem\u003e, 69\u003c/em\u003e(1), 1-7.\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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"USP11, prognosis, immunotherapy, pan-cancer, immune cell","lastPublishedDoi":"10.21203/rs.3.rs-3828450/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3828450/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003ePurpose\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe role of \u003cem\u003eUSP11\u003c/em\u003e as a crucial regulator in cancer has gained significant attention due to its deubiquitinating enzyme catalytic activity. However, a comprehensive evaluation of \u003cem\u003eUSP11\u003c/em\u003e in pan-cancer studies is currently lacking.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur analysis incorporates data from multiple sources, including five immunotherapy cohorts, thirty-three cohorts from The Cancer Genome Atlas (TCGA), and sixteen cohorts from the Gene Expression Omnibus (GEO), two of which were transcriptomic at the single-cell level.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur findings show that the aberrant expression of \u003cem\u003eUSP11\u003c/em\u003ewas found to be predictive of survival outcomes in various cancer types. And the highest frequency of genomic alterations occurred in uterine corpus endometrial carcinoma (UCEC), and single-cell transcriptome analysis of UCEC further revealed a significantly higher expression of \u003cem\u003eUSP11\u003c/em\u003e in plasmacytoid dendritic cells and mast cells. Notably, the expression of \u003cem\u003eUSP11\u003c/em\u003e was related to the infiltration levels of CD8+ T cells and natural killing (NK) activated cells. Furthermore, in the skin cutaneous melanoma (SKCM) phs000452 cohort, patients who had higher levels of \u003cem\u003eUSP11\u003c/em\u003e mRNA during immunotherapy experienced a significantly shorter median progression-free survival.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBased on our findings, \u003cem\u003eUSP11\u003c/em\u003e emerges as a promising molecular biomarker with potential implications for predicting patient prognosis and immunoreaction in pan-cancer.\u003c/p\u003e","manuscriptTitle":"Integrative and Comprehensive Pan-cancer Analysis of Ubiquitin Specific Peptidase 11 (USP11) As a Prognostic and Immunological Biomarker","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-05 17:50:36","doi":"10.21203/rs.3.rs-3828450/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8ce87005-8aa8-4f81-9830-53353db08507","owner":[],"postedDate":"January 5th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-07-12T00:38:59+00:00","versionOfRecord":{"articleIdentity":"rs-3828450","link":"https://doi.org/10.1016/j.heliyon.2024.e34523","journal":{"identity":"heliyon","isVorOnly":true,"title":"Heliyon"},"publishedOn":"2024-07-01 00:38:59","publishedOnDateReadable":"July 1st, 2024"},"versionCreatedAt":"2024-01-05 17:50:36","video":"","vorDoi":"10.1016/j.heliyon.2024.e34523","vorDoiUrl":"https://doi.org/10.1016/j.heliyon.2024.e34523","workflowStages":[]},"version":"v1","identity":"rs-3828450","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3828450","identity":"rs-3828450","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}