UTP23 is a promising prognostic biomarker and is associated with immune infiltration in breast cancer

UTP23 is a promising prognostic biomarker and is associated with immune infiltration in breast cancer | 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 UTP23 is a promising prognostic biomarker and is associated with immune infiltration in breast cancer Jindong Li, Siman Xie, Benteng Zhang, Weiping He, Yan Zhang, Huilian Hua, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2040046/v2 This work is licensed under a CC BY 4.0 License Status: Posted Version 2 posted You are reading this latest preprint version Show more versions Abstract Background Breast cancer is one of the malignant tumors with a high incidence and mortality rate among women worldwide, and its prevalence is increasing year by year, posing a serious health risk to women. UTP23 (UTP23 Small Subunit Processome Component) is a nucleolar protein that is essential for ribosome production. As we all know, disruption of ribosome structure and function results in improper protein function, affecting the body's normal physiological processes and promoting cancer growth. However, little research has shown a connection between UTP23 and cancer. Methods We analyzed the mRNA expression of UTP23 in normal tissue and breast cancer using The Cancer Genome Atlas (TCGA) database and Gene Expression Omnibus (GEO) database, and the protein expression of UTP23 using The Human Protein Atlas (HPA) database. Next, we examined the relationship between UTP23 high expression and Overall Survival (OS) using Kaplan-Meier Plotters and enriched 980 differentially expressed genes in UTP23 high and low expression samples using GO/KEGG and GSEA to identify potential biological functions of UTP23 and signaling pathways that it might influence. Finally, we also investigated the relationship between UTP23 and immune infiltration and examined the effect of UTP23 on the proliferation of human breast cancer cell lines by knocking down UTP23. Results We found that UTP23 levels in breast cancer patient samples were noticeably greater than those in healthy individuals and that high UTP23 levels were strongly linked with poor prognoses ( P = 0.008). Functional enrichment analysis revealed that UTP23 expression was connected to the humoral immune response. Besides, UTP23 expression was found to be positively correlated with immune cell infiltration. Furthermore, UTP23 knockdown has been shown to inhibit the proliferation of human breast cancer cells MDA-MB-231 and HCC-1806. Conclusion Taken together, our study demonstrated that UTP23 is a promising target in detecting and treating breast cancer and is intimately linked to immune infiltration. UTP23 TCGA Breast cancer Biomarker immune infiltration Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction Breast cancer is a frequent disease among women all over the world, with an average of one in four women getting the diagnosis. It has seriously threatened the health of women around the world [ 1 – 3 ] . The National Cancer Institute of the United States proposed the concept of tumor molecular typing in 1999; four types of breast cancer were identified luminal A, luminal B, human epidermal growth factor receptor2 enriched (HER2), and triple-negative/basal-like breast cancer [ 4 – 7 ] . The mainstay of breast cancer treatment has involved surgical resection, chemotherapy, radiation, and targeted therapy. Conventional chemotherapy drugs including paclitaxel、anthracyclines and alkylating agents, easily lead to systemic side effects and toxicities, whereas targeted drug therapy is expensive and out of reach for most families [ 8 , 9 ] . Moreover, patients who are resistant to conventional treatment will experience rapid declines in quality of life and survival rates [ 10 , 11 ] . Therefore, finding new prognostic and predictive biomarkers is critical for patients to improve outcomes. UTP23 (UTP23 Small Subunit Processome Component) is necessary for the early processing of 18S ribosomal RNA and is mildly associated with preribosomal particles [ 12 ] . Moreover, it was discovered that UTP23 has the predicted PIN domain [ 13 ] . Research shows that the PIN domain is an endoribonuclease that participates in RNA degradation and processing aspects. One of UTP23's most important functional modules is its N-terminal helix 1, which may play a role in rRNA binding to the pre-90S ribosome [ 14 , 15 ] . Cell homeostasis and ribosome synthesis are intimately linked, dysregulation of any of these processes is likely to cause cell death and uncontrolled cell growth [ 16 ] . UTP23 was previously recognized as one of the most significant nuclear regulatory genes [ 17 ] . Furthermore, there is evidence that ovarian cancer patients with UTP23 overexpression are significantly more sensitive to paclitaxel [ 18 ] . However, there are no studies on its role in breast cancer. To learn more about the part UTP23 plays in the emergence of breast cancer, this study examined UTP23 mRNA and protein expression in breast cancer patients by using data from the TCGA database and confirmed it as a potential target for breast cancer treatment. Furthermore, GO/KEGG analysis discovered that UTP23 was probably connected to the humoral immune response. Finally, we investigated UTP23 expression on a selection of breast cancer cell lines, showing that knockdown of UTP23 effectively inhibited the growth of either HCC1806 or MDA-MB-231 breast cancer cells. In conclusion, We determined that UTP23 is a very promising target for breast cancer therapy through database mining and bioinformatics analysis, which was further supported by in vitro knockdown of UTP23. Methods Cell lines Cells were purchased from ATCC, all identified by Short Tandem Repeat (STR), MDA-MB-231 culture conditions: DMEM/F12 medium (GIBCO, USA) plus 10% FBS, 37 degrees 5% CO2; HCC-1806 culture conditions: RPMI 1640 medium (GIBCO, USA) plus 10% FBS, 37 ℃ 5% CO2. Reagent Nontargeting siRNA (siNC) and siRNA (siUTP23#1 and siUTP23#2) and were purchased from Shanghai Generay Biotech Co., Ltd (Shanghai, China) and stored at -20 ℃ away from light. The sequence of NC is 5′-UUCUCCGAACGUGUCACGUTT-3′. The sequences of siUTP23#1 and siUTP23#2 are 5′-GGUUGUUUCUCCAGGUAAATT-3′and 5′-GGUAGUGUUUGGAUUGCAATT-3′. RNA isolation and qRT-PCR assay The primes were obtained from Shanghai Generay Biotech Co., Ltd (Shanghai, China). All reagents mentioned below were obtained from Vazyme Biotech (Nanjing, China). The total RNA was extracted from cells using TRIzol Reagent. The cDNA was reverse transcribed with the HiScript QRT SuperMix. The isolated mRNA quantity was measured with the SYBR Green master mix. The primes sequences used were: UTP23 forward GCTTCTTCCGCAACAACTTCG; UTP23 reverse CCTTTCCCAATGTTTCTAGCTCT; GAPDH forward TCACCACCATGGAGAAGGC; GAPDH reverse GCTAAGCAGTTGGTGGTGCA. The qRT-PCR was performed as previously described [ 19 ] . cell proliferation assay The cells infected with indicated siRNA were performed using 200,000 cells per well in a 6-well plate for 24h. Then cells were plated in a 96-well microplate with 2,000 cells and 200uL medium per well. On days 1,2,3,4, the MTT assay determined the values. MTT assays were performed as previously described [ 20 ] . Clone formation assays Cells in good condition at logarithmic growth stage were spotted into 6-well plates at a density of 1000 cells per well, cultured for 10 days, the supernatant was discarded, washed twice with PBS, and stained with 1% crystal violet solution. Public datasets and data processing RNA-seq data of breast cancer tissues were obtained from The Cancer Genome Atlas (TCGA) database. The Data were processed using the software R 3.6.3 version: the "ggplot2" package was used for data visualization, the “stat” package was used for single gene correlation analysis, and the “ComplexHeatmap” package was used to visualize the heatmap, “DESeq2” package was used for single gene difference analysis. Enrichment analysis with the “clusterProfile” package, and difference analysis with the “limma” package. Single gene difference analysis The breast cancer patients were split into two groups according to median UTP23 expression levels: the UTP23 high expression group and the UTP23 low expression group. R “DESeq2” package was used for single gene difference analysis. |log2(FC)|>1, and a P values < 0.05 were considered a statistically different result. Functional enrichment analysis The R “clusterProfiler” package was used for Gene ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. GSEA 2.0 was used to analyze gene sets derived from the MSigDBCollection or published gene signatures. Survival analysis The impact of UTP23 high expression on clinical outcomes of breast cancer patients were analyzed using Kaplan-Meier plots ( http://kmplot.com/analysis ). Immune cell infiltration analysis The association between UTP23 and immune cell infiltration was examined using the ssGSEA (single sample GSEA) algorithm. The relevance of related gene expression and immune cell infiltration was investigated through the Tumor Immune Estimation Resource (TIMER) database ( https://cistrome.shinyapps.io/timer ). Statistical analysis The results are the mean ± standard deviation (SD). A p-value of 0.05 was considered statistically significant on GraphPad Prism 8.0. Results UTP23 was upregulated in BRCA The pan-cancer analysis of UTP23 showed that UTP23 was significantly overexpressed in almost all tumors (Fig.1A), including Breast invasive carcinoma (BRCA)、Cholangiocarcinoma (CHOL)、Colon adenocarcinoma (COAD)、Lymphoid Neoplasm Diffuse Large B-cell Lymphoma (DLBC)、Esophageal carcinoma (ESCA)、Glioblastoma multiforme (GEM)、Head and Neck squamous cell carcinoma (HNBC)、Kidney renal clear cell carcinoma (KIRC)、Acute Myeloid Leukemia (LAWL)、Brain Lower Grade Glioma (LGG)、Liver hepatocellular carcinoma (LIHC)、Lung adenocarcinoma (LUAD)、Lung squamous cell carcinoma (LUSC)、Ovarian serous cystadenocarcinoma (OV)、Pancreatic adenocarcinoma (PAAD)、Rectum adenocarcinoma (READ)、Skin Cutaneous Melanoma (SKCM)、Stomach adenocarcinoma (STAD)、Testicular Germ Cell Tumors (TGCT)、Thyroid carcinoma (THCA)、Thymoma (THYM)、Uterine Corpus Endometrial Carcinoma (UCEC)、Uterine Carcinosarcoma (UCS). Analysis of the TCGA database showed that the expression of UTP23 mRNA in tumor tissues was much higher than that in normal tissues (Fig.1B) and their corresponding adjacent tissues (Fig.1C). The same results also appeared in the analysis of the GSE22820 and GSE36295 datasets (Fig.1D-E). Furthermore, we discovered that UTP23 levels were generally higher in white patients than in other ethnic groups, and there was no significant difference in UTP23 levels in tissues of patients who received radiation therapy versus those who did not (Fig.1F-G). The content of UTP23 protein in tumor tissue was also significantly higher than that in normal tissue (Fig.2). High UTP23 expression has adverse effects on BRCA survivors 1083 cancer patients were identified based on the median value for the expression level of UTP23 mRNA in BRCA, with 542 being categorized as high and 541 as low. A detailed description of the clinicopathological features can be found in Table 1. Kaplan-Meier analysis revealed that patients with high UTP23 expression in the TCGA-BRCA data set had poorer overall survival (OS) than those with low expression ( P =0.008, Fig.3A). We also analyzed the OS of T stage, N stage, and M stage, and found that UTP23 expression was strongly correlated with OS in early disease in the early stage of the disease, while the relationship with prognosis was not very significant in the later stage of the disease (Fig.3B-G). Subgroup analysis indicated that there was a significant correlation between OS and UTP23 expression in patients who did not receive radiation therapy, but not in patients who did (Fig.3H-I). Furthermore, multivariate analysis showed that PAM50 and age were also very substantially related to overall survival. Patients with Her2 positive and basal breast cancer had worse overall survival, and those older than 60 years also had worse overall survival than younger patients (Table.2). Table 1 Correlation between UTP23 expression and clinicopathologic characteristics of TCGA breast cancer patients. Characteristic Low expression of UTP23 High expression of UTP23 P value n 541 542 T stage, n (%) 0.011 T1 143 (13.2%) 134 (12.4%) T2 303 (28.1%) 326 (30.2%) T3 83 (7.7%) 56 (5.2%) T4 11 (1%) 24 (2.2%) N stage, n (%) 0.007 N0 275 (25.8%) 239 (22.5%) N1 178 (16.7%) 180 (16.9%) N2 42 (3.9%) 74 (7%) N3 42 (3.9%) 34 (3.2%) M stage, n (%) 0.984 M0 431 (46.7%) 471 (51.1%) M1 9 (1%) 11 (1.2%) Race, n (%) 0.007 Asian 28 (2.8%) 32 (3.2%) Black or African 113 (11.4%) 68 (6.8%) American White 375 (37.7%) 378 (38%) Age, n (%) 0.168 60 229 (21.1%) 253 (23.4%) Histological type, n (%) < 0.001 Infiltrating Ductal 333 (34.1%) 439 (44.9%) Carcinoma Infiltrating Lobular 155 (15.9%) 50 (5.1%) Carcinoma Table 2 The univariate and multivariate analyses of overall survival according to UTP23 expression Univariate analysis Multivariate analysis Characteristics Total(N) Hazard ratio (95% CI) P value Hazard ratio (95% CI) P value T stage 1079 T1&T2 906 Reference T3&T4 174 1.608 (1.110–2.329) 0.012 1.468 (0.933–2.308) 0.097 N stage 1063 N0&N1 872 Reference N2&N3 192 2.163 (1.472–3.180) < 0.001 2.024 (1.259–3.254) 0.004 M stage 922 M0 902 Reference M1 20 4.254 (2.468–7.334) < 0.001 1.962 (1.015–3.791) 0.045 PAM50 1042 LumA 562 Reference LumB 204 1.663 (1.088–2.541) 0.019 1.334 (0.840–2.118) 0.222 Her2 82 2.261 (1.325–3.859) 0.003 2.437 (1.340–4.431) 0.004 Basal 195 1.285 (0.833–1.981) 0.257 1.640 (1.012–2.656) 0.045 Age 1082 60 482 2.020 (1.465–2.784) < 0.001 2.262 (1.560–3.281) < 0.001 Race 993 Asian&Black or African 241 Reference American White 753 0.912 (0.615–1.350) 0.644 Functional and pathway enrichment analysis of UTP23 To investigate the specific mechanism of the role of UTP23 in breast cancer development and progression, we analyzed samples with high and low UTP23 expression and found 980 differentially expressed genes (DEGs, |log2(FC)|>1, p.adj<0.05), of which 675 genes were significantly upregulated while 305 genes were significantly downregulated. A volcano plot illustrates the DEGs' expression profiles (Fig.4A). Additionally, we analyzed the co-expressed genes of UTP23, in which RAD21 was the most associated gene with UTP23 expression (Fig.4B), and it has been previously shown to be associated with poor prognosis in breast cancer, which adds to the evidence that UTP23 may be associated with poor prognosis in breast cancer [21] . In the analysis of DEGs enriched in GO analysis and KEGG pathways, we found that the biological process in which UTP23 most likely to be involved included humoral immune respond、defense response to bacterium、antimicrobial humoral response, and the molecular function it was most likely to affect were endopeptidase inhibitor activity、hormone activity、receptor ligand activity and the cellular component it was most likely to affect were nucleosome、high-density lipoprotein particle、cornified envelope (Fig.4C). Genes associated with each signaling pathway were plotted in Fig.4D. The top 6 GO items and KEGG objects enriched for DEGs are shown in Fig.4E and Fig.4F, respectively. We also performed a GSEA analysis and found that the top 9 data sets with the most significant relationship with UTP23 were signaling by Rho GTPases, M phase, cell cycle checkpoints, neuronal system, deubiquitination, DNA repair, signaling by wnt, transmission across chemical synapses, chromatin-modifying enzymes (Table3, Fig.5). Furthermore, we draw UTP23-interaction proteins in the BRCA tissue network by using the STRING database (Fig.6A) and showed their co-expression scores with UTP23 (Fig.6B). Table 3 Reactome pathway enriched in high- and low-risk groups by using GESA. ID NES pvalue p.adjust FDR REACTOME_SIGNALING_BY_RHO_GTPASES 1.495 0.002 0.030 0.023 REACTOME_M_PHASE 2.118 0.002 0.030 0.023 REACTOME_CELL_CYCLE_CHECKPOINTS 2.364 0.002 0.030 0.023 REACTOME_NEURONAL_SYSTEM 1.868 0.002 0.030 0.023 REACTOME_DEUBIQUITINATION 1.729 0.002 0.030 0.023 REACTOME_DNA_REPAIR 1.896 0.002 0.030 0.023 REACTOME_SIGNALING_BY_WNT 1.537 0.002 0.030 0.023 REACTOME_TRANSMISSION_ACROSS_CHEMICAL_SYNAPSES 1.701 0.002 0.030 0.023 REACTOME_CHROMATIN_MODIFYING_ENZYMES 2.103 0.002 0.030 0.023 UTP23 expression correlates with immune cells infiltration Moreover, we used ssGSEA to examine whether UTP23 expression was associated with immune cell infiltration based on the finding that the UTP23 may participate in the humoral immune response. Figure.7A shows the relationship between immune cell infiltration and UTP23 mRNA expression. UTP23 expression was positively correlated with Th2 cells ( P <0.001, r=0.332) and Tcm ( P <0.001, r=0.332), and negatively correlated with pDC ( P <0.001, r=-0.456) and CD8 + T cells ( P <0.001, r=-0.253), while Tem ( P =0.983, r=0.001) had no relationship with UTP23 expression (Fig.7). Furthermore, the enrichment scores corroborate previous findings, with higher UTP23 expression associated with higher enrichment scores of Tcm, Th2 cells, T helper cells, and Tgd (Fig. 7G). Inhibition of UTP23 can effectively inhibit the proliferation of breast cancer cells To confirm that UTP23 is an important target for breast cancer treatment, we carried out qRT-PCR of UTP23 on multiple breast cancer cells, the results showed that the UTP23 expression in breast cancer was indeed higher than that in normal breast cells MCF10A, especially the triple-negative breast cancer cell lines HCC-1806, MDA-MB-231, etc (Fig.8A). Subsequently, we confirmed that knockdown of UTP23 can effectively inhibit the proliferation of HCC-1806 and MDA-MB-231 by growth curve assay (Fig.8D-E) and clone formation assay (Fig.8F-G). Discussion Ribosome synthesis is a complex, highly dynamic cellular metabolic process that requires many conserved assembly factors, and approximately fifty ribosomal proteins (r-protein) bind to ribosomal RNA (rRNA) during co-transcriptional processes [ 22 – 25 ] . UTP23 is a new small subunit processing component, which is a conserved protein factor involved in the early assembly of ribosomal small subunits and plays an important role in the ribosome assembly process, structurally important regions include the C-terminal tail, zinc finger, helix α1, and the highly conserved PIN domain [ 15 ] . Studies early on focused mainly on the structure and function of UTP23, with few attempting to determine how it contributes to tumorigenesis and progression [ 15 , 26 ] . Here, we analyzed UTP23 with the TCGA database, GEO database and HPA database and found that its mRNA and protein expression in breast cancer was significantly higher than that in normal tissues, and its expression did not differ significantly between radiation therapy and non- radiation therapy patients, which means that if a patient is treated with radiation, it does not make UTP23-targeted drugs any less effective in treating him. For those who are fighting breast cancer, this is crucial. In addition, through Kalan-Meier analysis, we found that poor patient outcomes were associated with high UTP23 expression in breast cancer, and UTP23 was more significantly associated with poor patient prognosis in the early stage of the disease, suggesting its potential as a diagnostic and prognostic indicator. In other words, it is possible to dramatically increase the early diagnosis rate of breast cancer by identifying UTP23 expression in the early stages of the disease. However, it is necessary to further verify whether UTP23 can serve as a useful diagnostic and prognostic marker for different stages of breast cancer progression. In the research, functional enrichment analysis revealed that UTP23 may be involved in the humoral immune response, defense response to the bacterium, antimicrobial humoral response, which suggested that UTP23 may be closely related to the human immune system. Besides, GSEA analysis showed that UTP23 expression was related to signaling by Rho GTPases, M phase, cell cycle checkpoints, neuronal system, deubiquitination, DNA repair, signaling by wnt, transmission across chemical synapses, and chromatin-modifying enzymes. ssGSEA showed that UTP23 expression has a positive correlation with the infiltration of immune cells, including Tcm, Th2 cells and T helper cells. We also explored gene networks connected to UTP23 with STRING, noticing that KRR1 got the highest score, which has been previously reported as a type of tumor-associated antigens (TAAs) and has the potential to be used as a target for tumor vaccines [ 27 ] . But whether UTP23 can play a role as a TAA in tumor immunotherapy needed further investigation. To further explore the specific role of UTP23 in breast cancer, we analyzed the mRNA expression of UTP23 in a series of human breast cancer cells by qRT-PCR, and the results showed that UTP23 was significantly higher in breast cancer cells than in human normal breast cells. UTP23 knockdown significantly inhibited the proliferation of human breast cancer cells HCC1806 and MDA-MB-231, proving that it is a very potential new target for breast cancer. We also highlight the need to further explore the specific mechanism of UTP23 in the progression of breast cancer. Cancer has always been the leading cause of death in humans. Although medical technology has enabled humans to overcome many diseases, cancer remains an untapped resource [ 28 ] . Novel tumor therapies, such as antibody-coupled drugs, immune checkpoint inhibitors, tumor vaccines, and adoptive cellular immunotherapy, have emerged in recent years [ 29 – 32 ] . The well-known CAR-T therapy is a type of relay immune cell therapy that entails isolating autologous tumor-infiltrating lymphocytes (TIL) or peripheral blood lymphocytes from tumor patients, sorting them, expanding them, activating them in vitro, and then re-transfusing them into the patient to obtain anti-tumor immunity [ 33 , 34 ] . The substantial relationship between UTP23 and immune infiltration examined in this work leads to the conclusion that further research on UTP23 is urgently required since it may have huge application in cellular immunotherapy. In recent years, due to COVID-19, people have paid unprecedented attention to vaccines, and tumor vaccines have also become a hot track in tumor immunity [ 35 ] . According to studies, tumor cells contain tumor antigens that the immune system can recognize to distinguish them from healthy ones [ 36 – 38 ] . By expressing particular, immunogenic tumor antigens (such as peptides, DNA, and RNA) with the assistance of adjuvants such cytokines and chemokines, which in turn kill and eliminate tumor cells, tumor vaccines can activate or increase the body's natural anti-tumor immunity [ 39 , 40 ] . Interestingly, KRR1, which has previously been identified as a novel tumor-associated antigen, received the highest score in the STRING database analysis of UTP23-interacting proteins. This suggests that UTP23 is likely to be a noveltumor-associated antigen as well. Furthermore, based on the results of the bioinformatics analysis, we carried out experimental validation and verified that knockdown of UTP23 could successfully reduce the proliferation of breast cancer cells. Conclusions According to our findings, UTP23 was strongly expressed in breast cancer tissues and associated with poor outcomes, suggesting it may be useful both as a diagnostic and prognostic marker for breast cancer. In addition, UTP23 also be related to immune infiltration, and future research should be conducted to investigate the role of UTP23 in the breast cancer immunotherapy. Finally, we confirmed by knocking down UTP23 in HCC-1806 and MDA-MB-231 cells that inhibition of UTP23 significantly inhibited the proliferation of breast cancer cells Thus, we highlight UTP23 as a promising target for treating breast cancer in this study. Declarations Ethics approval and consent to participate: Not applicable Consent for publication: Not applicable Availability of data and material: The datasets supporting the conclusion of this article are included within the article. Competing interests: The authors declare that they have no competing interests. Funding: The present study was partly supported by the Research Foundation of Taizhou People's Hospital (No. ZL202025). Authors’ contributions: Jindong Li: writing—original draft preparation. Siman Xie: data analyses and interpretation. Benteng Zhang: data analyses and interpretation. Weiping He: writing—review and editing. Yan Zhang: project conception and design. Huilian Hua: project conception and design. Li Yang: supervision. All authors participated and approved in writing or revising the manuscript. Acknowledgements : The authors have no acknowledgements. References Sperduto PW, Mesko S, Li J, et al. 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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-2040046","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":143565793,"identity":"e7851e8b-b70f-4e7d-9f89-f3fd4bd7a5a7","order_by":0,"name":"Jindong Li","email":"","orcid":"","institution":"Taizhou People's Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jindong","middleName":"","lastName":"Li","suffix":""},{"id":143565794,"identity":"d0405c2d-f253-4518-8abd-8d506e0933be","order_by":1,"name":"Siman Xie","email":"","orcid":"","institution":"China Pharmaceutical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Siman","middleName":"","lastName":"Xie","suffix":""},{"id":143565795,"identity":"18521f81-dd47-4e67-8eda-c4fdabe83ef8","order_by":2,"name":"Benteng Zhang","email":"","orcid":"","institution":"China Pharmaceutical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Benteng","middleName":"","lastName":"Zhang","suffix":""},{"id":143565796,"identity":"bde14ac4-6058-4ad5-b08d-063e1153fb54","order_by":3,"name":"Weiping He","email":"","orcid":"","institution":"Taizhou People's Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Weiping","middleName":"","lastName":"He","suffix":""},{"id":143565797,"identity":"9b6048bb-5f25-4889-ab32-87385febfacd","order_by":4,"name":"Yan Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1ElEQVRIiWNgGAWjYDACZgaGAzwMNnL87M0HDnyoIF5LmrFkz7HEgzPOEGsTD8PhRIMZPsaHeVuIUC3fzp144E3N4QQDCZ4PB3gbGOT5xQ7g12JwmHfDwTnH0vPMpXs3HJDcwWA4c3YCAS3MvBsO87BZF1vOObvhgOEZhgSD2wS0yDeDtPxjTtxwI+fBgcQ2IrQwAB12mLfNGaSF4cBBYrSA/TK3DxzIBgcbzkgQ9ot8/9nNH958A0fl489/Kmzk+aUJOQwNSJCmfBSMglEwCkYBdgAAZ01OpQBCUI0AAAAASUVORK5CYII=","orcid":"","institution":"Taizhou People's Hospital","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Yan","middleName":"","lastName":"Zhang","suffix":""},{"id":143565798,"identity":"427c6ca3-afb7-419c-b3c0-7de5e838f376","order_by":5,"name":"Huilian Hua","email":"","orcid":"","institution":"Taizhou People's Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Huilian","middleName":"","lastName":"Hua","suffix":""},{"id":143565799,"identity":"c17282f4-f899-488d-abb3-948fa4486770","order_by":6,"name":"Li Yang","email":"","orcid":"","institution":"Taizhou People's Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Li","middleName":"","lastName":"Yang","suffix":""}],"badges":[],"createdAt":"2022-09-07 05:29:12","currentVersionCode":2,"declarations":"","doi":"10.21203/rs.3.rs-2040046/v2","doiUrl":"https://doi.org/10.21203/rs.3.rs-2040046/v2","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":27674722,"identity":"2324d7fc-5664-4224-a022-c531af07ea10","added_by":"auto","created_at":"2022-10-12 14:24:03","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":249581,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eUTP23 mRNA in BRCA and other types of human cancers from TCGA data. \u003c/strong\u003eA) UTP23 expression levels in different tumor types from the TCGA database. B) Expression levels of UTP23 in BRCA (n=1109) and normal tissue (n=113) from the TCGA database. C) Expression levels of UTP23 in BRCA (n=112) and its paired adjacent tissues (n=112) from the TCGA database. D) Expression levels of UTP23 in BRCA (n=176) and normal tissue (n=10) from GSE22820 of the GEO database. E) Expression levels of UTP23 in BRCA (n=45) and normal tissue (n=5) from GSE36295 of the GEO database. F-G) Association with UTP23 mRNA expression and Race (n=994, P\u0026lt;0.001); Radiation therapy (n=997, P=0.518). *\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, **\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01, ***\u003cem\u003eP \u003c/em\u003e\u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-2040046/v2/d660396146daaea4fe2297d4.png"},{"id":27674723,"identity":"4651496b-3022-4806-a45f-87f0ee721a3a","added_by":"auto","created_at":"2022-10-12 14:24:03","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1380450,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003evalidation of UTP23 based on HPA database.\u003c/strong\u003e A) The IHC staining of UTP23 in the normal tissue from the HPA database. B) The IHC staining of UTP23 in the BRCA from the HPA database.\u003c/p\u003e","description":"","filename":"fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-2040046/v2/bc40e06cc6405c5468644f58.png"},{"id":27675525,"identity":"52733238-9a8e-4cc1-94a9-bd2677ecfaae","added_by":"auto","created_at":"2022-10-12 14:29:03","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":193237,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eOverall survival analysis with UTP23 mRNA expression. \u003c/strong\u003eA) Kaplan-Meier curves for overall survival in BRCA for all cases. B) T1\u0026amp;T2. C) T3\u0026amp;T4. D) N0\u0026amp;N1. E) N2\u0026amp;N3. F) M0. G) M1. H) Not receiving radiation therapy. \u0026nbsp;I) Receiving radiation therapy.\u003c/p\u003e","description":"","filename":"fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-2040046/v2/d8e278f52cd527307a202b71.png"},{"id":27676580,"identity":"63c561bd-829e-45a6-b16d-1636b9b41133","added_by":"auto","created_at":"2022-10-12 14:34:03","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1902124,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFunctional and pathway enrichment analyses of UTP23. \u003c/strong\u003eA) Volcano Plot of differentially expressed genes (DEGs). B) Heatmap showing the top 10 genes (5 positive-related, 5 negative-related) of DEGs. C) The GO enrichment and KEGG pathway analysis of 980 DEGs in the UTP23-high and low expression groups. D) Signaling pathway-protein interaction network of those DEGs. E) Chord diagram of top 6 GO items. F) Chord diagram of top 6 KEGG objects.\u003c/p\u003e","description":"","filename":"fig4.png","url":"https://assets-eu.researchsquare.com/files/rs-2040046/v2/455b299b9ccb6d85565b1bc7.png"},{"id":27674724,"identity":"dce14eb8-3be5-43d5-88c7-2db9674c21cd","added_by":"auto","created_at":"2022-10-12 14:24:03","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1365318,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEnrichment Plots by GSEA. \u003c/strong\u003eA) Enrichment of genes in the Rho GTPases signaling. B) Enrichment of genes in the M phase. C) Enrichment of genes in the cell cycle checkpoints.D) Enrichment of genes in the neuronal system. E) Enrichment of genes in deubiquitination. F) Enrichment of genes in the DNA repair. G) Enrichment of genes in the Wnt signaling. H) Enrichment of genes in the transmission across chemical synapses. I) Enrichment of genes in the chromatin-modifying enzymes.\u003c/p\u003e","description":"","filename":"fig5.png","url":"https://assets-eu.researchsquare.com/files/rs-2040046/v2/4c907f3d9e1615ecb89fd5bf.png"},{"id":27675524,"identity":"aa9fcb34-9280-4f58-8ca0-318e60adfc4b","added_by":"auto","created_at":"2022-10-12 14:29:03","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":630787,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eUTP23-interaction proteins in BRCA tissue.\u003c/strong\u003e A) UTP23-interaction proteins network. B) Annotation of UTP23-interacting proteins and their co-expression scores.\u003c/p\u003e","description":"","filename":"fig6.png","url":"https://assets-eu.researchsquare.com/files/rs-2040046/v2/ed65c55a1554060a7a405859.png"},{"id":27674728,"identity":"b8e9650e-6d18-44a4-8473-eaae6ff4648e","added_by":"auto","created_at":"2022-10-12 14:24:03","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":767517,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003essGSEA analyses UTP23 and the correlation of UTP23 expression with the immune infiltration level in BRCA.\u003c/strong\u003eA) The correlation between the infiltration of immune cells and the expression of UTP23. B-C) UTP23 expression significantly positively correlates with infiltration levels of Th2 cells and Tcm. D-E) UTP23 expression significantly negatively correlates with infiltration levels of pDC and CD8\u003csup\u003e+\u003c/sup\u003e T cells. F) UTP23 expression does not correlate with infiltration levels of Tem. G) Enrichment scores of multiple cell types of immune cell, infiltration in the UTP23-high and low expression groups. *\u003cem\u003eP\u003c/em\u003e\u0026nbsp;\u0026lt;\u0026nbsp;0.05, **\u003cem\u003eP\u003c/em\u003e\u0026nbsp;\u0026lt;\u0026nbsp;0.01, ***\u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026lt;\u0026nbsp;0.001.\u003c/p\u003e","description":"","filename":"fig7.png","url":"https://assets-eu.researchsquare.com/files/rs-2040046/v2/35962f8b1b2a0cd64745dbf4.png"},{"id":27675523,"identity":"c0c171f2-1628-40ab-b3d6-b7215704ab2c","added_by":"auto","created_at":"2022-10-12 14:29:03","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":602503,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eInhibition of UTP23 inhibits breast cancer proliferation. \u003c/strong\u003eA) Quantitative Real-time PCR analysis of UTP23 expression in a panel of breast cancer cell lines. B-C) Levels of UTP23 were measured by qRT-PCR. D-E) Growth curve of MDA-MB-231 and HCC-1806 cells transfected with control siRNA, UTP23 siRNA#1, and UTP23 siRNA#2. F-G) Clone formation assay of MDA-MB-231 and HCC-1806 cells transfected with control siRNA, UTP23 siRNA#1, and UTP23 siRNA#2. Bars and errors represent the means±SD of at least three independent experiments; statistically significant according to the student’s t-test; *\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, **\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01, ***\u003cem\u003eP \u003c/em\u003e\u0026lt; 0.001, and ****\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.0001.\u003c/p\u003e","description":"","filename":"fig8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2040046/v2/1c5d46e14ef1442dfd4270ff.jpg"},{"id":30704409,"identity":"4a6904c7-d092-41e9-9825-2ec74580d561","added_by":"auto","created_at":"2022-12-23 08:44:43","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3794447,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2040046/v2/556ee602-3d0b-4ef3-a5d6-d3fb6a25bca5.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"UTP23 is a promising prognostic biomarker and is associated with immune infiltration in breast cancer","fulltext":[{"header":"Introduction","content":"\u003cp\u003eBreast cancer is a frequent disease among women all over the world, with an average of one in four women getting the diagnosis. It has seriously threatened the health of women around the world\u003csup\u003e[\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. The National Cancer Institute of the United States proposed the concept of tumor molecular typing in 1999; four types of breast cancer were identified luminal A, luminal B, human epidermal growth factor receptor2 enriched (HER2), and triple-negative/basal-like breast cancer \u003csup\u003e[\u003cspan additionalcitationids=\"CR5 CR6\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. The mainstay of breast cancer treatment has involved surgical resection, chemotherapy, radiation, and targeted therapy. Conventional chemotherapy drugs including paclitaxel、anthracyclines and alkylating agents, easily lead to systemic side effects and toxicities, whereas targeted drug therapy is expensive and out of reach for most families\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. Moreover, patients who are resistant to conventional treatment will experience rapid declines in quality of life and survival rates \u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. Therefore, finding new prognostic and predictive biomarkers is critical for patients to improve outcomes.\u003c/p\u003e \u003cp\u003eUTP23 (UTP23 Small Subunit Processome Component) is necessary for the early processing of 18S ribosomal RNA and is mildly associated with preribosomal particles\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e. Moreover, it was discovered that UTP23 has the predicted PIN domain\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. Research shows that the PIN domain is an endoribonuclease that participates in RNA degradation and processing aspects. One of UTP23's most important functional modules is its N-terminal helix 1, which may play a role in rRNA binding to the pre-90S ribosome\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. Cell homeostasis and ribosome synthesis are intimately linked, dysregulation of any of these processes is likely to cause cell death and uncontrolled cell growth \u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e. UTP23 was previously recognized as one of the most significant nuclear regulatory genes \u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e. Furthermore, there is evidence that ovarian cancer patients with UTP23 overexpression are significantly more sensitive to paclitaxel \u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. However, there are no studies on its role in breast cancer.\u003c/p\u003e \u003cp\u003eTo learn more about the part UTP23 plays in the emergence of breast cancer, this study examined UTP23 mRNA and protein expression in breast cancer patients by using data from the TCGA database and confirmed it as a potential target for breast cancer treatment. Furthermore, GO/KEGG analysis discovered that UTP23 was probably connected to the humoral immune response. Finally, we investigated UTP23 expression on a selection of breast cancer cell lines, showing that knockdown of UTP23 effectively inhibited the growth of either HCC1806 or MDA-MB-231 breast cancer cells. In conclusion, We determined that UTP23 is a very promising target for breast cancer therapy through database mining and bioinformatics analysis, which was further supported by in vitro knockdown of UTP23.\u003c/p\u003e "},{"header":"Methods","content":" \u003cdiv id=\"Sec3\" class=\"Section3\"\u003e \u003ch2\u003eCell lines\u003c/h2\u003e \u003cp\u003eCells were purchased from ATCC, all identified by Short Tandem Repeat (STR), MDA-MB-231 culture conditions: DMEM/F12 medium (GIBCO, USA) plus 10% FBS, 37 degrees 5% CO2; HCC-1806 culture conditions: RPMI 1640 medium (GIBCO, USA) plus 10% FBS, 37 ℃ 5% CO2.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section3\"\u003e \u003ch2\u003eReagent\u003c/h2\u003e \u003cp\u003eNontargeting siRNA (siNC) and siRNA (siUTP23#1 and siUTP23#2) and were purchased from Shanghai Generay Biotech Co., Ltd (Shanghai, China) and stored at -20 ℃ away from light. The sequence of NC is 5\u0026prime;-UUCUCCGAACGUGUCACGUTT-3\u0026prime;. The sequences of siUTP23#1 and siUTP23#2 are 5\u0026prime;-GGUUGUUUCUCCAGGUAAATT-3\u0026prime;and 5\u0026prime;-GGUAGUGUUUGGAUUGCAATT-3\u0026prime;.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003eRNA isolation and qRT-PCR assay\u003c/h2\u003e \u003cp\u003eThe primes were obtained from Shanghai Generay Biotech Co., Ltd (Shanghai, China). All reagents mentioned below were obtained from Vazyme Biotech (Nanjing, China). The total RNA was extracted from cells using TRIzol Reagent. The cDNA was reverse transcribed with the HiScript QRT SuperMix. The isolated mRNA quantity was measured with the SYBR Green master mix. The primes sequences used were: UTP23 forward GCTTCTTCCGCAACAACTTCG; UTP23 reverse CCTTTCCCAATGTTTCTAGCTCT; GAPDH forward TCACCACCATGGAGAAGGC; GAPDH reverse GCTAAGCAGTTGGTGGTGCA. The qRT-PCR was performed as previously described\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003ecell proliferation assay\u003c/h2\u003e \u003cp\u003eThe cells infected with indicated siRNA were performed using 200,000 cells per well in a 6-well plate for 24h. Then cells were plated in a 96-well microplate with 2,000 cells and 200uL medium per well. On days 1,2,3,4, the MTT assay determined the values. MTT assays were performed as previously described\u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003eClone formation assays\u003c/h2\u003e \u003cp\u003eCells in good condition at logarithmic growth stage were spotted into 6-well plates at a density of 1000 cells per well, cultured for 10 days, the supernatant was discarded, washed twice with PBS, and stained with 1% crystal violet solution.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003ePublic datasets and data processing\u003c/h2\u003e \u003cp\u003eRNA-seq data of breast cancer tissues were obtained from The Cancer Genome Atlas (TCGA) database. The Data were processed using the software R 3.6.3 version: the \"ggplot2\" package was used for data visualization, the \u0026ldquo;stat\u0026rdquo; package was used for single gene correlation analysis, and the \u0026ldquo;ComplexHeatmap\u0026rdquo; package was used to visualize the heatmap, \u0026ldquo;DESeq2\u0026rdquo; package was used for single gene difference analysis. Enrichment analysis with the \u0026ldquo;clusterProfile\u0026rdquo; package, and difference analysis with the \u0026ldquo;limma\u0026rdquo; package.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003eSingle gene difference analysis\u003c/h2\u003e \u003cp\u003eThe breast cancer patients were split into two groups according to median UTP23 expression levels: the UTP23 high expression group and the UTP23 low expression group. R \u0026ldquo;DESeq2\u0026rdquo; package was used for single gene difference analysis. |log2(FC)|\u0026gt;1, and a P values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered a statistically different result.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003eFunctional enrichment analysis\u003c/h2\u003e \u003cp\u003eThe R \u0026ldquo;clusterProfiler\u0026rdquo; package was used for Gene ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. GSEA 2.0 was used to analyze gene sets derived from the MSigDBCollection or published gene signatures.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003eSurvival analysis\u003c/h2\u003e \u003cp\u003eThe impact of UTP23 high expression on clinical outcomes of breast cancer patients were analyzed using Kaplan-Meier plots (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://kmplot.com/analysis\u003c/span\u003e\u003cspan address=\"http://kmplot.com/analysis\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003eImmune cell infiltration analysis\u003c/h2\u003e \u003cp\u003eThe association between UTP23 and immune cell infiltration was examined using the ssGSEA (single sample GSEA) algorithm. The relevance of related gene expression and immune cell infiltration was investigated through the Tumor Immune Estimation Resource (TIMER) database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cistrome.shinyapps.io/timer\u003c/span\u003e\u003cspan address=\"https://cistrome.shinyapps.io/timer\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eThe results are the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD). A p-value of 0.05 was considered statistically significant on GraphPad Prism 8.0.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eUTP23 was upregulated in BRCA\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe pan-cancer analysis of UTP23 showed that UTP23 was significantly overexpressed in almost all tumors (Fig.1A), including Breast invasive carcinoma (BRCA)、Cholangiocarcinoma (CHOL)、Colon adenocarcinoma (COAD)、Lymphoid Neoplasm Diffuse Large B-cell Lymphoma (DLBC)、Esophageal carcinoma (ESCA)、Glioblastoma multiforme (GEM)、Head and Neck squamous cell carcinoma (HNBC)、Kidney renal clear cell carcinoma (KIRC)、Acute Myeloid Leukemia (LAWL)、Brain Lower Grade Glioma (LGG)、Liver hepatocellular carcinoma (LIHC)、Lung adenocarcinoma (LUAD)、Lung squamous cell carcinoma (LUSC)、Ovarian serous cystadenocarcinoma (OV)、Pancreatic adenocarcinoma (PAAD)、Rectum adenocarcinoma (READ)、Skin Cutaneous Melanoma (SKCM)、Stomach adenocarcinoma (STAD)、Testicular Germ Cell Tumors (TGCT)、Thyroid carcinoma (THCA)、Thymoma (THYM)、Uterine Corpus Endometrial Carcinoma (UCEC)、Uterine Carcinosarcoma (UCS). Analysis of the TCGA database showed that the expression of UTP23 mRNA in tumor tissues was much higher than that in normal tissues (Fig.1B) and their corresponding adjacent tissues (Fig.1C). The same results also appeared in the analysis of the GSE22820 and GSE36295 datasets (Fig.1D-E). Furthermore, we discovered that UTP23 levels were generally higher in white patients than in other ethnic groups, and there was no significant difference in UTP23 levels in tissues of patients who received radiation therapy versus those who did not (Fig.1F-G). The content of UTP23 protein in tumor tissue was also significantly higher than that in normal tissue (Fig.2).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHigh UTP23 expression has adverse effects on BRCA survivors\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e1083 cancer patients were identified based on the median value for the expression level of UTP23 mRNA in BRCA, with 542 being categorized as high and 541 as low. A detailed description of the clinicopathological features can be found in Table 1. Kaplan-Meier analysis revealed that patients with high UTP23 expression in the TCGA-BRCA data set had poorer overall survival (OS) than those with low expression (\u003cem\u003eP\u003c/em\u003e=0.008, Fig.3A). We also analyzed the OS of T stage, N stage, and M stage, and found that UTP23 expression was strongly correlated with OS in early disease in the early stage of the disease, while the relationship with prognosis was not very significant in the later stage of the disease (Fig.3B-G). Subgroup analysis indicated that there was a significant correlation between OS and UTP23 expression in patients who did not receive radiation therapy, but not in patients who did (Fig.3H-I). Furthermore, multivariate analysis showed that PAM50 and age were also very substantially related to overall survival. Patients with Her2 positive and basal breast cancer had worse overall survival, and those older than 60 years also had worse overall survival than younger patients (Table.2).\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" id=\"Tab1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eCorrelation between UTP23 expression and clinicopathologic characteristics of TCGA breast cancer patients.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCharacteristic\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLow expression of UTP23\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHigh expression of UTP23\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003en\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e541\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e542\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT stage, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e143 (13.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e134 (12.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e303 (28.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e326 (30.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e83 (7.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e56 (5.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11 (1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24 (2.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN stage, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e275 (25.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e239 (22.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e178 (16.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e180 (16.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42 (3.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e74 (7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42 (3.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34 (3.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eM stage, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.984\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eM0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e431 (46.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e471 (51.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eM1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9 (1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11 (1.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRace, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAsian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28 (2.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32 (3.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBlack or African\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e113 (11.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e68 (6.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAmerican\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWhite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e375 (37.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e378 (38%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.168\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;=60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e312 (28.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e289 (26.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e229 (21.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e253 (23.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHistological type, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInfiltrating Ductal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e333 (34.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e439 (44.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCarcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInfiltrating Lobular\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e155 (15.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e50 (5.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCarcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable border=\"1\" id=\"Tab2\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eThe univariate and multivariate analyses of overall survival according to UTP23 expression\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eUnivariate analysis\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMultivariate analysis\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal(N)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHazard ratio (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHazard ratio (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT stage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1079\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT1\u0026amp;T2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e906\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT3\u0026amp;T4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e174\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.608 (1.110\u0026ndash;2.329)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.012\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.468 (0.933\u0026ndash;2.308)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.097\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN stage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1063\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN0\u0026amp;N1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e872\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN2\u0026amp;N3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e192\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.163 (1.472\u0026ndash;3.180)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.024 (1.259\u0026ndash;3.254)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.004\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eM stage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e922\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eM0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e902\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eM1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.254 (2.468\u0026ndash;7.334)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.962 (1.015\u0026ndash;3.791)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.045\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePAM50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1042\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLumA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e562\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLumB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e204\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.663 (1.088\u0026ndash;2.541)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.019\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.334 (0.840\u0026ndash;2.118)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.222\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHer2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.261 (1.325\u0026ndash;3.859)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.003\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.437 (1.340\u0026ndash;4.431)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.004\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBasal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e195\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.285 (0.833\u0026ndash;1.981)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.257\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.640 (1.012\u0026ndash;2.656)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.045\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1082\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;=60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e601\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e482\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.020 (1.465\u0026ndash;2.784)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.262 (1.560\u0026ndash;3.281)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRace\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e993\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAsian\u0026amp;Black or African\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e241\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAmerican\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWhite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e753\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.912 (0.615\u0026ndash;1.350)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.644\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunctional and pathway enrichment analysis of UTP23\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo investigate the specific mechanism of the role of UTP23 in breast cancer development and progression, we analyzed samples with high and low UTP23 expression and found 980 differentially expressed genes (DEGs, |log2(FC)|\u0026gt;1, p.adj\u0026lt;0.05), of which 675 genes were significantly upregulated while 305 genes were significantly downregulated. A volcano plot illustrates the DEGs\u0026apos; expression profiles (Fig.4A). Additionally, we analyzed the co-expressed genes of UTP23, in which RAD21 was the most associated gene with UTP23 expression (Fig.4B), and it has been previously shown to be associated with poor prognosis in breast cancer, which adds to the evidence that UTP23 may be associated with poor prognosis in breast cancer\u003csup\u003e[21]\u003c/sup\u003e. In the analysis of DEGs enriched in \u0026nbsp;GO analysis and KEGG pathways, we found that the biological process in which UTP23 most likely to be involved included humoral immune respond、defense response to bacterium、antimicrobial humoral response, and the molecular function it was most likely to affect were endopeptidase inhibitor activity、hormone activity、receptor ligand activity and the cellular component it was most likely to affect were nucleosome、high-density lipoprotein particle、cornified envelope (Fig.4C). Genes associated with each signaling pathway were plotted in Fig.4D. The top 6 GO items and KEGG objects enriched for DEGs are shown in Fig.4E and Fig.4F, respectively. We also performed a GSEA analysis and found that the top 9 data sets with the most significant relationship with UTP23 were signaling by Rho GTPases, M phase, cell cycle checkpoints, neuronal system, deubiquitination, DNA repair, signaling by wnt, transmission across chemical synapses, chromatin-modifying enzymes (Table3, Fig.5). Furthermore, we draw UTP23-interaction proteins in the BRCA tissue network by using the STRING database (Fig.6A) and showed their co-expression scores with UTP23 (Fig.6B).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3\u0026nbsp;\u003c/strong\u003eReactome pathway enriched in high- and low-risk groups by using GESA.\u003c/p\u003e\n\u003ctable border=\"0\" cellpadding=\"0\" cellspacing=\"0\" width=\"558\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"54.91949910554562%\"\u003e\n \u003cp\u003e\u003cstrong\u003eID\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.449016100178891%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNES\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.880143112701253%\"\u003e\n \u003cp\u003e\u003cstrong\u003epvalue\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.343470483005367%\"\u003e\n \u003cp\u003e\u003cstrong\u003ep.adjust\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.407871198568873%\"\u003e\n \u003cp\u003e\u003cstrong\u003eFDR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"54.91949910554562%\"\u003e\n \u003cp\u003eREACTOME_SIGNALING_BY_RHO_GTPASES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.449016100178891%\"\u003e\n \u003cp\u003e1.495\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.880143112701253%\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.343470483005367%\"\u003e\n \u003cp\u003e0.030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.407871198568873%\"\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"54.91949910554562%\"\u003e\n \u003cp\u003eREACTOME_M_PHASE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.449016100178891%\"\u003e\n \u003cp\u003e2.118\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.880143112701253%\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.343470483005367%\"\u003e\n \u003cp\u003e0.030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.407871198568873%\"\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"54.91949910554562%\"\u003e\n \u003cp\u003eREACTOME_CELL_CYCLE_CHECKPOINTS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.449016100178891%\"\u003e\n \u003cp\u003e2.364\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.880143112701253%\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.343470483005367%\"\u003e\n \u003cp\u003e0.030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.407871198568873%\"\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"54.91949910554562%\"\u003e\n \u003cp\u003eREACTOME_NEURONAL_SYSTEM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.449016100178891%\"\u003e\n \u003cp\u003e1.868\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.880143112701253%\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.343470483005367%\"\u003e\n \u003cp\u003e0.030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.407871198568873%\"\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"54.91949910554562%\"\u003e\n \u003cp\u003eREACTOME_DEUBIQUITINATION\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.449016100178891%\"\u003e\n \u003cp\u003e1.729\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.880143112701253%\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.343470483005367%\"\u003e\n \u003cp\u003e0.030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.407871198568873%\"\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"54.91949910554562%\"\u003e\n \u003cp\u003eREACTOME_DNA_REPAIR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.449016100178891%\"\u003e\n \u003cp\u003e1.896\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.880143112701253%\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.343470483005367%\"\u003e\n \u003cp\u003e0.030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.407871198568873%\"\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"54.91949910554562%\"\u003e\n \u003cp\u003eREACTOME_SIGNALING_BY_WNT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.449016100178891%\"\u003e\n \u003cp\u003e1.537\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.880143112701253%\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.343470483005367%\"\u003e\n \u003cp\u003e0.030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.407871198568873%\"\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"54.91949910554562%\"\u003e\n \u003cp\u003eREACTOME_TRANSMISSION_ACROSS_CHEMICAL_SYNAPSES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.449016100178891%\"\u003e\n \u003cp\u003e1.701\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.880143112701253%\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.343470483005367%\"\u003e\n \u003cp\u003e0.030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.407871198568873%\"\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"54.91949910554562%\"\u003e\n \u003cp\u003eREACTOME_CHROMATIN_MODIFYING_ENZYMES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.449016100178891%\"\u003e\n \u003cp\u003e2.103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.880143112701253%\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.343470483005367%\"\u003e\n \u003cp\u003e0.030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.407871198568873%\"\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eUTP23 expression correlates with immune cells infiltration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMoreover, we used ssGSEA to examine whether UTP23 expression was associated with immune cell infiltration based on the finding that the UTP23 may participate in the humoral immune response. Figure.7A shows the relationship between immune cell infiltration and UTP23 mRNA expression. UTP23 expression was positively correlated with Th2 cells (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.001, r=0.332) and Tcm (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.001, r=0.332), and negatively correlated with pDC (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.001, r=-0.456) and CD8\u003csup\u003e+\u003c/sup\u003e T cells (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.001, r=-0.253), while Tem (\u003cem\u003eP\u003c/em\u003e=0.983, r=0.001) had no relationship with UTP23 expression (Fig.7). Furthermore, the enrichment scores corroborate previous findings, with higher UTP23 expression associated with higher enrichment scores of Tcm, Th2 cells, T helper cells, and Tgd (Fig. 7G).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInhibition of UTP23 can effectively inhibit the proliferation of breast cancer cells\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo confirm that UTP23 is an important target for breast cancer treatment, we carried out qRT-PCR of UTP23 on multiple breast cancer cells, the results showed that the UTP23 expression in breast cancer was indeed higher than that in normal breast cells MCF10A, especially the triple-negative breast cancer cell lines HCC-1806, MDA-MB-231, etc (Fig.8A). Subsequently, we confirmed that knockdown of UTP23 can effectively inhibit the proliferation of HCC-1806 and MDA-MB-231 by growth curve assay (Fig.8D-E) and clone formation assay (Fig.8F-G).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eRibosome synthesis is a complex, highly dynamic cellular metabolic process that requires many conserved assembly factors, and approximately fifty ribosomal proteins (r-protein) bind to ribosomal RNA (rRNA) during co-transcriptional processes\u003csup\u003e[\u003cspan additionalcitationids=\"CR23 CR24\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e. UTP23 is a new small subunit processing component, which is a conserved protein factor involved in the early assembly of ribosomal small subunits and plays an important role in the ribosome assembly process, structurally important regions include the C-terminal tail, zinc finger, helix α1, and the highly conserved PIN domain \u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. Studies early on focused mainly on the structure and function of UTP23, with few attempting to determine how it contributes to tumorigenesis and progression \u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eHere, we analyzed UTP23 with the TCGA database, GEO database and HPA database and found that its mRNA and protein expression in breast cancer was significantly higher than that in normal tissues, and its expression did not differ significantly between radiation therapy and non- radiation therapy patients, which means that if a patient is treated with radiation, it does not make UTP23-targeted drugs any less effective in treating him. For those who are fighting breast cancer, this is crucial. In addition, through Kalan-Meier analysis, we found that poor patient outcomes were associated with high UTP23 expression in breast cancer, and UTP23 was more significantly associated with poor patient prognosis in the early stage of the disease, suggesting its potential as a diagnostic and prognostic indicator. In other words, it is possible to dramatically increase the early diagnosis rate of breast cancer by identifying UTP23 expression in the early stages of the disease. However, it is necessary to further verify whether UTP23 can serve as a useful diagnostic and prognostic marker for different stages of breast cancer progression.\u003c/p\u003e \u003cp\u003eIn the research, functional enrichment analysis revealed that UTP23 may be involved in the humoral immune response, defense response to the bacterium, antimicrobial humoral response, which suggested that UTP23 may be closely related to the human immune system. Besides, GSEA analysis showed that UTP23 expression was related to signaling by Rho\u0026ensp;GTPases, M phase, cell cycle checkpoints, neuronal system, deubiquitination, DNA repair, signaling by wnt, transmission across chemical synapses, and chromatin-modifying enzymes. ssGSEA showed that UTP23 expression has a positive correlation with the infiltration of immune cells, including Tcm, Th2 cells and T helper cells. We also explored gene networks connected to UTP23 with STRING, noticing that KRR1 got the highest score, which has been previously reported as a type of tumor-associated antigens (TAAs) and has the potential to be used as a target for tumor vaccines\u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e. But whether UTP23 can play a role as a TAA in tumor immunotherapy needed further investigation.\u003c/p\u003e \u003cp\u003eTo further explore the specific role of UTP23 in breast cancer, we analyzed the mRNA expression of UTP23 in a series of human breast cancer cells by qRT-PCR, and the results showed that UTP23 was significantly higher in breast cancer cells than in human normal breast cells. UTP23 knockdown significantly inhibited the proliferation of human breast cancer cells HCC1806 and MDA-MB-231, proving that it is a very potential new target for breast cancer. We also highlight the need to further explore the specific mechanism of UTP23 in the progression of breast cancer.\u003c/p\u003e \u003cp\u003eCancer has always been the leading cause of death in humans. Although medical technology has enabled humans to overcome many diseases, cancer remains an untapped resource\u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e. Novel tumor therapies, such as antibody-coupled drugs, immune checkpoint inhibitors, tumor vaccines, and adoptive cellular immunotherapy, have emerged in recent years \u003csup\u003e[\u003cspan additionalcitationids=\"CR30 CR31\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/sup\u003e. The well-known CAR-T therapy is a type of relay immune cell therapy that entails isolating autologous tumor-infiltrating lymphocytes (TIL) or peripheral blood lymphocytes from tumor patients, sorting them, expanding them, activating them in vitro, and then re-transfusing them into the patient to obtain anti-tumor immunity\u003csup\u003e[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/sup\u003e. The substantial relationship between UTP23 and immune infiltration examined in this work leads to the conclusion that further research on UTP23 is urgently required since it may have huge application in cellular immunotherapy. In recent years, due to COVID-19, people have paid unprecedented attention to vaccines, and tumor vaccines have also become a hot track in tumor immunity\u003csup\u003e[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]\u003c/sup\u003e. According to studies, tumor cells contain tumor antigens that the immune system can recognize to distinguish them from healthy ones\u003csup\u003e[\u003cspan additionalcitationids=\"CR37\" citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]\u003c/sup\u003e. By expressing particular, immunogenic tumor antigens (such as peptides, DNA, and RNA) with the assistance of adjuvants such cytokines and chemokines, which in turn kill and eliminate tumor cells, tumor vaccines can activate or increase the body's natural anti-tumor immunity\u003csup\u003e[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]\u003c/sup\u003e. Interestingly, KRR1, which has previously been identified as a novel tumor-associated antigen, received the highest score in the STRING database analysis of UTP23-interacting proteins. This suggests that UTP23 is likely to be a noveltumor-associated antigen as well. Furthermore, based on the results of the bioinformatics analysis, we carried out experimental validation and verified that knockdown of UTP23 could successfully reduce the proliferation of breast cancer cells.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eAccording to our findings, UTP23 was strongly expressed in breast cancer tissues and associated with poor outcomes, suggesting it may be useful both as a diagnostic and prognostic marker for breast cancer. In addition, UTP23 also be related to immune infiltration, and future research should be conducted to investigate the role of UTP23 in the breast cancer immunotherapy. Finally, we confirmed by knocking down UTP23 in HCC-1806 and MDA-MB-231 cells that inhibition of UTP23 significantly inhibited the proliferation of breast cancer cells Thus, we highlight UTP23 as a promising target for treating breast cancer in this study.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate:\u0026nbsp;\u003c/strong\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u003c/strong\u003e Not applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material:\u0026nbsp;\u003c/strong\u003eThe datasets supporting the conclusion of this article are included within the article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u003c/strong\u003e The authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e The present study was partly supported by the Research Foundation of Taizhou People\u0026apos;s Hospital (No. ZL202025).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions:\u003c/strong\u003e Jindong Li: writing\u0026mdash;original draft preparation. Siman Xie: data analyses and interpretation. Benteng Zhang: data analyses and interpretation. Weiping He: writing\u0026mdash;review and editing. Yan Zhang: project conception and design. Huilian Hua: project conception and design. Li Yang: supervision. All authors participated and approved in writing or revising the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e: The authors have no acknowledgements.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSperduto PW, Mesko S, Li J, et al. Estrogen/progesterone receptor and HER2 discordance between primary tumor and brain metastases in breast cancer and its effect on treatment and survival[J]. Neurooncology. 2020;22(9):1359\u0026ndash;67.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMukama T, Kharazmi E, Xu X, et al. Risk-Adapted Starting Age of Screening for Relatives of Patients With Breast Cancer[J]. 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Cancer J Clin. 2020;70(2):86\u0026ndash;104.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"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":"UTP23, TCGA, Breast cancer, Biomarker, immune infiltration","lastPublishedDoi":"10.21203/rs.3.rs-2040046/v2","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2040046/v2","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eBreast cancer is one of the malignant tumors with a high incidence and mortality rate among women worldwide, and its prevalence is increasing year by year, posing a serious health risk to women. UTP23 (UTP23 Small Subunit Processome Component) is a nucleolar protein that is essential for ribosome production. As we all know, disruption of ribosome structure and function results in improper protein function, affecting the body's normal physiological processes and promoting cancer growth. However, little research has shown a connection between UTP23 and cancer.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe analyzed the mRNA expression of UTP23 in normal tissue and breast cancer using The Cancer Genome Atlas (TCGA) database and Gene Expression Omnibus (GEO) database, and the protein expression of UTP23 using The Human Protein Atlas (HPA) database. Next, we examined the relationship between UTP23 high expression and Overall Survival (OS) using Kaplan-Meier Plotters and enriched 980 differentially expressed genes in UTP23 high and low expression samples using GO/KEGG and GSEA to identify potential biological functions of UTP23 and signaling pathways that it might influence. Finally, we also investigated the relationship between UTP23 and immune infiltration and examined the effect of UTP23 on the proliferation of human breast cancer cell lines by knocking down UTP23.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eWe found that UTP23 levels in breast cancer patient samples were noticeably greater than those in healthy individuals and that high UTP23 levels were strongly linked with poor prognoses (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.008). Functional enrichment analysis revealed that UTP23 expression was connected to the humoral immune response. Besides, UTP23 expression was found to be positively correlated with immune cell infiltration. Furthermore, UTP23 knockdown has been shown to inhibit the proliferation of human breast cancer cells MDA-MB-231 and HCC-1806.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eTaken together, our study demonstrated that UTP23 is a promising target in detecting and treating breast cancer and is intimately linked to immune infiltration.\u003c/p\u003e","manuscriptTitle":"UTP23 is a promising prognostic biomarker and is associated with immune infiltration in breast cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":2,"date":"2022-10-12 14:24:01","doi":"10.21203/rs.3.rs-2040046/v2","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}},{"code":1,"date":"2022-09-09 18:58:02","doi":"10.21203/rs.3.rs-2040046/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":"d51b2bfe-1445-4f58-857d-430f21235e58","owner":[],"postedDate":"October 12th, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2022-12-23T08:44:29+00:00","versionOfRecord":[],"versionCreatedAt":"2022-10-12 14:24:01","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v2","identity":"rs-2040046","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2040046","identity":"rs-2040046","version":["v2"]},"buildId":"cBFmMYwuxLRRLfASyISRj","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}