Increased expression of UBE2T predicting poor survival of ovarian cancer: based on comprehensive analysis of UBE2s, clinical samples and the GEO database

Background (cid:0) Ubiquitin-conjugating enzymes E2 (UBE2) have been reported in the microenvironment of various malignant tumors, but their correlation with ovarian cancer remains elusive. Methods: The Oncomine, GEPIA, Kaplan-Meier Plotter, cBioPortal, and STRING databases were used to systematically analyze the expression pattern, prognostic value, genetic variation, and biological function of 12 members of the UBE2 gene family in ovarian cancer. UBE2T exhibited the greatest correlation with ovarian cancer and was thus further examined. Gene set enrichment analysis (GSEA), as well as analyses of function and pathway enrichment, somatic mutations, copy number variation, and methylation were performed, and the correlation with immune cell inltration was examined to explore the mechanism underlying aberrant UBE2T expression. Finally, the expression and prognostic value of UBE2T in ovarian cancer were veried by immunohistochemical evaluation of 131 clinical ovarian samples and the Gene Expression Omnibus (GEO) database (GSE51088, GSE73614, and GSE63885 datasets) analysis. Results: The mRNA levels of UBE2C , UBE2N , UBE2S , and UBE2T were signicantly upregulated in ovarian cancer compared with those in normal ovarian tissue. In patients with ovarian serous carcinoma, UBE2A , UBE2B , UBE2C , UBE2G , and UBE2T upregulation and UBE2R2 downregulation were associated with poor overall survival. Moreover, UBE2A , UBE2N , and UBE2T upregulation and UBE2G and UBE2R2 downregulation were associated with poor progression-free survival. Immunohistochemistry revealed that UBE2T was signicantly upregulated in ovarian malignant tumors compared with that in borderline tumors, benign tumors, and normal ovarian tissues, and its high expression was associated with poor prognosis. The Cox model showed that UBE2T upregulation was an independent risk factor affecting the prognosis of ovarian cancer (hazard ratio: 4.095, P= 0.029). The above results were veried in the GEO database. In addition, UBE2T was associated with specic immune cells and mainly involved in cell cycle-related events. Genomic analysis showed that TP53 and TTN mutations were associated with UBE2T expression. Gene copy number amplication and hypomethylation may be responsible for UBE2T upregulation in ovarian cancer. Conclusions: UBE2 family members may play a role in the development of ovarian cancer. Specically, UBE2T could serve as a new prognostic marker and therapeutic target for this disease. number of genes the statistical signicance. e Gene sets signicantly enriched in patients with high UBE2T expression (HALLMARK_E2F_TARGETS and HALLMARK_G2M_ CHECKPOINT). somatically mutated genes in patients with high UBE2T expression. somatically mutated genes UBE2T pathway analysis UBE2T high expression mutation the correlation between copy number variation and UBE2T expression. correlation between methylation


Background
Ovarian cancer (OC) is the most deadly malignancy of the female reproductive system and the fth leading cause of cancer-related death among women worldwide [1]. More than 70% of OC patients are diagnosed with advanced disease due to lack of typical clinical symptoms and effective diagnostic methods, which explains the high mortality rate of this disease. Despite major advances in surgical techniques, chemotherapy, and immunotherapy, the current treatments are still unsatisfactory, as the ve-year overall survival (OS) rate is approximately 30% [2]. Hence, the identi cation of biomarkers with high sensitivity and speci city, as well as the comprehension of their role in OC, are urgently needed.
The tumor microenvironment consists of multiple cellular and non-cellular components that support tumor growth and suppress antitumor response. The role of this system in tumor progression and its relevance for cancer immunotherapy have been extensively demonstrated [3]. Our previous analysis of genome-wide gene expression changes in HE4-transfected OC cells showed that ubiquitination is strongly involved in the development of malignant behavior. Recent ndings have also revealed that ubiquitination-related enzymes are important components of the tumor microenvironment. These enzymes include ubiquitin-activating (UBE1), ubiquitin-conjugating (UBE2), and ubiquitin-ligating (UBE3) enzymes, which facilitate ubiquitination and lead to proteasome-mediated protein degradation [4]. The UBE2 family is composed of 40 members, which are considered as pivotal factors in the ubiquitination cascade [5]. Some studies have reported that aberrant expression of UBE2s in tumors has an impact on tumor prognosis, suggesting that UBE2s are informative tumor markers for early diagnosis and prediction of prognosis [6][7][8]. However, the biological role and action mechanism of these enzymes in OC have not been fully elucidated. UBE2T, a member of the UBE2 family, was the rst to be de ned as a key factor in the Fanconi anemia pathway [9]. Several studies have con rmed that UBE2T plays a carcinogenic role in various types of cancer, including hepatocellular carcinoma, as well as lung, breast, stomach, bladder, and prostate cancer, but its expression level and prognostic value in OC are still unclear [10][11][12][13][14][15].
In this study, we selected 12 gene members of the UBE2 family with a possible relationship with OC, and used bioinformatics analysis to evaluate their expression and prognostic value. UBE2T exhibited the greatest correlation with OC, which was veri ed by immunohistochemistry (IHC) and GEO database analysis.
Moreover, the molecular function of UBE2T was explored. Our study provided valuable hints for the design of a new targeted therapy for OC.

Methods
Oncomine database analysis Oncomine (http://www. oncomine. org) is a web-based gene chip data-mining platform consisting of microarray databases covering 19 types of human cancer. It includes 715 tumor microarrays, as well as 86,733 cancer and normal tissue samples [16]. Oncomine can be used to identify genes with differential expression in cancers and their respective normal tissues. We used Oncomine to analyze the mRNA expression of UBE2s in different types of cancer. The standardized normalization and parameters were as follows: P value <0.01, fold change >2, and gene ranking in the top 10%.

GEPIA dataset analysis
Gene Expression Pro ling Interactive Analysis (GEPIA) (http://gepia.cancer-pku. cn/) is a database of RNA sequencing expression data from The Cancer Genome Atlas (TCGA) and Genotype-tissue Expression dataset (GTEx) projects, including 33 tumor types, 9,736 tumor samples, and 8,587 normal samples [17]. In this study, we used GEPIA to verify the differential expression of UBE2s in OC and normal ovarian tissues. In addition, the database was used to evaluate the correlations between different UBE2 members. P<0.05 indicated statistically signi cant differences.
GSE63885, based on the GPL570 platform (HG-U133_Plus_2; Affymetrix Human Genome U133 Plus 2.0 Array), contained 70 serous and 5 nonserous OC samples with complete clinical data [20]. All gene expression data were subjected to log2 transformation.

TCGA data extraction and analysis
Somatic mutations data corresponding to UBE2T high-low expression samples were downloaded from the TCGA-OV database. Data regarding OC-related gene copy number variations were downloaded from the cBioPortal (http://www.cbioportal.org/), and the samples were divided into four groups according to the copy number as follows: single deletions, diploid normal copy, low-ampli cation and high-ampli cation. Wilcoxon test was used to compare the expression of UBE2T between two groups. UBE2 genetic variations in ovarian serous cystadenocarcinoma (such as ampli cations, deep deletions, fusions, and mutations) were also analyzed in the cBioPortal database. OC methylation data were obtained from the Xena browser (https://xenabrowser.net/datapages/). Pearson correlation analysis was applied to evaluate the correlation between the methylation level and the expression of UBE2T.
This tool is used to verify the impact of biomarker genes identi ed from GEO, TCGA, and the Cancer Biomedical Informatics Grid project on survival [21]. In this study, the Kaplan-Meier plotter was used to evaluate the prognostic value of different UBE2 members. OC patients were separated into a high-expression or a low-expression group, the best cutoff values were determined by algorithms embedded in KM plotter. The relevant hazard ratios (HRs), 95% con dence intervals (CIs), and log-rank P values were calculated.

String protein network analysis
The Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database (https://string-db. org/) is an effective tool for the analysis of functional associations between proteins. It contains information on 9.64 million proteins and 13.8 million interactions in 2,031 species [22]. We used the STRING database to predict the upstream and downstream regulatory proteins of UBE2s and the regulatory relations between these proteins in Homo sapiens. Interactions with a combined score >0.7 (high con dence) were considered signi cant. This information was used to construct a protein-protein interaction (PPI) network, and the Gene Ontology (GO) biological process and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment were analyzed by ClueGO and CluePedia plug-ins in Cytoscape3.2.1.

Metascape enrichment and GSEA analysis
Page 5/37 The Metascape resource (http://metascape.org/gp/index. html) was utilized to predict the potential biological functions of target genes [23]. We used the cBioPortal database to identify genes that were coexpressed with UBE2T. The co-expressed genes with Spearman's correlation coe cient ≥0.35 or <−0.35 were imported into Metascape for GO and KEGG pathway enrichment analysis. In the TCGA-OV database, 379 OC cases were divided into a high-and a low-expression group according to the median expression value of UBE2T. GSEA 3.0 software was used to identify the potential hallmark between the two expression groups by using default weighted enrichment statistics. "h.all.v6.2.symbols.gmt" was set as gene set database, and gene permutations were set to 1000.

TISIDB immune analysis
The tumor-immune system interactions (TISIDB) database (http://cis. hku. hk/TISIDB) explores the function of genes and their role in tumor-immune interactions, and contains data on 998 genes related to antitumor immunity in 30 TCGA cancer types [24]. We employed the TISIDB database to predict correlations between UBE2T expression and tumor immune in ltrating cells, immune modulators, and major histocompatibility complex (MHC) molecules in OC.

Participants and specimens
A total of 131 ovarian para n-embedded tissue samples, surgically removed from inpatients at the

Immunohistochemistry
The rabbit anti-human polyclonal antibody against UBE2T (10105-2-AP, 1:75) was purchased from Proteintech (Chicago, IL, USA), and the Streptavidin-peroxidase (SP) immunohistochemistry kit was purchased from Zsbio (Beijing, China). Ovarian tissues were xed in 10% formalin, and 5-μm thick para n sections were prepared. The SP method was used to detect the expression of UBE2T. The presence of brownish-yellow staining on the cell membrane and in the cytoplasm was indicative of UBE2T positivity. A lung cancer sample was used as a positive control and phosphate-buffered saline was used instead of the antibody as a negative control. The staining intensity was classi ed as negative, light-yellow, brownishyellow, and dark-brown, and scored 0, 1, 2, and 3, respectively. Based on the percentage of positive cells, the following categories were created:<5%, 5%-25%, 26%-50%, 51-75%, and >75%, which scored 0, 1, 2, 3, and 4, respectively. The nal score was the product of the cell staining intensity score and the positive cell rate score: 0-2 was negative (-), 3-4 weakly positive (+), 5-8 moderately positive (++), and 9-12 strongly positive (+++). Negativity and weak positivity were considered indicative of low expression, whereas moderate and strong positivity corresponded to high expression. To minimize the errors, each tissue section was independently reviewed by two observers. Inconsistent results were reviewed by a third observer.
Statistical analysis SPSS 22.0 software (SPSS Inc., Chicago, IL, USA) was used for statistical analysis, GraphPad Prism 8.0 (La Jolla, CA, USA) and "survminer" R package were employed to generate gures. Chi squared and Fisher's exact tests were used to analyze counting data, while Student's t-test was used to analyze measurement data. Kaplan-Meier survival analysis and log-rank tests of the GEO database (GSE63885 and GSE73614) were used to assess the in uence of UBE2T on OC prognosis, and the GSE51088 dataset was used to compare the expression of UBE2T in different groups. Univariate and multivariate analyses were based on Cox proportional hazard regression models. P values <0.05 were de ned as indicative of statistical signi cance. The patients with malignant OC were followed up, and inpatient data and telephone contacts were available.
The last date of follow-up was November 30, 2019, and OS was de ned as the time interval between the date of surgery and the date of death or follow-up endpoint.
Next, we used the GEPIA database to compare the mRNA levels of different UBE2s in OC and normal ovarian tissues (Fig. 1b). The results indicated that the expression levels of UBE2C, UBE2F, UBE2N, UBE2S, and UBE2T were signi cantly higher in OC tissues than in normal ovarian tissues, whereas those of UBE2A, UBE2B, UBE2G, UBE2I, UBE2M, UBE2R2, and UBE2V2 were not signi cantly different between the two groups. Combined analysis of the GEPIA and Oncomine databases revealed that the expression of UBE2C, UBE2N, UBE2S, and UBE2T was upregulated in OC compared with that in normal tissues. cases from TCGA, Provisional) of ovarian serous cystadenocarcinoma were analyzed (Fig. 2a,b) Kaplan-Meier plotter analysis was applied to assess the relationship between the mRNA expression of individual UBE2 members and progression-free survival (PFS) in 1436 clinical OC patients (Fig. 3). We found that the mRNA levels of UBE2A, UBE2B, UBE2C, UBE2G, UBE2N, UBE2R2, and UBE2T were associated with OC prognosis, while those of the remaining members were not. Increased mRNA levels of UBE2A, UBE2B, UBE2C, UBE2N, and UBE2T, and decreased mRNA levels of UBE2G and UBE2R2 were signi cantly associated with poor prognosis.
Ovarian serous tumors represent the most common histological subtype among ovarian tumors. The prognostic value of UBE2 genes with expression levels signi cantly correlated with OC prognosis was further investigated in ovarian serous tumors. Increased mRNA levels of UBE2A (Fig. 4a), UBE2B (Fig. 4b), UBE2C (Fig. 4c), UBE2G (Fig. 4d), and UBE2T (Fig. 4g) were signi cantly correlated with poor OS in patients with ovarian serous tumors. Interestingly, increased expression of UBE2A (Fig. 5a) and UBE2G (Fig. 5d) was associated with shorter OS but longer PFS. Moreover, UBE2N (Fig. 5e) and UBE2T (Fig. 5g) upregulation was signi cantly correlated with poor PFS in patients with ovarian serous tumors, while UBE2R2 downregulation (Fig. 4f, 5f) was correlated with poor OS and PFS. Based on the above results, in patients with ovarian serous tumors, the expression levels of UBE2R2 and UBE2T were predictors of favorable and poor prognosis, respectively.
By integrating the results of Oncomine, GEPIA, and KM plotter, we observed that UBE2T was signi cantly upregulated in ovarian cancer compared to normal tissues, and that high UBE2T mRNA expression was signi cantly correlated with shorter OS and PFS in patients with ovarian serous tumors. Moreover, we assessed the prognostic value of UBE2T in relation to the pathological grade, FIGO stage, and TP53 status of ovarian serous tumors. Increased expression of UBE2T was correlated with poor OS in patients with tumors of all FIGO stages (P<0.05, Fig. 6c,d). Moreover, UBE2T upregulation predicted poor OS in patients with both mutated and wild-type TP53 (P<0.05, Fig. 6e,f). Although among patients with well/moderate and poor differentiation, those with high UBE2T expression tended to have shorter OS compared to patients with low UBE2T expression, the difference was not statistically signi cant (P>0.05, Fig. 6a,b).

Analysis of the interactions between UBE2 family members
Spearman's correlation coe cients were calculated by using the GEPIA platform to investigate the relationships between the expression levels of different UBE2 members in OC (Fig. 6g). The coe cients ranged from 0.013 (UBE2B vs. UBE2M) to 0.67 (UBE2C vs. UBE2S). The results indicated a moderate positive correlation between the expression levels of UBE2A, UBE2N, UBE2S, UBE2T, and UBE2V2 (0.3<r≤0.7), and a low positive correlation between the expression levels of UBE2C, UBE2F, UBE2M, and UBE2R2 (0<r≤0.3). However, UBE2B showed a weakly positive correlation with other UBE2 genes, in addition to a weakly negative correlation with UBE2C (r=-0.017).

Molecular mechanisms related to UBE2T expression in ovarian cancer
A total of 536 genes were obtained from the cBioPortal database, based on Spearman's correlation coe cients higher than 0.35 between their expression and that of UBE2T. Information about the coexpressed genes is shown in Additional le 2. The Metascape portal was used, and P<0.01 was set as the threshold value to screen the top GO annotations and KEGG pathway results regarding UBE2T and its interactors. The top 20 GO enrichment items were classi ed into three functional groups: biological process (BP, 13 items), molecular function (MF, 1 items), and cellular component (CC, 6 items) (Fig. 7a). Regarding the BP, the genes co-expressed with UBE2T were mainly involved in cell division, cell cycle phase transition, DNA replication, DNA repair, and DNA conformation changes. Based on the MF, the identi ed genes were mainly associated with catalytic activity, acting on DNA. In terms of CC, the genes were enriched in the following categories: chromosomal region, nuclear chromosome, spindle, microtubule organizing center, and replication fork. The top 14 KEGG pathways for genes that were co-expressed with UBE2T are shown in Fig.  7b. Among these, cell cycle signaling, DNA replication, spliceosome, Fanconi anemia, and p53 signaling pathways were found to be involved in OC tumorigenesis and progression. To further determine the relationship between the enriched terms, a similarity network was constructed, where terms with a similarity >0.3 were connected by edges. The network was visualized by using Cytoscape. Each node represented an enriched term and was colored according to its cluster (Fig. 7c), and P-value (Fig. 7d). In patients with high UBE2T expression, signi cantly upregulated gene sets with nominal P<0.05 and FDR<0.25 included "HALLMARK_E2F_TARGETS" and "HALLMARK_G2M_ CHECKPOINT". The enrichment plots are shown in Fig.   7e.
The presence of somatic mutations was investigated in cases with high and low UBE2T expression. TP53 and TTN were the top two mutated genes in both groups, and a high frequency of mutations in DST, CSMD1, MUC17, and NEB genes was speci cally found in patients with high UBE2T expression. Moreover, mutations in TOP2A, VPS13B, NF1, AHNAK, and FLG2 were signi cantly enriched in patients with low UBE2T expression (Fig. 7f,g). KEGG pathway analysis further demonstrated that in patients with high UBE2T expression the mutations mainly affected focal adhesion, calcium signaling, and ECM-receptor interactions (Fig. 7h). To explore the cause of UBE2T upregulation in OC, we analyzed its correlation with gene copy number and methylation level. We found that UBE2T expression increased with the copy number. Therefore, the high level of UBE2T could be partially attributed to gene ampli cation (Fig. 7i). The expression of UBE2T was negatively correlated with the methylation level, as hypomethylation was associated with high UBE2T expression (Pearson' s (372)=-0.125, P=0.0162, Fig. 7j).

Correlation between immune factors and UBE2T expression in ovarian cancer
Recently, immunotherapy has received increasing attention, becoming a new treatment strategy for ovarian cancer. We further explored the correlation between immune factors, including tumor-in ltrating lymphocytes  (Fig. 8h). Therefore, UBE2T may affect the immune activity in the OC microenvironment by regulating the above immune factors.
UBE2T expression in different ovarian tissues IHC staining demonstrated that UBE2T was mainly localized in the cytoplasm and the plasma membrane. The rates of positive and highly positive expression of UBE2T in ovarian epithelial malignant tumors (89.53% and 72.09%, respectively) were signi cantly higher than in ovarian epithelial borderline tumors (55.00% and 25.00%, respectively), ovarian epithelial benign tumors (26.37% and 13.34%, respectively), and normal ovarian tissues (20.00% and 10.00%, respectively, Fig. 9b,c). Consistently, GSE51088 analysis results showed a signi cant upregulation of UBE2T in malignant tumors compared to borderline tumors, benign tumors, and normal ovary (Fig. 9d).

Relationship between UBE2T expression and clinicopathological features of epithelial ovarian cancer
According to the median expression value of UBE2T, OC patients in GSE datasets were divided into a highexpression and a low-expression group. We found a signi cantly higher expression of UBE2T     Table 4). Furthermore, high UBE2T expression was con rmed to be a predictor of poor OS in the GSE73614 (P=0.035, Fig. 9f) and GSE63885 (P=0.03, Fig. 9g) datasets.
Moreover, application of the Cox regression model to the GSE63885 dataset con rmed high UBE2T expression as an independent risk factor for OS in OC patients (HR=1.717, P=0.031, Table 5). Discussion E2-conjugating enzymes play a central role in the formation and progression of various tumors [30]. Although the role of some UBE2 members in OC has been con rmed, to our knowledge, no studies have comprehensively analyzed the prognostic value and functional mechanism of the UBE2 family in OC. In this study, a systematic bioinformatics analysis was conducted to evaluate the potential role of UBE2 family genes as biomarkers of OC.
UBE2A and UBE2B are two RAD6 homologs originally identi ed in yeast and required for DNA repair [31]. They are both upregulated in OC compared to normal ovarian tissues, and their high expression is associated with poor prognosis in OC patients [6]. Downregulation of RAD6 was previously found to reduce the expression of the cancer stem cell markers, ALDH1A1 and SOX2, and to increase the sensitivity of OC cells to carboplatin [32]. UBE2C is a key regulator of cell cycle progression and mitosis [33]. It is highly expressed in epithelial ovarian cancer (EOC), especially in high-grade serous adenocarcinoma, and its high expression is an independent risk factor affecting the prognosis of EOC patients [34]. Another study showed that blocking UBE2C expression by RNA interference inhibits the growth of OC cell lines [35]. Two neddylation conjugating E2 enzymes, UBE2M and UBE2F, were found to play essential roles in paclitaxel (PTX)-induced cytotoxicity and tubulin polymerization in OC cell lines. A recent study reported that UBE2M and UBE2F knockdown impairs protein neddylation and reduces the antitumor activity of PTX in OC, suggesting new research directions on PTX chemoresistance [36]. The results of our bioinformatics analyses were consistent with previous reports. Kaplan-Meier plotter analysis showed that increased UBE2A and UBE2B expression was associated with poor PFS in OC patients and poor OS in patients with serous ovarian cancer. UBE2C expression was signi cantly higher in OC compared to normal tissues, and predicted poor prognosis.
Although UBE2F and UBE2M were signi cantly more expressed in OC compared to normal tissues, their levels had no correlation with prognosis.
UBE2I and UBE2N are involved in DNA repair and cell apoptosis [37,38]. UBE2I expression was higher in OC than in normal tissues, and correlated with clinicopathologic parameters. Moreover, UBE2I expression level was higher in ovarian serous carcinoma compared to ovarian myxoid carcinoma, endometrioid carcinoma, and clear cell carcinoma, and in advanced or poorly differentiated EOC compared to early or welldifferentiated EOC. Guo et al. reached similar conclusions, suggesting that UBE2I may be involved in the progression and histopathological differentiation of OC [39,40]. The downregulation of UBE2N in OC may be related to the acquisition of resistance to PTX through the DNMT1-CHFR-Aurora A pathway. Thus, UBE2N is a potential target of drugs aiming at reversing PTX resistance in patients with OC [41]. UBE2R2 plays a role in the ubiquitin-proteasome pathway [42], and the combination of UBE2R2-SMYD3-p53 signi cantly promotes p53 ubiquitination and degradation, thereby inducing EOC progression and metastasis [43]. However, some of our data were inconsistent with previous results. Oncomine analysis showed that UBE2I and UBE2N were signi cantly downregulated and upregulated in OC, respectively. UBE2N overexpression was associated with poor PFS in OC patients, while UBE2R2 downregulation correlated with poor OS and PFS in patients with ovarian serous tumors. We attribute these contradictory ndings to background heterogeneity between different databases, as well as to the limited number of samples.
Currently, little is known about the biological function of UBE2G, and even less about its role in tumors. Previous studies found that UBE2G is strongly expressed in the skeletal muscle and participates in the degradation of muscle-speci c proteins [44]. Our ndings demonstrated that the expression level of UBE2G in OC was signi cantly lower than in normal ovarian tissues, and that this downregulation was associated with poor PFS in OC patients, suggesting a tumor-suppressive role of this protein in OC. This possibility needs to be veri ed by further experiments.
Although there are few studies on the roles of UBE2S and UBE2V2 in OC, both genes are involved in the occurrence and progression of other tumors. UBE2S was reported to be aberrantly expressed in some gynecological tumors, including breast, cervical, and endometrial cancer, and its knockdown inhibits the proliferation of cancer cells and promotes apoptosis [45][46][47]. UBE2V2 overexpression seems to be involved in the pathogenesis of gastric cancer, and is signi cantly associated with poor prognosis in ER-positive/HER2negative breast cancer [48,49]. The current study demonstrated that both UBE2V2 and UBE2S were signi cantly upregulated in OC compared to normal tissues. However, there was no obvious correlation between their expression levels and prognosis.
UBE2T, also known as FANCT or HSPC150, is essential for FANCD2 monoubiquitination. It combines with a speci c ubiquitin E3 ligase to degrade speci c substrates, contributing to DNA repair in the Fanconi anemia pathway and playing a key role in cell proliferation and the maintenance of genomic stability [50]. Recently, UBE2T expression has been reported to be signi cantly upregulated in hepatocellular carcinoma [10], renal cell carcinoma [51], prostate cancer [15], breast cancer, and lung adenocarcinoma [52], and its high expression is associated with poor prognosis in patients. Consistently, after integrating the data on gene expression and prognostic value obtained from different databases, we found that UBE2T was signi cantly overexpressed in OC compared to normal ovarian tissues, and that its high expression was associated with poor prognosis in both OC and serous ovarian cancer.
Various studies have shown that speci c genetic alterations are related to cancer prognosis and potential predictors of cancer metastasis [53,54]. To verify the correlation between alterations in UBE2 genes and OC, the cBioPortal database was analyzed, revealing an overall rate of genetic alterations ranging from 13.5% to 28.47% in the OC dataset, and a percentage of individual gene alterations ranging from 0.9% to 4%. However, alterations in single UBE2 genes had no signi cant impact on OS or DFS, suggesting that these changes did not directly affect OC prognosis.
Since UBE2T displayed the greatest correlation with OC among UBE2 family members, its clinical effects in OC were further investigated. IHC staining and GEO database analysis con rmed that UBE2T expression was signi cantly increased in ovarian epithelial malignant tumors compared to ovarian epithelial borderline tumors, benign tumor, and normal tissues. The expression level of UBE2T in FIGO stage III-IV tumors was signi cantly higher than in FIGO stage I-II tumors, suggesting that UBE2T may promote OC progression. In addition, Cox multivariate analysis indicated high UBE2T expression as an independent risk factor affecting the prognosis of patients with EOC. Therefore, UBE2T is a candidate biomarker for the prediction of OC prognosis.
Multiple evidence indicates that UBE2T acts as an oncogene in a variety of tumors, but its molecular mechanism of action varies in different types of cancer. One study showed that UBE2T plays a role in the proliferation and invasion of hepatocellular carcinoma cells by regulating the G2/M transition of the cell cycle through the cyclin B1-CDK1 pathway [55], while another study found that UBE2T enhances p53 ubiquitination and degradation, promoting the carcinogenesis of hepatocellular carcinoma [10]. Furthermore, UBE2T downregulation reduces the activity of the PI3K/Akt signaling pathway, thus inhibiting the proliferation and migration of renal cell carcinoma [51] and osteosarcoma cells [56]. Moreover, UBE2T promotes breast cancer progression by directly regulating the BRCA1/BARD1 complex [12]. Furthermore, UBE2T promotes nasopharyngeal carcinoma cell proliferation, invasion, and metastasis by activating the AKT/GSK3β/β-catenin pathway [57]. Finally, UBE2T silencing in bladder or gastric cancer was found to induce cell cycle arrest in G2/M phase, thereby promoting cancer cell apoptosis and inhibiting tumor growth [13,14]. However, the role of UBE2T in OC has not yet been investigated.
To clarify the role of UBE2T in OC at the molecular level, the functions and relevant pathways of UBE2T coexpressed genes were investigated by enrichment analysis using the Metascape database. These genes were mainly involved in processes related to cell cycle, including cell division, cell cycle phase transition, DNA replication, and DNA repair, and predominantly implicated in cell cycle regulation, Fanconi anemia signaling, and p53 signaling. GSEA results revealed that patients with high UBE2C expression were signi cantly enriched in E2F_TARGETS and G2M_ CHECKPOINT. KEGG pathway analysis elucidated that the UBE2T high expression mutation group was signi cantly enriched in focal adhesion, calcium signaling, and ECM-receptor interactions. These processes are critical for tumor progression, which could explain the role and molecular function of UBE2T in OC.
In patients with high UBE2T expression, TP53, TTN, DST, FAT3, CSMD1, MUC16, and MUC17 exhibited a signi cant rate of alterations. TP53 mutation frequency in ovarian serous carcinomas has been reported to range from 50% and 80% [58]; ubiquitous TP53 mutations are characteristic of high-grade serous ovarian cancer (HGSOC) and related to relapse [59]. TTN encodes a large polypeptide expressed in many cancer cell types and involved in oncogenesis, and its mutation has been found in OC by using an the next-generation sequencing (NGS) library [60]. CSDM1 was present in the genome-wide homozygous deletion pro ling of HGSOC [61]. Gene expression pro ling showed that MUC16 and MUC17 are overexpressed in the majority of serous and mucinous ovarian carcinomas, respectively [62,63]. We hypothesize that mutations in these genes may be related to the high level of UBE2T expression in OC. In addition, gene ampli cation and hypomethylation were also identi ed as possible causes of UBE2T overexpression.
Growing evidence is being provided that the interaction between immune and cancer cells in the tumor microenvironment has an impact on tumor progression [30]. OC is an immunogenic tumor that can be recognized and attacked by the immune system. The accumulation of TILs in OC microenvironment is related to prolonged OS of the patients, while immune escape events are associated with poor prognosis [64,65]. In this study, the correlation between UBE2T expression and the levels of in ltrating immune cells was analyzed by using the TISIDB-OV database. Particularly strong correlations were found between UBE2T expression and the level of speci c TIL populations (eosinophils, Act_CD4, neutrophils, and memory B cells), immune inhibitors (CSF1R, TGFB1, KDR, and CD160), immune stimulators (NTSE, TNFSF14, and TNFSF15), and MHC molecules (HLA-E, HLA-DOA, HLA-DQA1, and HLA-DQB1). The in ltration of B cells in HGSOC promotes anti-tumor responses, and chemotherapy enhances memory B cell response. Hence, the presence of B cells is a predictor of improved survival in patients with OC [66]. CSF1R is overexpressed in OC and the determination of its circulating levels proved useful for disease detection and the evaluation of therapeutic e cacy [67]. It has been reported that the cells of ovarian carcinoma produce large amounts of TGF-β1, which facilitates their escape from the immune system, and the clinical e cacy of immunotherapy based on TGF-β1-silenced tumor vaccines for OC has been investigated [68]. We speculate that UBE2T overexpression may cause the reduction or depletion of immune cells, leading to tumor progression by immune escape.

Conclusion
In summary, our bioinformatics analyses highlighted a potential role of UBE2s in OC onset and progression. However, our ndings need to be supported by additional research and clinical data. IHC and GEO analysis con rmed that UBE2T was signi cantly upregulated in OC, and that its high expression was an independent risk factor and prognostic predictor for OC. UBE2T is also a potential biomarker for early diagnosis and a candidate immune-related therapeutic target for OC. However, the biological properties of UBE2T and the molecular events related to its overexpression in OC need to be further explored.         Molecular mechanisms related to UBE2T expression in ovarian cancer (Metascape). a Bar graph of GO enriched terms colored by P-values. b Bar graph of KEGG enriched terms colored by P-values. c Network of enriched terms colored according to clusters: terms belonging to the same cluster are more closely related to Page 33/37 each other. d Network of enriched terms colored according to P-value: the higher the number of genes in each term, the higher the statistical signi cance. e Gene sets signi cantly enriched in patients with high UBE2T expression (HALLMARK_E2F_TARGETS and HALLMARK_G2M_ CHECKPOINT). f Top 20 somatically mutated genes in patients with high UBE2T expression. g Top 20 somatically mutated genes in patients with low UBE2T expression. h KEGG pathway analysis in the UBE2T high expression mutation group. i Analysis of the correlation between copy number variation and UBE2T expression. j Analysis of the correlation between methylation and UBE2T expression