Identification of TNFRSF4 as a Diagnosis and Prognosis Biomarker Associated with Immune Microenvironment in Uterine Corpus Endometrial Carcinoma


 BackgroundThe interaction between tumor microenvironment (TME) and tumors offers various targets in mounting anti-tumor immunotherapies. However, the diagnostic and prognostic biomarkers in uterine corpus endometrial carcinoma (UCEC) are still limited. Here, we aimed to analyze the TME features and identify novel prognostic biomarkers for UCEC. MethodsESTIMATE, CIBERSORT, protein-protein interaction (PPI) network, univariate Cox regression, and functional enrichment analysis were performed to identify immune- and survival-related hub genes as well as possible molecular mechanisms. The limma package and the deconvolution algorithm were adopted to estimate the tumor-infiltrating immune cells (TICs) abundance and their relationship with the target gene. Tissue microarrays (TMAs) of UCEC were evaluated to validate protein expression of the identified immune markers, including TNFRSF4, CD4, and CD8. The receiver operating characteristic (ROC) curve was used to determine the efficacy of TNFRSF4 in diagnosing UCEC. ResultsTwo genes, TNFRSF4 and S1PR4, were screened out from 386 intersection differential expression gene (DEGs) shared by ImmuneScore and StromalScore in UCEC. Highlighted by TNFRSF4, we found that it was not only positively correlated with the TICs (mainly CD4+ T cells, CD8+ T cells, and Tregs) but significantly related to diagnosis and prognosis in patients of UCEC, both verified by data from the TCGA database and clinical samples. ConclusionsCollectively, TNFRSF4 could serve as a high-profile biomarker to robustly predict immune microenvironment, clinical diagnosis and prognosis for UCEC.

for uterine corpus cancer, the ve-year survival rate could achieve 80%, approximately [8] . In clinical ndings, all the survivors from the early stage are confronted with enormous risks for metastasis or recurrence. Once it happened, there is, in most cases, only less than four months left for the patients despite that the advanced therapeutic patterns such as neoadjuvant chemotherapy, radiotherapy, and target treatment are available [9] .
The tumor microenvironment (TME) is necessary for the living of tumor cells. It, either directly or indirectly, affects tumor occurrence and development through maintaining a quiescent state of immune contexture [10] , promoting tumor angiogenesis [11] , changing biological features of carcinoma [12] , or even regulating the cancer stem cell activity [13,14] . The orchestration of these changes not only engenders tumor survival and expansion by establishing a suitable TME but creates heterologous cell types within tumors by giving tumor selection for mutations [15] . Additionally, TME is inextricably linked to tumor immune suppression or activation. In addition to tumor cells, the stromal cells, chemokines, and cytokines, TME comprises innate immune cells (including macrophages, neutrophils, dendritic cells, myeloid-derived suppressor cells, and natural killer cells) and adaptive immune cells (including T cells and B cells) [16] . Both populations of immune cells innately modulate tumor cell-intrinsic and extrinsic processes within TME. To date, much work has been devoted to establishing moderate-resolution TME data for the analysis and characterization of the cross-talk between the tumor and its microenvironment, such as immunohistochemistry (IHC) and bulk tissue microarrays (TMAs) [17,18] . ESTIMATE (Estimation of Stromal and Immune cells in Malignant Tumor tissues using Expression data) is a highly rated tool developed by Kosuke Yoshihara and his co-workers for predicting tumor purity mainly based on the ssGSEA algorithm [19] , which has helped us to estimate the abundance of tumor-in ltrating lymphocytes and subsequently stratify patients for predicting clinical outcomes [20] . Increasing evidence has elucidated that the difference in the e cacy of tumor immunotherapy was mainly due to the heterogeneity of TME [21] . Even though most of these valuable methods and theories have been well applied and tested in many cancers, many works are still needed to make sense of the correlation between TME biological characteristics and the aberrant expression of immune-related genes in UCEC.
Therefore, more reliable and effective surrogate biomarkers are left to be explored to predict the pathological behavior of UCEC and enable the strati cation of patients according to immune-related criteria for improving prognostic and selecting appropriate adjuvant therapy to guide clinical decisions.
Here, we used the ESTIMATE algorithms and the tumor-in ltrating immune cells (TICs) pro le to perform a comprehensive analysis of TME and detect related gene expression in patients with UCEC, through which TNFRSF4 was identi ed to be associated with the diagnosis and prognosis of UCEC. Moreover, to support our ndings, clinical specimens were applied to validate the expression of TNFRSF4 in UCEC and adjacent normal tissues. The correlations of TNFRSF4 with clinicopathologic features, immune-related markers (including CD4 and CD8) were evaluated either. Overall, our results indicated that TNFRSF4 might be a diagnostic-and prognostic-valued biomarker, as a crucial role in TME of UCEC.

Raw Data Acquisition and Processing
Transcriptome RNA-seq data of 587 UCEC cases (552 tumor samples and 35 controls) and corresponding clinical information were obtained from the TCGA database (https://portal.gdc.cancer.gov/).

Estimation Evaluation and DEGs Generation
Estimate algorithm was applied to determine the immune-stromal component in TME of each sample utilizing estimate package in R software (https://r-forge.r-project.org/), the nal results of which were calculated as StromalScore, ImmuneScore, and ESTIMATEScore, representing the ratio of stromal cell, immune cell, and the summation of both cells, respectively [22] . The higher scores correspondingly indicated more elevated composition abundance. Based on the median of the StromalScore and ImmuneScore, samples were divided into either the high or low score groups, and survival analysis was carried out by R software loaded with survival and survminer packages [23] . 541 tumor samples were nally analyzed with complete survival information, and the survival curve was produced by the Kaplan-Meier method and compared between groups by the log-rank test. At the same time, clinical data were acquired and analyzed to ascertain the correlation between scores and clinical parameters, including age and grade. Then, differentially expressed genes (DEGs) of StromalScore and ImmuneScore between the high and low score groups were screened out by limma package [24] with false discovery rate (FDR) < 0.05 and |log fold change (FC)| > 1.

Functional Enrichment Analysis
DEGs were displayed and processed as heatmaps according to the expression level using the pheatmap package for clustering (https://CRAN.R-project.org/package=pheatmap). To determine the functional enrichment for gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways analysis, R packages (clusterPro ler, enrichplot, org.Hs.eg.db, and ggplot2) were utilized to identify gene functions and achieve visualization for functional pro les [25,26] , where q-value of less than 0.05 were considered signi cantly enriched.

PPI Network Construction and Cox Regression Analysis
Protein-protein interaction (PPI) network was produced through the online analytical database, STRING (https://string-db.org/cgi/input.pl) to clarify the interactive relation among genes (only were the edges and nodes kept when the con dence of interactive relationships > 0.4). To further con rm the highly interconnected genes, the MCODE plugin of Cytoscape (https://cytoscape.org/) was applied, and the interactive hub cluster was selected with 56 hub genes and 794 edges (degree cutoff = 0.2, K-core = 2). Additionally, Cox regression analysis was conducted by using R package survival. Genes with P-value < 0.05 were displayed.

TICs Pro le
The limma package in R was applied to normalize the data to evaluate the proportion of TICs, and then a standardized gene expression pro le was uploaded to CIBERSORT. The deconvolution algorithm was adopted to estimate the TIC abundance [27] . Only 235 tumor samples with P-value < 0.05 were screened out by quality ltering and applied to the following analysis. Immunohistochemistry (IHC) and evaluation of immunostaining IHC staining was performed as described previously [28,29] . Brie y, TMA specimens were depara nized, hydrated in xylol and ethanol, respectively, and subjected to heat mediated antigen retrieval with Tris/EDTA buffer (PH 9.0) at 95℃ for 10 min. Endogenous peroxidase activity was quenched by 0.3% hydrogen peroxide diluents before commencing with the IHC staining protocol. After blocking with normal goat serum (ZLI-9056, ZSGB-BIO, China), TMA sections were incubated with antibodies against TNFRSF4 (ab264465, 1:1000; Abcam), CD8A (encoding CD8) (85336S, 1:100; CST), and CD4 (4B12, 1:100; Leica), coated at 4℃ overnight, and with biotin-conjugated secondary reagents for 30 min. Human tonsil and placenta tissues were used as the positive/negative control with/without primary antibodies. At the end of the staining, whole TMA slides were digitally scanned at ×400 using a NanoZoomer S360 (Hamamatsu, Japan) for visualization.
This study was conducted according to the Reporting Recommendations for Tumor Marker Prognostic Studies (REMARK) guidelines [30] . To evaluate the immunostaining, two experienced pathologists were blinded to the patients' clinical outcomes and independently determined the scores using the semiquantitative immunoreactive Score by Remmele and Stegner [31] . Given that TNFRSF4, CD8 and CD4 were expressed in tumor-in ltrating lymphocytes (TILs), its scoring has used the method of Erminia Massarelli [32] by counting positive cells in the ve random square areas at 400× magni cation, and the expression of each marker was recorded as the density of positive cells/mm 2 . univariate Cox regression analysis were performed to assess survival and estimate the independent prognostic factors. Statistical signi cance was de ned when P-value was less than 0.05.

Research Thoughts and Structure of This Study
The analysis process of our study was displayed in the ow chart ( Fig. 1). On the whole, in order to evaluate the abundance of TICs and the component ratio of immune and stromal cells in UCEC samples, 587 cases of transcriptome RNA-seq data from the TCGA database, including 35 normal tissues and 552 tumor ones, were retrieved and processed by CIBERSORT and ESTIMATE algorithms. 386 DEGs shared by ImmuneScore and StromalScore were determined, which were further analyzed to screen out the hub network genes based on PPI network and MCODE scores. Meanwhile, univariate Cox regression was performed to ltrate out prognosis-related genes. Then, target genes were nally intersected and obtained by genes from the core cluster in the PPI network and the survival-correlated genes from univariate Cox regression analysis. TNFRSF4 and S1PR4 were determined, and we placed emphasis mainly on TNFRSF4 for the subsequent series of investigations. Then, we con rmed the prognostic signi cance and indicative role in TME remodeling of TNFRSF4 in UCEC, as well as the correlation of TNFRSF4 with the abundance of TICs.

Clinicopathologic Signi cance of the Estimate Scores
To reveal the relationship between the component of immune or stromal cells and the clinical outcomes, survival analysis was performed for ImmuneScore, StromalScore, and ESTIMATEScore, respectively. As known, the higher score estimated in ImmuneScore or StromalScore indicated a higher proportion of corresponding immune or stromal cells in TME. Besides, ESTIMATEScore represented the sum of ImmuneScore and StromalScore; in other words, the higher the score, the lower the purity of the tumor. As presented in Fig. 2A, the percentage of immune components was positively correlated with the overall survival rate despite that StromalScore and ESTIMATEScore made no difference (Fig. 2B, C). These results manifested that the immune composition in TME was a more reliable indicator of the prognosis for UCEC patients. To further estimate the correlation between the three scores and clinicopathologic parameters. We compared patients, groups by age or pathological grade of UCEC. Our results demonstrated that tumor of UCEC patients over 65 years old had lower StromalScore and ESTIMATEScore than younger patients (P < 0.01 and < 0.05, respectively) even though ImmuneScore did not differ in terms of age as shown in Fig. 3A, C, E. As for the pathological grade, it was worth noting that our data exhibited consistently decreasing trends in the three scores as tumor progressed even though not all comparisons among groups with different grade were statistically signi cant (Fig. 3B, D, F). Intriguingly, groups of grade showed lower ImmuneScore, StromalScore and ESTIMATEScore (P = 0.013, 0.023, and 0.0081, respectively) when compared with groups of grade . These results implied that advanced age (> 65) and higher grade of UCEC, especially grade , tended to had less immune and stromal component, possibly signi ed poor prognosis.
DEGs Shared by ImmuneScore and StromalScore and Functional Enrichment In order to explore the variation of gene pro le in the wake of the alteration of the immune or stromal component in TME, we compared the expression level of genes between the high-and low-score groups.
Among them, 716 DEGs were nally determined regarding ImmuneScore, including 552 up-regulated genes and 164 down-regulated genes. Similarly, there were 731 DEGs screened out from StromalScore, which mainly consisted of highly expressed genes. As shown in the heatmaps, the top 50 DEGs were displayed based on ImmuneScore and StromalScore, and the gene expression pattern showed an apparent difference between groups (Fig. 4A, B). After the intersection, 386 genes were shared in common by ImmuneScore and StromalScore, with 366 genes were up-regulated and the rest down-regulated (Fig. 4C, D). Then, functional enrichment analysis was carried out based on these overlapped genes. Our results indicated that the identi ed genes were chie y enriched in the regulation of lymphocyte activation, differentiation, and proliferation, as well as regulation of leukocyte cell-cell adhesion in the biological process (BP). About the cellular component (CC) of GO analysis, these genes encoding proteins were the main components of the external side of the plasma membrane, immunological synapse, granule membrane, and protein complex involved in cell adhesion. As for molecular function (MF), the DEGs prevailingly enrolled in immune or cytokine receptor activity and chemokine binding or activity (Fig. 4E). KEGG analysis revealed that chemokine signaling pathway and cytokine-cytokine receptor interaction were most relevant to these DEGs (Fig. 4F). It thus appeared that the overall function of these DEGs primarily focused on immune-related activities, which essentially suggested that the involvement of immune modulation was a notable feature of TME in UCEC.

Intersection Analysis of PPI Network and Cox Regression
So as to elucidate the underlying mechanism, we explored the PPI network constructed by the STRING database, and thus, the interaction between genes was identi ed. On this basis, the MCODE plugin of Cytoscape software was applied to seek the hub gene cluster. Finally, 56 genes were identi ed with 794 edges among these genes, displayed in Fig. 5A, and the bar plot in Fig. 5B showed the top 40 genes, ranked by the number of adjacent nodes. Additionally, to identify prognosis-related genes among 386 DEGs in UCEC, univariate Cox regression analysis was performed, and the results manifested that the expression level of 16 genes was highly correlated with the survival outcome as shown in the forest plot ( Fig. 5C). Subsequently, two genes, TNFRSF4 and S1PR4, were screened out after intersection analysis based on the above PPI network and univariate Cox regression analysis (Fig. 5D).
Prognostic Signi cance and Indicative Role in TME Remodeling of TNFRSF4 in UCEC According to previous studies on UCEC, we found a gene, namely TNFRSF4, barely reported on UCEC but was a high-pro le target on other cancers. TNFRSF4 was rst discovered on the surface of activated CD4 + T cells in rats, which belonged to tumor necrosis factor receptor family member [33] . TNFRSF4 played a vital role in immune regulation in multiple cancers as a crucial immune checkpoint. For example, it was once proved to be subjected to forkhead box P3 (Foxp3) to reduce the immunosuppression action of Tregs in breast cancer [34] . Similarly, TNFRSF4 also exhibited a promising future in targeted therapy towards other tumors such as gastric carcinoma, leukemia, and squamous cell carcinoma of the head and neck [35][36][37] . In the following analysis, our data indicated that TNFRSF4 showed higher expression level in tumor tissues than normal tissues either in unpaired or paired samples (P < 0.001 and P = 0.028, respectively), as shown in Fig. 6A, B. Interestingly, the expression level decreased as aged when compared with the younger group (Fig. 6C). Similarly, tumors of grade or exhibited a consistently less amount of TNFRSF4 when compared with tumors of grade (Fig. 6D). More importantly, the tumor samples were classi ed into two groups, the high or low-expression groups, according to the median expression of TNFRSF4 for survival analysis. Here, we found that increased expression of TNFRSF4 was signi cantly correlated with survival outcome (Fig. 6E). These results signi ed that TNFRSF4 seemed to act as a protective factor in UCEC; however, this so-called protection appeared to decline with aging and tumor progression inevitably. Considering that the expression of TNFRSF4 was positively related to the survival rate and negatively correlated with the age or grade of UCEC patients, GSEA was implemented for the high and low-expression groups of TNFRSF4. As shown in Fig. 6F, the genes in the TNFRSF4 highexpression group were chie y enriched in immune-related activities, such as IFN-γ response, IL-2/STAT5 signaling and IL-6/JAK/STAT3 signaling. In terms of the TNFRSF4 low-expression group, the genes were mainly involved in the G2M checkpoint, mitotic spindle, mTORC1 signaling and protein secretion (Fig. 6G). For C7 collection de ned by MSigDB, the immunologic gene sets, multiple immune functional gene sets enrolled in the high and low TNFRSF4 expression groups (Fig. 6H, I). These results suggested that TNFRSF4 might be a potential indicator for the status of TME.

Correlation of TNFRSF4 with the Abundance of TICs
Given the above ndings, the component of TICs of each sample was further estimated using the CIBERSORT algorithm to detect the pertinence relation of TNFRSF4 expression with the immune microenvironment and 22 kinds of immune cell pro les in UCEC samples were identi ed, as shown in the bar plot (Fig. 7A). It was observed that immune cells in UCEC were mainly composed of T cells and macrophagocytes. Besides, the correlation among the immune cells was also displayed. The results showed that the proportion of CD8 + T cells was negatively correlated with the presence of CD4 + memory resting T cells and M0 macrophagocytes (correlation coe cient = -0.55 or -0.63, respectively). Conversely, the content of CD8 + T cells was positively related to the scale of CD4 + memory activated T cells, as seen in Fig. 7B.
To verify the majors immune cells affected by the expression of TNFRSF4, the difference and correlation analysis were carried out and results demonstrated that a total of 5 kinds of TICs differed between the high and low-TNFRSF4 expression groups, including CD8 + T cells, regulatory T cells, resting dendritic cells, eosinophils, and neutrophils (Fig. 8A). Furthermore, seven kinds of immune cells were correlated with the expression of TNFRSF4 (three positively and four negatively), as observed in Fig. 8B. Finally, four types of immune cells were identi ed to be vitally interconnected with TNFRSF4 in view of the differential expression and degree of relevance via intersection analysis (Fig. 8A). These results further hinted that the expression of TNFRSF4 had much to do with the immune activity of TME in UCEC.

Identi cation of the Expression, Correlation, and Diagnostic Performance of TNFRSF4
From the TCGA mRNA expression pro les, we found that the expression of TNFRSF4 was positively correlated with CD4 (R = 0.51) and CD8A (R = 0.47) (Fig. 9A, B). In IHC staining, the proteins of TNFRSF4, CD4, and CD8 were over-expressed in UCEC tissues (mainly on tumor immune in ltrating cells) compared with normal tissues or even paired adjacent normal tissues (Fig. 9E, F). Moreover, these proteins exhibited a cytoplasmic and membranous staining pattern in the UCEC samples, which were consistent with the results in non-small cell lung cancer [32] . Representative images of TNFRSF4, CD4, and CD8 were shown in Fig. 9D. However, no statistical correlation was found between TNFRSF4 expression and clinicopathologic parameters, such as age and histological grade (Table 1). To assess the diagnostic value of TNFRSF4 in UCEC, the ROC diagnosis model was performed. Surprisingly, we uncovered that TNFRSF4 had a higher diagnostic signi cance in either the TCGA dataset (the AUC value = 0.715) or clinical specimens (the AUC value = 0.777) (Fig. 9C, G). Altogether, these data implied the potential role of TNFRSF4 in immune microenvironment remodeling and diagnostic performance for UCEC.

Discussion
Given the limited understanding of the details of TME for UCEC, especially the complicated and volatile characteristics of the immunological microenvironment, in this study, we comprehensively analyzed the alteration of TME and the composition of TICs in UCEC based on CIBERSORT algorithms. We then determined TNFRSF4 and SIRP4 as immune-and prognosis-related hub genes obtained by the intersection between Cox proportional hazards regression and PPI analysis. Finally, focused on TNFRSF4, we found that TNFRSF4 might be responsible for stably maintaining the immunomodulatory characteristics of TME, thus leading to a better prognosis, which was further validated in the TCGA dataset and patient cohort.
Exploring TME signature genes is a necessary step in uncovering intricate relationships among clinical features and molecular characteristics. ESTIMATE algorithm, as a classical tool, has been used to screen out potential novel biomarkers based on stromal and immune gene sets from each sample in the tumor expression matrix. Here, our data demonstrated that ImmuneScore had a more signi cant correlation with the overall survival rate than StromalScore and ESTIMATEScore, which suggested that immune microenvironment composition was more critical in modulating cancer progression and prognosis of UCEC patients. Besides, the correlation between the three scores and clinicopathologic parameters (age, histological, and grade) revealed that older patients with UCEC of higher grade showed more unfavorable clinical outcomes. This phenomenon could be explained that lower immune cell composition or less 'immune driven' genotype tended to appear in patients with advanced cancer [38] . By the way, Peigen Chen et al. demonstrated that the histological type extracted from the TCGA database was associated with immune and stromal scores in spite that both of which made no difference [39] .
To screen out novel genes related to TME in UCEC, 587 UCEC cases from the TCGA database were divided into the high and low-score groups based on the median of the StromalScore and ImmuneScore. Our results showed that 386 DEGs were shared in common by ImmuneScore and StromalScore, with 366 genes were up-regulated and 20 down-regulated. Next, to clarify the functional enrichment of these overlapped genes, GO terms and KEGG were used. In this part, we found that most of the intersection genes were chie y involved in immune modulation, including T cell activation, regulation of lymphocyte activation, immune receptor activity, and chemokine signaling pathway. Based on these results, it suggested that these DEGs played a vital role in modulating the immunological environment, and implied series of intricate interactions among non-neoplastic cells within the TME could impact tumor fate [16] .
To identify potential biomarkers, Cox regression and PPI network analysis were carried out in the present study. We identi ed two genes, TNFRSF4 and S1PR4, after intersection analysis based on the top 40 PPI genes cluster and 16 prognostic-related genes. Of them, TNFRSF4, also known as OX40 or CD134, a costimulatory molecule mainly expressed on CD4 + and CD8 + T cells, which belongs to the TNF-receptor superfamily [40] , has been selected for further investigation. Initially, evidence was brought by Paterson, D.J. [41] , who identi ed TNFRSF4 as a speci c marker of T-cell activation and survival when cross-linked with its ligand TNFSF4 [41,42] . Then emerging evidence highlighted that TNFRSF4 was a promising therapeutic target for T cell-mediated anti-tumor immunotherapy [37,43,44] . However, there are still some contradictory sides of TNFRSF4, especially that the prognostic effects of TNFRSF4 among different kinds of tumors are often poles apart. These paradoxical results could be explained by heterogeneous TME and homeostatic regulation of Tregs [45] . Encouragingly, the latest evidence reinforced the notion that immunotherapy with TNFRSF4 agonists would trigger a new trend in cancer therapies, and there was no sanitary approval for TNFRSF4 agonists up to now [46] . For example, TNFRSF4 stimulation plus a CTLA-4 blockade functioned as a potential therapeutic strategy to eradicate disseminated tumors by depleting tumor-speci c Tregs within the tumor-in ltrating lymphocytes in tumor-bearing mice [47] . In short, TNFRSF4 might act as a win-win path to reestablish T-cell antitumor activity [48] .
Intriguing, as a second-generation immune checkpoint molecule, there are relatively few studies on UCEC, but it was extremely favored in T cell-mediated anti-tumor immunotherapy in other cancers. It encourages us to explore whether TNFRSF4 can serve as a reliable prognostic biomarker. In our investigation, we found that an elevated expression of TNFRSF4 indicated a better prognosis but was negatively correlated with age or grade in UCEC patients from the TCGA database. Furthermore, GSEA ndings revealed that transcriptome signatures of immune-related activities were positively correlated with TNFRSF4 and chie y enriched in in ammatory signaling pathways (such as IFN-γ response) in the TNFRSF4 overexpressed group. To our knowledge, IFN-γ is a pleiotropic molecule with widely involved pro-and antitumorigenic effects [49] , and CD8 + T lymphocyte-mediated anti-tumor immunity relies on IFN-γ production for CD8 + T cells expansion [50] . At the same time, related signal pathways (e.g. G2M checkpoint, mitotic spindle, and mTORC1 signaling) that regulated tumor cell proliferation and survival were mainly enriched in the TNFRSF4 low-expression group. Accordingly, the low expression of TNFRSF4 might promote the progression of the tumor to some extent in the UCEC. Of course, further studies are needed to verify this speculation in vivo and in vitro.
Immune cell in ltration has held great promise as a new biomarker of prognosis in patients with different types of solid tumors, including endometrial cancer [51] , liver cancer [52] , colon cancer [53] , brain tumor [54] , and pancreatic ductal adenocarcinoma [55] . To further investigate the role of TNFRSF4 in TME, we rstly estimated the component of TICs in UCEC samples by the CIBERSORT algorithm. Consistent with previous studies, our results demonstrated that T cells and macrophagocytes were dominant in TME. The proportion of CD8 + T cells was positively related to the amount of CD4 + memory-activated T cells rather than CD4 + memory resting T cells and M0 macrophagocytes. As known, CD4 + T cell help is indispensable for sustaining CD8 + T cell function during chronic viral infection and engaged in antitumor activities by CD8 + cytotoxic T cells-dependent apoptosis [56,57] . In other words, from a clinical point of view, the presence of immune cell in ltration with high levels of cytotoxicity CD8 + T cells signi ed a favorable prognosis in cancers. Moreover, our study gured out that the expression of TNFRSF4 was positively correlated with CD4 + T cells, CD8 + T cells, and Tregs by bioinformatics analysis. Notably, we also found this relationship in UCEC via TMA validation except for Tregs. Based on our results, further con rming this correlation and evaluating the prognostic signi cance of TNFRSF4 in large-scale cohort studies are of great importance.
Inspired by these results, to further verify the protein expression of TNFRSF4 in clinical specimens, commercialized TMAs, consisted of 85 UCEC patients (including 36 paired cases), were validated. Our result showed that TNFRSF4 was overexpressed in cancer tissues when compared with paired adjacent normal tissues, and it was mainly located in the TILs within the tumor stroma. It's worth noting that the distinct location of TNFRSF4, as previously described, indicated different prognostic value. For example, the expression of TNFRSF4 on TILs was correlated with favorable prognosis in human cancers, including advanced gastric cancer, non-small cell lung cancer, ovarian carcinoma, malignant melanoma, and colorectal cancer [32,[58][59][60][61] , but the expression on cancer cells was associated with a poorer outcome including hepatocellular carcinoma and cutaneous squamous cell carcinoma [62,63] . Furthermore, TNFRSF4-positive cells were also observed to interact with CD4 + and CD8 + T cells in our study, which would be due to the role of TNFRSF4 in the proliferation of CD4 + and CD8 + T cells, as well as the survival of antigen-speci c memory T cells [64][65][66] . Additionally, one unexpected result by ROC curve analysis suggested that TNFRSF4 expression could serve as a potential indicator of favorable diagnosis among patients with UCEC, and this diagnosis performance was better than both CD4 and CD8.
Our current study has limitations. First, given the inherent biases produced by TMAs, those results must be interpreted cautiously and improved in future studies. Second, the coexpression and function of TNFRSF4 within the component of TME are unde ned; multiplexed quantitative immuno uorescence is essential to make up for it. Apart from the above limitations, the clinical prognostic value of TNFRSF4 was not validated in cohorts with UCEC patients as well, but these endeavors are underway in our team.

Conclusions
In summary, we identi ed a novel molecule in UCEC, TNFRSF4, and an elevated expression of it tends to signify favorable clinical outcomes, closely related to the abundance of CD4 + and CD8 + T cells. As an immune-related gene, it held feasible and reliable diagnostic and prognostic management in patients with UCEC. Notably, our study suggested that a deep understanding of TNFRSF4 might unravel a clue for the optimal chance of therapeutic success against UCEC.

Declarations
Ethics approval and consent to participate The research scheme of this study has been reviewed and approved by the Ethics Committee of Peking Union Medical College Hospital (ethics, S-K973), and following the guidelines approved by the Institutional Review Board of our hospital.

Consent for publication
Not applicable.

Availability of data and materials
All data generated during this research are included in this published manuscript.

Con ict of interest
The authors declare no con ict of interest. The ow diagram of the research design. This ow chart presented a comprehensive bioinformatics analysis and cohort validation to screen out the putative target gene, TNFRSF4, and investigate its clinicopathologic signi cance in UCEC.      Identi cation of target genes for correlation, expression, and diagnostic performance in UCEC. A, B.
Correlation of TNFRSF4 with CD4 and CD8A. C. ROC curves analyzing the diagnostic performance of TNFRSF4, CD4 and CD8A for UCEC patients in the TCGA. D. Representative IHC stating for TNFRSF4, CD4 and CD8 in UCEC and adjacent normal tissue. ×200 magni cation (scale bar = 100 μm). E, F. Violin plots visualizing the quanti cation of TNFRSF4, CD4 and CD8 in UCEC and adjacent normal tissue. G. ROC curves validating the diagnostic performance of TNFRSF4, CD4 and CD8 in UCEC clinical specimens.