Bioinformatic Profiling Identifies Prognosis-Related Genes in the Immune Microenvironment of Endometrial Carcinoma

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It is a promising strategy to quantify immune patterns of EC for predicting prognosis and therapeutic efficiency. Here, we attempted to identify the possible immune microenvironment-related prognostic markers of EC. Methods: We obtained the RNA sequencing and corresponding clinical data of EC from TCGA database. Then, 3 immune scores based on the Estimation of STromal and immune cells in MAlignant Tumor tissues using Expression data (ESTIMATE) algorithm were computed. Correlation between above ESTIMATE scores and other immune-related scores, molecular subtypes, prognosis, and gene mutation status (including BRCA and TP53) were further analyzed. Afterwards, gene modules associated with the ESTIMATE scores were screened out through hierarchical clustering analysis and WGCNA. Differentially expressed analysis was performed and genes shared by the most relevant modules were found out. KEGG pathway enrichment analysis was conducted to explore the biological functions of those genes. Survival analysis was carried out to identify prognostic immune-related genes and GSE17025 database was used as external dataset for further validation. Results: The immune-related scores based on ESTIMATE algorithm was closely related to the immune microenvironment of EC. 3 gene modules that had the closest correlations with 3 ESTIMATE scores were obtained. 109 immune-related genes were preliminarily found out and 29 pathways were significantly enriched, most of which were associated with immune response. Univariate survival analysis revealed that there were 14 genes positively associated wicth both OS and PFS. Among which, 11 genes showed marked correlations with ImmuneScore values in GSE17025 database. Conclusion: Our current study profiled the immune status and identified 14 novel immune-related prognostic biomarkers for EC. Our findings may help to investigate the complicated tumor microenvironment and develop novel individualized therapeutic targets for EC. Cell Communication and Signaling Endometrial carcinoma Immune TCGA Survival analysis Prognosis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Background The global morbidity and mortality of endometrial carcinoma (EC) shows an increasing trend in recent years [ 1 , 2 ]. In China, EC ranks the second place in terms of morbidity among all malignant tumors of female genital system, for whom the 5-year survival rate is 55.1% [ 3 ]. At present, FIGO staging and histological classification are still the chief factors applied to patient stratification and prognosis prediction in EC [ 4 , 5 ]. Over the past few past decades, great individual differences have been found in the outcomes of EC treatments due to the tumor heterogeneity, which is partially dependent on the molecular biological features of the primary lesion [ 6 ]. Moreover, quite a few patients show distinct responses to adjuvant therapy even though they are at the same or familiar clinical stage [ 7 ]. Thus, more effective approaches are needed in order to hierarchically classify patients into high- or low- risk subgroups for monitoring and optimizing the treatment of EC [ 8 ]. As in many other types of cancer, immunotherapy is currently recognized as a novel promising therapeutic option in EC [ 9 ]. Interactions between different infiltrating immune cells and EC cells in tumor microenvironment (TME) significantly affect tumor progression and recurrence [ 10 ]. Besides, EC exhibits a unique immune profile and can be used to construct suitable models for exploring molecular crosstalk between immune and tumor cells [ 11 ]. Great progresses have been attained in the past few years, making it possible to recognize novel molecular therapeutic targets within the microenvironment of EC [ 12 ]. At the same time, several immune-related factors have been identified to predict patient prognosis, which emphasizes the significance of certain immune status on EC outcomes [ 13 ]. Nonetheless, the vast majority of existing studies are pre-clinical basic experiments or have limited available clinical information, while study with a large sample size has not been carried out so far [ 14 ]. Consequently, several immune scoring systems were developed based on the immune-related gene expression patterns through integrated analysis of The Cancer Genome Atlas (TCGA) database to explore the relationships between tumor cells and immunocytes in TME, as well as to quantify the immune microenvironment for each individual cancer case. Of them, the inflammation-based index was reported to be associated with the local immune response and prognosis in various cancers, including pancreatic cancer, colorectal cancer [ 15 ], non-small cell lung cancer [ 16 ] and tongue cancer [ 17 ] and so on. However, limited studies have been designed in an attempt to develop an immune-related prognostic signature for EC. In the current study, we obtained the RNA sequencing (RNA-seq) and corresponding clinical data of EC from TCGA database. Then, we calculated 3 immune scores for each EC sample based on the Estimation of STromal and immune cells in MAlignant Tumor tissues using Expression data (ESTIMATE) algorithm and analyzed the correlation between ESTIMATE immune score and molecular features of EC. Thereafter, gene modules associated with the immune scores were identified using weighted gene co-expression network analysis (WGCNA), and finally, 14 novel prognostic immune-related genes were screened out which were further validated in the GSE17025 database. Materials And Methods Data collection and immune score calculation All the data we used in our study are publicly accessible at TCGA and NCBI GEO (accession number: GSE17025) database. Firstly, GDC application programming interface was utilized to download clinical follow-up, RNA-seq and SNP data. RNA-Seq FPKM values were subsequently converted into Transcripts PerKilobase Million (TPM) files. The expression levels of 13 previously published immune metagenes [ 18 ] were calculated as the median of log2-transformed expression levels of clustered genes for each sample. Meanwhile, the infiltration of 6 immune cells (B cells, CD4 + T cells, CD8 + T cells, neutrophils, macrophages and dendritic cells) were calculated and downloaded from the TIMER [ 19 ] ( https://cistrome.shinyapps.io/timer/ ) database. In addition, the ESTIMATEScore, StromalScore and ImmuneScore values for each sample were computed by the ESTIMATE function of R package. Assessment of the correlation between ESTIMATE scores and immune status Correlation analysis was carried out to assess the relevance between 3 ESTIMATE immune scores, expression of 13 immune metagenes and the infiltration status of 6 immune cells. Moreover, the correlation of 3 ESTIMATE immune scores, 13 immune metagenes expression and the infiltration of 6 immune cells were further analyzed among the 4 previously reported EC subtypes respectively. The Correlation Between Estimate Scores And Patient Prognosis We firstly conducted survival analysis using Kaplan-Meier method with survival function of R package to explore the overall survival (OS) of above mentioned 4 EC subtypes respectively. Afterwards, patients were divided into high- and low-score groups according to the median of 3 ESTIMATE immune scores. Then, the differences of OS between these groups were examined through Kaplan-Meier method with survival package under R environment. Exploration of the association between ESTIMATE scores and gene mutation The mutation data of BRCA2, BRCA1 and TP53 were extracted from TCGA derived SNP dataset and processed with Mutect. Then, the correlation between different mutation status of these genes and ESTIMATE immune scores were analyzed using Wilcox.test Package under R environment. Furthermore, patients were divided into high- and low- tumor mutation burden (TMB) groups according to the median of TMB value. The correlation between TMB status and ESTIMATE immune scores were assessed using Wilcox.test Package under R environment. Identifying Immune Score-related Gene Modules Through Wgcna Firstly, transcripts that had at least 75% TPM greater than 1 and median absolute deviation (MAD) greater than the median were selected for following analysis according to the expression patterns. Then, samples were clustered through hierarchical clustering method with the distance > 80000 considered as cut-off value of outlier sample. The distance between each 2 transcripts was computed according to Pearson correlation coefficient, then the R package WGCNA [ 20 ] was used to establish the weight co-expression network, and co-expression modules were selected at the soft threshold of 10 in order to ensure the constructed co-expression network conformed to the scale-free network. That was to say, the node/k connectivity (log(k)) logarithm was negatively correlated with logarithm in terms of the occurrence probability of node (log(P(k)), and the correlation coefficient was > 0.8. The appropriate β value was also selected to ensure the scale-free network. Then, the expression matrix was converted into the adjacent one, followed by further conversion into the topological one for gene clustering on the basis of topological overlap matrix (TOM), in accordance with the average linkage hierarchical cluster approach following the mixed dynamic shear tree standards. In addition, more than 30 genes had been selected for every gene network module. The dynamic shear approach was also utilized to determine gene modules, and eigengenes values of all modules were calculated. Cluster analysis was then carried out on the modules, adjacent modules were fused together to obtain a novel one, and appropriate minModuleSize, deepSplit, and height values were assigned. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis was performed to explore the potential biological functions of genes within these 3 modules using the ClusterProfiler package under R environment, with the significant FDR level of < 0.05. Then, the associations between the recognized gene modules and ESTIMATEScore, StromalScore as well as ImmuneScore values were calculated, respectively, in order to mine the gene modules having highest correlation. Construction Of Gene Interaction Network And Functional Analyses We separated patients into two groups equally based on the ImmuneScore and ESTIMATEScore. The DESeq2 function of R package was utilized to screen different expressed genes (DEGs) between these groups. The cut-off criterion for DEGs was set as p 1. Genes shared by the most relevant module, DEGs of ImmuneScore and ESTIMATEScore groups were finally screened out. All genes were subsequently mapped into the String database [ 21 ] separately using the STRINGdb package under R environment, with the threshold of > 0.4 to obtain the gene-gene interaction. Cytoscape was used for visualization. At the same time, the R package clusterprofile [ 22 ] was utilized to carry out KEGG enrichment analyses for visualizing the signaling pathways affected by genes. The prognostic value of each immune-related gene was calculated using survival package under R environment and was further verified with an independent external GEO dataset (GSE17025) through Pearson correlation analysis. Results The immune scoring system based on the ESTIMATE algorithm was closely related to the immune microenvironment of EC The expression levels of 13 immune metagenes (Metascore), infiltration status of 6 immunocytes (immusocre), and 3 immune-related scores based on ESTIMATE algorithm (ESTIMATEScore, StromalScor, and ImmuneScore). Additionally, Spearman correlation coefficient was employed for quantifying the relationships between these scoring systems (Fig. 1 ). Our results suggested that, immune-related scores calculated using the ESTIMATE algorithm had an average internal correlation > 0.8 and also high correlations with the other 2 algorithms. Above findings indicated that immune-related scores computed based on the ESTIMATE algorithm was closely related to the immune microenvironment of EC. Besides, ESTIMATEScore, ImmuneScore, and StromalScore among the 4 recognized subtypes [ 23 ] had also been examined (Fig. 2 A-C). It was clear that, differences in ESTIMATEScore and ImmuneScore levels among different molecular subtypes of EC were statistically significant. It was noteworthy that copy-number high subtype had the lowest scores among these 4 subtypes. Moreover, distribution of 6 immunocytes infiltration (Fig. 2 D-I) and 13 metagenes expression (Fig.S1) among these 4 subtypes was also analyzed. Statistical significances were observed in 6 out of 13 metagenes expression and 5 out of 6 immunocytes infiltration. To assess the relationship between 3 ESTIMATE scores and patient prognosis, the prognosis of above 4 subtypes was firstly examined. As shown in Fig. 3 A, difference in patient prognosis across those 4 subtypes was statistically significant, among which, the copy − number high subtype had the poorest prognosis. Next, all samples were classified according to median of ESTIMATE scores followed by survival analysis (Fig. 3 B-C). Obviously, the prognosis for samples with the high ESTIMATEScore and ImmuneScore was much better than that with low scores, suggesting that ESTIMATE immune scoring system might be used as novel promising markers to predict the prognosis for EC. Afterwards, correlations between those 3 ESTIMATE scores and 3 independent prognostic gene mutations (BRCA2, BRCA1 and TP53) were explored [ 24 , 25 ]. As a result, 3 immune-related scores of different mutant groups were generally higher when compared with those of wild-type groups, especially in TP53 and BRCA1 subgroups (Fig. 4 A-I). Subsequently, the TMB of each sample was computed, and the relationships between 3 ESTIMATE scores and TMB level were analyzed. As presented in Fig. 4 J-L, the ESTIMATEScore and ImmuneScore showed significant positive correlation with TMB. To sum up, the immune-related scores based on ESTIMATE algorithm was closely related to the immune microenvironment of EC and could be used to be the optimal immune scoring methods for prognosis prediction. Identification Of Immune Score-related Gene Modules By Wgcna 14131 transcripts were firstly selected to performed hierarchical clustering analysis (Fig. 5 A). After excluding outlier data, 579 samples were finally used to construct a weight co-expression network through WGCNA (Fig. 5 B, C). Then, gene modules were examined by dynamic shear, and all the recognized modules were further clustered. Next, adjacent gene modules were fused to form a new one, with minModuleSize, deepSplit and height set at 30, 2 and 0.25, respectively. 17 modules were ultimately obtained (Fig. 5 D, E), and 5362 transcripts were assigned to 16 co-expression modules except the grey module. Correlations between eigenvectors of these 16 modules and 3 ESTIMATE scores were then calculated (Fig. 5 E), from which we could see that the red (170 genes), purple (55 genes) and tan (49 genes) modules had the closest correlations with 3 ESTIMATE scores, with the average correlation coefficients > 0.5. As following, we performed KEGG pathway enrichment analysis to explore the potential biological functions of the 274 genes within these 3 modules. According to the results,there were 23 and 54 pathways enriched to the purple module and red module, respectively (Fig. 6 ). It was easy to find that the genes were primary enriched to immune-related pathways, including T cell differentiation, primary immunodeficiency, chemokine signaling pathway, cytokine-cytokine receptor interaction, B cell receptor signaling pathway and so on. Thus, it can be inferred that the ESTIMATE score-related genes may closely associate with the immune microenvironment of EC. Identification Of Immune Microenvironment-related Prognostic Genes To seek out the most immune-related genes, correlations Qbetween previously obtained ESTIMATE score-related modules and genes were computed. As illustrated in Fig. 7 , the correlation coefficients were distributed in a bimodal manner, with the intersection point value of 0.77. Based on this, 136 genes with the maximum correlation coefficients with those 3 modules greater than 0.77 were screened out. According to the previously described screening method, 379 DEGs were identified among high- and low-ESTIMATEScore subgroups (Fig. 8 A, B) using the R package DESeq2 [ 26 ]. At the meantime, 526 DEGs were acquired among ImmuneScore-high and -low subgroups (Fig. 8 C, D). The results were presented in a volcano plot and a heatmap for each subgroup. Obviously, the DEGs showed distinct expression patterns in low-ESTIMATEScore subgroup compared with high-ESTIMATEScore subgroup. Similar results were also observed in low- and high-ImmuneScore subgroups. To further investigate the immune-related genes, we integrated above 3 gene sets (136 genes with the maximum correlation coefficients with 3 modules, 379 DEGs in high- and low-ESTIMATEScore subgroups and 526 DEGs in high- and low-ImmuneScore subgroups). After intersection, 133 common genes were obtained and 24 genes belonged to 13 metagenes were eliminated subsequently. Finally, 109 immune-related genes were preliminarily found out (Fig. 9 A). Subsequently, KEGG enrichment analyses was conducted with clusterProfiler package under R environment. As shown in Fig. 9 B, 29 pathways were significantly enriched, most of which were associated with immune response. Afterwards, the R STRINGdb package [ 27 ] was utilized to analyze the protein-protein interaction network of those 109 genes. All these genes were mapped to String database in order to acquire the relationship network and 67 nodes were finally obtained (Fig. 9 C). As is depicted in Fig. 9 D, the degree value of each node was high (5.49 on average), which demonstrated the close association between those immune-related genes. Then, univariate survival analysis was performed to identify the immune microenvironment-related prognostic markers using the survival and expression data of above 109 genes. The results revealed that there were 34 and 19 genes associated with OS and PFS, respectively. Among which, 14 genes were found to be positively correlated with both OS and PFS (Table 1, Fig. 10 ). Validation Of Immune-related Prognostic Genes Using An External Database The independent dataset GSE17025 [ 28 ] was chosen and normalized expression matrix was downloaded. Then, the R package ESTIMATE was used to calculate the sample ImmuneScore values. Pearson correlation coefficients between expression levels of 12 genes (2/14 genes were excluded due to unavailable expression data) and ImmuneScore values were further determined. As illustrated in Fig. 11 , 11 genes (except S1PR4) showed marked correlations with ImmuneScore values, which was consistent with our previous findings. Discussion Until recently, the 5-year survival of advanced or recurrent EC is still not optimistic. Even though postoperative adjuvant therapy (chemotherapy and/or radiotherapy) can improve patient outcome, great individual differences in therapeutic effect exist due to the biological heterogeneity of EC cells. Hence, there is a critical need for reliable prognostic biomarkers to evaluate the risk of cancer progression and develop the patient-tailored treatment strategy. With the rapid development of next generation sequencing techniques, numbers of novel molecular biomarkers have been identified, which were of great help to the personalized treatment of EC [ 8 , 29 ]. Nevertheless, most studies are conducted on animal models, surgical cancer tissues samples or cell lines under in vitro conditions. Tumor microenvironment, which is comprised of tumor cells, stromal cells and the secreted inflammatory mediators and cytokines, is a complex system and supports various tumor biological behaviors, including tumor genesis, progression, metastasis and so on. Stromal cells are mainly made up of immunocytes, fibroblasts, mesenchymal cells and tumor-associated endothelial cells. The abnormal infiltration of stromal cells, especially for immunocytes (such as neutrophils, monocytes and lymphocytes), has been verified in numerous studies [ 11 , 30 ]. For now, great attention has been paid to the association between immune system and tumor biology [ 31 ]. A growing number of studies have not only revealed the interaction between EC cells and the host immune system, but also enhanced the efficacy of immunotherapies [ 32 ]. As mentioned before, EC has been widely accepted as a kind of immunogenic malignancy. The treatment of EC has reached a new milestone through artificially manipulating the tumor immune microenvironment. Therefore, it is of great importance for us to explore the immune-related prognostic and therapeutic biomarkers for EC [ 33 ]. In current study, we used the RNA-seq data downloaded from TCGA database to calculated 3 immune-related scores based on ESTIMATE algorithm, which showed marked correlation with the immune status, survival time, prognosis-related gene mutations, and molecular subtypes of EC. Next, ESTIMATE immune score-related gene modules were obtained by means of WGCNA. 109 immune-related genes were then screened out using differential expression analysis and their functions were examined through enrichment analysis. Survival analysis was then performed and 14 novel immune-related prognostic genes were finally screened out, among which, 12 genes were further validated in GSE17025 dataset and consistent results were obtained. Our newly discovered 14 immune-related prognostic markers include WAS, GZMH, CD7, NKG7, LINC01871, TRAC, CD8A, TRBC2, CD3E, IGSF6, RASAL3, ITGAL, S1PR4 and CCL4. Unfortunately, only a few studies have explored the roles of these genes in EC. Among these, 4 genes (CD8A, CD3E, CCL4 and ITGAL) were reported to have close relationship with tumor immune microenvironment and to be involved in various pathological processes of EC [ 34 – 36 ]. TRBC2 encodes a specific region of the T-cell receptor beta-2 chain [ 37 ] and has been identified as a promising biomarker for the distinction of multiple cancer types, including breast cancer, colorectal cancer, glioblastoma, hepatobiliary cancer, lung cancer and pancreatic cancer and so on [ 38 ]. It is worth mentioning that, our findings demonstrated that TRBC2 had the most significant correlation with both OS and PFS of EC patients. Nevertheless, no studies have validated the specific role of TRBC2 in EC yet. Thus, it really deserves further study to elucidate the clinical importance and underlying molecular mechanism of TRBC2 in EC. Conclusion Briefly, our current study focuses on gene features associated with EC immune microenvironment. According to our findings, these genes participate in the progression of EC, and affect patient prognosis. Our work helps to investigate the complicated interactions in EC microenvironment. At the same time, our work may help to develop novel potential prognostic biomarkers and therapeutic targets for EC. Abbreviations EC Endometrial Carcinoma ESTIMATE MAlignant Tumor Tissues using Expression Data FIGO The International Federation of Gynecology and Obstetrics TME Tumor Microenvironment TCGA The Cancer Genome Atlas WGCNA Weighted Gene Co-expression Network Analysis GEO Gene Expression Omnibus TPM Transcripts PerKilobase Million OS Overall Survival PFS Progression-free Survival TMB Tumor Mutation Burden MAD Median Absolute Deviation TOM Topological Overlap Matrix KEGG Kyoto Encyclopedia of Genes and Genomes DEGs Different Expressed Genes Declarations Abbreviations EC Endometrial Carcinoma ESTIMATE MAlignant Tumor Tissues using Expression Data FIGO The International Federation of Gynecology and Obstetrics TME Tumor Microenvironment TCGA The Cancer Genome Atlas WGCNA Weighted Gene Co-expression Network Analysis GEO Gene Expression Omnibus TPM Transcripts PerKilobase Million OS Overall Survival PFS Progression-free Survival TMB Tumor Mutation Burden MAD Median Absolute Deviation TOM Topological Overlap Matrix KEGG Kyoto Encyclopedia of Genes and Genomes DEGs Different Expressed Genes Declarations Ethical approval and consent to participate Not applicable. Consent for publication Not applicable. Availability of data and materials The data generated are included in the manuscript and supplementary data. All the data we used in our study are publicly accessible at TCGA and NCBI GEO database. Competing Interests The authors declare no conflicts of interest. Funding This work was supported by Chinese National Natural Science Foundation (81902629). Authors’ Contributions Pu Cheng and Jiong Ma conceived, designed the study, analyzed the data and prepared the manuscript, Xia Zheng and Chunxia Zhou collected the data and organized the figures, Xuejun Chen reviewed and edited the manuscript. All authors read and approved the final manuscript. Acknowledgements Not applicable. 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Tables Gene symbol Gene name p value (OS) p value (PFS) WAS Wiskott-Aldrich snydrome 0.005103711 0.033465715 GZMH Granzyme H 0.008033252 0.006877271 CD7 CD7 molecule 0.010859632 0.006642321 NKG7 Natural killer cell granule protein 7 0.000412089 0.030792653 LINC01871 Long intergenic non-protein coding RNA 1871 0.000140799 0.007739673 TRAC T cell receptor alpha constant 4.40E-05 0.009042146 CD8A CD8a molecule 0.003341004 0.012404753 TRBC2 T cell receptor beta constant 1.03E-05 0.001855756 CD3E CD3e molecule 0.000106431 0.002676012 IGSF6 Immunoglobulin superfamily menmer 6 0.00173721 0.043427798 RASAL3 RAS protein activator like 3 0.006739461 0.039618006 ITGAL Inergrin subunit alpha L 0.004923864 0.041623366 S1PR4 Sphingsine-1-phosphate receptor 4 0.007261307 0.029160931 CCL4 C-C motif chemokine ligand 4 0.001976119 0.034895726 Supplementary Files FigureS1.pdf Cite Share Download PDF Status: Published Journal Publication published 15 Jun, 2021 Read the published version in Scientific Reports → Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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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-30550","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research","associatedPublications":[],"authors":[{"id":655791,"identity":"134fbdca-5610-44d1-9ac7-42eea934613a","order_by":0,"name":"Pu Cheng","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA30lEQVRIie3RIQvCQBTA8RsHZzmXN8TvcDJwRfwsN4TFlRWDjoGwpn3gh7hoPBm4cpgdK0tnsRgNojc1b7MJ3j/ce+H90gGg0/1iHKD30osBUcOIuxPMvyYWfY12YpaZrK67ZUCKcxViMBkyDmXVROyj745SkYekpNTBwHcYRy5pIkRgNOgnB4+VlCuSeYxjZLWSe02KfazIoyMxkoXHThAowtuJLdDYXic8tIUPRlsyc9IMjRuJKaC0bkkUmHkuyWU+HW7ylWwknzKqHkRenwk73KuimsCq27FOp9P9W0/K3EwXnToZagAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0001-6358-5219","institution":"second affiliated hospital of Zhejiang university","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Pu","middleName":"","lastName":"Cheng","suffix":""},{"id":655792,"identity":"c4cef9a1-05af-4e5e-ad76-2532ca357f58","order_by":1,"name":"Jiong Ma","email":"","orcid":"","institution":"Zhejiang University School of Medicine Second Affiliated Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jiong","middleName":"","lastName":"Ma","suffix":""},{"id":655793,"identity":"8e8f285f-ba7d-4d65-bbff-5f61215b9b8e","order_by":2,"name":"Xia Zheng","email":"","orcid":"","institution":"Zhejiang University School of Medicine Second Affiliated Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xia","middleName":"","lastName":"Zheng","suffix":""},{"id":655794,"identity":"9a08f78c-3b52-4a62-80eb-37d6512277f6","order_by":3,"name":"Chunxia Zhou","email":"","orcid":"","institution":"Zhejiang University School of Medicine Second Affiliated Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Chunxia","middleName":"","lastName":"Zhou","suffix":""},{"id":655795,"identity":"6884151c-731d-4689-b17b-5eb22aa90b82","order_by":4,"name":"Xuejun Chen","email":"","orcid":"","institution":"Zhejiang University School of Medicine Second Affiliated Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xuejun","middleName":"","lastName":"Chen","suffix":""}],"badges":[],"createdAt":"2020-05-21 05:26:58","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-30550/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-30550/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-021-92091-5","type":"published","date":"2021-06-15T20:46:34+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":1308015,"identity":"afafcafe-fe9d-43f1-b2ad-1ea2d36e29a3","added_by":"auto","created_at":"2020-06-11 16:40:55","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":430775,"visible":true,"origin":"","legend":"Correlations between ESTIMATE immune score and other immune scores for EC patients. (A) Correlations among the 3 ESTIMATE immune scores. (B) Cor-relations between 3 ESTIMATE immune scores and 13 metagenes scores for EC pa-tients. (C) Correlations between 3 ESTIMATE immune scores and 6 immunocyte in-filtration scores for EC patients. Coefficients of Spearman correlation were displayed color-coded, so as to demonstrate the negative (red) or positive (blue) association.","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-30550/v1/Figure1.jpg"},{"id":1308016,"identity":"56d3c834-68b8-4666-b428-c005d4e3c8d7","added_by":"auto","created_at":"2020-06-11 16:40:55","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":779031,"visible":true,"origin":"","legend":"Distribution of ESTIMATEScore (A), ImmuneScore(B), StromalScore (C) and infiltration score of B cell (D), CD4+T cell (E), CD8+T cell (F), Neutrophil (G), Macrophage (H), Dendritic cell (I) among 4 different molecular subtypes (Copy−number high, Copy−number low, MSI and POLE).","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-30550/v1/Figure2.jpg"},{"id":1308017,"identity":"6fc5afbf-79c0-4b0f-96f1-c4ed1359cef3","added_by":"auto","created_at":"2020-06-11 16:40:55","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":201164,"visible":true,"origin":"","legend":"Prognosis of EC patients with different molecular subtypes (A), ImmuneScores (B) and ESTIMATEScores (C).","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-30550/v1/Figure3.jpg"},{"id":1308018,"identity":"6276a977-d540-43da-b921-3cd9f63a8204","added_by":"auto","created_at":"2020-06-11 16:40:55","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":656955,"visible":true,"origin":"","legend":"Correlations between ESTIMATE immune scores and gene mutations. The StromalScore in TP53 (A), BRCA1 (D) and BRCA2 (G) non-mutation and mutation groups. ImmuneScore in TP53 (B), BRCA1 (E) and BRCA2 (H) non-mutation and mutation groups. ESTIMATEScore in TP53 (C), BRCA1 (F) and BRCA2 (I) non-mutation and mutation groups. StromalScore (J), ImmuneScore (K) and ESTI-MATEScore (L) in TMB-high and -low groups.","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-30550/v1/Figure4.jpg"},{"id":1308019,"identity":"686c1455-6379-4377-bc32-ee66daa821c0","added_by":"auto","created_at":"2020-06-11 16:40:55","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":706414,"visible":true,"origin":"","legend":"Immune score-related gene modules identified via WGCNA. (A) Cluster analysis of samples. (B, C) Network topological analysis for different soft-thresholding powers. (D) Module colors and gene dendrogram. (E) Correlations between different gene modules and 3 ESTIMATEScores.","description":"","filename":"Figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-30550/v1/Figure5.jpg"},{"id":1308020,"identity":"77c59346-59ff-4e25-a705-6982cda634c0","added_by":"auto","created_at":"2020-06-11 16:40:55","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":753552,"visible":true,"origin":"","legend":"The KEGG pathway enrichment analysis of the genes in purple (A) and red (B) module.","description":"","filename":"Figure6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-30550/v1/Figure6.jpg"},{"id":1308021,"identity":"e983b51d-87ee-4052-b693-8f63f0e16f39","added_by":"auto","created_at":"2020-06-11 16:40:56","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":139580,"visible":true,"origin":"","legend":"The distribution of maximum correlation coefficients of 274 module-related genes.","description":"","filename":"Figure7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-30550/v1/Figure7.jpg"},{"id":1308022,"identity":"fa9f41d1-2b4b-406e-9474-2e32ce979be5","added_by":"auto","created_at":"2020-06-11 16:40:56","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":687454,"visible":true,"origin":"","legend":"Gene expression profile of high- and low-ImmuneScore /ESTIMATEScore groups. (A, C) Volcanic maps. (B, D) Heatmap.","description":"","filename":"Figure8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-30550/v1/Figure8.jpg"},{"id":1308023,"identity":"8f27f3f8-0b71-4edc-9076-43276d4198f2","added_by":"auto","created_at":"2020-06-11 16:40:56","extension":"jpg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":833526,"visible":true,"origin":"","legend":"Mining of the immune-related genes. (A) The co-expressed genes that were markedly correlated with ImmuneScore/ESTIMATEScore. (B) KEGG enrichment analysis of the 109 genes. (C) Protein-protein interaction network of the 109 genes. (D) The degree value of each node within protein-protein interaction network.","description":"","filename":"Figure9.jpg","url":"https://assets-eu.researchsquare.com/files/rs-30550/v1/Figure9.jpg"},{"id":1308024,"identity":"a380fde2-c2da-4ad0-ad8a-ffdd68ded935","added_by":"auto","created_at":"2020-06-11 16:40:56","extension":"jpg","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":843267,"visible":true,"origin":"","legend":"Correlations between 14 novel immune-related genes and prognosis of EC patients (OS and PFS).","description":"","filename":"Figure10.jpg","url":"https://assets-eu.researchsquare.com/files/rs-30550/v1/Figure10.jpg"},{"id":1308025,"identity":"54100edf-1383-409b-adb9-3db5c445a296","added_by":"auto","created_at":"2020-06-11 16:40:56","extension":"jpg","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":771847,"visible":true,"origin":"","legend":"Correlations between 12 immune-related prognostic genes and Im-muneScore/ESTIMATEScore within GSE17025 dataset.","description":"","filename":"Figure11.jpg","url":"https://assets-eu.researchsquare.com/files/rs-30550/v1/Figure11.jpg"},{"id":13539067,"identity":"468bb116-3b65-4634-a5da-91d9b8acaebb","added_by":"auto","created_at":"2021-09-17 01:41:53","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2165917,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-30550/v1/23deb419-e398-42f2-b096-0bd441487b79.pdf"},{"id":1852510,"identity":"88de22c3-b8e9-4a4f-bccc-d606af688efe","added_by":"auto","created_at":"2020-08-09 13:52:46","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1355635,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-30550/v1/FigureS1.pdf"}],"financialInterests":"","formattedTitle":"\u003cp\u003eBioinformatic Profiling Identifies Prognosis-Related Genes in the Immune Microenvironment of Endometrial Carcinoma\u003c/p\u003e","fulltext":[{"header":"Background","content":" \u003cp\u003eThe global morbidity and mortality of endometrial carcinoma (EC) shows an increasing trend in recent years [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. In China, EC ranks the second place in terms of morbidity among all malignant tumors of female genital system, for whom the 5-year survival rate is 55.1% [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. At present, FIGO staging and histological classification are still the chief factors applied to patient stratification and prognosis prediction in EC [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Over the past few past decades, great individual differences have been found in the outcomes of EC treatments due to the tumor heterogeneity, which is partially dependent on the molecular biological features of the primary lesion [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Moreover, quite a few patients show distinct responses to adjuvant therapy even though they are at the same or familiar clinical stage [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Thus, more effective approaches are needed in order to hierarchically classify patients into high- or low- risk subgroups for monitoring and optimizing the treatment of EC [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAs in many other types of cancer, immunotherapy is currently recognized as a novel promising therapeutic option in EC [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Interactions between different infiltrating immune cells and EC cells in tumor microenvironment (TME) significantly affect tumor progression and recurrence [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Besides, EC exhibits a unique immune profile and can be used to construct suitable models for exploring molecular crosstalk between immune and tumor cells [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Great progresses have been attained in the past few years, making it possible to recognize novel molecular therapeutic targets within the microenvironment of EC [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. At the same time, several immune-related factors have been identified to predict patient prognosis, which emphasizes the significance of certain immune status on EC outcomes [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Nonetheless, the vast majority of existing studies are pre-clinical basic experiments or have limited available clinical information, while study with a large sample size has not been carried out so far [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eConsequently, several immune scoring systems were developed based on the immune-related gene expression patterns through integrated analysis of The Cancer Genome Atlas (TCGA) database to explore the relationships between tumor cells and immunocytes in TME, as well as to quantify the immune microenvironment for each individual cancer case. Of them, the inflammation-based index was reported to be associated with the local immune response and prognosis in various cancers, including pancreatic cancer, colorectal cancer [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], non-small cell lung cancer [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] and tongue cancer [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] and so on. However, limited studies have been designed in an attempt to develop an immune-related prognostic signature for EC.\u003c/p\u003e \u003cp\u003eIn the current study, we obtained the RNA sequencing (RNA-seq) and corresponding clinical data of EC from TCGA database. Then, we calculated 3 immune scores for each EC sample based on the Estimation of STromal and immune cells in MAlignant Tumor tissues using Expression data (ESTIMATE) algorithm and analyzed the correlation between ESTIMATE immune score and molecular features of EC. Thereafter, gene modules associated with the immune scores were identified using weighted gene co-expression network analysis (WGCNA), and finally, 14 novel prognostic immune-related genes were screened out which were further validated in the GSE17025 database.\u003c/p\u003e "},{"header":"Materials And Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData collection and immune score calculation\u003c/h2\u003e \u003cp\u003eAll the data we used in our study are publicly accessible at TCGA and NCBI GEO (accession number: GSE17025) database. Firstly, GDC application programming interface was utilized to download clinical follow-up, RNA-seq and SNP data. RNA-Seq FPKM values were subsequently converted into Transcripts PerKilobase Million (TPM) files. The expression levels of 13 previously published immune metagenes [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] were calculated as the median of log2-transformed expression levels of clustered genes for each sample. Meanwhile, the infiltration of 6 immune cells (B cells, CD4\u0026thinsp;+\u0026thinsp;T cells, CD8\u0026thinsp;+\u0026thinsp;T cells, neutrophils, macrophages and dendritic cells) were calculated and downloaded from the TIMER [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cistrome.shinyapps.io/timer/\u003c/span\u003e\u003c/span\u003e) database. In addition, the ESTIMATEScore, StromalScore and ImmuneScore values for each sample were computed by the ESTIMATE function of R package.\u003c/p\u003e \u003cp\u003e \u003cb\u003eAssessment of the correlation between ESTIMATE scores and immune status\u003c/b\u003e \u003c/p\u003e \u003cp\u003eCorrelation analysis was carried out to assess the relevance between 3 ESTIMATE immune scores, expression of 13 immune metagenes and the infiltration status of 6 immune cells. Moreover, the correlation of 3 ESTIMATE immune scores, 13 immune metagenes expression and the infiltration of 6 immune cells were further analyzed among the 4 previously reported EC subtypes respectively.\u003c/p\u003e \u003c/div\u003e \n\u003ch2\u003eThe Correlation Between Estimate Scores And Patient Prognosis\u003c/h2\u003e\n \u003cp\u003eWe firstly conducted survival analysis using Kaplan-Meier method with survival function of R package to explore the overall survival (OS) of above mentioned 4 EC subtypes respectively. Afterwards, patients were divided into high- and low-score groups according to the median of 3 ESTIMATE immune scores. Then, the differences of OS between these groups were examined through Kaplan-Meier method with survival package under R environment.\u003c/p\u003e \u003cp\u003e \u003cb\u003eExploration of the association between ESTIMATE scores and gene mutation\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe mutation data of BRCA2, BRCA1 and TP53 were extracted from TCGA derived SNP dataset and processed with Mutect. Then, the correlation between different mutation status of these genes and ESTIMATE immune scores were analyzed using Wilcox.test Package under R environment. Furthermore, patients were divided into high- and low- tumor mutation burden (TMB) groups according to the median of TMB value. The correlation between TMB status and ESTIMATE immune scores were assessed using Wilcox.test Package under R environment.\u003c/p\u003e \n\u003ch2\u003eIdentifying Immune Score-related Gene Modules Through Wgcna\u003c/h2\u003e\n \u003cp\u003eFirstly, transcripts that had at least 75% TPM greater than 1 and median absolute deviation (MAD) greater than the median were selected for following analysis according to the expression patterns. Then, samples were clustered through hierarchical clustering method with the distance\u0026thinsp;\u0026gt;\u0026thinsp;80000 considered as cut-off value of outlier sample. The distance between each 2 transcripts was computed according to Pearson correlation coefficient, then the R package WGCNA [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] was used to establish the weight co-expression network, and co-expression modules were selected at the soft threshold of 10 in order to ensure the constructed co-expression network conformed to the scale-free network. That was to say, the node/k connectivity (log(k)) logarithm was negatively correlated with logarithm in terms of the occurrence probability of node (log(P(k)), and the correlation coefficient was \u0026gt;\u0026thinsp;0.8. The appropriate β value was also selected to ensure the scale-free network. Then, the expression matrix was converted into the adjacent one, followed by further conversion into the topological one for gene clustering on the basis of topological overlap matrix (TOM), in accordance with the average linkage hierarchical cluster approach following the mixed dynamic shear tree standards. In addition, more than 30 genes had been selected for every gene network module. The dynamic shear approach was also utilized to determine gene modules, and eigengenes values of all modules were calculated. Cluster analysis was then carried out on the modules, adjacent modules were fused together to obtain a novel one, and appropriate minModuleSize, deepSplit, and height values were assigned. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis was performed to explore the potential biological functions of genes within these 3 modules using the ClusterProfiler package under R environment, with the significant FDR level of \u0026lt;\u0026thinsp;0.05. Then, the associations between the recognized gene modules and ESTIMATEScore, StromalScore as well as ImmuneScore values were calculated, respectively, in order to mine the gene modules having highest correlation.\u003c/p\u003e \n\u003ch2\u003eConstruction Of Gene Interaction Network And Functional Analyses\u003c/h2\u003e\n \u003cp\u003eWe separated patients into two groups equally based on the ImmuneScore and ESTIMATEScore. The DESeq2 function of R package was utilized to screen different expressed genes (DEGs) between these groups. The cut-off criterion for DEGs was set as \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and |log2(Foldchange)|\u0026gt;1. Genes shared by the most relevant module, DEGs of ImmuneScore and ESTIMATEScore groups were finally screened out. All genes were subsequently mapped into the String database [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] separately using the STRINGdb package under R environment, with the threshold of \u0026gt;\u0026thinsp;0.4 to obtain the gene-gene interaction. Cytoscape was used for visualization. At the same time, the R package clusterprofile [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] was utilized to carry out KEGG enrichment analyses for visualizing the signaling pathways affected by genes. The prognostic value of each immune-related gene was calculated using survival package under R environment and was further verified with an independent external GEO dataset (GSE17025) through Pearson correlation analysis.\u003c/p\u003e "},{"header":"Results","content":" \u003cp\u003e \u003cb\u003eThe immune scoring system based on the ESTIMATE algorithm was closely related to the immune microenvironment of EC\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe expression levels of 13 immune metagenes (Metascore), infiltration status of 6 immunocytes (immusocre), and 3 immune-related scores based on ESTIMATE algorithm (ESTIMATEScore, StromalScor, and ImmuneScore). Additionally, Spearman correlation coefficient was employed for quantifying the relationships between these scoring systems (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Our results suggested that, immune-related scores calculated using the ESTIMATE algorithm had an average internal correlation\u0026thinsp;\u0026gt;\u0026thinsp;0.8 and also high correlations with the other 2 algorithms. Above findings indicated that immune-related scores computed based on the ESTIMATE algorithm was closely related to the immune microenvironment of EC.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eBesides, ESTIMATEScore, ImmuneScore, and StromalScore among the 4 recognized subtypes [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] had also been examined (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA-C). It was clear that, differences in ESTIMATEScore and ImmuneScore levels among different molecular subtypes of EC were statistically significant. It was noteworthy that copy-number high subtype had the lowest scores among these 4 subtypes. Moreover, distribution of 6 immunocytes infiltration (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD-I) and 13 metagenes expression (Fig.S1) among these 4 subtypes was also analyzed. Statistical significances were observed in 6 out of 13 metagenes expression and 5 out of 6 immunocytes infiltration.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo assess the relationship between 3 ESTIMATE scores and patient prognosis, the prognosis of above 4 subtypes was firstly examined. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA, difference in patient prognosis across those 4 subtypes was statistically significant, among which, the copy\u0026thinsp;\u0026minus;\u0026thinsp;number high subtype had the poorest prognosis. Next, all samples were classified according to median of ESTIMATE scores followed by survival analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB-C). Obviously, the prognosis for samples with the high ESTIMATEScore and ImmuneScore was much better than that with low scores, suggesting that ESTIMATE immune scoring system might be used as novel promising markers to predict the prognosis for EC.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAfterwards, correlations between those 3 ESTIMATE scores and 3 independent prognostic gene mutations (BRCA2, BRCA1 and TP53) were explored [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. As a result, 3 immune-related scores of different mutant groups were generally higher when compared with those of wild-type groups, especially in TP53 and BRCA1 subgroups (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA-I). Subsequently, the TMB of each sample was computed, and the relationships between 3 ESTIMATE scores and TMB level were analyzed. As presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eJ-L, the ESTIMATEScore and ImmuneScore showed significant positive correlation with TMB.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo sum up, the immune-related scores based on ESTIMATE algorithm was closely related to the immune microenvironment of EC and could be used to be the optimal immune scoring methods for prognosis prediction.\u003c/p\u003e \n\u003ch2\u003eIdentification Of Immune Score-related Gene Modules By Wgcna\u003c/h2\u003e\n \u003cp\u003e14131 transcripts were firstly selected to performed hierarchical clustering analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). After excluding outlier data, 579 samples were finally used to construct a weight co-expression network through WGCNA (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB, C). Then, gene modules were examined by dynamic shear, and all the recognized modules were further clustered. Next, adjacent gene modules were fused to form a new one, with minModuleSize, deepSplit and height set at 30, 2 and 0.25, respectively. 17 modules were ultimately obtained (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD, E), and 5362 transcripts were assigned to 16 co-expression modules except the grey module. Correlations between eigenvectors of these 16 modules and 3 ESTIMATE scores were then calculated (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE), from which we could see that the red (170 genes), purple (55 genes) and tan (49 genes) modules had the closest correlations with 3 ESTIMATE scores, with the average correlation coefficients\u0026thinsp;\u0026gt;\u0026thinsp;0.5.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAs following, we performed KEGG pathway enrichment analysis to explore the potential biological functions of the 274 genes within these 3 modules. According to the results,there were 23 and 54 pathways enriched to the purple module and red module, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). It was easy to find that the genes were primary enriched to immune-related pathways, including T cell differentiation, primary immunodeficiency, chemokine signaling pathway, cytokine-cytokine receptor interaction, B cell receptor signaling pathway and so on. Thus, it can be inferred that the ESTIMATE score-related genes may closely associate with the immune microenvironment of EC.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \n\u003ch2\u003eIdentification Of Immune Microenvironment-related Prognostic Genes\u003c/h2\u003e \u003cp\u003eTo seek out the most immune-related genes, correlations Qbetween previously obtained ESTIMATE score-related modules and genes were computed. As illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, the correlation coefficients were distributed in a bimodal manner, with the intersection point value of 0.77. Based on this, 136 genes with the maximum correlation coefficients with those 3 modules greater than 0.77 were screened out.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAccording to the previously described screening method, 379 DEGs were identified among high- and low-ESTIMATEScore subgroups (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA, B) using the R package DESeq2 [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. At the meantime, 526 DEGs were acquired among ImmuneScore-high and -low subgroups (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eC, D). The results were presented in a volcano plot and a heatmap for each subgroup. Obviously, the DEGs showed distinct expression patterns in low-ESTIMATEScore subgroup compared with high-ESTIMATEScore subgroup. Similar results were also observed in low- and high-ImmuneScore subgroups.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo further investigate the immune-related genes, we integrated above 3 gene sets (136 genes with the maximum correlation coefficients with 3 modules, 379 DEGs in high- and low-ESTIMATEScore subgroups and 526 DEGs in high- and low-ImmuneScore subgroups). After intersection, 133 common genes were obtained and 24 genes belonged to 13 metagenes were eliminated subsequently. Finally, 109 immune-related genes were preliminarily found out (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSubsequently, KEGG enrichment analyses was conducted with clusterProfiler package under R environment. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eB, 29 pathways were significantly enriched, most of which were associated with immune response. Afterwards, the R STRINGdb package [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] was utilized to analyze the protein-protein interaction network of those 109 genes. All these genes were mapped to String database in order to acquire the relationship network and 67 nodes were finally obtained (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eC). As is depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eD, the degree value of each node was high (5.49 on average), which demonstrated the close association between those immune-related genes.\u003c/p\u003e \u003cp\u003eThen, univariate survival analysis was performed to identify the immune microenvironment-related prognostic markers using the survival and expression data of above 109 genes. The results revealed that there were 34 and 19 genes associated with OS and PFS, respectively. Among which, 14 genes were found to be positively correlated with both OS and PFS (Table\u0026nbsp;1, Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \n\u003ch2\u003eValidation Of Immune-related Prognostic Genes Using An External Database\u003c/h2\u003e\n \u003cp\u003eThe independent dataset GSE17025 [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] was chosen and normalized expression matrix was downloaded. Then, the R package ESTIMATE was used to calculate the sample ImmuneScore values. Pearson correlation coefficients between expression levels of 12 genes (2/14 genes were excluded due to unavailable expression data) and ImmuneScore values were further determined. As illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003e, \u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003e genes (except S1PR4) showed marked correlations with ImmuneScore values, which was consistent with our previous findings.\u003c/p\u003e "},{"header":"Discussion","content":" \u003cp\u003eUntil recently, the 5-year survival of advanced or recurrent EC is still not optimistic. Even though postoperative adjuvant therapy (chemotherapy and/or radiotherapy) can improve patient outcome, great individual differences in therapeutic effect exist due to the biological heterogeneity of EC cells. Hence, there is a critical need for reliable prognostic biomarkers to evaluate the risk of cancer progression and develop the patient-tailored treatment strategy.\u003c/p\u003e \u003cp\u003eWith the rapid development of next generation sequencing techniques, numbers of novel molecular biomarkers have been identified, which were of great help to the personalized treatment of EC [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Nevertheless, most studies are conducted on animal models, surgical cancer tissues samples or cell lines under \u003cem\u003ein vitro\u003c/em\u003e conditions.\u003c/p\u003e \u003cp\u003eTumor microenvironment, which is comprised of tumor cells, stromal cells and the secreted inflammatory mediators and cytokines, is a complex system and supports various tumor biological behaviors, including tumor genesis, progression, metastasis and so on. Stromal cells are mainly made up of immunocytes, fibroblasts, mesenchymal cells and tumor-associated endothelial cells. The abnormal infiltration of stromal cells, especially for immunocytes (such as neutrophils, monocytes and lymphocytes), has been verified in numerous studies [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. For now, great attention has been paid to the association between immune system and tumor biology [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. A growing number of studies have not only revealed the interaction between EC cells and the host immune system, but also enhanced the efficacy of immunotherapies [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. As mentioned before, EC has been widely accepted as a kind of immunogenic malignancy. The treatment of EC has reached a new milestone through artificially manipulating the tumor immune microenvironment. Therefore, it is of great importance for us to explore the immune-related prognostic and therapeutic biomarkers for EC [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn current study, we used the RNA-seq data downloaded from TCGA database to calculated 3 immune-related scores based on ESTIMATE algorithm, which showed marked correlation with the immune status, survival time, prognosis-related gene mutations, and molecular subtypes of EC. Next, ESTIMATE immune score-related gene modules were obtained by means of WGCNA. 109 immune-related genes were then screened out using differential expression analysis and their functions were examined through enrichment analysis. Survival analysis was then performed and 14 novel immune-related prognostic genes were finally screened out, among which, 12 genes were further validated in GSE17025 dataset and consistent results were obtained.\u003c/p\u003e \u003cp\u003eOur newly discovered 14 immune-related prognostic markers include WAS, GZMH, CD7, NKG7, LINC01871, TRAC, CD8A, TRBC2, CD3E, IGSF6, RASAL3, ITGAL, S1PR4 and CCL4. Unfortunately, only a few studies have explored the roles of these genes in EC. Among these, 4 genes (CD8A, CD3E, CCL4 and ITGAL) were reported to have close relationship with tumor immune microenvironment and to be involved in various pathological processes of EC [\u003cspan additionalcitationids=\"CR35\" citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. TRBC2 encodes a specific region of the T-cell receptor beta-2 chain [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e] and has been identified as a promising biomarker for the distinction of multiple cancer types, including breast cancer, colorectal cancer, glioblastoma, hepatobiliary cancer, lung cancer and pancreatic cancer and so on [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. It is worth mentioning that, our findings demonstrated that TRBC2 had the most significant correlation with both OS and PFS of EC patients. Nevertheless, no studies have validated the specific role of TRBC2 in EC yet. Thus, it really deserves further study to elucidate the clinical importance and underlying molecular mechanism of TRBC2 in EC.\u003c/p\u003e "},{"header":"Conclusion","content":" \u003cp\u003eBriefly, our current study focuses on gene features associated with EC immune microenvironment. According to our findings, these genes participate in the progression of EC, and affect patient prognosis. Our work helps to investigate the complicated interactions in EC microenvironment. At the same time, our work may help to develop novel potential prognostic biomarkers and therapeutic targets for EC.\u003c/p\u003e \u003cp\u003e \u003cb\u003eAbbreviations\u003c/b\u003e \u003c/p\u003e \u003cp\u003eEC Endometrial Carcinoma\u003c/p\u003e \u003cp\u003eESTIMATE MAlignant Tumor Tissues using Expression Data\u003c/p\u003e \u003cp\u003eFIGO The International Federation of Gynecology and Obstetrics\u003c/p\u003e \u003cp\u003eTME Tumor Microenvironment\u003c/p\u003e \u003cp\u003eTCGA The Cancer Genome Atlas\u003c/p\u003e \u003cp\u003eWGCNA Weighted Gene Co-expression Network Analysis\u003c/p\u003e \u003cp\u003eGEO Gene Expression Omnibus\u003c/p\u003e \u003cp\u003eTPM Transcripts PerKilobase Million\u003c/p\u003e \u003cp\u003eOS Overall Survival\u003c/p\u003e \u003cp\u003ePFS Progression-free Survival\u003c/p\u003e \u003cp\u003eTMB Tumor Mutation Burden\u003c/p\u003e \u003cp\u003eMAD Median Absolute Deviation\u003c/p\u003e \u003cp\u003eTOM Topological Overlap Matrix\u003c/p\u003e \u003cp\u003eKEGG Kyoto Encyclopedia of Genes and Genomes\u003c/p\u003e \u003cp\u003eDEGs Different Expressed Genes\u003c/p\u003e \u003cp\u003e \u003cb\u003eDeclarations\u003c/b\u003e \u003c/p\u003e "},{"header":"Abbreviations","content":"\u003cp\u003eEC\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; \u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; Endometrial Carcinoma\u003c/p\u003e\n\u003cp\u003eESTIMATE \u0026nbsp;\u0026nbsp;\u0026nbsp; MAlignant Tumor Tissues using Expression Data\u003c/p\u003e\n\u003cp\u003eFIGO\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; The International Federation of Gynecology and Obstetrics\u003c/p\u003e\n\u003cp\u003eTME\u0026nbsp;\u0026nbsp;\u0026nbsp; \u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; Tumor Microenvironment\u003c/p\u003e\n\u003cp\u003eTCGA \u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; The Cancer Genome Atlas\u003c/p\u003e\n\u003cp\u003eWGCNA \u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; Weighted Gene Co-expression Network Analysis\u003c/p\u003e\n\u003cp\u003eGEO\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; Gene Expression Omnibus\u003c/p\u003e\n\u003cp\u003eTPM\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; Transcripts PerKilobase Million\u003c/p\u003e\n\u003cp\u003eOS\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; Overall Survival\u003c/p\u003e\n\u003cp\u003ePFS\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; Progression-free Survival\u003c/p\u003e\n\u003cp\u003eTMB\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; Tumor Mutation Burden\u003c/p\u003e\n\u003cp\u003eMAD\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; Median Absolute Deviation\u003c/p\u003e\n\u003cp\u003eTOM\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; Topological Overlap Matrix\u003c/p\u003e\n\u003cp\u003eKEGG\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; Kyoto Encyclopedia of Genes and Genomes\u003c/p\u003e\n\u003cp\u003eDEGs\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; Different Expressed Genes\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data generated are included in the manuscript and supplementary data. All the data we used in our study are publicly accessible at TCGA and NCBI GEO database.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003cbr /\u003e\u003c/strong\u003eThe authors declare no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003cbr /\u003e\u003c/strong\u003eThis work was supported by Chinese National Natural Science Foundation (81902629).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePu Cheng and Jiong Ma conceived, designed the study, analyzed the data and prepared the manuscript, Xia Zheng and Chunxia Zhou collected the data and organized the figures, Xuejun Chen reviewed and edited the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContributor information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePu Cheng, Email: [email protected]\u003c/p\u003e\n\u003cp\u003eJiong Ma, Email: [email protected]\u003c/p\u003e\n\u003cp\u003eXia Zheng, Email [email protected]\u003c/p\u003e\n\u003cp\u003eChunxia Zhou, Email: [email protected]\u003c/p\u003e\n\u003cp\u003eXuejun Chen, Email: [email protected]\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e \u003cspan\u003eBray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. 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Scientific reports. 2019;9(1):3284.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eSafonov A, Jiang T, Bianchini G, Gyorffy B, Karn T, Hatzis C, Pusztai L. Immune Gene Expression Is Associated with Genomic Aberrations in Breast Cancer. Cancer research. 2017;77(12):3317\u0026ndash;24.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eLi T, Fan J, Wang B, Traugh N, Chen Q, Liu JS, Li B, Liu XS. TIMER: A Web Server for Comprehensive Analysis of Tumor-Infiltrating Immune Cells. Cancer research. 2017;77(21):e108\u0026ndash;10.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eLangfelder P, Horvath S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinform. 2008;9:559.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eSzklarczyk D, Morris JH, Cook H, Kuhn M, Wyder S, Simonovic M, Santos A, Doncheva NT, Roth A, Bork P, et al. 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Risk of endometrial carcinoma associated with BRCA mutation. Gynecol Oncol. 2001;80(3):395\u0026ndash;8.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eLiu Z, Wan G, Heaphy C, Bisoffi M, Griffith JK, Hu CA. A novel loss-of-function mutation in TP53 in an endometrial cancer cell line and uterine papillary serous carcinoma model. Mol Cell Biochem. 2007;297(1\u0026ndash;2):179\u0026ndash;87.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eLove MI, Huber W, Anders S: \u003cb\u003eModerated estimation of fold change and dispersion for RNA\u003c/b\u003e-\u003cb\u003eseq data with DESeq2\u003c/b\u003e. \u003cem\u003eGenome biology\u003c/em\u003e 2014, \u003cb\u003e15\u003c/b\u003e(12):550.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eJeanquartier F, Jean-Quartier C, Holzinger A. Integrated web visualizations for protein-protein interaction databases. 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The Implication and Significance of Beta 2 Microglobulin: A Conservative Multifunctional Regulator. Chin Med J. 2016;129(4):448\u0026ndash;55.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eZhang YH, Huang T, Chen L, Xu Y, Hu Y, Hu LD, Cai Y, Kong X. Identifying and analyzing different cancer subtypes using RNA-seq data of blood platelets. Oncotarget. 2017;8(50):87494\u0026ndash;511.\u003c/span\u003e \u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003ctable border=\"#0000;\" width=\"913\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd width=\"155\"\u003e\n\u003cp\u003eGene symbol\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"433\"\u003e\n\u003cp\u003eGene name\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"159\"\u003e\n\u003cp\u003e\u003cem\u003ep\u003c/em\u003e value (OS)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"167\"\u003e\n\u003cp\u003ep value (PFS)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"155\"\u003e\n\u003cp\u003eWAS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"433\"\u003e\n\u003cp\u003eWiskott-Aldrich snydrome\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"159\"\u003e\n\u003cp\u003e0.005103711\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"167\"\u003e\n\u003cp\u003e0.033465715\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"155\"\u003e\n\u003cp\u003eGZMH\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"433\"\u003e\n\u003cp\u003eGranzyme H\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"159\"\u003e\n\u003cp\u003e0.008033252\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"167\"\u003e\n\u003cp\u003e0.006877271\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"155\"\u003e\n\u003cp\u003eCD7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"433\"\u003e\n\u003cp\u003eCD7 molecule\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"159\"\u003e\n\u003cp\u003e0.010859632\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"167\"\u003e\n\u003cp\u003e0.006642321\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"155\"\u003e\n\u003cp\u003eNKG7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"433\"\u003e\n\u003cp\u003eNatural killer cell granule protein 7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"159\"\u003e\n\u003cp\u003e0.000412089\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"167\"\u003e\n\u003cp\u003e0.030792653\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"155\"\u003e\n\u003cp\u003eLINC01871\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"433\"\u003e\n\u003cp\u003eLong intergenic non-protein coding RNA 1871\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"159\"\u003e\n\u003cp\u003e0.000140799\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"167\"\u003e\n\u003cp\u003e0.007739673\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"155\"\u003e\n\u003cp\u003eTRAC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"433\"\u003e\n\u003cp\u003eT cell receptor alpha constant\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"159\"\u003e\n\u003cp\u003e4.40E-05\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"167\"\u003e\n\u003cp\u003e0.009042146\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"155\"\u003e\n\u003cp\u003eCD8A\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"433\"\u003e\n\u003cp\u003eCD8a molecule\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"159\"\u003e\n\u003cp\u003e0.003341004\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"167\"\u003e\n\u003cp\u003e0.012404753\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"155\"\u003e\n\u003cp\u003eTRBC2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"433\"\u003e\n\u003cp\u003eT cell receptor beta constant\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"159\"\u003e\n\u003cp\u003e1.03E-05\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"167\"\u003e\n\u003cp\u003e0.001855756\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"155\"\u003e\n\u003cp\u003eCD3E\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"433\"\u003e\n\u003cp\u003eCD3e molecule\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"159\"\u003e\n\u003cp\u003e0.000106431\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"167\"\u003e\n\u003cp\u003e0.002676012\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"155\"\u003e\n\u003cp\u003eIGSF6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"433\"\u003e\n\u003cp\u003eImmunoglobulin superfamily menmer 6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"159\"\u003e\n\u003cp\u003e0.00173721\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"167\"\u003e\n\u003cp\u003e0.043427798\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"155\"\u003e\n\u003cp\u003eRASAL3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"433\"\u003e\n\u003cp\u003eRAS protein activator like 3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"159\"\u003e\n\u003cp\u003e0.006739461\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"167\"\u003e\n\u003cp\u003e0.039618006\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"155\"\u003e\n\u003cp\u003eITGAL\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"433\"\u003e\n\u003cp\u003eInergrin subunit alpha L\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"159\"\u003e\n\u003cp\u003e0.004923864\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"167\"\u003e\n\u003cp\u003e0.041623366\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"155\"\u003e\n\u003cp\u003eS1PR4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"433\"\u003e\n\u003cp\u003eSphingsine-1-phosphate receptor 4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"159\"\u003e\n\u003cp\u003e0.007261307\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"167\"\u003e\n\u003cp\u003e0.029160931\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"155\"\u003e\n\u003cp\u003eCCL4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"433\"\u003e\n\u003cp\u003eC-C motif chemokine ligand 4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"159\"\u003e\n\u003cp\u003e0.001976119\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"167\"\u003e\n\u003cp\u003e0.034895726\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Endometrial carcinoma, Immune, TCGA, Survival analysis, Prognosis","lastPublishedDoi":"10.21203/rs.3.rs-30550/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-30550/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Endometrial carcinoma (EC) is a common malignancy of female genital system which exhibits a unique immune profile. It is a promising strategy to quantify immune patterns of EC for predicting prognosis and therapeutic efficiency. Here, we attempted to identify the possible immune microenvironment-related prognostic markers of EC. \u003c/p\u003e\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e We obtained the RNA sequencing and corresponding clinical data of EC from TCGA database. Then, 3 immune scores based on the Estimation of STromal and immune cells in MAlignant Tumor tissues using Expression data (ESTIMATE) algorithm were computed. Correlation between above ESTIMATE scores and other immune-related scores, molecular subtypes, prognosis, and gene mutation status (including BRCA and TP53) were further analyzed. Afterwards, gene modules associated with the ESTIMATE scores were screened out through hierarchical clustering analysis and WGCNA. Differentially expressed analysis was performed and genes shared by the most relevant modules were found out. KEGG pathway enrichment analysis was conducted to explore the biological functions of those genes. Survival analysis was carried out to identify prognostic immune-related genes and GSE17025 database was used as external dataset for further validation.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e The immune-related scores based on ESTIMATE algorithm was closely related to the immune microenvironment of EC. 3 gene modules that had the closest correlations with 3 ESTIMATE scores were obtained. 109 immune-related genes were preliminarily found out and 29 pathways were significantly enriched, most of which were associated with immune response. Univariate survival analysis revealed that there were 14 genes positively associated wicth both OS and PFS. Among which, 11 genes showed marked correlations with ImmuneScore values in GSE17025 database.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eOur current study profiled the immune status and identified 14 novel immune-related prognostic biomarkers for EC. Our findings may help to investigate the complicated tumor microenvironment and develop novel individualized therapeutic targets for EC.\u003c/p\u003e","manuscriptTitle":"Bioinformatic Profiling Identifies Prognosis-Related Genes in the Immune Microenvironment of Endometrial Carcinoma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2020-06-11 16:40:54","doi":"10.21203/rs.3.rs-30550/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":"897b1c17-cdcd-4629-9863-814a3e893da6","owner":[],"postedDate":"June 11th, 2020","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":117910,"name":"Cell Communication and Signaling"}],"tags":[],"updatedAt":"2021-07-27T20:46:34+00:00","versionOfRecord":{"articleIdentity":"rs-30550","link":"https://doi.org/10.1038/s41598-021-92091-5","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2021-06-15 20:46:34","publishedOnDateReadable":"June 15th, 2021"},"versionCreatedAt":"2020-06-11 16:40:54","video":"","vorDoi":"10.1038/s41598-021-92091-5","vorDoiUrl":"https://doi.org/10.1038/s41598-021-92091-5","workflowStages":[]},"version":"v1","identity":"rs-30550","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-30550","identity":"rs-30550","version":["v1"]},"buildId":"WrCJVZZCHTDjtuVLN7oU0","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}