Identication of the Prognostic LncRNA Biomarkers and Comprehensive Analysis of LncRNA-Mediated ceRNA Network for Uterine Corpus Endometrial Carcinoma

Given that long non-coding RNAs (lncRNAs) involved in the tumor initiation or progression of the endometrium and that competing endogenous RNA (ceRNA) plays an important role in increasingly more biological processes, lncRNA-mediated ceRNA is likely to function in the pathogenesis of uterine corpus endometrial carcinoma (UCEC). Our present study aimed to explore the potential molecular mechanisms for the prognosis of UCEC through an lncRNA-mediated ceRNA network. The transcriptome proles and corresponding clinical proles of UCEC dataset were retrieved from CPTAC and TCGA databases respectively. Differentially expressed genes (DEGs) in UCEC samples were identied via R” package. Then, an integrated bioinformatics analysis including functional enrichment analysis, tumor inltrating immune cell(TIIC) analysis, Kaplan-Meier curve, Cox regression analysis were conducted to analyze the prognostic biomarkers. an lncRNA-mediated ceRNA network reveals the molecular mechanism that facilitates UCEC pathological progress. LncRNAs including DGCR5, LINC00443, C2orf48, LINC00483, TRBV11-2 and MEG8 involved in lncRNA-miRNA-mRNA regulatory network were identied as promising diagnostic, therapeutic or prognostic biomarkers. Further studies are warranted to explore meaningful biological functional pathways underlying these lncRNA roles for UCEC.


Introduction
Uterine corpus endometrial cancer (UCEC), one of globally common gynecological malignancies, presents a possibly upward trend of with the increase of obese women. [1][2][3]The choice for treatment of UCEC has considerable exploration and development prospects for the perspective of molecular biology. In previous studies, risk factors including p53 expression [4], and estrogen receptor (ER) and progesterone receptor (PR) expression [5], as well as clinical treatment manners have been identi ed [6]. The discovery of these factors provides access to take advantage of underlying therapeutic biomarkers for personalized treatment strategies.MiRNA is a family of small non-coding RNA molecules of about 21 to 25 nucleotides long. miRNA inhibits translation of targeted mRNA or affects its stability by speci c identi cations, and down regulates its expression by combining at its 3'UTR site [7]. The abnormal miRNA expression in development of tumors has been con rmed by many studies [8]. LncRNA is known to play a role as key signal transduction mediators in the occurrence, progression and treatment of numerous malignancies [9][10][11]. According to the ceRNA hypothesis [12], lncRNA is a molecular sponge of miRNA, which suppresses the activity of miRNA by binding with microRNA response element (MRE) and down regulates the expression of the target genes indirectly. Based on this argument, the ceRNA network has been extensively researched and veri ed in lung cancer [13], breast cancer [14] and so on, while there have been little discussion on lncRNA-mediated ceRNA networks of UCEC.
Therefore, in this study, we retrieved and analyzed lncRNA expression in UCEC from TCGA and CPTAC database separately and performed an integrated bioinformatics analysis including functional enrichment analysis, Tumor In ltrating Immune Cell (TIIC) analysis, and constructed the UCEC-speci c ceRNA network and gured out the underlying association between those ceRNAs and the progression of UCEC.

Data Collection
Transcriptional and clinical data of UCEC in both The Cancer Genome Atlas data portal(TCGA; https://portal.gdc.cancer.gov/) and Clinical Proteomic Tumor Analysis Consortium(CPTAC; https://cptacdata-portal.georgetown.edu/data-use-agreement) were retrieved. There were 587 samples downloaded from TCGA, containing 555 UCEC patients and 32 normal specimens, and 116 samples downloaded from CPTAC, containing 101 UCEC patients and 15 normal specimens, contributing to the UCEC and normal control group as a cohort. The clinical features of UCEC patients from 2 databases were respectively shown ( Table 1). 12 other samples of originally 555 UCEC samples in TCGA database were omitted in this whole study due to missing associated information. Therefore, actually a total of 575 samples from TCGA were conducted to the further analyses. The clinical features of UCEC patients including age, gender, race, pathology stage, histological type and vital status were extracted. Transcriptome data were annotated with the Genecode website (https://www.gencodegenes.org/). No samples were excluded when to screen for differentially expressed RNAs (DERNAs, including three ones: differentially expressed long noncoding RNA(DElncRNA), differentially expressed microRNA (DEmiRNA), differentially expressed messager RNA(DEmRNA)). Both databases were publically available and were released in compliance with ethical approvals; therefore no further application from University Ethics Committee was obtained.

Identi cation of DEGs
The "edge R" was utilized to identify differentially expressed genes (DEGs) between the normal samples and UCEC patients by the criteria (false discovery rate (FDR) or adjusted P <0.01, and | log 2 FC | > 2(FC is fold change)). All of the statistical tests were conducted and the heatmap and volcano plot were displayed by ggplot2 package in R software package (version:4.0.3; https://www.r-project.org/) and statistical signi cance was de ned as a P-value < 0.05 unless otherwise stated.

Construction of Protein-Protein Interaction Network
In this study, a total of 1064 and 917 DEGs ltered by | log 2 FC | >3 from the two databases were subjected to perform protein-protein interaction(PPI) network analysis using Search Tool for the Retrieval of Interacting Genes (STRING; https://string-db.org/) [15]. An interaction with a combined score by default >0.4 was considered statistically signi cant. Cytoscape (version 3.8.2) is a bioinformatics software platform publically used for visualizing molecular interaction networks [16]. To nd hub genes actively participated in UCEC progression, we employed the maximal clique centrality (MCC) algorithm to represent 20 key mRNAs with important biological functions via CytoHubba in Cytoscape [17].
Tumor In ltrating Immune Cells Pro ling To characterize proportions of tumor in ltrating immune cells(TIICs) in UCEC, CIBERSORT (http://cibersort. stanford. edu/) algorithm in combination with a LM22 gene signature matrix was used to assess the relative fractions of 22 invasive immune cell subtypes in each UCEC sample. Regarding the results of the algorithm, we also accumulated the percentage of each immune cell theoretically calculated from each patient sample and presented those top ranked immune cell types in bar graphs.
Worthy to point out, if these UCEC samples lack of associated transcriptome information, they were then omitted. Additionally, to investigate the immune in ltration landscape of UCEC, gene set enrichment analysis (ssGSEA) was performed by GSVA package in R (https://bioconductor.org/ packages/release/bioc/html/GSVA.html) to calculate the score of immune in ltration in each sample on the basis of immune cell-speci c gene expression levels. Standardized pro les of gene expression data in both groups were extracted and immune scores were evaluated, scoring types including different cell clusters and respective expression values corresponding to different colors.
Furthermore, on the foundation of overall ceRNA network, we rstly conducted survival analyses and Cox regression analyses of these genes, both results of which were considered as hub genes in our ceRNA network. Thereafter, we input these hub genes to visualize their lncRNA-miRNA-mRNA regulatory relationships via Cytohubba (http://hub.iis.sinica.edu.tw/cytohubba) in Cytoscape. Eventually, we constructed a novel ceRNA subnetwork composed of lncRNA-miRNA-mRNA pairs, which may provide prognostic molecular values.

Survival Analysis and LncRNA-mediated Prognostic Model Construction
On one hand, survival R was operated for survival analysis of all DERNAs in the ceRNA network. Kaplan-Meier (K-M) curves were plotted with DERNAs by log-rank test. On the other hand, we identi ed the lncRNAs linked with total survival (p < 0. 05) to act as prognostic lncRNA signature candidates and then imported them for Cox regression analyses. According to the median risk score, the UCEC patients were divided into the high risk and low risk groups. To evaluate the accuracy of models survival-related DElncRNAs, we carried out the receiver operating characteristic (ROC) curve analysis (UCEC data in CPTAC using 3 years as the predicted time, UCEC data in TCGA using 5 years as the predicted time), along with the area under the receiver operating characteristic curve (AUC) analysis at a criterion of AUC > 0.7. Besides, we further retrieved analyses of survival-related lncRNAs in the GEPIA database(GEPIA; http://gepia.cancer-pku.cn/index.html) by p values ≦0.1(although 0.11 was also considered as signi cant in this study).Subsequently, we compared these results of survival-related lncRNAs with our analyses thus to validate and prove its reliability.

Validation of Dysregulated mRNAs via HPA and GEPIA databases
The Human Protein Atlas Portal (HPA) (www.proteinatlas.org) [21] which contains different genes in speci c cancer types and publicly available information of Immunohistonchemistry(IHC) staining was used for survival analyses. Gene Expression Pro ling Interactive Analysis, an interactive website, composed of 9736 patients and 8587 normal samples from TCGA and GTEx projects (The Genotype-Tissue Expression project) were utilized for the analysis of RNA sequencing expression. According to prognostic lncRNA-mRNA or mRNA-miRNA signatures in the lncRNA-mediated network, we took 33 mRNAs resulted from UCEC data of TCGA into two external databases mentioned above for further retrieval and analysis.

Functional and Pathway Enrichment Analysis
To analyze functions represented in two pro les of identi ed DEmRNAs in the ceRNA network, Gene Ontology (GO), Kyoto Encyclopedia of genes and Genomes (KEGG) were performed by cluster Pro ler R package and plotted by GO plot R package and KEGG plot R package. Three methods were utilized to enrich meaningful biological pathways by the standard of p valve less than 0.05.

Construction of PPI Network
The 1981 DEmRNAs (| log 2 FC | >3) were further selected to construct PPI network to select hub genes that play crucial roles in UCEC genesis. Given the large number of DEmRNAs in this module, we used MCC algorithm in "Cytohubba" of Cytoscape software to visualize and select hub genes in the PPI network. The top 20 high score genes in in CPTAC were shown ( Fig.2A). Similarly, the top 20 high score genes in TCGA belong to Histone cluster 1 H family which was shown (Fig.2B).
Two graphs separately describe the top 20 most dynamic hub genes and their intersection relationships evaluated by MCC algorithm in "Cytohubba". These sub-graphs of these selected mRNA-coding protein nodes are shown from highly essential (red) to essential (yellow).

TIICs Enrichment Analysis
Using CIBERSORT algorithm, we evaluated 101 tumor transcriptome pro les from CPTAC database and 543 tumor transcriptome pro les from TCGA database (Fig.3A,3B). In addition, we also performed ssGSEA analysis by GSVA package to score the corresponding TIICs in each simple sample, and nally we found that TIICs in both CPTAC and TCGA database sources expressed well (Fig.3C,3D). On account of the top ve immune cell components in UCEC patients by CIBERSORT algorithm, 2 bar graphs were then visualized. In CPTAC, there were abundant CD8 T cells (28.5%) and plasma cells (20.5%) and other TIICs. In TCGA samples, naive CD4 cells (18.9%) and CD8 cells (15.2%) were well in ltrated, accompanied by slightly increased activated NK cells (10.2%), plasma cells (9.1%) and macrophages M0(7.5%) (Fig.3E,3F).
Construction of ceRNA Network and hub LncRNA-miRNA-mRNA subnetwork In order to better comprehend the interactions of mRNAs, lncRNAs, and miRNAs in UCEC, we constructed an lncRNA-mediated ceRNA regulatory network. To begin with, 1741 DElncRNAs in CPTAC database succeeded to match with 123 lncRNAs in the miRCODE database. Considering 123 of 1741 DElncRNAs could interact with DEmiRNAs, 36 miRNAs in both miRCODE and CPTAC database were selected to construct lncRNA-miRNA pairs. Meanwhile, to interplay with 204 DEmiRNAs acquired from CPTAC database, we retrieved 1420 mRNAs in three databases (miRTarBase, miRDB and TargetScan). The 1420 miRNA-targeted mRNAs predicted in these databases were intersected with 2463 DEmRNAs thus to obtain 124 miRNA-targeted mRNAs belonging to CPTAC database. Finally, an lncRNA-mediated ceRNA network consisting of 36 miRNAs, 123 lncRNAs and 124 mRNAs were achieved (Fig.4A). Meanwhile, the same work ow of UCEC-speci c ceRNA network construction was repeated in data from TCGA. We obtained an lncRNA-mediated ceRNA network consisting of 38 miRNAs, 83 lncRNAs and 110 mRNAs (Fig.4B).
Furthermore, cytohubba was applied to visualize our extracted hub genes composing of lncRNA, miRNA and mRNAs and derived regulatory ceRNA network thus to identify potentially prognostic molecular pathways of UCEC in CPTAC and TCGA (Fig.4C,4D). A total of 6 hub lncRNA-miRNA-mRNA regulatory relationships from 2 databases were shown (Table 2). Moreover, coincident ceRNA results in the overall ceRNA network from both CPTAC and TCGA databases were shown in the Venn diagram (Fig.4E). In order to gure out the effects of interactions for survival between lncRNAs, miRNAs and mRNAs, we imported survival-related data of UCEC and genes in ceRNA to analyze its prognosis. Survival R were operated for DERNAs signi cantly correlated with overall survival in the ceRNA network  (Fig.6A-6E).
To further identify DElncRNAs with prognostic features in a more accurate way, multi-Cox regression analyses and corresponded ROC curves were carried out. After eliminating some samples lacking in survival time, 94 complete samples in CPTAC were divided into the high-risk (n=47) and low-risk (n=47) groups (cutoff value = -0. 78) and 543 samples with complete survival information in TCGA into the highrisk (n=272) and low-risk (n=272) cohort by median value (cutoff value= -0. 18; one sample of survival data was just in the median and counted in both groups). We performed a multi-factor COX regression analysis and a global survival analysis of the model thus separately identi ed two lncRNAs of 3-year survival data in CPTAC and eight lncRNA prognosis candidates of 5-year survival UCEC data in TCGA by p < 0.05 (Fig.7A,7B,7C,7F)). Receiver operating characteristic (ROC) curves tested the in uence on their lncRNA signatures associated with overall survival in UCEC. Area under ROC curve of 3-year survival rate (AUC) and 5-year survival rate (AUC) were respectively 0.967 and 0.751. (Fig.7B,7E) Besides, multivariate cox regression analysis of totally 10 prognostic lncRNAs associated with overall survival in UCEC patients generated from 2 databases were shown in Table 3.  In the multivariate Cox regression analysis derived from CPTAC database, 2 lncRNAs including MEG8 and TRBV11_2 were identi ed to construct the OS prediction model. OS-related prediction model=(0.007280976* expression value of MEG8)+(0.027816351* expression value of TRBV11_2). We divided the 94 UCEC cases into the high and low-risk groups according to the median values of the OS-related prediction model.

Validations of survival analysis and mRNA Expression at the Transcriptional Level
To further demonstrate the prognostic signi cance of 33 mRNAs screened from the ceRNA network, we selected external databases for survival analysis and validation with IHC images. Firstly, we input 33 screened mRNAs into HPA database (version 20.1; https://www.Proteinatlas.org/about/assays+annotation#tcga_survival) to validate whether they were associated with the prognosis of UCEC. Consequences revealed that 8 mRNAs(CBX2, CCL22, CCNE1, DLX4, IGFBP5, NR3C1, SOX11, POLQ) highly expressed in UCEC were closely related with its prognosis(log rank P values 0.001). Subsequently, we retrieved overall survival analyses of 8 mRNAs generated from GEPIA by ltered criteria of P values ≤0.1(although 0.11 was also considered as signi cant in this study) and veri ed 5 mRNAs (CCNE1, CCL22, NR3C1, IGFBP5 and POLQ).
Based on two previous steps for external veri cations, two IHC images of the last screened mRNAs (CCNE1, NR3C1) in the HPA database approved the same results (Fig.8A). Survival validations of 5 mRNAs including CCNE1, CCL22, NR3C1, IGFBP5 and POLQ from GEPIA were shown in the Fig.8B-8F. In this study, we identi ed 5 survival-related mRNAs, there were no related IHC samples of CCL22, IGFBP5 and POLQ but CCNE1 and NR3C1 to further validate in the HPA database. The translational expression level of CCNE1 and NR3C1 was positively linked with disease status, as they were up-regulated in UCEC samples.

Enrichment Analyses of Functional Pathways
To elucidate the biological functions represented in two pro les of identi ed DEmRNAs, we performed enrichment analyses mainly by "cluster pro ler", with the standard of p <0. 05. In this study, GO analyses disclosed that top signi cant GO terms (p < 0. 05) commonly obtained from UCEC data in CPTAC (Fig.9A,9B) and TCGA database (Fig.9E,9F). The KEGG analyses revealed that what closely related to DEmRNAs originated from CPTAC were mainly enriched in pathways such as "cellular senescence", "proteoglycans in cancer" and "microRNAs in cancer" (Fig.9C,9D)). The KEGG results derived from DEmRNAs in TCGA database were shown (Fig.9G,9H). The top 20 GO and KEGG results for TCGA and CPTAC database were provided in supplement Table 2.

Discussion
Recently, with the increase of obese women, UCEC has become one of the leading gynecologic tumors [22]. Although some diagnostic markers like CA125, CA199, and CEA are clinically used, survival results are not optimistic after routine diagnosis and therapy. Therefore, it is worthy to discover and analyze biomarkers for prognosis prediction of UCEC. LncRNAs have increasingly seized the attention of cancer research elds because of serving as regulating biomarkers [23]. But due to experimental complexity, functional studies related to lncRNAs have limitations to carry out in comparison with those of protein-encoding RNAs. As is illustrated in accumulating researches, molecular mechanisms underlying ceRNA network provide an explanation for carcinogenesis and its associated development. LncRNAs act as key components of ceRNA family, through miRNA response elements (MREs), compete with molecules binding to the same miRNAs to achieve regulation of expression levels between each other.
As our lncRNA-mediated ceRNA network of UCEC respectively constructed from the CPTAC and TCGA databases indicated, there were a total of 23 lncRNAs, 9 miRNAs, and 33 mRNAs correlated with the overall survival results and served as promising biomarkers for predicting prognosis of UCEC.
Conventional prognostic model constructions often make inadequate risk groupings and estimates of clinical outcomes [24,25]. Nonetheless, this ceRNA hypothesis provides us a novel predictive insight from the angle of heterogeneity between UCEC patients to analyze overall survival (OS) results. For bioinformatics analysis conducted in multiple databases, it is common practice to combine sample pro les from multiple databases for further analysis after standardized quality control treatment.
However, the follow-up time length of UCEC pro les from CPTAC is shorter than that from TCGA database. So we calculated on the level of three-year survival results thus didn't combine it with that from TCGA database.
In our present study, MIR205HG and ADARB2-AS1 were signi cantly correlated with survival. High expression of MIR205HG was found at the rst time to predict a good prognosis, while high expression of ADARB2-AS1 had an opposite effect on the survival outcome of patients. Dong  cancer cell proliferation and migration and activated apoptosis. LncRNA MIR205HG also acts as a ceRNA to accelerate tumor growth and progression in cervical cancer through spongiform Mir-122-5p [27].
ADARB2-AS1 has been reported as a prognostic related lncRNA in UCEC [28], which again echoed the reliability of our results.
In addition, in order to make our results convincing, we put lncRNA biomarkers into GEPIA for external veri cation. The results showed that 5 highly expressed lncRNAs of DGCR5, GLIS3-AS1, UPK1A-AS1, MEG8, TPTEP1 indicated poor prognosis, which is in agreement with our results. To clarify our ndings, we comprehensively analyzed survival results of lncRNA-regulated mRNAs in GEPIA and HPA. Through these external databases, we also concluded that high expression of CCL22, CCNE1, IGFBP5, NR3C1 and POLQ genes were associated with poor prognosis of UCEC. Furthermore, we mirrored methods in foregoing study [29] to identify our mRNA results and obtained poor survival results of NR3C1 and CCNE1 by validations of IHC images, which suggested their tumor promoter roles. Some researches has veri ed that CCNE1 ampli cation is associated with aggressive potential in UCEC tumorigenesis [30][31][32]. CCNE1, known as Cyclin E1, is a member of Cyclins to function as regulators of CDK kinases. The protein encoded by Cyclin belongs to the highly conserved Cyclin family, characterized in its dramatic periodicity in protein abundance through cell cycle. With respect to other carcinoma progression, patients with overexpressed CCNE1 were reportedly at increased threat for poor endings of cervical cancer [33] and triplenegative breast cancer [34]. Functioned as regulatory genes in the downstream, mRNA CCNE1 and NR3C1 brought potential reference values for our presently identi ed lncRNA-mediated ceRNA pathways.
Furthermore, on the basis of overall ceRNA network, we constructed a novel prognostic ceRNA subnetwork, which were composed of lncRNA-miRNA-mRNA axes. We rstly conducted survival analysis and multivariate analysis on lncRNAs in the ceRNA network derived from TCGA database, and identi ed them as hub genes in the following subnetwork. We rstly identi ed these key lncRNAs in the ceRNA network of TCGA, then paired their corresponding key miRNAs. Then we matched the key miRNAs with survival-related mRNAs thus to construct the survival-related subnetwork. For survival analysis in CPTAC database, although we failed to identify survival-related mRNAs and miRNAs, we surprisingly discovered that survival-related mRNAs and miRNAs in TCGA database also existed in the ceRNA network constructed by CPTAC database. Therefore, we chose to use these mRNAs, miRNAs and key lncRNAs derived from CPTAC to jointly construct survival-related subnetworks of CPTAC. In our constructed ceRNA subnetwork, there were 6 lncRNA-mediated lncRNA-miRNA-mRNA axes to role in survival outcomes of UCEC patients. A novel survival-related lncRNA DGCR5 could up-regulate CCNE1 expression by binding to miR195, miR383 and miR424, although DGCR5 had not been directly recorded in UCEC tumorigenesis procedures. DiGeorge syndrome critical region gene 5 (DGCR5), a molecular sponge to regulate cancerous signaling pathways, has been previously discovered to be extremely dysregulated in various tumors and induce the malignant phenotypes of oragans such as liver, pancreas and lungs.etc. Except for DGCR5, lncRNA LINC00443, LINC00483, C2orf48, TRBV11-2 and MEG8 were identi ed by multivariate Cox regression analysis, which reveals more accurate ability to predict prognosis.
From multivariate Cox regression analysis in our constructed ceRNA network, we totally identi ed 10 lncRNA prognostic signature candidates to predict the survival events of UCEC patients. As shown in our ceRNA subnetwork diagram for TCGA, lncRNAs such as LINC00443, C2orf48, LINC00483 could regulate DLX4 and NR3C1 expression by binding to miR-183. LINC00443, LINC00483 and C2orf48 were previously proved to promote carcinoma progression [35], the same consequence of which were validated by our experiment. DLX4 has been shown to cause tumor migration, invasion, and metastasis [36]. Previous in vivo studies on UCEC reported that DLX4 promoted cell proliferation, migration, and suggested poor prognosis, which is consistent with our ndings [37]. NR3C1 encodes glucocorticoid receptors to affect glucocorticoid response and participates in other transcription regulatory procedures. Former study on miRNA-mRNA regulatory network in UCEC found that over expression of mRNA NR3C1 led to poor prognosis [38,39]. Previously in vivo experiments proved that over-expressed LINC00483 promoted UCEC tumorigenesis, the mechanism of which was mainly to sponge with miR-508-3p to regulate RGS17 expression levels [40]. In other cancer researches, LINC00483 also acted as a strong ceRNA molecule and exhibited its regulatory ability to mediate tumor progression and prognosis, such as lung adenocarcinoma [41] and gastric cancer [42]. Our clinical survival and transcriptome results revealed that patients with over-expression of LINC00443, C2orf48 and LINC00483 had poor prognostic outcomes. The two lncRNAs ltered in multi-Cox regression analysis in CPTAC, TRBV11-2 and MEG8 provided potential pathways to explain ceRNA regulatory network despite of none associated reports for UCEC. Firstly, they could up-regulate SOX11 expression by binding to hsa-mir-363 to bring about poor prognosis, and survival related mRNA SOX11 hypermethylation was reported as a tumor biomarker in UCEC [43]. Secondly, MEG8 competed with has-mir-424 to CBX2 and CCNE1 expression as well as competing with has-mir-183 to up-regulate DLX4 and NR3C1 expression. Therefore, we predicted that TRBV11-2 and MEG8 worked as prognostic ceRNAs to up-regulate SOX11, CCNE1 and NR3C1 expression thus resulting in poor prognosis, suggesting that these lncRNAs could promote UCEC development. Furthermore, LINC00028 existed in our survival analysis results rooted from both databases, and the over-expression of LINC00028 indicated a poor prognosis. Besides, LINC00028 has been reported to be involved in ceRNA regulatory network of osteosarcoma recurrence [44]. However, its mechanisms related to UCEC pathogenesis remains unclear.
As illustrated in former studies [45,46], UCEC carcinogenesis is promoted by cell cycle acceleration. Similarly, our KEGG analyses in both databases indicated that DEmRNAs were mostly enriched in pathways such as microRNAs in cancer, cellular senescence and proteoglycans in cancer.
Deoxyribonucle, and deoxyRNA were identi ed in tumor and adjacent normal tissue samples in a large cohort of UCEC patients.
Even though we identi ed the molecular mechanism of ceRNA from 587 samples in TCGA and 116 samples in CPTAC database, there was still a limited sample size for more reliable biomarkers that hindered us from incorporating data pro les originated from CPTAC and TCGA database into a comprehensive study ideally. Concerning the pilot study limited by failure to closely link the analysis of the two databases, multicentric studies are supposed to carry out to support our new researches inevitably.

Conclusion
Using data obtained from CPTAC and TCGA, we screened out lncRNA prognostic signatures on the basis of ceRNA completely composed of hub genes. Besides, an lncRNA-mediated ceRNA network reveals the molecular mechanism that facilitates UCEC pathological progress. LncRNAs including DGCR5, LINC00443, C2orf48, LINC00483, TRBV11-2 and MEG8 involved in lncRNA-miRNA-mRNA regulatory network were identi ed as promising diagnostic, therapeutic or prognostic biomarkers. Further studies are warranted to explore meaningful biological functional pathways underlying these lncRNA roles for UCEC.      Kaplan-Meier curves of DERNAs and overall survival rate in UCEC samples. LncRNA signature prognostic risk models for UCEC. For CPTAC dataset, Kaplan-Meier curves showed that low-risk group had a lower mortality rate than high-risk group (P=0.0046<0.05) (A

Supplementary Files
This is a list of supplementary les associated with this preprint. Click to download.