Identification and Validation of Hub Genes in Uterine Corpus Endometrioid Carcinoma

doi:10.21203/rs.3.rs-2090822/v1

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Identification and Validation of Hub Genes in Uterine Corpus Endometrioid Carcinoma

https://doi.org/10.21203/rs.3.rs-2090822/v1

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Uterine Corpus Endometrioid Carcinoma (UCEC) is one of the malignant tumors with high incidence of female reproductive system. Despite significant progress in molecular biology research and treatment of UCEC, the molecular mechanisms of UCEC tumorigenesis have not been fully elucidated. The purpose of this research was to identify differentially expressed genes (DEGs) association with the pathogenesis of UCEC, and to further define available prognostic biomarkers and immunotherapy targets for UCEC. RNA expression datasets and clinical data for UCEC patients were collected from the UCSC Xena database and The Cancer Genome Atlas (TCGA) database. 174 UCEC tissues and 23 normal endometrial tissues were screened using PCA and LIMMA methods. Analysis of GO enrichment indicated that the up-regulated DEGs were mainly involved in tissue development, cell cycle regulation and epithelial development. Subsequently, DEGs was identified by weighted gene co-expression network analysis (WGCNA). DEGs in blue modules were determined to be significantly positively correlated with UCEC, conversely, black modules were significantly negatively correlated with UCEC. Finally, 16 genes were identified by WGCNA, and further Kaplan-Meier (KM) analysis revealed that 5 of the 16 genes were significantly negatively correlated with overall survival (OS) namely hub genes, including AURKA, CCNE1, IQGAP3, TTK and UBE2C. Further, the expression of hub gene was verified by GEO dataset and IHC analysis in the Human Protein Atlas. Finally, potential immunotherapeutic targets and approaches were revealed by calculating immune scores for immune infiltration, immune cell infiltration, and immune cell regulator across the 5 hub genes. In conclusion, In conclusion, this study probed into the molecular mechanisms of UCEC development in depth, and identifies 5 promising prognostic biomarkers and immunotherapy targets in UCEC progression, providing directions for the treatment of UCEC.

Uterine Corpus Endometrioid Carcinoma

Hub Genes

Weighted gene co-expression network analysis

Prognosis

Immunotherapy

The incidence of endometrial cancer ranks second among the malignant tumors of female reproductive system in China, but it ranks first in the United States and other western developed countries ^[1–2]. Epidemiological evidence confirms that obesity, hypertension, and diabetes are high-risk factors for endometrial cancer ^[3]. At present, endometrial cancer is divided into two types, type I and type II, according to the different pathogenesis and biological behavior characteristics. Type I accounts for most endometrial cancers, most of which are endometrioid adenocarcinomas, and a few are mucinous adenocarcinomas. Type II endometrial cancer includes rare pathological types such as serous carcinoma, clear cell carcinoma, and carcinosarcoma ^[4]. The most common type of endometrial cancer is endometrioid adenocarcinoma. Because endometrioid adenocarcinoma progresses slowly and often has early symptoms such as abnormal uterine bleeding, most patients can be diagnosed early ^[5–6]. Most of the patients in the early stage can obtain a high clinical remission rate through surgery and supplemental radiation or chemotherapy, and the 5-year survival rate of patients is as high as 80%. However, in some patients, there is poor pathological differentiation and metastasis of the tumor, and the patient will have postoperative recurrence, which will eventually lead to the death of the patient. This also makes it difficult for patients with endometrial cancer to be cured ^[7–8].

Therefore, in order to further understand the mechanism of occurrence and development of endometrial cancer, more new candidate genes need to be mined to play the role of early diagnosis and treatment.

Weighted gene co-expression network analysis (WGCNA) is a very practical and systemic biology method used to describe gene association patterns between different patient, which can be used to identify highly co-varied gene sets. And ultimately identify biomarkers or therapeutic targets based on the interconnectivity of gene sets and the link between genome and phenotype^[9]. Therefore, the aim of this study was to appraise the hub genes associated with endometrial cancer prognosis and immunotherapy based on WGCNA by bioinformatics analysis method.

Data collection and pre-processing

The ‘uterine corpus endometrioid carcinoma (UCEC)’ cohort RNA expression data and clinical information were downloaded on 16 Apr 2022 from TCGA (https://www.cancer.gov/about-nci/organization/ccg/research/structural-genomics/tcga) and UCSC Xena database (https://xenabrowser.net/datapages/). The RNA expression data were obtained from 201 UCEC patients, including 177 tumor samples and 24 normal tissue samples. For Principal Component Analysis (PCA), we use the R package stats (version 3.6.0) for analysis, specifically, we first perform z-score on the expression profile, and further use the prcomp function to perform dimensionality reduction analysis to obtain the reduced dimensionality matrix.

Identification Of Ucec Degs And Go Functional Enrichment

Limma ^[10] (linear models for microarray data) is a differential expression genes screening method based on generalized linear models, here we use the R package limma (version 3.40.6) for differential analysis to obtain differentially expressed genes between UCEC and endometrium tissues. differentially expressed genes were defined as those showing |log2(fold change) | > 2 and FDR < 0.05. Volcano plots of differentially expressed genes and the GO annotations of DEGs using the Sanger box tools, a free online platform for data analysis (http://vip.sangerbox.com/). P values of < 0.05 and an FDR of < 0.25 were considered statistically significant.

Wgcna And Identification Hub Genes

A weighted adjacency matrix was constructed using a power function A mn=|C mn|^β (C mn = Pearson's correlation between Gene m and Gene n; A mn = adjacency between Gene m and Gene n; β = soft-thresholding parameter). WGCNA analysis methods and procedures refer to published articles^[11]. Based on Hub gene screening from example (DOI: 10.3389/fonc.2018.00374) ^[12], we calculated the expression correlation with genes to obtain gene significance (GS), and calculated the expression correlation of module feature vectors and genes to obtain module membership (MM). Setting the cut-off criteria (|MM| > 0.8 and |GS| > 0.6), those genes with high connectivity in the clinically significant module were identified as hub genes.

Hub Genes Survival Analysis

We used the R software package survival (version: 3.2-7), then integrated survival time, survival status and hub genes expression data, and evaluated the prognostic significance of each gene based on Cox regression analysis and Kaplan–Meier analysis(http://kmplot.com/analysis/).

Validation Hub Genes By Mrna Expression And Immunohistochemical Methods

The unified and standardized GEO dataset (GSE17025) downloaded from the Sanger box tools (http://vip.sangerbox.com/), and further extracted AURKA, CCNE1, IQGAP3, TTK, UBE2C, LOC134466, NPAS4, and TBC1D2B gene expression data in all patients. Finally, the expression data of eight genes were obtained and drawn in Split violin plot .

The Human Protein Atlas(HPA)is a Swedish-based program website with the aim to map all the human proteins in cells, tissues, and organs using an integration of various omics technologies (https://www.proteinatlas.org). The content and images of the HPA database are open access. In this study, the protein expression levels of 5 hub genes between normal endometrial tissues with UCEC tissues were compared and plotted.

Hub Genes Mutation Landscape Analysis

We mapped the genomes of 5 hub genes including mutations, copy number variants (CNVs), and mRNA expression z-scores (log RNASeqV2 RSEM) using data from 373 UCEC samples of the Uterine Corpus Endometrioid Carcinoma dataset (TCGA, Nature 2013) ^[13]. Then further used the Mutation Mapper tool to map the mutation landscape of 5 hub genes. The data analysis using CBioPortal (http://www.cbioportal.org/).

Hub Genes Immune Infiltration Analysis

We downloaded the unified and standardized pan-cancer dataset from the Sanger box tools (http://vip.sangerbox.com/) (PANCAN, N = 19131, G = 60499), and further we extracted AURKA, CCNE1, IQGAP3, TTK and UBE2C gene expression data in each sample. Then screened the sample sources, including Primary Blood Derived Cancer - Peripheral Blood (TCGA-LAML), Primary Tumor, Metastatic of TCGA-SKCM, Primary Blood Derived Cancer - Bone Marrow, Primary Solid Tumor, Recurrent Blood Derived Cancer - Bone Marrow samples, further log2(x + 0.001) transformation was performed on each expression value. And extracted the gene expression profile of each tumor from it, map the expression profile to GeneSymbol, and further use the R software package ESTIMATE (version 1.0.13, https://bioinformatics.mdanderson.org/public-software/estimate/, DOI: 10.1038/ncomms3612) ^[14] to calculate the stromal, immune, and ESTIMATE scores of each patient in each tumor were obtained according to gene expression value. We used the corr.test function of the R package psych (version 2.1.6) to calculate the Pearson's correlation coefficient between the hub genes and the immune infiltration score and sixty immune checkpoint pathway genes (including 24 inhibitory and 36 stimulatory) (The Immune Landscape of Cancer, DOI:10.1016/j.immuni.2018.03.023) ^[15–16] to determine the immune infiltration score of the hub genes.

Using CIBERSOR Method (CIBERSOR, DOI:10.1038/nmeth.3337) ^[17] to evaluate the associations between hub genes expression and 22 types of immune cell infiltration scores of 178 UCEC samples.

Data pre-processing

RNA sequencing data and clinical information from the TCGA uterine corpus endometrioid carcinoma (UCEC) (Table 1 and Table S1-2). After filtering based on PCA and 3 UCEC tumors and 1 normal sample had been excluded (Fig. 1A), two principal components distinguished tumor samples (13.77% of first component, PCA1) from normal samples (8.31% of second component, PCA2). Therefore, the gene expression profiles of 23 normal samples and 174 tumor samples were finally used for subsequent research.

Table 1

The clinical information for TCGA UCEC dataset
Characteristics	LIVING(N = 458)	DECEASED(N = 91)	Total(N = 549)	p-value	FDR
Age
Mean ± SD	63.20 ± 10.94	67.71 ± 11.43	63.95 ± 11.14
Median[min-max]	63.00[31.00,90.00]	68.00[35.00,90.00]	64.00[31.00,90.00]
Stage				2.60E-12	2.90E-11
Stage I	311(56.65%)	32(5.83%)	343(62.48%)
Stage II	43(7.83%)	9(1.64%)	52(9.47%)
Stage III	91(16.58%)	33(6.01%)	124(22.59%)
Stage IV	13(2.37%)	17(3.10%)	30(5.46%)
Hypertension				0.52	1
NO	154(34.00%)	27(5.96%)	181(39.96%)
YES	224(49.45%)	48(10.60%)	272(60.04%)
Diabetes				0.51	1
NO	255(60.57%)	51(12.11%)	306(72.68%)
YES	92(21.85%)	23(5.46%)	115(27.32%)
Histological_Type				1.90E-06	1.30E-05
Endometrioid	363(66.12%)	49(8.93%)	412(75.05%)
Mixed	16(2.91%)	6(1.09%)	22(4.01%)
Serous	79(14.39%)	36(6.56%)	115(20.95%)
Grade				1.50E-06	1.20E-05
G1	97(17.67%)	2(0.36%)	99(18.03%)
G2	108(19.67%)	14(2.55%)	122(22.22%)
G3	253(46.08%)	75(13.66%)	328(59.74%)
Peritoneal Wash				3.60E-08	3.60E-07
negative	308(74.94%)	45(10.95%)	353(85.89%)
positive	33(8.03%)	25(6.08%)	58(14.11%)

Identification Of Degs In Ucec And Go Enrichment Analysis

Comparing 23 normal and 174 UCEC samples, we identified 5476 DEGs (FDR < 0.05) with 2254 up-regulated genes and 3222 down-regulated genes (Fig. 1B and 1C, Table S3). GO enrichment analysis results shown that the up-regulated DEGs were mainly involved in tissue development, cell cycle regulation and epithelium development (Fig. 1D). In contrast, the downregulated DEGs were mainly involved in system development, regulation of multicellular organismal process, regulation of localization, cell adhesion and extracellular matrix (Fig. 1E).

Wgcna And Identification Of Key Modules And Candidate Hub Genes

A total of 5476 DEGs and clinical information from 549 UCEC patients was constructed by WGCNA. Using cluster analysis method to check the quality of the data from the 549 samples (the cut-off threshold value show height < 200)(Fig. 2A). 6 clinical variables were applied in the WGCNA (Fig. 2A): disease status (Tumor-Normal), stage, age, histological type and grade. Setting the soft threshold power β to 6 and the independence degree to 0.86 (Fig. 2D). This procedure yielded 13 co-expression modules: greenyellow, magenta, tan, black, brown, cyan, pink, blue, green, grey, yellow, purple and salmon (Fig. 2C, E and F).

Eigengene analysis indicated that the results in the blue module was strongly correlated positively with UCEC (correlation coefficient = 0.81, P = 9.9e-48), while the results in the black module strongly highly correlated negatively with UCEC (correlation coefficient=-0.83, P = 1.6e-51) (Fig. 2B). The above results were further confirmed by the analysis of hierarchical clustering, heatmaps, and adjacency relationships (Fig. 2F). Finally, a total of 289 genes in the blue and black modules were identified as candidate genes(Fig. 2G, H).

Hub Gene Expression And Correlation With Survival

Next, we examined potential associations between genes expression and patient OS for the 289 genes in the blue and black modules. Among them, a total of 16 candidate genes were screened (Fig. 3A). The blue module genes AURKA, CCNE1, IQGAP3, TTK and UBE2C, and the black module genes LOC134466, NPAS4, and TBC1D2B were associated with prognosis (Fig. 3B–I).

mRNA expression and Immunohistochemical validated AURKA, CCNE1, IQGAP3, TTK and UBE2C as hub genes

Using Sangerbox website and Split violin plot tool, we confirmed that the expression of five hub genes (AURKA, CCNE1, IQGAP3, TTK and UBE2C) were significantly different between normal and UCEC tissues. AURKA, CCNE1, IQGAP3, TTK and UBE2C were up regulated in UCEC, might contribute to tumorigenesis in UCEC (Fig. 4A).

And then we tried to explore the immunohistochemical results of five hub genes in UCEC by the HPA. AURKA, CCNE1and IQGAP3 were not detected in endometrium tissues, whereas low and high expressions of them were observed in UCEC tissues (Fig. 4B, C, D). We also found that low or medium expressions of TTK and UBE2C were detected in endometrium tissues, however, medium and high expressions of them were observed in UCEC tissues (Fig. 4E and F). Taken together, this results demonstrate that both the transcriptional and translational levels of AURKA, CCNE1, IQGAP3, TTK and UBE2C are elevated in UCEC patients.

Mutation Landscape Of Five Hub Genes

Further using the OncoPrint view in the CBioPortal database, the mutations in the five hub genes were visualized. 48% of patients showed mutations in AURKA, CCNE1, IQGAP3, TTK and UBE2C. IQGAP3 had the highest mutation rate (15%), including missense and splice mutations (Fig. 5A). TTK had the highest rate of somatic mutation (5.2%), and the most common mutations were missense mutations and deletions (Fig. 5B).

Immune Infiltration Level Of Five Hub Genes

Firstly, we obtained the immune infiltration scores of 178 UCEC samples and calculated the Pearson's correlation coefficient between 5 hub genes and immune infiltration scores using the corr.test function of the R package psych (version 2.1.6). The results showed AURKA (R=-0.24, p = 1.0e-3), CCNE1 (R=-0.08, p = 0.27), IQGAP3 (R=-0.32, p = 9.9e-6), TTK (R=-0.38, p = 1.6e-7) and UBE2C (R=-0.17, p = 0.02). We found that the expression of AURKA, IQGAP3, TTK and UBE2C was significantly negatively correlated with immune infiltration in UCEC (Fig. 6A).

Secondly, we used the corr.test function of the R package psych (version 2.1.6) to calculate the Pearson's correlation coefficient between 5 hub genes and 22 types of immune cell infiltration scores in UCEC. We observed that 5 hub genes expression was significantly negatively correlated with T cells regulatory (Tregs) in UCEC. AURKA expression was also negatively correlated with Dendritic cells resting, but positively correlated with Macrophages M1. CCNE1 expression was negatively correlated with T cells CD4 memory resting and Neutrophils, but positively correlated with B cells memory and Macrophages M0. IQGAP3 expression was negatively correlated with Dendritic cells resting, but positively correlated with B cells naive. TTK expression was also negatively correlated with T cells CD8. UBE2C expression was negatively correlated with T cells CD4 memory resting, Dendritic cells resting and Neutrophils, but positively correlated with Macrophages M0 (Fig. 6B).

Finally, we calculated the Pearson correlations of five hub genes and sixty immune checkpoint pathway genes (including 24 inhibitory and 36 stimulatory). In 24 inhibitory immune checkpoint pathways, we observed that at less 3 of 5 hub genes expression was significantly positively correlated with HMGB1, ICOS, CCL5 and IFNA2. In 36 stimulatory immune checkpoint pathways, we observed that at less 3 of 5 hub genes expression was significantly positively correlated with EDNRB, CD276, VEGFB and IL4 (Fig. 6C).

In China, endometrial cancer is a common reproductive system tumor, and the main treatment methods are surgical resection, chemotherapy and radiotherapy. Although most early-stage endometrial cancers can be cured, there is still a lack of effective treatment methods for locally advanced and progressive endometrial cancer, especially effective molecular diagnosis and molecular therapy. Therefore, exploring the molecular mechanisms involved in the occurrence and development of endometrial cancer is of great significance. In this study, we used WGCNA to screen potential hub genes related to the prognosis and immunotherapy of UCEC.

A total of 5476 differentially expressed gene, 13 modules and 16 candidate genes identified by WGCNA analysis. Among the 16 candidates genes, AURKA, CCNE1, IQGAP3, TTK and UBE2C were significantly associated with poor prognosis. Therefore, we defined the AURKA, CCNE1, IQGAP3, TTK and UBE2C genes as "final" hub genes.

Aurora kinase A (AURKA) is a member of the serine/threonine kinase family whose activation is required for the process of cell division by regulating mitosis. based on the TCGA database, the expression of AURKA was significantly higher in cancer samples than in normal control samples. AURKA may promotes tumorigenesis via cell proliferation, epithelial-mesenchymal transition (EMT), metastasis, apoptosis and self-renewal of cancer stem cells ^[18]. AURKA may play a role in impaired cell viability and enhanced apoptosis in UCEC ^[19]. Further research found that AURKA inhibitor Alisertib may treat UCEC, revealing that AURKA is a promising therapeutic target ^[20]. Liu found that AURKA is highly expressed in HCC and predicted 8 AURKA-related genes with prognostic value, and then successfully constructed a gene signature. These gene signatures are associated with immune cell infiltration and immune checkpoints shown potential therapeutic point in HCC patients ^[21].

Interestingly, although this study and Yuan's study ^[20] analyzed different TCGA data using similar methods and tools, AURKA was screened and identified consistently as a key gene for predicting prognosis in UCEC. This study found that the expression of AURKA was significantly negatively correlated with immune infiltration, T cells regulatory (Tregs) and Dendritic cells resting in UCEC. HMGB1, CD40LG, TNFSF9, ICOS, CD28, CD40, IL2, CCL5, CD80, CXCL10, TNFRSF4 and TNFSF4 may be the stimulatory immune checkpoints of AURKA in UCEC. And EDNRB, C10ORF54, VEGFB, VTCN1, IL13, KIR2DL1, CTLA4 and TGFB1 may be the inhibitory immune checkpoint of AURKA in UCEC.

CCNE1 is a member of the highly conserved cyclin family. In CCNE1-overexpressing tumor cells, aberrant p53 further lost control of the G1-S checkpoint, increasing the dependence on S phase and, more importantly, survival dependence on the G2-M cell cycle checkpoint ^[22–24]. CCNE1 is an oncogene in many tumors, and its elevated expression was further found to be associated with poor outcomes and platinum resistance ^[25–27]. In this study, CCNE1 expression was negatively correlated with T cells CD4 memory resting and Neutrophils, but positively correlated with B cells memory and Macrophages M0 in UCEC. HMGB1, CX3CL1, TLR4, ICOS, CD27, BTN3A1, BTN3A2, PRF1, CCL5, TNFRSF9, ICAM1 and IFNA1/2 may be the stimulatory immune checkpoints of CCNE1. And EDNRB, CD276, ADORA2A, IL12A, IL4, KIR2DL1, IL10, SLAMF7, TGFB1, TIGIT and IDO1 may be the inhibitory immune checkpoint of CCNE1 in UCEC.

The protein encoded by IQGAP3 belong to the Rho family, which play critical roles in the development and progression of several cancers. Studies have found that the expression of IQGAP3 is increased in high-grade serous ovarian cancer, and the proliferation, migration and invasion of ovarian cancer cell are inhibited when IQGAP3 is silenced ^[28]. Interestingly, high expression of IQGAP3 appears to be associated with tumor mutational burden, microsatellite instability, immune cell infiltration, and immunomodulatory agents, with integral roles in the progression and immune response of various human cancers ^[29]. IQGAP3 expression was negatively correlated with Dendritic cells resting, but positively correlated with B cells naive. HMGB1, ICOSLG, TNFSF9, ICOS, CD28, CD40, BTN3A1/2, IL2, CCL5, TNFRSF9, IL2RA, CXCL9, INF and IFNA2 may be the stimulatory immune checkpoints of IQGAP3. And C10ORF54, CD276, VEGFB, IL4, KIR2DL3, IL10, TIGIT, BTLA and IDO1 may be the inhibitory immune checkpoint of IQGAP3 in UCEC.

TTK encodes a dual-specificity protein kinase that is involved in cell proliferation. The expression of TTK promotes cell proliferation, migration and invasion, suggesting that TTK may be an oncogene ^[30–31]. Inhibition of TTK expression caused chromosomal missegregation and tumor cell death, conversely, HER2-positive breast and hepatocellular carcinomas were associated with poor prognosis after overexpression of TTK ^[32–33]. The abnormal expression of TTK is involved in breast cancer cell cycle pathway, DNA replication pathway, and P53 signaling pathway ^[34]. TTK expression was also negatively correlated with T cells CD8. HMGB1, SELP, CD70, CD28, ICOS, BTN3A2, GZMA, CCL5, IL2RA, CXCL9, IL1A and IFNA1/2 may be the stimulatory immune checkpoints of IQGAP3. And EDNRB, VEGFA, PDCD1, IL13, KIR2DL3 and SLAMF7 may be the inhibitory immune checkpoint of TTK in UCEC.

UBE2C (ubiquitin-conjugating enzyme E2C) encodes a protein that is associated with diseases such as methotrexate-associated lymphoid hyperplasia and complement component 7 deficiency. Gene Ontology (GO) annotations reveals that the main functions of UBE2C are ligase activity and ubiquitin protein ligase binding ^[35]. The abnormal expression of UBE2C gene has a clear relationship with the prognosis, clinical features, immunity, methylation of pan-cancer, suggest that UBE2C-encoded protein may contribute to cancer progression ^[36]. Overexpression of UBE2C is associated with breast cancer recurrence and is involved in estrogen-dependent/independent proliferation in early HR+/HER2- breast cancer ^[37]. UBE2C expression was negatively correlated with T cells CD4 memory resting, Dendritic cells resting and Neutrophils, but positively correlated with Macrophages M0. HMGB1, CD40LG, SELP, ENTPD1, TLR4, ICOSLG, CD40 and IL1A/B may be the stimulatory immune checkpoints of UBE2C. And CD276, LAG3, VEGFA/B, IL12A, IL4 and CTLA4 may be the inhibitory immune checkpoint of UBE2C in UCEC.

In summary, all those hub genes were reported to play pivotal roles in the tissue development and cell cycle regulation of several types of tumors [18, 22–24, 28, 30–31, 36]. The 5 hub genes in this study are related to the prognosis and immune infiltration of UCEC and can be used as indicators of prognosis and immunotherapy. However, the conclusions of this study are only from bioinformatics analysis, and to be more convincing, further studies on the carcinogenic mechanism of UCEC are required.

DEG, differentially expressed gene; EMT, epithelial-mesenchymal transition; GO, Gene Ontology; GS, gene significance; MM, module membership; TCGA, The Cancer Genome Atlas; UCEC, uterine corpus endometrioid carcinoma; WGCNA, weighted gene co-expression network analysis.

Ethics approval and consent to participate

Not Applicable.

Consent for publication

Not applicable.

Availability of data and materials

The datasets used for the analysis in this study were derived from the TCGA public repository (https://portal.gdc.cancer.gov/) and the UCSC Xena database (https://xenabrowser.net/datapages/). Source material used in the study is included in the article or supplementary material. Further enquiries may be made to the corresponding author if reasonably requested.

Competing interests

The authors state that they have no competing interests.

Funding

This research was funded by the ‘Basic and Applied Basic Research Fund Project of Guangdong Province’ (funding No. 2020A1515010082); Science and Technology Planning Project of Guangzhou (funding No.202102010003); the Project for Key Medicine Discipline Construction of Guangzhou Municipality (No. 2021-2023-17) and Guangzhou High-level Clinical Key Specialty (No. 2019-2021-06).

Authors' contributions

JIANQI LI, YANFNG HUANG and XIUJIE SHENG wrote the main manuscript text and JIANQI LI, YANFNG HUANG prepared figures 1-6 and table 1. All authors reviewed the manuscript.

Acknowledgements

Thanks for the Sanger box tools, a free online platform for data analysis (http://vip.sangerbox.com/)

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No competing interests reported.

TableS1.TCGA.UCEC.samplemaphiseqv2
Table S1. TCGA.UCEC.sampleMap_HiSeqV2
TableS2.GSE17025GPL5701111XML.matrix.txt
Table S2. GSE17025_GPL570_1_11_1_XML.matrix
TableS3.degUCEC.txt
Table S3. deg_UCEC.txt
TableS4.hubgenesexpression.xlsx
Table S4. hub genes expression.xlsx
TableS5.hubGenelist.docx
Table S5. hub_Gene list.docx

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Identification and Validation of Hub Genes in Uterine Corpus Endometrioid Carcinoma

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Abstract

Figures

Introduction

Methods

Data collection and pre-processing

Identification Of Ucec Degs And Go Functional Enrichment

Wgcna And Identification Hub Genes

Hub Genes Survival Analysis

Validation Hub Genes By Mrna Expression And Immunohistochemical Methods

Hub Genes Mutation Landscape Analysis

Hub Genes Immune Infiltration Analysis

Results

Data pre-processing

Identification Of Degs In Ucec And Go Enrichment Analysis

Wgcna And Identification Of Key Modules And Candidate Hub Genes

Hub Gene Expression And Correlation With Survival

mRNA expression and Immunohistochemical validated AURKA, CCNE1, IQGAP3, TTK and UBE2C as hub genes

Mutation Landscape Of Five Hub Genes

Immune Infiltration Level Of Five Hub Genes

Discussion

Conclusion

Abbreviations

Declarations

References

Additional Declarations

Supplementary Files

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