Relationship between insulin resistance and endometrial cancer was revealed by gene screening

doi:10.21203/rs.3.rs-864828/v2

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Relationship between insulin resistance and endometrial cancer was revealed by gene screening

https://doi.org/10.21203/rs.3.rs-864828/v2

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Background: Epidemiological studies have found, the occurrence of endometrial cancer (EC) is closely related to metabolic diseases, and insulin resistance (IR) plays an important role in the pathogenesis of endometrium, but the specific pathogenesis is still unclear. The purpose of this study is to reveal the relationship between insulin resistance and endometrial carcinoma.

Methods: We analyzed one endometrial carcinoma database (GSE106191) and one insulin-resistant database (GSE63992), with GEO database and Venny online analysis tool, and P-value< 0.05 was thought about as statistically significant, then, we found an add-up to 148 different genes. Functional enrichment analysis of these genes using DAVID showed that they were participated in transcription factor activity, signaling pathways and response to factors, etc. Then used Cytoscape to establish Protein-Protein Interaction network, and got 25 hub genes. Then further analyzed the expression differences, histological differences, survival analysis, immune infiltration and genetic alteration of 25 hub genes.

Results: Of the 25 hub genes, 9 hub genes (KLF5, BRD4, E2F2, LYN, HES1, YY1, IGSF3, TRAF3 and GJB3) were over-expressed in endometrial carcinoma, 11hub genes (TRO, NEURL2, PTEN, RARA, RYBP, OGT, FBXW7, PIK3C3, PIK3C2B, ING3 and DDX58) were under-expressed, and there were no significant difference among 5 hub genes (MYL12A, RAD21, TGFBRAP1, IRAK2 and PTPN1).The results showed that the survival time of patients expressing OGT, IGSF3, TRO, NEURL2 and PIK3C2B may be significantly and closely related to EC, and the percentage of gene changes of five central genes ranged from 3% to 10% of a single gene, was also related to the infiltration of seven kinds of immune cells in endometrial carcinoma.

Conclusion: The five key genes (OGT, IGSF3, PIK3C2B, TRO and NEURL2) may be involved in immune infiltration in the progression of endometrial carcinoma, and there is also a certain mutation probability in gene mutation. Insulin resistance may be a trigger in the pathogenesis of endometrial cancer.

bioinformatics analysis

endometrial cancer

insulin resistance

prognostic value

Bioinformatics analysis shows that insulin resistance is closely related to the pathogenesis of endometrial cancer, and its specific pathogenesis may be related to the transcription and mutation of key genes.

Endometrial cancer (EC) occupies an important place among the three common malignant tumors in gynecology, accounting for one of the three malignant tumors of the female reproductive system, which is more common in postmenopausal or menopausal women¹. In the United States, the new incidence of endometrial carcinoma ranks fourth among female malignant tumors, accounting for 7% of new cancers every year, and 10 170 patients died². Over the last few years, the incidence and death rates in China have been increasing year by year, and now the morbidity ranks second only to cervical cancer, with the incidence and death rates being 63.4/100,000 and 21.8/100,000 separately³, and the onset age tends to be younger, which seriously affect women's physical and mental health and quality of life. Despite the rapid development of auxiliary examination methods of endometrial cancer, and patients with early diagnosis of disease are related to good prognosis, but there are still 5% ~ 15% patients with recurrence risk, who with recurrent or metastatic diseases have limited treatment options and short overall survival time, they are high mortality and poor survival rate⁴. Besides, the development of endometrial cancer is usually related to some metabolic diseases in confirmed patients, such as obesity, diabetes and long-term use of estrogen, etc.

Evidence-based medicines have confirmed that the risk of endometrial cancer is related to metabolic diseases such as overweight or obesity, insulin resistance and impaired glucose toleration. Among them, obesity and diabetes have been proved to significantly increase the incidence of endometrial cancer⁵. In recent years, the relationship between endometrial cancer and insulin resistance (IR) has been paid increasing attention. Epidemiological studies have shown that insulin resistance exists at the early stage of endometrial hyperplasia, also the increase of insulin is positively correlated with endometrial hyperplasia, atypical hyperplasia and endometrial cancer⁶, however, the exact pathogenesis of insulin resistance helping increase the development of endometrial cancer has not been fully elucidated. The increase of insulin levels induced by insulin resistance promotes cell proliferation and leads to the direct or indirect change of sex hormone levels, which may be the pathogenic relationship between insulin resistance and endometrial cancer⁷. And insulin sensitizer has potential anti-tumor effect. It is considered that insulin sensitizers can inhibit mitochondrial breathing and lung related chain complex 1⁸, inhibit oxidative phosphorylation and reduce ATP production, thus reducing the energy supply and blocking the metabolism of tumor cells⁹. However, this theory is still controversial, which indicates that insulin regulation may be a potential treatment strategy for endometrial cancer.

Although epidemiology has confirmed that insulin resistance is intimate related to the occuring and progress of endometrial carcinoma, the details of the pathophysiological methods are still hard to catch. In this article, we aim to prove the potential pathophysiological relationship between endometrial cancer and insulin resistance. In addition, we also discussed the function, prognosis and potential mechanism of pivotal genes in endometrial carcinoma.

2 . 1 | Acquisition of data

The gene expression files of GSE63992 and GSE106191 were downloaded from the Gene Expression Omnibus (GEO, https://www.ncbi.nlm.nih.gov/geo/) database, which is a gene expression data warehouse and online resources for retrieving gene expression data from any species or man-made sources. The GSE63992 dataset included the gene expression profiles of nine insulin-resistant and nine insulin-sensitive obese subjects¹⁰. The GSE106191 dataset involved the gene expression profiles of 66 endometrial cancer patients, including sixty-four endometrial carcinoma patients and thirty-three endometrial hyperplasia controls,submitted by Sugiyama Y¹¹,et al. After analyzing genes with GEO2R, an online tool for GEO, then, we got all genes with difference expression.

2 . 2 | Acquisition of differentially expressed genes (DEGs)

The primary genetic databases were extracted, and the condition of |log-Fold Change| >0, P-value< 0.05 was carefully thought about as statistically significant for differentially expressed genes. The overlapping DEGs of IR and EC were used for further analysis, then we got 148 eligible difference genes to get analysed. In addition, we set log Fold Change over 0 as up-regulated threshold, and below 0 as down-regulated threshold.

2 . 3 | Enrichment analysis and MOCOD analysis of DEGs

Gene Ontology (GO) mainly includes three branches: biological processes, molecular functions and cellular components¹². Kyoto Encyclopedia of Genes and Genomes (KEGG) can link the gene catalogue obtained from the completely sequenced genome with the system function at higher level of cells, species and ecosystems. We explore the remarkably enriched GO terms and KEGG pathways of DEGs in the David online tool(https:// david.ncifcrf.gov/), which supplies research scholars with a complete and thorough set of functional note tools to comprehend the biological importance behind large amounts of gene lists¹³. According to David online tool analysis, the differential genes,which P-value < 0.05 were thought to be statistically significant. Then, we used another online tool, called Metascape, to determine the most significal MOCODE.

2 . 4 | Protein-Protein Interaction (PPI) network,acquisition of hub genes

Search Tool , GeneMANIA (http://genemania.org), which can build the PPI network of gene expression products, and which is a flexible web and supports 9 kinds of organisms¹⁴. We assess the relationship of different genes, mapped differences of all genes in GeneMANIA. Then, we imported PPI network information dates into the software Cytoscape 3.8.2 to explore. And to clarify the hub gene in the PPI network by used cytoHubba, which is a software in Cytoscape.

2 . 5 | Differences of hub gene expression

UALCAN (http://ualcan.path.uab.edu/cgi-bin/ualcan-res.pl), is a nice website that incorporates different RNA data and patient medicine-based data from TCGA and provides survival analysis, used to analysis the expression difference of hub genes¹⁵. We imported hub genes to analyze their expression in endometrial carcinoma, and we consider P-value >＝ 0.005 is significant.

2 . 6 | The histological analysis of hub genes

The Human Protein Atlas (HPA,https://www.proteinatlas.org/), which uses the integration of various omics techniques, including protein omics, transcriptomics and systems biology based on mass spectrometry, antibody-based imaging and mass spectrometry-based imaging, all human protein in cells, tissues and organs can be drawn. We analysis the expression of the hub genes on HPA dataset in EC. The hub genes were analyzed by histopathological to assess differences in expression.

2 . 7 | Analysis of survival in hub genes in endometrial cancer

Survival analysis for the hub genes, which had expression differences, were also analyzed using web-based tool UALCAN (http://ualcan.path.uab.edu). For all survival analyses, log-rank P < 0.05 was believed to be statistically significant. And we considered genes, which had survival significance, as candidate genes for further analysis.

2 . 8 | Genetic alteration analysis of candidate genes

Gene alteration analysis of EC tissuses were performed using CBioPortal for Cancer Genomics, CBioPortal (http://www.cbioportal.org/) provides information related to the integrative analysis of complex cancer genomics and clinical profiles from105 different cancer types within the TCGA pipeline¹⁶. We used CBioPortal to analyse Genetic alterations of candidate genes.

2 . 9 | Tumor-Infiltrating Immune Cells analysis of candidate genes

Comprehensive analysis of the relationship between the expression of candidate genes and tumor-infiltrating immune expression using TIMER (https://cistrome.shinyapps.io/timer/), a web server can render services for complete and thorough analysis of tumor-infiltrating immune cells, such as B cells, CD8+ T cells, CD4+ T cells, neutrophils, dendritic cells and macrophages¹⁷.

3 . 1 | Identification of differential genes in IR and EC

In the GSE63992 data set, we got 594 up-regulated and 526 down-regulated genes to be identified from duodenal samples of insulin-sensitive and insulin-resistant obese subjects, which are compared with IR patients and IS group (Figure 1a,c). And in the dataset GSE106191, 3111 up-regulated and 2590 down-regulated genes were obtained in EC group compared with endometrial hyperplasia controls group (Figure 1b ,d). As shown in Figure 1e and Table 1, there were included 81 intersectional upregulated genes and 67 intersectional downregulated genes in IR and EC.

3 . 2 | The gene ontology analysis and pathway of differential genes

We used David online tool to analysis the DEGs in detail, the conclusions of GO analysis indicated that the up-regulated gene changes in biological processes (BP) included significant increasement of several metabolic processes (regulation of DNA-binding transcription factor activity, pattern recognition receptor signaling pathway etc.), as the down-regulated genes were predominantly concentrated in response to glucose, regulation of myeloid leukocyte differentiation and endocytosis (Figure 2a. and Table 2). The molecular function (MF) enrichment analysis indicated the function of the genes mainly concentrated on significant enrichment of binding-related function (protein binding, transcription factor activity binding, and transcription factor binding), as shown in Figure 2b. and Table 2. The changes of cellular component (CC) included significant enrichment of genes related to the cytoplasm and nucleoplasm (Figure 2c. and Table 2). And the changes in KEGG pathway were significantly enriched in NF-kappa B signaling pathway, small cell lung cancer and Epstein-Barr virus infection (Table 3 and Figure 2d).

3 . 3 | Comprehensive analysis of PPI Network

According to the information on GeneMANIA database. There were 148 DEGs to be found, including 81 up- and 67 down-regulated genes in the overall number of 6821 genes, and the PPI network was built with 1364 edges (Figure 3a). We used the Metascape (http://metascape.org), which is a gratuitous online tool for gene annotation and analysis, to further analyze the PPI network, and selected the top three most significant modules (Figure 3b-d). We also carefully studied the function annotation of the genes in the modules, separately. According to the results of enrichment analysis, the gene functions of these three modules were mainly related to the cell cycle process, actin cytoskeleton organization,ligand binding and protein polyubiquitination (Figure 3e).

Cytoscape is an open source software platform for inquiring about complex networks and assembling these with any type of attribute data. Cytoscape was used to find hub genes. The default limits were as follows: a degree cut-off ≥2, node score cutoff ≥0.2, K-score ≥2 and max depth of 100 for selecting hub genes. Twenty-five hub genes related to EC and insulin resistance were analyzed by multiple algorithms , including Degree, EcCentricity, Closeness, Radiality and MNC(Maximum Neighborhood Component), and there were used for further analysis¹⁸. We selected the top 30 genes of these five algorithms respectively, and then selected the common 25 genes. The hub genes were PTEN, YY1, KLF5, RARA, ING3, HES1, BRD4, OGT, E2F2, IGSF3, FBXW7, LYN, PIK3C3, TRAF3, RAD21, PTPN1, PIK3C2B, DDX58, GJB3, TRO, NEURL2, MYL12A, IRAK2, TGFBRAP1, RYBP, respectively. For the connection between EC and IR, the results were revealed in Table 4.

3 . 4 | Expression difference of hub genes in EC

We compared the protein expression difference of the twenty-five hub genes between EC and normal tissues by using the UALCAN dataset. We discovered that the protein expression levels of KLF5, BRD4, E2F2, LYN, HES1, YY1, IGSF3, TRAF3 and GJB3 were much higher in EC tissues than in normal tissues, and the protein expression levels of TRO, NEURL2, PTEN, RARA, RYBP, OGT, FBXW7, PIK3C3, PIK3C2B, ING3 and DDX58 were clearly lower in EC tissues than in normal tissues, whereas the protein expression levels of MYL12A, RAD21, TGFBRAP1, IRAK2 and PTPN1 were not significantly different between EC and normal tissues, as shown in Figure 4.

3 . 5 | The histological analysis of hub genes

We compared the twenty-five hub genes between EC and healthy tissues by using the HPA dataset to analysis the expression, Figure 5. The results were the same as UALCAN, the expression levels of KLF5, BRD4, E2F2, LYN, YY1, IGSF3 and TRAF3 were higher in EC tissues than in normal tissues, and the expression levels of TRO, NEURL2, PTEN, RYBP, OGT, FBXW7, PIK3C3, PIK3C2B, ING3 and DDX58 were lower in EC tissues than in normal tissues, whereas the expression levels of MYL12A, RAD21, TGFBRAP1, IRAK2 and PTPN1 were not much different between EC and normal tissues. But we did not find RARA, GJB3 and HES1 in HPA.

3 . 6 | Survival analysis of the hub genes based on the TCGA dataset

Further analysis was conducted on the above the hub genes, which had expression difference. The survival curves of these genes were analyzed in TCGA endometrial cancer database in Ualcan network tool. TCGA endometrial cancer database contains data of 35 normal patients, 409 patients with endometrioid, 115 patients with serous and 22 patients with mixed serous and endometrioid. The results showed the effects of these genes in patients survival,OGT(P=0.0045), IGSF3(P=0.0025), TRO(P=0.00012), NEURL2(P=0.0054) and PIK3C2B(P=0.048) is significantly strongly related to EC (P < 0.05), as shown in Figure 6, and the survival curve showed that patients with high expression of these genes have a significantly worse than patients with low expression. And we think about genes, which existed on both insulin resistance and endometrial cancer and had survival significant, as candidate genes.

3 . 7 | Genetic alteration analysis in patients with EC

We analyzed the genetic alterations of OGT, IGSF3, TRO, NEURL2 and PIK3C2B in EC by using cBioPortal online tool. A total of 1449 patients from three datasets of endometrium cancer, which were Uterine Corpus Endometrial Carcinoma (TCGA, Firehose Legacy),Uterine Corpus Endometrial Carcinoma (TCGA, Nature 2013) and Uterine Corpus Endometrial Carcinoma (TCGA, PanCancer Atlas),were carefully studied. Among the three datasets analyzed, the gene set pathway was changed in 237 (24.6%) of the queried samples, and alterations ranging from 25.52% (135/529) to 13.42% (73/544) were found for the gene sets submitted for analysis (Figure 7a). The percentages of genetic alterations in the five hub genes for EC varied from 3% to 10% for individual genes (OGT, 5%; IGSF3, 4%; TRO, 7%; NEURL2, 3% and PIK3C2B, 10%). For OGT, most of the changes are missense mutation, IGSF3 is missense mutation and amplification, TRO is missense mutation, truncation mutation, amplification and deep deletion, NEURL2 is mainly amplification, and PIK3C2B is missense mutation and amplification (Figure 7b). The results of Kaplan–Meier plotter and log-rank test pointed to no significant difference in overall survival (OS) and disease-free survival (DFS) between the cases with changes in one of the query genes and those without alterations in any query genes (P-values, 0.444 and 0.215, respectively; Figure 7c,d).

3 . 8 | Analysis of Tumor-Infiltrating Immune Cells using TIMER tool

Comprehensive analysis of the relationship between the expression of OGT, IGSF3, TRO, NEURL2, PIK3C2B and tumor-infiltrating Immune using TIMER. The results showed that OGT, IGSF3, TRO, NEURL2 and PIK3C2B are related to the infiltration of seven kinds of immune cells in endometrial carcinoma. OGT is related to the infiltration of CD8+ T cells and neutrophils (Figure 8a), IGSF3 is related to the infiltration of B cells and CD4+ T cells (Figure 8b), TRO is related to the infiltration of CD8+ T cells and CD4+ T cells (Figure 8c), NEURL2 is related to the infiltration of CD8+ T cells (Figure 8d), and PIK3C2B is related to the infiltration of CD4+ T cells, neutrophils and dendritic cells(Figure 8e).

In our study, we explore the risk factors for EC. We received 64 EC samples and 33 normal samples from GSE106191 in GEO database, and 9 insulin-resistant samples and 9 insulin-sensitive samples in GSE63992 dataset. At last, we got 148 different genes, 81 up- and 67 down-regulated genes. The results of GO analysis showed that DEGs were mainly related to DNA-binding transcription factor activity, pattern recognition receptor signaling pathway and response to glucose. The results of KEGG pathways were significantly enriched in NF-kappa B signaling pathway, small cell lung cancer and Epstein-Barr virus infection. Moreover the twenty-five hub genes were found in DEGs, which are: PIK3C2B, OGT, PTEN, YY1, KLF5, RARA, ING3, HES1, BRD4, TRO, E2F2, IGSF3, FBXW7, LYN, GJB3, PIK3C3, TRAF3, RYBP, IRAK2, NEURL2, RAD21, PTPN1, DDX58, MYL12A and TGFBRAP1. According to the survival analysis of hub genes, five out of the twenty-five hub genes, namely, OGT, IGSF3, PIK3C2B, TRO and NEURL2 are significantly correlated with EC.

The functions of each hub gene were different. OGT exists in healthy endometrium tissues and endometrium cancer tissues. Low expression of OGT was associated with prognosis in the endometrium tumor (P = 1.6616E-06). OGT were reported that it is overactivated in many kinds of cancers and is involved in the advanced progression of cancer¹⁹, the increase of O-GlcNAc modification of cell protein is a common feature of tumor cells, and the glycosylation level of O-GlcNAc in tumor cells with higher deterioration degree is higher²⁰, and stably interacts with and modifies all three TET proteins,and help produce TET to form O-GlcNAcylated TET and can also interact with TET to form a complex with the ability to further modify chromatin which participated in controlling embryonic development and cancer progression²¹, OGT is highly dependent on glucose and this modification generally becomes more abundant in various metabolic pathways in cancer cells²². OGT controls ovarian cancer cell motility through the RhoA/ROCK signal pathway²³. However, studies suggest that TET is related to the onset and progression of endometrium cancer²⁴. In another study demonstrated,OGT plays a very important role for maintenance and stress response of HSCs through OGT-PINK1 axis²⁵, however,PINK1 can lead to mitochondrial dysfunction by inhibiting a number of mitochondrial genes in 30% of endometrial cancers²⁶.

IGSF3 (Immunoglobulin Superfamily Member 3) is a protein coding gene. The protein encoded by this gene is an immunoglobulin-like membrane protein containing several V-type Ig-like domains. IGSF3 can active the the NF-κB signaling pathway in hepatocellular carcinoma progression²⁷. The presence of tumor necrosis within primary tumors is result to hypoxia, angiogenesis and inflammation responses,the majority of them associated with NF-kB²⁸. We have detected abnormal expression of NF-κB signaling pathway in various cancer types²⁹, and it is generally believed that NF-κB should be a valuable pharmaceutical target³⁰. In another medical literature said that NF-κB RelB protein is highly expressed in EC samples and cell lines, and mainly increased in endometrioid adenocarcinoma³¹. So, IGSF3 may represent a considerable molecular target for EC.

PIK3C2B is strongly related to tumour stage and vessel invasion, which was closely and positively connected with prognosis in colon cancer³². In addition, PIK3C2B can regulate different cell functions, such as cell proliferation, invasion and cancer progression³³. PIK3C2B can control cell invasion partly by regulating the MEK/ERK pathway and partly by activating additional signalling pathways that modulate prostate cancer cell migration³³. And in a previous study, PIK3C2B had impacts on prognostic OS of ovarian cancer(OC), resulting in a reduction in the median survival time of OC by 7 to 10 months³⁴. Then PIK3C2B participates in PI3K-Akt signaling pathway, and PI3K network belongs to the most common events in human cancers³⁴, Philip et al. reported that inhibition of PI3K/Akt/mTOR signaling increased the sensitivity of endometrial carcinoma cells to some targeted therapies³⁵, so blocking PIK3C2B may be an immunotherapeutic pathway for the treatment of endometrial carcinoma.

TRO gene encodes a membrane protein that mediates cell adhesion between trophoblastic cells and the epithelial cells of the endometriu, it can promote the proliferation of embryonic cells and the invasion of trophoblast cells into endometrium. It is worth noting that the structure of TRO is exactly the same as that of MAGE-D3 carboxy-terminal functional region in MAGE tumor gene family, suggesting that TRO may have special biological functions³⁶. The data from TCGA are discussed by bioinformatics method, and it is found that TRO may play a negative role in the prognosis of 13 cancer types, such as breast cancer, head and neck squamous cell carcinoma and non-small cell lung cancer. They believe that down-regulation of TRO affects the growth of cancer cells³⁷. However, the invasion, metastasis and rapid proliferation of malignant tumors are just like the adhesion between trophoblast cells and endometrial cells, invasion into endometrial matrix and subsequent rapid proliferation during embryo development and implantation. In recent years, the mechanism of this gene in tumors is seldom studied, which needs further exploration.

NEURL2 is an asymmetric division gene for normal stem cells, which is very important to maintain the stem cells of normal stem cells and tumor stem cells³⁸. NEURL2 can participate in the development of lung cancer by mediating the activity of twist 2. Moreover, the analysis of TCGA shows that the expression of twist 2 is positively correlated with the development of lung adenocarcinoma, while NEURL2 is negatively correlated with the development of lung adenocarcinoma. However, the relationship between Neurl2 and endometrial cancer is still unclear, because it is related to the prognosis of endometrial cancer patients, and it can be used as the research direction of follow-up treatment.

IR is a kind of biological effect reduction characterized by reduced glucose uptake and inhibition of glucose output by adipose tissue and muscle, the main manifestations are that the sensitivity of liver, muscle, fat and other target tissues to insulin decreases and the uptake and utilization of glucose decreases³⁹. However, with the prolongation of IR, the normal blood glucose level of the body cannot be maintained, resulting in abnormal blood glucose metabolism and other metabolic disorders, which in turn increases the risk of various diseases. And IR may play a crucial role in the development and metastasis of tumors, the increase of IGF-1 levels and hyperinsulinemia in obese and overweight people promote the transformation of endometrial tumors⁴⁰. IR can affect the level of sex hormones in EC patients, the increase of insulin level can inhibit the synthesis of sex hormones in liver and promote the increase of free estrogen and male hormones, while the increase of estrogen level can promote endometrial hyperplasia. Under the long-term stimulation of estrogen, the endometrium changes from hyperplasia to canceration, which increases the risk of postoperative recurrence and death. Insulin not only plays a crucial role in metabolism, but also can inhibit apoptosis and promote cell proliferation. Insulin-like growth factor is abundantly expressed in the intimal tissue of EC patients, which activates the downstream signal pathway, induces intimal hyperplasia and promotes the growth of EC cells. IR serves a crucial role in the development and progression of endometrial tumors.

For the sake of exploring the potential mechanism of insulin resistance on the pathogenesis of endometrial cancer, we analyzed the enrichment, expression, mutation and immune infiltration of related genes. The results showed that five key genes (OGT, IGSF3, PIK3C2B, TRO and NEURL2) mainly affected the prognosis of endometrial cancer patients.

In this study, the relationship between insulin resistance and endometrial cancer was systematically analyzed, and these results indicate that OGT, IGSF3, PIK3C2B, TRO and NEURL2 may play a pivotal role in the occurrence and development of endometrial cancerous tissue, which provides a unique perspective for the therapeutic regimen of endometrial cancer and may be a new therapeutic target for endometrial cancer.

EC, endometrial cancer; IR, insulin resistance; IS, insulin sensitive; DEGS, differentially expressed genes; PPI, Protein-Protein Interaction.

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Availability of data and materials

GEO database publicly provides all the data supporting the findings of this study, which was at https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc at =GSE106191 and GSE63992.

Competing interests

Not applicable.

Funding

This work was supported by the Hubei Provincial Health Commission Joint Fund Key Project, China (Grant No. WJ2019H500).

Authors' contributions

D.D.D.: Conceptualization, Methodology, Writing - original draft. Q.Z.: Supervision, Validation. Z.H. and H.X.H.: Data curation, Visualization, Investigation. M.Z.Z.: Supervision, Writing - review & editing.

Acknowledgements

Not applicable.

Data sharing and data accessibility

GEO database publicly provides all the data supporting the findings of this study , which was at https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc at =GSE106191 and GSE63992. The Human Protein Atlas IHC data and IHC images provide the images and dates supporting the findings of this study, which was at https://www.proteinatlas.org/. The TCGA dataset(s) provides the images and dates supporting the findings of this study, which was at https://www.tcga.org/. The UALCAN dataset(s) provides the images and dates supporting the findings of this study, which was at http://ualcan.path.uab.edu/analysis.html.

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TABLE 1. Identification of the up-regulated and down-regulated differential genes

DEGs

Genes Name

Up-regulated

GIP TRIM31 ZG16 TRIM15 GNL3L LAMB3 E2F2 TRNP1 YY1 ITPRIPL2 AGO2 MYL12A TRAF3 AKAP7 ZIC1

INF2 CFB AAK1 PIK3C2B PITX1 OTUD7A

IRAK2 FAM107B GPR35 LYN PDCD7 KDM2A TGFBRAP1 RAD21 UBXN2A TYMS IGSF3 USP9X KCNQ4 BRD4

DENR PTPN1 EDN1 UBE2H RNF19A FAM170A

RRP1 TJP2 EPT1 TPM4 MAPKAPK3 UBE3C ARHGAP5 ZFP91 ERC1 SDCCAG3 FRMD8 ZNF92 SPATA5 VEZT

TCF20 GNB1L SPN HES1 CLIC5 MSL3 HRH3

KLF5 GCNT2 FBXW7 CNGB1 UHMK1 BTBD7 SPATA8 GJB3 SLC7A13 SMCR8 CHRNA4 DDX58 ADGRF1 IL4I1 GPR31 SIM2 MYO19 PCDHGA11 CXORF38

Down-regulated

SLC9B2 KCNN3 SLC25A17 FKBP7 FMNL3 MVB12B MOSPD1 ZFP92 NEURL2 P2RY14 RHOBTB1 MAGED1 VPS16

RARA MAP4 ZXDA LOC202181 SCAMP4

COG5 PTGER3 EIF3J-AS1 PFKL SUPT3H LSS LTBP4 SMARCD3 NEK1 ADAM12 PIK3C3 CAB39L MIB1 GPALPP1 VPS13B TCF21 ANKRD50 OGT

COL21A1 TMEM178A NPHP4 ZBED8 LINC00938 CAMTA1 ELF2 PTEN LMO4 RYBP GPX8 RERGL SEC63 ZFP90

ZCCHC11 MEG3 UNC45A FAM118A

CCDC80 AMPH AZI2 SERPINF1 MYO9A ING3 RUNX1T1 TTC5 MAGED2 TRO ZNF704 PLA2R1 PCDHGA1

TABLE 2. Gene ontology analysis of biological processes,cellular components and molecular functional components of different expressed genes

Expression

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Relationship between insulin resistance and endometrial cancer was revealed by gene screening

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1 | Introduction

2 | Materials And Methods

3 | Results

4 | Discussion

5 | Conclusion

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