Background
Endometrial cancer is the most common malignancy of the female genital tract [
1]. Endometrial cancer predominately affects post-menopausal women, however 15–25% of cases are diagnosed before menopause. Endometrial cancer is not amenable to screening, hence effective management is required once diagnosed.
Due to the limitation of experiment, Bioinformatics Analysis has been widely applied to screen and analyze genes in linkage to various types of cancer progression [
2,
3]. For example, Omer et al. released that the homeostasis of cholesterol greatly contributed to the development of cancers by using TCGA database [
4]. Many genes with similar expression patterns actually affect each other and even have a regulatory relationship [
5]. Most studies only focus on the differential expression of genes, but ignore the chain between genes. Weighted gene expression network analysis (WGCNA) is a systematic biological method which is utilized to describe the pattern of gene association between different samples [
6]. It can be used to identify highly synergistically altered gene sets and candidate biomarker genes or therapeutic targets based on the associations of gene sets and associations between gene sets and phenotypes. Recently WGCNA is comprehensively used in cancer-related research [
7]. For instance, Zhou et al. revealed that TOP2A might be used as a potential biological target for the prognosis and progression of pancreatic ductal adenocarcinoma [
8]. Wang et al. found that ASPM may cause cirrhosis, then further produce hepatocellular carcinoma [
9]. In this research, we first screened differentially expressed genes by using WGCNA-based systems biology methods, and then constructed PPI network and co-expression network of genes. Ultimately we discovered the essential genes and pathways involved in the carcinogenic mechanism of EC [
10].
Discussion
Endometrial cancer is the most common malignancy of the female genital tract. Endometrial cancer predominately affects post-menopausal women, however 15–25% of cases are diagnosed before menopause. Endometrial cancer is not amenable to screening, hence needs to be managed effectively as soon as diagnosis is made. Therefore, it is very important to research biomarkers and related regulatory pathways that affect the development of EC. In this study, we examined the gene expression profile of GSE17025 including 91 tumor tissue samples and 12 normal tissue samples to identify the molecular mechanism of EC and seek some biomarkers. Bioinformatics analysis of these biological factors is used to seek genes that are beneficial to treatment.
In these findings, we identified 1737 DEGs associated with EC including 690 up-regulated genes and 1048 down-regulated genes using p < 0.05 and |logFC| ≥ 1 as the cutoff criteria. We utilized PPI network analysis and WGCNA analysis to select PPI and gene co-expression modules that were linked to the clinical development of EC. Furthermore, we conducted functional and pathway analysis to seek biological pathways that may have an impact on EC.
According to the GO analysis of DEGs, we found that upregulated DEGs were significantly enriched in CXCR chemokine receptor binding. The down-regulated genes were highly enriched in collagen binding. KEGG analysis showed that the upregulated genes were mostly enriched in IL-17 signaling pathway. The down-regulated genes were mostly enriched in MAPK signaling pathway. The PPI network was built on the DEGs that was analyzed by STRING website and Cytoscape software. Referring to cutoff of a k-core of 2, we found 8 clusters. Among the 8 clusters, cluster 1 got the highest score, included 32 nodes and 477 edges in this subnetwork.
WGCNA analysis showed that 11 modules possessed relevant expression pattern. For each module exceedingly, GO enrichment analysis was functioned to explore the biological process. The most relevant turquoise module contains 590 key genes. Through GO analysis, they were mainly enriched in microtubule motor activity. In KEGG analysis, we could find that DEGs in turquoise module were highly enriched in Cell cycle.
Sixty-five genes with the high connectivity in 590 genes of turquoise module were taken. Based on the PPI network, we screened 27 genes. Survival analysis of these 27 genes was performed by using the TCGA database, and 11 genes with significance were finally obtained. GEPIA website was used to verify these 11 genes. We found that these 11 genes were highly expressed in cancer tissues, but the expression levels of these genes in normal tissues were significantly lower than that in cancer tissues. We also used UALCAN to confirm the results, and we found that the expression levels of these 11 genes were higher in various subtypes of EC than in normal tissues. Interestingly, we further analyzed these 11 genes by using the TCGA database and the R language package and found that ANLN, DLGAP5, FOXM1, NCAPH, RAD54L, and RRM2 have mutational significance, and survival analysis showed that the survival rate of patients with the 5 mutant genes was higher than that of patients without gene mutations except RRM2. These results suggested that these five genes may influence the development of EC through the mechanism of mutation.
Anillin (ANLN), an actin-binding protein, was required for cytokinesis. Zhou W et al. found that knockdown of ANLN inhibits the growth and migration of human breast cancer cells [
21]. Wang G et al. found overexpression of ANLN was correlated with colorectal cancer progression and poor prognosis [
22]. Xia L et al. found that ANLN functions as a key candidate gene in cervical cancer through bioinformatics analysis [
2]. Zeng S et al. used Transcriptome sequencing and then found that ANLN was a promising prognostic biomarker in bladder urothelial carcinoma [
23].
DLGAP5 was also a frequently studied gene. Chen et al. suggested that DLGAP5 may be involved in the regulation of ovarian cancer as a key target of NOTCH3 [
24]. Liu R et al. found that DLGAP5 was related to estrogen receptor positive breast cancer through WGCNA [
25]. Liao et al. indicated that Silencing of DLGAP5 significant inhibits the proliferation and invasion of hepatocellular carcinoma cells [
26]. Stangeland et al. found that DLGAP5 may be a biological target of glioblastoma stem cells through bioinformatics analysis [
27]. Schneideret al. demonstrated that DLGAP5 was a specific mitosis-associated genes correlate with poor prognosis for non-small cell lung cancer patients [
28].
The transcription factor Forkhead box protein M1 (FOXM1) was a preferred anticancer target, due to its significance in execution of mitosis, cell cycle progression, as well as other signal pathways leading to tumorigenesis [
29]. Cui et al. demonstrated that FOXM1 promotes the Warburg effect and pancreatic cancer progression via transactivation of LDHA expression [
30].
Ribonucleotide reductase subunit M2 (RRM2) has been shown to be a meaningful advance factor for advanced non-small cell lung cancer and breast cancer [
31,
32]. Grolmusz et al. also found that Cell cycle dependent RRM2 may serve as proliferation marker and pharmaceutical target in adrenocortical cancer [
33]. Wang et al. found that Increased expression of RRM2 by human papillomavirus E7 oncoprotein promotes angiogenesis in cervical cancer [
34]. For gastric cancer cell, overexpression of RRM2 promotes their invasiveness via AKT/NF-κB signaling pathway [
35]. NCAPH was proved highly expressed in colorectal cancer cell lines comparing with normal human colonic epithelial cells, and many NCAPH mutations in colorectal cancer patients were identified [
36], but it was not detailed in EC. Similarly, RAD54L had also been shown to mutate in multiple tumors [
37,
38].
ASPM proved to be positively correlated with the progress and poor prognosis of both prostate cancer and hepatocellular carcinoma [
39,
40]. Wang et al. showed that ASPM promoted aggressiveness of pancreatic tumor [
41]. ASPM has also been proven to affect the prognosis of patients with ovarian cancer in many ways [
42,
43]. CDCA8 was often studied in bladder cancer and breast cancer, CDCA8 and FOXM1 appeared to be related in breast cancer [
44,
45]. The specific mechanism of CDCA8 in EC was still unclear.
Holliday Junction Recognition Protein (HJURP) was a centromeric histone chaperone involved in de novo histone H3 variant CenH3 (CENP-A) recruitment. Cao et al. demonstrated that Silencing of HJURP induced dysregulation of cell cycle and ROS metabolism in bladder cancer cells via PPARγ-SIRT1 feedback loop [
46]. HJURP proved to be an independent prognostic factor in Serous Ovarian Carcinoma and breast cancer [
47,
48]. Chen et al. proved that HJURP promoted hepatocellular carcinoma proliferation by destabilizing p21 via the MAPK/ERK1/2 and AKT/GSK3β signaling pathways [
49]. After Hu et al. knocked down NuF2 by siRNA, the proliferation of pancreatic cancer cells was inhibited [
50]. NUF2 was certified to participate in the tumorigenicity of colon cancer cells [
51]. Fu HL et al. found that silencing NUF2 inhibited the growth of osteosarcoma cells and promoted its apoptosis [
52]. Experiments in glioma cells and hepatocellular carcinoma cells had the same results [
53,
54]. The effects of HMMR had been mentioned in breast cancer and glioblastoma [
55,
56].
Conclusion
Through a series of comprehensive analysis of bioinformatics, we could roughly screen the hub genes and pathways related to the progression of EC, and target therapy for the extractive hub genes including ANLN, DLGAP5, FOXM1, NCAPH, RAD54L, RRM2, ASPM, CDCA8, HJURP, HMMR and NUF2. They might greatly promote the prognosis of EC. However, these key genes and pathways still need to be tested in a large quantity of clinical specimens, and need to be analyzed and validated in combination with the individual conditions of clinical patients in order to finally determine the biological targets that were most beneficial to endometrial cancer.
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