Identification of molecular markers associated with the progression and prognosis of endometrial cancer: a bioinformatic study

Jinhui Liu,Shan Wu,Siyue Li,Sipei Nie,Jie Zhang,Mingming Feng,Wenjun Cheng,Hui Wang,Jiangnan Qiu

doi:10.1186/s12935-020-1140-3

Jinhui Liu, Shan Wu + Show 7 more

Open Access

https://doi.org/10.1186/s12935-020-1140-3

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Abstract

BackgroundEndometrial cancer (EC) is one kind of women cancers. Bioinformatic technology could screen out relative genes which made targeted therapy becoming conventionalized.MethodsGSE17025 were downloaded from GEO. The genomic data and clinical data were obtained from TCGA. R software and bioconductor packages were used to identify the DEGs. Clusterprofiler was used for functional analysis. STRING was used to assess PPI information and plug-in MCODE to screen hub modules in Cytoscape. The selected genes were coped with functional analysis. CMap could find EC-related drugs that might have potential effect. Univariate and multivariate Cox proportional hazards regression analyses were performed to predict the risk of each patient. Kaplan–Meier curve analysis could compare the survival time. ROC curve analysis was performed to predict value of the genes. Mutation and survival analysis in TCGA database and UALCAN validation were completed. Immunohistochemistry staining from Human Protein Atlas database. GSEA, ROC curve analysis, Oncomine and qRT-PCR were also performed.ResultsFunctional analysis showed that the upregulated DEGs were strikingly enriched in chemokine activity, and the down-regulated DEGs in glycosaminoglycan binding. PPI network suggested that NCAPG was the most relevant protein. CMap identified 10 small molecules as possible drugs to treat EC. Cox analysis showed that BCHE, MAL and ASPM were correlated with EC prognosis. TCGA dataset analysis showed significantly mutated BHCE positively related to EC prognosis. MAL and ASPM were further validated in UALCAN. All the results demonstrated that the two genes might promote EC progression. The profile of ASPM was confirmed by the results from immunohistochemistry. ROC curve demonstrated that the mRNA levels of two genes exhibited difference between normal and tumor tissues, indicating their diagnostic efficiency. qRT-PCR results supported the above results. Oncomine results showed that DNA copy number variation of MAL was significantly higher in different EC subtypes than in healthy tissues. GSEA suggested that the two genes played crucial roles in cell cycle.ConclusionBCHE, MAL and ASPM are tumor-related genes and can be used as potential biomarkers in EC treatment.

Highlights

Endometrial cancer (EC) is one kind of women cancers
We identified the Differentially expressed genes (DEGs) in The Cancer Genome Atlas (TCGA) using the edegr package with FDR < 0.01 and |log2fold change (FC)| ≥ 2
In TCGA, we screened out 2614 DEGs from EC samples, which contained 1644 up-regulated and 970 down-regulated. (Additional file 4: Figure S4)

Summary

Introduction

Endometrial cancer (EC) is one kind of women cancers. Bioinformatic technology could screen out relative genes which made targeted therapy becoming conventionalized. In 2015, the American Cancer Society (ACS) predicted that the number of new EC cases was 54,870, and 10,170 of them died. This means that in the past 20 years, the mortality of EC has almost doubled. No useful tool is available to screen EC, accurate and early diagnosis is critical for the treatment of EC patients [3,4,5,6]. In recent years, based on the rapid development of high-throughput sequencing technology and increasingly complete public databases, The Cancer Genome Atlas (TCGA) database and Gene Expression Omnibus (GEO) database has collected a large number of clinical, pathological, and biological data from patients with malignancies [8, 9]. Based on cox prognosis analysis, TCGA data, website validation and qRT-PCR, we determined the hub gene and pathways which might be related to EC [13]

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Conclusion