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

Abstract

Background: Endometrial cancer (EC) is one kind of women cancers. Bioinformatic technology could screen out relative genes which made targeted therapy becoming conventionalized. Methods: GSE17025 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. Results: Functional 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. Conclusion: BCHE, MAL and ASPM are tumor-related genes and can be used as potential biomarkers in EC treatment.

Bioinformatic Analysis Identifing FGF1 Gene as a New Prognostic Indicator in Clear Cell Renal Cell Carcinoma 

“…GEO and TCGA database have been two most commonly used public databases for worldwide researchers to explore the genetic abnormalities in various cancers [36][37][38][39]. In the study, four different cDNA expression pro les GSE53757, GSE53000, GSE71963 and GSE68417 were picked from GEO database based on their sample number for further analyzing the differently expressed genes in ccRCC comparing to normal renal tissue, and the result revealed 192 genes that were shared in four pro les including 39 up-regulated and 153 down-regulated genes.…”

Identification of 6 Hub Proteins and Protein Risk Signature of Colorectal Cancer

“…To measure the performance of our hub protein risk signature, the receiver operating characteristic (ROC) curve and the corresponding areas under the ROC curve (AUC) were produced using the R “survivalROC” packages [ 19 ]. The univariate and multivariate Cox proportional hazard regression analyses were performed to evaluate the independent prognostic potential of protein risk signature.…”

Screening of Hub Genes Associated with Pulmonary Arterial Hypertension by Integrated Bioinformatic Analysis

“…The "clusterprofile" package in R software can perform statistical analysis and visualization of functional clustering of gene collections [19]. The KEGG pathway enrichment analysis of DEGs was performed using the "clusterprofiler" package [20]. Gene Ontology is a widely used ontology in the field of bioinformatics, which covers three aspects of biology: biological process (BP), cellular component (CC), and molecular function (MF) [21].…”