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Bioinformatic screening for candidate biomarkers and their prognostic values in endometrial cancer.

ABSTRACT: BACKGROUND:Endometrial cancer is a common gynecological cancer with annually increasing incidence worldwide. However, the biomarkers that provide prognosis and progression for this disease remain elusive. RESULTS:Two eligible human endometrial cancer datasets (GSE17025 and GSE25405) were selected for the study. A total of 520 differentially expressed mRNAs and 30 differentially expressed miRNAs were identified. These mRNAs were mainly enriched in cell cycle, skeletal system development, vasculature development, oocyte maturation, and oocyte meiosis signalling pathways. A total of 160 pairs of differentially expressed miRNAs and mRNAs, including 22 differentially expressed miRNAs and 71 overlapping differentially expressed mRNAs, were validated in endometrial cancer samples using starBase v2.0 project. The prognosis analysis revealed that Cyclin E1 (CCNE1, one of the 82 hub genes, which correlated with hsa-miR-195 and hsa-miR-424) was significantly linked to a worse overall survival in endometrial cancer patients. CONCLUSIONS:The hub genes and differentially expressed miRNAs identified in this study might be used as prognostic biomarkers for endometrial cancer and molecular targets for its treatment.

SUBMITTER: Li Y

PROVIDER: S-EPMC7510080 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature

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Bioinformatic screening for candidate biomarkers and their prognostic values in endometrial cancer.

Li Yaowei Y Li Li L

BMC genetics 20200922 1

<h4>Background</h4>Endometrial cancer is a common gynecological cancer with annually increasing incidence worldwide. However, the biomarkers that provide prognosis and progression for this disease remain elusive.<h4>Results</h4>Two eligible human endometrial cancer datasets (GSE17025 and GSE25405) were selected for the study. A total of 520 differentially expressed mRNAs and 30 differentially expressed miRNAs were identified. These mRNAs were mainly enriched in cell cycle, skeletal system develo ...[more]

PMID: 32962636

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Project description:Background Glioblastoma (GBM) is one of the most common and malignant primary brain tumors in adults, with high mortality rates and limited treatment. Based on bioinformatic analyses, this study aimed to identify biomarkers and relevant molecular pathways that may serve as potential targets for the treatment of GBM. Methods Expression profiles were downloaded from the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) database; nine GBM samples and three normal samples were extracted from the GSE104267 dataset. Differentially-expressed messenger RNA (mRNA) and long non-coding RNA (lncRNA) were screened from the preprocessed dataset. The clusterProfiler package in R was used to perform a biological process (BP) analysis of gene ontology (GO), and a Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis was performed separately in upregulated and downregulated groups. A competing endogenous RNA (ceRNA) network was constructed using Cytoscape. Based on data downloaded from The Cancer Genome Atlas (TCGA), Kaplan-Meier (K-M) survival curves were established. Real-time quantitative reverse transcription polymerase chain reaction (qRT-PCR) was performed to evaluate IL10RB antisense RNA 1 (IL10RB-AS1) expression in GBM tissue compared with that in normal brain tissue. Results A total of 253 differentially-expressed genes (DEGs) were obtained. Based on BP and KEGG enrichment annotation analyses, 11 lncRNA-related pathways were identified through function prediction analysis. A competing endogenous RNA (ceRNA) subnetwork, including 21 nodes and 29 regulatory pairs, was then constructed. Based on the clinical data of GBM in TCGA, one survival-related DEG, IL10RB-AS1, was identified using the log-rank statistical test. K-M survival curves of IL10RB-AS1 and expression levels of IL10RB-AS1 in both GBM and normal brain tissue were obtained. Conclusions Through the combination of bioinformatic analyses, one survival-related differentially-expressed lncRNA, IL10RB-AS1, was identified. This, along with several related signaling pathways and ceRNA systems that were elucidated in GBM have potential prognostic value and might offer new possibilities for the treatment of GBM.

Dataset Information

Bioinformatic screening for candidate biomarkers and their prognostic values in endometrial cancer.

Publications

Bioinformatic screening for candidate biomarkers and their prognostic values in endometrial cancer.

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