Dataset Information

A three miRNAs signature predicts survival in cervical cancer using bioinformatics analysis.

ABSTRACT: Growing evidences showed that a large number of miRNAs were abnormally expressed in cervical cancer tissues and played irreplaceable roles in tumorigenesis, progression and metastasis. The aim of the present study was to identify the differential miRNAs expression between cervical cancer and normal cervical tissues by analyzing the high-throughput miRNA data downloaded from TCGA database. Additionally, we evaluated the prognostic values of the differentially expressed miRNAs and constructed a three-miRNA signature that could effectively predict patient survival. According to the cut-off criteria (P?2FC|?>?2.0), a total of 78 differentially expressed miRNAs were identified between cervical cancer tissues and matched normal tissues, including 37 up-regulated miRNAs and 41 down-regulated miRNAs. The Kaplan-Meier survival method revealed the prognostic function of the three miRNAs (miRNA-145, miRNA-200c, and miRNA-218-1). Univariate and multivariate Cox regression analysis showed that the three-miRNA signature was an independent prognostic factor in cervical cancer. The functional enrichment analysis suggested that the target genes of three miRNAs may be involved in various pathways related to cancer, including MAPK, AMPK, focal adhesion, cGMP-PKG, wnt, and mTOR signaling pathway. Taken together, the present study suggested that three-miRNA signature could be used as a prognostic marker in cervical cancer.

SUBMITTER: Liang B

PROVIDER: S-EPMC5514022 | biostudies-literature | 2017 Jul

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

A three miRNAs signature predicts survival in cervical cancer using bioinformatics analysis.

Liang Bin B Li Yunhui Y Wang Tianjiao T

Scientific reports 20170717 1

Growing evidences showed that a large number of miRNAs were abnormally expressed in cervical cancer tissues and played irreplaceable roles in tumorigenesis, progression and metastasis. The aim of the present study was to identify the differential miRNAs expression between cervical cancer and normal cervical tissues by analyzing the high-throughput miRNA data downloaded from TCGA database. Additionally, we evaluated the prognostic values of the differentially expressed miRNAs and constructed a th ...[more]

PMID: 28717180

Similar Datasets

Project description:Objective:We identified differentially expressed microRNAs (DEMs) between esophageal carcinoma (ESCA) tissues and normal esophageal tissues. We then constructed a novel three-miRNA signature to predict the prognosis of ESCA patients using bioinformatics analysis. Materials and Methods. We combined two microarray profiling datasets from the Gene Expression Omnibus (GEO) database and RNA-seq datasets from the Cancer Genome Atlas (TCGA) database to analyze DEMs in ESCA. The clinical data from 168 ESCA patients were selected from the TCGA database to assess the prognostic role of the DEMs. The TargetScan, miRDB, miRWalk, and DIANA websites were used to predict the miRNA target genes. Functional enrichment analysis was conducted using the Database for Annotation, Visualization, and Integrated Discovery (David), and protein-protein interaction (PPI) networks were obtained using the Search Tool for the Retrieval of Interacting Genes database (STRING). Results:With cut-off criteria of P < 0.05 and |log2FC|?>?1.0, 33 overlapping DEMs, including 27 upregulated and 6 downregulated miRNAs, were identified from GEO microarray datasets and TCGA RNA-seq count datasets. The Kaplan-Meier survival analysis indicated that a three-miRNA signature (miR-1301-3p, miR-431-5p, and miR-769-5p) was significantly associated with the overall survival of ESCA patients. The results of univariate and multivariate Cox regression analysis showed that the three-miRNA signature was a potential prognostic factor in ESCA. Furthermore, the gene functional enrichment analysis revealed that the target genes of the three miRNAs participate in various cancer-related pathways, including viral carcinogenesis, forkhead box O (FoxO), vascular endothelial growth factor (VEGF), human epidermal growth factor receptor 2 (ErbB2), and mammalian target of rapamycin (mTOR) signaling pathways. In the PPI network, three target genes (MAPK1, RB1, and CLTC) with a high degree of connectivity were selected as hub genes. Conclusions:Our results revealed that a three-miRNA signature (miR-1301-3p, miR-431-5p, and miR-769-5p) is a potential novel prognostic biomarker for ESCA.

Project description:Pancreatic ductal adenocarcinoma (PDAC) is the third leading cause of cancer death in the US. Despite multiple large-scale genetic sequencing studies, identification of predictors of patient survival remains challenging. We performed a comprehensive assessment and integrative analysis of large-scale gene expression datasets, across multiple platforms, to enable discovery of a prognostic gene signature for patient survival in pancreatic cancer. PDAC RNA-Sequencing data from The Cancer Genome Atlas was stratified into Survival+ (>2-year survival) and Survival-(<1-year survival) cohorts (n = 47). Comparisons of RNA expression profiles between survival groups and normal pancreatic tissue expression data from the Gene Expression Omnibus generated an initial PDAC specific prognostic differential expression gene list. The candidate prognostic gene list was then trained on the Australian pancreatic cancer dataset from the ICGC database (n = 103), using iterative sampling based algorithms, to derive a gene signature predictive of patient survival. The gene signature was validated in 2 independent patient cohorts and against existing PDAC subtype classifications. We identified 707 candidate prognostic genes exhibiting differential expression in tumor versus normal tissue. A substantial fraction of these genes was also found to be differentially methylated between survival groups. From the candidate gene list, a 5-gene signature (ADM, ASPM, DCBLD2, E2F7, and KRT6A) was identified. Our signature demonstrated significant power to predict patient survival in two distinct patient cohorts and was independent of AJCC TNM staging. Cross-validation of our gene signature reported a better ROC AUC (≥ 0.8) when compared to existing PDAC survival signatures. Furthermore, validation of our signature through immunohistochemical analysis of patient tumor tissue and existing gene expression subtyping data in PDAC, demonstrated a correlation to the presence of vascular invasion and the aggressive squamous tumor subtype. Assessment of these genes in patient biopsies could help further inform risk-stratification and treatment decisions in pancreatic cancer.

Dataset Information

A three miRNAs signature predicts survival in cervical cancer using bioinformatics analysis.

Publications

A three miRNAs signature predicts survival in cervical cancer using bioinformatics analysis.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets