Estimating survival time of patients with glioblastoma multiforme and characterization of the identified microRNA signatures

clear cell renal cell carcinoma (ccRcc) is the most representative subtype of renal cancer. circRnA acts as a kind of ceRnA to play a role in regulating microRnA (miRnA) in many cancers. However, the potential pathogenesis role of the regulatory network among circRnA/miRnA/mRnA is not clear and has not been fully explored. CircRNA expression profile data were obtained from GEO datasets, and the differentially expressed circRNAs (DECs) were identified through utilizing R package (Limma) firstly. Secondly, miRNAs that were regulated by these circRNAs were predicted by using Cancerspecific circRNA database and Circular RNA Interactome. Thirdly, some related genes were identified by intersecting targeted genes, which was predicted by a web tool (miRWalk) and differentially expressed genes, which was obtained from TCGA datasets. Function enrichment was analyzed, and a PPI network was constructed by Cytoscape software and DAVID web set. Subsequently, ten hub-genes were screened from the network, and the overall survival time in patients of ccRcc with abnormal expression of these hub-genes were completed by GEPIA web set. In the last, a circRNA/miRNA/ mRnA regulatory network was constructed, and potential compounds and drug which may have the function of anti ccRCC were forecasted by taking advantage of CMap and PharmGKB datasets. Six DECs (hsa_circ_0029340, hsa_circ_0039238, hsa_circ_0031594, hsa_circ_0084927, hsa_circ_0035442, hsa_circ_0025135) were obtained and six miRNAs (miR-1205, miR-657, miR-587, miR-637, miR-1278, miR-548p) which are regulated by three circRNAs (hsa_circ_0084927, hsa_circ_0035442, hsa_ circ_0025135) were also predicted. Then 497 overlapped genes regulated by these six miRNAs above had been predicted, and function enrichment analysis revealed these genes are mainly linked with some regulation functions of cancers. Ten hub-genes (PTGER3, ADCY2, APLN, CXCL5, GRM4, MCHR1, NPY5R, CXCR4, ACKR3, MTNR1B) have been screened from a PPI network. PTGER3, ADCY2, CXCL5, GRM4 and APLN were identified to have a significant effect on the overall survival time of patients with ccRCC. Furthermore, one compound (josamycin) and four kinds of drugs (capecitabine, hmgcoa reductase inhibitors, ace Inhibitors and bevacizumab) were confirmed as potential therapeutic options for ccRcc by cMap analysis and pharmacogenomics analysis. this study implies the potential pathogenesis of the regulatory network among circRnA/miRnA/mRnA and provides some potential therapeutic options for ccRcc. Renal cancer is common cancer, and the incidence rates in males and females are 5% and 3%, respectively 1. Clear cell renal cell carcinoma (ccRCC) accounts for 70-80% of renal cancer, which is the most representative

Section: Discussionmentioning

confidence: 99%

Construct a circRNA/miRNA/mRNA regulatory network to explore potential pathogenesis and therapy options of clear cell renal cell carcinoma

Bai

Yan

et al. 2020

“…The feature selection algorithm IBCGA uses an intelligent evolutionary algorithm 82 to solve the large parameter optimization problem. IBCGA has been successfully applied in several bioinformatics problems, including the prediction of human ubiquitination sites 83 , the prediction of the regulatory roles of cyclic AMP receptor proteins 84 and the estimation of survival time for cancer patients 85 , 86 .…”

Section: Methodsmentioning

confidence: 99%

Identifying a miRNA signature for predicting the stage of breast cancer

Sathipati

2018

Self Cite

Breast cancer is a heterogeneous disease and one of the most common cancers among women. Recently, microRNAs (miRNAs) have been used as biomarkers due to their effective role in cancer diagnosis. This study proposes a support vector machine (SVM)-based classifier SVM-BRC to categorize patients with breast cancer into early and advanced stages. SVM-BRC uses an optimal feature selection method, inheritable bi-objective combinatorial genetic algorithm, to identify a miRNA signature which is a small set of informative miRNAs while maximizing prediction accuracy. MiRNA expression profiles of a 386-patient cohort of breast cancer were retrieved from The Cancer Genome Atlas. SVM-BRC identified 34 of 503 miRNAs as a signature and achieved a 10-fold cross-validation mean accuracy, sensitivity, specificity, and Matthews correlation coefficient of 80.38%, 0.79, 0.81, and 0.60, respectively. Functional enrichment of the 10 highest ranked miRNAs was analysed in terms of Kyoto Encyclopedia of Genes and Genomes and Gene Ontology annotations. Kaplan-Meier survival analysis of the highest ranked miRNAs revealed that four miRNAs, hsa-miR-503, hsa-miR-1307, hsa-miR-212 and hsa-miR-592, were significantly associated with the prognosis of patients with breast cancer.

“…The parameter tuning of IBCGA was same with the previous study 34, 35 . The customized IBCGA for obtaining the m -miRNA signature where r start ≤ m ≤ r end is described below.…”

Section: Methodsmentioning

confidence: 99%

Identifying the miRNA signature associated with survival time in patients with lung adenocarcinoma using miRNA expression profiles

Sathipati

2017

Self Cite

Lung adenocarcinoma is a multifactorial disease. MicroRNA (miRNA) expression profiles are extensively used for discovering potential theranostic biomarkers of lung cancer. This work proposes an optimized support vector regression (SVR) method called SVR-LUAD to simultaneously identify a set of miRNAs referred to the miRNA signature for estimating the survival time of lung adenocarcinoma patients using their miRNA expression profiles. SVR-LUAD uses an inheritable bi-objective combinatorial genetic algorithm to identify a small set of informative miRNAs cooperating with SVR by maximizing estimation accuracy. SVR-LUAD identified 18 out of 332 miRNAs using 10-fold cross-validation and achieved a correlation coefficient of 0.88 ± 0.01 and mean absolute error of 0.56 ± 0.03 year between real and estimated survival time. SVR-LUAD performs well compared to some well-recognized regression methods. The miRNA signature consists of the 18 miRNAs which strongly correlates with lung adenocarcinoma: hsa-let-7f-1, hsa-miR-16-1, hsa-miR-152, hsa-miR-217, hsa-miR-18a, hsa-miR-193b, hsa-miR-3136, hsa-let-7g, hsa-miR-155, hsa-miR-3199-1, hsa-miR-219-2, hsa-miR-1254, hsa-miR-1291, hsa-miR-192, hsa-miR-3653, hsa-miR-3934, hsa-miR-342, and hsa-miR-141. Gene ontology annotation and pathway analysis of the miRNA signature revealed its biological significance in cancer and cellular pathways. This miRNA signature could aid in the development of novel therapeutic approaches to the treatment of lung adenocarcinoma.