Bioinformatic Analysis Divulged Novel Prognostic Circulating MicroRNAs and Their Potential Target Genes in Breast Cancer.

Breast cancer (BC) is both an inherited and environmental-based disease which is the leading cause of death among women. Early detection of BC can prevent invasion and metastasis in patients. Currently, researchers endeavor to find non-invasive biological markers from body fluids. Circulating non-coding RNAs such as microRNAs (miRNAs) can potentially be valuable prognostic and detective biomarkers. To identify novel miRNA-based biomarkers, we utilized bioinformatic tools. To reach this goal, the miRNA expression profiles of GSE31309, GSE 44,281, GSE98181, and GSE118782 were analyzed through a limma package of R. Target gene prediction of differentially expressed miRNAs, called differentially expressed miRNAs (DEMs), between samples of healthy individuals and BC patients was implemented through Multimir package of R. Functional enrichment analysis of predicted target genes through Enrich R (online database) revealed that most of the genes are enriched in the mitochondrial outer membrane for cellular component, intrinsic apoptotic signaling regulations for biological processes, transcription co-receptor activity for molecular functions, and dopaminergic synapse pathway. Furthermore, our survival analysis results revealed that miR-29c and mir-361 have the potential to serve as prognostic biomarkers.

Novel Insight Into the Association Between Obesity and Hepatocellular Carcinoma Occurrence and Recurrence: High-Throughput Microarray Data Set Analysis of Differentially Expressed Genes.

PURPOSE: This study aims to identify potential biomarkers of hepatocellular carcinoma (HCC) occurrence/recurrence and obesity, along with the molecular mechanisms that involve these biomarkers. METHODS: Three microarray data sets, namely GSE18897, GSE25097, and GSE36376 (genetic suppressor elements associated with obesity, tumor, and recurrence, respectively), were downloaded from Gene Expression Omnibus database to be investigated for their expression as differentially expressed genes (DEGs) in HCC and obesity. The functional and pathway enrichment analysis of these DEGs were identified by the Database for Annotation Visualization and Integrated Discovery. The protein-protein interaction network analysis was performed with STRING online tool and Cytoscape software. RESULTS: One hundred sixty common DEGs were screened. We found that these genes were associated with certain pathways such as metabolic pathways, terpenoid backbone biosynthesis, and adipocytokine signaling pathway. The involvements of 10 genes, including RPS16, RPS7, CCT3, HNRNPA2B1, EIF4G1, PSMC4, NHP2, EGR1, FDPS, and MCM4, were identified in the subnetwork. HNRNPA2B1 and RPS7 in the GSE18897 data set, RPS16, RPS7, CCT3, HNRNPA2B1, PSMC4, NHP2, FDPS, and MCM4 in the GSE25097 data set, and RPS16, RPS7, CCT3, HNRNPA2B1, EIF4G1, PSMC4, NHP2, FDPS, and MCM4 in the GSE36376 data set exhibited positive fold changes. CONCLUSION: These DEGs and pathways could be of diagnostic value as potential biomarkers involved in the pathogenesis of HCC, pertaining to both obesity and HCC occurrence/recurrence.