Integrated analysis of multiple bioinformatics studies to identify microRNA-target gene-transcription factor regulatory networks in retinoblastoma

Abstract

Background: In children, retinoblastoma (RB) is one of the most common primary malignant ocular tumors and has a poor prognosis and high mortality. To understand the molecular mechanisms of RB, we identified microRNAs (miRNAs), key genes and transcription factors (TFs) using bioinformatics analysis to build potential miRNA-gene-TF networks.Methods: We collected three gene expression profiles and one miRNA expression profile from the Gene Expression Omnibus (GEO) database. We used the limma R package to identify overlapping differentially expressed genes (DEGs) and differentially expressed miRNAs in RB tissues compared to noncancer tissues.The robust rank aggregation (RRA) method was implemented to identify key genes among the DEGs. Then, miRNA-key gene-TF networks were built using the online tools TransmiR and miRTarBase. Next, we used RT-qPCR to confirm the results. Results:We identified 180 DEGs in RB tissues compared to nontumor tissues using integrative analysis, among which 109 genes were upregulated and 71 were downregulated. Gene ontology (GO) analysis revealed that these DEGs were primarily involved with chromosome segregation, condensed chromosome and DNA replication origin binding. The most highly enriched pathways obtained in Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were cell cycle, DNA replication, homologous recombination, P53 signaling pathway and pyrimidine metabolism. Furthermore, two key differentially expressed miRNAs (DEMs) were also established: let-7a and let-7b. Finally, the potential regulatory networks of miRNA-target gene-TFs were examined.Conclusions: This study identified key genes and built miRNA-target gene-TF regulatory networks in RB, which will deepen our understanding of the molecular mechanisms involved in the development of RB.These key genes and miRNAs may be potential targets and biomarkers for RB diagnosis and therapy.

Retinoblastoma gene expression profiling based on bioinformatics analysis

“…Researchers have discussed the most important prognostic factors and potential mechanisms of RB through the use of existing data on RB and bioinformatics methods [ 8 , 9 ]. For example, an analysis conducted by Wen et al [ 10 ] identified two critical microRNA targets in RB: let-7a and let-7b by analyzing a variety of bioinformatics studies and identifying microRNA-target gene-transcription factor regulatory networks in RB. According to Gao et al [ 11 ] the long noncoding RNA (lncRNA) MEG3 may play a role in tumor suppression in RB, and the activation of Lnc00152 by Sp1 induces EMT through the miR-30d/SOX9/ZEB2 pathway and enhances the invasion and metastasis of RB cells through this pathway.…”

Pharmacological effects and mechanisms of bee venom and its main components: Recent progress and perspective

“…In the past 10 years, bioinformatic analyses, e.g., Encyclopedia of Genes and Genomes (KEGG) pathway, disease ontology semantic and enrichment (DOSE) and protein-protein interaction (PPI) analyses, etc., have been widely used in the investigation fields of genomics and proteomics, due to that they can comprehensively discover the biological mysteries of large and complex biological data accounting for physiological and pathological alternations of organism, or changes of organism in response to external stimuli ( Yu et al, 2015 ; Wen et al, 2022 ). For the bioinformatic analyses, differentially expressed miRNAs (differentially expressed genes (DEGs) or differentially expressed proteins (DEPs) from omics experiments are screened firstly, and KEGG and disease ontology (DO) databases can then be called online by R language platform with the screened DEGs or DEPs to identify enriched pathways and related diseases usually using a two-tailed Fisher’s exact test.…”