Weighted genes associated with the progression of retinoblastoma: Evidence from bioinformatic analysis

Mechanisms underlying the development of malignant retinoblastoma (RB) remain largely unknown. The purpose of this study was to identify weighted genes that are associated with the progression of RB and to assess the usefulness of bioinformatic analysis in RB research. Bioinformatic analysis was performed to construct weighted gene co-expression and protein-protein interaction (PPI) networks and to predict long non-coding RNA (lncRNA)microRNA (miRNA)-mRNA regulatory networks. RNA extracted from RB and adjacent retinal tissue was used to validate the results obtained from bioinformatic analysis, using a semi-quantitative PCR (qPCR) assay. Twentyone modules were generated from 5000 most variably expressed genes. Both the light-yellow and red modules were significantly associated with the cellular anaplastic grade of RB. The genes clustered in the light-yellow module included protocadherin beta (PCDHBs) family members. The red module included 5 hub genes involved in cell division. According to the hypothesis that lncRNA may serve as a competing endogenous RNA (ceRNA) for miRNAs and modulates mRNA expression, a network was constructed between lncRNA metastasisassociated lung adenocarcinoma transcript 1 (MALAT1) and cell division-related mRNAs. PCR analysis using 23 tumor tissues and 5 adjacent retinal tissue showed increased expression of PCDHB5 in tumor samples, and supported the predicted upregulation of mitotic checkpoint serine/threonine kinase (BUB1) by MALAT1 via miR495-3p. Our study highlights the importance of bioinformatic analysis in identifying potential markers and mechanisms associated with the malignant transformation of RB, and provides evidence to suggest that PCDHB5 and the ceRNA regulatory network of MALAT1/miR-495-3p/BUB1 are involved in the progression of RB.

Automated summary

This study aimed to identify weighted genes associated with the progression of malignant retinoblastoma (RB) and assess the utility of bioinformatic analysis in RB research. Results showed that PCDHB5 and the ceRNA regulatory network of MALAT1/miR-495-3p/BUB1 are involved in the progression of RB, highlighting the importance of bioinformatic analysis in identifying potential markers and mechanisms associated with RB transformation.