Identification of hub genes and pathways associated with retinoblastoma based on co-expression network analysis

Abstract

ABSTRACT. The objective of this paper was to identify hub genes and pathways associated with retinoblastoma using centrality analysis of the co-expression network and pathway-enrichment analysis. The coexpression network of retinoblastoma was constructed by weighted gene co-expression network analysis (WGCNA) based on differentially expressed (DE) genes, and clusters were obtained through the molecular complex detection (MCODE) algorithm. Degree centrality analysis of the co-expression network was performed to explore hub genes present in retinoblastoma. Pathway-enrichment analysis was performed using the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. Validation of hub gene expression in retinoblastoma was performed by reverse transcription-polymerase chain reaction (RT-PCR) analysis. The coexpression network based on 221 DE genes between retinoblastoma and normal controls consisted of 210 nodes and 3965 edges, and 5 clusters of the network were evaluated. By assessing the centrality analysis of the co- (2015) expression network, 21 hub genes were identified, such as SNORD115-41, RASSF2, and SNORD115-44. According to RT-PCR analysis, 16 of the 21 hub genes were differently expressed, including RASSF2 and CDCA7, and 5 were not differently expressed in retinoblastoma compared to normal controls. Pathway analysis showed that genes in 2 clusters were enriched in 3 pathways: purine metabolism, p53 signaling pathway, and melanogenesis. In this study, we successfully identified 16 hub genes and 3 pathways associated with retinoblastoma, which may be potential biomarkers for early detection and therapy for retinoblastoma.

Co-expression network analysis identified six hub genes in association with progression and prognosis in human clear cell renal cell carcinoma (ccRCC)

“…The algorithm, weighted gene co-expression network analysis (WGCNA), can construct free-scale gene co-expression networks to explore the relationships between different gene sets or between gene sets and clinical features [7] . WGCNA has been widely applied to finding the hub genes associated with clinical feature in different cancer types [8] , [9] , [10] .…”

Identifying biomarkers of papillary renal cell carcinoma associated with pathological stage by weighted gene co-expression network analysis

“…COL5A2 , HOXB1 , CENP-E , MYCN and BCL-2 were predicted to be associated with endometrial cancer progression via Hedgehog signaling and other cancer-related pathways [11]. Additionally, SRASSF2 and CDCA7 were identified as potential biomarkers for retinoblastoma [12]. …”

Comprehensive Analysis of Serum Small Extracellular Vesicles-Derived Coding and Non-Coding RNAs from Retinoblastoma Patients for Identifying Regulatory Interactions

“…PI3k-AKT, HIF1, ErbB, and P53 signaling pathways are enriched terms. These are the known signaling pathways that drive RB tumorigenesis [ 57 , 94 , 95 ]. The hub genes EZH2 and ZEB are well-studied epigenetic regulators in RB.…”