Identification of co‑expression modules and hub genes of retinoblastoma via co‑expression analysis and protein‑protein interaction networks

Abstract

retinoblastoma is a common intraocular malignant tumor in children. However, the molecular and genetic mechanisms of retinoblastoma remain unclear. The gene expression dataset GSe110811 was retrieved from Gene expression omnibus. after preprocessing, coexpression modules were constructed by weighted gene coexpression network analysis (WGcna), and modules associated with clinical traits were identified. In addition, functional enrichment analysis was performed for genes in the indicated modules, and protein-protein interaction (PPi) networks and subnetworks were constructed based on these genes. eight coexpression modules were constructed through WGcna. of these, the yellow module had the highest association with severity and age (r=0.82 and P=3e-07; r=0.72 and P=3e-05). The turquoise module had the highest association with months (r=-0.63 and P=5e-04). The genes in the two modules participate in multiple pathways of retinoblastoma, and by combining the PPi network and subnetworks; 10 hub genes were identified in the two modules. The present study identified coexpression modules and hub genes associated with clinical traits of retinoblastoma, providing novel insight into retinoblastoma progression.

In silico Identification of Hypoxic Signature followed by reverse transcription-quantitative PCR Validation in Cancer Cell Lines

“…A growing number of studies have identified DEGs during hypoxia in different cancer cell types using RNA-Seq analysis [ 3 - 5 ] . However, their findings only represent the genetic characteristics of specific tumor cells during hypoxia.…”

Gene co-expression network analysis revealed novel biomarkers for ovarian cancer

“…A system-based approach is needed to gain a comprehensive view of cancer, and the power of differential co-expression networks was previously shown in cancer research ( Gov and Arga, 2017 ) . The construction and analysis of differential co-expression networks have been widely used to discover gene modules and hub genes in various cancers, including retinoblastoma ( Mao et al, 2020 ), pituitary adenoma ( Aydin and Arga, 2019 ), ovarian ( Gov and Arga, 2017 ; Gov, 2020 ), lung ( Liao et al, 2020 ) , and cervical cancers ( Kori et al, 2019 ) .…”

Towards a global investigation of transcriptomic signatures through co-expression networks and pathway knowledge for the identification of disease mechanisms

“…Though it is standard practice to perform enrichment analysis using pathway and gene set databases (e.g. KEGG and Gene Ontology ( 19 )) on gene lists from co-expression networks such as those from a particular disease module ( 20 , 21 ) for mechanistic insights, this approach ignores the topology of the network as it exclusively relies upon sets of genes rather than the network structure. In a recent study, Paci et al.…”