Table4_The Regulatory Network and Role of the circRNA-miRNA-mRNA ceRNA Network in the Progression and the Immune Response of Wilms Tumor Based on RNA-.DOCX (13.74 kB)
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Table4_The Regulatory Network and Role of the circRNA-miRNA-mRNA ceRNA Network in the Progression and the Immune Response of Wilms Tumor Based on RNA-Seq.DOCX

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posted on 26.04.2022, 04:41 authored by Xiao-Mao Tian, Bin Xiang, Zhao-Xia Zhang, Yan-Ping Li, Qin-Lin Shi, Mu-jie Li, Qi Li, Yi-Hang Yu, Peng Lu, Feng Liu, Xing Liu, Tao Lin, Da-Wei He, Guang-Hui Wei

Circular RNA (circRNA), which is a newly discovered non-coding RNA, has been documented to play important roles in miRNA sponges, and the dysregulation of which is involved in cancer development. However, circRNA expression profiles and their role in initiation and progression of Wilms tumor (WT) remain largely unclear at present. Here, we used paired WT samples and high-throughput RNA sequencing to identify differentially expressed circRNAs (DE-circRs) and mRNAs (DE-mRs). A total of 314 DE-circRs and 1612 DE-mRs were identified. The expression of a subset of differentially expressed genes was validated by qRT–PCR. A complete circRNA-miRNA-mRNA network was then constructed based on the common miRNA targets of DE-circRs and DE-mRs identified by miRanda prediction tool. The Gene set enrichment analysis (GSEA) indicated that several signaling pathways involving targeted DE-mRs within the ceRNA network were associated with cell cycle and immune response, which implies their participation in WT development to some extent. Subsequently, these targeted DE-mRs were subjected to implement PPI analysis and to identify 10 hub genes. Four hub genes were closely related to the survival of WT patients. We then filtered prognosis-related hub genes by Cox regression and least absolute shrinkage and selection operator (LASSO) regression analysis to construct a prognosis-related risk score system based on a three-gene signature, which showed good discrimination and predictive ability for WT patient survival. Additionally, we analyzed the mutational landscape of these genes and the associations between their expression levels and those of immune checkpoint molecules and further demonstrated their potential impact on the efficacy of immunotherapy. qRT–PCR and western blotting (WB) analysis were used to validate key differentially expressed molecules at the RNA and protein levels, respectively. Besides these, we selected a key circRNA, circEYA1, for function validation. Overall, the current study presents the full-scale expression profiles of circRNAs and the circRNA-related ceRNA network in WT for the first time, deepening our understanding of the roles and downstream regulatory mechanisms of circRNAs in WT development and progression. We further constructed a useful immune-related prognostic signature, which could improve clinical outcome prediction and guide individualized treatment.