Exploration of retinoblastoma pathogenesis with bioinformatics
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ABSTRACT: Background
Differentially expressed genes (DEGs) from retinoblastoma (RB) tissues play key roles in the progression of RB. However, the role of DEGs in different subtypes and stages of RB has not yet been systematically analyzed. Methods
In this study, the DEGs for tumor and adjacent from 3 RB data sets GSE24673, GSE97508, and GSE110811 were analyzed with regard to the different subtypes and stages of the disease. Results
Through comparison with adjacent tissues, a total of 78 upregulated genes and 155 downregulated genes from the RB tissues were identified across the 3 data sets. Gene set enrichment analysis (GSEA) showed that the 3 representative genes CDK1, CDC20, and BUB1, which were all upregulated, could promote the cell cycle in RB. Compared with adjacent tissues in GSE97508, a total of 19 gigantol-targeted genes were predicted to be upregulated in invasive RB tissues. On the other hand, DEGs for tumor and adjacent from 3 RB data sets GSE24673, GSE97508, and GSE110811 were integrated with regard to invasiveness and stages of the disease, and another 19 DEGs were subsequently identified. Among these genes, UHRF1 was the only identified upregulated gene, while the other 18 were all downregulated genes. Cell Counting Kit-8 (CCK-8) experiment and GSEA results showed that UHRF1 can promote the proliferation and invasion of RB. Conversely, the downregulated representative gene CADM1 is a tumor suppressor gene, which can inhibit the progression of RB. Conclusions
This study indicated that the verified DEGs are continuously and consistently expressed in different subtypes and stages of RB. These DEGs may be the key to understanding the development and invasion of RB.
SUBMITTER: Zhang Y
PROVIDER: S-EPMC8797805 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:PurposeRetinoblastoma (RB) sets the paradigm for hereditary cancer syndromes, for which medical care can change depending on the results of genetic testing. In this study, we screened constitutional mutations in the RB1 gene via a method combining DNA sequencing and multiplex ligation-dependent probe amplification (MLPA), and performed a preliminary exploration of genotype-phenotype correlations.MethodsThe peripheral blood of 85 retinoblastoma probands, including 39 bilateral and 46 unilateral, was collected, and genomic DNA was extracted. DNA sequencing was conducted first. MLPA analysis was applied for patients with bilateral RB with negative sequencing results and unilateral probands whose age at diagnosis was less than 1 year old.ResultsThirty-four distinct mutations were identified in 40 (47.1%) of the 85 probands (36 bilateral and four unilateral), of which 20% (8/40) was identified by MLPA. The total detection rate in bilateral cases was 92.3% (36/39). Of the total mutations identified, 77.5% (31/40) probands with a mean age of 10.7 months at diagnosis had null mutations, and 22.5% (9/40) with a mean age of 13.5 months at diagnosis had in-frame mutations. Of the 31 probands with null mutations, bilateral RB accounted for 96.8% (30/31). Of the nine probands with in-frame mutations, 66.7% had bilateral RB. There were seven new mutations of RB1 identified in this report, including six null mutations and one missense mutation. Clinical staging of the tumor did not show obvious differences between patients with null mutations and in-frame mutations.ConclusionsOur results confirm that the type of mutation is related to age of onset and the laterality, but not staging of the retinoblastoma tumor. MLPA is a reliable method for detecting gross deletion or duplication of the RB1 gene. The combination of sequencing and MLPA improves the clinical diagnosis of RB.
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Project description:Retinoblastoma (RB) is the commonest malignant tumor of the infant retina. Besides genetic changes, epigenetic events are also considered to implicate the occurrence of RB. This study aimed to identify significantly altered protein-coding genes, DNA methylation, microRNAs (miRNAs), long noncoding RNAs (lncRNAs), and their molecular functions and pathways associated with RB, and investigate the epigenetically regulatory mechanism of DNA methylation modification and non-coding RNAs on key genes of RB via bioinformatics method.We obtained multi-omics data on protein-coding genes, DNA methylation, miRNAs, and lncRNAs from the Gene Expression Omnibus database. We identified differentially expressed genes (DEGs) using the Limma package in R, discerned their biological functions and pathways using enrichment analysis, and conducted the modular analysis based on protein-protein interaction network to identify hub genes of RB. Survival analyses based on The Cancer Genome Atlas clinical database were performed to analyze prognostic values of key genes of RB. Subsequently, we identified the differentially methylated genes, differentially expressed miRNAs (DEMs) and lncRNAs (DELs), and intersected them with key genes to analyze possible targets of the underlying epigenetic regulatory mechanisms. Finally, the ceRNA network of lncRNAs-miRNAs-mRNAs was constructed using Cytoscape.A total of 193 DEGs, 74 differentially methylated-DEGs (DM-DEGs), 45 DEMs, 5 DELs were identified. The molecular pathways of DEGs were enriched in cell cycle, p53 signaling pathway, and DNA replication. A total of 10 key genes were identified and found significantly associated with poor survival outcome based on survival analyses, including CDK1, BUB1, CCNB2, TOP2A, CCNB1, RRM2, KIF11, KIF20A, NDC80, and TTK. We further found that hub genes MCM6 and KIF14 were differentially methylated, key gene RRM2 was targeted by DEMs, and key genes TTK, RRM2, and CDK1 were indirectly regulated by DELs. Additionally, the ceRNA network with 222 regulatory associations was constructed to visualize the correlations between lncRNAs-miRNAs-mRNAs.This study presents an integrated bioinformatics analysis of genetic and epigenetic changes that may be associated with the development of RB. Findings may yield many new insights into the molecular biomarker candidates and epigenetically regulatory targets of RB.
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