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Retinoblastoma gene expression profiling based on bioinformatics analysis

BACKGROUND: Retinoblastoma (RB) is frequently occurring malignant tumors that originate in the retina, and their exact cause and development mechanisms are yet to be fully comprehended. In this study, we identified possible biomarkers for RB and delved into the molecular mechanics linked with such m...

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Autores principales: Mao, Jun, Lu, Mingzhi, Lu, Siduo, Xing, Yiqiao, Xu, Xuejiao, Chen, Ying, Xu, Huirong, Zuo, Wei, Zhou, Jingwen, Du, Wei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10183129/
https://www.ncbi.nlm.nih.gov/pubmed/37179305
http://dx.doi.org/10.1186/s12920-023-01537-4
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author Mao, Jun
Lu, Mingzhi
Lu, Siduo
Xing, Yiqiao
Xu, Xuejiao
Chen, Ying
Xu, Huirong
Zuo, Wei
Zhou, Jingwen
Du, Wei
author_facet Mao, Jun
Lu, Mingzhi
Lu, Siduo
Xing, Yiqiao
Xu, Xuejiao
Chen, Ying
Xu, Huirong
Zuo, Wei
Zhou, Jingwen
Du, Wei
author_sort Mao, Jun
collection PubMed
description BACKGROUND: Retinoblastoma (RB) is frequently occurring malignant tumors that originate in the retina, and their exact cause and development mechanisms are yet to be fully comprehended. In this study, we identified possible biomarkers for RB and delved into the molecular mechanics linked with such markers. METHODS: In this study GSE110811 and GSE24673 were analyzed. Weighted gene co-expression network analysis (WGCNA) was applied to screen modules and genes associated with RB. By overlapping RB-related module genes with differentially expressed genes (DEGs) between RB and control samples, differentially expressed retinoblastoma genes (DERBGs) were acquired. A gene ontology (GO) enrichment analysis and a kyoto encyclopedia of genes and genomes (KEGG) enrichment analysis were conducted to explore the functions of these DERBGs. To study the protein interactions of DERBGs, a protein–protein interaction (PPI) network was constructed. Hub DERBGs were screened using the least absolute shrinkage and selection operator (LASSO) regression analysis, as well as the random forest (RF) algorithm. Additionally, the diagnostic performance of RF and LASSO methods was evaluated using receiver operating characteristic (ROC) curves and single-gene gene set enrichment analysis (GSEA) was conducted to explore the potential molecular mechanisms involved with these Hub DERBGs. In addition, the competing endogenous RNA (ceRNA) regulatory network of Hub DERBGs was constructed. RESULT: About 133 DERBGs were found to be associated with RB. GO and KEGG enrichment analyses revealed that the important pathways of these DERBGs. Furthermore, the PPI network revealed 82 DERBGs interacting with each other. By RF and LASSO methods, PDE8B, ESRRB, and SPRY2 were identified as Hub DERBGs in patients with RB. From the expression assessment of Hub DERBGs, it was found that the levels of expression of PDE8B, ESRRB, and SPRY2 were significantly decreased in the tissues of RB tumors. Secondly, single-gene GSEA revealed a connection between these 3 Hub DERBGs and oocyte meiosis, cell cycle, and spliceosome. Finally, the ceRNA regulatory network revealed that hsa-miR-342-3p, hsa-miR-146b-5p, hsa-miR-665, and hsa-miR-188-5p may play a central role in the disease. CONCLUSION: Hub DERBGs may provide new insight into RB diagnosis and treatment based on the understanding of disease pathogenesis. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12920-023-01537-4.
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spelling pubmed-101831292023-05-15 Retinoblastoma gene expression profiling based on bioinformatics analysis Mao, Jun Lu, Mingzhi Lu, Siduo Xing, Yiqiao Xu, Xuejiao Chen, Ying Xu, Huirong Zuo, Wei Zhou, Jingwen Du, Wei BMC Med Genomics Research BACKGROUND: Retinoblastoma (RB) is frequently occurring malignant tumors that originate in the retina, and their exact cause and development mechanisms are yet to be fully comprehended. In this study, we identified possible biomarkers for RB and delved into the molecular mechanics linked with such markers. METHODS: In this study GSE110811 and GSE24673 were analyzed. Weighted gene co-expression network analysis (WGCNA) was applied to screen modules and genes associated with RB. By overlapping RB-related module genes with differentially expressed genes (DEGs) between RB and control samples, differentially expressed retinoblastoma genes (DERBGs) were acquired. A gene ontology (GO) enrichment analysis and a kyoto encyclopedia of genes and genomes (KEGG) enrichment analysis were conducted to explore the functions of these DERBGs. To study the protein interactions of DERBGs, a protein–protein interaction (PPI) network was constructed. Hub DERBGs were screened using the least absolute shrinkage and selection operator (LASSO) regression analysis, as well as the random forest (RF) algorithm. Additionally, the diagnostic performance of RF and LASSO methods was evaluated using receiver operating characteristic (ROC) curves and single-gene gene set enrichment analysis (GSEA) was conducted to explore the potential molecular mechanisms involved with these Hub DERBGs. In addition, the competing endogenous RNA (ceRNA) regulatory network of Hub DERBGs was constructed. RESULT: About 133 DERBGs were found to be associated with RB. GO and KEGG enrichment analyses revealed that the important pathways of these DERBGs. Furthermore, the PPI network revealed 82 DERBGs interacting with each other. By RF and LASSO methods, PDE8B, ESRRB, and SPRY2 were identified as Hub DERBGs in patients with RB. From the expression assessment of Hub DERBGs, it was found that the levels of expression of PDE8B, ESRRB, and SPRY2 were significantly decreased in the tissues of RB tumors. Secondly, single-gene GSEA revealed a connection between these 3 Hub DERBGs and oocyte meiosis, cell cycle, and spliceosome. Finally, the ceRNA regulatory network revealed that hsa-miR-342-3p, hsa-miR-146b-5p, hsa-miR-665, and hsa-miR-188-5p may play a central role in the disease. CONCLUSION: Hub DERBGs may provide new insight into RB diagnosis and treatment based on the understanding of disease pathogenesis. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12920-023-01537-4. BioMed Central 2023-05-13 /pmc/articles/PMC10183129/ /pubmed/37179305 http://dx.doi.org/10.1186/s12920-023-01537-4 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Mao, Jun
Lu, Mingzhi
Lu, Siduo
Xing, Yiqiao
Xu, Xuejiao
Chen, Ying
Xu, Huirong
Zuo, Wei
Zhou, Jingwen
Du, Wei
Retinoblastoma gene expression profiling based on bioinformatics analysis
title Retinoblastoma gene expression profiling based on bioinformatics analysis
title_full Retinoblastoma gene expression profiling based on bioinformatics analysis
title_fullStr Retinoblastoma gene expression profiling based on bioinformatics analysis
title_full_unstemmed Retinoblastoma gene expression profiling based on bioinformatics analysis
title_short Retinoblastoma gene expression profiling based on bioinformatics analysis
title_sort retinoblastoma gene expression profiling based on bioinformatics analysis
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10183129/
https://www.ncbi.nlm.nih.gov/pubmed/37179305
http://dx.doi.org/10.1186/s12920-023-01537-4
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