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Identification of therapeutic targets for osteosarcoma by integrating single-cell RNA sequencing and network pharmacology

Background: Osteosarcoma (OS) is a common primary tumor with extensive heterogeneity. In this study, we used single-cell RNA sequencing (scRNA-seq) and network pharmacology to analyze effective targets for Osteosarcoma treatment. Methods: The cell heterogeneity of the Osteosarcoma single-cell datase...

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Autores principales: Wang, Yan, Qin, Di, Gao, Yiyao, Zhang, Yunxin, Liu, Yao, Huang, Lihong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9853455/
https://www.ncbi.nlm.nih.gov/pubmed/36686663
http://dx.doi.org/10.3389/fphar.2022.1098800
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author Wang, Yan
Qin, Di
Gao, Yiyao
Zhang, Yunxin
Liu, Yao
Huang, Lihong
author_facet Wang, Yan
Qin, Di
Gao, Yiyao
Zhang, Yunxin
Liu, Yao
Huang, Lihong
author_sort Wang, Yan
collection PubMed
description Background: Osteosarcoma (OS) is a common primary tumor with extensive heterogeneity. In this study, we used single-cell RNA sequencing (scRNA-seq) and network pharmacology to analyze effective targets for Osteosarcoma treatment. Methods: The cell heterogeneity of the Osteosarcoma single-cell dataset GSE162454 was analyzed using the Seurat package. The bulk-RNA transcriptome dataset GSE36001 was downloaded and analyzed using the CIBERSORT algorithm. The key targets for OS therapy were determined using Pearson’s correlation analysis. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed on key targets. The DeepDR algorithm was used to predict potential drugs for Osteosarcoma treatment. Molecular docking analysis was performed to verify the binding abilities of the predicted drugs and key targets. qRT-PCR assay was used to detect the expression of key targets in osteoblasts and OS cells. Results: A total of 21 cell clusters were obtained based on the GSE162454 dataset, which were labeled as eight cell types by marker gene tagging. Four cell types (B cells, cancer-associated fibroblasts (CAFs), endothelial cells, and plasmocytes) were identified in Osteosarcoma and normal tissues, based on differences in cell abundance. In total, 17 key targets were identified by Pearson’s correlation analysis. GO and KEGG analysis showed that these 17 genes were associated with immune regulation pathways. Molecular docking analysis showed that RUNX2, OMD, and CD4 all bound well to vincristine, dexamethasone, and vinblastine. The expression of CD4, OMD, and JUN was decreased in Osteosarcoma cells compared with osteoblasts, whereas RUNX2 and COL9A3 expression was increased. Conclusion: We identified five key targets (CD4, RUNX2, OMD, COL9A3, and JUN) that are associated with Osteosarcoma progression. Vincristine, dexamethasone, and vinblastine may form a promising drug–target pair with RUNX2, OMD, and CD4 for Osteosarcoma treatment.
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spelling pubmed-98534552023-01-21 Identification of therapeutic targets for osteosarcoma by integrating single-cell RNA sequencing and network pharmacology Wang, Yan Qin, Di Gao, Yiyao Zhang, Yunxin Liu, Yao Huang, Lihong Front Pharmacol Pharmacology Background: Osteosarcoma (OS) is a common primary tumor with extensive heterogeneity. In this study, we used single-cell RNA sequencing (scRNA-seq) and network pharmacology to analyze effective targets for Osteosarcoma treatment. Methods: The cell heterogeneity of the Osteosarcoma single-cell dataset GSE162454 was analyzed using the Seurat package. The bulk-RNA transcriptome dataset GSE36001 was downloaded and analyzed using the CIBERSORT algorithm. The key targets for OS therapy were determined using Pearson’s correlation analysis. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed on key targets. The DeepDR algorithm was used to predict potential drugs for Osteosarcoma treatment. Molecular docking analysis was performed to verify the binding abilities of the predicted drugs and key targets. qRT-PCR assay was used to detect the expression of key targets in osteoblasts and OS cells. Results: A total of 21 cell clusters were obtained based on the GSE162454 dataset, which were labeled as eight cell types by marker gene tagging. Four cell types (B cells, cancer-associated fibroblasts (CAFs), endothelial cells, and plasmocytes) were identified in Osteosarcoma and normal tissues, based on differences in cell abundance. In total, 17 key targets were identified by Pearson’s correlation analysis. GO and KEGG analysis showed that these 17 genes were associated with immune regulation pathways. Molecular docking analysis showed that RUNX2, OMD, and CD4 all bound well to vincristine, dexamethasone, and vinblastine. The expression of CD4, OMD, and JUN was decreased in Osteosarcoma cells compared with osteoblasts, whereas RUNX2 and COL9A3 expression was increased. Conclusion: We identified five key targets (CD4, RUNX2, OMD, COL9A3, and JUN) that are associated with Osteosarcoma progression. Vincristine, dexamethasone, and vinblastine may form a promising drug–target pair with RUNX2, OMD, and CD4 for Osteosarcoma treatment. Frontiers Media S.A. 2023-01-06 /pmc/articles/PMC9853455/ /pubmed/36686663 http://dx.doi.org/10.3389/fphar.2022.1098800 Text en Copyright © 2023 Wang, Qin, Gao, Zhang, Liu and Huang. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Pharmacology
Wang, Yan
Qin, Di
Gao, Yiyao
Zhang, Yunxin
Liu, Yao
Huang, Lihong
Identification of therapeutic targets for osteosarcoma by integrating single-cell RNA sequencing and network pharmacology
title Identification of therapeutic targets for osteosarcoma by integrating single-cell RNA sequencing and network pharmacology
title_full Identification of therapeutic targets for osteosarcoma by integrating single-cell RNA sequencing and network pharmacology
title_fullStr Identification of therapeutic targets for osteosarcoma by integrating single-cell RNA sequencing and network pharmacology
title_full_unstemmed Identification of therapeutic targets for osteosarcoma by integrating single-cell RNA sequencing and network pharmacology
title_short Identification of therapeutic targets for osteosarcoma by integrating single-cell RNA sequencing and network pharmacology
title_sort identification of therapeutic targets for osteosarcoma by integrating single-cell rna sequencing and network pharmacology
topic Pharmacology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9853455/
https://www.ncbi.nlm.nih.gov/pubmed/36686663
http://dx.doi.org/10.3389/fphar.2022.1098800
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