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

Yan Wang; Di Qin; Yiyao Gao; Yunxin Zhang; Yao Liu; Lihong Huang

doi:10.3389/fphar.2022.1098800

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

Front Pharmacol. 2023 Jan 6:13:1098800. doi: 10.3389/fphar.2022.1098800. eCollection 2022.

Authors

Yan Wang¹, Di Qin², Yiyao Gao¹, Yunxin Zhang³, Yao Liu², Lihong Huang²

Affiliations

¹ Science Research Center, China-Japan Union Hospital of Jilin University, Changchun, China.
² Department of Geriatrics, China-Japan Union Hospital of Jilin University, Changchun, China.
³ Department of Gastrointestinal Colorectal and Anal Surgery, China-Japan Union Hospital of Jilin University, Changchun, China.

Abstract

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.

Keywords: molecular docking; network pharmacology; osteosarcoma; single-cell RNA sequencing; therapeutic target.