Prediction and Identification of GPCRs Targeting for Drug Repurposing in Osteosarcoma

Manli Tan; Shangzhi Gao; Xiao Ru; Maolin He; Jinmin Zhao; Li Zheng

doi:10.3389/fonc.2022.828849

Prediction and Identification of GPCRs Targeting for Drug Repurposing in Osteosarcoma

Front Oncol. 2022 Apr 7:12:828849. doi: 10.3389/fonc.2022.828849. eCollection 2022.

Authors

Manli Tan¹, Shangzhi Gao², Xiao Ru², Maolin He², Jinmin Zhao^{1

3

4}, Li Zheng^{1

4}

Affiliations

¹ Guangxi Engineering Center in Biomedical Materials for Tissue and Organ Regeneration, The First Affiliated Hospital of Guangxi Medical University, Nanning, China.
² Collaborative Innovation Center of Regenerative Medicine and Medical Biological Resources Development and Application of Guangxi Medical University, Nanning, China.
³ Department of Orthopaedics Trauma and Hand Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning, China.
⁴ Guangxi Key Laboratory of Regenerative Medicine, The First Affiliated Hospital of Guangxi Medical University, Nanning, China.

Abstract

Background: Osteosarcoma (OS) is a malignant bone tumor common in children and adolescents. The 5-year survival rate is only 67-69% and there is an urgent need to explore novel drugs effective for the OS. G protein-coupled receptors (GPCRs) are the common drug targets and have been found to be associated with the OS, but have been seldom used in OS.

Methods: The GPCRs were obtained from GPCRdb, and the GPCRs expression profile of the OS was downloaded from the UCSC Xena platform including clinical data. 10-GPCRs model signatures related to OS risk were identified by risk model analysis with R software. The predictive ability and pathological association of the signatures in OS were explored by bio-informatics analysis. The therapeutic effect of the target was investigated, followed by the investigation of the targeting drug by the colony formation experiment were.

Results: We screened out 10 representative GPCRs from 50 GPCRs related to OS risk and established a 10-GPCRs prognostic model (with CCR4, HCRTR2, DRD2, HTR1A, GPR158, and GPR3 as protective factors, and HTR1E, OPN3, GRM4, and GPR144 as risk factors). We found that the low-risk group of the model was significantly associated with the higher survival probability, with the area under the curve (AUC) of the ROC greater than 0.9, conforming with the model. Moreover, both risk-score and metastasis were the independent risk factor of the OS, and the risk score was positively associated with the metastatic. Importantly, the CD8 T-cells were more aggregated in the low-risk group, in line with the predict survival rate of the model. Finally, we found that DRD2 was a novel target with approved drugs (cabergoline and bromocriptine), and preliminarily proved the therapeutic effects of the drugs on OS. These novel findings might facilitate the development of OS drugs.

Conclusion: This study offers a satisfactory 10-GPCRs model signature to predict the OS prognostic, and based on the model signature, candidate targets with approved drugs were provided.

Keywords: G protein-coupled receptors (GPCRs); bioinformatics analysis; drug targets; osteosarcoma; risk model.