AUTHOR=Zeng Ziliang , Li Wenpeng , Zhang Di , Zhang Chi , Jiang Xu , Guo Rui , Wang Zheyu , Yang Canchun , Yan Haolin , Zhang Zhilei , Wang Qiwei , Huang Renyuan , Zhao Qiancheng , Li Bo , Hu Xumin , Gao Liangbin 

TITLE=Development of a Chemoresistant Risk Scoring Model for Prechemotherapy Osteosarcoma Using Single-Cell Sequencing

JOURNAL=Frontiers in Oncology

VOLUME=Volume 12 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2022.893282

DOI=10.3389/fonc.2022.893282

ISSN=2234-943X

ABSTRACT=Background:
Chemoresistance is one of the leading causes that severely limits the success of osteosarcoma treatment. Evaluating chemoresistance before chemotherapy poses a new challenge for researchers. We established an effective prechemotherapy chemoresistance risk scoring model using single-cell sequencing.
Methods:
We comprehensively analyzed osteosarcoma data from TARGET-OS and GSE162454. A chemoresistant tumor cluster was identified using gene set enrichment analysis and AUCell scoring. Its differentiated trajectory was achieved with inferCNV and pseudotime analysis. Ligand–receptor interactions were annotated with iTALK. Furthermore, we established a chemoresistance risk scoring model using LASSO regression based on markers of chemoresistant tumor cells. Then, the performance was verified for its discriminatory ability and calibration.
Results:
C14 and C25 were identified as chemoresistant tumor cells, which showed both osteogenic function and tumor stemness. It was characterized for differentiated potential into a proliferative state and a bridge role between tumor cells and the osteosarcoma environment. The chemoresistance risk scoring model showed good discriminatory ability and calibration (AUC_train=0.82; AUC_valid=0.84). Compared with the classic bulk RNA-based model, it showed more robust performance in validating the environment (AUC_(valid-scRNA)=0.84; AUC_(valid-bulk DEGs)=0.54).
Conclusions:
Our work provided insights into understanding the chemoresistant tumor cells in osteosarcoma and using single-cell sequencing in the establishment of a chemoresistance risk scoring model. The model showed good discriminatory ability and calibration and provided us with a feasible way to evaluate chemoresistance in prechemotherapy osteosarcoma.