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
Department of Orthopedics, Sun Yat-sen Memorial Hospital, Guangzhou, China.
Front Oncol. 2022 May 18;12:893282. doi: 10.3389/fonc.2022.893282. eCollection 2022.
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 chemoresistance risk scoring model for prechemotherapy osteosarcoma using single-cell sequencing.
We comprehensively analyzed osteosarcoma data from the bulk mRNA sequencing dataset TARGET-OS and the single-cell RNA sequencing (scRNA-seq) dataset GSE162454. Chemoresistant tumor clusters were identified using 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 scRNA-seq-based markers of chemoresistant tumor clusters. The TARGET-OS dataset was used as the training group, and the bulk mRNA array dataset GSE33382 was used as the validation group. Finally, the performance was verified for its discriminatory ability and calibration.
Using bulk RNA data, we found that osteogenic expression was upregulated in chemoresistant osteosarcoma as compared to chemosensitive osteosarcoma. Then, we transferred the bulk RNA findings to scRNA-seq and noticed osteosarcoma tumor clusters C14 and C25 showing osteogenic cancer stem cell expression patterns, which fit chemoresistant characteristics. C14 and C25 possessed bridge roles in interactions with other clusters. On the one hand, they received various growth factor stimulators and could potentially transform into a proliferative state. On the other hand, they promote local tumor angiogenesis, bone remodeling and immunosuppression. Next, we identified a ten-gene signature from the C14 and C25 markers and constructed a chemoresistant risk scoring model using LASSO regression model. Finally, we found that chemoresistant osteosarcoma had higher chemoresistance risk score and that the model showed good discriminatory ability and calibration in both the training and validation groups ( = 0.82; = 0.84). Compared with that of the classic bulk RNA-based model, it showed more robust performance in validation environment ( 0.84; = 0.54).
Our work provides insights into understanding chemoresistant osteosarcoma tumor cells and using single-cell sequencing to establish 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.
化疗耐药是严重限制骨肉瘤治疗成功的主要原因之一。在化疗前评估化疗耐药性给研究人员带来了新的挑战。我们使用单细胞测序为化疗前骨肉瘤建立了一种有效的化疗耐药风险评分模型。
我们全面分析了来自批量mRNA测序数据集TARGET-OS和单细胞RNA测序（scRNA-seq）数据集GSE162454的骨肉瘤数据。使用富集分析和AUCell评分识别化疗耐药肿瘤簇。通过inferCNV和伪时间分析实现其分化轨迹。用iTALK注释配体-受体相互作用。此外，我们基于化疗耐药肿瘤簇的scRNA-seq标记，使用LASSO回归建立了化疗耐药风险评分模型。TARGET-OS数据集用作训练组，批量mRNA阵列数据集GSE33382用作验证组。最后，验证了其鉴别能力和校准性能。
使用批量RNA数据，我们发现与化疗敏感的骨肉瘤相比，化疗耐药的骨肉瘤中骨生成表达上调。然后，我们将批量RNA的研究结果转移到scRNA-seq中，发现骨肉瘤肿瘤簇C14和C25呈现骨生成癌干细胞表达模式，符合化疗耐药特征。C14和C25在与其他簇的相互作用中起桥梁作用。一方面，它们接受各种生长因子刺激物并可能转变为增殖状态。另一方面，它们促进局部肿瘤血管生成、骨重塑和免疫抑制。接下来，我们从C14和C25标记中鉴定出一个十基因特征，并使用LASSO回归模型构建了化疗耐药风险评分模型。最后，我们发现化疗耐药的骨肉瘤具有更高的化疗耐药风险评分，并且该模型在训练组和验证组中均显示出良好的鉴别能力和校准（=0.82；=0.84）。与基于经典批量RNA的模型相比，它在验证环境中表现出更强的性能（0.84；=0.54）。
我们的工作为理解化疗耐药的骨肉瘤肿瘤细胞以及使用单细胞测序建立化疗耐药风险评分模型提供了见解。该模型显示出良好的鉴别能力和校准，并为我们提供了一种评估化疗前骨肉瘤化疗耐药性的可行方法。
