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

Abstract

BackgroundChemoresistance 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.MethodsWe 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.ResultsUsing 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 (AUCtrain = 0.82; AUCvalid = 0.84). Compared with that of the classic bulk RNA-based model, it showed more robust performance in validation environment (AUCvalid-scRNA = 0.84; AUCvalid-bulk DEGs = 0.54).ConclusionsOur 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.

Identification of novel prognostic biomarkers for osteosarcoma: a bioinformatics analysis of differentially expressed genes in the mesenchymal stem cells from single-cell sequencing data set

“…Zhou et al examined the scRNA-seq landscape of intratumoral heterogeneity and revealed an immunosuppressive microenvironment in advanced OS ( 17 ). Recently, Zeng et al developed a chemo-resistant risk-scoring model for pre-chemotherapy OS by analyzing a scRNA-seq database ( 18 ).…”

Comprehensive analysis of potential cellular communication networks in advanced osteosarcoma using single-cell RNA sequencing data

“…Mounting clinical and experimental data suggests that osteosarcoma stem cells, which originate from mesenchymal stem cells, may be the biological genesis of osteosarcomas and demonstrate osteoblastic differentiation, producing malignant osteoid ( Brown et al, 2017 ; Xi et al, 2000 ). In addition, osteosarcoma is strongly connected with the osteoblastic lineage and displays osteogenic differentiation-related activities in proliferation, extracellular matrix secretion, and induction of ossification ( Zeng et al, 2022 ). So, in this study, potential cellular communication networks among mesenchymal stem cells, proliferating osteoblastic cells, and osteoblastic cells were identified through comprehensive analysis of osteosarcoma single-cell RNA sequencing (scRNA-seq), illustrating the complex regulatory network in the advanced osteosarcoma microenvironment.…”

Integrated single-cell RNA-seq analysis identifies immune heterogeneity associated with KRAS/TP53 mutation status and tumor-sideness in colorectal cancers

“…The CellphoneDB ( 46 ) algorithm and R ‘iTALK’ (version 0.1.0) ( 47 ) package were used to evaluate the crosstalk across the immunological and non-immunological cellular components in normal mucosa, tumor adjacent and tumor tissue. The quantification of cell-cell communications by calculating the expression of immune checkpoint genes using ‘iTALK’ has been reported in previous studies ( 48 , 49 ). The expression level of immune checkpoints is a key factor in the remodeling of tumor immune microenvironment and immunotherapy response, and the quantile or custom-defined values of PD-L1 expression were associated with patient outcomes after immunotherapy in lung cancers and colorectal cancers ( 50 , 51 ).…”