Single-Cell Transcriptomics Reveals the Complexity of the Tumor Microenvironment of Treatment-Naive Osteosarcoma

Liu, Yun; Feng, Wei; Dai, Yan; Bao, Mengying; Yuan, Zhenchao; He, Mingwei; Qin, Zhaojie; Liao, Shijie; He, Jun; Huang, Qian; Yu, Zhenyuan; Zeng, Yanyu; Guo, Binqian; Huang, Rong; Yang, Rongliang; Jiang, Yonghua; Liao, Jinling; Xiao, Zengming; Zhan, Xinli; Lin, Chengsen; Xu, Jiake; Ye, Yu; Ma, Jie; Wei, Qingjun; Mo, Zengnan

doi:10.3389/fonc.2021.709210

Cited by 81 publications

(161 citation statements)

References 111 publications

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“…By analyzing differentiation gene signatures in cell clones of chondroblastic osteosarcomas, these authors revealed that malignant chondroblastic osteosarcoma cells could be transdifferentiated into malignant osteoblastic cells. Liu and colleagues further demonstrated the presence of particular osteoblastic clones within bone tumors that are at the starting position of differentiation paths [ 117 ]. This was the first evidence in patients that osteosarcoma cell clones may not develop independently but are linked by hierarchical relationships and derive or “differentiate” from each other as shown in other types of tumors [ 118 , 119 ].…”

Section: Mechanisms Of Metastasis Formationmentioning

confidence: 99%

Metastatic Progression of Osteosarcomas: A Review of Current Knowledge of Environmental versus Oncogenic Drivers

Odri

Tchicaya-Bouanga

Yoon

et al. 2022

Cancers

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Metastases of osteosarcomas are heterogeneous. They may grow simultaneously with the primary tumor, during treatment or shortly after, or a long time after the end of the treatment. They occur mainly in lungs but also in bone and various soft tissues. They can have the same histology as the primary tumor or show a shift towards a different differentiation path. However, the metastatic capacities of osteosarcoma cells can be predicted by gene and microRNA signatures. Despite the identification of numerous metastasis-promoting/predicting factors, there is no efficient therapeutic strategy to reduce the number of patients developing a metastatic disease or to cure these metastatic patients, except surgery. Indeed, these patients are generally resistant to the classical chemo- and to immuno-therapy. Hence, the knowledge of specific mechanisms should be extended to reveal novel therapeutic approaches. Recent studies that used DNA and RNA sequencing technologies highlighted complex relations between primary and secondary tumors. The reported results also supported a hierarchical organization of the tumor cell clones, suggesting that cancer stem cells are involved. Because of their chemoresistance, their plasticity, and their ability to modulate the immune environment, the osteosarcoma stem cells could be important players in the metastatic process.

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Section: Mechanisms Of Metastasis Formationmentioning

confidence: 99%

Metastatic Progression of Osteosarcomas: A Review of Current Knowledge of Environmental versus Oncogenic Drivers

Odri

Tchicaya-Bouanga

Yoon

et al. 2022

Cancers

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show abstract

“…scRNA sequencing data were obtained from GSE162454 ( ) from the Gene Expression Omnibus (GEO) database ( 9 ). GSE162454 data were collected from conventional osteosarcoma biopsy samples taken before chemotherapy, and OS1-6 cases were included in our study.…”

Section: Methodsmentioning

confidence: 99%

“…Made available with technological advances, single-cell RNA sequencing (scRNA-seq) can resolve gene expression at the individual cell level and enable us to better explore and identify chemoresistant cells. Liu Y’s and Zhou Y’s first applied scRNA-seq to reveal the landscapes of tumor environments in osteosarcoma ( 9 , 10 ). Comparing the two techniques, scRNA-seq has higher sensitivity but higher cost, whereas bulk RNA-seq has more samples and reliably reflects the characteristics among populations.…”

Section: Introductionmentioning

confidence: 99%

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

Zeng

Zhang

et al. 2022

Front. Oncol.

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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.

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“…For example, Liu et al. showed that single-cell transcriptomics elucidated the complexity of the tumor microenvironment of treatment-naive osteosarcoma ( 13 ). Zhou et al.…”

Section: Introductionmentioning

confidence: 99%

Identification of Cell Subpopulations and Interactive Signaling Pathways From a Single-Cell RNA Sequencing Dataset in Osteosarcoma: A Comprehensive Bioinformatics Analysis

Dou

et al. 2022

Front. Oncol.

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Osteosarcoma is a type of highly aggressive bone tumor arising from primitive cells of mesenchymal origin in adults and is associated with a high rate of tumor relapse. However, there is an urgent need to clarify the molecular mechanisms underlying osteosarcoma development. The present study performed integrated bioinformatics analysis in a single-cell RNA sequencing dataset and explored the potential interactive signaling pathways associated with osteosarcoma development. Single-cell transcriptomic analysis of osteosarcoma tissues was performed by using the Seurat R package, the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of differentially expressed genes was performed by using the clusterProfiler R package, and the cell–cell interaction analysis was performed by using the CellPhoneDB package. Our results showed that 11 clustered cell types were identified across 11 osteosarcoma tissues, with cell types including “osteoblastic”, “myeloid”, “osteoblastic_proli”, “osteoclast”, and “tumor-infiltrating lymphocytes (TILs)” as the main types. The DEGs between different cell types from primary, metastatic, and recurrent osteosarcomas were mainly enriched in the GO terms including “negative regulation of hydrolase activity”, “regulation of peptidase activity”, “regulation of binding”, “negative regulation of proteolysis”, and “negative regulation of peptidase activity” and in the KEGG pathways including “transcriptional misregulation in cancer”, “cellular senescence”, “apoptosis”, “FoxO signaling pathway”, “cell cycle”, “NF-kappa B signaling pathway”, “p53 signaling pathway”, “pentose phosphate pathway”, and “protein export”. For the cell–cell communication network analysis, the different interaction profiles between cell types were detected among primary, metastatic, and recurrent osteosarcomas. Further exploration of the KEGG pathway revealed that these ligand/receptor interactions may be associated with the NF-κB signaling pathway and its interacted mediators. In conclusion, the present study for the first time explored the scRNA-seq dataset in osteosarcoma, and our results revealed the 11 clustered cell types and demonstrated the novel cell–cell interactions among different cell types in primary, metastatic, and recurrent osteosarcomas. The NF-κB signaling pathway may play a key role in regulating the TME of osteosarcoma. The present study may provide new insights into understanding the molecular mechanisms of osteosarcoma pathophysiology.

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Single-Cell Transcriptomics Reveals the Complexity of the Tumor Microenvironment of Treatment-Naive Osteosarcoma

Cited by 81 publications

References 111 publications

Metastatic Progression of Osteosarcomas: A Review of Current Knowledge of Environmental versus Oncogenic Drivers

Metastatic Progression of Osteosarcomas: A Review of Current Knowledge of Environmental versus Oncogenic Drivers

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

Identification of Cell Subpopulations and Interactive Signaling Pathways From a Single-Cell RNA Sequencing Dataset in Osteosarcoma: A Comprehensive Bioinformatics Analysis

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