Dataset Information

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

ABSTRACT: Osteosarcoma (OS), which occurs most commonly in adolescents, is associated with a high degree of malignancy and poor prognosis. In order to develop an accurate treatment for OS, a deeper understanding of its complex tumor microenvironment (TME) is required. In the present study, tissues were isolated from six patients with OS, and then subjected to single-cell RNA sequencing (scRNA-seq) using a 10× Genomics platform. Multiplex immunofluorescence staining was subsequently used to validate the subsets identified by scRNA-seq. ScRNA-seq of six patients with OS was performed prior to neoadjuvant chemotherapy, and data were obtained on 29,278 cells. A total of nine major cell types were identified, and the single-cell transcriptional map of OS was subsequently revealed. Identified osteoblastic OS cells were divided into five subsets, and the subsets of those osteoblastic OS cells with significant prognostic correlation were determined using a deconvolution algorithm. Thereby, different transcription patterns in the cellular subtypes of osteoblastic OS cells were reported, and key transcription factors associated with survival prognosis were identified. Furthermore, the regulation of osteolysis by osteoblastic OS cells via receptor activator of nuclear factor kappa-B ligand was revealed. Furthermore, the role of osteoblastic OS cells in regulating angiogenesis through vascular endothelial growth factor-A was revealed. C3_TXNIP⁺ macrophages and C5_IFIT1⁺ macrophages were found to regulate regulatory T cells and participate in CD8⁺ T cell exhaustion, illustrating the possibility of immunotherapy that could target CD8⁺ T cells and macrophages. Our findings here show that the role of C1_osteoblastic OS cells in OS is to promote osteolysis and angiogenesis, and this is associated with survival prognosis. In addition, T cell depletion is an important feature of OS. More importantly, the present study provided a valuable resource for the in-depth study of the heterogeneity of the OS TME.

SUBMITTER: Liu Y

PROVIDER: S-EPMC8335545 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:BackgroundLiver carcinoma generally presents as an immunosuppressive microenvironment that promotes tumor evasion. The intercellular crosstalk of immune cells significantly influences the construction of an immunosuppressive microenvironment. This study aimed to investigate the important interactions between immune cells and their targeting drugs in liver carcinoma, by using single-cell and bulk transcriptomic data.MethodsSingle-cell and bulk transcriptomic data were retrieved from Gene Expression Omnibus (GSE159977, GSE136103, and GSE125449) and The Cancer Genome Atlas (TGCA-LIHC), respectively. Quality control, dimension reduction, clustering, and annotation were performed according to the Scanpy workflow based on Python. Cell-cell interactions were explored using the CellPhone database and CellChat. Trajectory analysis was executed using a partition-based graph abstraction method. The transcriptomic factors (TFs) were predicted using single-cell regulatory network inference and clustering (SCENIC). The target genes from TFs were used to establish a related score based on the TCGA cohort; this score was subsequently validated by survival, gene set enrichment, and immune cell infiltration analyses. Drug prediction was performed based on the Cancer Therapeutics Response Portal and PRISM Repurposing datasets.ResultsThirty-one patients at four different states, including health, hepatitis, cirrhosis, and cancer, were enrolled in this study. After dimension reduction and clustering, twenty-two clusters were identified. Cell-cell interaction analyses indicated that macrophage-naive CD4 + T cell interaction significantly affect cancerous state. In brief, macrophages interact with naive CD4 + T cells via different pathways in different states. The results of SCENIC indicated that macrophages present in cancer cells were similar to those present during cirrhosis. A macrophage-naive CD4 + T cell (MNT) score was generated by the SCENIC-derived target genes. Based on the MNT score, five relevant drugs (inhibitor of polo-like kinase 1, inhibitor of kinesin family member 11, dabrafenib, ispinesib, and epothilone-b) were predicted.ConclusionsThis study reveals the crucial role of macrophage-naive CD4 + T cell interaction in the immunosuppressive microenvironment of liver carcinoma. Tumor-associated macrophages may be derived from cirrhosis and can initiate liver carcinoma. Predictive drugs that target the macrophage-naive CD4 + T cell interaction may help to improve the immunosuppressive microenvironment and prevent immune evasion. The relevant mechanisms need to be further validated in experiments and cohort studies.

Project description:The present study used single-cell RNA sequencing (scRNA-seq) to characterize the cellular composition of ovarian carcinosarcoma (OCS) and identify its molecular characteristics. scRNA-seq was performed in resected primary OCS for an in-depth analysis of tumor cells and the tumor microenvironment. Immunohistochemistry staining was used for validation. The scRNA-seq data of OCS were compared with those of high-grade serous ovarian carcinoma (HGSOC) tumors and other OCS tumors. Both malignant epithelial and malignant mesenchymal cells were observed in the OCS patient of this study. We identified four epithelial cell subclusters with different biological roles. Among them, epithelial subcluster 4 presented high levels of breast cancer type 1 susceptibility protein homolog (BRCA1) and DNA topoisomerase 2-α (TOP2A) expression and was related to drug resistance and cell cycle. We analyzed the interaction between epithelial and mesenchymal cells and found that fibroblast growth factor (FGF) and pleiotrophin (PTN) signalings were the main pathways contributing to communication between these cells. Moreover, we compared the malignant epithelial and mesenchymal cells of this OCS tumor with our previous published HGSOC scRNA-seq data and OCS data. All the epithelial subclusters in the OCS tumor could be found in the HGSOC samples. Notably, the mesenchymal subcluster C14 exhibited specific expression patterns in the OCS tumor, characterized by elevated expression of cytochrome P450 family 24 subfamily A member 1 (CYP24A1), collagen type XXIII α1 chain (COL23A1), cholecystokinin (CCK), bone morphogenetic protein 7 (BMP7), PTN, Wnt inhibitory factor 1 (WIF1), and insulin-like growth factor 2 (IGF2). Moreover, this subcluster showed distinct characteristics when compared with both another previously published OCS tumor and normal ovarian tissue. This study provides the single-cell transcriptomics signature of human OCS, which constitutes a new resource for elucidating OCS diversity.

Dataset Information

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

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets