| 作者：姚希官,叶冬平.基于单细胞RNA测序分析化疗对骨肉瘤微环境的影响[J].安徽医药,待发表. |
| 基于单细胞RNA测序分析化疗对骨肉瘤微环境的影响 |
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| 投稿时间：2024-03-20 录用日期：2024-04-08 |
| DOI： |
| 中文关键词: 单细胞RNA测序 骨肉瘤 化疗 肿瘤微环境 分化轨迹 分子分型 |
| 英文关键词: |
| 基金项目:广州市科技计划项目(202102010111) |
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| 摘要点击次数: 202 |
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| 中文摘要: |
| 背景：骨肉瘤的复发率高，生存率低，被认为是最常见的恶性骨肿瘤。到目前为止，这种疾病的准确发病机制和最佳治疗方法都没有明确的指南。近年来，单细胞RNA测序技术已成功运用于骨肉瘤的研究。然而，关于单细胞RNA测序技术在骨肉瘤化疗下的研究相关较少。截至目前，单细胞RNA测序技术在骨肉瘤化疗与非化疗的对比性研究成果几乎为零。
目的：利用单细胞RNA测序技术在骨肉瘤细胞所获得的研究数据，构建化疗与非化疗骨肉瘤细胞的单细胞转录图谱，鉴别骨肉瘤细胞的主要细胞亚群及其单细胞图谱，揭示化疗对骨肉瘤免疫微环境的影响，进而从单细胞水平寻找到一个或多个骨肉瘤的治疗靶点。
方法：从公共数据库获取了8例经单细胞RNA测序出的骨肉瘤细胞数据。通过软件分析，对获取数据标准化、细胞亚群分析、细胞类型鉴定等，构建出骨肉瘤的单细胞表达图谱并得到相关亚群标记基因。再利用基因功能分析、信号通路分析、拟时序分析等，进一步探究不同细胞亚群在骨肉瘤化疗微环境中的功能。
结果：获取了4例化疗和4例非化疗经单细胞RNA测序的骨肉瘤样本，样本共包含约 10 万个骨肉瘤单细胞，经过质控筛选后，80552 个细胞被用于进一步分析。根据它们的基因谱和典型标记，在化疗组与非化疗组骨肉瘤细胞的对比下，总结了成骨性骨肉瘤细胞、髓系骨肉瘤细胞、破骨细胞、增生性骨肉瘤细胞、T细胞和NK细胞、内皮细胞、巨噬细胞的亚群及其特异性标记基因。其中，成骨性骨肉瘤细胞、髓系骨肉瘤细胞、增殖性骨肉瘤细胞和NK/T细胞占据相对较高的比例，而且与未经化疗组相比较，化疗组表现出的异质性也主要体现在这几种细胞族群中。最后，通过UMAP分析确定了4个成骨性骨肉瘤细胞亚群、4个髓系骨肉瘤细胞子簇、5个增殖性骨肉瘤细胞亚簇和4个NK/T细胞亚簇。
结论：构建了骨肉瘤细胞的关键细胞族群及其分化轨迹，发现了可能影响骨肉瘤疾病进展的关键基因。揭示了氧化磷酸化、上皮间质转化和血管生成与抗肿瘤恶化、转移和化疗耐药密切相关。本研究成果对骨肉瘤细胞及其微环境特性有了更深入的了解，将为骨肉瘤在未来的治疗中提供新的见解。 |
| 英文摘要: |
| Background：Osteosarcoma is a malignant bone tumor that has a high recurrence rate and low survival rate.Currently, there are no definitive guidelines regarding the precise pathogenesis of this disease and the optimal treatment.In recent years, single-cell RNA sequencing technology has been successfully used in the study of osteosarcoma. However, there are fewer studies related to single-cell RNA sequencing technology under chemotherapy for osteosarcoma.Currently, there is a lack of comparative studies on the effectiveness of single-cell RNA sequencing technology for chemotherapy versus non-chemotherapy in osteosarcoma.
Objective: Using research data obtained from single-cell RNA sequencing technology in osteosarcoma cells, to construct single-cell transcriptional profiles of chemotherapy-treated and non-chemotherapy-treated osteosarcoma cells, to identify the major cellular subpopulations of osteosarcoma cells and their single-cell profiles, to elucidate the effect of chemotherapy on the immune microenvironment of osteosarcoma, and to search for one or more therapeutic targets for osteosarcoma at the single-cell level.
Methods: Public databases were used to obtain single-cell RNA sequencing data from 8 cases of osteosarcoma. The acquired data were normalized and analyzed using software to identify cell subpopulations, perform cell type identification, and construct single-cell expression profiles of osteosarcoma. Relevant subpopulation marker genes were obtained. Gene function analysis, signaling pathway analysis, and Pseudo-Time analysis were used to explore the functions of different cell subpopulations in the microenvironment of osteosarcoma chemotherapy.
Results：RNA sequencing samples were obtained from eight osteosarcoma cases, four of which received chemotherapy and four of which did not. In total, the samples contained approximately 100,000 osteosarcoma cells. After quality control screening, 80,552 cells were used for further analysis.The comparison between osteosarcoma cells in the chemotherapy group and the non-chemotherapy group was based on their gene profiles and typical markers. The subpopulations of osteogenic osteosarcoma cells, myeloid osteosarcoma cells, osteoblasts, proliferative osteosarcoma cells, T and NK cells, endothelial cells, and macrophages, along with their specific marker genes, were summarized. The chemotherapy group exhibited higher proportions of osteogenic osteosarcoma cells, medullary osteosarcoma cells, proliferative osteosarcoma cells, and NK/T cells compared to the chemotherapy-na?ve group. This heterogeneity was mainly reflected in these cell populations.UMAP analysis identified four subpopulations of osteogenic osteosarcoma cells, four subclusters of myeloid osteosarcoma cells, five subclusters of proliferative osteosarcoma cells, and four subclusters of NK/T cells.
Conclusions: The study constructed differentiation trajectories for key cellular populations of osteosarcoma cells and identified genes that may influence disease progression. The study revealed that oxidative phosphorylation, epithelial mesenchymal transition, and angiogenesis are closely related to anti-tumor deterioration, metastasis, and chemotherapy resistance. The findings offer a deeper understanding of osteosarcoma cells and their microenvironmental properties, providing new insights into future therapies for osteosarcoma. |
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