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全基因组测序和单细胞测序为骨肉瘤患者确定了一种基于巨噬细胞和脂质代谢相关特征的预后模型。

Bulk and single-cell sequencing identified a prognostic model based on the macrophage and lipid metabolism related signatures for osteosarcoma patients.

作者信息

Lin Zili, Wu Ziyi, Luo Wei

机构信息

Department of Orthopaedics, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, PR China.

Department of Orthopaedics, the Second Xiangya Hospital, Central South University, Changsha, Hunan, 410011, PR China.

出版信息

Heliyon. 2024 Feb 18;10(4):e26091. doi: 10.1016/j.heliyon.2024.e26091. eCollection 2024 Feb 29.

DOI:10.1016/j.heliyon.2024.e26091
PMID:38404899
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10884844/
Abstract

The introduction of multidrug combination chemotherapy has significantly advanced the long-term survival prospects for osteosarcoma (OS) patients over the past decades. However, the escalating prevalence of chemoresistance has emerged as a substantial impediment to further advancements, necessitating the formulation of innovative strategies. Our present study leveraged sophisticated bulk and single-cell sequencing techniques to scrutinize the OS immune microenvironment, unveiling a potential association between the differentiation state of macrophages and the efficacy of OS chemotherapy. Notably, we observed that a heightened presence of lipid metabolism genes and pathways in predifferentiated macrophages, constituting the major cluster of OS patients exhibiting a less favorable response to chemotherapy. Subsequently, we developed a robust Macrophage and Lipid Metabolism (MLMR) risk model and a nomogram, both of which demonstrated commendable prognostic predictive performance. Furthermore, a comprehensive investigation into the underlying mechanisms of the risk model revealed intricate associations with variations in the immune response among OS patients. Finally, our meticulous drug sensitivity analysis identified a spectrum of potential therapeutic agents for OS, including AZD2014, Sapitinib, Buparlisib, Afuresertib, MIRA-1, and BIBR-1532. These findings significantly augment the therapeutic arsenal available to clinicians managing OS, presenting a promising avenue for elevating treatment outcomes.

摘要

在过去几十年中,多药联合化疗的引入显著改善了骨肉瘤(OS)患者的长期生存前景。然而,化疗耐药性的日益普遍已成为进一步进展的重大障碍,因此需要制定创新策略。我们目前的研究利用先进的批量和单细胞测序技术来仔细研究OS免疫微环境,揭示了巨噬细胞的分化状态与OS化疗疗效之间的潜在关联。值得注意的是,我们观察到在预分化巨噬细胞中脂质代谢基因和通路的存在增加,这些巨噬细胞构成了对化疗反应较差的OS患者的主要群体。随后,我们开发了一个强大的巨噬细胞与脂质代谢(MLMR)风险模型和一个列线图,两者都表现出值得称赞的预后预测性能。此外,对风险模型潜在机制的全面研究揭示了与OS患者免疫反应变化的复杂关联。最后,我们细致的药物敏感性分析确定了一系列用于OS的潜在治疗药物,包括AZD2014、西替尼、布帕利西布、阿福雷西布、MIRA-1和BIBR-1532。这些发现显著增加了临床医生治疗OS可用的治疗手段,为提高治疗效果提供了一条有希望的途径。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ce58/10884844/fd32711614f4/mmcfigs2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ce58/10884844/09178f38ff47/gr1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ce58/10884844/437bcf1325c2/gr2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ce58/10884844/b705e62599d5/gr3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ce58/10884844/08b41e3ba434/gr4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ce58/10884844/2e9b3f856e12/gr5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ce58/10884844/5a142a30952c/gr6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ce58/10884844/3177bfb8a2ad/mmcfigs1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ce58/10884844/fd32711614f4/mmcfigs2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ce58/10884844/09178f38ff47/gr1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ce58/10884844/437bcf1325c2/gr2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ce58/10884844/b705e62599d5/gr3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ce58/10884844/08b41e3ba434/gr4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ce58/10884844/2e9b3f856e12/gr5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ce58/10884844/5a142a30952c/gr6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ce58/10884844/3177bfb8a2ad/mmcfigs1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ce58/10884844/fd32711614f4/mmcfigs2.jpg

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Comprehensive single-cell transcriptomic and proteomic analysis reveals NK cell exhaustion and unique tumor cell evolutionary trajectory in non-keratinizing nasopharyngeal carcinoma.
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