• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

单细胞转录组分析表明,肿瘤微环境中的细胞是脑转移黑色素瘤和颅外黑色素瘤转移之间的主要鉴别因素。

Single-cell transcriptome analysis suggests cells of the tumor microenvironment as a major discriminator between brain and extracranial melanoma metastases.

作者信息

Grützmann Konrad, Seifert Michael

机构信息

Institute for Medical Informatics and Biometry (IMB), Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany.

出版信息

Biol Direct. 2025 Sep 16;20(1):97. doi: 10.1186/s13062-025-00691-2.

DOI:10.1186/s13062-025-00691-2
PMID:40954493
Abstract

BACKGROUND

Despite therapeutic advances, metastatic melanoma, and particularly brain metastasis (MBM), remains a lethal burden for patients. Existing single-cell studies offer a more detailed view of melanoma and its microenvironment, which is crucial to improve diagnosis and treatment.

RESULTS

We here present a computational reanalysis of single-nucleus data comparing 15 MBM and 10 extracranial melanoma metastases (ECM), considering recent best practice recommendations. We used cell type-specific pseudobulking and omit imputation during patient integration to gain complementary insights. Interestingly, our analysis revealed high homogeneity in tumor cell expression profiles within and between MBM and ECM. However, MBM displayed even higher homogeneity but a more flexible energy metabolism, suggesting a specific metastatic adaptation to the putatively more restricted brain microenvironment. While tumor cells were homogeneous, the metastasis microenvironment, especially lymphocytes and related immune-tumor interaction pathways, exhibited greater divergence between MBM and ECM. Overall, this suggests that major differences between MBM and ECM are potentially driven by variations in their microenvironment. Finally, a comparison of single-cell data to previous bulk studies, including their deconvoluted putative cell types, showed significant differences, potentially causing divergent conclusions.

CONCLUSION

Our study contributed to refine the understanding of differences between MBM and ECM, suggesting these are potentially more influenced by their local microenvironments. Future research and therapies could possibly focus on the metabolic flexibility of melanoma brain metastases and patient-specific immune pathway alterations.

摘要

背景

尽管治疗取得了进展,但转移性黑色素瘤,尤其是脑转移瘤(MBM),仍然是患者的致命负担。现有的单细胞研究提供了对黑色素瘤及其微环境更详细的见解,这对于改善诊断和治疗至关重要。

结果

我们在此对单核数据进行了计算重新分析,比较了15例MBM和10例颅外黑色素瘤转移灶(ECM),同时考虑了近期的最佳实践建议。我们在患者整合过程中使用了细胞类型特异性伪批量分析并省略了插补,以获得互补的见解。有趣的是,我们的分析揭示了MBM和ECM内部以及之间肿瘤细胞表达谱的高度同质性。然而,MBM表现出更高的同质性但能量代谢更灵活,这表明其对假定更受限的脑微环境有特定的转移适应性。虽然肿瘤细胞是同质的,但转移微环境,尤其是淋巴细胞和相关的免疫-肿瘤相互作用途径,在MBM和ECM之间表现出更大的差异。总体而言,这表明MBM和ECM之间的主要差异可能是由它们微环境的变化驱动的。最后,将单细胞数据与以前的批量研究(包括它们解卷积后的假定细胞类型)进行比较,发现存在显著差异,这可能导致不同的结论。

结论

我们的研究有助于完善对MBM和ECM之间差异的理解,表明这些差异可能更多地受到其局部微环境的影响。未来的研究和治疗可能会聚焦于黑色素瘤脑转移瘤的代谢灵活性和患者特异性免疫途径改变。

相似文献

1
Single-cell transcriptome analysis suggests cells of the tumor microenvironment as a major discriminator between brain and extracranial melanoma metastases.单细胞转录组分析表明,肿瘤微环境中的细胞是脑转移黑色素瘤和颅外黑色素瘤转移之间的主要鉴别因素。
Biol Direct. 2025 Sep 16;20(1):97. doi: 10.1186/s13062-025-00691-2.

本文引用的文献

1
Technical and Biological Biases in Bulk Transcriptomic Data Mining for Cancer Research.癌症研究中批量转录组数据挖掘的技术和生物学偏差
J Cancer. 2025 Jan 1;16(1):34-43. doi: 10.7150/jca.100922. eCollection 2025.
2
E2F1-induced autocrine IL-6 inflammatory loop mediates cancer-immune crosstalk that predicts T cell phenotype switching and therapeutic responsiveness.E2F1 诱导的自分泌 IL-6 炎症环介导了癌症-免疫串扰,预测了 T 细胞表型转换和治疗反应性。
Front Immunol. 2024 Oct 31;15:1470368. doi: 10.3389/fimmu.2024.1470368. eCollection 2024.
3
Learning generalizable visual representation via adaptive spectral random convolution for medical image segmentation.通过自适应谱随机卷积学习可泛化的视觉表示用于医学图像分割
Comput Biol Med. 2023 Oct 24;167:107580. doi: 10.1016/j.compbiomed.2023.107580.
4
PCaseek: ultraspecific urinary tumor DNA detection using deep learning for prostate cancer diagnosis and Gleason grading.PCaseek:利用深度学习进行超特异性尿肿瘤DNA检测以诊断前列腺癌并进行Gleason分级。
Cell Discov. 2024 Sep 3;10(1):90. doi: 10.1038/s41421-024-00710-y.
5
The classification of melanocytic gene signatures.黑素细胞基因特征分类。
Pigment Cell Melanoma Res. 2024 Nov;37(6):854-863. doi: 10.1111/pcmr.13189. Epub 2024 Jul 28.
6
Advancing immunotherapy for melanoma: the critical role of single-cell analysis in identifying predictive biomarkers.推进黑色素瘤免疫疗法:单细胞分析在鉴定预测性生物标志物方面的关键作用。
Front Immunol. 2024 Jul 4;15:1435187. doi: 10.3389/fimmu.2024.1435187. eCollection 2024.
7
Proliferating macrophages in human tumours show characteristics of monocytes responding to myelopoietic growth factors.人类肿瘤中的增殖巨噬细胞表现出对造血生长因子有反应的单核细胞的特征。
Front Immunol. 2024 Jun 5;15:1412076. doi: 10.3389/fimmu.2024.1412076. eCollection 2024.
8
Personalized identification and characterization of genome-wide gene expression differences between patient-matched intracranial and extracranial melanoma metastasis pairs.患者匹配的颅内和颅外黑色素瘤转移瘤对之间全基因组基因表达差异的个性化识别与表征。
Acta Neuropathol Commun. 2024 Apr 24;12(1):67. doi: 10.1186/s40478-024-01764-5.
9
Regulatory T cells subgroups in the tumor microenvironment cannot be overlooked: Their involvement in prognosis and treatment strategy in melanoma.肿瘤微环境中的调节性T细胞亚群不容忽视:它们在黑色素瘤预后和治疗策略中的作用。
Environ Toxicol. 2024 Oct;39(10):4512-4530. doi: 10.1002/tox.24247. Epub 2024 Mar 26.
10
Network-based analysis of heterogeneous patient-matched brain and extracranial melanoma metastasis pairs reveals three homogeneous subgroups.基于网络的异质性患者匹配脑和颅外黑色素瘤转移对分析揭示了三个同质亚组。
Comput Struct Biotechnol J. 2024 Feb 17;23:1036-1050. doi: 10.1016/j.csbj.2024.02.013. eCollection 2024 Dec.