• 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

解析鞘脂代谢基因在骨肉瘤进展和微环境中的作用，构建预后标志物。

Dissecting the effect of sphingolipid metabolism gene in progression and microenvironment of osteosarcoma to develop a prognostic signature.

机构信息

Department of Orthopedics, Renmin Hospital of Wuhan University, Wuhan, China.

Department of Biomedical Sciences, College of Veterinary Medicine and Life Sciences, City University of Hong Kong, Kowloon Tong, Hong Kong SAR, China.

出版信息

Front Endocrinol (Lausanne). 2022 Oct 14;13:1030655. doi: 10.3389/fendo.2022.1030655. eCollection 2022.

DOI:10.3389/fendo.2022.1030655
PMID:36313783
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9613955/
Abstract

Sphingolipid metabolism (SM) fuels tumorigenesis and the malignant progression of osteosarcoma (OS), which leads to an unfavorable prognosis. Elucidating the molecular mechanisms underlying SM in osteosarcoma and developing a SM-based prognostic signature could be beneficial in the clinical setting. This study included 88 frozen OS samples to recognize the vital SM-relevant genes in the development of OS utilizing univariate Cox regression. The Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis was conducted on the SM- relevant genes to minimize the risk of overfitting. The prognostic signature was generate utilizing the multivariable Cox regression analysis and was verified in the validation cohort. Moreover, cellular and molecular mechanisms associated with SM have an unfavorable prognosis for OS patients and have been widely studied. Resultantly, an SM-based prognostic risk model was established according to critical prognostic genes (CBS, and , which had an excellent ability to predict the prognosis of OS patients (AUC for the train cohort was 0.887 and AUC for validation cohort was 0.737). The high-risk OS patients identified based on this prognostic signature had significantly poor immune microenvironment, indicated by significantly low immune score (mean=216.290 ± 662.463), reduced infiltrations of 25 immune cells, including NK cells (LogFC= -0.3597), CD8+T cells ((LogFC=-0.2346), Cytolytic activity ((LogFC=-0.1998), etc. The immunosuppressive microenvironment could be due to dysregulated SM of glycolipids. Further, a nomogram was constructed by integrating the SM-based prognostic signature and clinical paraments to facilitate clinical application. The nomogram could accurately predict the prognosis of OS invalids. Collectively, this study clarified the function of SM in the development of OS and helped develop a tool for risk stratification based on SM-related genes with application in clinical settings. The results of our study will aid in identifying high-risk patients and provide individualized treatments.

摘要

鞘脂代谢（SM）为骨肉瘤（OS）的发生和恶性进展提供动力，导致预后不良。阐明骨肉瘤中 SM 的分子机制，并开发基于 SM 的预后标志物，可能对临床有帮助。本研究纳入 88 例冷冻骨肉瘤样本，利用单因素 Cox 回归识别骨肉瘤发生发展中重要的 SM 相关基因。对 SM 相关基因进行最小绝对收缩和选择算子（LASSO）回归分析，以最小化过度拟合的风险。利用多变量 Cox 回归分析生成预后标志物，并在验证队列中进行验证。此外，与 SM 相关的细胞和分子机制与骨肉瘤患者的不良预后相关，已被广泛研究。因此，根据关键预后基因（CBS 和 ）建立了基于 SM 的预后风险模型，该模型具有很好的预测骨肉瘤患者预后的能力（训练队列的 AUC 为 0.887，验证队列的 AUC 为 0.737）。根据该预后标志物确定的高危骨肉瘤患者的免疫微环境明显较差，免疫评分明显较低（均值=216.290±662.463），25 种免疫细胞浸润减少，包括 NK 细胞（LogFC=-0.3597）、CD8+T 细胞（LogFC=-0.2346）、细胞溶解活性（LogFC=-0.1998）等。免疫抑制微环境可能是由于糖脂的 SM 失调所致。进一步通过整合基于 SM 的预后标志物和临床参数构建列线图，以方便临床应用。该列线图可以准确预测骨肉瘤患者的预后。总之，本研究阐明了 SM 在骨肉瘤发生发展中的作用，并有助于开发基于 SM 相关基因的风险分层工具，应用于临床。我们的研究结果将有助于识别高危患者，并提供个体化治疗。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a68a/9613955/f788c369ea10/fendo-13-1030655-g015.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a68a/9613955/274269497793/fendo-13-1030655-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a68a/9613955/d7f8c96584c7/fendo-13-1030655-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a68a/9613955/2e5c5eb0e578/fendo-13-1030655-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a68a/9613955/5636d8766e7d/fendo-13-1030655-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a68a/9613955/f68e03e57180/fendo-13-1030655-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a68a/9613955/d329d47ca388/fendo-13-1030655-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a68a/9613955/175d798d6cf2/fendo-13-1030655-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a68a/9613955/2d953dcc94cb/fendo-13-1030655-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a68a/9613955/1953d95362f1/fendo-13-1030655-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a68a/9613955/dab38d2cc5e5/fendo-13-1030655-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a68a/9613955/c86204ce8590/fendo-13-1030655-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a68a/9613955/3125a8978064/fendo-13-1030655-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a68a/9613955/1a7a47937322/fendo-13-1030655-g013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a68a/9613955/dee845099b20/fendo-13-1030655-g014.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a68a/9613955/f788c369ea10/fendo-13-1030655-g015.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a68a/9613955/274269497793/fendo-13-1030655-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a68a/9613955/d7f8c96584c7/fendo-13-1030655-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a68a/9613955/2e5c5eb0e578/fendo-13-1030655-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a68a/9613955/5636d8766e7d/fendo-13-1030655-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a68a/9613955/f68e03e57180/fendo-13-1030655-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a68a/9613955/d329d47ca388/fendo-13-1030655-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a68a/9613955/175d798d6cf2/fendo-13-1030655-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a68a/9613955/2d953dcc94cb/fendo-13-1030655-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a68a/9613955/1953d95362f1/fendo-13-1030655-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a68a/9613955/dab38d2cc5e5/fendo-13-1030655-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a68a/9613955/c86204ce8590/fendo-13-1030655-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a68a/9613955/3125a8978064/fendo-13-1030655-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a68a/9613955/1a7a47937322/fendo-13-1030655-g013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a68a/9613955/dee845099b20/fendo-13-1030655-g014.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a68a/9613955/f788c369ea10/fendo-13-1030655-g015.jpg

相似文献

1
Dissecting the effect of sphingolipid metabolism gene in progression and microenvironment of osteosarcoma to develop a prognostic signature.解析鞘脂代谢基因在骨肉瘤进展和微环境中的作用，构建预后标志物。
Front Endocrinol (Lausanne). 2022 Oct 14;13:1030655. doi: 10.3389/fendo.2022.1030655. eCollection 2022.
2
Prediction of Prognosis and Immunotherapy Response with a Novel Natural Killer Cell Marker Genes Signature in Osteosarcoma.基于新型自然杀伤细胞标志物基因特征预测骨肉瘤的预后和免疫治疗反应。
Cancer Biother Radiopharm. 2024 Sep;39(7):502-516. doi: 10.1089/cbr.2023.0103. Epub 2023 Oct 27.
3
Identification of an EMT-related gene-based prognostic signature in osteosarcoma.识别骨肉瘤中 EMT 相关基因的预后特征。
Cancer Med. 2023 Jun;12(11):12912-12928. doi: 10.1002/cam4.5942. Epub 2023 Apr 27.
4
Exploring the immune microenvironment of osteosarcoma through T cell exhaustion-associated gene expression: a study on prognosis prediction.通过 T 细胞耗竭相关基因表达探索骨肉瘤的免疫微环境：一项预后预测研究。
Front Immunol. 2023 Dec 15;14:1265098. doi: 10.3389/fimmu.2023.1265098. eCollection 2023.
5
Development and validation of neutrophil extracellular traps-derived signature to predict the prognosis for osteosarcoma patients.中性粒细胞胞外诱捕网衍生标志物的建立与验证及其对骨肉瘤患者预后评估的价值
Int Immunopharmacol. 2024 Jan 25;127:111364. doi: 10.1016/j.intimp.2023.111364. Epub 2023 Dec 14.
6
Comprehensive analysis of prognostic tumor microenvironment-related genes in osteosarcoma patients.骨肉瘤患者预后肿瘤微环境相关基因的综合分析。
BMC Cancer. 2020 Aug 27;20(1):814. doi: 10.1186/s12885-020-07216-2.
7
The mitochondrial energy metabolism pathway-related signature predicts prognosis and indicates immune microenvironment infiltration in osteosarcoma.线粒体能量代谢途径相关特征可预测骨肉瘤的预后，并提示免疫微环境浸润。
Medicine (Baltimore). 2023 Nov 17;102(46):e36046. doi: 10.1097/MD.0000000000036046.
8
Identification and characterization of aging/senescence-induced genes in osteosarcoma and predicting clinical prognosis.鉴定和特征分析骨肉瘤中与衰老/衰老相关的基因，并预测临床预后。
Front Immunol. 2022 Oct 5;13:997765. doi: 10.3389/fimmu.2022.997765. eCollection 2022.
9
Ferroptosis-related lncRNAs guiding osteosarcoma prognosis and immune microenvironment.铁死亡相关 lncRNAs 指导骨肉瘤预后和免疫微环境。
J Orthop Surg Res. 2023 Oct 19;18(1):787. doi: 10.1186/s13018-023-04286-3.
10
Identification and verification of a BMPs-related gene signature for osteosarcoma prognosis prediction.鉴定和验证与 BMPs 相关的基因特征，用于骨肉瘤预后预测。
BMC Cancer. 2023 Feb 22;23(1):181. doi: 10.1186/s12885-023-10660-5.

引用本文的文献

1
Integrated multi-omics analysis identifies lipid metabolism biomarkers in ONFH and reveals therapeutic potential of retinoic acid.综合多组学分析鉴定出股骨头坏死中的脂质代谢生物标志物并揭示了视黄酸的治疗潜力。
Sci Rep. 2025 Aug 21;15(1):30716. doi: 10.1038/s41598-025-13703-y.
2
Feasibility of machine learning-based modeling and prediction to assess osteosarcoma outcomes.基于机器学习的建模和预测评估骨肉瘤预后的可行性。
Sci Rep. 2025 May 19;15(1):17386. doi: 10.1038/s41598-025-00179-z.
3
Metabolic reprogramming and immune microenvironment characteristics in laryngeal carcinoma: advances in immunotherapy.

本文引用的文献

1
Hypoxia inducible lncRNA-CBSLR modulates ferroptosis through m6A-YTHDF2-dependent modulation of CBS in gastric cancer.低氧诱导长非编码 RNA-CBSLR 通过 m6A-YTHDF2 依赖的 CBS 调控在胃癌中调控铁死亡。
J Adv Res. 2021 Oct 5;37:91-106. doi: 10.1016/j.jare.2021.10.001. eCollection 2022 Mar.
2
The Role of Sphingolipids Metabolism in Cancer Drug Resistance.鞘脂代谢在癌症耐药中的作用。
Front Oncol. 2021 Dec 23;11:807636. doi: 10.3389/fonc.2021.807636. eCollection 2021.
3
Tumor purity as a prognosis and immunotherapy relevant feature in cervical cancer.
喉癌中的代谢重编程与免疫微环境特征：免疫治疗进展
Front Immunol. 2025 Apr 30;16:1589243. doi: 10.3389/fimmu.2025.1589243. eCollection 2025.
4
Nonlinear association between hematocrit levels and short-term all-cause mortality in ICU patients with acute pancreatitis: insights from a retrospective cohort study.急性胰腺炎重症监护病房患者血细胞比容水平与短期全因死亡率之间的非线性关联：一项回顾性队列研究的见解
BMC Gastroenterol. 2025 Mar 19;25(1):186. doi: 10.1186/s12876-025-03764-8.
5
Multi-omic validation of the cuproptosis-sphingolipid metabolism network: modulating the immune landscape in osteosarcoma.多组学验证铜死亡-鞘脂代谢网络：调节骨肉瘤中的免疫景观。
Front Immunol. 2024 Jun 25;15:1424806. doi: 10.3389/fimmu.2024.1424806. eCollection 2024.
6
Advances in prognostic models for osteosarcoma risk.骨肉瘤风险预后模型的进展。
Heliyon. 2024 Mar 26;10(7):e28493. doi: 10.1016/j.heliyon.2024.e28493. eCollection 2024 Apr 15.
7
Integrated single-cell sequencing, spatial transcriptome sequencing and bulk RNA sequencing highlights the molecular characteristics of parthanatos in gastric cancer.单细胞测序、空间转录组测序和批量 RNA 测序的综合分析突出了胃癌中 parthanatos 的分子特征。
Aging (Albany NY). 2024 Mar 18;16(6):5471-5500. doi: 10.18632/aging.205658.
8
Exploring the relationship between metabolism and immune microenvironment in osteosarcoma based on metabolic pathways.基于代谢途径探讨骨肉瘤中代谢与免疫微环境的关系。
J Biomed Sci. 2024 Jan 12;31(1):4. doi: 10.1186/s12929-024-00999-7.
9
Machine learning-enhanced insights into sphingolipid-based prognostication: revealing the immunological landscape and predictive proficiency for immunomotherapy and chemotherapy responses in pancreatic carcinoma.机器学习增强对基于鞘脂的预后分析的见解：揭示胰腺癌免疫治疗和化疗反应的免疫格局及预测能力。
Front Mol Biosci. 2023 Oct 31;10:1284623. doi: 10.3389/fmolb.2023.1284623. eCollection 2023.
10
Uncovering the immune microenvironment and molecular subtypes of hepatitis B-related liver cirrhosis and developing stable a diagnostic differential model by machine learning and artificial neural networks.揭示乙型肝炎相关肝硬化的免疫微环境和分子亚型，并通过机器学习和人工神经网络建立稳定的诊断鉴别模型。
Front Mol Biosci. 2023 Sep 22;10:1275897. doi: 10.3389/fmolb.2023.1275897. eCollection 2023.
肿瘤纯度作为宫颈癌的预后和免疫治疗相关特征。
Aging (Albany NY). 2021 Nov 29;13(22):24768-24785. doi: 10.18632/aging.203714.
4
The Immune Landscape of Osteosarcoma: Implications for Prognosis and Treatment Response.骨肉瘤的免疫景观：对预后和治疗反应的影响。
Cells. 2021 Jul 2;10(7):1668. doi: 10.3390/cells10071668.
5
S1P signaling, its interactions and cross-talks with other partners and therapeutic importance in colorectal cancer.鞘氨醇-1-磷酸（S1P）信号传导、其与其他伙伴的相互作用和相互影响以及在结直肠癌中的治疗重要性。
Cell Signal. 2021 Oct;86:110080. doi: 10.1016/j.cellsig.2021.110080. Epub 2021 Jul 7.
6
Cancer-Associated Glycosphingolipids as Tumor Markers and Targets for Cancer Immunotherapy.癌症相关糖脂作为肿瘤标志物和癌症免疫治疗靶点。
Int J Mol Sci. 2021 Jun 7;22(11):6145. doi: 10.3390/ijms22116145.
7
Highly recurrent CBS epimutations in gastric cancer CpG island methylator phenotypes and inflammation.高度复发的 CBS 表观遗传学突变与胃癌 CpG 岛甲基化表型和炎症有关。
Genome Biol. 2021 Jun 1;22(1):167. doi: 10.1186/s13059-021-02375-2.
8
Gangliosides as Signaling Regulators in Cancer.神经节苷脂作为癌症中的信号调节剂。
Int J Mol Sci. 2021 May 11;22(10):5076. doi: 10.3390/ijms22105076.
9
Pathways of immune exclusion in metastatic osteosarcoma are associated with inferior patient outcomes.转移性骨肉瘤中免疫排斥的途径与患者预后不良相关。
J Immunother Cancer. 2021 May;9(5). doi: 10.1136/jitc-2020-001772.
10
A computational approach to analyse the amino acid variants of GLB1 protein causing GM1 Gangliosidosis.一种分析导致 GM1 神经节苷脂贮积症的 GLB1 蛋白氨基酸变异的计算方法。
Metab Brain Dis. 2021 Mar;36(3):499-508. doi: 10.1007/s11011-020-00650-y. Epub 2021 Jan 4.