Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Non-LTR Retrotransposons03:18

Non-LTR Retrotransposons

11.9K
As the name suggests, non-LTR retrotransposons lack the long terminal repeats characteristic of the LTR retrotransposons. Additionally, both LTR and non-LTR retrotransposons use distinct mechanisms of mobilization. Non-LTR retrotransposons are further divided into two classes - Long interspersed nuclear elements (LINEs) and short interspersed nuclear elements (SINEs), both of which occur abundantly in most mammals, including humans. Some of the active non-LTR retrotransposons in humans are L1...
11.9K

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Comparison of PEG-Based and Non-PEG Bowel Preparation for Emergency Colonoscopy in Acute Lower Gastrointestinal Bleeding: A Real-World Cohort Study with IPTW Analysis.

Digestive diseases and sciences·2026
Same author

The Evolving Landscape of Terahertz Biosensing: From Sensitivity to Precision.

ACS applied materials & interfaces·2026
Same author

Corrigendum to "Management of atypical cartilaginous tumors (ACT): An Italian Sarcoma Group (ISG) consensus document" [Crit. Rev. Oncol. Hematol. 223 (2026) 105358].

Critical reviews in oncology/hematology·2026
Same author

Multi-phase hybrid metabolomics framework identifies clinically applicable plasma signatures for early detection of gastric cancer.

Nature communications·2026
Same author

Management of atypical cartilaginous tumors (ACT): an Italian Sarcoma Group (ISG) consensus document.

Critical reviews in oncology/hematology·2026
Same author

Clinical Characteristics at the Diagnosis of New Primary Melanoma in Italy: A Multicenter Retrospective Study Before and After the COVID-19 Pandemic.

Journal of clinical medicine·2026

相关实验视频

Updated: Sep 20, 2025

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
07:13

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

202

基于分子特征的分类 retroperitoneal 脂质肉瘤:一个前性队列研究.

Mengmeng Xiao1,2, Xiangji Li2,3, Fanqin Bu3

  • 1Department of Retroperitoneal Tumor Surgery, Peking University People's Hospital, Beijing, China.

eLife
|May 23, 2025
PubMed
概括

这项研究基于分子特征确定了两种不同类型的逆皮质脂肉瘤 (RPLS),改善了患者分层. 一种新的生物标志物分类提供了一种具有成本效益的方法,用于指导RPLS中的治疗决策.

关键词:
对于LEP来说,这是一个很好的选择.在 PTTG1 的基础上.癌症生物学 癌症生物学人类 人类 人类 人类 人类 人类 人类医学 医学 医学 医学 医学分子分类的分子分类.后皮皮质脂肪肉瘤 (retroperitoneal liposarcoma) 是一种癌症.

更多相关视频

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.9K
On-Site Sampling and Extraction of Brain Tumors for Metabolomics and Lipidomics Analysis
06:48

On-Site Sampling and Extraction of Brain Tumors for Metabolomics and Lipidomics Analysis

Published on: May 31, 2020

6.0K

相关实验视频

Last Updated: Sep 20, 2025

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
07:13

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

202
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.9K
On-Site Sampling and Extraction of Brain Tumors for Metabolomics and Lipidomics Analysis
06:48

On-Site Sampling and Extraction of Brain Tumors for Metabolomics and Lipidomics Analysis

Published on: May 31, 2020

6.0K

科学领域:

  • 在瘤学瘤学.
  • 分子生物学分子生物学
  • 基因组学就是基因组学.

背景情况:

  • 复原皮质脂肪肉瘤 (RPLS) 是一种罕见且具有攻击性的恶性瘤,分子异质性不明.
  • 对于监测RPLS进展和临床结果的生物标志物有限.

研究的目的:

  • 阐明RPLS的分子格局,并确定不同的患者亚型.
  • 开发一个临床相关和成本效益的RPLS分类系统.

主要方法:

  • 在88名RPLS患者身上进行了RNA测序,以确定失调的基因和途径.
  • 使用无监督集群和非负矩阵因子化来定义RPLS亚型.
  • 基于LEP和PTTG1生物标志物的分类模型在241名患者中使用免疫组织化学验证.

主要成果:

  • 确定了两种不同的RPLS亚型,其特点是独特的分子特征,瘤微环境和临床结果 (总体存活率和无病存活率).
  • 使用LEP和PTTG1生物标志物的简化分类实现了高精度 (AUC>0.99).
  • 被归类为LEP+和PTTG1-的患者表现出较少的攻击性特征和改善的OS (HR=0.41) 和DFS (HR=0.60).

结论:

  • 这项研究呈现了迄今为止RPLS最大的基因表达格局.
  • 建立了基于免疫组织化学的RPLS分子分类,证明了临床相关性和成本效益.
  • 开发的分类可以帮助指导RPLS患者的治疗决策.