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相关概念视频

lncRNA - Long Non-coding RNAs02:39

lncRNA - Long Non-coding RNAs

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In humans, more than 80% of the genome gets transcribed. However, only around 2% of the genome codes for proteins. The remaining part produces non-coding RNAs which includes ribosomal RNAs, transfer RNAs, telomerase RNAs, and regulatory RNAs, among other types. A large number of regulatory non-coding RNAs have been classified into two groups depending upon their length – small non-coding RNAs, such as microRNA, which are less than 200 nucleotides in length, and long non-coding RNA...
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相关实验视频

Updated: Jun 16, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
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使用基于机器学习的基因预测淋巴瘤预后,与乳化相关的基因.

Miao Zhu1, Qin Xiao2, Xinzhen Cai3

  • 1Department of Hematology, Northern Jiangsu People's Hospital Affiliated to Yangzhou University, Yangzhou 225001, China; The Key Laboratory of Syndrome Differentiation and Treatment of Gastric Cancer of the State, Administration of Traditional Chinese Medicine, Yangzhou University, Yangzhou 225001, China; Yangzhou Hematology Laboratory, Yangzhou 225001, China.

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乳化,一种新的修饰,影响淋巴瘤预后和药物反应. 一种新的乳化风险评分模型有助于分层患者,并指导扩散大B细胞淋巴瘤的治疗选择.

关键词:
扩散大的B细胞淋巴瘤.在 HNRNPH1 的情况下,HNRNPH1乳化 乳化 乳化机器学习是机器学习.风险评分 风险评分 风险评分

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科学领域:

  • 在瘤学瘤学.
  • 生物化学 生物化学
  • 分子生物学分子生物学

背景情况:

  • 乳化是一种涉及乳酸的翻译后修饰 (PTM),与固体瘤进展有关.
  • 虽然在淋巴瘤患者中观察到高乳酸水平,但乳酸化作用在很大程度上仍未得到研究.
  • 这项研究解决了关于乳糖化在淋巴瘤中的参与的知识差距.

研究的目的:

  • 为了识别与淋巴瘤相关的乳化相关基因.
  • 评估这些基因在扩散性大B细胞淋巴瘤 (DLBCL) 的预后和预测价值.
  • 根据乳化标志物开发一个风险分层模型.

主要方法:

  • 对DLBCL中乳化相关基因表达的TCGA和GEO数据集的分析.
  • 开发使用COX回归的预后风险评分模型.
  • 通过细胞和小鼠模型对关键基因的功能验证.
  • 在临床淋巴瘤样本中检查乳糖化.

主要成果:

  • 确定了70个与乳化相关的基因,与DLBCL预后有显著的关联.
  • 开发了一种乳化风险评分模型,与患者的治疗结果和免疫透相关.
  • 高风险患者表现出化学抗药性,但对免疫治疗有反应.
  • 鉴定出HNRNPH1是影响预后,细胞亡和细胞周期的关键调节者.

结论:

  • 乳化在DLBCL预后,免疫微环境和治疗反应中发挥着重要作用.
  • 开发的风险评分模型促进了患者分层和个性化治疗策略.
  • HNRNPH1是一个关键的乳化调节剂,影响DLBCL患者的治疗结果.