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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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MicroRNAs01:22

MicroRNAs

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MicroRNA (miRNA) are short, regulatory RNA transcribed from introns (non-coding regions of a gene) or intergenic regions (stretches of DNA present between genes). Several processing steps are required to form biologically active, mature miRNA. The initial transcript, called primary miRNA (pri-mRNA), base-pairs with itself, forming a stem-loop structure. Within the nucleus, an endonuclease enzyme, called Drosha, shortens the stem-loop structure into hairpin-shaped pre-miRNA. After the pre-miRNA...
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相关实验视频

Updated: Jun 30, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
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Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

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使用可解释的机器学习模型识别与瘤免疫治疗反应相关的microRNAs.

Dong-Yeon Nam1, Je-Keun Rhee2

  • 1Department of Bioinformatics & Life Science, Soongsil University, Seoul, Republic of Korea.

Scientific reports
|March 15, 2024
PubMed
概括

预测免疫治疗反应至关重要. 这项研究表明,microRNAs (miRNAs) 可以预测患者对免疫检查点阻塞疗法的反应,识别关键miRNA生物标志物,以便更好地选择治疗.

科学领域:

  • 在瘤学瘤学.
  • 免疫学 免疫学 免疫学
  • 遗传学 是一个遗传学.

背景情况:

  • 预测患者对瘤免疫疗法的反应,特别是免疫检查点阻塞 (ICB),对于优化治疗疗效和最大限度地减少不良影响至关重要.
  • 目前用于预选可能受益于ICB治疗的患者的方法仍然是临床瘤学的重大挑战.

研究的目的:

  • 研究微RNAs (miRNAs) 作为预测生物标志物的潜力,用于患者对瘤免疫检查点阻塞 (ICB) 治疗的反应.
  • 开发和验证使用miRNA表达特征的机器学习模型,用于预测各种癌症类型的ICB治疗结果.

主要方法:

  • 基于19种癌症类型的miRNA表达数据,构建随机森林模型来预测ICB治疗反应.
  • 应用夏普利添加式扩展 (SHAP) 来解释模型预测并确定单个miRNAs的贡献.
  • 分析被高度重要的miRNA准的途径,以了解它们在免疫反应中的作用.

主要成果:

  • 使用一组有限的重要miRNAs的机器学习模型实现了与使用整个miRNA表达特征的模型相比的预测性能.
  • 在SHAP分析中,确定了特定的miRNAs,这些miRNAs对于预测ICB反应至关重要.
  • 这些关键miRNA所准的基因与与瘤相关的和与免疫相关的生物学途径有显著的关联.

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相关实验视频

Last Updated: Jun 30, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
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Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

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结论:

  • 微RNA表达数据对预测患者对瘤免疫疗法的反应具有重大潜力.
  • 选择的信息性miRNAs可以作为可靠的生物标志物来评估免疫疗法的有效性,从而促进对潜在治疗机制的理解.