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相关概念视频

Cancer-Critical Genes II: Tumor Suppressor Genes01:05

Cancer-Critical Genes II: Tumor Suppressor Genes

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Genes usually encode proteins necessary for the proper functioning of a healthy cell. Mutations can often cause changes to the gene expression pattern, thereby altering the phenotype.
When the function of certain critical genes, especially those involved in cell cycle regulation and cell growth signaling cascades, gets disrupted, it upsets the cell cycle progression. Such cells with unchecked cell cycles start proliferating uncontrollably and eventually develop into tumors.
Such genes that act...
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Cancers Originate from Somatic Mutations in a Single Cell02:21

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Cancer arises from mutations in the critical genes that allow healthy cells to escape cell cycle regulation and acquire the ability to proliferate indefinitely. Though originating from a single mutation event in one of the originator cells, cancer progresses when the mutant cell lines continue to gain more and more mutations, and finally, become malignant. For example, chronic myelogenous leukemia (CML) develops initially as a non-lethal increase in white blood cells, which progressively...
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Tumor suppressor genes are normal genes that can slow down cell division, repair DNA mistakes, or program the cells for apoptosis in case of irreparable damage. Hence, they play an essential role in preventing the proliferation of damaged cells.
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相关实验视频

Updated: Jun 23, 2025

Comparative Lesions Analysis Through a Targeted Sequencing Approach
08:16

Comparative Lesions Analysis Through a Targeted Sequencing Approach

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SIG:基于图的癌症亚型分层化与基因突变结构信息结构信息.

Chengcheng Zhang, Wei Li, Ming Deng

    IEEE/ACM transactions on computational biology and bioinformatics
    |June 14, 2024
    PubMed
    概括

    这项研究引入了SIG,这是一种用于癌症亚型聚类的新方法,利用基因突变结构信息. 通过揭示突变基因之间的隐藏关联,SIG增强了癌症基因组分析,改善了亚型分层.

    科学领域:

    • 计算生物学是一种计算生物学.
    • 基因组学就是基因组学.
    • 生物信息学是一种生物信息学.

    背景情况:

    • 实体瘤呈现高维,稀疏和小样本大小的数据,挑战使用基因组数据的癌症亚型分层.
    • 现有的聚类方法往往忽略了患者基因矩阵中突变基因之间的关键关联.
    • 基因突变结构信息隐含癌症亚型信号,为改善聚类提供了潜力.

    研究的目的:

    • 引入一种新的方法,图中结构信息 (SIG),用于增强癌症亚型集群.
    • 利用基因突变的结构信息来提高癌症患者分层的准确性.
    • 通过结合基因间突变关联来解决当前方法的局限性.

    主要方法:

    • 通过在个体患者样本中建立突变基因之间的对联关系来构建图.
    • 通过将所有突变基因之间的这些关联结合起来,生成结构信息图.
    • 整合体质瘤基因组数据与丰富的基因网络并应用网络传播用于患者集群.

    主要成果:

    • SIG有效地捕获和利用基因突变结构信息用于癌症亚型集群.
    • 与最先进的 (SOTA) 方法相比,该方法显示出更高的集群性能.
    • 对卵巢和肺腺癌 (LUAD) 数据集的验证实验证实了SIG的有效性.

    更多相关视频

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    Testing Targeted Therapies in Cancer using Structural DNA Alteration Analysis and Patient-Derived Xenografts
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    相关实验视频

    Last Updated: Jun 23, 2025

    Comparative Lesions Analysis Through a Targeted Sequencing Approach
    08:16

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    Visualizing Genetic Variants, Short Targets, and Point Mutations in the Morphological Tissue Context with an RNA In Situ Hybridization Assay
    10:57

    Visualizing Genetic Variants, Short Targets, and Point Mutations in the Morphological Tissue Context with an RNA In Situ Hybridization Assay

    Published on: August 14, 2018

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    Testing Targeted Therapies in Cancer using Structural DNA Alteration Analysis and Patient-Derived Xenografts
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    Testing Targeted Therapies in Cancer using Structural DNA Alteration Analysis and Patient-Derived Xenografts

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    结论:

    • 基因突变结构信息是癌症亚型分层的有价值的,以前未被充分利用的资源.
    • 通过结合基于网络的基因关联,SIG方法在癌症聚类方面取得了重大进展.
    • SIG有可能提高我们对癌症异质性的理解,并指导个性化治疗策略.