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相关概念视频

The Tumor Microenvironment02:17

The Tumor Microenvironment

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Every normal cell or tissue is embedded in a complex local environment called stroma, consisting of different cell types, a basal membrane, and blood vessels. As normal cells mutate and develop into cancer cells, their local environment also changes to allow cancer progression. The tumor microenvironment (TME) consists of a complex cellular matrix of stromal cells and the developing tumor. The cross-talk between cancer cells and surrounding stromal cells is critical to disrupt normal tissue...
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相关实验视频

Updated: Jun 14, 2025

A Clinical Metaproteomics Workflow Implemented within Galaxy Bioinformatics Platform to Analyze Host-Microbiome Interactions Underlying Human Disease
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使用PRISM重新检查癌症微生物组.

Bassel C Ghaddar1, Martin J Blaser2, Subhajyoti De1

  • 1Center for Systems and Computational Biology, Rutgers Cancer Institute, Rutgers University; 195 Albany St., New Brunswick, New Jersey 08901.

bioRxiv : the preprint server for biology
|February 3, 2025
PubMed
概括

一个新的计算工具PRISM精确地识别了人类基因组数据中的微生物,揭示了与瘤相关的细菌与胰腺癌进展和复发风险有关.

科学领域:

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

背景情况:

  • 癌症微生物组的作用受到争论,需要对人类基因组学进行先进的微生物分析.
  • 在低生物质样本中精确识别微生物对于癌症研究至关重要.

研究的目的:

  • 开发和验证PRISM,一种用于精确识别和清除低生物质测序数据中的微生物的计算方法.
  • 通过使用CPTAC和TCGA数据集,应用PRISM来分析各种癌症类型的瘤相关微生物.

主要方法:

  • 微生物识别和消毒的PRISM计算方法.
  • 在大型精选数据集 (62,006个种群) 上对PRISM进行基准测试.
  • 从CPTAC和TCGA数据集中分析了8种癌症类型.

主要成果:

  • 在识别真假阳性和真假阳性分类系统方面,PRISM表现出色.
  • 在胃肠道瘤中检测到丰富的微生物群 (CPTAC).
  • 在胰腺瘤中发现的细菌与改变的葡萄糖蛋白质组,吸烟史和复发风险相关.
  • 在其他癌症类型和TCGA数据中微生物信号稀缺,可能是由于测序参数.

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相关实验视频

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Predictive Immune Modeling of Solid Tumors
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结论:

  • PRISM提高了对人类基因组数据微生物分析的信心.
  • 已确定与瘤相关的微生物具有潜在的分子和临床意义.
  • 该研究强调了PRISM在揭示微生物在癌症,特别是胰腺癌中的作用方面的实用性.