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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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Updated: Sep 11, 2025

Microbiota Analysis Using Two-step PCR and Next-generation 16S rRNA Gene Sequencing
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Challenges and Opportunities in Analyzing Cancer-Associated Microbiomes.

Minghao Chia1,2, Mihai Pop3, Steven L Salzberg4,5,6,7

  • 1Genome Institute of Singapore (GIS), Agency for Science, Technology and Research (A*STAR), Genome, Singapore, Republic of Singapore.

Cancer Research
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Summary
This summary is machine-generated.

Cancer-associated microbiome research uses advanced sequencing to find biomarkers and treatments. This review covers computational challenges and opportunities in analyzing these microbiomes for better cancer care.

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Quantitative Mass Spectrometric Profiling of Cancer-cell Proteomes Derived From Liquid and Solid Tumors
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Area of Science:

  • Microbiology
  • Bioinformatics
  • Oncology

Background:

  • Cancer-associated microbiome research is rapidly advancing due to high-throughput sequencing.
  • Microbiomes offer potential for non-invasive cancer biomarkers and novel therapeutic strategies.

Purpose of the Study:

  • To review computational challenges and opportunities in analyzing cancer-associated microbiomes.
  • To discuss sequencing-driven strategies for taxonomic and functional characterization.
  • To highlight considerations for database selection and host-microbiome interaction inference.

Main Methods:

  • Review of current computational tools and analysis strategies for microbiome data.
  • Discussion of strengths and limitations in identifying contamination and bias.
  • Exploration of statistical, metabolic, and network inference techniques.

Main Results:

  • Current tools have limitations in resolving species/strains and identifying contamination.
  • Database selection is critical for accurate metagenomic analysis.
  • Spatial and single-cell technologies, along with AI, are enhancing microbiome insights.

Conclusions:

  • Addressing computational challenges is key to advancing cancer microbiome research.
  • Robust analysis methods are needed to manage large datasets and generate hypotheses.
  • Future directions include integrating multi-omics data and AI for deeper understanding.