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A toggle clamp is a mechanical device commonly used for holding and clamping objects in various applications, such as woodworking, metalworking, and assembly operations. Consider a toggle clamp subjected to a force of 200 N at the handle. The vertical clamping force can be calculated, provided the dimensions of the toggle clamp are known.
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Machine Learning Identifies Stemness Features Associated with Oncogenic Dedifferentiation.

Tathiane M Malta1, Artem Sokolov2, Andrew J Gentles3

  • 1Henry Ford Health System, Detroit, MI 48202, USA; University of São Paulo, Ribeirão Preto-SP 14049, Brazil.

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Summary
This summary is machine-generated.

This study introduces novel stemness indices to measure cancer dedifferentiation using machine learning. These indices reveal links between cancer stemness, the tumor microenvironment, and metastasis, offering new therapeutic targets for tumor differentiation.

Keywords:
The Cancer Genome Atlascancer stem cellsdedifferentiationepigenomicgenomicmachine learningpan-cancerstemness

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Area of Science:

  • Oncology
  • Genomics
  • Bioinformatics

Background:

  • Cancer progression is characterized by dedifferentiation and acquisition of stem-cell-like traits.
  • Assessing the degree of oncogenic dedifferentiation is crucial for understanding cancer progression.

Purpose of the Study:

  • To develop novel stemness indices for quantifying oncogenic dedifferentiation.
  • To identify new biological mechanisms and therapeutic targets associated with cancer stemness.

Main Methods:

  • Utilized a one-class logistic regression (OCLR) machine-learning algorithm.
  • Extracted transcriptomic and epigenetic features from stem cells and differentiated cells.
  • Applied stemness indices to analyze tumor microenvironment, metastatic tumors, and single-cell data.

Main Results:

  • Identified novel biological mechanisms underlying the dedifferentiated oncogenic state.
  • Found correlations between cancer stemness, immune checkpoint expression, and immune cell infiltration.
  • Observed that dedifferentiated phenotype is more prominent in metastatic tumors.
  • Revealed intra-tumor molecular heterogeneity using single-cell data analysis.

Conclusions:

  • Novel stemness indices provide a quantitative measure of oncogenic dedifferentiation.
  • Cancer stemness is linked to the tumor immune microenvironment and metastatic potential.
  • The developed indices can identify new therapeutic strategies targeting tumor differentiation.