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Related Concept Videos

Tumor Progression02:07

Tumor Progression

Tumor progression is a phenomenon where the pre-formed tumor acquires successive mutations to become clinically more aggressive and malignant. In the 1950s, Foulds first described the stepwise progression of cancer cells through successive stages.
Colon cancer is one of the best-documented examples of tumor progression. Early mutation in the APC gene in colon cells causes a small growth on the colon wall called a polyp. With time, this polyp grows into a benign, pre-cancerous tumor. Further...
Tumor Progression02:07

Tumor Progression

Tumor progression is a phenomenon where the pre-formed tumor acquires successive mutations to become clinically more aggressive and malignant. In the 1950s, Foulds first described the stepwise progression of cancer cells through successive stages.
Colon cancer is one of the best-documented examples of tumor progression. Early mutation in the APC gene in colon cells causes a small growth on the colon wall called a polyp. With time, this polyp grows into a benign, pre-cancerous tumor. Further...
mTOR Signaling and Cancer Progression03:03

mTOR Signaling and Cancer Progression

The mammalian target of rapamycin or mTOR protein was discovered in 1994 due to its direct interaction with rapamycin. The protein gets its name from a yeast homolog called TOR. The mTOR protein complex in mammalian cells plays a major role in balancing anabolic processes such as the synthesis of proteins, lipids, and nucleotides and catabolic processes, such as autophagy in response to environmental cues, such as availability of nutrients and growth factors.
The mTOR pathway or the...
mTOR Signaling and Cancer Progression03:03

mTOR Signaling and Cancer Progression

The mammalian target of rapamycin or mTOR protein was discovered in 1994 due to its direct interaction with rapamycin. The protein gets its name from a yeast homolog called TOR. The mTOR protein complex in mammalian cells plays a major role in balancing anabolic processes such as the synthesis of proteins, lipids, and nucleotides and catabolic processes, such as autophagy in response to environmental cues, such as availability of nutrients and growth factors.
The mTOR pathway or the...
Cancer Survival Analysis01:21

Cancer Survival Analysis

Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
Adaptive Mechanisms in Cancer Cells02:53

Adaptive Mechanisms in Cancer Cells

Cancer cells accumulate genetic changes at an abnormally rapid rate due to the defects in the DNA repair mechanisms. From an evolutionary perspective, such genetic instability is advantageous for cancer development. Mutant cell lines accumulate a series of beneficial mutations that contribute to their progression into cancer.
Some of the advantages that cancer cells have on normal cells include - enhanced ability to divide without terminally differentiating, induce new blood vessel formation,...

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Related Experiment Video

Updated: Jun 19, 2026

Dissociation of Human and Mouse Tumor Tissue Samples for Single-cell RNA Sequencing
05:58

Dissociation of Human and Mouse Tumor Tissue Samples for Single-cell RNA Sequencing

Published on: August 16, 2024

Disease progression and solid tumor survival: a transcriptome decoherence model.

Adrian E Platts1, Claudia Lalancette, Benjamin R Emery

  • 1The Center for Molecular Medicine and Genetics, Wayne State University School of Medicine, Detroit, MI 48201, USA.

Molecular and Cellular Probes
|October 20, 2009
PubMed
Summary
This summary is machine-generated.

We introduce Decoherence Gene Pair Models (DGPMs) to analyze gene expression variance. DGPMs reveal how gene network topology changes during cancer progression, becoming sparser in advanced tumors.

Related Experiment Videos

Last Updated: Jun 19, 2026

Dissociation of Human and Mouse Tumor Tissue Samples for Single-cell RNA Sequencing
05:58

Dissociation of Human and Mouse Tumor Tissue Samples for Single-cell RNA Sequencing

Published on: August 16, 2024

Area of Science:

  • Genomics
  • Systems Biology
  • Bioinformatics

Background:

  • Gene expression analysis typically excludes genes with high variance across replicates.
  • These 'unstable' transcripts can exhibit coordinated behavior, especially in disease states.

Purpose of the Study:

  • To develop and validate a novel model for analyzing gene networks using covariant gene pairs.
  • To investigate how gene network topology changes during cancer metastasis and progression.

Main Methods:

  • Developed the Decoherence Gene Pair Models (DGPMs) based on in-vitro data.
  • Tested DGPMs against various solid tumor datasets to assess network topology changes.

Main Results:

  • DGPMs demonstrated changes in network topology that correlate with the metastatic transition.
  • The model revealed a transition from richly connected networks in healthy tissues to sparser networks in advanced tumors.

Conclusions:

  • Covariant gene pairs can effectively model network dynamics during disease progression.
  • DGPMs offer a new approach to understanding cancer metastasis through network topology shifts.