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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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Spontaneous mutations arise infrequently during DNA replication due to errors in the process. A key factor behind these errors is tautomeric shifts in nitrogenous bases, where bases transition from keto to enol forms or amino to imino forms. This shift can alter base-pairing rules, leading to mutations. Additionally, reactive oxygen species (ROS) arising from aerobic metabolism can damage DNA, resulting in depurination (loss of a purine base) or depyrimidination (loss of a pyrimidine base).
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Several factors can increase the risk of cancer in an individual. About 50% of cancer cases can be prevented by adopting a healthy lifestyle, regular exercise, eating healthy, and following a modest cancer prevention diet. Epidemiological studies have consistently shown that populations with vegetable and fruit-rich diets have reduced the incidence of cancer. On the other hand, populations who have a diet rich in animal fat, red meat, junk food, or high calories are predisposed to cancer.
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Single Droplet Digital Polymerase Chain Reaction for Comprehensive and Simultaneous Detection of Mutations in Hotspot Regions
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Predicting DNA mutations during cancer evolution.

Juan Carlos Martinez1, Nelson Lopez-Jimenez2, Tao Meng3

  • 11 School of Computing and Information Sciences, Florida International University, Miami, Florida, USA.

International Journal of Bioinformatics Research and Applications
|November 13, 2015
PubMed
Summary

This study introduces a computational framework to predict genetic mutations in acute myeloid leukemia (AML) patients. The framework accurately forecasts mutation sites, demonstrating its potential for advancing bioinformatics and clinical research in cancer.

Keywords:
AMLDNA mutationsNGSacute myeloid leukaemiabioinformaticscancer evolutiongenome sequencingmutation prediction

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Biological systems are complex information processors, posing challenges for hypothesis testing.
  • Physiological complexities in bio-systems hinder the development and validation of behavioral hypotheses.

Purpose of the Study:

  • To evaluate a computational framework using longitudinal acute myeloid leukemia (AML) patient data.
  • To predict genomic regions likely to mutate at relapse using pre-relapse sequencing data.

Main Methods:

  • Utilized published data from a longitudinal AML study.
  • Processed next-generation sequencing (NGS) data from normal and malignant tissues.
  • Predicted mutation regions before relapse and compared with actual relapse sequencing data.

Main Results:

  • Achieved a high correlation coefficient of 0.9816 ± 0.009 at a 95% confidence interval.
  • Demonstrated strong agreement between the framework's predictions and actual mutation data.
  • Validated the computational framework's predictive accuracy.

Conclusions:

  • The proposed computational framework shows high performance in predicting genomic mutations in AML.
  • This validated framework offers new avenues for bioinformatics research and clinical applications in oncology.
  • The study highlights the potential of computational approaches in understanding complex diseases like AML.