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Hidden Markov model for defining genomic changes in lung cancer using gene expression data.

Chiang-Ching Huang1, Jeremy M G Taylor, David G Beer

  • 1Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, Illinois 60611-4402, USA. huangcc@northwestern.edu

Omics : a Journal of Integrative Biology
|October 31, 2006
PubMed
Summary

This study introduces a hidden Markov model (HMM) to map gene expression, identifying chromosomal regions linked to lung cancer development and progression. The HMM accurately pinpoints abnormal expression zones, aiding in understanding carcinogenesis.

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Area of Science:

  • Genomics
  • Cancer Research
  • Bioinformatics

Background:

  • Gene expression patterns correlate with chromosomal position, forming a 'transcriptome map'.
  • Regional gene expression changes, potentially due to chromosomal alterations like amplification or deletion, are crucial in cancer research.
  • Identifying these regions is vital for understanding cancer development.

Purpose of the Study:

  • To develop a hidden Markov model (HMM) for identifying chromosomal regions with correlated gene expression.
  • To apply this HMM to lung cancer data to find regions of abnormal gene expression.
  • To associate these abnormal expression regions with clinical parameters in lung cancer.

Main Methods:

  • Development of a hidden Markov model (HMM) for analyzing gene expression data.

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  • Estimation of model parameters using maximum penalized likelihood.
  • Application of the HMM to a lung cancer microarray dataset (86 human lung adenocarcinomas).

    • Main Results:

    • The HMM identified several chromosomal regions with abnormal gene expression in lung adenocarcinomas.
    • Identified regions align with known recurrent amplification or deletion sites in lung cancer.
    • Abnormal expression regions show associations with tumor stage, differentiation, and survival.

    Conclusions:

    • Genes within identified abnormal expression regions may significantly contribute to lung carcinogenesis.
    • The developed HMM is a valuable tool for precisely locating abnormal expression regions for further research.
    • This approach aids in understanding the genomic underpinnings of lung cancer.