Molecular footprints of human lung cancer progression

Cancer Sci. 2004 Mar;95(3):197-204. doi: 10.1111/j.1349-7006.2004.tb02203.x.

Abstract

Lung cancer is the leading cause of cancer-related death in the world. To understand the molecular processes and pathways of, and contributing factors to lung cancer progression, genetic alterations in various progression stages of lung cancer cells have been studied, since these alterations can be regarded as molecular footprints representing the individual processes of multistage lung carcinogenesis. The results indicate that defects in both the p53 and RB/p16 pathways are essential for the malignant transformation of lung epithelial cells. Several other genes, such as K-ras, PTEN and MYO18B, are genetically altered less frequently than p53 and RB/p16 in lung cancer cells, suggesting that alterations in these genes are associated with further malignant progression or unique phenotypes in a subset of lung cancer cells. However, it is still unclear what genes control the metastatic potential of lung cancer cells. Further analyses of molecular footprints in lung cancer cells, in particular in the cells of metastatic sites, will give us valuable information to fully understand the process of lung cancer progression, and to find novel ways of controlling it. Molecular footprints at the sites of p53 mutations and p16 deletions further indicate that DNA repair activities for G:C to T:A transversion and non-homologous end-joining of DNA double-strand breaks play important roles in the accumulation of genetic alterations in lung cancer cells. Thus, identification of environmental as well as genetic factors inducing or suppressing the occurrence of such alterations would be a clue to find novel ways of lung cancer prevention.

Publication types

  • Research Support, Non-U.S. Gov't
  • Review

MeSH terms

  • Disease Progression*
  • Gene Expression Regulation, Neoplastic
  • Genes, Tumor Suppressor
  • Lung Neoplasms / classification
  • Lung Neoplasms / genetics*
  • Lung Neoplasms / pathology
  • Models, Genetic