Literature DB >> 8616111

Molecular genetic tumor markers in the early diagnosis and screening of non-small-cell lung cancer.

D R Jacobson1, C L Fishman, N E Mills.   

Abstract

BACKGROUND: Little progress has been made in decreasing lung cancer mortality by applying conventional methods to early diagnosis and screening. Recent advances in molecular oncology, however, have provided tools which may be of use in this area. Many genes involved in controlling cell growth and differentiation are abnormal in lung cancer cells. Such genes include K-ras, p53, rb, myc, her2/neu, and probably one or more tumor suppressor genes on chromosome 3p. The involvement of these genes in lung cancer is reviewed. The K-ras oncogene contains a mutation in codon 12 in many cases of non-small-cell lung cancer, particularly adenocarcinoma, and is thus a potentially useful lung cancer tumor marker. DESIGN; We have developed a highly sensitive, simple assay for ras mutations, and applied it to bronchoalveolar lavage fluid obtained from patients undergoing evaluation for suspected lung cancer.
RESULTS: In many cases, the ras assay was more sensitive than routine cytology and histopathology, demonstrating that this is a potentially clinically useful assay.
CONCLUSION: Molecular genetic tumor markers, including mutations in ras and other genes, and/or immunohistochemical tumor markers, may provide tools which can be applied to bronchoalveolar lavage fluid or sputum, for use in diagnostic tests and in screening programs. The use of such markers may lead to decreased lung cancer mortality.

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Year:  1995        PMID: 8616111     DOI: 10.1093/annonc/6.suppl_3.s3

Source DB:  PubMed          Journal:  Ann Oncol        ISSN: 0923-7534            Impact factor:   32.976


  2 in total

1.  Polymorphisms in miRNA binding site: new insight into small cell lung cancer susceptibility.

Authors:  Hong-yu Liu; Jun Chen
Journal:  Acta Pharmacol Sin       Date:  2011-08-29       Impact factor: 6.150

2.  An in silico analytical study of lung cancer and smokers datasets from gene expression omnibus (GEO) for prediction of differentially expressed genes.

Authors:  Atif Noorul Hasan; Mohammad Wakil Ahmad; Inamul Hasan Madar; B Leena Grace; Tarique Noorul Hasan
Journal:  Bioinformation       Date:  2015-05-28
  2 in total

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