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http://dx.doi.org/10.7314/APJCP.2013.14.3.2037

Importance of Serum SELDI-TOF-MS Analysis in the Diagnosis of Early Lung Cancer  

Simsek, Cebrail (Chest Disease Department, Ataturk Chest Disease and Surgery Research and Education Hospital)
Sonmez, Ozlem (Chest Disease Department, Ataturk Chest Disease and Surgery Research and Education Hospital)
Yurdakul, Ahmet Selim (Chest Disease Department, Faculty of Medicine, Gazi University)
Ozmen, Fusun (Medical Oncology Department, Numune Education and Researh Hospital)
Zengin, Nurullah (Medical Oncology Department, Dr Abdurrahman Yurtaslan Education and Research Hospital)
Keyf, Atilla Isan (Chest Disease Department, Ataturk Chest Disease and Surgery Research and Education Hospital)
Kubilay, Dilek (Biochemistry Department, Dr Abdurrahman Yurtaslan Education and Research Hospital)
GUlbahar, Ozlem (Biochemistry Department, Faculty of Medicine, Gazi University)
Karatayli, Senem Ceren (Instıtute of Hepatology, Faculty of Medicine, Ankara University)
Bozdayi, Mithat (Instıtute of Hepatology, Faculty of Medicine, Ankara University)
Ozturk, Can (Chest Disease Department, Faculty of Medicine, Gazi University)
Publication Information
Asian Pacific Journal of Cancer Prevention / v.14, no.3, 2013 , pp. 2037-2042 More about this Journal
Abstract
Background: Different methods of diagnosis have been found to be inefficient in terms of screening and early diagnosis of lung cancer. Cancer cells produce proteins whose serum levels may be elevated during the early stages of cancer development. Therefore, those proteins may be recognized as potential cancer markers. The aim of this study was to differentiate healthy individuals and lung cancer cases by analyzing their serum protein profiles and evaluate the efficacy of this method in the early diagnosis of lung cancer. Materials and Methods: 170 patients with lung cancer, 53 under high risk of lung cancer, and 47 healthy people were included in our study. Proteomic analysis of the samples was performed with the SELDI-TOF-MS approach. Results: The most discriminatory peak of the high risk group was 8141. When tree classification analysis was performed between lung cancer and the healthy control group, 11547 was determined as the most discriminatory peak, with a sensitivity of 85.5%, a specificity of 89.4%, a positive predictive value (PPV) of 96.7% and a negative predictive value (NPV) of 62.7%. Conclusions: We determined three different protein peaks 11480, 11547 and 11679 were only present in the lung cancer group. The 8141 peak was found in the high-risk group, but not in the lung cancer and control groups. These peaks may prove to be markers of lung cancer which suggests that they may be used in the early diagnosis of lung cancer.
Keywords
Lung cancer; biomarkers; proteomic; SELDI; diagnostic tools; bioinformatics;
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