Objective: To improve diagnostic methods to screen biomarkers for early diagnosis in lung adenocarcinoma by employing laser capture microdissection (LCM) and surface-enhanced laser desorption ionization time-of-flight mass spectrometry (SELDI-TOF-MS) and support vector machine (SVM).
Methods: Frozen sections of thickness 8 microm were made using 6 cases of fresh lung cancer tissues and 4 cases of matched normal lung tissues. The sections were stained by improved HE solution. The homogeneous adenocarcinoma cells and normal cells were collected by LCM in each sample, and then SELDI profiles based on PBS II+SELDI-TOF-MS (IMAC protein chip) were analyzed using SVM.
Results: High quality cell samples were obtained by LCM quickly and precisely from normal specimens and diseased tissues without interstitium, inflammation and necrosis. Eighty four differential protein peaks were found. Top ten of them were identified as candidate biomarkers; six proteins were significantly weakly expressed in lung cancer tissue compared to normal tissues, but the other four protein were over-expressed (P < 0.05). Every candidate biomarker has undergone the blind-cross-test. Each of them can separate the lung cancer from normal samples with a sensitivity of 100% and a specificity of 100%. The 3191 m/z was considered as disease marker of lung adenocarcinoma.
Conclusion: The method combined LCM with SELDI-TOF-MS may be able to screen potential biomarkers to distinguish lung cancer from healthy tissue with high sensitivity and specificity, which could improve early diagnosis for lung cancer.
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