| Summary: | 碩士 === 長庚大學 === 生物醫學研究所 === 101 === Non-small cell lung cancer (NSCLC) is the most common type of lung cancer, which is the prominent cause of the death in Taiwan. Because of organ proximal and smaller dilution effects, pleural effusion (PE) would be a good source for biomarker discovery in NSCLC. Herein, we used one-dimensional gel electrophoresis combined with nano liquid-chromatography tandem mass spectrometry (GeLC-MS/MS) to generate the most comprehensive PE proteome databases from six types of PEs, including malignant PE (MPE from lung , breast, and gastric cancers), lung para-malignant (PMPE) and benign diseases (tuberculosis and pneumonia). The spectral counting method was used for comparative proteome analysis. In order to distinguish malignancy from benign diseases, we selected 36 candidate proteins with higher spectral counts (2-fold changes) in lung MPE than those in benign PEs. We then set 4 criteria, including (I) more than two times spectral counts in lung MPE than that in lung PMPE, (II) compare with ONCOMONE lung adenocarcinoma datasets, (III) functional classification and literature search for novelty in lung cancer, and (IV) the availability of commercialized ELISA kits to narrow down the promising candidates. Preliminarily, we selected three potential biomarkers for further verifications by ELISA and Western blot analyses. We find the trend of concentration ratio in ELISA is consistent with the MS ratio. Then we use ROC curve to present our data, and the result best AUC of single marker in PE-001 is 0.843, and the AUC in PE-002 is 0.782, and the AUC in
viii
PE-003 is 0.750. But the AUC is higher than each marker alone when the PE-001 and PE-002 markers are combined. The overall survival analysis revealed that PE-001 would be a potential prognosis marker for NSCLC. We also determined the sera levels of three potential biomarkers in healthy control and lung cancer patients, however, the protein levels in sera were not correlated to those in PEs. In this study, we confirmed that these label-free quantitative proteome can produce potential biomarkers, providing a useful datasets to search NSCLC biomarker in future.
|
|---|
