Comparative proteome analysis of human adenocarcinoma

Abstract

This study was designed to use comparative proteomics technology to find the differentially expressed proteins between human lung adenocarcinoma and paired normal tumor-adjacent lung tissues. The total proteins of 20 human lung adenocarcinoma tissues and paired normal tumor-adjacent lung tissues were separated by means of immobilized pH gradient-based two-dimensional gel electrophoresis (2-DE) and Coomassie Blue staining. The differentially expressed proteins were analyzed with image analysis software and then identified by matrix-assisted laser desorption/ionization time of flight mass spectrometry (MALDI-TOF-MS) and quadrupole time of flight mass spectrometry (Q-TOF-MS/MS). (1) Well-resolved, highly reproducible 2-DE patterns of human lung adenocarcinoma and paired normal tumor adjacent lung tissues were obtained. (2) PDQUEST 2D image analysis software was used to analysis 20 cases of lung adenocarcinoma and paired normal lung tissues, 1006 +/- 54 spots were matched among tumor tissues and normal lung tissues. Twenty-eight differentially expressed spots were screened. (3) Twenty non-redundant differentially expressed proteins were identified by mass spectrometry, thirteen proteins were up-regulated, and seven proteins were down-regulated, some proteins were involved in the regulation of cell signal transduction or metabolized enzyme of cell. (4) To validate the results screened by proteome research, immunohistochemistry was used to validate several lung adenocarcinoma differentially expressed proteins including 14-3-3 sigma, annexin 1, and manganese superoxide dismutase. The results showed that these three proteins were really differentially expressed between lung adenocarcinoma and paired normal lung tissues. These results will provide scientific foundation and new clue for the research of human lung adenocarcinoma.

Quantitative Proteomics Reveals Regulation of Karyopherin Subunit Alpha-2 (KPNA2) and Its Potential Novel Cargo Proteins in Nonsmall Cell Lung Cancer

“…The process listed in the second significant network was most consistent with our previous finding that knockdown of KPNA2 reduces proliferation and migration in lung cancer cells (23). Notably, 13 proteins in the second significant network (CSTA, CDC45L, FN1, FOSL1, JUP, PKP3, PLAU, RBL1, SEPT9, SFN, S100A7, and UHRF1) are reported to be dysregulated, whereas the other 13 proteins (ALDH1A3, ARPP19, CBS, CLNS1A, DDX11, FLG, GALNT7, HMGCS1, KPNA4, PELP1, PNPLA6, RRM2, and TLE3) are not dysregulated in lung cancer (Table II) (37)(38)(39)(40)(41)(42)(43)(44)(45)(46)(47)(48)(49)(50)(51)(52). Therefore, the quantitative whole proteome data set may facilitate our B, CL1-5 cells were cultured in SILAC-labeling media and transfected with control and KPNA2 siRNA, respectively.…”

Preliminary exploration on the serum biomarkers of bloodstream infection with carbapenem‐resistant Klebsiella pneumoniae based on mass spectrometry

“…Matrix assisted laser desorption ionization time‐of‐flight mass spectrometry (MALDI‐TOF MS) is often applied to identify new disease biomarkers. This approach has proved successful for identifying many new diagnostic markers, for example, in cancers of the breast, lung, and liver along with autoimmune diseases and other diseases 23–25 . However, to our best knowledge, it has not been used previously to identify specific biomarkers associated with drug‐resistant bacterial infections.…”

“…This approach has proved successful for identifying many new diagnostic markers, for example, in cancers of the breast, lung, and liver along with autoimmune diseases and other diseases. [23][24][25] However, to our best knowledge, it has not been used previously to identify specific biomarkers associated with drug-resistant bacterial infections. Considering the complex backgrounds of clinical BSI patients, we first employed a mouse BSI model to avoid confounding factors that could affect the biomarker discovery process.…”