Open AccessComparative mitochondrial proteomic analysis of human large cell lung cancer cell lines with different metastasis potential
Author(s) -
Liu Zhenkun,
Xu Song,
Li Lu,
Zhong Xiaorong,
Chen Chun,
Fan Yaguang,
Shen Wang,
Zu Lingling,
Xue Feng,
Wang Min,
Zhou Qinghua
Publication year - 2019
Publication title -
thoracic cancer
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.823
H-Index - 28
eISSN - 1759-7714
pISSN - 1759-7706
DOI - 10.1111/1759-7714.13052
Subject(s) - proteome , metastasis , proteomics , lung cancer , cancer research , cancer , mitochondrion , cancer cell , microbiology and biotechnology , biology , western blot , pathology , medicine , bioinformatics , biochemistry , gene , genetics
Background Lung cancer is a highly aggressive cancer with a poor prognosis and is associated with distant metastasis; however, there are no clinically recognized biomarkers for the early diagnosis and prediction of lung cancer metastasis. We sought to identify the differential mitochondrial protein profiles and understand the molecular mechanisms governing lung cancer metastasis. Methods Mitochondrial proteomic analysis was performed to screen and identify the differential mitochondrial protein profiles between human large cell lung cancer cell lines with high (L‐9981) and low (NL‐9980) metastatic potential by two‐dimensional differential gel electrophoresis. Western blot was used to validate the differential mitochondrial proteins from the two cells. Bioinformatic proteome analysis was performed using the Mascot search engine and messenger RNA expression of the 37 genes of the differential mitochondrial proteins were detected by real‐time PCR. Results Two hundred and seventeen mitochondrial proteins were differentially expressed between L‐9981 and NL‐9980 cells ( P < 0.05). Sixty‐four analyzed proteins were identified by matrix‐assisted laser desorption/ionization‐time of flight mass spectrometry coupled with database interrogation. Ontology analysis revealed that these proteins were mainly involved in the regulation of translation, amino acid metabolism, tricarboxylic acid cycle, cancer invasion and metastasis, oxidative phosphorylation, intracellular signaling pathway, cell cycle, and apoptosis. Conclusion Our results suggest that the incorporation of more samples and new datasets will permit the definition of a collection of proteins as potential biomarkers for the prediction and diagnosis of lung cancer metastasis.