STEAP1 facilitates metastasis and epithelial-mesenchymal transition of lung adenocarcinoma via the JAK2/STAT3 signaling pathway.

Six-transmembrane epithelial antigen of prostate-1 (STEAP1) is a relatively newly identified gene target from prostate cancer, breast cancer, and gastric cancer. However, functions of STEAP1 in lung adenocarcinoma (LUAD) are still unknown. In the present study, we explored the molecular and cellular mechanisms of STEAP1 in LUAD. Western blot and Q-PCR were conducted to detect the protein and mRNA expressions respectively. The cell proliferation was tested by CCK8 assay. The effects of STEAP1 on the metastasis and epithelial-mesenchymal transition (EMT) of LUAD were evaluated by EdU assay, wound healing assay, and transwell migratory assay. H1650, H358, HCC827, H1299, H23, A549, H1693 were selected as human LUAD cell lines in the study. Results have shown that STEAP1 expression was up-regulated in LUAD cells compared with normal lung epithelial cells. Knockdowning of STEAP1 suppressed the proliferation, migration, and invasion of LUAD epithelial cells. Importantly, after comparing the proliferation, migration, and invasion of LUAD to the corresponding control groups treated in STAT3 inhibitor ADZ1480, we found that STEAP1 regulates EMT via Janus kinase 2/signal transducer and activator of transcription 3 (JAK2/STAT3) signaling pathway. In conclusion, STEAP1 can serve as a therapeutic target, and it may have important clinical implications for LUAD treatment.

Biomarker discovery and identification from non-small cell lung cancer sera.

Currently, serum biomarkers might usually be thought not to be used for early detection of lung cancer by some researchers. In this study, we used a highly optimized ClinProt-matrix-assisted laser desorption/ionization time-of flight mass spectrometer (MALDI-TOF-MS) to screen non-small cell lung carcinoma (NSCLC) markers in serum. A training set of spectra derived from 45 NSCLC patients, 24 patients with benign lung diseases (BLDs) and 21 healthy individuals, was used to develop a proteomic pattern that discriminated cancer from non-cancer effectively. A test set, including 74 cases (29 NSCLC patients and 45 controls), was used to validate this pattern. After cross-validation, the classifier showed sensitivity and specificity, 86.20% and 80.00%, respectively. Remarkably, 100% of early stage serum samples could be correctly classified as lung cancer. Furthermore, the differential peptides of 1865Da and 4209Da were identified as element of component 3 and eukaryotic peptide chain release factor GTP-binding subunit ERF, respectively. The patterns we described and peptides we identified may have clinical utility as surrogate markers for detection and classification of NSCLC.

Study of differential proteins in lung adenocarcinoma using laser capture microdissection combined with liquid chip-mass spectrometry technology.

BACKGROUND: In recent years the proportion of lung adenocarcinoma (adCA) which occurs in lung cancer patients has increased. Using laser capture microdissection (LCM) combined with liquid chip-mass spectrometry technology, we aimed to screen lung cancer biomarkers by studying the proteins in the tissues of adCA. METHODS: We used LCM and magnetic bead based weak cation exchange (MB-WCX) to separate and purify the homogeneous adCA cells and normal cells from six cases of fresh adCA and matched normal lung tissues. The proteins were analyzed and identified by matrix assisted laser desorption/ionization time-of-fight mass spectrometry (MALDI-OF-MS). We screened for the best pattern using a radial basic function neural network algorithm. RESULTS: About 2.895 × 10(6) and 1.584 × 10(6) cells were satisfactorily obtained by LCM from six cases of fresh lung adCA and matched normal lung tissues, respectively. The homogeneities of cell population were estimated to be over 95% as determined by microscopic visualization. Comparing the differentially expressed proteins between the lung adCA and the matched normal lung group, 221 and 239 protein peaks, respectively, were found in the mass-to-charge ration (M/Z) between 800 Da and 10 000 Da. According to t test, the expression of two protein peaks at 7521.5 M/Z and 5079.3 M/Z had the largest difference between tissues. They were more weakly expressed in the lung adCA compared to the matched normal group. The two protein peaks could accurately separate the lung adCA from the matched normal lung group by the sample distribution chart. A discriminatory pattern which can separate the lung adCA from the matched normal lung tissue consisting of three proteins at 3358.1 M/Z, 5079.3 M/Z and 7521.5 M/Z was established by a radial basic function neural network algorithm with a sensitivity of 100% and a specificity of 100%. CONCLUSIONS: Differential proteins in lung adCA were screened using LCM combined with liquid chip-mass spectrometry technology, and a biomarker model was established. It is possible that this technology is going to become a powerful tool in screening and early diagnosis of lung adCA.

Serum peptidome profiling in patients with lung cancer.

Serum peptide profiling is a promising approach for classification of cancer versus noncancer samples. In this study, we aimed to search for discriminating peptide patterns in serum samples between lung cancer patients and healthy controls. The magnetic beads-based weak cation-exchange chromatography followed by matrix-assisted laser desorption and ionization time-of-flight mass spectrometry (MALDI-TOF MS) was used in this study to identify patients with lung cancer. In total, serum samples from 64 lung cancer patients (32 for training set and 32 for testing set), 64 healthy controls (32 for training set and 32 for testing set), and 10 COPD patients (for disease control) were analyzed in this study. The mass spectra data analyzed with ClinProTools software was used to distinguish between cancer patients and healthy individuals based on three different algorithm models (GA, SNN, and QC). In the training set, patients with lung cancer could be identified with the mean sensitivity of 98.9% and specificity of100%. Similar results could be obtained from testing set, showing 87% sensitivity and 84.8% specificity. Screening for serum peptide patterns using MALDI-TOF MS showed high sensitivity and specificity in identifying patients with lung cancer.

[Application of liquid chip-mass spectrometry technology for screening serum biomarker proteins in lung squamous cell carcinoma].

OBJECTIVE: To screen the serum biomarker proteins of lung squamous cell carcinoma (SCCs) by liquid chip-mass spectrometry technology. METHODS: All serum samples, including 34 SCCs, 46 benign lung diseases (BLDs) and 44 healthy individuals, were analyzed by CLINPROT system in order to study the serum protein expression profiles. Then the discriminatory proteins were detected by FlexAnalysis 3.0 software. Biomarkers were identified by liquid chromatography-tandem mass spectrometry (LCMS/MS). RESULTS: Comparing the differential serum expression proteins between SCCs and healthy individuals, and SCCs and BLDs respectively. Ninety-six differential protein peaks [mass-to-charge ration (M/Z) between 800 and 10 000] were found between SCCs and healthy individuals. In these protein peaks, the expression of protein peaks at 4054.13 M/Z and 4267.46 M/Z had the largest difference between them. The two protein peaks could accurately separate SCCs from healthy individuals by the frame of axes. Similarly, 99 differential protein peaks were automatically detected between SCCs and BLDs. In these protein peaks, the expression of protein peaks at 5065.27 M/Z and 4054.02 M/Z had the largest difference between them. The two protein peaks could accurately separate SCCs from BLDs by the frame of axes. Identified by LC-MS/MS, 1778 M/Z and 1865 M/Z might be assayed jointly and corresponded to complements C3 fragment or C3f precursor. CONCLUSIONS: Differential protein expressions existed between SCCs versus healthy individuals and SCCs versus BLD patients. It is feasible to screen the diagnostic serum biomarkers of SCC with a high sensitivity and specificity by using CLINPROT system.

[The value of (18)F-FDG PET/CT in differential diagnosis of benign and malignant pulmonary lesions: a Meta-analysis.].

OBJECTIVE: Using Meta analysis to evaluate the value of (18)F-FDG PET/CT ((18)fluorine-fluorodeoxyglucose Positron emission tomography/computed tomography) in differentiating between benign and malignant pulmonary lesions. METHODS: Relevant documentations from PubMed and other 5 databases from 1980 to 2008 were searched, and the eligible literatures according to the inclusive criteria were selected. The statistical information and quality of science were assessed and classified. The data were analyzed using Meta-Disc1.4 software. The diagnostic value of PET/CT in distinguishing benign from malignant pulmonary lesions was evaluated by the pooled sensitivity, specificity, the likelihood ratio (LR) and summary receiver operating characteristic curve (SROC curve) statistical indicators. RESULTS: Seven literatures were collected including 5 in English and 2 in Chinese, and 795 cases were included in the study. Heterogeneity test showed that the homogeneity of the study was good. By using deterministic models to analyze the data, the value of the weighted sensitivity was 95% (93% - 97%), the specificity was 77% (71% - 82%), the positive likelihood ratio was 4.12, negative likelihood ratio was 0.08, and the SROC area under the curve (area under curve, AUC) was 94%. CONCLUSION: PET/CT is of high diagnostic value in differentiation between benign and malignant lung lesions, but large sized, multicenter, prospective studies are needed to assess its clinical value more accurately.