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.

Is the Prophylactic Use of Hepatoprotectants Necessary in Anti-Tuberculosis Treatment?

BACKGROUND: Liver injury is one of the serious side effects of anti-tuberculosis (TB) drugs. It is controversial whether hepatoprotectant prophylaxis is efficient and safe in anti-TB treatment, so we aimed to assess the efficacy and safety of hepatoprotectant prophylaxis in patients who had received anti-TB treatment. METHODS: PubMed, the Cochrane library, Embase, Ovid, Springer link, Wiley, Elsevier, Web of Science, and the Karger Online Journal were systematically searched prior to April 2016 for articles related to hepatoprotectant prophylaxis in the treatment of TB. A meta-analysis was conducted to estimate the effect of hepatoprotective agents on liver function and adverse events (AEs) in patients who had received anti-TB drugs. The primary outcomes were changes in alanine transaminase (ALT) and aspartate transaminase (AST) levels. The other outcomes were drug-induced liver injury (DILI) and AEs. RESULTS: In our review, 6 trials that involved 1,227 patients were included. Our analysis indicated that hepatoprotective agents exerted protective effects on liver function in patients who had received anti-TB drugs (weighted mean difference, WMD = -7.81, 95% CI [-12.26, -3.37], p = 0.0006 [ALT]; WMD = -7.07, 95% CI [-11.43, -2.72], p = 0.001 [AST]) in any age group. However, in the subgroup analysis of treatment duration, the use of hepatoprotective agents was not associated with significant changes in ALT and AST levels after 2 weeks of treatment and exhibited a positive effect on liver function after 4 weeks of treatment. Moreover, the use of hepatoprotectants significantly decreased the number of DILI cases (risk ratio, RR 0.50, 95% CI [0.34-0.73], p = 0.0004). However, the use of hepatoprotectants led to similar AEs in the control groups (RR 1.07, 95% CI [0.82-1.39], p = 0.62). CONCLUSIONS: The use of hepatoprotective drugs may prevent liver injury in patients who are receiving anti-TB drugs without any significant AEs 4 weeks after the initiation of hepatoprotective medication.

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.

Detection of lung adenocarcinoma using magnetic beads based matrix-assisted laser desorption/ionization time-of-flight mass spectrometry serum protein profiling.

BACKGROUND: Recently, due to the rapid development of proteomic techniques, great advance has been made in many scientific fields. We aimed to use magnetic beads (liquid chip) based matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS) technology to screen distinctive biomarkers for lung adenocarcinoma (adCA), and to establish the diagnostic protein profiles. METHODS: Using weak cation exchange magnetic beads (MB-WCX) to isolate and purify low molecular weight proteins from sera of 35 lung adCA, 46 benign lung diseases (BLDs) and 44 healthy individuals. The resulting spectra gained by anchor chip-MALDI-TOF-MS were analyzed by ClinProTools and a pattern recognition genetic algorithm (GA). RESULTS: In the working mass range of 800 - 10 000 Da, 99 distinctive peaks were resolved in lung adCA versus BLDs, while 101 peaks were resolved in lung adCA versus healthy persons. The profile gained by GA that could distinguish adCA from BLDs was comprised of 4053.88, 4209.57 and 3883.33 Da with sensitivity of 80%, specificity of 93%, while that could separate adCA from healthy control was comprised of 2951.83 Da and 4209.73 Da with sensitivity of 94%, specificity of 95%. The sensitivity provided by carcinoembryonic antigen (CEA) in this experiment was significantly lower than our discriminatory profiles (P < 0.005). We further identified a eukaryotic peptide chain release factor GTP-binding subunit (eRF3b) (4209 Da) and a complement C3f (1865 Da) that may serve as candidate biomarkers for lung adCA. CONCLUSION: Magnetic beads based MALDI-TOF-MS technology can rapidly and effectively screen distinctive proteins/polypeptides from sera of lung adCA patients and controls, which has potential value for establishing a new diagnostic method for lung adCA.

[Application of laser capture microdissection and surface-enhanced laser desorption ionization time-of-flight mass spectrometry to screen biomarkers for early diagnosis of lung adenocarcinoma].

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.

[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.

[Use of laser capture microdissection and surface-enhanced laser desorption ionization time-of-flight mass spectrometry to screen differential proteins in lung adenocarcinoma and lung squamous carcinoma].

OBJECTIVE: To screen biomarkers for classification in lung adenocarcinoma and lung squamous carcinoma by using laser capture microdissection (LCM) and surface-enhanced laser desorption ionization time-of-flight mass spectrometry (SELDI-TOF-MS) and support vector machine (SVM). METHODS: Six specimens of lung adenocarcinoma tissues and seven specimens of lung squamous carcinoma tissues obtained during operation were made into frozen sections and stained by improved H-E solution. About 1.2 x 10(5) of homogeneous adenocarcinoma cells and 1.4 x 10(5) of homogeneous lung squamous carcinoma cells were collected using LCM. Then SELDI profiles based on PBS II(+)SELDI-TOF-MS (IMAC protein chip) and the data were analyzed by support vector machine (SVM). RESULTS: Eighty seven differential protein peaks were found and top ten of them were identified as candidate biomarkers. The expression levels of 6 proteins among them with the molecular weights of 3333, 3592, 3848, 5036, 5191, and 5211 respectively in the lung squamous cancer tissues were weaker than those in the adenocarcinoma tissues, and the expression levels of 4 proteins with the molecular weights of 2505, 4004, 4847, and 11 412 in the lung squamous carcinoma tissues were stronger than those in the adenocarcinoma tissues. The expression of the protein with the molecular weight of 4847 in the squamous cancer was significantly stronger than that in the adenocarcinoma (p + 0.032). A discriminatory pattern consisting of 3 proteins with the molecular weights of 4847, 11 412, and 3592 was established with a sensitivity of 100% and a specificity of 100% respectively in separating adenocarcinoma from squamous carcinoma. CONCLUSION: There is a difference in protein component between adenocarcinoma and squamous carcinoma. LCM combined with SELDI-TOF-MS help screen a biomarker pattern to distinguish lung adenocarcinoma from lung squamous carcinoma with high sensitivity and specificity.