Methyl-CpG Binding Domain Protein 2 Inhibits the Malignant Characteristic of Lung Adenocarcinoma through the Epigenetic Modulation of 10 to 11 Translocation 1 and miR-200s.

It has been reported that disorders of epigenetic modulation play a critical role in carcinogenesis. Methyl-CpG binding domain protein 2 (MBD2) is known to act as an epigenetic modulator in various types of tumors; however, the role of MBD2 in lung adenocarcinoma (LUAD) remains unclear. Herein, we demonstrated the down-regulation of MBD2 in LUAD compared with adjacent nontumor tissues. The down-regulation of MBD2 in LUAD was correlated with metastasis and poor survival. In addition, MBD2 inhibited tumor metastasis by maintaining the expression of the miR-200s, which suppressed the invasive properties of tumors. Also, MBD2 positively correlated with 5-hydroxymethylcytosine content in the promoter of miR-200s. The conventional view is that MBD2 acts as a transcriptional suppressor. However, the data revealed that MBD2 may act as a transcriptional activator by recruiting 10 to 11 translocation 1 (TET1) and forming a chromatin-remodeling complex. The MBD2-TET1 complex locates to the TET1 promoter and removes the methyl residues in this region, thereby activating TET1 transcription. TET1 also acted as a tumor suppressor in LUAD. Taken together, the data demonstrate the correlation between MBD2, miR-200s, and TET1, and tumor suppressive effect of MBD2 through up-regulation of TET1 and the miR-200s.

Effective Removal of Tetracycline Antibiotics from Water using Hybrid Carbon Membranes.

Antibiotic residues in drinking water have become a global problem, especially in developing countries. However, effective purification of water contaminated by antibiotics remains a great challenge. Here, we investigated the removing of tetracycline by carbon nanomaterials with different structures and surface functionalities. The result shows that a membrane of thick graphene oxide (GO) and activated carbon (AC) with a thickness of 15 µm can effectively remove 98.9% of tetracycline hydrochloride (TCH) from water by vacuum filtration. Structural analysis indicated that the AC nanoparticles were uniformly inserted into the GO interstitial sites without any aggregations. Also, GO sheets were loosened by the encapsulated AC nanoparticles, leading to the formation of numerous tiny pores (3-10 nm) that acted as channels for fluid passage, whereas the carbons and chemical groups on the GO surface adsorbed TCH. GO/AC membrane exhibits the best adsorption efficiency among the investigated materials, including pure GO, AC, carbon nanotube (CNT), and CNT/AC and GO/CNT hybrids.

Predictive factors for lymph node metastasis in clinical stage IA lung adenocarcinoma.

BACKGROUND: Systematic lymph node dissection or sampling in all patients with clinical stage IA lung adenocarcinoma is currently controversial because the risk of lymph node metastasis is unclear. This study aimed to determine the predictive factors for lymph node metastasis in patients with clinical stage IA lung adenocarcinoma. METHODS: The records of 651 consecutive patients with clinical stage IA lung adenocarcinoma who underwent surgical resection were retrospectively reviewed. The tumors were categorized according to preoperative computed tomography findings as nonsolid (pure ground-glass opacity), part solid, or pure solid. Positron emission tomography with evaluation of the maximum standardized uptake value was performed in 219 patients. Clinicopathologic factors predicting hilar and mediastinal lymph node metastasis were identified by univariate and multivariate analyses. RESULTS: Tumors were classified as nonsolid in 55 patients (8.4%), part solid in 292 (44.9%), and pure solid in 304 (46.7%). Sixty-nine patients (10.6%) had lymph node metastasis, including 43 (6.6%) with pN1 and 26 (4.0%) with pN2. Ground-glass opacity status (part solid or pure solid), serum carcinoembryonic antigen level (>5 ng/dL), histologic subtype (acinar predominant, papillary predominant, micropapillary predominant, or solid predominant), and maximum standardized uptake value (>5) were identified as significant predictors of lymph node metastasis. CONCLUSIONS: Systematic lymph node dissection should be performed in patients with clinical stage IA lung adenocarcinoma with part-solid or pure-solid tumors, especially those with a carcinoembryonic antigen level exceeding 5 ng/dL and a maximum standardized uptake value exceeding 5. The intraoperative diagnosis of histologic subtype may help to identify patients in whom systematic lymph node dissection can be omitted.

Factors that predict lymph node status in clinical stage T1aN0M0 lung adenocarcinomas.

BACKGROUND: To identify patients in whom systematic lymph node dissection would be suitable, preoperative diagnosis of the biological invasiveness of lung adenocarcinomas through the classification of these T1aN0M0 lung adenocarcinomas into several subgroups may be warranted. In this retrospective study, we sought to determine predictive factors of lymph node status in clinical stage T1aN0M0 lung adenocarcinomas. METHODS: We retrospectively reviewed the records of 273 consecutive patients undergone surgical resection of clinical stage T1aN0M0 lung adenocarcinomas at Shanghai Chest Hospital, from January 2011 to December 2012. Preoperative computed tomography findings of all 273 patients were reviewed and their tumors categorized as pure GGO, GGO with minimal solid components (<5 mm), part-solid (solid parts >5 mm), or purely solid. Relevant clinicopathologic features were investigated to identify predictors of hilar or mediastinal lymph node metastasis using univariate or multiple variable analysis. RESULTS: Among the 273 eligible clinical stage T1aN0M0 lung adenocarcinomas examined on thin-section CT, 103 (37.7%) were pure GGO, 118 (43.2%) GGO with minimal solid components, 13 (4.8%) part-solid (solid parts >5 mm, five GGO predominant and eight solid predominant), and 39 (14.3%) pure solid. There were 18 (6.6%) patients with lymph node metastasis. Incidence of N1 and N2 nodal involvement was 11 (6.6%) and seven (2.6%) patients, respectively. All patients with pure GGO and GGO with minimal solid components (<5 mm) tumors were pathologically staged N0. Multivariate analyses showed that the following factors significantly predicted lymph node metastasis for T1a lung adenocarcinomas: symptoms at presentation, GGO status, and abnormal carcinoembryonic antigen (CEA) titer. Multivariate analyses also showed that the following factors significantly predicted lymph node metastasis for pure solid tumors: air bronchogram sign, tumor size, symptoms at presentation, and abnormal CEA titer. CONCLUSIONS: The patients of clinical stage T1aN0M0 lung adenocarcinomas with pure GGO and GGO with minimal solid components tumors were pathologically staged N0 and systematic lymph node dissection should be avoided. But systematic lymph node dissection should be performed for pure solid tumors or part-solid, especially in patients with CEA greater than 5 ng/mL or symptoms at presentation, because of the high possibility of lymph node involvement.

[Correlation analysis between imaging features and lymph node metastasis in T1a lung adenocarcinoma].

OBJECTIVE: To analyze the data of patients with clinical stage T1a lung adenocarcinoma and find the predictive factors associated with lymph node metastasis. METHODS: From January to June 2012, 271 patients with small nodules of peripheral lung adenocarcinoma were enrolled in the retrospective review. There were 105 male and 112 female patients, with an average age of (61 ± 11)years (range 32-85 years). The data were collected including age, gender, smoking history, carcinoembryonic antigen(CEA), imaging findings, surgical procedure, pleural involvement, symptoms, tumor size, pathological classification, pathologic stage, maximum standardized uptake value(SUVmax) and lymph node metastasis. The predictive factors of lymph node metastasis in clinical factors were detected by univariate and multivariate analysis. RESULTS: By preoperative thin-section CT, 35 patients were categorized as pure ground-grass opacity(GGO), 11 cases of atypical adenomatous hyperplasia, 24 cases of adenocarcinoma in situ, with no lymph node metastasis. Categorized as mixed ground-glass opacities in 89 patients, 84 patients (94.4%) had no lymph node metastasis, only 5 patients (6.0%) with lymph node metastasis. Categorized as solid nodules in 93 patients, a total of 28 cases (30.1%) had lymph node metastasis. There were statistically significant difference between three groups (χ(2) = 23.41, P < 0.001) . By univariate analysis, we found that the predictive factors of lymph node metastasis were as follows: tumor size > 1 cm (χ(2) = 9.021, P < 0.003) , imaging performance with mixed GGO or solid nodules (χ(2) = 23.41, P < 0.000) , CEA > 5 µg/L (χ(2) = 15.541, P < 0.000) and PET-CT SUVmax > 5 (χ(2) = 0.644, P < 0.000). By multivariate analysis, we found that imaging performance (mixed GGO or solid nodules) was the independent predictor of lymph node metastasis in clinical factors (OR = 166.116, 95%CI:18.161-25.19, P < 0.001) . CONCLUSIONS: Patients of pure GGO generally do not have lymph node metastasis. Tumor diameter > 1 cm, imaging findings with the mixed GGO or solid nodules, carcinoembryonic antigen CEA > 5 µg/L, PET-CT SUVmax > 5 are predictive factors of lymph node metastasis in which imaging is independent predictor.

A classification method based on principal components of SELDI spectra to diagnose of lung adenocarcinoma.

PURPOSE: Lung cancer is the leading cause of cancer death worldwide, but techniques for effective early diagnosis are still lacking. Proteomics technology has been applied extensively to the study of the proteins involved in carcinogenesis. In this paper, a classification method was developed based on principal components of surface-enhanced laser desorption/ionization (SELDI) spectral data. This method was applied to SELDI spectral data from 71 lung adenocarcinoma patients and 24 healthy individuals. Unlike other peak-selection-based methods, this method takes each spectrum as a unity. The aim of this paper was to demonstrate that this unity-based classification method is more robust and powerful as a method of diagnosis than peak-selection-based methods. RESULTS: The results showed that this classification method, which is based on principal components, has outstanding performance with respect to distinguishing lung adenocarcinoma patients from normal individuals. Through leaving-one-out, 19-fold, 5-fold and 2-fold cross-validation studies, we found that this classification method based on principal components completely outperforms peak-selection-based methods, such as decision tree, classification and regression tree, support vector machine, and linear discriminant analysis. CONCLUSIONS AND CLINICAL RELEVANCE: The classification method based on principal components of SELDI spectral data is a robust and powerful means of diagnosing lung adenocarcinoma. We assert that the high efficiency of this classification method renders it feasible for large-scale clinical use.