Livin and Fas-FasL can inhibit cell apotosis. Recent studies show Livin is overexpressed in lung cancer and promotes tumor development. Fas -FasL is expressed abnormally in lung cancer. Aberrant expression of Livin and Fas-FasL may play synergetic roles in lung carcinogenesis, and may be new targets for diagnosis and gene treatment of lung cancer.

Background and purpose:MicroRNAs (miRNAs, miR) are directly involved in cancer initiation, progression and metastasis. Alterations of miRNAs expression in cancer tissue may be reflected in circulation. We attempted to investigate the expression and clinical signiifcance of plasma miR-221 in patients with small cell lung cancer (SCLC). The plasma and tissues levels of miR-221 in 51 SCLC patients and 20 controls were evaluated and compared among various clinicopathological characteristics. Methods: Confirmation of higher miR-221 levels in primary SCLC tissues than normal lung tissues. Evaluation of plasma miR-221 concentrations by comparing results from 51 consecutive SCLC patients and 20 healthy volunteers. Evaluation of the assay for monitoring tumour dynamics in SCLC patients. Results:Expression of miR-221 was signiifcantly higher in SCLC tissues than in para-carcinoma tissues and normal lung tissues. Plasma miR-221 concentrations were signiifcantly higher in SCLC patients than those in normal people (P<0.05). The expression of plasma miR-221 was not associated with gender, and age (P>0.05), correlated with significantly chemosensitivity, overall survival and clinical stage (P<0.05). Cox regression analysis indicated that plasma miR-221 expression and disease stage were found to be significantly independent prognostic factors for the SCLC patients. Conclusion: Plasma miR-221 could be a useful biomarker for cancer detection, monitoring tumour dynamics and predicting malignant outcomes in SCLC patients, and may contribute to clinical decision making in treatments.

Background and purpose:Lung cancer is the leading cause of cancer-related deaths worldwide. Approximately 15%of all histological types consist of small cell lung cancer (SCLC). Chemotherapy is one of the major treatment methods. Though the current front-line standard chemotherapy regimen for SCLC is active in most SCLC cases, however, the disease recurs shortly after the ifrst successful treatment with multi-drug resistance phenotype. Our previously study showed that zinc ifnger protein X-linked (ZFX) was overexpressed in SCLC cells. This study aimed to investigate the expression of ZFX in SCLC tissues, and to clear their possible associations with clinical parameters and provide basis for therapy of SCLC. Methods:A total of 98 surgical specimens of small cell lung cancer were collected. The expression of ZFX was examined by quantiifcational real-time polymerase chain reaction in 78 specimens taken from patients with complete clinical data. Results:The expression of ZFX was signiifcantly increased in extensive stages than in limited stages. The expression of ZFX was associated with tumor stage, the sensitivity of chemotherapy, and survival times (all P<0.05), no data was found correlated with gender and ages (P>0.05). Conclusion: ZFX expression might be associated with the development of SCLC, and may be a potential prognosis predictor.

Objective To investigate the expressions of Wnt-1 and FZD-1 in small cell lung cancer(SCLC) patients and their relations with chemotherapy resistance,clinical feature and prognosis.Methods Peripheral blood specimens were collected from 41 SCLC patients.The expression of Wnt-1 and FZD-1 in peripheral blood monouclear cells (PBMC) were detected.The relationship among the expression of Wnt-1 and FZD-1,clinicopathologic feature and prognosis was analyzed.Results The relative expression of Wnt-1 and FZD-1 in chemotherapy resistant group was significantly higher than that in chemotherapy sensitive group (all P ＜ 0.05).The expression of Wnt-1was positively correlated with that of FZD-1 (r =0.186,P ＜ 0.05).The FZD-1expression level was not correlated with patients' age,sex and smoking history (all P ＞ 0.05),but closely with clinic-staging (P =0.008).The Wnt-1 expression level was not correlated with patients' clinical features (all P ＞ 0.05).There was statistical difference in median survival time between Wnt-1 and FZD-1 high-expression group and low-expression group.Conclusions Wnt-1 and FZD-1relationships with chemotherapy resistance and prognosis.Wnt-1 and FZD-1 may act as an important role in chemotherapy resistance of SCLC and could be served as indicators for the chemotherapy resistance and outcome assessment of SCLC.

BACKGROUND:Early detection and accurate staging diagnosis of heart failure are the basis of good clinical therapy efficacy. Due to lack of simple and effective staging model for the diagnosis of heart failure, it is difficult to diagnose heart failure in clinics, leading to poor control of heart failure. OBJECTIVE:To establish the disease staging model based on Adaboost and SVM for heart failure, and improve the accuracy of diagnosis and staging of heart failure. METHODS:A total of 194 cases were roled into this study, including heart failure patients and healthy physical examination persons. According to the stage standards formulated by American Colege of Cardiology and American Heart Association, specific clinical feature parameters closely related to heart failure were colected and selected. Based on clinical diagnosis results and using Adaboost model and SVM model, we trained the models for heart failure diagnosis and staging, thus obtaining diagnosis model. RESULTS AND CONCLUSION: The parameters included stroke volume, cardiac output, left ventricular ejection fraction, left atrial diameter, left ventricular internal diameter at end-systole, N-terminal pro-brain natriuretic peptide and heart rate variability. As for the Adaboost model, its sensitivity and specificity was 100% and 94.4%, respectively. At the same time the SVM model had good sensitivity and specificity, 86.5% and 89.4% respectively. Adaboost classification model can be accurate in the diagnosis of heart failure symptoms, the accuracy reached 89.36%. On the basis of the diagnosis of heart failure, the SVM classification model is effective in staging the severity of heart failure, staging accuracy for staging B and C was 86.49% and 81.48%, respectively. The findings indicate that, combining Adaboost and SVM machine learning models could provide an accurate diagnosis and staging model for heart failure.