Objective To observe the protective effect of N-NAC on radiation-induced lung injury. Methods 86 cases of thoracic neoplasm patients were chosen and randomly divided into two groups,group RT +N(n =43)and group RT(n =43).Two groups were observed by CT after radiotherapy.Acute and chronical toxicities were graded by RTOG.TGF-β1,IL-1,IL-4,TNF were observed before and after the radiotherapy.Results After 3 monthsof radiotherapy,RTOG≥2 was 23.4%(RT +N),while RTOG≥2 was 53.1%(RT).there was significant differencebetween the two groups(P <0.01).At 6,9 and 12 months,fibrosis was present in 28.4%,25.4%,22.4% receivingRT vs 58.4%,54.4%,52.4% receiving RT +N,there was significant difference between the two groups(P <0.05).TGF-β1,IL-1,IL-4,TNF were observed which showed that The RT +N were lower than RT.Conclusion N-NAC can reduce incidence rate of lung injury in radiotherapy,and can reduce the content and the release of TGF-β1,IL-1,IL-4,TNF.

Objective To clone the cDNA encoding human HMGB1, express it in E. coli, and identify its biological activity. Methods Human HMGB1 cDNA was amplified by RT-PCR and cloned into vector pUC19. After sequence analysis, the cDNA was ligated into prokaryotic expression vector pQE-80L and induced by IPTG to express HMGB1. The protein was purified with Ni~(2+)-NTA chromatography and polymyxin B affinity column. To identify the function of purified protein, the product was co-cultured with THP1 cells. Results Recombinant expression plasmid pQE-80L/HMGB1 was constructed successfully. After purification, the protein purity reached 96%. The recombinant HMGB1 stimulated THP1 to secrete TNF-? . Conclusion The highly purified HMGB1 was obtained successfully, which showed biological activity. These results lay the foundation for further research on the function of human HMGB1.

Objective: A prognostic risk model for lung adenocarcinoma patients was established based on the cancer genome atlas(TCGA) database to explore the prognostic performance of autophagy related gene risk model for lung adenocarcinoma patients and its correlation with immune microenvironment． Methods: Clinical information and transcriptome data of lung adenocarcinoma patients were downloaded and extracted from TCGA database，and 232 autophagy-related genes were screened from the human autophagy database．cox regression analysis was used to screen out four autophagy genes independently associated with prognosis．The prognostic prediction model of lung adenocarcinoma was constructed by risk score ，and the performance of prediction model was evaluated by ROC curve．The relationship between risk scores and tumor immune microenvironment was explored using ESTIMATE ( estimation of stromal and immune cells in malignant tumour tissues using expression data) and CIBERSORT algo- rithms. Results: Thirty differentially expressed autophagy-related genes were identified in lung adenocarcinoma， of which four autophagy genes (BIRC5，ERO1A，ITGB4，NLRC4 ) could predict the prognosis of the patients. Grouped by risk score，the Kaplan-Meier analysis demonstrated that the survival rate of high-risk group was signifi- cantly lower than that of low-risk group(P＜0. 000 1) ．The ROC curve proved the accuracy of the model in predic- ting the prognosis of lung adenocarcinoma ( AUC = 0. 757 ) ．The ESTIMATE and CIBERSORT analyses revealed that the risk scoring model was associated with multiple immune cells and immune infiltrates in the tumor microenvi- ronment． Conclusion Compared with clinical data，the autophagy gene prognostic risk model can better predict the prognosis of patients with lung adenocarcinoma．In the high-risk group，CD4 + memory quiescent cells can im- prove prognosis in lung adenocarcinoma patients.

Objective: To construct nomogram to predict the risk of bone metastasis in patients with non-small cell lung cancer(NSCLC). Methods: The clinical data of NSCLC patients diagnosed in the hospital were retrospectively analyzed, including the occurrence of bone metastasis, age, gender, pathological type, smoking status, PS score, TN stage, metastasis of other sites before bone metastasis, carcinoembryonic antigen(CEA) level, alpha fetoprotein(AFP) level, serum calcium(Ca2+), serum phosphorus(P), alkaline phosphatase(ALP) level, which were determined by univariate and multivariate logistic regression analysis. Receiver operating characteristic curve(ROC) and decision curve analysis were used, DCA was used to verify the accuracy and clinical benefit of the model, and nomogram was used to visualize the model. Results: Area under the ROC curve(AUC) showed that in the modeling group(n=138) and the validation group(n=92), the AUC value predicted by combined indicators(age, gender, pathological type, CEA, ALP)(modeling group=0.792, validation group=0.629) was higher than that predicted by single indicator. Conclusion The prediction model constructed in this study has good effect and can provide reference for clinical screening of high-risk patients with bone metastasis of NSCLC.