Tobacco carcinogen induces tryptophan metabolism and immune suppression via induction of indoleamine 2,3-dioxygenase 1.

Indoleamine 2,3-dioxygenase 1 (IDO1), the enzyme that catabolizes tryptophan (Trp) metabolism to promote regulatory T cells (Tregs) and suppress CD8+ T cells, is regulated by several intrinsic signaling pathways. Here, we found that tobacco smoke, a major public health concern that kills 8 million people each year worldwide, induced IDO1 in normal and malignant lung epithelial cells in vitro and in vivo. The carcinogen nicotine-derived nitrosaminoketone (NNK) was the tobacco compound that upregulated IDO1 via activation of the transcription factor c-Jun, which has a binding site for the IDO1 promoter. The NNK receptor α7 nicotinic acetylcholine receptor (α7nAChR) was required for NNK-induced c-Jun activation and IDO1 upregulation. In A/J mice, NNK reduced CD8+ T cells and increased Tregs. Clinically, smoker patients with non-small-cell lung cancer (NSCLC) exhibited high IDO1 levels and low Trp/kynurenine (Kyn) ratios. In NSCLC patients, smokers with lower IDO1 responded better to anti-PD1 antibody treatment than those with higher IDO1. These data indicate that tobacco smoke induces IDO1 to catabolize Trp metabolism and immune suppression to promote carcinogenesis, and lower IDO1 might be a potential biomarker for anti-PD1 antibodies in smoker patients, whereas IDO1-high smoker patients might benefit from IDO1 inhibitors in combination with anti-PD1 antibodies.

[Inpatient Care Expenditure of Terminally Ill Patients in the Geriatric Ward of Peking Union Medical College Hospital].

Objective To describe the inpatient care expenditure of the terminally ill patients in the geriatric ward of Peking Union Medical College Hospital and facilitate future research on the economic outcomes of hospice and palliative care.Methods The histories of patients admitted to the Department of Geriatrics of Peking Union Medical College Hospital during 2018 were reviewed by trained doctors.According to the diagnosis and overall health state,terminally ill patients were selected and enrolled in the study.Demographics,health and disease information,prescriptions,and expenditure details were retrieved from the HIS system.Results In 2018,35 patients were terminally ill and eligible for hospice care,including 20 males and 15 females,with the average age of(78±8)years(59-91 years),the average age-adjusted Charlson Comorbidity Index of 10±3,and the median Barthel index of 40(10,70).These patients had malignant tumor(23 cases),heart failure(4 cases),end-stage renal disease(1 case),end-stage liver disease(2 cases),dementia(4 cases)and other severe diseases(3 cases).The patients received standard care within the scope of internal medicine and geriatrics.Finally,8 patients died during hospitalization,and 27 were discharged alive.The 35 patients had the median length of stay of 15(12,23)days,the median inpatient expenditure of CNY 21 500(13 800,37 600),and the median daily expenditure of CNY 1425(970,2503).The percentage of expenditure was(28.5±12.3)% for medication,(33.2±18.0)% for tests and examinations,and 11.5%(6.4%,15.8%)for accommodation and medical services.The medications for symptom control costed CNY(77±58)per day on average,accounting for(5.2±3.5)% of the total expenditure.Conclusions The inpatient expenditure for terminally ill patients in the tertiary grade A hospital was higher than that reported in community hospitals providing hospice care.In terms of expenditure constitution,the money spent on medications and tests/examinations were similar,and the percentage of expenditure on medications for symptom control was low.There is a need for further research on the economic impact of hospice and palliative care among terminally ill patients in China.

Lnc-GAN1 expression is associated with good survival and suppresses tumor progression by sponging mir-26a-5p to activate PTEN signaling in non-small cell lung cancer.

BACKGROUND: Long non-coding RNAs (lncRNAs) play vital roles in the development and progression of non-small-cell lung cancer (NSCLC); however, the role of most lncRNAs in NSCLC remains unknown. This study explored the clinical significance, biological function and underlying mechanism of lnc-GAN1 in NSCLC. METHODS: With a custom lncRNA microarray we found that lnc-GAN1 is markedly downregulated in NSCLC tissues. Then lnc-GAN1 expression level was measured using qRT-PCR in NSCLC tissues and cell lines. Survival was assessed using the Kaplan-Meier method. The biological functions of lnc-GAN1 in lung cancer cells were evaluated in vitro and in vivo. RNA fluorescence in situ hybridization and subcellular localization assays revealed the subcellular distribution of lnc-GAN1 in cells. Bioinformatic analysis was adopted to predict miRNAs and signaling pathways regulated by lnc-GAN1. RNA immunoprecipitation and Dual-luciferase reporter assays were used to assess the interaction between lnc-GAN1 and miR-26a-5p in lung cancer cells. RESULTS: lnc-GAN1 is downregulated in HCC tissues and associated with larger tumor size and poor overall survival and disease-free survival; its ectopic expression suppresses cell proliferation, colony formation, and cell cycle progression and induces apoptosis in NSCLC cells; it also inhibits tumor growth in the NSCLC xenograft model. We further proved that lnc-GAN1 is localized in cytoplasm and transcribed independently from its parental gene GAN. Mechanistically, lnc-GAN1 acts as a sponge for miR-26a-5p by two seed sequences, and the two non-coding RNAs have a negative relationship in NSCLC tissues; we further prove that PTEN is a direct target of miR-26a-5p and lnc-GAN1 inhibits cell cycle signaling pathway by activating PTEN, whose expression level correlated negatively with miR-26a-5p level but positively with lnc-GAN1 level in NSCLC samples. CONCLUSIONS: Lnc-GAN1 is downregulated and associated with poor survival of NSCLC patients, and mechanistically acts as a tumor suppressor via sponging and inhibiting miR-26a-5p to upregulate PTEN. This study provides a potential prognostic biomarker and treatment target for NSCLC.

Identification of a 4-lncRNA signature predicting prognosis of patients with non-small cell lung cancer: a multicenter study in China.

BACKGROUND: Previous findings have indicated that the tumor, nodes, and metastases (TNM) staging system is not sufficient to accurately predict survival outcomes in patients with non-small lung carcinoma (NSCLC). Thus, this study aims to identify a long non-coding RNA (lncRNA) signature for predicting survival in patients with NSCLC and to provide additional prognostic information to TNM staging system. METHODS: Patients with NSCLC were recruited from a hospital and divided into a discovery cohort (n = 194) and validation cohort (n = 172), and detected using a custom lncRNA microarray. Another 73 NSCLC cases obtained from a different hospital (an independent validation cohort) were examined with qRT-PCR. Differentially expressed lncRNAs were determined with the Significance Analysis of Microarrays program, from which lncRNAs associated with survival were identified using Cox regression in the discovery cohort. These prognostic lncRNAs were employed to construct a prognostic signature with a risk-score method. Then, the utility of the prognostic signature was confirmed using the validation cohort and the independent cohort. RESULTS: In the discovery cohort, we identified 305 lncRNAs that were differentially expressed between the NSCLC tissues and matched, adjacent normal lung tissues, of which 15 are associated with survival; a 4-lncRNA prognostic signature was identified from the 15 survival lncRNAs, which was significantly correlated with survivals of NSCLC patients. This signature was further validated in the validation cohort and independent validation cohort. Moreover, multivariate Cox analysis demonstrates that the 4-lncRNA signature is an independent survival predictor. Then we established a new risk-score model by combining 4-lncRNA signature and TNM staging stage. The receiver operating characteristics (ROC) curve indicates that the prognostic value of the combined model is significantly higher than that of the TNM stage alone, in all the cohorts. CONCLUSIONS: In this study, we identified a 4-lncRNA signature that may be a powerful prognosis biomarker and can provide additional survival information to the TNM staging system.

A Predictive Model for Estimation Risk of Proliferative Lupus Nephritis.

BACKGROUND: Lupus nephritis (LN) is classified by renal biopsy into proliferative and nonproliferative forms, with distinct prognoses, but renal biopsy is not available for every LN patient. The present study aimed to establish an alternate tool by building a predictive model to evaluate the probability of proliferative LN. METHODS: In this retrospective cohort with biopsy-proven LN, 382 patients in development cohort, 193 in internal validation cohort, and 164 newly diagnosed patients in external validation cohort were selected. Logistic regression model was established, and the concordance statistics (C-statistics), Akaike information criterion (AIC), integrated discrimination improvement, Hosmer-Lemeshow test, and net reclassification improvement were calculated to evaluate the performance and validation of models. RESULTS: The prevalence of proliferative LN was 77.7% in the whole cohort. A model, including age, gender, systolic blood pressure, hemoglobin, proteinuria, hematuria, and serum C3, performed well on good-of-fit and discrimination in the development chohort to predict the risk of proliferative LN (291 for AIC and 0.84 for C-statistics). In the internal and external validation cohorts, this model showed good capability for discrimination and calibration (0.84 and 0.82 for C-statistics, and 0.99 and 0.75 for P values, respectively). CONCLUSION: This study developed and validated a model including demographic and clinical indices to evaluate the probability of presenting proliferative LN to guide therapeutic decisions and outcomes.