Socio-Economic Predictors and Distribution of Tuberculosis Incidence in Beijing, China: A StudyUsing a Combination of Spatial Statistics and GIS Technology

Evidence shows that multiple factors, such as socio-economic status and access to health care facilities, affect tuberculosis (TB) incidence. However, there is limited literature available with respect to the correlation between socio-economic/health facility factors and tuberculosis incidence. This study aimed to explore the relationship between TB incidence and socio-economic/health service predictors in the study settings. A retrospective spatial regression analysis was carried out based on new sputum smear-positive pulmonary TB cases in Beijing districts. Global Moran’s I analysis was adopted to detect the spatial dependency followed by spatial regression models (spatial lag model, and spatial error model) along with the ordinary least square model were applied to examine the correlation between TB incidence and predictors. A high incidence of TB was seen in densely populated districts in Beijing, e.g., Haidian, Mentougou, and Xicheng. After comparing the R2, log-likelihood, and Akaike information criterion (AIC) values among three models, the spatial error model (R2 = 0.413; Log Likelihood = −591; AIC = 1199.76) identified the best model fit for the spatial regression model. The study showed that the number of beds in health institutes (p \u3c 0.001) and per capita gross domestic product (GDP) (p = 0.025) had a positive effect on TB incidence, whereas population density (p \u3c 0.001) and migrated population (p \u3c 0.001) had an adverse impact on TB incidence in the study settings. High TB incidence districts were detected in urban and densely populated districts in Beijing. Our findings suggested that socio-economic predictors influence TB incidence. These findings may help to guide TB control programs and promote targeted intervention

The Association between Environmental Factors and Scarlet Fever Incidence in Beijing Region: Using GIS and Spatial Regression Models

(1) Background: Evidence regarding scarlet fever and its relationship with meteorological, including air pollution factors, is not very available. This study aimed to examine the relationship between ambient air pollutants and meteorological factors with scarlet fever occurrence in Beijing, China. (2) Methods: A retrospective ecological study was carried out to distinguish the epidemic characteristics of scarlet fever incidence in Beijing districts from 2013 to 2014. Daily incidence and corresponding air pollutant and meteorological data were used to develop the model. Global Moran’s I statistic and Anselin’s local Moran’s I (LISA) were applied to detect the spatial autocorrelation (spatial dependency) and clusters of scarlet fever incidence. The spatial lag model (SLM) and spatial error model (SEM) including ordinary least squares (OLS) models were then applied to probe the association between scarlet fever incidence and meteorological including air pollution factors. (3) Results: Among the 5491 cases, more than half (62%) were male, and more than one-third (37.8%) were female, with the annual average incidence rate 14.64 per 100,000 population. Spatial autocorrelation analysis exhibited the existence of spatial dependence; therefore, we applied spatial regression models. After comparing the values of R-square, log-likelihood and the Akaike information criterion (AIC) among the three models, the OLS model (R2 = 0.0741, log likelihood = −1819.69, AIC = 3665.38), SLM (R2 = 0.0786, log likelihood = −1819.04, AIC = 3665.08) and SEM (R2 = 0.0743, log likelihood = −1819.67, AIC = 3665.36), identified that the spatial lag model (SLM) was best for model fit for the regression model. There was a positive significant association between nitrogen oxide (p = 0.027), rainfall (p = 0.036) and sunshine hour (p = 0.048), while the relative humidity (p = 0.034) had an adverse association with scarlet fever incidence in SLM. (4) Conclusions: Our findings indicated that meteorological, as well as air pollutant factors may increase the incidence of scarlet fever; these findings may help to guide scarlet fever control programs and targeting the intervention

Mutant-allele fraction heterogeneity is associated with non-small cell lung cancer patient survival

Genetic intratumor heterogeneity is associated with tumor occurrence, development and overall outcome. The present study aims to explore the association between mutant-allele fraction (MAF) heterogeneity and patient overall survival in lung cancer. Somatic mutation data of 939 non-small cell lung cancer (NSCLC) cases were obtained from The Cancer Genome Atlas. Entropy-based mutation allele fraction (EMAF) score was used to describe the uncertainty of individual somatic mutation patterns and to further analyze the association with patient overall survival. Results indicated that association between EMAF and overall survival was significant in the discovery set [hazard ratio (H)R=1.62; 95% confidence interval (CI): 1.08–2.41; P=0.018] and replication set (HR=1.63; 95% CI: 1.11–2.37; P=0.011). In addition, EMAF was also significantly different in lung adenocarcinoma and squamous cell carcinoma. Furthermore, a significant difference was indicated in early-stage patients. Results from c-index analysis indicated that EMAF improved the model predictive performance on the 3-year survival beyond that of traditional clinical staging, particularly in early-stage patients. In conclusion, EMAF successfully reflected MAF heterogeneity among patients with NSCLC. Additionally, EMAF improved the predictive performance in early-stage patient prognosis beyond that of traditional clinical staging. In clinical application, EMAF appears to identify a subset of early-stage patients with a poor prognosis and therefore may help inform clinical decisions regarding the application of chemotherapy after surgery

A multi-omic study reveals BTG2 as a reliable prognostic marker for early-stage non-small cell lung cancer

B-cell translocation gene 2 (BTG2) is a tumour suppressor protein known to be downregulated in several types of cancer. In this study, we investigated a potential role for BTG2 in early-stage non-small cell lung cancer (NSCLC) survival. We analysed BTG2 methylation data from 1230 early-stage NSCLC patients from five international cohorts, as well as gene expression data from 3038 lung cancer cases from multiple cohorts. Three CpG probes (cg01798157, cg06373167, cg23371584) that detected BTG2 hypermethylation in tumour tissues were associated with lower overall survival. The prognostic model based on methylation could distinguish patient survival in the four cohorts [hazard ratio (HR) range, 1.51-2.21] and the independent validation set (HR=1.85). In the expression analysis, BTG2 expression was positively correlated with survival in each cohort (HR range, 0.28-0.68), which we confirmed with meta-analysis (HR=0.61, 95% CI 0.54-0.68). The three CpG probes were all negatively correlated with BTG2 expression. Importantly, an integrative model of BTG2 methylation, expression and clinical information showed better predictive ability in the training set and validation set. In conclusion, the methylation and integrated prognostic signatures based on BTG2 are stable and reliable biomarkers for early-stage NSCLC. They may have new applications for appropriate clinical adjuvant trials and personalized treatments in the future

Epigenetic modifications in KDM lysine demethylases associate with survival of early-stage NSCLC

BACKGROUND: KDM lysine demethylase family members are related to lung cancer clinical outcomes and are potential biomarkers for chemotherapeutics. However, little is known about epigenetic alterations in KDM genes and their roles in lung cancer survival. METHODS: Tumor tissue samples of 1230 early-stage non-small cell lung cancer (NSCLC) patients were collected from the five independent cohorts. The 393 methylation sites in KDM genes were extracted from epigenome-wide datasets and analyzed by weighted random forest (Ranger) in discovery phase and validation dataset, respectively. The variable importance scores (VIS) for the sites in top 5% of both discovery and validation sets were carried forward for Cox regression to further evaluate the association with patient's overall survival. TCGA transcriptomic data were used to evaluate the correlation with the corresponding DNA methylation. RESULTS: DNA methylation at sites cg11637544 in KDM2A and cg26662347 in KDM1A were in the top 5% of VIS in both discovery phase and validation for squamous cell carcinomas (SCC), which were also significantly associated with SCC survival (HRcg11637544 = 1.32, 95%CI, 1.16-1.50, P = 1.1 × 10-4; HRcg26662347 = 1.88, 95%CI, 1.37-2.60, P = 3.7 × 10-3), and correlated with corresponding gene expression (cg11637544 for KDM2A, P = 1.3 × 10-10; cg26662347 for KDM1A P = 1.5 × 10-5). In addition, by using flexible criteria for Ranger analysis followed by survival classification tree analysis, we identified four clusters for adenocarcinomas and five clusters for squamous cell carcinomas which showed a considerable difference of clinical outcomes with statistical significance. CONCLUSIONS: These findings highlight the association between somatic DNA methylation in KDM genes and early-stage NSCLC patient survival, which may reveal potential epigenetic therapeutic targets

SIPA1L3 methylation modifies the benefit of smoking cessation on lung adenocarcinoma survival: an epigenomic-smoking interaction analysis

Smoking cessation prolongs survival and decreases mortality of patients with non‐small‐cell lung cancer (NSCLC). In addition, epigenetic alterations of some genes are associated with survival. However, potential interactions between smoking cessation and epigenetics have not been assessed. Here, we conducted an epigenome‐wide interaction analysis between DNA methylation and smoking cessation on NSCLC survival. We used a two‐stage study design to identify DNA methylation-smoking cessation interactions that affect overall survival for early‐stage NSCLC. The discovery phase contained NSCLC patients from Harvard, Spain, Norway, and Sweden. A histology‐stratified Cox proportional hazards model adjusted for age, sex, clinical stage, and study center was used to test DNA methylation-smoking cessation interaction terms. Interactions with false discovery rate‐q ≤ 0.05 were further confirmed in a validation phase using The Cancer Genome Atlas database. Histology‐specific interactions were identified by stratification analysis in lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) patients. We identified one CpG probe (cg02268510SIPA1L3) that significantly and exclusively modified the effect of smoking cessation on survival in LUAD patients [hazard ratio (HR)interaction = 1.12; 95% confidence interval (CI): 1.07-1.16; P = 4.30 × 10-7]. Further, the effect of smoking cessation on early‐stage LUAD survival varied across patients with different methylation levels of cg02268510SIPA1L3. Smoking cessation only benefited LUAD patients with low methylation (HR = 0.53; 95% CI: 0.34-0.82; P = 4.61 × 10-3) rather than medium or high methylation (HR = 1.21; 95% CI: 0.86-1.70; P = 0.266) of cg02268510SIPA1L3. Moreover, there was an antagonistic interaction between elevated methylation of cg02268510SIPA1L3 and smoking cessation (HRinteraction = 2.1835; 95% CI: 1.27-3.74; P = 4.46 × 10−3). In summary, smoking cessation benefited survival of LUAD patients with low methylation at cg02268510SIPA1L3. The results have implications for not only smoking cessation after diagnosis, but also possible methylation‐specific drug targeting

Trans-omics biomarker model improves prognostic prediction accuracy for early-stage lung adenocarcinoma

Limited studies have focused on developing prognostic models with trans-omics biomarkers for early-stage lung adenocarcinoma (LUAD). We performed integrative analysis of clinical information, DNA methylation, and gene expression data using 825 early-stage LUAD patients from 5 cohorts. Ranger algorithm was used to screen prognosis-associated biomarkers, which were confirmed with a validation phase. Clinical and biomarker information was fused using an iCluster plus algorithm, which significantly distinguished patients into high- and low-mortality risk groups (Pdiscovery = 0.01 and Pvalidation = 2.71×10-3). Further, potential functional DNA methylation-gene expression-overall survival pathways were evaluated by causal mediation analysis. The effect of DNA methylation level on LUAD survival was significantly mediated through gene expression level. By adding DNA methylation and gene expression biomarkers to a model of only clinical data, the AUCs of the trans-omics model improved by 18.3% (to 87.2%) and 16.4% (to 85.3%) in discovery and validation phases, respectively. Further, concordance index of the nomogram was 0.81 and 0.77 in discovery and validation phases, respectively. Based on systematic review of published literatures, our model was superior to all existing models for early-stage LUAD. In summary, our trans-omics model may help physicians accurately identify patients with high mortality risk

Analysis of Dyakonov surface waves existing at the interface of an isotropic medium and a conductor-backed uniaxial slab

In this paper, Dyakonov surface waves (Dyakonov SWs) existing at the interface between a semi-infinite isotropic medium and a conductor-backed uniaxial slab are analyzed with the help of an exponential-matrix method. The boundary conditions at the interface are formulated using eigenvalues and eigenvectors of two partnering media. Based on this, the existence region of Dyakonov SWs is formulated and proven to be highly dependent on the thickness of the uniaxial slab. Some relevant characteristics of the propagating Dyakonov SWs, such as the distribution of the propagation constant, and the electric- and magnetic-field distributions, are introduced and investigated. In addition, this method can be applied to analyze other finite thickness structures.Published versio