A nomogram to predict lung cancer in pulmonary lesions for tuberculosis infection patients.

Similar clinical features make the differential diagnosis difficult, particularly between lung cancer and pulmonary tuberculosis (TB), without pathological evidence for patients with concomitant TB infection. Our study aimed to build a nomogram to predict malignant pulmonary lesions applicable to clinical practice. We retrospectively analyzed clinical characteristics, imaging features, and laboratory indicators of TB infection patients diagnosed with lung cancer or active pulmonary TB at Xiangya Hospital of Central South University. A total of 158 cases from January 1, 2018 to May 30, 2019 were included in the training cohort. Predictive factors for lung cancer were screened by a multiple-stepwise logistic regression analysis. A nomogram model was established, and the discrimination, stability, and prediction performance of the model were analyzed. A total of 79 cases from June 1, 2019, to December 30, 2019, were used as the validation cohort to verify the predictive value of the model. Eight predictor variables, including age, pleural effusion, mediastinal lymph node, the number of positive tumor markers, the T cell spot test for TB, pulmonary lesion morphology, location, and distribution, were selected to construct the model. The corrected C-statistics and the Brier scores were 0.854 and 0.130 in the training cohort, and 0.823 and 0.163 in the validation cohort. Calibration plots showed good performance, and decision curve analysis indicated a high net benefit. In conclusion, the nomogram model provides an effective method to calculate the probability of lung cancer in TB infection patients, and it has excellent discrimination, stability, and prediction performance in detecting a malignant diagnosis of undiagnosed pulmonary lesions.

The association of smoking on the increased risk of osteoporotic fracture: Results from a cross-sectional study and two-sample Mendelian randomization.

INTRODUCTION: We conducted analyses of the association between smoking and osteoporosis and osteoporotic fractures using a secondary dataset analysis of the National Health and Nutrition Examination Survey (NHANES) database and the two-sample Mendelian randomization (MR) method. METHODS: The associations between smoking and osteoporosis or osteoporotic fractures were analyzed using weighted logistic regression models for both univariate and multivariable analyses using pooled 1999-2018 NHANES data. The summary-level data of genome-wide association studies (GWAS) of smoking and osteoporosis were extracted from the IEU Open GWAS project. The inverse variance weighted method was used as the main method for the two-sample MR analysis. RESULTS: We obtained the following main findings based on the NHANES data: smoking was associated with osteoporosis according to the analyses of 30856 participants (OR=1.21; 95% CI: 1.06-1.39, p=0.004); smoking was associated with hip osteoporotic fracture according to the analyses of 30928 participants (OR=1.47; 95% CI: 1.14-1.90, p=0.004); smoking was associated with wrist osteoporotic fracture according to the analyses of 30923 participants (OR=1.33; 95% CI: 1.18-1.49, p<0.001); and smoking was associated with spine osteoporotic fracture according to the analyses of 30910 participants (OR=1.43, 95% CI: 1.18-1.73, p<0.001). In addition, we confirmed the potential causal effect of smoking on the risk of osteoporotic fracture (OR=24.5; 95% CI: 1.11-539, p=0.043) by conducting two-sample MR analyses. CONCLUSIONS: Smoking was associated with increased risks of both osteoporosis and osteoporotic fracture. Smoking showed a potential causal effect on the risk of osteoporotic fracture.

Comprehensive analyses reveal the prognosis and biological function roles of chromatin regulators in lung adenocarcinoma.

The present study explored the prognosis and biological function roles of chromatin regulators (CRs) in patients with lung adenocarcinoma (LUAD). Using transcriptome profile and clinical follow-up data of LUAD dataset, we explored the molecular classification, developed, and validated a CR prognostic model, built an individual risk scoring system in LUAD, and compared the clinical and molecular characteristics between different subtypes and risk stratifications. We investigated the chemotherapy sensitivity and predicted potential immunotherapy response. Lastly, we collected the clinical samples and validated the prognosis and potential function role of NAPS2. Our study indicated that LUAD patients could be classified into two subtypes that had obviously different clinical background and molecular features. We constructed a prognostic model with eight CR genes, which was well validated in several other population cohort. We built high- and low-risk stratifications for LUAD patients. Patients from high-risk group were totally different from low-risk groups in clinical, biological function, gene mutation, microenvironment, and immune infiltration levels. We idented several potential molecular compounds for high-risk group treatment. We predicted that high-risk group may have poor immunotherapy response. We finally found that Neuronal PAS Domain Protein 2 (NPAS2) involved in the progression of LUAD via regulating cell adhesion. Our study indicated that CR involved in the progression of LUAD and affect their prognosis. Different therapeutic strategies should be developed for different molecular subtypes and risk stratifications. Our comprehensive analyses uncover specific determinants of CRs in LUAD and provides implications for investigating disease-associated CRs.

Identification of Novel Prognostic Biomarkers Relevant to Immune Infiltration in Lung Adenocarcinoma.

Background: Programmed death ligand-1 (PD-L1) is a biomarker for assessing the immune microenvironment, prognosis, and response to immune checkpoint inhibitors in the clinical treatment of lung adenocarcinoma (LUAD), but it does not work for all patients. This study aims to discover alternative biomarkers. Methods: Public data were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). Weighted gene co-expression network analysis (WGCNA) and gene ontology (GO) were used to determine the gene modules relevant to tumor immunity. Protein-protein interaction (PPI) network and GO semantic similarity analyses were applied to identify the module hub genes with functional similarities to PD-L1, and we assessed their correlations with immune infiltration, patient prognosis, and immunotherapy response. Immunohistochemistry (IHC) and hematoxylin and eosin (H&E) staining were used to validate the outcome at the protein level. Results: We identified an immune response-related module, and two hub genes (PSTPIP1 and PILRA) were selected as potential biomarkers with functional similarities to PD-L1. High expression levels of PSTPIP1 and PILRA were associated with longer overall survival and rich immune infiltration in LUAD patients, and both were significantly high in patients who responded to anti-PD-L1 treatment. Compared to PD-L1-negative LUAD tissues, the protein levels of PSTPIP1 and PILRA were relatively increased in the PD-L1-positive tissues, and the expression of PSTPIP1 and PILRA positively correlated with the tumor-infiltrating lymphocytes. Conclusion: We identified PSTPIP1 and PILRA as prognostic biomarkers relevant to immune infiltration in LUAD, and both are associated with the response to anti-PD-L1 treatment.