Background Pulmonary hypertension (PH) is a life-threatening complication of chronic obstructive pulmonary disease (COPD). Timely diagnosis of PH in COPD patients is vital to achieve proper treatment; however, there is no algorithm to identify those at high risk. We aimed to develop a predictive model for PH in patients with COPD that provides individualized risk estimates. Methods A total of 527 patients with COPD who were admitted to our hospital between May 2019 and December 2020 were retrospectively enrolled in this study. Using echocardiographic results as a standard, patients were stratified into a moderate- or high-PH probability group and a low-PH probability group. They were randomly grouped into either the training set (n = 368 patients) or validation set (n = 159 patients) in a ratio of 7:3. We utilized the least absolute shrinkage and selection operator (LASSO) regression model to select the feature variables. The characteristic variables selected in the LASSO regression were analyzed using multivariable logistic regression to construct the predictive model. The predictive model was displayed using a nomogram. We used the receiver operating characteristic curve, calibration curve, and clinical decision curve analysis (DCA) to evaluate model performance, and internal validation was assessed. Results The predictive factors included in the prediction model were Global Initiative for Chronic Obstructive Lung Disease (GOLD) stage, emphysema, PaCO 2 , NT-pro-BNP, red blood cell (RBC) distribution width-standard deviation (RDW-SD), and neutrophil/lymphocyte ratio (NLR). The predictive model yielded an area under the curve (AUC) of 0.770 (95% confidence interval [CI], 0.719–0.820); in the internal validation, the AUC was 0.741 (95% CI, 0.659–0.823). The predictive model was well calibrated, and the DCA showed that the proposed nomogram had strong clinical applicability. Conclusion This study showed that a simple nomogram could be used to calculate the risk of PH in patients with COPD which can be useful for the individualized clinical management of COPD patients who may be occur with PH. Further studies need to be confirmed by larger sample sizes and validated in the stable COPD population.

Background. ANLN and miR-30a-5p may be involved in the progression of lung adenocarcinoma (LUAD). However, their underlying mechanism in LUAD has not been completely comprehended. Methods. Differential expression analysis, binding site prediction, and survival analysis were conducted by bioinformatics approaches. ANLN mRNA and miR-30a-5p expression were detected by qRT-PCR. ANLN protein expression was detected by western blot. Cell behaviors in LUAD were examined by functional experiments. Results. ANLN was activated in LUAD cells in terms of mRNA and protein. High ANLN level was positively correlated with poor prognosis. Enforced ANLN stimulated protumorigenesis LUAD cell behaviors. miR-30a-5p could target ANLN mRNA, as revealed and verified through assays. Remarkably low miR-30a-5p expression was observed in LUAD cells, and it could repress ANLN expression. The accelerated cell behaviors by overexpression of ANLN were counteracted by upregulating miR-30a-5p. Conclusion. Overall, miR-30a-5p remarkably restrained the malignant progression of LUAD cells by constraining ANLN expression. Thus, ANLN and miR-30a-5p could be novel therapeutic targets of LUAD.

Small cell lung cancer (SCLC) is a highly invasive and fatal malignancy. Research at the present stage implied that the expression of immune-related genes is associated with the prognosis in SCLC. Accordingly, it is essential to explore effective immune-related molecular markers to judge prognosis and treat SCLC. Our research obtained SCLC dataset from Gene Expression Omnibus (GEO) and subjected mRNAs in it to differential expression analysis. Differentially expressed mRNAs (DEmRNAs) were intersected with immune-related genes to yield immune-related differentially expressed genes (DEGs). The functions of these DEGs were revealed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. Thereafter, we categorized 3 subtypes of immune-related DEGs via K-means clustering. Kaplan-Meier curves analyzed the effects of 3 subtypes on SCLC patients’ survival. Single-sample gene set enrichment analysis (ssGSEA) and ESTIMATE validated that the activation of different immune gene subtypes differed significantly. Finally, an immune-related-7-gene assessment model was constructed by univariate-Lasso-multiple Cox regression analyses. Riskscores, Kaplan-Meier curves, receiver operating characteristic (ROC) curves, and independent prognostic analyses validated the prognostic value of the immune-related-7-gene assessment model. As suggested by GSEA, there was a prominent difference in cytokine-related pathways between high- and low-risk groups. As the analysis went further, we discovered a statistically significant difference in the expression of human leukocyte antigen (HLA) proteins and costimulatory molecules expressed on the surface of CD274, CD152, and T lymphocytes in different groups. In a word, we started with immune-related genes to construct the prognostic model for SCLC, which could effectively evaluate the clinical outcomes and offer guidance for the treatment and prognosis of SCLC patients.