HTR2B as a novel biomarker of chronic obstructive pulmonary disease with lung squamous cell carcinoma.

Chronic obstructive pulmonary disease (COPD) is often associated with lung squamous cell carcinoma (LUSC), which has the same etiology (smoking, inflammation, oxidative stress, microenvironmental changes, and genetics). Smoking, inflammation, and airway remodeling are the most important and classical mechanisms of COPD comorbidity in LUSC patients. Cancer can occur during repeated airway damage and repair (airway remodeling). Changes in the inflammatory and immune microenvironments, which can cause malignant transformation of some cells, are currently being revealed in both LUSC and COPD patients. We obtained the GSE76925 dataset from the Gene Expression Omnibus database. Screening for possible COPD biomarkers was performed using the LASSO regression model and a random forest classifier. The compositional patterns of the immune cell fraction in COPD patients were determined using CIBERSORT. HTR2B expression was analyzed using validation datasets (GSE47460, GSE106986, and GSE1650). HTR2B expression in COPD cell models was determined via real-time quantitative PCR. Epithelial-mesenchymal transition (EMT) marker expression levels were determined after knocking down or overexpressing HTR2B. HTR2B function and mechanism in LUSC were analyzed with the Kaplan‒Meier plotter database. HTR2B expression was inhibited to detect changes in LUSC cell proliferation. A total of 1082 differentially expressed genes (DEGs) were identified in the GSE76925 dataset (371 genes were significantly upregulated, and 711 genes were significantly downregulated). Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis indicated that the DEGs were mainly enriched in the p53 signaling and ß-alanine metabolism pathways. Gene Ontology enrichment analysis indicated that the DEGs were largely related to transcription initiation from the RNA polymerase I promoter and to the regulation of mononuclear cell proliferation. The LASSO regression model and random forest classifier results revealed that HTR2B, DPYS, FRY, and CD19 were key COPD genes. Immune cell infiltration analysis indicated that these genes were closely associated with immune cells. Analysis of the validation sets suggested that HTR2B was upregulated in COPD patients. HTR2B was significantly upregulated in COPD cell models, and its upregulation was associated with increased EMT marker expression. Compared with that in bronchial epithelial cells, HTR2B expression was upregulated in LUSC cells, and inhibiting HTR2B expression led to the inhibition of LUSC cell proliferation. In conclusions, HTR2B might be a new biomarker and therapeutic target in COPD patients with LUSC.

Single-cell RNA sequencing integrated with bulk RNA sequencing analysis identifies a tumor immune microenvironment-related lncRNA signature in lung adenocarcinoma.

BACKGROUND: Recently, long non-coding RNAs (lncRNAs) have been demonstrated as essential roles in tumor immune microenvironments (TIME). Nevertheless, researches on the clinical significance of TIME-related lncRNAs are limited in lung adenocarcinoma (LUAD). METHODS: Single-cell RNA sequencing and bulk RNA sequencing data are integrated to identify TIME-related lncRNAs. A total of 1368 LUAD patients are enrolled from 6 independent datasets. An integrative machine learning framework is introduced to develop a TIME-related lncRNA signature (TRLS). RESULTS: This study identified TIME-related lncRNAs from integrated analysis of single­cell and bulk RNA sequencing data. According to these lncRNAs, a TIME-related lncRNA signature was developed and validated from an integrative procedure in six independent cohorts. TRLS exhibited a robust and reliable performance in predicting overall survival. Superior prediction performance barged TRLS to the forefront from comparison with general clinical features, molecular characters, and published signatures. Moreover, patients with low TRLS displayed abundant immune cell infiltration and active lipid metabolism, while patients with high TRLS harbored significant genomic alterations, high PD-L1 expression, and elevated DNA damage repair (DDR) relevance. Notably, subclass mapping analysis of nine immunotherapeutic cohorts demonstrated that patients with high TRLS were more sensitive to immunotherapy. CONCLUSIONS: This study developed a promising tool based on TIME-related lncRNAs, which might contribute to tailored treatment and prognosis management of LUAD patients.

Development of a novel predictive model for interstitial lung disease in ANCA-associated vasculitis prognostications within the Chinese population.

Antineutrophil cytoplasmic antibody vasculitis-associated interstitial lung disease (AAV-ILD) is a potentially life-threatening disease. However, very little research has been done on the condition's mortality risk. Hence, our objective is to find out the factors influencing the prognosis of AAV-ILD and employ these findings to create a nomogram model. Patients with AAV-ILD who received treatment at the First Affiliated Hospital of Zhengzhou University during the period from March 1, 2011, to April 1, 2022 were selected for this research. The development of nomogram entailed a synergistic integration of univariate, Lasso, and multivariate Cox regression analyses. Internal validation ensued through bootstrap techniques involving 1000 re-sampling iterations. Discrimination and calibration were assessed utilizing Harrell's C-index, receiver operating characteristic (ROC) curve, and calibration curve. Model performance was evaluated through integrated discrimination improvement (IDI), net reclassification improvement (NRI), and likelihood ratio test. The net benefit of the model was evaluated using decision curve analysis (DCA). A cohort comprising 192 patients was enrolled for analysis. Throughout observation period, 32.29% of the population died. Key factors such as cardiac involvement, albumin, smoking history, and age displayed substantial prognostic relevance in AAV-ILD. These factors were incorporated to craft a predictive nomogram. Impressively, the model exhibited robust performance, boasting a Harrell's C index of 0.826 and an AUC of 0.940 (95% CI 0.904-0.976). The calibration curves depicted a high degree of harmony between predicted outcomes and actual observations. Significantly enhancing discriminative ability compared to the ILD-GAP model, the nomogram was validated through the IDI, NRI, and likelihood ratio test. DCA underscored the superior predictive value of the predictive model over the ILD-GAP model. The internal validation further affirmed this efficacy, with a mean Harrell's C-index of 0.815 for the predictive model. The nomogram model can be employed to predict the prognosis of patients with AAV-ILD. Moreover, the model performance is satisfactory. In the future, external datasets could be utilized for external validation.