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.

Gastrointestinal symptoms of long COVID-19 related to the ectopic colonization of specific bacteria that move between the upper and lower alimentary tract and alterations in serum metabolites.

BACKGROUND: Since the coronavirus disease 2019 (COVID-19) outbreak, many COVID-19 variants have emerged, causing several waves of pandemics and many infections. Long COVID-19, or long-term sequelae after recovery from COVID-19, has aroused worldwide concern because it reduces patient quality of life after rehabilitation. We aimed to characterize the functional differential profile of the oral and gut microbiomes and serum metabolites in patients with gastrointestinal symptoms associated with long COVID-19. METHODS: We prospectively collected oral, fecal, and serum samples from 983 antibiotic-naïve patients with mild COVID-19 and performed a 3-month follow-up postdischarge. Forty-five fecal and saliva samples, and 25 paired serum samples were collected from patients with gastrointestinal symptoms of long COVID-19 at follow-up and from healthy controls, respectively. Eight fecal and saliva samples were collected without gastrointestinal symptoms of long COVID-19 at follow-up. Shotgun metagenomic sequencing of fecal samples and 2bRAD-M sequencing of saliva samples were performed on these paired samples. Two published COVID-19 gut microbiota cohorts were analyzed for comparison. Paired serum samples were analyzed using widely targeted metabolomics. RESULTS: Mild COVID-19 patients without gastrointestinal symptoms of long COVID-19 showed little difference in the gut and oral microbiota during hospitalization and at follow-up from healthy controls. The baseline and 3-month samples collected from patients with gastrointestinal symptoms associated with long COVID-19 showed significant differences, and ectopic colonization of the oral cavity by gut microbes including 27 common differentially abundant genera in the Proteobacteria phylum, was observed at the 3-month timepoint. Some of these bacteria, including Neisseria, Lautropia, and Agrobacterium, were highly related to differentially expressed serum metabolites with potential toxicity, such as 4-chlorophenylacetic acid, 5-sulfoxymethylfurfural, and estradiol valerate. CONCLUSIONS: Our study characterized the changes in and correlations between the oral and gut microbiomes and serum metabolites in patients with gastrointestinal symptoms associated with long COVID-19. Additionally, our findings reveal that ectopically colonized bacteria from the gut to the oral cavity could exist in long COVID-19 patients with gastrointestinal symptoms, with a strong correlation to some potential harmful metabolites in serum.

Construction of a diagnostic signature and immune landscape of pulmonary arterial hypertension.

Background: Molecular biomarkers are widely used for disease diagnosis and exploration of pathogenesis. Pulmonary arterial hypertension (PAH) is a rapidly progressive cardiopulmonary disease with delayed diagnosis. Studies were limited regarding molecular biomarkers correlated with PAH from a broad perspective. Methods: Two independent microarray cohorts comprising 73 PAH samples and 36 normal samples were enrolled in this study. The weighted gene co-expression network analysis (WGCNA) was performed to identify the key modules associated with PAH. The LASSO algorithm was employed to fit a diagnostic model. The latent biology mechanisms and immune landscape were further revealed via bioinformatics tools. Results: The WGCNA approach ultimately identified two key modules significantly associated with PAH. For genes within the two models, differential expression analysis between PAH and normal samples further determined nine key genes. With the expression profiles of these nine genes, we initially developed a PAH diagnostic signature (PDS) consisting of LRRN4, PI15, BICC1, PDE1A, TSHZ2, HMCN1, COL14A1, CCDC80, and ABCB1 in GSE117261 and then validated this signature in GSE113439. The ROC analysis demonstrated outstanding AUCs with 0.948 and 0.945 in two cohorts, respectively. Besides, patients with high PDS scores enriched plenty of Th17 cells and neutrophils, while patients with low PDS scores were dramatically related to mast cells and B cells. Conclusion: Our study established a robust and promising signature PDS for diagnosing PAH, with key genes, novel pathways, and immune landscape offering new perspectives for exploring the molecular mechanisms and potential therapeutic targets of PAH.

Bioinformatic analysis and preliminary validation of potential therapeutic targets for COVID-19 infection in asthma patients.

BACKGROUND: Severe acute respiratory syndrome coronavirus 2 causes coronavirus disease 19 (COVID-19). The number of confirmed cases of COVID-19 is also rapidly increasing worldwide, posing a significant challenge to human safety. Asthma is a risk factor for COVID-19, but the underlying molecular mechanisms of the asthma-COVID-19 interaction remain unclear. METHODS: We used transcriptome analysis to discover molecular biomarkers common to asthma and COVID-19. Gene Expression Omnibus database RNA-seq datasets (GSE195599 and GSE196822) were used to identify differentially expressed genes (DEGs) in asthma and COVID-19 patients. After intersecting the differentially expressed mRNAs, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed to identify the common pathogenic molecular mechanism. Bioinformatic methods were used to construct protein-protein interaction (PPI) networks and identify key genes from the networks. An online database was used to predict interactions between transcription factors and key genes. The differentially expressed long noncoding RNAs (lncRNAs) in the GSE195599 and GSE196822 datasets were intersected to construct a competing endogenous RNA (ceRNA) regulatory network. Interaction networks were constructed for key genes with RNA-binding proteins (RBPs) and oxidative stress-related proteins. The diagnostic efficacy of key genes in COVID-19 was verified with the GSE171110 dataset. The differential expression of key genes in asthma was verified with the GSE69683 dataset. An asthma cell model was established with interleukins (IL-4, IL-13 and IL-17A) and transfected with siRNA-CXCR1. The role of CXCR1 in asthma development was preliminarily confirmed. RESULTS: By intersecting the differentially expressed genes for COVID-19 and asthma, 393 common DEGs were obtained. GO and KEGG enrichment analyses of the DEGs showed that they mainly affected inflammation-, cytokine- and immune-related functions and inflammation-related signaling pathways. By analyzing the PPI network, we obtained 10 key genes: TLR4, TLR2, MMP9, EGF, HCK, FCGR2A, SELP, NFKBIA, CXCR1, and SELL. By intersecting the differentially expressed lncRNAs for COVID-19 and asthma, 13 common differentially expressed lncRNAs were obtained. LncRNAs that regulated microRNAs (miRNAs) were mainly concentrated in intercellular signal transduction, apoptosis, immunity and other related functional pathways. The ceRNA network suggested that there were a variety of regulatory miRNAs and lncRNAs upstream of the key genes. The key genes could also bind a variety of RBPs and oxidative stress-related genes. The key genes also had good diagnostic value in the verification set. In the validation set, the expression of key genes was statistically significant in both the COVID-19 group and the asthma group compared with the healthy control group. CXCR1 expression was upregulated in asthma cell models, and interference with CXCR1 expression significantly reduced cell viability. CONCLUSIONS: Key genes may become diagnostic and predictive biomarkers of outcomes in COVID-19 and asthma. Video Abstract.

Integrative bioinformatics analysis to explore a robust diagnostic signature and landscape of immune cell infiltration in sarcoidosis.

Background: The unknown etiology of sarcoidosis with variable clinical features leads to delayed diagnosis and limited therapeutic strategies. Hence, exploring the latent mechanisms and constructing an accessible and reliable diagnostic model of sarcoidosis is vital for innovative therapeutic approaches to improve prognosis. Methods: This retrospective study analyzed transcriptomes from 11 independent sarcoidosis cohorts, comprising 313 patients and 400 healthy controls. The weighted gene co-expression network analysis (WGCNA) and differentially expressed gene (DEG) analysis were performed to identify molecular biomarkers. Machine learning was employed to fit a diagnostic model. The potential pathogenesis and immune landscape were detected by bioinformatics tools. Results: A 10-gene signature SARDS consisting of GBP1, LEF1, IFIT3, LRRN3, IFI44, LHFPL2, RTP4, CD27, EPHX2, and CXCL10 was further constructed in the training cohorts by the LASSO algorithm, which performed well in the four independent cohorts with the splendid AUCs ranging from 0.938 to 1.000. The findings were validated in seven independent publicly available gene expression datasets retrieved from whole blood, PBMC, alveolar lavage fluid cells, and lung tissue samples from patients with outstanding AUCs ranging from 0.728 to 0.972. Transcriptional signatures associated with sarcoidosis revealed a potential role of immune response in the development of the disease through bioinformatics analysis. Conclusions: Our study identified and validated molecular biomarkers for the diagnosis of sarcoidosis and constructed the diagnostic model SARDS to improve the accuracy of early diagnosis of the disease.

Use of machine learning models to predict prognosis of combined pulmonary fibrosis and emphysema in a Chinese population.

BACKGROUND: Combined pulmonary fibrosis and emphysema (CPFE) is a novel clinical entity with a poor prognosis. This study aimed to develop a clinical nomogram model to predict the 1-, 2- and 3-year mortality of patients with CPFE by using the machine learning approach, and to validate the predictive ability of the interstitial lung disease-gender-age-lung physiology (ILD-GAP) model in CPFE. METHODS: The data of CPFE patients from January 2015 to October 2021 who met the inclusion criteria were retrospectively collected. We utilized LASSO regression and multivariable Cox regression analysis to identify the variables associated with the prognosis of CPFE and generate a nomogram. The Harrell's C index, the calibration curve and the area under the receiver operating characteristic (ROC) curve (AUC) were used to evaluate the performance of the nomogram. Then, we performed likelihood ratio test, net reclassification improvement (NRI), integrated discrimination improvement (IDI) and decision curve analysis (DCA) to compare the performance of the nomogram with that of the ILD-GAP model. RESULTS: A total of 184 patients with CPFE were enrolled. During the follow-up, 90 patients died. After screening out, diffusing lung capacity for carbon monoxide (DLCO), right ventricular diameter (RVD), C-reactive protein (CRP), and globulin were found to be associated with the prognosis of CPFE. The nomogram was then developed by incorporating the above five variables, and it showed a good performance, with a Harrell's C index of 0.757 and an AUC of 0.800 (95% CI 0.736-0.863). Moreover, the calibration plot of the nomogram showed good concordance between the prediction probabilities and the actual observations. The nomogram also improved the discrimination ability of the ILD-GAP model compared to that of the ILD-GAP model alone, and this was substantiated by the likelihood ratio test, NRI and IDI. The significant clinical utility of the nomogram was demonstrated by DCA. CONCLUSION: Age, DLCO, RVD, CRP and globulin were identified as being significantly associated with the prognosis of CPFE in our cohort. The nomogram incorporating the 5 variables showed good performance in predicting the mortality of CPFE. In addition, although the nomogram was superior to the ILD-GAP model in the present cohort, further validation is needed to determine the clinical utility of the nomogram.