CD74, a novel predictor for bronchopulmonary dysplasia in preterm infants

Prediction of prognostic and immune therapy response in lung adenocarcinoma based on MHC-I-related genes

OBJECTIVES

The study investigated the prognostic and immune predictive potential of major histocompatibility complex class I (MHC-I) in lung adenocarcinoma (LUAD).

MATERIALS AND METHODS

With The Cancer Genome Atlas (TCGA)-LUAD and Gene Expression Omnibus datasets (GSE26939, GSE72094) as the training and validation sets, respectively, we used Cox regression analysis to construct a prognostic model, and verified independence of riskscore. The predictive capacity of the model was assessed in both sets using the receiver operating characteristic curve and Kaplan-Meier survival curves. Immune analysis was performed by using ssGSEA. Additionally, immune checkpoint blockade therapy was assessed by using immunophenoscore, Tumor Immune Dysfunction and Exclusion score. Based on the cMAP database, effective small molecule compounds were predicted.

RESULTS

A prognostic model was established based on 8 MHC-I-related genes, and the predictive capacity of the model was accurate. Immune analysis results revealed that patients classified as high-risk had lower levels of immune cell infiltration and impaired immune function. The low-risk group possessed a better response to immune checkpoint blockade therapy. Theobromine and pravastatin were identified as having great potential in improving the prognosis of LUAD.

CONCLUSION

Overall, the study revealed MHC-I-related molecular prognostic biomarkers as robust indicators for LUAD prognosis and immune therapy response.

Risk of asthma in preterm infants with bronchopulmonary dysplasia: a systematic review and meta-analysis

BACKGROUND

This study aimed to systematically review and meta-analyze the available literature on the association between preterm infant bronchopulmonary dysplasia (BPD) and pre-adulthood asthma.

METHODS

Studies examining the association between BPD and asthma in children and adolescents were systematically reviewed, and a meta-analysis was conducted. We searched Scopus, Embase, Web of Science, PubMed, and Cochrane Library from the database inception to March 26, 2022. The pooled odds ratio (OR) estimate was used in our meta-analysis to calculate the correlation between BPD and the probability of developing asthma before adulthood. Stata 12.0 was used to conduct the statistical analysis.

RESULTS

The correlation between asthma and BPD in preterm newborns was examined in nine studies. We used a random effect model to pool the OR estimate. Our results indicated a marked increase in the risk of subsequent asthma in preterm infants with BPD [OR = 1.73, 95% confidence interval (CI) = 1.43-2.09]. Moreover, there was no obvious heterogeneity across the studies (P = 0.617, I² = 0%). The pooled OR remained stable and ranged from 1.65 (95% CI = 1.35-2.01) to 1.78 (95% CI = 1.43-2.21). Regarding publication bias, the funnel plot for asthma risk did not reveal any noticeable asymmetry. We further performed Begg's and Egger's tests to quantitatively evaluate publication bias. There was no evidence of a publication bias for asthma risk (P > |Z| = 0.602 for Begg's test, and P > |t| = 0.991 for Egger's test).

CONCLUSIONS

Our findings indicate that preterm infants with BPD have a much higher risk of developing asthma in the future (OR = 1.73, 95% CI = 1.43-2.09). Preterm infants with BPD may benefit from long-term follow-up.

The Transcriptome Characteristics of Severe Asthma From the Prospect of Co-Expressed Gene Modules

Rationale: Severe asthma is a heterogeneous disease with multiple molecular mechanisms. Gene expression studies of asthmatic bronchial epithelial cells have provided biological insights and underscored possible pathological mechanisms; however, the molecular basis in severe asthma is still poorly understood.

Objective: The objective of this study was to identify the features of asthma and uncover the molecular basis of severe asthma in distinct molecular phenotype.

Methods: The k-means clustering and differentially expressed genes (DEGs) were performed in 129 asthma individuals in the Severe Asthma Research Program. The DEG profiles were analyzed by weighted gene co-expression network analysis (WGCNA), and the expression value of each gene module in each individual was annotated by gene set variation analysis (GSVA).

Results: Expression analysis defined five stable asthma subtype (AS): 1) Phagocytosis-Th2, 2) Normal-like, 3) Neutrophils, 4) Mucin-Th2, and 5) Interferon-Th1 and 15 co-expressed gene modules. “Phagocytosis-Th2” enriched for receptor-mediated endocytosis, upregulation of Toll-like receptor signal, and myeloid leukocyte activation. “Normal-like” is most similar to normal samples. “Mucin-Th2” preferentially expressed genes involved in O-glycan biosynthesis and unfolded protein response. “Interferon-Th1” displayed upregulation of genes that regulate networks involved in cell cycle, IFN gamma response, and CD8 TCR. The dysregulation of neural signal, REDOX, apoptosis, and O-glycan process were related to the severity of asthma. In non-TH2 subtype (Neutrophils and Interferon-Th1) with severe asthma individuals, the neural signals and IL26-related co-expression module were dysregulated more significantly compared to that in non-severe asthma. These data infer differences in the molecular evolution of asthma subtypes and identify opportunities for therapeutic development.

Conclusions: Asthma is a heterogeneous disease. The co-expression analysis provides new insights into the biological mechanisms related to its phenotypes and the severity.