CD8A is a Promising Biomarker Associated with Immunocytes Infiltration in Hyperoxia-Induced Bronchopulmonary Dysplasia

Effect of Oridonin on Experimental Animal Model of Bronchopulmonary Dysplasia

Bronchopulmonary dysplasia (BPD) is a serious disease that occurs in premature and low-birth-weight infants. In recent years, the incidence of BPD has not decreased, and there is no effective treatment for it. Oridonin (Ori) is a traditional Chinese medicine with a wide range of biological activities, especially pharmacological and anti-inflammatory. It is well known that inflammation plays a key role in BPD. However, the therapeutic effect of Ori on BPD has not been studied. Therefore, in the present study, we will observe the anti-inflammatory activity of Ori in an experimental animal model of BPD. Here, we showed that Ori could significantly decrease hyperoxia-induced alveolar injury, inhibit neutrophil recruitment, myeloperoxidase concentrations, and release inflammatory factors in BPD neonatal rats. Taken together, the experimental results suggested that Ori can significantly improve BPD in neonatal rats by inhibiting inflammatory response.

Joint effects of CD8A and ICOS in Long QT Syndrome (LQTS) and Beckwith-Wiedemann Syndrome (BWS)

BACKGROUND

Long QT Syndrome (LQTS) and Beckwith-Wiedemann Syndrome (BWS) are complex disorders with unclear origins, underscoring the need for in-depth molecular investigations into their mechanisms. The main aim of this study is to identify the shared key genes between LQTS and BWS, shedding light on potential common molecular pathways underlying these syndromes.

METHODS

The LQTS and BWS datasets are available for download from the GEO database. Differential expression genes (DEGs) were identified. Weighted gene co-expression network analysis (WGCNA) was used to detect significant modules and central genes. Gene enrichment analysis was performed. CIBERSORT was used for immune cell infiltration analysis. The predictive protein interaction (PPI) network of core genes was constructed using STRING, and miRNAs regulating central genes were screened using TargetScan.

RESULTS

Five hundred DEGs associated with Long QT Syndrome and Beckwith-Wiedemann Syndrome were identified. GSEA analysis revealed enrichment in pathways such as T cell receptor signaling, MAPK signaling, and adrenergic signaling in cardiac myocytes. Immune cell infiltration indicated higher levels of memory B cells and naive CD4 T cells. Four core genes (CD8A, ICOS, CTLA4, LCK) were identified, with CD8A and ICOS showing low expression in the syndromes and high expression in normal samples, suggesting potential inverse regulatory roles.

CONCLUSION

The expression of CD8A and ICOS is low in long QT syndrome and Beckwith-Wiedemann syndrome, indicating their potential as key genes in the pathogenesis of these syndromes. The identification of shared key genes between LQTS and BWS provides insights into common molecular mechanisms underlying these disorders, potentially facilitating the development of targeted therapeutic strategies.

The fatty acid-related gene signature stratifies poor prognosis patients and characterizes TIME in cutaneous melanoma

BACKGROUND

The aim of this study is to build a prognostic model for cutaneous melanoma (CM) using fatty acid-related genes and evaluate its capacity for predicting prognosis, identifying the tumor immune microenvironment (TIME) composition, and assessing drug sensitivity.

METHODS

Through the analysis of transcriptional data from TCGA-SKCM and GTEx datasets, we screened for differentially expressed fatty acids-related genes (DEFAGs). Additionally, we employed clinical data from TCGA-SKCM and GSE65904 to identify genes associated with prognosis. Subsequently, utilizing all the identified prognosis-related fatty acid genes, we performed unsupervised clustering analysis using the ConsensusClusterPlus R package. We further validated the significant differences between subtypes through survival analysis and pathway analysis. To predict prognosis, we developed a LASSO-Cox prognostic signature. This signature's predictive ability was rigorously examined through multivariant Cox regression, survival analysis, and ROC curve analysis. Following this, we constructed a nomogram based on the aforementioned signature and evaluated its accuracy and clinical utility using calibration curves, cumulative hazard rates, and decision curve analysis. Using this signature, we stratified all cases into high- and low-risk groups and compared the differences in immune characteristics and drug treatment responsiveness between these two subgroups. Additionally, in this study, we provided preliminary confirmation of the pivotal role of CD1D in the TIME of CM. We analyzed its expression across various immune cell types and its correlation with intercellular communication using single-cell data from the GSE139249 dataset.

RESULTS

In this study, a total of 84 DEFAGs were identified, among which 18 were associated with prognosis. Utilizing these 18 prognosis-related genes, all cases were categorized into three subtypes. Significant differences were observed between subtypes in terms of survival outcomes, the expression of the 18 DEFAGs, immune cell proportions, and enriched pathways. A LASSO-Cox regression analysis was performed on these 18 genes, leading to the development of a signature comprising 6 DEFAGs. Risk scores were calculated for all cases, dividing them into high-risk and low-risk groups. High-risk patients exhibited significantly poorer prognosis than low-risk patients, both in the training group (p < 0.001) and the test group (p = 0.002). Multivariate Cox regression analysis indicated that this signature could independently predict outcomes [HR = 2.03 (1.69-2.45), p < 0.001]. The area under the ROC curve for the training and test groups was 0.715 and 0.661, respectively. Combining risk scores with clinical factors including metastatic status and patient age, a nomogram was constructed, which demonstrated significant predictive power for 3  and 5 years patient outcomes. Furthermore, the high and low-risk subgroups displayed differences in the composition of various immune cells, including M1 macrophages, M0 macrophages, and CD8+ T cells. The low-risk subgroup exhibited higher StromalScore, ImmuneScore, and ESTIMATEScore (p < 0.001) and demonstrated better responsiveness to immune therapy for patients with PD1-positive and CTLA4-negative or positive expressions (p < 0.001). The signature gene CD1D was found to be mainly expressed in monocytes/macrophages and dendritic cells within the TIME. Through intercellular communication analysis, it was observed that cases with high CD1D expression exhibited significantly enhanced signal transductions from other immune cells to monocytes/macrophages, particularly the (HLA-A/B/C/E/F)-CD8A signaling from natural killer (NK) cells to monocytes/macrophages (p < 0.01).

CONCLUSIONS

The prognostic signature constructed in this study, based on six fatty acid-related genes, exhibits strong capabilities in predicting patient outcomes, identifying the TIME, and assessing drug sensitivity. This signature can aid in patient risk stratification and provide guidance for clinical treatment strategies. Additionally, our research highlights the crucial role of CD1D in the CM's TIME, laying a theoretical foundation for future related studies.

Bioinformatics analyses of immune-related genes and immune infiltration associated with lung ischemia-reperfusion injury

BACKGROUND

Ischemia-reperfusion injury (IRI) is a significant complication that can occur following lung transplantation and is known to contribute to poor prognosis. Our research aimed to investigate the potential molecular targets and mechanisms involved in lung IRI (LIRI), in order to improve our understanding of this condition.

METHOD

We downloaded gene expression datasets (GSE127003 and GSE18995) linked to LIRI from the GEO database. Using WGCNA, we identified LIRI-related modules. Functional enrichment analyses were performed on the modules showing significant correlation with LIRI. Core immune-related genes (IRGs) were identified and validated using the GSE18995 dataset. A rat LIRI model was established to validate the expression changes of core IRGs. The LIRI groups were subjected to 60 min of warm ischemia followed by 120 min of reperfusion. Additionally, the xCell algorithm was used to characterize the immune landscape and analyze the relationships between hub IRGs and infiltrating immune cells.

RESULTS

A total of 483 genes from the turquoise module were identified through WGCNA, with a predominant enrichment in immune- and inflammation-related pathways. Three IRGs (PTGS2, CCL2, and RELB) were found to be up-regulated after reperfusion in both GSE127003 and GSE18995 datasets, and this was further confirmed using the rat LIRI model. The xCell analysis revealed that immune score, CD8+ naive T cells, eosinophils, neutrophils, NK cells, and Tregs were upregulated after reperfusion. PTGS2, CCL2, and RELB showed positive correlations with CD8+ naive T cells, monocytes, neutrophils, and Tregs.

CONCLUSION

PTGS2, CCL2, and RELB were found to be potential biomarkers for LIRI. Immune and microenvironment scores were higher after reperfusion compared to before reperfusion. PTGS2, CCL2, and RELB appear to play a crucial role in the development of LIRI and may contribute to it by increasing the number of immune cells. Our findings offer new perspectives on potential treatment targets and the pathogenesis of LIRI.