Identification and validation of potential biomarkers related to oxidative stress in idiopathic pulmonary fibrosis.

Idiopathic pulmonary fibrosis (IPF) is a chronic, progressive, fibrotic interstitial pneumonia with a poor prognosis and a pathogenesis that has not been fully elucidated. Oxidative stress is closely associated with IPF. In this research, we aimed to identify reliable diagnostic biomarkers associated with the oxidative stress through bioinformatics techniques. The gene expression profile data from the GSE70866 dataset was retrieved from the gene expression omnibus (GEO) database. We extracted 437 oxidative stress-related genes (ORGs) from gene set enrichment analysis (GSEA). The GSE141939 dataset was used for single-cell RNA-seq analysis to identify the expression of diagnostic genes in different cell clusters. A total of 10 differentially expressed oxidative stress-related genes (DE-ORGs) were screened. Subsequently, SOD3, CD36, ACOX2, RBM11, CYP1B1, SNCA, and MPO from the 10 DE-ORGs were identified as diagnostic genes based on random forest algorithm with randomized least absolute shrinkage and selection operator (LASSO) regression. A nomogram was constructed to evaluate the risk of disease. The decision curve analysis (DCA) and clinical impact curves indicated that the nomogram based on these seven biomarkers had extraordinary predictive power. Immune cell infiltration analysis results revealed that DE-ORGs were closely related to various immune cells, especially CYP1B1 was in positive correlation with monocytes and negative correlation with macrophages M1. Single-cell RNA-seq analysis showed that CYP1B1 was mainly associated with macrophages, and SNCA was mainly associated with basal cells. CYP1B1 and SNCA were diagnostic genes associated with oxidative stress in IPF.

Du X ，Ma Z ，Xing Y ，Feng L ，Li Y ，Dong C ，Ma X ，Huo R ，Tian X ... -  《-》

Erratum: Eyestalk Ablation to Increase Ovarian Maturation in Mud Crabs.

《Jove-Journal of Visualized Experiments》

[Exploration of key ferroptosis-related genes as therapeutic targets for sepsis based on bioinformatics and the depiction of their immune profiles characterization].

Li M ，Mei Y ，Pan A 《-》

Establishment and validation of a prognostic model for idiopathic pulmonary fibrosis based on mitochondrial-related genes.

The prognosis for patients diagnosed with idiopathic pulmonary fibrosis (IPF) is exceedingly grim, and there are currently no pharmacological interventions available that effectively reduce mortality rates. Emerging evidence underscores the intimate connection between mitochondrial dysfunction and the onset and advancement of IPF. However, there remains a scarcity of prognostic models for assessing the risk associated with mitochondrial-related genes in IPF. This study aims to develop a comprehensive prognostic model for IPF that incorporates mitochondrial-related genes to enhance risk assessment and guide clinical decision-making. Two IPF-related microarray expression profiling datasets (GSE28042 and GSE70866) accompanied with survival data were acquired from the Gene Expression Omnibus (GEO) database. The "limma" R package was used to identify differentially expressed mitochondrial-related genes between normal samples and IPF samples. The prognostic model was constructed using univariate Cox regression, the least absolute shrinkage and selection operator (LASSO) Cox regression analyses, and multivariate Cox regression analysis. Multivariate independent prognostic analysis was conducted to ascertain whether the risk score could serve as an independent prognostic factor for predicting clinicopathological outcomes. A nomogram was employed to forecast the survival probability of IPF patients, providing valuable support for clinical decision-making processes. The CIBERSORT algorithm was utilized to examine discrepancies in immune cell infiltration within the model. The expression of genes screened from the prognostic model was validated in external data sets and western blot assays. We developed a prognostic model for mitochondrial-related risks, incorporating ARMCX2 and ACOT11, and subsequently validated its predictive efficacy in the validation set. The IPF samples were stratified into high-risk and low-risk groups based on the median of the risk score. According to Kaplan-Meier curve analysis, the high-risk group exhibited inferior outcomes compared to the low-risk group. The time-dependent receiver operating characteristic (ROC) analysis demonstrated the accurate prognostic capability of the risk model for IPF. A nomogram, accompanied by calibration curves, was presented to predict 1-, 2-, and 3-year survival in IPF patients. The risk model we employed not only unveiled significant disparities in functional enrichment between the high-risk and low-risk groups, but also demonstrated a robust correlation with the infiltration of specific immune cells. In this study, the mitochondrial-related prognostic model incorporating ARMCX2 and ACOT11 demonstrates potential clinical utility for informing decision-making in IPF patients and offers valuable insights for future therapeutic interventions.

Wang X ，Yang L ，Wang Y ，Dou X ，Li Y ，Wang K ，Zhang H ... -  《-》

Identification of an immune-related gene panel for the diagnosis of pulmonary arterial hypertension using bioinformatics and machine learning.

This study aimed to screen an immune-related gene (IRG) panel and develop a novel approach for diagnosing pulmonary arterial hypertension (PAH) utilizing bioinformatics and machine learning (ML). Gene expression profiles were retrieved from the Gene Expression Omnibus (GEO) database to identify differentially expressed immune-related genes (IRG-DEGs). We employed five machine learning algorithms-LASSO, random forest (RF), boosted regression trees (BRT), XGBoost, and support vector machine recursive feature elimination (SVM-RFE) to identify biomarkers derived from IRG-DEGs associated with the diagnosis of PAH, incorporating them into the IRG-DEGs panel. Validation of these biomarker levels in lung tissue was conducted in a hypoxia-induced mouse model of PAH, investigating the correlation between AIMP1, IL-15, GLRX, SOD1, Fulton's index (RVHI), and the ratio of pulmonary artery medial thickness to external diameter (MT%). Subsequently, we developed a nomogram model based on the IRG-DEGs panel in lung tissue for diagnosing PAH. The expression, distribution, and pseudotime analysis of these biomarkers across various immune cell types were assessed using single-cell sequencing datasets. Finally, we evaluated the diagnostic utility of the nomogram model based on the IRG-DEGs panel in peripheral blood mononuclear cells (PBMCs) for diagnosing PAH. A total of 36 upregulated and 17 downregulated IRG-DEGs were identified in lung tissue from patients with PAH. AIMP1, IL-15, GLRX, and SOD1 were subsequently selected as novel immune-related biomarkers for PAH through the aforementioned machine learning algorithms and incorporated into the IRG-DEGs panel. Experimental results from mice with PAH validated that the expression levels of AIMP1, IL-15, and GLRX in lung tissue were elevated, while SOD1 expression was significantly reduced. Additionally, GLRX and AIMP1 exhibited positive correlations with Fulton's index (RVHI). The expression levels of GLRX, IL-15, and AIMP1 showed positive correlations with MT%, whereas SOD1 exhibited negative correlations with MT%. Analysis of single-cell sequencing data further revealed that the levels of IRG-DEG panel members gradually increased during the pseudotime trajectory from PBMCs to macrophages, correlating with macrophage activation. The area under the curve (AUC) for diagnosing PAH using a nomogram model based on the IRG-DEGs panel derived from lung tissue samples and PBMCs was ≥0.969 and 0.900, respectively. We developed an IRG-DEGs panel containing AIMP1, IL-15, GLRX, and SOD1, which may facilitate the diagnosis of pulmonary arterial hypertension (PAH). These findings provide novel insights that may enhance diagnostic and therapeutic approaches for PAH.

Xiong P ，Huang Q ，Mao Y ，Qian H ，Yang Y ，Mou Z ，Deng X ，Wang G ，He B ，You Z ... -  《-》