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Development and Validation of the Prognostic Index Based on Inflammation-Related Gene Analysis in Idiopathic Pulmonary Fibrosis.

ABSTRACT: Background: Historically, idiopathic pulmonary fibrosis (IPF) was considered a chronic inflammation disorder, but this conception was reassessed in the past decades. Our understanding of the role of inflammation in IPF and its association with clinical significance remained incomplete. Methods: We downloaded mRNA expression data of peripheral blood mononuclear cells (PBMCs) from the Gene Expression Omnibus (GEO) repository. Inflammation-related genes (IRGs) expressed differently between IPF and control (CTRL) were determined. In this study, we systemically analyzed the expression of differently expressed IRGs by comprehensive bioinformatic analysis, and then investigated their potential prognostic values. The related prognostic gene expressions were verified in our cohort. Results: 110 differently expressed IRGs were identified in this study, including 64 upregulated and 46 downregulated IRGs. Three IRGs (S100A12, CCR7, and TNFSF4) were identified as potential hub genes for prognosis. Those genes were subsequently subjected to the construction of the prognostic models. In the results, IPF patients categorized as high risk demonstrated a poor overall survival rate compared to patients categorized as low risk. Based on this prognostic model, the area under the curve (AUC) of the survival-dependent receiver operator characteristic (ROC) for 1-year, 2-year, and 3-year survival rates was 0.611, 0.695, and 0.681, respectively, in the GSE28042 cohort. These observations were validated in the GSE27957 cohort, confirming the good prognostic effect of this model. The expression of the three genes was validated in our cohort. We also conducted a nomogram based on the three IRGs' mRNA for quantitative IPF prognosis. Conclusion: Three IRGs (S100A12, CCR7, and TNFSF4) were identified as potential markers for the prognosis of IPF.

SUBMITTER: Lu Y

PROVIDER: S-EPMC8339426 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Aging plays a significant role in the occurrence and development of idiopathic pulmonary fibrosis (IPF). In this study, we aimed to identify and verify potential aging-associated genes involved in IPF using bioinformatic analysis. The mRNA expression profile dataset GSE150910 available in the Gene Expression Omnibus (GEO) database and R software were used to identify the differentially expressed aging-related genes involved in IPF. Hub gene expression was validated by other GEO datasets. Gene ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed on differentially expressed aging-related genes. Subsequently, aging-related genes were further screened using three techniques (least absolute shrinkage and selection operator (LASSO) regression, support vector machine, and random forest), and the receiver operating characteristic curves were plotted based on screening results. Finally, real-time quantitative polymerase chain reaction (qRT-PCR) was performed to verify the RNA expression of the six differentially expressed aging-related genes using the blood samples of patients with IPF and healthy individuals. Sixteen differentially expressed aging-related genes were detected, of which the expression of 12 were upregulated and four were downregulated. GO and KEGG enrichment analyses indicated the presence of several enriched terms related to senescence and apoptotic mitochondrial changes. Further screening by LASSO regression, support vector machine, and random forest identified six genes (IGF1, RET, IGFBP2, CDKN2A, JUN, and TFAP2A) that could serve as potential diagnostic biomarkers for IPF. Furthermore, qRT-PCR analysis indicated that among the above-mentioned six aging-related genes, only the expression levels of IGF1, RET, and IGFBP2 in patients with IPF and healthy individuals were consistent with the results of bioinformatic analysis. In conclusion, bioinformatics analysis identified 16 potential aging-related genes associated with IPF, and clinical sample validation suggested that among these, IGF1, RET, and IGFBP2 might play a role in the incidence and prognosis of IPF. Our findings may help understand the pathogenesis of IPF.

Project description:BackgroundLung cancer is the most common malignant tumor in the world, and its prognosis is still not optimistic. The aim of this study was to establish an immune-related gene (IRG) prognostic index (IRGPI) for lung adenocarcinoma (LUAD) based on IRGs, and to explore the prognosis, molecular and immune features, and response to immune checkpoint inhibitor (ICI) therapy in IRGPI-classified different subgroups of LUAD.MethodsBased on the LUAD transcriptome RNA-sequencing data in TCGA database, the differentially expressed genes (DEGs) were selected. Subsequently, DEGs were intersected with IRGs to obtain differentially expressed immune-related genes (DEIRGs). Weighted gene co-expression network analysis (WGCNA) identified hub genes in DEIRGs. Finally, univariate and multivariate Cox regression analyses were used to build an IRGPI model. Subsequently, TCGA patients were divided into high- and low-risk groups, and the survival of patients in different groups was further analyzed. Besides, we validated the molecular and immune characteristics, relationship with immune checkpoints, angiogenesis-related genes, and immune subtypes distribution in different subgroups. Meanwhile, we further validated the response to ICI therapy in different subgroups.ResultsThe IRGPI was constructed based on 13 DEIRGs. Compared with the low-risk group, overall survival (OS) was lower in the high-risk group, and the high-risk score was independently associated with poorer OS. Besides, the high-risk score was associated with cell cycle pathway, high mutation rate of TP53 and KRAS, high infiltration of M0 macrophages, and immunosuppressive state, and these patients had poorer prognosis but the TIDE score of the high-risk group was lower than that of the other group, which means that the high-risk group could benefit more from ICI treatment. In contrast, the low-risk score was related to low mutation rate of TP53 and KRAS, high infiltration of plasma cells, and immunoactive state, and these patients had better prognosis but the low-risk group less benefit from ICI treatment based on the results of TIDE score.ConclusionsIRGPI is a prospective biomarker based on IRGs that can distinguish high- and low-risk groups to predict patient prognosis, help characterize the tumor immune microenvironment, and evaluate the benefit of ICI therapy in LUAD.

Project description:Background Idiopathic pulmonary fibrosis (IPF) is a chronic progressive interstitial lung disease with limited therapeutic options. Recent studies have demonstrated that chemokines play a vital role in IPF pathogenesis. In the present study, we explored whether the gene signature associated with chemokines could be used as a reliable biological marker for patients with IPF. Methods Chemokine-related differentially expressed genes (CR-DEGs) in IPF and control lung tissue samples were identified using data from the Gene Expression Omnibus database. A chemokine-related signature of the diagnostic model was established using the LASSO-Cox regression. In addition, unsupervised cluster analysis was conducted using consensus-clustering algorithms. The CIBERSORT algorithm was used to calculate immune cell infiltration across patient subgroups. Finally, we established a mouse model of bleomycin-induced pulmonary fibrosis and a model of fibroblasts treated with TGFβ1. Expression levels of chemokine-related signature genes were determined using real-time quantitative polymerase chain reaction (RT-qPCR). Results We established a chemokine-related eleven-gene signature of a diagnostic model consisting of CXCL2, CCRL2, ARRB1, XCL1, GRK5, PPBP, CCL19, CCL13, CCL11, CXCL6, and CXCL13, which could easily distinguish between IPF patients and controls. Additionally, we identified two subtypes of IPF samples based on chemokine-related gene expression. Pulmonary function parameters and stromal scores were significantly higher in subtype 1 than in subtype 2. Several immune cell types, especially plasma cells and macrophages, differ significantly between the two subtypes. RT-qPCR results showed that the expression levels of Cxcl2 and Ccl2 increased considerably in bleomycin-induced mice. Meanwhile, Arrb1, Ccrl2, Grk5, and Ppbp expression was significantly reduced. Furthermore, multiple chemokine-related genes were altered in TGFβ1 or TNFα-induced fibroblast cells. Conclusions A novel chemokine-related eleven-signature of diagnostic model was developed. These genes are potential biomarkers of IPF and may play essential roles in its pathogenesis.

Project description:BackgroundIdiopathic pulmonary fibrosis (IPF) is a chronic, progressive, and fatal fibrotic pulmonary disease with unknow etiology. Owing to lack of reliable prognostic biomarkers and effective treatment measures, patients with IPF usually exhibit poor prognosis. The aim of this study is to establish a risk score prognostic model for predicting the prognosis of patients with IPF based on autophagy-related genes.MethodsThe GSE70866 dataset was obtained from the gene expression omnibus (GEO) database. The autophagy-related genes were collected from the Molecular Signatures Database (MSigDB). Gene enrichment analysis for differentially expressed genes (DEGs) was performed to explore the function of DEGs. Univariate, least absolute shrinkage and selection operator (LASSO), as well as multivariate Cox regression analyses were conducted to identify a multi-gene prognostic model. Receiver operating characteristic (ROC) curve was applied to assess the prediction accuracy of the model. The expression of genes screened from the prognostic model was validated in clinical samples and human lung fibroblasts by qPCR and western blot assays.ResultsAmong the 514 autophagy-related genes, a total of 165 genes were identified as DEGs. These DEGs were enriched in autophagy-related processes and pathways. Based on the univariate, LASSO, and multivariate Cox regression analyses, two genes (MET and SH3BP4) were included for establishing the risk score prognostic model. According to the median value of the risk score, patients with IPF were stratified into high-risk and low-risk groups. Patients in high-risk group had shorter overall survival (OS) than low-risk group in both training and test cohorts. Multivariate regression analysis indicated that prognostic model can act as an independent prognostic indicator for IPF. ROC curve analysis confirmed the reliable predictive value of prognostic model. In the validation experiments, upregulated MET expression and downregulated SH3BP4 expression were observed in IPF lung tissues and TGF-β1-activated human lung fibroblasts, which is consistent with results from microarray data analysis.ConclusionThese findings indicated that the risk score prognostic model based on two autophagy-related genes can effectively predict the prognosis of patients with IPF.

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Development and Validation of the Prognostic Index Based on Inflammation-Related Gene Analysis in Idiopathic Pulmonary Fibrosis.

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