Dataset Information

Construction of prediction model of inflammation related genes in idiopathic pulmonary fibrosis and its correlation with immune microenvironment.

ABSTRACT:

Background

The role of inflammation in the formation of idiopathic pulmonary fibrosis (IPF) has gained a lot of attention recently. However, the involvement of genes related to inflammation and immune exchange environment status in the prognosis of IPF remains to be further clarified. The objective of this research is to establish a new model for the prediction of the overall survival (OS) rate of inflammation-related IPF.

Methods

Gene Expression Omnibus (GEO) was employed to obtain the three expression microarrays of IPF, including two from alveolar lavage fluid cells and one from peripheral blood mononuclear cells. To construct the risk assessment model of inflammation-linked genes, least absolute shrinkage and selection operator (lasso), univariate cox and multivariate stepwise regression, and random forest method were used. The proportion of immune cell infiltration was evaluated by single sample Gene Set Enrichment Analysis (ssGSEA) algorithm.

Results

The value of genes linked with inflammation in the prognosis of IPF was analyzed, and a four-genes risk model was constructed, including tpbg, Myc, ffar2, and CCL2. It was highlighted by Kaplan Meier (K-M) survival analysis that patients with high-risk scores had worse overall survival time in all training and validation sets, and univariate and multivariate analysis highlighted that it has the potential to act as an independent risk indicator for poor prognosis. ROC analysis showed that the prediction efficiency of 1-, 3-, and 5-year OS time in the training set reached 0.784, 0.835, and 0.921, respectively. Immune infiltration analysis showed that Myeloid-Derived Suppressor Cells (MDSC), macrophages, regulatory T cells, cd4+ t cells, neutrophils, and dendritic cells were more infiltrated in the high-risk group than in the low-risk group.

Conclusion

Inflammation-related genes can be well used to evaluate the IPF prognosis and impart a new idea for the treatment and follow-up management of IPF patients.

SUBMITTER: Yin YQ

PROVIDER: S-EPMC9806212 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Construction of prediction model of inflammation related genes in idiopathic pulmonary fibrosis and its correlation with immune microenvironment.

Yin Ying-Qiu YQ Peng Feng F Situ Hui-Jing HJ Xie Jun-Ling JL Tan Liming L Wei Jie J Jiang Fang-Fang FF Zhang Shan-Qiang SQ Liu Jun J

Frontiers in immunology 20221219

<h4>Background</h4>The role of inflammation in the formation of idiopathic pulmonary fibrosis (IPF) has gained a lot of attention recently. However, the involvement of genes related to inflammation and immune exchange environment status in the prognosis of IPF remains to be further clarified. The objective of this research is to establish a new model for the prediction of the overall survival (OS) rate of inflammation-related IPF.<h4>Methods</h4>Gene Expression Omnibus (GEO) was employed to obta ...[more]

PMID: 36601116

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Project description:BackgroundIdiopathic pulmonary fibrosis (IPF) is a highly fatal lung disease of unknown etiology with a median survival after diagnosis of only 2-3 years. Its poor prognosis is due to the limited therapy options available as well as the lack of effective prognostic indicators. This study aimed to construct a novel prognostic signature for IPF to assist in the personalized management of IPF patients during treatment.MethodsDifferentially-expressed genes (DEGs) in IPF patients versus healthy individuals were analyzed using the "limma" package of R software. Immune-related genes (IRGs) were obtained from the ImmPort database. Univariate Cox regression analysis was adopted to screen significantly prognostic IRGs for IPF patients. Multiple Cox regression analysis was used to identify optimal prognostic IRGs and construct a prognostic signature.ResultsCompared with healthy individuals, there were a total of 52 prognosis-related DEGs in the bronchoalveolar lavage (BAL) samples of IPF patients, of which 37 genes were identified as IRGs. Of these, five genes (CXCL14, SLC40A1, RNASE3, CCR3, and RORA) were significantly associated with overall survival (OS) in IPF patients, and were utilized for establishment of the prognostic signature. IPF patients were divided into high- and low-risk groups based on the prognostic signature. Marked differences in the OS probability were observed between high- and low-risk IPF patients. The area under curves (AUCs) of the receiver operating characteristic (ROC) curve for the prognostic signature in the training and validation cohorts were 0.858 and 0.837, respectively. The expression levels between RNASE3 and SLC40A1 (P<0.01, r=0.394), between RORA and CXCL14 (P<0.01, r=-0.355), between CCR3 and CXCL14 (P<0.01, r=0.258), as well as between RNASE3 and CCR3 (P<0.01, r=0.293) were significantly correlated.ConclusionsWe developed a validated and reproducible IRG-based prognostic signature that should be helpful in the personalized management of patients with IPF, providing new insights into the relationship between the immune system and IPF.

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Project description:BackgroundIdiopathic pulmonary fibrosis (IPF) is a severe lung condition, and finding better ways to diagnose and treat the disease is crucial for improving patient outcomes. Our study sought to develop an artificial neural network (ANN) model for IPF and determine the immune cell types that differed between the IPF and control groups.MethodsFrom the Gene Expression Omnibus (GEO) database, we first obtained IPF microarray datasets. To conduct protein-protein interaction (PPI) networks and enrichment analyses, differentially expressed genes (DEGs) were screened between tissues of patients with IPF and tissues of controls. Afterward, we identified the important feature genes associated with IPF using random forest (RF) analysis, and then constructed and validated a prediction ANN mode. In addition, the proportions of immune cells were quantified using cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT) analysis, which was performed on microarray datasets based on gene expression profiling.ResultsA total of 11 downregulated and 36 upregulated DEGs were identified. PPI networks and enrichment analyses were carried out; the immune system and extracellular matrix were the subjects of the enrichments. Using RF analysis, the significant feature genes LRRC17, COMP, ASPN, CRTAC1, POSTN, COL3A1, PEBP4, IL13RA2, and CA4 were identified. The nine feature gene scores were integrated into the ANN to develop a diagnostic prediction model. The receiver operating characteristic (ROC) curves demonstrated the strong diagnostic ability of the ANN in predicting IPF in the training and testing sets. An analysis of IPF tissues in comparison to normal tissues revealed a reduction in the infiltration of natural killer cells resting, monocytes, macrophages M0, and neutrophils; conversely, the infiltration of T cells CD4 memory resting, mast cells, and macrophages M0 increased.ConclusionLRRC17, COMP, ASPN, CRTAC1, POSTN, COL3A1, PEBP4, IL13RA2, and CA4 were determined as key feature genes for IPF. The nine feature genes in the ANN model will be extremely important for diagnosing IPF. It may be possible to use differentiated immune cells from IPF samples in comparison to normal samples as targets for immunotherapy in patients with IPF.

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Dataset Information

Construction of prediction model of inflammation related genes in idiopathic pulmonary fibrosis and its correlation with immune microenvironment.

Background

Methods

Results

Conclusion

Publications

Construction of prediction model of inflammation related genes in idiopathic pulmonary fibrosis and its correlation with immune microenvironment.

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