Investigation of a Hypoxia-Immune-Related Microenvironment Gene Signature and Prediction Model for Idiopathic Pulmonary Fibrosis

CD4+T and CD8+T cells profile in lung inflammation and fibrosis: targets and potential therapeutic drugs

Pulmonary fibrosis is an interstitial lung disease characterized by chronic progressive fibrosis. It is associated with fibrocyte proliferation and collagen deposition, leading to severe, irreversible lung function decline. Despite extensive research, the diagnosis and treatment of pulmonary fibrosis are complicated and have no effective treatment. During the formation of pulmonary fibrosis, immune dysregulation by inflammatory cell infiltration is the key driver of pulmonary fibrosis. Recently, single-cell sequencing analysis of silicosis mice showed that various cells in the alveolar immune microenvironment are involved in forming pulmonary fibrosis, such as macrophages, fibroblasts, epithelial cells, etc. Among them, T cell subpopulations in silicosis mice were significantly activated, indicating that T lymphocyte subsets play an essential role in the process of pulmonary fibrosis. More and more pulmonary clinical studies show that T lymphocytes in the lung immune microenvironment play an important and multifaceted role. This article summarizes the role of CD4+T cells and CD8+T cells in pulmonary fibrosis. This article provides some new insight into the potential therapy target that can delay the process of pulmonary fibrosis by regulating the proportions of different subpopulations of T lymphocytes and some related signaling pathways.

Super-Enhancer Target Gene CBP/p300-Interacting Transactivator With Glu/Asp-Rich C-Terminal Domain, 2 Cooperates With Transcription Factor Forkhead Box J3 to Inhibit Pulmonary Vascular Remodeling

The function of super-enhancers (SEs) in pulmonary hypertension (PH), especially in the proliferation of pulmonary artery smooth muscle cells (PASMCs), is currently unknown. We identified SEs-targeted genes in PASMCs with chromatin immunoprecipitation (ChIP)-sequence by H3K27ac antibody and proved that CBP/p300-interacting transactivator with Glu/Asp-rich C-terminal domain, 2 (CITED2) is an SEs-targeted gene through bioinformatics prediction, ChIP-PCR, dual-luciferase reporter gene assays and other experimental methods. We also found that the expression of CITED2 and the transcription factor Forkhead Box J3 (FOXJ3) was reduced in hypoxic mouse PASMCs. In addition, the expression of CITED2 and FOXJ3 also decreased in both the patients with idiopathic pulmonary arterial hypertension (iPAH) and the human PASMCs exposed to hypoxia. The decreased expression of CITED2 was reversed by co-transfection of FOXJ3 and SEs plasmids. Overexpressing of CITED2 attenuated the PASMCs proliferation induced by hypoxia. Lentiviral overexpression of CITED2 also reversed hypoxia-induced pulmonary hypertension mice model. Mechanically, the expression of CITED2 by affecting by FOXJ3, which binding with three SEs located in the about 2000 bp of TSS. In conclusion, we first identified that CITED2 is a kind of SEs-targeted gene, modulated by FOXJ3. The FOXJ3/SEs/CITED2 axis may become a new therapeutic target of PH.

Integrating cellular experiments, single-cell sequencing, and machine learning to identify endoplasmic reticulum stress biomarkers in idiopathic pulmonary fibrosis

BACKGROUND

Idiopathic Pulmonary Fibrosis (IPF) presents a severe respiratory challenge with a poor prognosis due to the lack of reliable biomarkers. Recent evidence suggests that Endoplasmic Reticulum Stress (ERS) may be associated with IPF pathogenesis. This study focuses on uncovering ERS-associated biomarkers for IPF.

METHODS

Sequencing data from diverse datasets were analyzed, utilizing differential gene expression analysis and Weighted Gene Co-expression Network Analysis (WGCNA). Endoplasmic Reticulum Stress (ERS)-related genes were extracted from the GeneCards database. Hub genes were identified through Protein-Protein Interaction (PPI) analysis. Diagnostic and prognostic models were developed using machine learning algorithms and validated across both training and validation sets. Additionally, techniques such as Cell-type Identification by Estimating Relative Subsets of RNA Transcripts and single-cell RNA sequencing were employed to identify potential IPF-related cells. These findings were further investigated to elucidate their underlying mechanisms through in vitro experiments.

RESULTS

Differentially expressed genes, WGCNA-identified blue module genes, and ERS-related genes extracted from the GeneCards database were intersected, and the resulting genes were used to construct diagnostic and prognostic models. Validation using multiple datasets indicated that both the diagnostic and prognostic models possess strong predictive capabilities. PPI analysis highlighted SPP1 as a potential hub gene in IPF. Moreover, M2 macrophages were found in higher quantities in the lung tissue of IPF patients, with a significant increase in SPP1-expressing M2 macrophages compared to the control group. In vitro experiments demonstrated that exogenous SPP1 inhibited the proliferation and migration of M2 macrophages and promoted apoptosis within a certain concentration range.

CONCLUSION

This study identifies ERS-related biomarkers in IPF, highlighting SPP1 and M2 macrophages. The resulting diagnostic and prognostic models offer strong predictive capabilities, unveiling new therapeutic avenues.

Prognostic Function and Immunologic Landscape of a Predictive Model Based on Five Senescence-Related Genes in IPF Bronchoalveolar Lavage Fluid

BACKGROUND

Idiopathic pulmonary fibrosis (IPF) is a type of interstitial lung disease characterized by unknown causes and a poor prognosis. Recent research indicates that age-related mechanisms, such as cellular senescence, may play a role in the development of this condition. However, the relationship between cellular senescence and clinical outcomes in IPF remains uncertain.

METHODS

Data from the GSE70867 database were meticulously analyzed in this study. The research employed differential expression analysis, as well as univariate and multivariate Cox regression analysis, to pinpoint senescence-related genes (SRGs) linked to prognosis and construct a prognostic risk model. The model's clinical relevance and its connection to potential biological processes were systematically assessed in training and testing datasets. Additionally, the expression location of prognosis-related SRGs was identified through immunohistochemical staining, and the correlation between SRGs and immune cell infiltration was deduced using the GSE28221 dataset.

RESULT

The prognostic risk model was constructed based on five SRGs (cellular communication network factor 1, CYR61, stratifin, SFN, megakaryocyte-associated tyrosine kinase, MATK, C-X-C motif chemokine ligand 1, CXCL1, LIM domain, and actin binding 1, LIMA1). Both Kaplan-Meier (KM) curves (p = 0.005) and time-dependent receiver operating characteristic (ROC) analysis affirmed the predictive accuracy of this model in testing datasets, with respective areas under the ROC curve at 1-, 2-, and 3-years being 0.721, 0.802, and 0.739. Furthermore, qRT-RCR analysis and immunohistochemical staining verify the differential expression of SRGs in IPF samples and controls. Moreover, patients in the high-risk group contained higher infiltration levels of neutrophils, eosinophils, and M1 macrophages in BALF, which appeared to be independent indicators of poor prognosis in IPF patients.

CONCLUSION

Our research reveals the effectiveness of the 5 SRGs model in BALF for risk stratification and prognosis prediction in IPF patients, providing new insights into the immune infiltration of IPF progression.

Anoikis and SPP1 in idiopathic pulmonary fibrosis: integrating bioinformatics, cell, and animal studies to explore prognostic biomarkers and PI3K/AKT signaling regulation

OBJECTIVE

This study aims to explore the relevance of anoikis in idiopathic pulmonary fibrosis (IPF) and identify associated biomarkers and signaling pathways.

METHOD

Unsupervised consensus cluster analysis was employed to categorize IPF patients into subtypes. We utilized Weighted Gene Co-Expression Network Analysis (WGCNA) and Protein-Protein Interaction network construction to identify anoikis-related modules and key genes. A prognostic signature was developed using Lasso and multivariate Cox regression analysis. Single-cell sequencing assessed hub gene expression in various cell types, and both cell and animal experiments confirmed IPF-related pathways.

RESULTS

We identified two distinct anoikis-associated subtypes with differing prognoses. WGCNA revealed essential hub genes, with SPP1 being prominent in the anoikis-related signature. The anoikis-related signature is effective in determining the prognosis of patients with IPF. Single-cell sequencing highlighted significant differences in SPP1 expression, notably elevated in fibroblasts derived from IPF patients. In vivo and in vitro experiments demonstrated that SPP1 enhances fibrosis in mouse lung fibroblasts by regulating p27 through the PI3K/Akt pathway.

CONCLUSION

Our research demonstrates a robust prognostic signature associated with anoikis and highlights SPP1 as a pivotal regulator of the PI3K/AKT signaling pathway in pulmonary fibrosis.

Development and validation of a novel model to predict pulmonary embolism in cardiology suspected patients: A 10-year retrospective analysis

As there are no predictive models for pulmonary embolism (PE) in patients with suspected PE at cardiology department. This study developed a predictive model for the probability of PE development in these patients. This retrospective analysis evaluated data from 995 patients with suspected PE at the cardiology department from January 2012 to December 2021. Patients were randomly divided into the training and validation cohorts (7:3 ratio). Using least absolute shrinkage and selection operator regression, optimal predictive features were selected, and the model was established using multivariate logistic regression. The features used in the final model included clinical and laboratory factors. A nomogram was developed, and its performance was assessed and validated by discrimination, calibration, and clinical utility. Our predictive model showed that six PE-associated variables (age, pulse, systolic pressure, syncope, D-dimer, and coronary heart disease). The area under the curve - receiver operating characteristic curves of the model were 0.721 and 0.709 (95% confidence interval: 0.676-0.766 and 0.633-0.784), respectively, in both cohorts. We also found good consistency between the predictions and real observations in both cohorts. In decision curve analysis, the numerical model had a good net clinical benefit. This novel model can predict the probability of PE development in patients with suspected PE at cardiology department.

Immune factors have a complex causal regulation on pulmonary fibrosis: Insights from a two-sample Mendelian randomization analysis

Pulmonary fibrosis is a chronic, progressive lung disease characterized by excessive scarring of lung tissue, and its pathophysiological mechanisms have not been fully elucidated. Immune cells play a key role in many diseases, and this study aims to explore the causal link between immune cell characteristics and pulmonary fibrosis using Mendelian randomization. Utilizing the public GWAS database Open GWAS, this study collected whole-genome association study datasets of peripheral blood immune phenotypes and summary data of GWAS related to pulmonary fibrosis. Through Mendelian randomization (MR) analysis, we identified single nucleotide polymorphisms (SNPs) significantly associated with immune traits as instrumental variables. After pleiotropy and heterogeneity tests, causal effects were assessed using methods such as inverse-variance weighted (IVW), weighted median, and MR-Egger. Comprehensive MR analysis indicated a significant causal relationship between various immune cell types, including regulatory T cells (Tregs), natural killer (NK) cells, and specific monocyte subgroups, with the risk of pulmonary fibrosis. Specifically, phenotypes such as Activated & resting Treg %CD4+, CCR2-positive monocytes, and CD16-CD56 positive NK cells were associated with a reduced risk of pulmonary fibrosis. In contrast, CD8 + T cell subgroups were associated with an increased risk. This study provides evidence of a causal relationship between immune cell characteristics and pulmonary fibrosis, highlighting the protective role of regulatory T cells and specific NK cell subgroups, as well as the potential harm of CD8 + T cell subgroups. These findings offer new insights into the immunoregulatory mechanisms of pulmonary fibrosis and the development of novel therapeutic strategies.

Investigation of a UPR-Related Gene Signature Identifies the Pro-Fibrotic Effects of Thrombospondin-1 by Activating CD47/ROS/Endoplasmic Reticulum Stress Pathway in Lung Fibroblasts

Unfolded protein response (UPR) signaling and endoplasmic reticulum (ER) stress have been linked to pulmonary fibrosis. However, the relationship between UPR status and pulmonary function and prognosis in idiopathic pulmonary fibrosis (IPF) patients remains largely unknown. Through a series of bioinformatics analyses, we established a correlation between UPR status and pulmonary function in IPF patients. Furthermore, thrombospondin-1 (TSP-1) was identified as a potential biomarker for prognostic evaluation in IPF patients. By utilizing both bulk RNA profiling and single-cell RNA sequencing data, we demonstrated the upregulation of TSP-1 in lung fibroblasts during pulmonary fibrosis. Gene set enrichment analysis (GSEA) results indicated a positive association between TSP-1 expression and gene sets related to the reactive oxygen species (ROS) pathway in lung fibroblasts. TSP-1 overexpression alone induced mild ER stress and pulmonary fibrosis, and it even exacerbated bleomycin-induced ER stress and pulmonary fibrosis. Mechanistically, TSP-1 promoted ER stress and fibroblast activation through CD47-dependent ROS production. Treatment with either TSP-1 inhibitor or CD47 inhibitor significantly attenuated BLM-induced ER stress and pulmonary fibrosis. Collectively, these findings suggest that the elevation of TSP-1 during pulmonary fibrosis is not merely a biomarker but likely plays a pathogenic role in the fibrotic changes in the lung.

A comprehensive cuproptosis score and associated gene signatures reveal prognostic and immunological features of idiopathic pulmonary fibrosis

Background

Cuproptosis, the most recently identified and regulated cell death, depends on copper ions in vivo. Copper regulates the pathogenesis of Idiopathic pulmonary fibrosis (IPF), but the mechanism of action underlying cuproptosis in IPF remains unclear.

Methods

We identified three cuproptosis patterns based on ten cuproptosis-related genes using unsupervised consensus clustering. We quantified these patterns using a PCA algorithm to construct a cuproptosis score. ssGSEA and the Cibersort algorithm assessed the immune profile of IPF patients. GSEA and GSVA were used to analyze the functional differences in different molecular patterns. Drug susceptibility prediction based on cuproptosis scores and meaningful gene markers was eventually screened in combination with external public data sets,in vitro experiments and our cases.

Results

Of the three types of cuproptosis-related clusters identified in the study, patients in the clusterA, geneclusterB, and score-high groups showed improved prognoses. Moreover, each cluster exhibited differential immune characteristics, with the subtype showing a poorer prognosis associated with an immune overreaction. Cuproptosis score can be an independent risk factor for predicting the prognosis of IPF patients. GSEA showed a significant functional correlation between the score and cuproptosis. The genes AKAP9, ANK3, C6orf106, LYRM7, and MBNL1, were identified as prognostic-related signatures in IPF patients. The functional role of immune regulation in IPF was further explored by correlating essential genes with immune factors. Also, the nomogram constructed by cumulative information from gene markers and cuproptosis score showed reliable clinical application.

Conclusions

Cuproptosis patterns differ significantly in the prognosis and immune characteristics of IPF patients. The cuproptosis score and five gene signatures can provide a reliable reference in the prognosis and diagnosis of IPF.

Prevalence and clinical impacts of obstructive sleep apnea in patients with idiopathic pulmonary fibrosis: A single-center, retrospective study

BACKGROUND

Idiopathic pulmonary fibrosis (IPF) is an interstitial lung disease with chronic, progressive lung fibrosis with a poor prognosis. Recent studies have reported a high prevalence of obstructive sleep apnea (OSA) in IPF patients and an association with poor prognosis. This study aimed to evaluate the prevalence, risk factors, and clinical effects on mortality of OSA in patients with IPF.

METHODS

Clinical data were retrospectively analyzed in 167 patients with IPF at Haeundae-Paik Hospital, Republic of Korea. A type 4 portable device was used to monitor OSA, and an apnea-hypopnea index of 5 events per sleep hour and above was diagnosed as OSA.

RESULTS

The mean follow-up period and age were 26.9 months and 71.4 years, respectively, with male predominance. OSA was confirmed in 108 patients (64.7%). Mild OSA was the most common (62.1%). Independent risk factors for OSA in the multivariate logistic regression analysis were age (odds ratio [OR] 1.07, 95% confidence interval [CI] 1.02-1.13, p = 0.007), body weight (OR 1.05, 95% CI 1.02-1.09, p = 0.002), and risk based on the Berlin questionnaire (OR 2.76, 95% CI 1.12-6.80, p = 0.028). Shorter six-minute walk distance (6MWD) (hazard ratio [HR] 1.00, 95% CI: 1.00-1.00, p < 0.001), acute exacerbation (AE) (HR 13.83, 95% CI: 5.71-33.47, p < 0.001), and higher percentage of cumulative time with oxygen saturation below 90% in total sleep time (HR 1.08, 95% CI: 1.02-1.14, p = 0.007) were risk factors for mortality in IPF patients in the Cox regression analysis.

CONCLUSION

Approximately two-thirds of the IPF patients had OSA. Older age, higher body weight, and high risk based on the Berlin questionnaire were independent risk factors for OSA in IPF patients. Shorter 6MWD, experience of AE, and night hypoxemia during sleep were associated with a higher risk of mortality in patients with IPF.

A Novel 5-Methylcytosine- and Immune-Related Prognostic Signature Is a Potential Marker of Idiopathic Pulmonary Fibrosis

Idiopathic pulmonary fibrosis (IPF) is the most common and highly lethal pulmonary interstitial lung disease. The current study is aimed at investigating reliable markers suitable for the treatment and identification of IPF. This study constructed the first 5-methylcytosine- (m5C-) and immune-related prognostic signature (m5CPS) based on coexpressed genes of m5C regulatory genes and immune-related genes. The m5CPS was established using the training cohort (n = 68) and verified using the test (n = 44) and validation (n = 64) cohorts. The area under the curve (AUC) values were utilized to evaluate the accuracy of m5CPS in predicting the survival of IPF patients. The Kaplan-Meier curves and Cox regression analyses were used to assess the prognostic effect of m5CPS. The AUC was utilized to evaluate the reliability of m5CPS in distinguishing IPF patients from healthy individuals. In terms of the results, m5CPS could predict the one-, three-, and five-year survival rates of IPF patients with high accuracy (AUC = .803–.973). In fact, m5CPS is not only an independent indicator of the poor prognosis of IPF patients (hazard ratio > 1; p < .05) but can also distinguish IPF patients from healthy individuals (AUC = .862). Also, m5CPS may affect the immune response and inflammatory response, and it was positively associated with the infiltration levels of active mast cells (p < .05). In sum, the current study establishes a novel m5CPS for IPF and reveals the role of m5CPS as a reliable marker for predicting the prognosis and disease status of IPF patients.

Identification and immune characteristics of molecular subtypes related to fatty acid metabolism in idiopathic pulmonary fibrosis

Background

Although fatty acid metabolism has been confirmed to be involved in the pathological process of idiopathic pulmonary fibrosis (IPF), systematic analyses on the immune process mediated by fatty acid metabolism-related genes (FAMRGs) in IPF remain lacking.

Methods

The gene expression data of 315 patients with IPF were obtained from Gene Expression Omnibus database and were divided into the training and verification sets. The core FAMRGs of the training set were identified through weighted gene co-expression network analysis. Then, the fatty acid metabolism-related subtypes in IPF were identified on the basis of k-means unsupervised clustering. The scores of fatty acid metabolism and the expression of the fibrosis biomarkers in different subtypes were compared, and functional enrichment analysis was carried out on the differentially expressed genes between subtypes. A random forest model was used to select important FAMRGs as diagnostic markers for distinguishing between subtypes, and a line chart model was constructed and verified by using other datasets and rat models with different degrees of pulmonary fibrosis. The difference in immune cell infiltration among subtypes was evaluated with CIBERSORT, and the correlation between core diagnostic markers and immune cells were analyzed.

Results

Twenty-four core FAMRGs were differentially expressed between the training set and normal samples, and IPF was divided into two subtypes. Significant differences were observed between the two subtypes in biological processes, such as linoleic acid metabolism, cilium movement, and natural killer (NK) cell activation. The subtype with high fatty acid metabolism had more severe pulmonary fibrosis than the other subtype. A reliable construction line chart model based on six diagnostic markers was constructed, and ABCA3 and CYP24A1 were identified as core diagnostic markers. Significant differences in immune cell infiltration were found between the two subtypes, and ABCA3 and CYP24A1 were closely related to NK cells.

Conclusion

Fatty acid metabolism and the immune process that it mediates play an important role in the occurrence and development of IPF. The analysis of the role of FAMRGs in IPF may provide a new potential therapeutic target for IPF.

Bias and Reporting Quality of Clinical Prognostic Models for Idiopathic Pulmonary Fibrosis: A Cross-Sectional Study

Objective

This study aims to evaluate the risk of bias (ROB) and reporting quality of idiopathic pulmonary fibrosis (IPF) prediction models by assessing characteristics of these models.

Methods

The development and/or validation of IPF prognostic models were identified via an electronic search of PubMed, Embase, and Web of Science (from inception to 12 August, 2021). Two researchers independently assessed the risk of bias (ROB) and reporting quality of IPF prediction models based on the Prediction model Risk Of Bias Assessment Tool (PROBAST) and Transparent Reporting of a multivariable prognostic model for Individual Prognosis or Diagnosis (TRIPOD) checklist.

Results

Twenty prognostic model studies for IPF were included, including 7 (35%) model development and external validation studies, 8 (40%) development studies, and 5 (25%) external validation studies. According to PROBAST, all studies were appraised with high ROB, because of deficient reporting in the domains of participants (45.0%) and analysis (67.3%), and at least 55% studies were susceptible to 4 of 20 sources of bias. For the reporting quality, none of them completely adhered to the TRIPOD checklist, with the lowest mean reporting score for the methods and results domains (46.6% and 44.7%). For specific items, eight sub-items had a reporting rate ≥80% and adhered to the TRIPOD checklist, and nine sub-items had a very poor reporting rate, less than 30%.

Conclusion

Studies adhering to PROBAST and TRIPOD checklists are recommended in the future. The reproducibility and transparency can be improved when studies completely adhere to PROBAST and TRIPOD checklists.

Development and Validation of a Novel Gene Signature for Predicting the Prognosis of Idiopathic Pulmonary Fibrosis Based on Three Epithelial-Mesenchymal Transition and Immune-Related Genes

Background: Increasing evidence has revealed that epithelial–mesenchymal transition (EMT) and immunity play key roles in idiopathic pulmonary fibrosis (IPF). However, correlation between EMT and immune response and the prognostic significance of EMT in IPF remains unclear.

Methods: Two microarray expression profiling datasets (GSE70866 and GSE28221) were downloaded from the Gene Expression Omnibus (GEO) database. EMT- and immune-related genes were identified by gene set variation analysis (GSVA) and the Estimation of STromal and Immune cells in MAlignant Tumors using Expression data (ESTIMATE) algorithm. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed to investigate the functions of these EMT- and immune-related genes. Cox and least absolute shrinkage and selection operator (LASSO) regression analyses were used to screen prognostic genes and establish a gene signature. Gene Set Enrichment Analysis (GSEA) and Cell-type Identification By Estimating Relative Subsets Of RNA Transcripts (CIBERSORT) were used to investigate the function of the EMT- and immune-related signatures and correlation between the EMT- and immune-related signatures and immune cell infiltration. Quantitative real-time polymerase chain reaction (qRT-PCR) was performed to investigate the mRNA expression of genes in the EMT- and immune-related signatures.

Results: Functional enrichment analysis suggested that these genes were mainly involved in immune response. Moreover, the EMT- and immune-related signatures were constructed based on three EMT- and immune-related genes (IL1R2, S100A12, and CCL8), and the K–M and ROC curves presented that the signature could affect the prognosis of IPF patients and could predict the 1-, 2-, and 3-year survival well. Furthermore, a nomogram was developed based on the expression of IL1R2, S100A12, and CCL8, and the calibration curve showed that the nomogram could visually and accurately predict the 1-, 2-, 3-year survival of IPF patients. Finally, we further found that immune-related pathways were activated in the high-risk group of patients, and the EMT- and immune-related signatures were associated with NK cells activated, macrophages M0, dendritic cells resting, mast cells resting, and mast cells activated. qRT-PCR suggested that the mRNA expression of IL1R2, S100A12, and CCL8 was upregulated in whole blood of IPF patients compared with normal samples.

Conclusion: IL1R2, S100A12, and CCL8 might play key roles in IPF by regulating immune response and could be used as prognostic biomarkers of IPF.

The Combined Model of CX3CR1-Related Immune Infiltration Genes to Evaluate the Prognosis of Idiopathic Pulmonary Fibrosis

Background

High expression of chemokine (C-X3-C motif) receptor 1 (CX3CR1) was shown to contribute to the progression of many fibrotic diseases. However, there is still no study for the role of CX3CR1 in idiopathic pulmonary fibrosis (IPF). Therefore, we aimed to identify CX3CR1-related immune infiltration genes (IIGs) in IPF and establish a combined risk model to evaluate the prognosis of IPF.

Methods

A discovery cohort of IPF patients (GSE70867) was downloaded from the Gene Expression Omnibus dataset. We identified the composition of 22 kinds of immune cells infiltration by CIBERSORT. The Cox regression model with the LASSO method was used for identifying prognostic genes and developing CX3CR1-related IIGs. Kaplan–Meier was applied to plot the survival curve of prognosis model. Peripheral blood mononuclear cell (PBMC) and bronchoalveolar lavage fluid (BALF) were collected to be tested by quantitative reverse transcriptase-PCR (qRT-PCR) from 15 clinical samples, including 8 healthy controls (HC), 4 patients with usual interstitial pneumonia (UIP) and 3 patients with pulmonary fibrosis (FIB).

Results

We found that high expression of CX3CR1 in BALF contributed to the poor prognosis in IPF patients. ALR4C, RAB37, GPR56, MARCKS, PXN and RASSF2 were identified as CX3CR1-related IIGs, which were highly expressed in PBMC of UIP/FIB patients than that of HC. Moreover, the expression of PXN was higher in FIB patients’ PBMC than that of UIP ones. In the cohort of IPF patients, high infiltration of activated NK cells in BALF caused poor survival compared to low infiltration group. The infiltration of activated NK was regulated by CX3CR1-related IIGs. The combined risk model predicted that high expression of CX3CR1-related IIGs and high infiltrated activated NK cells caused poor prognosis in IPF patients.

Conclusion

We identified a group of CX3CR1-related IIGs in IPF patients. This combined risk model provided new insights in the prognosis and therapy of IPF.

Fatty acid metabolism-related genes in bronchoalveolar lavage fluid unveil prognostic and immune infiltration in idiopathic pulmonary fibrosis

BACKGROUND

Idiopathic pulmonary fibrosis (IPF) is a chronic and progressive condition with an unfavorable prognosis. A recent study has demonstrated that IPF patients exhibit characteristic alterations in the fatty acid metabolism in their lungs, suggesting an association with IPF pathogenesis. Therefore, in this study, we have explored whether the gene signature associated with fatty acid metabolism could be used as a reliable biological marker for predicting the survival of IPF patients.

METHODS

Data on the fatty acid metabolism-related genes (FAMRGs) were extracted from databases like Kyoto Encyclopedia of Genes and Genomes (KEGG), Hallmark, and Reactome pathway. The GSE70866 dataset with information on IPF patients was retrieved from the Gene Expression Omnibus (GEO). Next, the consensus clustering method was used to identify novel molecular subgroups. Gene Set Enrichment Analysis (GSEA) was performed to understand the mechanisms involved. The Cell-type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT) algorithm was used to evaluate the level of immune cell infiltration in the identified subgroups based on gene expression signatures of immune cells. Finally, the Least Absolute Shrinkage and Selection Operator (LASSO) regression and multivariate Cox regression analysis were performed to develop a prognostic risk model.

RESULTS

The gene expression signature associated with fatty acid metabolism was used to create two subgroups with significantly different prognoses. GSEA reveals that immune-related pathways were significantly altered between the two subgroups, and the two subgroups had different metabolic characteristics. High infiltration of immune cells, mainly activated NK cells, monocytes, and activated mast cells, was observed in the subgroup with a poor prognosis. A risk model based on FAMRGs had an excellent ability to predict the prognosis of IPF. The nomogram constructed using the clinical features and the risk model could accurately predict the prognosis of IPF patients.

CONCLUSION

The fatty acid metabolism-related gene expression signature could be used as a potential biological marker for predicting clinical outcomes and the level of infiltration of immune cells. This could eventually enhance the accuracy of the treatment of IPF patients.

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

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.