Biomarkers In Idiopathic Pulmonary Fibrosis Research Articles

Molecular and Genetic Biomarkers in Idiopathic Pulmonary Fibrosis: Where Are We Now?

Idiopathic pulmonary fibrosis (IPF) represents a chronic progressive fibrotic interstitial lung disease of unknown cause with an ominous prognosis. It remains an unprecedent clinical challenge due to its delayed diagnosis and unpredictable clinical course. The need for accurate diagnostic, prognostic and predisposition biomarkers in everyday clinical practice becomes more necessary than ever to ensure prompt diagnoses and early treatment. The identification of such blood biomarkers may also unravel novel drug targets against IPF development and progression. So far, the role of diverse blood biomarkers, implicated in various pathogenetic pathways, such as in fibrogenesis (S100A4), extracellular matrix remodelling (YKL-40, MMP-7, ICAM-1, LOXL2, periostin), chemotaxis (CCL-18, IL-8), epithelial cell injury (KL-6, SP-A, SP-D), autophagy and unfolded protein response has been investigated in IPF with various results. Moreover, the recent progress in genetics in IPF allows for a better understanding of the underlying disease mechanisms. So far, the causative mutations in pulmonary fibrosis include mutations in telomere-related genes and in surfactant-related genes, markers that could act as predisposition biomarkers in IPF. The aim of this review is to provide a comprehensive overview from the bench to bedside of current knowledge and recent insights on biomarkers in IPF, and to suggest future directions for research. Large-scale studies are still needed to confirm the exact role of these biomarkers.

Identifying potential biomarkers of idiopathic pulmonary fibrosis through machine learning analysis

Idiopathic pulmonary fibrosis (IPF) is the most common and serious type of idiopathic interstitial pneumonia, characterized by chronic, progressive, and low survival rates, while unknown disease etiology. Until recently, patients with idiopathic pulmonary fibrosis have a poor prognosis, high mortality, and limited treatment options, due to the lack of effective early diagnostic and prognostic tools. Therefore, we aimed to identify biomarkers for idiopathic pulmonary fibrosis based on multiple machine-learning approaches and to evaluate the role of immune infiltration in the disease. The gene expression profile and its corresponding clinical data of idiopathic pulmonary fibrosis patients were downloaded from Gene Expression Omnibus (GEO) database. Next, the differentially expressed genes (DEGs) with the threshold of FDR < 0.05 and |log2 foldchange (FC)| > 0.585 were analyzed via R package “DESeq2” and GO enrichment and KEGG pathways were run in R software. Then, least absolute shrinkage and selection operator (LASSO) logistic regression, support vector machine-recursive feature elimination (SVM-RFE) and random forest (RF) algorithms were combined to screen the key potential biomarkers of idiopathic pulmonary fibrosis. The diagnostic performance of these biomarkers was evaluated through receiver operating characteristic (ROC) curves. Moreover, the CIBERSORT algorithm was employed to assess the infiltration of immune cells and the relationship between the infiltrating immune cells and the biomarkers. Finally, we sought to understand the potential pathogenic role of the biomarker (SLAIN1) in idiopathic pulmonary fibrosis using a mouse model and cellular model. A total of 3658 differentially expressed genes of idiopathic pulmonary fibrosis were identified, including 2359 upregulated genes and 1299 downregulated genes. FHL2, HPCAL1, RNF182, and SLAIN1 were identified as biomarkers of idiopathic pulmonary fibrosis using LASSO logistic regression, RF, and SVM-RFE algorithms. The ROC curves confirmed the predictive accuracy of these biomarkers both in the training set and test set. Immune cell infiltration analysis suggested that patients with idiopathic pulmonary fibrosis had a higher level of B cells memory, Plasma cells, T cells CD8, T cells follicular helper, T cells regulatory (Tregs), Macrophages M0, and Mast cells resting compared with the control group. Correlation analysis demonstrated that FHL2 was significantly associated with the infiltrating immune cells. qPCR and western blotting analysis suggested that SLAIN1 might be a signature for the diagnosis of idiopathic pulmonary fibrosis. In this study, we identified four potential biomarkers (FHL2, HPCAL1, RNF182, and SLAIN1) and evaluated the potential pathogenic role of SLAIN1 in idiopathic pulmonary fibrosis. These findings may have great significance in guiding the understanding of disease mechanisms and potential therapeutic targets in idiopathic pulmonary fibrosis.

Changes in lung epithelial cell volatile metabolite profile induced by pro-fibrotic stimulation with TGF-β1

Volatile organic compounds (VOCs) have shown promise as potential biomarkers in idiopathic pulmonary fibrosis. Measuring VOCs in the headspace of in vitro models of lung fibrosis may offer a method of determining the origin of those detected in exhaled breath. The aim of this study was to determine the VOCs associated with two lung cell lines (A549 and MRC-5 cells) and changes associated with stimulation of cells with the pro-fibrotic cytokine, transforming growth factor (TGF)-β1. A dynamic headspace sampling method was used to sample the headspace of A549 cells and MRC-5 cells. These were compared to media control samples and to each other to identify VOCs which discriminated between cell lines. Cells were then stimulated with the TGF-β1 and samples were compared between stimulated and unstimulated cells. Samples were analysed using thermal desorption-gas chromatography-mass spectrometry and supervised analysis was performed using sparse partial least squares-discriminant analysis (sPLS-DA). Supervised analysis revealed differential VOC profiles unique to each of the cell lines and from the media control samples. Significant changes in VOC profiles were induced by stimulation of cell lines with TGF-β1. In particular, several terpenoids (isopinocarveol, sativene and 3-carene) were increased in stimulated cells compared to unstimulated cells. VOC profiles differ between lung cell lines and alter in response to pro-fibrotic stimulation. Increased abundance of terpenoids in the headspace of stimulated cells may reflect TGF-β1 cell signalling activity and metabolic reprogramming. This may offer a potential biomarker target in exhaled breath in IPF.

Analysis of tissue lipidomics and computed tomography pulmonary fat attenuation volume (CTPFAV ) in idiopathic pulmonary fibrosis.

There is increasing interest in the role of lipids in processes that modulate lung fibrosis with evidence of lipid deposition in idiopathic pulmonary fibrosis (IPF) histological specimens. The aim of this study was to identify measurable markers of pulmonary lipid that may have utility as IPF biomarkers. IPF and control lung biopsy specimens were analysed using a unbiased lipidomic approach. Pulmonary fat attenuation volume (PFAV) was assessed on chest CT images (CTPFAV ) with 3D semi-automated lung density software. Aerated lung was semi-automatically segmented and CTPFAV calculated using a Hounsfield-unit (-40 to -200HU) threshold range expressed as a percentage of total lung volume. CTPFAV was compared to pulmonary function, serum lipids and qualitative CT fibrosis scores. There was a significant increase in total lipid content on histological analysis of IPF lung tissue (23.16 nmol/mg) compared to controls (18.66 mol/mg, p = 0.0317). The median CTPFAV in IPF was higher than controls (1.34% vs. 0.72%, p < 0.001) and CTPFAV correlated significantly with DLCO% predicted (R2 = 0.356, p < 0.0001) and FVC% predicted (R2 = 0.407, p < 0.0001) in patients with IPF. CTPFAV correlated with CT features of fibrosis; higher CTPFAV was associated with >10% reticulation (1.6% vs. 0.94%, p = 0.0017) and >10% honeycombing (1.87% vs. 1.12%, p = 0.0003). CTPFAV showed no correlation with serum lipids. CTPFAV is an easily quantifiable non-invasive measure of pulmonary lipids. In this pilot study, CTPFAV correlates with pulmonary function and radiological features of IPF and could function as a potential biomarker for IPF disease severity assessment.

Identifying oxidative stress-related biomarkers in idiopathic pulmonary fibrosis in the context of predictive, preventive, and personalized medicine using integrative omics approaches and machine-learning strategies.

Idiopathic pulmonary fibrosis (IPF) is a rare interstitial lung disease with a poor prognosis that currently lacks effective treatment methods. Preventing the acute exacerbation of IPF, identifying the molecular subtypes of patients, providing personalized treatment, and developing individualized drugs are guidelines for predictive, preventive, and personalized medicine (PPPM/3PM) to promote the development of IPF. Oxidative stress (OS) is an important pathological process of IPF. However, the relationship between the expression levels of oxidative stress-related genes (OSRGs) and clinical indices in patients with IPF is unclear; therefore, it is still a challenge to identify potential beneficiaries of antioxidant therapy. Because PPPM aims to recognize and manage diseases by integrating multiple methods, patient stratification and analysis based on OSRGs and identifying biomarkers can help achieve the above goals. Transcriptome data from 250 IPF patients were divided into training and validation sets. Core OSRGs were identified in the training set and subsequently clustered to identify oxidative stress-related subtypes. The oxidative stress scores, clinical characteristics, and expression levels of senescence-associated secretory phenotypes (SASPs) of different subtypes were compared to identify patients who were sensitive to antioxidant therapy to conduct differential gene functional enrichment analysis and predict potential therapeutic drugs. Diagnostic markers between subtypes were obtained by integrating multiple machine learning methods, their expression levels were tested in rat models with different degrees of pulmonary fibrosis and validation sets, and nomogram models were constructed. CIBERSORT, single-cell RNA sequencing, and immunofluorescence staining were used to explore the effects of OSRGs on the immune microenvironment. Core OSRGs classified IPF into two subtypes. Patients classified into subtypes with low oxidative stress levels had better clinical scores, less severe fibrosis, and lower expression of SASP-related molecules. A reliable nomogram model based on five diagnostic markers was constructed, and these markers' expression stability was verified in animal experiments. The number of neutrophils in the immune microenvironment was significantly different between the two subtypes and was closely related to the degree of fibrosis. Within the framework of PPPM, this work comprehensively explored the role of OSRGs and their mediated cellular senescence and immune processes in the progress of IPF and assessed their capabilities aspredictors of high oxidative stress and disease progression,targets of the vicious loop between regulated pulmonary fibrosis and OS for targeted secondary and tertiary prevention, andreferences for personalized antioxidant and antifibrotic therapies. The online version contains supplementary material available at 10.1007/s13167-023-00334-4.

Identification of potential biomarkers for idiopathic pulmonary fibrosis and validation of TDO2 as a potential therapeutic target.

Idiopathic pulmonary fibrosis (IPF) is a progressive interstitial lung disease with a high mortality rate. On this basis, exploring potential therapeutic targets to meet the unmet needs of IPF patients is important. To explore novel hub genes for IPF therapy. Here, we used public datasets to identify differentially expressed genes between IPF patients and healthy donors. Potential targets were considered based on multiple bioinformatics analyses, especially the correlation between hub genes and carbon monoxide diffusing capacity of carbon monoxide, forced vital capacity, and patient survival rate. The mRNA levels of the hub genes were determined through quantitative real-time polymerase chain reaction. We found that TDO2 was upregulated in IPF patients and predicted poor prognosis. Surprisingly, single-cell RNA sequencing data analysis revealed significant enrichment of TDO2 in alveolar fibroblasts, indicating that TDO2 may participate in the regulation of proliferation and survival. Therefore, we verified the upregulated expression of TDO2 in an experimental mouse model of transforming growth factor-β (TGF-β)-induced pulmonary fibrosis. Furthermore, the results showed that a TDO2 inhibitor effectively suppressed TGF-β-induced fibroblast activation. These findings suggest that TDO2 may be a potential target for IPF treatment. Based on transcription factors-microRNA prediction and scRNA-seq analysis, elevated TDO2 promoted the IPF proliferation of fibroblasts and may be involved in the P53 pathway and aggravate ageing and persistent pulmonary fibrosis. We provided new target genes prediction and proposed blocking TGF-β production as a potential treatment for IPF.

Identification of immune biomarkers associated with basement membranes in idiopathic pulmonary fibrosis and their pan-cancer analysis.

Idiopathic pulmonary fibrosis (IPF) is a chronic progressive interstitial lung disease of unknown etiology, characterized by diffuse alveolitis and alveolar structural damage. Due to the short median survival time and poor prognosis of IPF, it is particularly urgent to find new IPF biomarkers. Previous studies have shown that basement membranes (BMs) are associated with the development of IPF and tumor metastasis. However, there is still a lack of research on BMs-related genes in IPF. Therefore, we investigated the expression level of BMs genes in IPF and control groups, and explored their potential as biomarkers for IPF diagnosis. In this study, the GSE32537 and GSE53845 datasets were used as training sets, while the GSE24206, GSE10667 and GSE101286 datasets were used as validation sets. In the training set, seven immune biomarkers related to BMs were selected by differential expression analysis, machine learning algorithm (LASSO, SVM-RFE, Randomforest) and ssGSEA analysis. Further ROC analysis confirmed that seven BMs-related genes played an important role in IPF. Finally, four immune-related Hub genes (COL14A1, COL17A1, ITGA10, MMP7) were screened out. Then we created a logistic regression model of immune-related hub genes (IHGs) and used a nomogram to predict IPF risk. The nomogram model was evaluated to have good reliability and validity, and ROC analysis showed that the AUC value of IHGs was 0.941 in the training set and 0.917 in the validation set. Pan-cancer analysis showed that IHGs were associated with prognosis, immune cell infiltration, TME, and drug sensitivity in 33 cancers, suggesting that IHGs may be potential targets for intervention in human diseases including IPF and cancer.

Machine learning-based prediction of candidate gene biomarkers correlated with immune infiltration in patients with idiopathic pulmonary fibrosis.

This study aimed to identify candidate gene biomarkers associated with immune infiltration in idiopathic pulmonary fibrosis (IPF) based on machine learning algorithms. Microarray datasets of IPF were extracted from the Gene Expression Omnibus (GEO) database to screen for differentially expressed genes (DEGs). The DEGs were subjected to enrichment analysis, and two machine learning algorithms were used to identify candidate genes associated with IPF. These genes were verified in a validation cohort from the GEO database. Receiver operating characteristic (ROC) curves were plotted to assess the predictive value of the IPF-associated genes. The cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT) algorithm was used to evaluate the proportion of immune cells in IPF and normal tissues. Additionally, the correlation between the expression of IPF-associated genes and the infiltration levels of immune cells was examined. A total of 302 upregulated and 192 downregulated genes were identified. Functional annotation, pathway enrichment, Disease Ontology and gene set enrichment analyses revealed that the DEGs were related to the extracellular matrix and immune responses. COL3A1, CDH3, CEBPD, and GPIHBP1 were identified as candidate biomarkers using machine learning algorithms, and their predictive value was verified in a validation cohort. Additionally, ROC analysis revealed that the four genes had high predictive accuracy. The infiltration levels of plasma cells, M0 macrophages and resting dendritic cells were higher and those of resting natural killer (NK) cells, M1 macrophages and eosinophils were lower in the lung tissues of patients with IPF than in those of healthy individuals. The expression of the abovementioned genes was correlated with the infiltration levels of plasma cells, M0 macrophages and eosinophils. COL3A1, CDH3, CEBPD, and GPIHBP1 are candidate biomarkers of IPF. Plasma cells, M0 macrophages and eosinophils may be involved in the development of IPF and may serve as immunotherapeutic targets in IPF.

Endothelin-1 as a Biomarker of Idiopathic Pulmonary Fibrosis and Interstitial Lung Disease Associated with Autoimmune Diseases.

The aim of this study was to determine the role of endothelin-1 (ET-1), a molecule involved in multiple vascular and fibrosing abnormalities, as a biomarker of interstitial lung disease (ILD), as well as its use for the differential diagnosis between idiopathic pulmonary fibrosis (IPF) and ILD associated with autoimmune diseases (AD-ILD), using a large and well-defined cohort of patients with ILD. A total of 112 patients with IPF, 91 patients with AD-ILD (28 rheumatoid arthritis (RA), 26 systemic sclerosis, 20 idiopathic inflammatory myositis and 17 interstitial pneumonia with autoimmune features) and 44 healthy controls were included. ET-1 serum levels were determined by enzyme-linked immunosorbent assay. A significant increase in ET-1 levels was found in patients with IPF compared to controls. Likewise, AD-ILD patients also showed higher ET-1 levels than controls when the whole cohort was stratified by the type of AD. Similar ET-1 levels were found in IPF and AD-ILD patients, regardless of the underlying AD. Interestingly, increased ET-1 levels were correlated with worse lung function in IPF and RA-ILD patients. Our study supports that serum ET-1 may be useful as a biomarker of ILD, although it could not help in the differential diagnosis between IPF and AD-ILD. Moreover, ET-1 levels may be associated with ILD severity.

A potential three-gene-based diagnostic signature for idiopathic pulmonary fibrosis.

Background: Idiopathic pulmonary fibrosis (IPF) is a life-threatening disease whose etiology remains unknown. This study aims to explore diagnostic biomarkers and pathways involved in IPF using bioinformatics analysis. Methods: IPF-related gene expression datasets were retrieved and downloaded from the NCBI Gene Expression Omnibus database. Differentially expressed genes (DEGs) were screened, and weighted correlation network analysis (WGCNA) was performed to identify key module and genes. Functional enrichment analysis was performed on genes in the clinically significant module. Then least absolute shrinkage and selection operator (LASSO) logistic regression and support vector machine-recursive feature elimination (SVM-RFE) algorithms were run to screen candidate biomarkers. The expression and diagnostic value of the biomarkers in IPF were further validated in external test datasets (GSE110147). Results: 292 samples and 1,163 DEGs were screened to construct WGCNA. In WGCNA, the blue module was identified as the key module, and 59 genes in this module correlated highly with IPF. Functional enrichment analysis of blue module genes revealed the importance of extracellular matrix-associated pathways in IPF. IL13RA2, CDH3, and COMP were identified as diagnostic markers of IPF via LASSO and SVM-RFE. These genes showed good diagnostic value for IPF and were significantly upregulated in IPF. Conclusion: This study indicates that IL13RA2, CDH3, and COMP could serve as diagnostic signature for IPF and might offer new insights in the underlying diagnosis of IPF.

B4GALT1 as a New Biomarker of Idiopathic Pulmonary Fibrosis.

Idiopathic pulmonary fibrosis (IPF) is a disease characterized by progressive scarring of the lung that involves the pulmonary interstitium. The disease may rapidly progress, leading to respiratory failure, and the long-term survival is poor. There are no accurate biomarkers available so far. Our aim was to evaluate the expression of the B4GALT1 in patients with IPF. Analysis of B4GALT1 gene expression was performed in silico on two gene sets, retrieved from the Gene Expression Omnibus database. Expression of B4GALT1 was then evaluated, both at the mRNA and protein levels, on lung specimens obtained from lung biopsies of 4 IPF patients, on one IPF-derived human primary cell and on 11 cases of IPF associated with cancer. In silico re-analysis demonstrated that the B4GALT1 gene was overexpressed in patients and human cell cultures with IPF (p = 0.03). Network analysis demonstrated that B4GALT1 upregulation was correlated with genes belonging to the EMT pathway (p = 0.01). The overexpression of B4GALT1 was observed, both at mRNA and protein levels, in lung biopsies of our four IPF patients and in the IPF-derived human primary cell, in other fibrotic non-lung tissues, and in IPF associated with cancer. In conclusion, our results indicate that B4GALT1 is overexpressed in IPF and could represent a novel marker of this disease.

Prognostic analysis of m6A-related genes as potential biomarkers in idiopathic pulmonary fibrosis.

Idiopathic pulmonary fibrosis (IPF) is a progressive, fatal lung disease with limited treatment options. N6-methyladenosine (m6A) is a reversible RNA modification and has been implicated in various biological processes. However, there are few studies on m6A in IPF. This project mainly explores the prognostic value of m6A-related genes as potential biomarkers in IPF, in order to establish a set of accurate prognostic prediction model. In this study, we used GSE28042 dataset in GEO database to screen out 218 m6A-related candidate genes with high IPF correlation and high differential expression through differentially expressed gene analysis, WGCNA and m6A correlation analysis. The genes associated with the prognosis of IPF were screened out by univariate Cox regression analysis, LASSO analysis, and multivariate Cox regression analysis, and the multivariate Cox model of prognostic risk of related genes was constructed. We found that RBM11, RBM47, RIC3, TRAF5 and ZNF14 were key genes in our model. Finally, the prognostic prediction ability and independent prognostic characteristics of the risk model were evaluated by survival analysis and independent prognostic analysis, and verified by the GSE93606 dataset, which proved that the prognostic risk model we constructed has a strong and stable prediction efficiency.

Identification of diagnostic gene biomarkers related to immune infiltration in patients with idiopathic pulmonary fibrosis based on bioinformatics strategies.

The study aims to identify potential diagnostic markers of idiopathic pulmonary fibrosis (IPF) and analyze the significance of immune cell infiltration in this pathology. Download two publicly available gene expression profiles (GSE10667 and GSE24206 datasets) from the GEO database including 48 Idiopathic pulmonary fibrosis (IPF) samples and 21 human control samples and select for distinctly expressed genes (DEG) from them. Lasso regression model and support vector machine recursive feature elimination S,V,R,F analysis were used to check candidate biomarkers. The area under the subject's work characteristic curve (AUC) value is used to evaluate its recognition ability. The GSE53845 dataset (40 IPF patients and 8 controls) continue to validate the expression level and diagnostic value of biomarkers in IPF. Comprehensive analysis of immune infiltrated cells of IPF was performed using R software and immune cell infiltration estimation analysis tool- deconvolution algorithm (CIBERSORT). 43 DEGs were identified in total. The identified DEGs mostly involve pneumonia, lung disease, collagen disease, obstructive pulmonary disease and other diseases. The activation of IL-17 signaling pathways, amoebic disease, interaction of viral proteins with cytokines and cytokine receptors, protein digestion and absorption, and flaccid hormone signaling pathways in IPF were different from the control group. The expression degree of CRTAC1, COL10A1, COMP, RPS4Y1, IGFL2, NECAB1, SCG5, SLC6A4, and SPP1 in IPF tissue were prominently higher than the normal group. Immune cell infiltration analysis showed that CRTAC1, COL10A1, COMP, IGFL2, NECAB1, SCG5, SLC6A4, and SPP1 were associated with monocytes, plasma cells, neutrophils, and regulatory (treg) T cells. CRTAC1, COL10A1, COMP, IGFL2, NECAB1, SCG5, SLC6A4, and SPP1 can be used as diagnostic markers for IPF, providing new ideas for the future study of IPF occurrence and molecular mechanisms.

Idiopathic pulmonary fibrosis: Diagnosis, biomarkers and newer treatment protocols

Idiopathic pulmonary fibrosis (IPF) is a chronic, progressive lung condition marked by lung scarring that progresses over time and with usual interstitial pneumonia histology (UIP). It is linked to a worsening cough, dyspnea, and a worse quality of life. Around 3 million persons worldwide suffer from IPF, and the prevalence rises sharply with advancing age. The detection of the UIP pattern, generally using high-resolution CT; lung biopsy may be necessary in certain individuals; the diagnostic approach also includes the elimination of other interstitial lung illnesses or overlapping problems. The UIP pattern is mostly bilateral, peripheral, and basal, with clusters of subpleural cystic airspaces and reticular alterations linked to traction bronchiectasis.Although there are still many uncertainties about how to define susceptibility, it is believed that the molecular mechanisms causing IPF reflect an abnormal reparative response to repeated alveolar epithelial damage in an aging genetically sensitive individual. With the availability of two pharmacotherapeutic drugs, pirfenidone and nintedanib, that slow physiological advancement and potentially increase progression-free survival, significant progress has been made in our knowledge of the clinical treatment of IPF. The goal of current research is to develop early biomarkers for IPF that may include circulating variables, demographic information, and imaging data.

Exploring Idiopathic Pulmonary Fibrosis Biomarker by Simultaneous Two-Photon Fluorescence Imaging of Cysteine and Peroxynitrite.

Idiopathic pulmonary fibrosis (IPF) has been characterized as a chronic inflammatory disease that leads to irreversible damage to pulmonary function. However, there is no specific IPF biomarker that can be used to distinguish IPF and not pneumonia. Endoplasmic reticulum (ER) stress is prominent in IPF. To search for a specific biomarker of IPF, we developed two ER-targeting two-photon (TP) fluorescent probes, TPER-ONOO and TPER-Cys, for peroxynitrite (ONOO-) and cysteine (Cys) imaging, respectively. A significant increase in Cys levels in the lungs was discovered only in mice with IPF, which implied that Cys might be an IPF biomarker candidate. Furthermore, we uncovered the mechanism of glutathione (GSH) deficiency in IPF, which was not due to Cys shortage but instead was attributable to impaired glutamate cysteine ligase and glutathione synthetase activities via ONOO--induced post-transcriptional modification. This work has potential to provide a new method for IPF early diagnosis and drug efficacy evaluation.

Bioinformatic identification and analysis of immune-related chromatin regulatory genes as potential biomarkers in idiopathic pulmonary fibrosis.

BackgroundIdiopathic pulmonary fibrosis (IPF) is a disease with a very poor prognosis. The search for new IPF biomarkers is particularly urgent due to the uncertainty of the mechanisms and treatment. Studies have shown that chromatin regulators (CRs) are involved in the development of IPF and are associated with tumor immunity. However, there are no studies on immune-related CRs in IPF. Therefore, we conducted a systematic study to analyze the expression levels and immune correlation of CRs in IPF tissues and normal tissues and to explore their potential as diagnostic biomarkers.MethodsGSE53845, GSE179781 and GSE24206 datasets from Gene Expression Omnibus (GEO) database were merged into an integrated dataset as the training set; GSE70866 was used as the validation dataset. The cr-related differentially expressed genes (DEGs) between normal and IPF tissues were identified using the “Limma” software package. Weighted gene co-expression network analysis (WGCNA) was performed using the “WGCNA” package to screen eigengenes, which were intersected with DEGs to identify hub genes. The “ggcorrplot” package was used to analyze the correlation between hub genes and immunity, and immune-related hub genes were defined as immHub. A logistic regression model was constructed using immHub as the independent variable and whether the diagnosis was IPF as the dependent variable.ResultsOne hundred and sixty-nine DEGs were identified between IPF and normal tissues. wGCNA identified 3 key modules in brown, green and yellow genes that were present in all 3 modules and met module membership (MM) >0.8 and gene significance (GS) >0.5 were called signature genes (n=390). Four intersecting genes were obtained by intersecting DEGs with signature genes (PADI4, IGFBP7, GADD45A, and SETBP1) all associated with immunity were defined as immHub genes Logistic regression models were constructed based on immHub genes. The area under the curve (AUC) of the ROC curve is used to evaluate the diagnostic accuracy of the logistic regression model for IPF. The AUC in the ROC analysis was 0.771 for the training dataset, and 0.759 for the validation dataset.ConclusionsPADI4, IGFBP7 and GADD45A may be biomarkers for IPF, which will provide assistance in the diagnosis, treatment and prognostic assessment of IPF patients, and provide an important basis for future studies on the relationship between CRs genes and IPF.

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.

Soluble CD163 is produced by monocyte-derived and alveolar macrophages, and is not associated with the severity of idiopathic pulmonary fibrosis.

The soluble form of the membrane hemoglobin scavenger receptor CD163 (sCD163), released by shedding, is a strong marker for macrophage activation. Serum sCD163 levels rise in several acute inflammatory states and some fibrosing diseases. Monocyte-derived macrophages (MoDM) differentiated by macrophage colony-stimulating factor (M-MoDM) contribute to the pathophysiology of idiopathic pulmonary fibrosis (IPF), an irreversible and rapidly fatal interstitial lung disease. Since M-MoDM express high membrane CD163 levels, we thus postulated that sCD163 could be a relevant biomarker for macrophage activation in IPF. We found that M-MoDM constitutively released higher amounts of sCD163 (49.5 ± 24.5 ng/ml) than monocytes (0.45 ± 0.32 ng/ml) or MoDM differentiated with granulocyte macrophage-stimulating factor (2.24 ± 0.98 ng/ml). The basal production of sCD163 by M-MoDM was increased following stimulation with lipopolysaccharide (123.4 ± 54.9 ng/ml) or ATP (168.9 ± 41.8 ng/ml). The sCD163 release was controlled by metalloproteases but not through ADAM17 activation. Moreover, CD163-positive macrophages and sCD163 were detected in pulmonary tissues and alveolar fluids of Caucasian patients with IPF, respectively. IPF alveolar macrophages constitutively secreted sCD163 amounts (67.6 ± 44.6 ng/µg RNA) which were significantly higher than those released by alveolar macrophages isolated from controls (19.2 ± 7.6 ng/µg RNA) or patients with other interstitial lung disease (31.5 ± 16.6 ng/µg RNA). However, the concentrations of sCD163 in blood serum collected from 155 patients with IPF did not correlate with the severity of their disease. In conclusion, our results show that M-MoDM constituted a pertinent model to study the regulation of sCD163 production. Yet, serum sCD163 values could not provide a prognostic biomarker for IPF in our cohort.

Molecularly imprinted polymer-based extended-gate field-effect transistor (EG-FET) chemosensor for selective determination of matrix metalloproteinase-1 (MMP-1) protein

A conducting molecularly imprinted polymer (MIP) film was integrated with an extended-gate field-effect transistor (EG-FET) transducer to determine epitopes of matrix metalloproteinase-1 (MMP-1) protein biomarker of idiopathic pulmonary fibrosis (IPF) selectively. Most suitable epitopes for imprinting were selected with Basic Local Alignment Search Tool software. From a pool of MMP-1 epitopes, the two, i.e., MIAHDFPGIGHK and HGYPKDIYSS, the relatively short ones, most promising for MMP-1 determination, were selected, mainly considering their advantageous outermost location in the protein molecule and stability against aggregation. MIPs templated with selected epitopes of the MMP-1 protein were successfully prepared by potentiodynamic electropolymerization and simultaneously deposited as thin films on electrodes. The chemosensors, constructed of MIP films integrated with EG-FET, proved useful in determining these epitopes even in a medium as complex as a control serum. The limit of detection for the MIAHDFPGIGHK and HGYPKDIYSS epitope was ∼60 and 20 nM, respectively. Moreover, the chemosensors selectively recognized whole MMP-1 protein in the 50–500 nM concentration range in buffered control serum samples.

THE TRANSLATIONAL POSSIBILITY OF TARGETING LncRNAs AS A THERAPEUTIC STRATEGY FOR IDIOPATHIC PULMONARY FIBROSIS

Idiopathic pulmonary fibrosis (IPF) is an interstitial lung disease (ILD). IPF causes stiffness in the lungs which makes it difficult to breathe and get oxygen to the bloodstream. IPF is a particularly severe form of lung fibrosis with no completely known etiology and a median survival of 2.5−3.5 years after diagnosis. The phenotypic changes in the lung fibroblasts are believed to contribute to the development of idiopathic pulmonary fibrosis. Long intergenic non-coding RNAs (LncRNAs) have been identified as novel regulators of gene expression and protein activity. In non-stimulated cells, it showed reduced proliferation and inflammation but no difference in the fibrotic response of IPF fibroblasts. These functional changes in non-stimulated cells were associated with changes in the expression of the histone marks, H3K4me1, H3K4me3, and H3K27ac indicating a possible involvement of epigenetics. Following activation with TGF-β1 and IL-1β, it demonstrated an increased fibrotic but reduced inflammatory response in IPF fibroblasts. No significant difference in proliferation following PDGF exposure was observed. The LncRNAs, LINC00960, and LINC01140 were upregulated in IPF fibroblasts. Knockdown studies showed that LINC00960 and LINC01140 were positive regulators of proliferation in both control and IPF fibroblasts but had no effect on the fibrotic response. Knockdown of LINC01140 but not LINC00960 increased the inflammatory response, which was greater in IPF compared to control fibroblasts. Overall, this review study tries to emphasize the role of LncRNAs as regulators of proliferation and inflammation in human lung fibroblast, a biomarker in IPF, and a novel treatment approach.