Biomarkers In Idiopathic Pulmonary Fibrosis Research Articles

Exploring ABHD5 as a Lipid-Related Biomarker in Idiopathic Pulmonary Fibrosis: Integrating Machine Learning, Bioinformatics, and In Vitro Experiments.

Recent studies increasingly suggest a connection between lipids and idiopathic pulmonary fibrosis (IPF). This study was aimed at exploring potential lipid-related biomarkers for IPF and uncovering the mechanisms underlying pulmonary fibrosis. IPF-related datasets were retrieved from the GEO database, and the ComBat algorithm was used to merge multiple datasets and eliminate batch effects. Weighted gene co-expression network analysis (WGCNA) was utilized to identify modules and genes associated with IPF. Potential hub genes were determined by intersecting these genes with lipid-related genes from the GeneCards database. A machine learning-based integrative approach was developed to construct diagnostic and prognostic signatures, which were validated across several datasets. Additionally, single-cell sequencing data was used to validate the expression differences of core IPF-related genes across various cell types. The effect of ABHD5 on fibroblasts was assessed using the cell counting kit-8, 5-ethynyl-2'-deoxyuridine, and cell scratch assays. The expression levels of fibrotic factors were measured using real-time quantitative polymerase chain reaction and western blot analysis. WGCNA identified a red module potentially related to IPF, and the intersection with lipid-related genes yielded 51 hub genes. These genes were used to build diagnostic and prognostic models that demonstrated robust validation capabilities across multiple datasets. Single-cell sequencing analysis revealed low expression of ABHD5 in the lung tissues of IPF patients, with a higher proportion of fibroblasts exhibiting low ABHD5 expression. Cell experiments showed that under the influence of TGF-β1, knockdown of ABHD5 slightly promoted fibroblast proliferation. Additionally, fibroblasts with low ABHD5 expression exhibited enhanced migratory capabilities and secreted more fibrotic factors. Lipid-related diagnostic and prognostic models for IPF were developed, and ABHD5 may serve as a potential biomarker. Low ABHD5 expression could potentially accelerate the progression of pulmonary fibrosis.

Blood lipid profiles as a prognostic biomarker in idiopathic pulmonary fibrosis

BackgroundDysregulation of lipid metabolism is implicated in the pathogenesis of idiopathic pulmonary fibrosis (IPF). However, the association between the blood lipid profiles and the prognosis of IPF is not well defined. We aimed to identify the impacts of lipid profiles on prognosis in patients with IPF.MethodsClinical data of 371 patients with IPF (145 and 226 in the derivation and validation cohorts, respectively), including serum lipid profiles (total cholesterol, triglyceride, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, apolipoprotein A-I [Apo A-I], and apolipoprotein B), were retrospectively collected. The association with mortality was analyzed using the Cox proportional hazard model.ResultsIn the derivation cohort, the mean age was 67.5 years, 86.2% were men, and 30.3% died during the follow-up (median: 18.0 months). Non-survivors showed lower lung function and greater gender-age-physiology scores than survivors. Among the serum lipid profiles, the levels of triglyceride and Apo A-I were significantly lower in non-survivors than in survivors. In the multivariate Cox analysis, low Apo A-I levels (< 140 mg/dL) were independently associated with the risk of mortality (hazard ratio 3.910, 95% confidence interval 1.170-13.069; P = 0.027), when adjusted for smoking history, body mass index, GAP score, and antifibrotic agent use. In both derivation and validation cohorts, patients with low Apo A-I levels (< 140 mg/dL) had worse survival (median survival: [derivation] 34.0 months vs. not reached, P = 0.003; [validation] 40.0 vs. 53.0 months, P = 0.027) than those with high Apo A-I levels in the Kaplan–Meier survival analysis.ConclusionsOur results indicate that low serum Apo A-1 levels are an independent predictor of mortality in patients with IPF, suggesting the utility of serum Apo A-I as a prognostic biomarker in IPF.

Novel AT2 Cell Subpopulations and Diagnostic Biomarkers in IPF: Integrating Machine Learning with Single-Cell Analysis.

Idiopathic pulmonary fibrosis (IPF) is a long-term condition with an unidentified cause, and currently there are no specific treatment options available. Alveolar epithelial type II cells (AT2) constitute a heterogeneous population crucial for secreting and regenerative functions in the alveolus, essential for maintaining lung homeostasis. However, a comprehensive investigation into their cellular diversity, molecular features, and clinical implications is currently lacking. In this study, we conducted a comprehensive examination of single-cell RNA sequencing data from both normal and fibrotic lung tissues. We analyzed alterations in cellular composition between IPF and normal tissue and investigated differentially expressed genes across each cell population. This analysis revealed the presence of two distinct subpopulations of IPF-related alveolar epithelial type II cells (IR_AT2). Subsequently, three unique gene co-expression modules associated with the IR_AT2 subtype were identified through the use of hdWGCNA. Furthermore, we refined and identified IPF-related AT2-related gene (IARG) signatures using various machine learning algorithms. Our analysis demonstrated a significant association between high IARG scores in IPF patients and shorter survival times (p-value < 0.01). Additionally, we observed a negative correlation between the percent predicted diffusing capacity for lung carbon monoxide (% DLCO) and increased IARG scores (cor = -0.44, p-value < 0.05). The cross-validation findings demonstrated a high level of accuracy (AUC > 0.85, p-value < 0.01) in the prognostication of patients with IPF utilizing the identified IARG signatures. Our study has identified distinct molecular and biological features among AT2 subpopulations, specifically highlighting the unique characteristics of IPF-related AT2 cells. Importantly, our findings underscore the prognostic relevance of specific genes associated with IPF-related AT2 cells, offering valuable insights into the advancement of IPF.

Identification of CFH and FHL2 as biomarkers for idiopathic pulmonary fibrosis.

Idiopathic pulmonary fibrosis (IPF) is a fatal disease of unknown etiology with a poor prognosis, characterized by a lack of effective diagnostic and therapeutic interventions. The role of immunity in the pathogenesis of IPF is significant, yet remains inadequately understood. This study aimed to identify potential key genes in IPF and their relationship with immune cells by integrated bioinformatics analysis and verify by in vivo and in vitro experiments. Gene microarray data were obtained from the Gene Expression Omnibus (GEO) for differential expression analysis. The differentially expressed genes (DEGs) were identified and subjected to functional enrichment analysis. By utilizing a combination of three machine learning algorithms, specific genes associated with idiopathic pulmonary fibrosis (IPF) were pinpointed. Then their diagnostic significance and potential co-regulators were elucidated. We further analyzed the correlation between key genes and immune infiltrating cells via single-sample gene set enrichment analysis (ssGSEA). Subsequently, a single-cell RNA sequencing data (scRNA-seq) was used to explore which cell types expressed key genes in IPF samples. Finally, a series of in vivo and in vitro experiments were conducted to validate the expression of candidate genes by western blot (WB), quantitative real-time PCR (qRT-PCR), and immunohistochemistry (IHC) analysis. A total of 647 DEGs of IPF were identified based on two datasets, including 225 downregulated genes and 422 upregulated genes. They are closely related to biological functions such as cell migration, structural organization, immune cell chemotaxis, and extracellular matrix. CFH and FHL2 were identified as key genes with diagnostic accuracy for IPF by three machine learning algorithms. Analysis using ssGSEA revealed a significant association of both CFH and FHL2 with diverse immune cells, such as B cells and NK cells. Further scRNA-seq analysis indicated CFH and FHL2 were specifically upregulated in human IPF tissues, which was confirmed by in vitro and in vivo experiments. In this study, CFH and FHL2 have been identified as novel potential biomarkers for IPF, with potential diagnostic utility in future clinical applications. Subsequent investigations into the functions of these genes in IPF and their interactions with immune cells may enhance comprehension of the disease's pathogenesis and facilitate the identification of therapeutic targets.

Modified blood cell GAP model as a prognostic biomarker in idiopathic pulmonary fibrosis.

The Gender, Age and Physiology (GAP) model is a simple mortality prediction tool in patients with idiopathic pulmonary fibrosis that uses demographic and physiological variables available at initial evaluation. White blood cell variables may have associations with idiopathic pulmonary fibrosis outcomes. We evaluated whether incorporating blood cell counts in modified GAP (cGAP) models would improve outcome prediction in patients with idiopathic pulmonary fibrosis. This retrospective analysis included pooled data from phase 3 randomised trials of pirfenidone in idiopathic pulmonary fibrosis (ASCEND, CAPACITY 004, CAPACITY 006). Study outcomes (disease progression, all-cause mortality, all-cause hospitalisation, respiratory-related hospitalisation) were evaluated during the initial 1-year period. Shared frailty models were used to evaluate associations between continuous and categorical baseline white and red blood cell parameters and study outcomes in a bivariate context, and to evaluate the impact of adding continuous monocyte count (cGAP1) or white and red blood cell parameters (cGAP2) to traditional GAP variables in a multivariable context based on C-statistics changes. Data were pooled from 1247 patients (pirfenidone, n=623; placebo, n=624). Significant associations (bivariate analyses) were idiopathic pulmonary fibrosis progression with neutrophil and eosinophil counts; all-cause mortality with monocyte and neutrophil counts; all-cause hospitalisation with monocyte count, neutrophil count and haemoglobin level; and respiratory-related hospitalisation with monocyte count, neutrophil count and haemoglobin level. In multivariate analyses, C-statistics were highest for the cGAP2 model for each of the outcomes. Modified GAP models incorporating monocyte counts alone or plus other white and red blood cell variables may be useful to improve prediction of outcomes in patients with idiopathic pulmonary fibrosis.

An extended-gate field-effect transistor (EG-FET) signal transducing combined with epitope molecular imprinting for selective chemosensing of chosen idiopathic pulmonary fibrosis (IPF) biomarkers

We propose a relatively rapid and straightforward procedure for the determination of chosen idiopathic pulmonary fibrosis (IPF) predictive protein biomarkers, vis., matrix metalloproteinase-1 (MMP-1) and surfactant-associated protein-A (SP-A), suitable for their determination in bodily fluids. IPF is a lethal lung disease, annually causing nearly as many deaths as breast cancer. Moreover, the coronavirus recently triggered acute exacerbation of patients suffering from IPF. Here, epitopes specific to the chosen IPF biomarkers were molecularly imprinted in a polymer as an alternative to imprinting fragile whole biomarkers’ molecules. A thin molecularly imprinted polymer (MIP) film-based recognition units were integrated by electropolymerization with gates of extended-gate field-effect transistors (EG-FETs) to fabricate biomarker-selective chemical sensors suitable as point-of-care testing (POCT) diagnostic devices. Either AQDDIDGIQAI or FKGNKYWAVQGQNV single-epitope imprinting allowed determining the MMP-1 biomarker in the 10 to 250 nM linear dynamic concentration range with a LOD of 7.5 or 2 nM, respectively. Simultaneous imprinting of three MMP-1 epitopes, vis., MIAHDFPGIGHK, HGYPKDIYSS, and FKGNKYWAVQGQNV, decreased this LOD to 0.1 nM, allowing for determining this biomarker in serum. Moreover, by FSSNGQSIT or YSDGTPVNYTNWYR single-epitope imprinting, chemosensors for SP-A were fabricated to show the versatility of epitope imprinting. These chemosensors selectively determined the SP-A biomarker with the LOD of 1.9 or 2.9 nM, allowing its determination in bodily fluids.

Identification and validation of mutual hub genes in idiopathic pulmonary fibrosis and rheumatoid arthritis-associated usual interstitial pneumonia

ObjectivesThe study aims at exploring common hub genes and pathways in idiopathic pulmonary fibrosis (IPF) and rheumatoid arthritis-associated usual interstitial pneumonia (RA-UIP) through integrated bioinformatics analyses. MethodsThe GSE199152 dataset containing lung tissue samples from IPF and RA-UIP patients was acquired from the Gene Expression Omnibus (GEO) database. The identification of overlapping differentially expressed genes (DEGs) in IPF and RA-UIP was carried out through R language. Protein–protein interaction (PPI) network analysis and module analysis were applied to filter mutual hub genes in the two diseases. Enrichment analyses were also conducted to analyze the possible biological functions and pathways of the overlapped DEGs and hub genes. The diagnostic value of key genes was assessed with R language, and the expressions of these genes in pulmonary cells of IPF and rheumatoid arthritis-associated interstitial lung disease (RA-ILD) patients were analyzed with single cell RNA-sequencing (scRNA-seq) datasets. The expression levels of hub genes were validated in blood samples from patients, specimens of human lung fibroblasts, lung tissue samples from mice, as well as external GEO datasets. ResultsFour common hub genes (THBS2, TIMP1, POSTN, and CD19) were screened. Enrichment analyses showed that the abnormal expressions of DEGs and hub genes may be connected with the onset of IPF and RA-UIP by regulating the progression of fibrosis. ScRNA-seq analyses illustrated that for both IPF and RA-ILD patients, THBS2, TIMP1, and POSTN were mainly expressed in lung fibroblasts, while CD19 was uniquely high-expressed in B cells. The qRT-PCR and immunohistochemistry (IHC) results verified that the expression levels of hub genes were mostly in accordance with the findings obtained from the bioinformatics analyses. ConclusionThough IPF and RA-UIP are distinct diseases, they may to some extent have mutual pathogenesis in the development of fibrosis. THBS2, TIMP1, POSTN, and CD19 may be the potential biomarkers of IPF and RA-UIP, and intervention on related pathways of these genes could offer new strategies for the precision treatment of IPF and RA-UIP.

Baseline Blood Levels of Mucin-1 Are Associated with Crucial On-Treatment Adverse Outcomes in Patients with Idiopathic Pulmonary Fibrosis Receiving Antifibrotic Pirfenidone.

Mucin-1 is a multi-functional glycoprotein expressed by type II alveolocytes and may be detectable in the circulation following pulmonary fibrosis. The prognostic utility of baseline pre-treatment blood levels of mucin-1 in patients with idiopathic pulmonary fibrosis (IPF) receiving antifibrotics has not yet been fully established. We retrospectively studied a cohort of patients (from two hospitals) with IPF who were receiving pirfenidone for >12 weeks. Baseline blood mucin-1 levels were measured via sandwich enzyme-linked immunosorbent assays. We investigated the performance of mucin-1 levels in longitudinally predicting the risks of acute exacerbation of IPF (AE-IPF) and severe adverse outcomes (SAO), including lung transplantation and death. Seventy patients were included; 20 developed AE-IPF; and 31 had SAO during the follow-up period. Patients with baseline mucin-1 levels ≥2.5 ng/mL had enhanced risks of AE-IPF (adjusted hazard ratio [aHR], 14.07; 95% confidence interval [CI], 4.26-46.49) and SAO within 2 years (aHR, 7.87; 95% CI, 2.86-21.70) and anytime during the follow-up (aHR, 4.68; 95% CI, 2.11-10.39). The risks increased across subgroups with increasing mucin-1 levels. Patients in the "mucin-1 ≥ 2.5" group also exhibited an accelerated decline in DLCO. This study supports baseline blood mucin-1 levels as a biomarker for IPF that predicts adverse outcomes during pirfenidone treatment.

Identification of cuproptosis-related diagnostic biomarkers in idiopathic pulmonary fibrosis.

Idiopathic pulmonary fibrosis (IPF) is a progressive and fatal lung disease with clinical and pathological heterogeneity. Recent studies have identified cuproptosis as a novel cell death mechanism. However, the role of cuproptosis-related genes in the pathogenesis of IPF is still unclear. Two IPF datasets of the Gene Expression Omnibus database were studied. Mann-Whitney U test, correlation analysis, functional enrichment analyses, single-sample gene set enrichment analysis, CIBERSORT, unsupervised clustering, weighted gene co-expression network analysis, and receiver operating characteristic curve analysis were used to conduct our research. The dysregulated cuproptosis-related genes and immune responses were identified between IPF patients and controls. Two cuproptosis-related molecular clusters were established in IPF, the high immune score group (C1) and the low immune score group (C2). Significant heterogeneity in immunity between clusters was revealed by functional analyses results. The module genes with the strongest correlation to the 2 clusters were identified by weighted gene co-expression network analysis results. Seven hub genes were found using the Cytoscape software. Ultimately, 2 validated diagnostic biomarkers of IPF, CDKN2A and NEDD4, were obtained. Subsequently, the results were validated in GSE47460. Our investigation illustrates that CDKN2A and NEDD4 may be valid biomarkers that were useful for IPF diagnosis and copper-related clustering.

Analysis of expression profile of long non-coding RNA in patients with idiopathic and COVID-19-induced pulmonary fibrosis

Introduction. Idiopathic pulmonary fibrosis (IPF) comprises an interstitial lung disease with unclear pathogenesis, rapid progression, and no effective treatment. Pulmonary fibrosis is reported to be one of the most severe complications induced by a new coronavirus infection COVID-19. The mechanisms triggering pulmonary fibrosis and leading to its rapid progression remain substantially unclear. Evidence suggests that immune and genetic factors contribute to the development of this disease. Among the latter, the role of long non-coding RNAs (dnRNAs) has been actively studied to date. Materials and methods. Considering the role of TP53TG1, LINC00342, H19, MALAT1, DNM3OS, and MEG3 dnRNAs as regulators of signaling pathways associated with fibroblast activation and epithelial-mesenchymal transition, the authors analyzed the expression level of selected dnRNAs in lung tissue and blood mononuclear cells of patients with IPF (N = 12), post-COVID-19 pulmonary fibrosis (N = 14), and in control group (N = 27). Results and discussion. Blood mononuclear cells in patients with IPF and post-COVID-19 PF revealed similar patterns of TP53TG1 and MALAT1 dnRNA expression. The level of relative expression of MALAT1 was significantly higher in patients with IPF (Fold Change=3.207, P = 0.0005) and with post-COVID-19 PF (Fold Change=9.854, P = 0.0003), while the relative expression level of TP53TG1 reduced in patients with IPF (Fold Change=0.4308, P = 0.0313) and with post-COVID-19 PF (Fold Change=0.1888, P = 0.0003 in blood mononuclear cells, Fold Change=0.1791, P = 0.0237 in lung tissue). Increased expression of DNM3OS in blood mononuclear cells (Fold Change=12.899, P = 0.0016) and lung tissue (Fold Change=9.527, P = 0.0001), LINC00342 (Fold Change=2.221, P = 0.0309) in blood mononuclear cells was revealed only in patients with IPF. Conclusion. Evaluation of the dnRNA expression profile of TP53TG1, LINC00342, MALAT1 and DNM3OS in blood mononuclei can be used as an informative and non-invasive biomarker in IPF and post COVID-19 PF.

PTK2-associated gene signature could predict the prognosis of IPF

Idiopathic pulmonary fibrosis (IPF) is a progressive and fatal lung disease with a poor prognosis. Current/available clinical prediction tools have limited sensitivity and accuracy when evaluating clinical outcomes of IPF. Research has shown that focal adhesion kinase (FAK), produced by the protein tyrosine kinase 2 (PTK2) gene, is crucial in IPF development. FAK activation is a characteristic of lesional fibroblasts; Thus, FAK may be a valuable therapeutic target or prognostic biomarker for IPF. This study aimed to create a gene signature based on PTK2-associated genes and microarray data from blood cells to predict disease prognosis in patients with IPF. PTK2 levels were found to be higher in lung tissues of IPF patients compared to healthy controls, and PTK2 inhibitor Defactinib was found to reduce TGFβ-induced FAK activation and increase α-smooth muscle actin. Although the blood PTK2 levels were higher in IPF patients, blood PTK level alone could not predict IPF prognosis. From 196 PTK2-associated genes, 11 genes were prioritized to create a gene signature (PTK2 molecular signature) and a risk score system using univariate and multivariate Cox regression analysis. Patients were divided into high-risk and low-risk groups using PTK2 molecular signature. Patients in the high-risk group experienced decreased survival rates compared to patients in the low-risk group across all discovery and validation cohorts. Further functional enrichment and immune cell proportion analyses revealed that the PTK2 molecular signature strongly reflected the activation levels of immune pathways and immune cells. These findings suggested that PTK2 is a molecular target of IPF and the PTK2 molecular signature is an effective IPF prognostic biomarker.

Predictive Biomarkers in Idiopathic Pulmonary Fibrosis: A Comprehensive Analysis for Early Diagnosis and Patient Stratification

Predictive Biomarkers in Idiopathic Pulmonary Fibrosis: A Comprehensive Analysis for Early Diagnosis and Patient Stratification

Exploring the Role of Biomarkers Associated with Alveolar Damage and Dysfunction in Idiopathic Pulmonary Fibrosis-A Systematic Review.

Idiopathic pulmonary fibrosis (IPF) is one of the most aggressive forms of interstitial lung diseases (ILDs), marked by an ongoing, chronic fibrotic process within the lung tissue. IPF leads to an irreversible deterioration of lung function, ultimately resulting in an increased mortality rate. Therefore, the focus has shifted towards the biomarkers that might contribute to the early diagnosis, risk assessment, prognosis, and tracking of the treatment progress, including those associated with epithelial injury. We conducted this review through a systematic search of the relevant literature using established databases such as PubMed, Scopus, and Web of Science. Selected articles were assessed, with data extracted and synthesized to provide an overview of the current understanding of the existing biomarkers for IPF. Signs of epithelial cell damage hold promise as relevant biomarkers for IPF, consequently offering valuable support in its clinical care. Their global and standardized utilization remains limited due to a lack of comprehensive information of their implications in IPF. Recognizing the aggressive nature of IPF among interstitial lung diseases and its profound impact on lung function and mortality, the exploration of biomarkers becomes pivotal for early diagnosis, risk assessment, prognostic evaluation, and therapy monitoring.

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.