Identification of Hub Genes and Pathways Associated With Idiopathic Pulmonary Fibrosis via Bioinformatics Analysis

Construction of an artificial neural network diagnostic model and investigation of immune cell infiltration characteristics for idiopathic pulmonary fibrosis

BACKGROUND

Idiopathic pulmonary fibrosis (IPF) is a severe lung condition, and finding better ways to diagnose and treat the disease is crucial for improving patient outcomes. Our study sought to develop an artificial neural network (ANN) model for IPF and determine the immune cell types that differed between the IPF and control groups.

METHODS

From the Gene Expression Omnibus (GEO) database, we first obtained IPF microarray datasets. To conduct protein-protein interaction (PPI) networks and enrichment analyses, differentially expressed genes (DEGs) were screened between tissues of patients with IPF and tissues of controls. Afterward, we identified the important feature genes associated with IPF using random forest (RF) analysis, and then constructed and validated a prediction ANN mode. In addition, the proportions of immune cells were quantified using cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT) analysis, which was performed on microarray datasets based on gene expression profiling.

RESULTS

A total of 11 downregulated and 36 upregulated DEGs were identified. PPI networks and enrichment analyses were carried out; the immune system and extracellular matrix were the subjects of the enrichments. Using RF analysis, the significant feature genes LRRC17, COMP, ASPN, CRTAC1, POSTN, COL3A1, PEBP4, IL13RA2, and CA4 were identified. The nine feature gene scores were integrated into the ANN to develop a diagnostic prediction model. The receiver operating characteristic (ROC) curves demonstrated the strong diagnostic ability of the ANN in predicting IPF in the training and testing sets. An analysis of IPF tissues in comparison to normal tissues revealed a reduction in the infiltration of natural killer cells resting, monocytes, macrophages M0, and neutrophils; conversely, the infiltration of T cells CD4 memory resting, mast cells, and macrophages M0 increased.

CONCLUSION

LRRC17, COMP, ASPN, CRTAC1, POSTN, COL3A1, PEBP4, IL13RA2, and CA4 were determined as key feature genes for IPF. The nine feature genes in the ANN model will be extremely important for diagnosing IPF. It may be possible to use differentiated immune cells from IPF samples in comparison to normal samples as targets for immunotherapy in patients with IPF.

Identification of Hub Genes and Prediction of Targeted Drugs for Rheumatoid Arthritis and Idiopathic Pulmonary Fibrosis

There is a potential link between rheumatoid arthritis (RA) and idiopathic pulmonary fibrosis (IPF). The aim of this study is to investigate the molecular processes that underlie the development of these two conditions by bioinformatics methods. The gene expression samples for RA (GSE77298) and IPF (GSE24206) were retrieved from the Gene Expression Omnibus (GEO) database. After identifying the overlapping differentially expressed genes (DEGs) for RA and IPF, we conducted functional annotation, protein-protein interaction (PPI) network analysis, and hub gene identification. Finally, we used the hub genes to predict potential medications for the treatment of both disorders. We identified 74 common DEGs for further analysis. Functional analysis demonstrated that cellular components, biological processes, and molecular functions all played a role in the emergence and progression of RA and IPF. Using the cytoHubba plugin, we identified 7 important hub genes, namely COL3A1, SDC1, CCL5, CXCL13, MMP1, THY1, and BDNF. As diagnostic indicators for RA, SDC1, CCL5, CXCL13, MMP1, and THY1 showed favorable values. For IPF, COL3A1, SDC1, CCL5, CXCL13, THY1, and BDNF were favorable diagnostic markers. Furthermore, we predicted 61 Chinese and 69 Western medications using the hub genes. Our research findings demonstrate a shared pathophysiology between RA and IPF, which may provide new insights for more mechanistic research and more effective treatments. These common pathways and hub genes identified in our study offer potential opportunities for developing more targeted therapies that can address both disorders.

Multi-omics integration reveals a nonlinear signature that precedes progression of lung fibrosis

Objectives

Idiopathic pulmonary fibrosis (IPF) is a devastating progressive interstitial lung disease with poor outcomes. While decades of research have shed light on pathophysiological mechanisms associated with the disease, our understanding of the early molecular events driving IPF and its progression is limited. With this study, we aimed to model the leading edge of fibrosis using a data-driven approach.

Methods

Multiple omics modalities (transcriptomics, metabolomics and lipidomics) of healthy and IPF lung explants representing different stages of fibrosis were combined using an unbiased approach. Multi-Omics Factor Analysis of datasets revealed latent factors specifically linked with established fibrotic disease (Factor1) and disease progression (Factor2).

Results

Features characterising Factor1 comprised well-established hallmarks of fibrotic disease such as defects in surfactant, epithelial-mesenchymal transition, extracellular matrix deposition, mitochondrial dysfunction and purine metabolism. Comparatively, Factor2 identified a signature revealing a nonlinear trajectory towards disease progression. Molecular features characterising Factor2 included genes related to transcriptional regulation of cell differentiation, ciliogenesis and a subset of lipids from the endocannabinoid class. Machine learning models, trained upon the top transcriptomics features of each factor, accurately predicted disease status and progression when tested on two independent datasets.

Conclusion

This multi-omics integrative approach has revealed a unique signature which may represent the inflection point in disease progression, representing a promising avenue for the identification of therapeutic targets aimed at addressing the progressive nature of the disease.

Development of a Novel Biomarker for the Progression of Idiopathic Pulmonary Fibrosis

The progression of idiopathic pulmonary fibrosis (IPF) is diverse and unpredictable. We identified and validated a new biomarker for IPF progression. To identify a candidate gene to predict progression, we assessed differentially expressed genes in patients with advanced IPF compared with early IPF and controls in three lung sample cohorts. Candidate gene expression was confirmed using immunohistochemistry and Western blotting of lung tissue samples from an independent IPF clinical cohort. Biomarker potential was assessed using an enzyme-linked immunosorbent assay of serum samples from the retrospective validation cohort. We verified that the final candidate gene reflected the progression of IPF in a prospective validation cohort. In the RNA-seq comparative analysis of lung tissues, CD276, COL7A1, CTSB, GLI2, PIK3R2, PRAF2, IGF2BP3, and NUPR1 were up-regulated, and ADAMTS8 was down-regulated in the samples of advanced IPF. Only CTSB showed significant differences in expression based on Western blotting (n = 12; p < 0.001) and immunohistochemistry between the three groups of the independent IPF cohort. In the retrospective validation cohort (n = 78), serum CTSB levels were higher in the progressive group (n = 25) than in the control (n = 29, mean 7.37 ng/mL vs. 2.70 ng/mL, p < 0.001) and nonprogressive groups (n = 24, mean 7.37 ng/mL vs. 2.56 ng/mL, p < 0.001). In the prospective validation cohort (n = 129), serum CTSB levels were higher in the progressive group than in the nonprogressive group (mean 8.30 ng/mL vs. 3.00 ng/mL, p < 0.001). After adjusting for baseline FVC, we found that CTSB was independently associated with IPF progression (adjusted OR = 2.61, p < 0.001). Serum CTSB levels significantly predicted IPF progression (AUC = 0.944, p < 0.001). Serum CTSB level significantly distinguished the progression of IPF from the non-progression of IPF or healthy control.

Automatic cell-type harmonization and integration across Human Cell Atlas datasets

Harmonizing cell types across the single-cell community and assembling them into a common framework is central to building a standardized Human Cell Atlas. Here, we present CellHint, a predictive clustering tree-based tool to resolve cell-type differences in annotation resolution and technical biases across datasets. CellHint accurately quantifies cell-cell transcriptomic similarities and places cell types into a relationship graph that hierarchically defines shared and unique cell subtypes. Application to multiple immune datasets recapitulates expert-curated annotations. CellHint also reveals underexplored relationships between healthy and diseased lung cell states in eight diseases. Furthermore, we present a workflow for fast cross-dataset integration guided by harmonized cell types and cell hierarchy, which uncovers underappreciated cell types in adult human hippocampus. Finally, we apply CellHint to 12 tissues from 38 datasets, providing a deeply curated cross-tissue database with ∼3.7 million cells and various machine learning models for automatic cell annotation across human tissues.

Unagi: Deep Generative Model for Deciphering Cellular Dynamics and In-Silico Drug Discovery in Complex Diseases

Human diseases are characterized by intricate cellular dynamics. Single-cell sequencing provides critical insights, yet a persistent gap remains in computational tools for detailed disease progression analysis and targeted in-silico drug interventions. Here, we introduce UNAGI, a deep generative neural network tailored to analyze time-series single-cell transcriptomic data. This tool captures the complex cellular dynamics underlying disease progression, enhancing drug perturbation modeling and discovery. When applied to a dataset from patients with Idiopathic Pulmonary Fibrosis (IPF), UNAGI learns disease-informed cell embeddings that sharpen our understanding of disease progression, leading to the identification of potential therapeutic drug candidates. Validation via proteomics reveals the accuracy of UNAGI's cellular dynamics analyses, and the use of the Fibrotic Cocktail treated human Precision-cut Lung Slices confirms UNAGI's predictions that Nifedipine, an antihypertensive drug, may have antifibrotic effects on human tissues. UNAGI's versatility extends to other diseases, including a COVID dataset, demonstrating adaptability and confirming its broader applicability in decoding complex cellular dynamics beyond IPF, amplifying its utility in the quest for therapeutic solutions across diverse pathological landscapes.

iDESC: identifying differential expression in single-cell RNA sequencing data with multiple subjects

BACKGROUND

Single-cell RNA sequencing (scRNA-seq) technology has enabled assessment of transcriptome-wide changes at single-cell resolution. Due to the heterogeneity in environmental exposure and genetic background across subjects, subject effect contributes to the major source of variation in scRNA-seq data with multiple subjects, which severely confounds cell type specific differential expression (DE) analysis. Moreover, dropout events are prevalent in scRNA-seq data, leading to excessive number of zeroes in the data, which further aggravates the challenge in DE analysis.

RESULTS

We developed iDESC to detect cell type specific DE genes between two groups of subjects in scRNA-seq data. iDESC uses a zero-inflated negative binomial mixed model to consider both subject effect and dropouts. The prevalence of dropout events (dropout rate) was demonstrated to be dependent on gene expression level, which is modeled by pooling information across genes. Subject effect is modeled as a random effect in the log-mean of the negative binomial component. We evaluated and compared the performance of iDESC with eleven existing DE analysis methods. Using simulated data, we demonstrated that iDESC had well-controlled type I error and higher power compared to the existing methods. Applications of those methods with well-controlled type I error to three real scRNA-seq datasets from the same tissue and disease showed that the results of iDESC achieved the best consistency between datasets and the best disease relevance.

CONCLUSIONS

iDESC was able to achieve more accurate and robust DE analysis results by separating subject effect from disease effect with consideration of dropouts to identify DE genes, suggesting the importance of considering subject effect and dropouts in the DE analysis of scRNA-seq data with multiple subjects.

Idiopathic Pulmonary Fibrosis Molecular Substrates Revealed by Competing Endogenous RNA Regulatory Networks

Idiopathic pulmonary fibrosis (IPF) is a chronic progressive fibrotic disease of the lung with poor prognosis. Fibrosis results from remodeling of the interstitial tissue. A wide range of gene expression changes are observed, but the role of micro RNAs (miRNAs) and circular RNAs (circRNA) is still unclear. Therefore, this study aimed to establish an messenger RNA (mRNA)-miRNA-circRNA competing endogenous RNA (ceRNA) regulatory network to uncover novel molecular signatures using systems biology tools. Six datasets were used to determine differentially expressed genes (DEGs) and miRNAs (DEmiRNA). Accordingly, protein-protein, mRNA-miRNA, and miRNA-circRNA interactions were constructed. Modules were determined and further analyzed in the Drug Gene Budger platform to identify potential therapeutic compounds. We uncovered common 724 DEGs and 278 DEmiRNAs. In the protein-protein interaction network, TMPRSS4, ESR2, TP73, CLEC4E, and TP63 were identified as hub protein coding genes. The mRNA-miRNA interaction network revealed two modules composed of ADRA1A, ADRA1B, hsa-miR-484 and CDH2, TMPRSS4, and hsa-miR-543. The DEmiRNAs in the modules further analyzed to propose potential circRNA regulators in the ceRNA network. These results help deepen the understanding of the mechanisms of IPF. In addition, the molecular leads reported herein might inform future innovations in diagnostics and therapeutics research and development for IPF.

Identification of diagnostic hub genes related to neutrophils and infiltrating immune cell alterations in idiopathic pulmonary fibrosis

Background

There is still a lack of specific indicators to diagnose idiopathic pulmonary fibrosis (IPF). And the role of immune responses in IPF is elusive. In this study, we aimed to identify hub genes for diagnosing IPF and to explore the immune microenvironment in IPF.

Methods

We identified differentially expressed genes (DEGs) between IPF and control lung samples using the GEO database. Combining LASSO regression and SVM-RFE machine learning algorithms, we identified hub genes. Their differential expression were further validated in bleomycin-induced pulmonary fibrosis model mice and a meta-GEO cohort consisting of five merged GEO datasets. Then, we used the hub genes to construct a diagnostic model. All GEO datasets met the inclusion criteria, and verification methods, including ROC curve analysis, calibration curve (CC) analysis, decision curve analysis (DCA) and clinical impact curve (CIC) analysis, were performed to validate the reliability of the model. Through the Cell Type Identification by Estimating Relative Subsets of RNA Transcripts algorithm (CIBERSORT), we analyzed the correlations between infiltrating immune cells and hub genes and the changes in diverse infiltrating immune cells in IPF.

Results

A total of 412 DEGs were identified between IPF and healthy control samples, of which 283 were upregulated and 129 were downregulated. Through machine learning, three hub genes (ASPN, SFRP2, SLCO4A1) were screened. We confirmed their differential expression using pulmonary fibrosis model mice evaluated by qPCR, western blotting and immunofluorescence staining and analysis of the meta-GEO cohort. There was a strong correlation between the expression of the three hub genes and neutrophils. Then, we constructed a diagnostic model for diagnosing IPF. The areas under the curve were 1.000 and 0.962 for the training and validation cohorts, respectively. The analysis of other external validation cohorts, as well as the CC analysis, DCA, and CIC analysis, also demonstrated strong agreement. There was also a significant correlation between IPF and infiltrating immune cells. The frequencies of most infiltrating immune cells involved in activating adaptive immune responses were increased in IPF, and a majority of innate immune cells showed reduced frequencies.

Conclusion

Our study demonstrated that three hub genes (ASPN, SFRP2, SLCO4A1) were associated with neutrophils, and the model constructed with these genes showed good diagnostic value in IPF. There was a significant correlation between IPF and infiltrating immune cells, indicating the potential role of immune regulation in the pathological process of IPF.

Processing of carbon-reinforced construction materials releases PM2.5 inducing inflammation and (secondary) genotoxicity in human lung epithelial cells and fibroblasts

Building demolition following domestic fires or abrasive processing after thermal recycling can release particles harmful for the environment and human health. To mimic such situations, particles release during dry-cutting of construction materials was investigated. A reinforcement material consisting of carbon rods (CR), carbon concrete composite (C³) and thermally treated C³ (ttC³) were physicochemically and toxicologically analyzed in monocultured lung epithelial cells, and co-cultured lung epithelial cells and fibroblasts at the air-liquid interface. C³ particles reduced their diameter to WHO fibre dimensions during thermal treatment. Caused by physical properties or by polycyclic aromatic hydrocarbons and bisphenol A found in the materials, especially the released particles of CR and ttC³ induced an acute inflammatory response and (secondary) DNA damage. Transcriptome analysis indicated that CR and ttC³ particles carried out their toxicity via different mechanisms. While ttC³ affected pro-fibrotic pathways, CR was mostly involved in DNA damage response and in pro-oncogenic signaling.

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

Objective

This study aimed to identify candidate gene biomarkers associated with immune infiltration in idiopathic pulmonary fibrosis (IPF) based on machine learning algorithms.

Methods

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.

Results

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.

Conclusion

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.

Diagnostic and prognostic value of secreted phosphoprotein 1 for idiopathic pulmonary fibrosis: a systematic review and meta-analysis

BackgroundThere is an increasing number of studies on the diagnostic and prognostic biomarkers associated with IPF. The purpose of this study was to explore the diagnostic and prognostic value of secreted phosphoprotein 1 (SPP1) in IPF.MethodsUsing five database, appropriate studies were included. Pooled sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR) and 95% confidence intervals (CIs) were calculated. Pooled hazard ratios (HRs) and 95% CIs related to prognosis were calculated.ResultsThirteen studies were included in the meta-analyses. The pooled sensitivity, specificity, PLR, NLR and DOR were 0.84 (95% CI 0.72-0.91), 0.89 (95% CI 0.83-0.94), 7.94 (95% CI 4.63-13.62), 0.18 (95% CI 0.10-0.33), 43.08 (95% CI 15.88-116.84) for SPP1 in the differential diagnosis of IPF and healthy people. The pooled sensitivity, specificity, PLR, NLR and DOR were 0.97 (95% CI 0.57-1.00), 0.93 (95% CI 0.73-0.98), 13.87 (95% CI 3.26-58.99), 0.03 (95% CI 0-0.68), 446.91 (95% CI 21.02-9504.41) for SPP1 to differentiate IPF and lung cancer patients. High SPP1 expression predicts poor prognosis for IPF patients (HR= 1.42, 95% CI = 1.27 and 1.58, P < 0.001).ConclusionsSPP1 is a potential diagnostic and prognostic biomarker for IPF patients.

Co-expression of fibrotic genes in inflammatory bowel disease; A localized event?

Introduction

Extracellular matrix turnover, a ubiquitous dynamic biological process, can be diverted to fibrosis. The latter can affect the intestine as a serious complication of Inflammatory Bowel Diseases (IBD) and is resistant to current pharmacological interventions. It embosses the need for out-of-the-box approaches to identify and target molecular mechanisms of fibrosis.

Methods and results

In this study, a novel mRNA sequencing dataset of 22 pairs of intestinal biopsies from the terminal ileum (TI) and the sigmoid of 7 patients with Crohn's disease, 6 with ulcerative colitis and 9 control individuals (CI) served as a validation cohort of a core fibrotic transcriptomic signature (FIBSig), This signature, which was identified in publicly available data (839 samples from patients and healthy individuals) of 5 fibrotic disorders affecting different organs (GI tract, lung, skin, liver, kidney), encompasses 241 genes and the functional pathways which derive from their interactome. These genes were used in further bioinformatics co-expression analyses to elucidate the site-specific molecular background of intestinal fibrosis highlighting their involvement, particularly in the terminal ileum. We also confirmed different transcriptomic profiles of the sigmoid and terminal ileum in our validation cohort. Combining the results of these analyses we highlight 21 core hub genes within a larger single co-expression module, highly enriched in the terminal ileum of CD patients. Further pathway analysis revealed known and novel inflammation-regulated, fibrogenic pathways operating in the TI, such as IL-13 signaling and pyroptosis, respectively.

Discussion

These findings provide a rationale for the increased incidence of fibrosis at the terminal ileum of CD patients and highlight operating pathways in intestinal fibrosis for future evaluation with mechanistic and translational studies.

Relationship between asporin and extracellular matrix behavior: A literature review

Asporin (ASPN), as a member of the small leucine-rich repeat proteoglycan family, is a type of protein that is found in the extracellular matrix. Collagen deposition or transformation is involved in a variety of pathological processes. ASPN is identified in cancerous tissue, pathological cardiac tissue, articular cartilage, keloid, and fibrotic lung tissue, and it has a role in the development of cancer, cardiovascular, bone and joint, keloid, and pulmonary fibrosis by interfering with collagen metabolism. This review article summarizes the data on ASPN expressions in mouse and human and highlights that overexpress of ASPN might play a role in a variety of diseases. Although our knowledge of ASPN is currently limited, these instances may help us better understand how it interacts with diseases.

Maximizing Small Biopsy Patient Samples: Unified RNA-Seq Platform Assessment of over 120,000 Patient Biopsies

Despite its wide-ranging benefits, whole-transcriptome or RNA exome profiling is challenging to implement in a clinical diagnostic setting. The Unified Assay is a comprehensive workflow wherein exome-enriched RNA-sequencing (RNA-Seq) assays are performed on clinical samples and analyzed by a series of advanced machine learning-based classifiers. Gene expression signatures and rare and/or novel genomic events, including fusions, mitochondrial variants, and loss of heterozygosity were assessed using RNA-Seq data generated from 120,313 clinical samples across three clinical indications (thyroid cancer, lung cancer, and interstitial lung disease). Since its implementation, the data derived from the Unified Assay have allowed significantly more patients to avoid unnecessary diagnostic surgery and have played an important role in guiding follow-up decisions regarding treatment. Collectively, data from the Unified Assay show the utility of RNA-Seq and RNA expression signatures in the clinical laboratory, and their importance to the future of precision medicine.

Identification of Critical Genes and Pathways for Influenza A Virus Infections via Bioinformatics Analysis

Influenza A virus (IAV) requires the host cellular machinery for many aspects of its life cycle. Knowledge of these host cell requirements not only reveals molecular pathways exploited by the virus or triggered by the immune system but also provides further targets for antiviral drug development. To uncover critical pathways and potential targets of influenza infection, we assembled a large amount of data from 8 RNA sequencing studies of IAV infection for integrative network analysis. Weighted gene co-expression network analysis (WGCNA) was performed to investigate modules and genes correlated with the time course of infection and/or multiplicity of infection (MOI). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed to explore the biological functions and pathways of the genes in 5 significant modules. Top hub genes were identified using the cytoHubba plugin in the protein interaction network. The correlation between expression levels of 7 top hub genes and time course or MOI was displayed and validated, including BCL2L13, PLSCR1, ARID5A, LMO2, NDRG4, HAP1, and CARD10. Dysregulated expression of these genes potently impacted the development of IAV infection through modulating IAV-related biological processes and pathways. This study provides further insights into the underlying molecular mechanisms and potential targets in IAV infection.

Matrix Metalloproteinases and Their Inhibitors in Pulmonary Fibrosis: EMMPRIN/CD147 Comes into Play

Pulmonary fibrosis (PF) is characterized by aberrant extracellular matrix (ECM) deposition, activation of fibroblasts to myofibroblasts and parenchymal disorganization, which have an impact on the biomechanical traits of the lung. In this context, the balance between matrix metalloproteinases (MMPs) and their tissue inhibitors of metalloproteinases (TIMPs) is lost. Interestingly, several MMPs are overexpressed during PF and exhibit a clear profibrotic role (MMP-2, -3, -8, -11, -12 and -28), but a few are antifibrotic (MMP-19), have both profibrotic and antifibrotic capacity (MMP7), or execute an unclear (MMP-1, -9, -10, -13, -14) or unknown function. TIMPs are also overexpressed in PF; hence, the modulation and function of MMPs and TIMP are more complex than expected. EMMPRIN/CD147 (also known as basigin) is a transmembrane glycoprotein from the immunoglobulin superfamily (IgSF) that was first described to induce MMP activity in fibroblasts. It also interacts with other molecules to execute non-related MMP aactions well-described in cancer progression, migration, and invasion. Emerging evidence strongly suggests that CD147 plays a key role in PF not only by MMP induction but also by stimulating fibroblast myofibroblast transition. In this review, we study the structure and function of MMPs, TIMPs and CD147 in PF and their complex crosstalk between them.