Dataset Information

Identification of Hub Genes in Idiopathic Pulmonary Fibrosis and Their Association with Lung Cancer by Bioinformatics Analysis.

ABSTRACT:

Background

Idiopathic pulmonary fibrosis (IPF) is a chronic, progressive, and irreversible disease with a high mortality rate worldwide. However, the etiology and pathogenesis of IPF have not yet been fully described. Moreover, lung cancer is a significant complication of IPF and is associated with increased mortality. Nevertheless, identifying common genes involved in developing IPF and its progression to lung cancer remains an unmet need. The present study aimed to identify hub genes related to the development of IPF by meta-analysis. In addition, we analyzed their expression and their relationship with patients' progression in lung cancer.

Method

Microarray datasets GSE24206, GSE21369, GSE110147, GSE72073, and GSE32539 were downloaded from Gene Expression Omnibus (GEO). Next, we conducted a series of bioinformatics analysis to explore possible hub genes in IPF and evaluated the expression of hub genes in lung cancer and their relationship with the progression of different stages of cancer.

Results

A total of 1888 differentially expressed genes (DEGs) were identified, including 1105 upregulated and 783 downregulated genes. The 10 hub genes that exhibited a high degree of connectivity from the PPI network were identified. Analysis of the KEGG pathways showed that hub genes correlate with pathways such as the ECM-receptor interaction. Finally, we found that these hub genes are expressed in lung cancer and are associated with the progression of different stages of lung cancer.

Conclusions

Based on the integration of GEO microarray datasets, the present study identified DEGs and hub genes that could play an essential role in the pathogenesis of IPF and its association with the development of lung cancer in these patients, which could be considered potential diagnostic biomarkers or therapeutic targets for the disease.

SUBMITTER: Velazquez-Enriquez JM

PROVIDER: S-EPMC10604190 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature

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Publications

Identification of Hub Genes in Idiopathic Pulmonary Fibrosis and Their Association with Lung Cancer by Bioinformatics Analysis.

Velázquez-Enríquez Juan Manuel JM Reyes-Avendaño Itayetzi I Santos-Álvarez Jovito Cesar JC Reyes-Jiménez Edilburga E Vásquez-Garzón Verónica Rocío VR Baltiérrez-Hoyos Rafael R

Advances in respiratory medicine 20231012 5

<h4>Background</h4>Idiopathic pulmonary fibrosis (IPF) is a chronic, progressive, and irreversible disease with a high mortality rate worldwide. However, the etiology and pathogenesis of IPF have not yet been fully described. Moreover, lung cancer is a significant complication of IPF and is associated with increased mortality. Nevertheless, identifying common genes involved in developing IPF and its progression to lung cancer remains an unmet need. The present study aimed to identify hub genes r ...[more]

PMID: 37887075

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Project description:Background: Lung cancer is the most common comorbidity of idiopathic pulmonary fibrosis. Thus there is an urgent need for the research of IPF and carcinogenesis Objective: The objective of this study was to explore hub genes which are common in pulmonary fibrosis and lung cancer progression through bioinformatic analysis. Methods: All the analysis was performed in R software. Differentially expressed genes (DEGs) were explored by comparing gene expression profiles between IPF tissues and healthy lung tissues from GSE24206, GSE53845, GSE101286 and GSE110147 datasets. Venn Diagram analysis was used to identify the overlapping genes, while GO and KEGG pathway enrichment analysis were used to explore the biological functions of the DEGs using clusterprofiler package. Hub genes were identified by analyzing protein-protein interaction networks using Cytoscape software. Nomogram was constructed using the rms package. Tumor immune dysfunction and exclusion (TIDE) and Genomics of Drug Sensitivity in Cancer (GDSC) analysis was used to quantify the immunotherapy and chemotherapy sensitivity of non-small cell lung cancer (NSCLC) patients. Results: COL1A1, COL3A1, MMP1, POSTN1 and TIMP3 were identified as the top five hub genes. The five hub genes were used to construct a diagnostic nomogram that was validated in another IPF dataset. Since the hub genes were also associated with lung cancer progression, we found that the nomogram also had diagnostic value in NSCLC patients. These five genes achieved a statistically difference of overall survival in NSCLC patients (p < 0.05). The expression of the five hub genes was mostly enriched in fibroblasts. Fibroblasts and the hub genes also showed significant ability to predict the susceptibility of NSCLC patients to chemotherapy and immunotherapy. Conclusion: We identified five hub genes as potential biomarkers of IPF and NSCLC progression. This finding may give insight into the underlying molecular mechanisms of IPF and lung cancer progression and provides potential targets for developing new therapeutic agents for IPF patients.

Project description:Background: Idiopathic pulmonary fibrosis (IPF) is a chronic respiratory disease characterized by peripheral distribution of bilateral pulmonary fibrosis that is more pronounced at the base. IPF has a short median survival time and a poor prognosis. Therefore, it is necessary to identify effective prognostic indicators to guide the treatment of patients with IPF. Methods: We downloaded microarray data of bronchoalveolar lavage cells from the Gene Expression Omnibus (GEO), containing 176 IPF patients and 20 controls. The top 5,000 genes in the median absolute deviation were classified into different color modules using weighted gene co-expression network analysis (WGCNA), and the modules significantly associated with both survival time and survival status were identified as prognostic modules. We used Lasso Cox regression and multivariate Cox regression to search for hub genes related to prognosis from the differentially expressed genes (DEGs) in the prognostic modules and constructed a risk model and nomogram accordingly. Moreover, based on the risk model, we divided IPF patients into high-risk and low-risk groups to determine the biological functions and immune cell subtypes associated with the prognosis of IPF using gene set enrichment analysis and immune cell infiltration analysis. Results: A total of 153 DEGs located in the prognostic modules, three (TPST1, MRVI1, and TM4SF1) of which were eventually defined as prognostic hub genes. A risk model was constructed based on the expression levels of the three hub genes, and the accuracy of the model was evaluated using time-dependent receiver operating characteristic (ROC) curves. The areas under the curve for 1-, 2-, and 3-year survival rates were 0.862, 0.885, and 0.833, respectively. The results of enrichment analysis showed that inflammation and immune processes significantly affected the prognosis of patients with IPF. The degree of mast and natural killer (NK) cell infiltration also increases the prognostic risk of IPF. Conclusions: We identified three hub genes as independent molecular markers to predict the prognosis of patients with IPF and constructed a prognostic model that may be helpful in promoting therapeutic gains for IPF patients.

Project description:BackgroundIdiopathic pulmonary fibrosis (IPF) is a chronic and progressive interstitial lung disease, characterized by a decline in lung function. To date, the pathophysiologic mechanisms associated with lung dysfunction remain unclear, and no effective therapy has been identified to improve lung function.MethodsIn the present study, we used weighted gene co-expression network analysis (WGCNA) to identify key modules and hub genes associated with lung function in IPF. Three datasets, containing clinical information, were downloaded from Gene Expression Omnibus. WGCNA was performed on the GSE32537 dataset. Differentially expressed gene s (DEGs) between IPF patients and healthy controls were also identified to filter hub genes. The relationship between hub genes and lung function was then validated using the GSE47460 and GSE24206 datasets.ResultsThe red module, containing 267 genes, was positively correlated with the St. George's Respiratory Questionnaire score (r = 0.37, p < 0.001) and negatively correlated with the percent predicted forced vital capacity (FVC% predicted) (r = - 0.46, p < 0.001) and the percent predicted diffusion capacity of the lung for carbon monoxide (Dlco% predicted) (r = - 0.42, p < 0.001). Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analysis suggested that the genes in the red module were primarily involved in inflammation and immune pathways. Based on Module Membership and Gene Significance, 32 candidate hub genes were selected in the red module to construct a protein-protein interaction network . Based on the identified DEGs and the degree of connectivity in the network, we identified three hub genes, including interleukin 6 (IL6), suppressor of cytokine signaling-3 (SOCS3), and serpin family E member 1 (SERPINE1). In the GSE47460 dataset, Spearman correlation coefficients between Dlco% predicted and expression levels of IL6, SERPINE1, SOCS3 were -0.32, -0.41, and -0.46, respectively. Spearman correlation coefficients between FVC% predicted and expression levels of IL6, SERPINE1, SOCS3 were -0.29, -0.33, and -0.27, respectively. In the GSE24206 dataset, all three hub genes were upregulated in patients with advanced IPF.ConclusionWe identified three hub genes that negatively correlated with the lung function of IPF patients. Our results provide insights into the pathogenesis underlying the progressive disruption of lung function, and the identified hub genes may serve as biomarkers and potential therapeutictargets for the treatment of IPF patients.

Project description:Background: Idiopathic pulmonary fibrosis (IPF) is a fatal and irreversible interstitial lung disease. The specific mechanisms involved in the pathogenesis of IPF are not fully understood, while metabolic dysregulation has recently been demonstrated to contribute to IPF. This study aims to identify key metabolism-related genes involved in the progression of IPF, providing new insights into the pathogenesis of IPF. Methods: We downloaded four datasets (GSE32537, GSE110147, GSE150910, and GSE92592) from the Gene Expression Omnibus (GEO) database and identified differentially expressed metabolism-related genes (DEMRGs) in lung tissues of IPF by comprehensive analysis. Then, we performed GO, KEGG, and Reactome enrichment analyses of the DEMRGs. Subsequently, key DEMRGs were identified by machine-learning algorithms. Next, miRNAs regulating these key DEMRGs were predicted by integrating the GSE32538 (IPF miRNA dataset) and the miRWalk database. The Cytoscape software was used to visualize miRNA-mRNA regulatory networks. In addition, the relative levels of immune cells were assessed by the CIBERSORT algorithm, and the correlation of key DEMRGs with immune cells was calculated. Finally, the mRNA expression of the key DEMRGs was validated in two external independent datasets and an in vivo experiment. Results: A total of 101 DEMRGs (51 upregulated and 50 downregulated) were identified. Six key DEMRGs (ENPP3, ENTPD1, GPX3, PDE7B, PNMT, and POLR3H) were further identified using two machine-learning algorithms (LASSO and SVM-RFE). In the lung tissue of IPF patients, the expression levels of ENPP3, ENTPD1, and PDE7B were upregulated, and the expression levels of GPX3, PNMT, and POLR3H were downregulated. In addition, the miRNA-mRNA regulatory network of key DEMRGs was constructed. Then, the expression levels of key DEMRGs were validated in two independent external datasets (GSE53845 and GSE213001). Finally, we verified the key DEMRGs in the lung tissue of bleomycin-induced pulmonary fibrosis mice by qRT-PCR. Conclusion: Our study identified key metabolism-related genes that are differentially expressed in the lung tissue of IPF patients. Our study emphasizes the critical role of metabolic dysregulation in IPF, offers potential therapeutic targets, and provides new insights for future studies.

Project description:Background: Idiopathic pulmonary arterial hypertension (IPAH) is a life-threatening disease. Growing evidence indicated that IPAH is a chronic immune disease. This study explored the molecular mechanisms and T cell infiltration of IPAH using integrated bioinformatics methods. Methods: Gene expression profiles of dataset GSE113439 were downloaded from the Gene Expression Omnibus and analyzed using R. Protein-protein interaction (PPI) network and gene set enrichment analysis (GSEA) were established by NetworkAnalyst. Gene Ontology enrichment analysis was performed using ClueGO. Transcription factors of differentially expressed genes (DEGs) were estimated using iRegulon. Transcription factors and selected hub genes were verified by real-time polymerase chain reaction (qPCR) in the lung tissues of rats with pulmonary artery hypertension. The least absolute shrinkage and selection operator regression model and the area under the receiver operating characteristic curve (AUC) were applied jointly to identify the crucial hub genes. Moreover, immune infiltration in IPAH was calculated using ImmuCellAI, and the correlation between key hub genes and immune cells was analyzed using R. Results: A total of 512 DEGs were screened, and ten hub genes and three transcription factors were filtered by the DEG PPI network. The DEGs were mainly enriched in mitotic nuclear division, chromosome organization, and nucleocytoplasmic transport. The ten hub genes and three transcription factors were confirmed by qPCR. Moreover, MAPK6 was identified as the most potent biomarker with an AUC of 100%, and ImmuCellAI immune infiltration analysis showed that a higher proportion of CD4-naive T cells and central memory T cells (Tcm) was apparent in the IPAH group, whereas the proportions of cytotoxic T cells (Tc), exhausted T cells (Tex), type 17 T helper cells, effector memory T cells, natural killer T cells (NKT), natural killer cells, gamma-delta T cells, and CD8 T cells were lower. Finally, MAPK6 was positively correlated with Tex and Tcm, and negatively correlated with Tc and NKT. Conclusion: MAPK6 was identified as a crucial hub gene to discriminate IPAH from the normal group. Dysregulated immune reactions were identified in the lung tissue of patients with IPAH.

Project description:BackgroundThere 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.MethodsWe 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.ResultsA 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.ConclusionOur 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.

Dataset Information

Identification of Hub Genes in Idiopathic Pulmonary Fibrosis and Their Association with Lung Cancer by Bioinformatics Analysis.

Background

Method

Results

Conclusions

Publications

Identification of Hub Genes in Idiopathic Pulmonary Fibrosis and Their Association with Lung Cancer by Bioinformatics Analysis.

Similar Datasets

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