Identification of Hub Genes and Pathways Associated With Idiopathic Pulmonary Fibrosis via Bioinformatics Analysis.
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ABSTRACT:
SUBMITTER: Wan H
PROVIDER: S-EPMC8406749 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:IntroductionThe coronavirus disease 2019 (COVID-19), emerged in late 2019, was caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The risk factors for idiopathic pulmonary fibrosis (IPF) and COVID-19 are reported to be common. This study aimed to determine the potential role of differentially expressed genes (DEGs) common in IPF and COVID-19.Materials and methodsBased on GEO database, we obtained DEGs from one SARS-CoV-2 dataset and five IPF datasets. A series of enrichment analysis were performed to identify the function of upregulated and downregulated DEGs, respectively. Two plugins in Cytoscape, Cytohubba and MCODE, were utilized to identify hub genes after a protein-protein interaction (PPI) network. Finally, candidate drugs were predicted to target the upregulated DEGs.ResultsA total of 188 DEGs were found between COVID-19 and IPF, out of which 117 were upregulated and 71 were downregulated. The upregulated DEGs were involved in cytokine function, while downregulated DEGs were associated with extracellular matrix disassembly. Twenty-two hub genes were upregulated in COVID-19 and IPF, for which 155 candidate drugs were predicted (adj.P.value < 0.01).ConclusionIdentifying the hub genes aberrantly regulated in both COVID-19 and IPF may enable development of molecules, encoded by those genes, as therapeutic targets for preventing IPF progression and SARS-CoV-2 infections.
| S-EPMC8769324 | biostudies-literature
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.
| S-EPMC9038140 | biostudies-literature
Project description:BackgroundIdiopathic 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.MethodMicroarray 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.ResultsA 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.ConclusionsBased 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.
| S-EPMC10604190 | biostudies-literature
Project description:ObjectiveThe aim of this study is the identification of hub genes associated with idiopathic pulmonary arterial hypertension (IPAH).Materials and methodsGSE15197 gene expression data was downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified by screening IPAH patients and controls. The 5,000 genes with the greatest variances were analyzed using a weighted gene co-expression network analysis (WGCNA). Modules with the strongest correlation with IPAH were chosen, followed by a functional enrichment analysis. Protein-protein interaction (PPI) networks were constructed to identify hub gene candidates using calculated degrees. Real hub genes were found from the overlap of DEGs and candidate hub genes. microRNAs (miRNAs) targeting real hub genes were found by screening miRNet 2.0. The most important IPAH miRNAs were identified.ResultsThere were 4,395 DEGs identified. WGCNA indicated that green and brown modules associated most strongly with IPAH. Functional enrichment analysis showed that green and brown module genes were mainly involved in protein digestion and absorption and proteoglycans in cancer, respectively. The top ten candidate hub genes in green and brown modules were identified, respectively. After overlapping with DEGs, 11 real hub genes were identified: EP300, MMP2, CDH2, CDK2, GNG10, ALB, SMC2, DHX15, CUL3, BTBD1, and LTN1. These genes were expressed with significant differences in IPAH versus controls, indicating a high diagnostic ability. The miRNA-gene network showed that hsa-mir-1-3p could associate with IPAH.ConclusionEP300, MMP2, CDH2, CDK2, GNG10, ALB, SMC2, DHX15, CUL3, BTBD1, and LTN1 may play essential roles in IPAH. Predicted miRNA hsa-mir-1-3p could regulate gene expression in IPAH. Such hub genes may contribute to the pathology and progression in IPAH, providing potential diagnostic and therapeutic opportunities for IPAH patients.
| S-EPMC8117102 | biostudies-literature
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.
| S-EPMC7485506 | biostudies-literature
Project description:Idiopathic pulmonary fibrosis is a chronic and irreversible respiratory disease with a high incidence worldwide and no specific treatment. Currently, the etiology and pathogenesis of this disease remain largely unknown. In main purpose of this study, bioinformatics analysis was used to uncover key genes and pathways related to idiopathic pulmonary fibrosis (IPF). Gene expression profiles of GSE2052 and GSE35145 were obtained. After combining the 2 chip groups; then, we normalized the data, eliminating batch difference. R software was used to process and to screen differentially expressed genes (DEGs) between the IPF and normal tissues. Then, functional enrichment analysis of these DEGs was carried out, and a protein-protein interaction network (PPI) was also constructed. A total of 276 DEGs (152 up and 134 down-regulated genes) were identified in the IPF lung samples. The PPI network was established with 227 nodes and 763 edges. The top 10 hub genes were CAM1, CDH1, CXCL12, JUN, CTGF, SERPINE1, CXCL1, EDN1, COL1A2, and SPARC. Analyzing the PPI network modules with close interaction, the 3 key modules in the whole PPI network were screened out. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways enriched for the module containing DEGs contained the viral protein interaction with cytokine and the cytokine receptor, the TNF signaling pathway, and the chemokine signaling pathway. The identified key genes and pathways may play an important role in the occurrence and development of IPF, and may be expected to be biomarkers or therapeutic targets for the diagnosis of IPF.
| S-EPMC7478566 | biostudies-literature
Project description:The coronavirus disease 2019 (COVID-19) has developed into a global health crisis. Pulmonary fibrosis, as one of the complications of SARS-CoV-2 infection, deserves attention. As COVID-19 is a new clinical entity that is constantly evolving, and many aspects of disease are remain unknown. The datasets of COVID-19 and idiopathic pulmonary fibrosis were obtained from the Gene Expression Omnibus. The hub genes were screened out using the Random Forest (RF) algorithm depending on the severity of patients with COVID-19. A risk prediction model was developed to assess the prognosis of patients infected with SARS-CoV-2, which was evaluated by another dataset. Six genes (named NELL2, GPR183, S100A8, ALPL, CD177, and IL1R2) may be associated with the development of PF in patients with severe SARS-CoV-2 infection. S100A8 is thought to be an important target gene that is closely associated with COVID-19 and pulmonary fibrosis. Construction of a neural network model was successfully predicted the prognosis of patients with COVID-19. With the increasing availability of COVID-19 datasets, bioinformatic methods can provide possible predictive targets for the diagnosis, treatment, and prognosis of the disease and show intervention directions for the development of clinical drugs and vaccines.
| S-EPMC10725713 | biostudies-literature
Project description: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.
| S-EPMC10987927 | biostudies-literature
Project description:ObjectiveThe pathogenesis of idiopathic pulmonary fibrosis (IPF) remains unclear. We sought to identify IPF-related genes that may participate in the pathogenesis and predict potential targeted traditional Chinese medicines (TCMs).MethodsUsing IPF gene-expression data, Wilcoxon rank-sum tests were performed to identify differentially expressed genes (DEGs). Protein-protein interaction (PPI) networks, hub genes, and competitive endogenous RNA (ceRNA) networks were constructed or identified by Cytoscape. Quantitative polymerase chain reaction (qPCR) experiments in TGF-β1-induced human fetal lung (HFL) fibroblast cells and a pulmonary fibrosis mouse model verified gene reliability. The SymMap database predicted potential TCMs targeting IPF. The reliability of TCMs was verified in TGF-β1-induced MRC-5 cells.MaterialsMultiple gene-expression profile data of normal lung and IPF tissues were downloaded from the Gene Expression Omnibus database. HFL fibroblast cells and MRC-5 cells were purchased from Wuhan Procell Life Science and Technology Co., Ltd. (Wuhan, China). C57BL/12 mice were purchased from Beijing Vital River Laboratory Animal Technology Co., Ltd. (Beijing, China).ResultsIn datasets GSE134692 and GSE15197, DEGs were identified using Wilcoxon rank-sum tests (both p < 0.05). Among them, 1885 DEGs were commonly identified, and 87% (1640 genes) had identical dysregulation directions (binomial test, p < 1.00E-16). A PPI network with 1623 nodes and 8159 edges was constructed, and 18 hub genes were identified using the Analyze Network plugin in Cytoscape. Of 18 genes, CAV1, PECAM1, BMP4, VEGFA, FYN, SPP1, and COL1A1 were further validated in the GeneCards database and independent dataset GSE24206. ceRNA networks of VEGFA, SPP1, and COL1A1 were constructed. The genes were verified by qPCR in samples of TGF-β1-induced HFL fibroblast cells and pulmonary fibrosis mice. Finally, Sea Buckthorn and Gnaphalium Affine were predicted as potential TCMs for IPF. The TCMs were verified by qPCR in TGF-β1-induced MRC-5 cells.ConclusionThis analysis strategy may be useful for elucidating novel mechanisms underlying IPF at the transcriptome level. The identified hub genes may play key roles in IPF pathogenesis and therapy.
| S-EPMC10552267 | biostudies-literature
Project description:Glioblastoma (GBM) is the most common type of malignant brain tumor, and is associated with poor patient prognosis. A comprehensive understanding of the molecular mechanism underlying GBM may help to guide the identification of novel diagnoses and treatment targets. The gene expression profile of the GSE4290 GBM dataset was analyzed in order to identify differentially expressed genes (DEGs). Enriched pathways were identified through Gene Ontology and the Kyoto Encyclopedia of Genes and Genomes analyses. A protein-protein interaction network was constructed in order to identify hub genes and for module analysis. Expression and survival analyses were conducted in order to screen and validate critical genes. A total of 1,801 DEGs were recorded, including 620 upregulated and 1,181 downregulated genes. Upregulated DEGs were enriched in the terms 'mitotic cell cycle process', 'mitotic cell cycle' and 'cell cycle process'. Downregulated genes were enriched in 'transsynaptic signaling', 'anterograde transsynaptic signaling' and 'synaptic signaling'. A total of 15 hub genes, which displayed a high degree of connectivity, were selected. These genes included vascular endothelial growth factor A, cyclin-dependent kinase 1 (CDK1), cell-division cycle protein 20 (CDC20), aurora kinase A (AURKA), and budding uninhibited by benzimidazoles 1 (BUB1). The identified DEGs and hub genes may help guide investigations on the mechanisms underlying the development and progression of GBM. CDK1, CDC20, AURKA and BUB1, which are involved in cell cycle pathways, may be potential targets in the diagnosis and therapy of GBM.
| S-EPMC6312941 | biostudies-literature