Identification of hub genes and key pathways of paraquat-induced human embryonic pulmonary fibrosis by bioinformatics analysis and in vitro studies

Abstract Background: Paraquat is highly toxic pesticide, which usually led to acute lung injury and subsequently develop pulmonary fibrosis, the exact mechanisms of PQ-induced lung fibrosis remain largely unclear and no specific drugs for this disease have been approved. Methods: Our study aimed to identify its potential mechanism though modeling study in vitro and bioinformatics analysis. Gene expression datasets associated with PQ-induced lung fibrosis were obtained from the Gene Expression Omnibus and differentially expressed genes (DEGs) were identified using GEO2R. Functional enrichment analyses were performed using the Database for Annotation. Results: The DEGs in the two datasets, of which 92 overlapping genes were found in two microarray datasets. Functional analysis demonstrated that the 92 DEGs were enriched in the ‘TNF signaling pathway’, ‘CXCR chemokine receptor binding’, and ‘core promoter binding’. Moreover, nine hub genes were identified from a protein‑protein interaction network. Conclusions: This integrative analysis firstly identified candidate genes and pathways in PQ-induced lung fibrosis, as well as benefit to research novel approaches for treating for control of PQ-induced pulmonary fibrosis.

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Bioinformatics Analysis Identifies Potential Ferroptosis Key Genes in the Pathogenesis of Pulmonary Fibrosis

Frontiers in Genetics ◽

10.3389/fgene.2021.788417 ◽

2022 ◽

Vol 12 ◽

Author(s):

Jie He ◽

Xiaoyan Li ◽

Mi Yu

Keyword(s):

Random Forest ◽

Pulmonary Fibrosis ◽

Aerobic Exercise ◽

Signaling Pathway ◽

Bioinformatics Analysis ◽

Treadmill Training ◽

Normal Lung ◽

Random Forest Algorithm ◽

Hub Genes ◽

Qrt Pcr

Objective: Ferroptosis has an important role in developing pulmonary fibrosis. The present project aimed to identify and validate the potential ferroptosis-related genes in pulmonary fibrosis by bioinformatics analyses and experiments.Methods: First, the pulmonary fibrosis tissue sequencing data were obtained from Gene Expression Omnibus (GEO) and FerrDb databases. Bioinformatics methods were used to analyze the differentially expressed genes (DEGs) between the normal control group and the pulmonary fibrosis group and extract ferroptosis-related DEGs. Hub genes were screened by enrichment analysis, protein-protein interaction (PPI) analysis, and random forest algorithm. Finally, mouse pulmonary fibrosis model was made for performing an exercise intervention and the hub genes’ expression was verified through qRT-PCR.Results: 13 up-regulated genes and 7 down-regulated genes were identified as ferroptosis-related DEGs by comparing 103 lung tissues with idiopathic pulmonary fibrosis (IPF) and 103 normal lung tissues. PPI results indicated the interactions among these ferroptosis-related genes. Kyoto Encyclopedia of Genes and Genomes (KEGG) Pathway enrichment and Genome-Ontology (GO) enrichment analyses showed that these ferroptosis-related genes involved in the organic anion transport, response to hypoxia, response to decrease oxygen level, HIF-1 signaling pathway, renal cell carcinoma, and arachidonic acid metabolism signaling pathway. The confirmed genes using PPI analysis and random forest algorithm included CAV1, NOS2, GDF15, HNF4A, and CDKN2A. qRT-PCR of the fibrotic lung tissues from the mouse model showed that the mRNA levels of NOS2 and GDF15 were up-regulated, while CAV1 and CDKN2A were down-regulated. Also, treadmill training led to an increased expression of CAV1 and CDKN2A and a decrease in the expression of NOS2 and GDF15.Conclusion: Using bioinformatics analysis, 20 potential genes were identified to be associated with ferroptosis in pulmonary fibrosis. CAV1, NOS2, GDF15, and CDKN2A were demonstrated to be influencing the development of pulmonary fibrosis by regulating ferroptosis. These findings suggested that, as an aerobic exercise treatment, treadmill training reduced ferroptosis in the pulmonary fibrosis tissues, and thus, reduces inflammation in the lungs. Aerobic exercise training initiate concomitantly with induction of pulmonary fibrosis reduces ferroptosis in lung. These results may develop our knowledge about pulmonary fibrosis and may contribute to its treatment.

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Survival-based bioinformatics analysis to identify hub genes and key pathways in non-small cell lung cancer

Translational Cancer Research ◽

10.21037/tcr.2019.06.35 ◽

2019 ◽

Vol 8 (4) ◽

pp. 1188-1198

Author(s):

Chunliang Liu ◽

Yu Chen ◽

Yuqi Deng ◽

Yu Dong ◽

Jixuan Jiang ◽

...

Keyword(s):

Lung Cancer ◽

Small Cell Lung Cancer ◽

Cell Lung Cancer ◽

Bioinformatics Analysis ◽

Small Cell ◽

Small Cell Lung ◽

Hub Genes ◽

Key Pathways

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Identifying Hub Genes and Key Pathways Associated with Thyroid Cancer by Integrated Bioinformatics Analysis

10.1145/3473258.3473274 ◽

2021 ◽

Author(s):

Jingyan Diao ◽

Degang Zhu

Keyword(s):

Thyroid Cancer ◽

Bioinformatics Analysis ◽

Hub Genes ◽

Key Pathways

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In Vitro Studies on the Impact of Ergothioneine on Idiopathic Pulmonary Fibrosis Markers in Human Lung Epithelial Cells

10.1164/ajrccm-conference.2019.199.1_meetingabstracts.a2141 ◽

2019 ◽

Author(s):

C.C. Koay ◽

M.A. Selo ◽

C.G. Clerkin ◽

C. Ehrhardt

Keyword(s):

Epithelial Cells ◽

Idiopathic Pulmonary Fibrosis ◽

Pulmonary Fibrosis ◽

Human Lung ◽

In Vitro Studies ◽

Lung Epithelial Cells ◽

Lung Epithelial ◽

The Impact ◽

Human Lung Epithelial Cells

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Identification of Key Pathways and Genes in Dementia via Integrated Bioinformatics Analysis

10.1101/2021.04.18.440371 ◽

2021 ◽

Author(s):

Basavaraj Mallikarjunayya Vastrad ◽

Chanabasayya Mallikarjunayya Vastrad

Keyword(s):

Gene Regulatory Network ◽

Regulatory Network ◽

Bioinformatics Analysis ◽

Functional Enrichment ◽

Ppi Network ◽

Hub Genes ◽

Hub Gene ◽

Network Modules ◽

Gene Regulatory ◽

Key Pathways

To provide a better understanding of dementia at the molecular level, this study aimed to identify the genes and key pathways associated with dementia by using integrated bioinformatics analysis. Based on the expression profiling by high throughput sequencing dataset GSE153960 derived from the Gene Expression Omnibus (GEO), the differentially expressed genes (DEGs) between patients with dementia and healthy controls were identified. With DEGs, we performed a series of functional enrichment analyses. Then, a protein protein interaction (PPI) network, modules, miRNA hub gene regulatory network and TF hub gene regulatory network was constructed, analyzed and visualized, with which the hub genes miRNAs and TFs nodes were screened out. Finally, validation of hub genes was performed by using receiver operating characteristic curve (ROC) analysis and RT PCR. A total of 948 DEGs were screened out, among which 475 genes were up regulated; while 473 were down regulated. Functional enrichment analyses indicated that DEGs were mainly involved in defense response, ion transport, neutrophil degranulation and neuronal system. The hub genes (CDK1, TOP2A, MAD2L1, RSL24D1, CDKN1A, NOTCH3, MYB, PWP2, WNT7B and HSPA12B) were identified from PPI network, modules, miRNA hub gene regulatory network and TF hub gene regulatory network. We identified a series of key genes along with the pathways that were most closely related with dementia initiation and progression. Our results provide a more detailed molecular mechanism for the advancement of dementia, shedding light on the potential biomarkers and therapeutic targets.

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Microengineered 3D pulmonary interstitial mimetics highlight a critical role for matrix degradation in idiopathic pulmonary fibrosis

10.1101/2020.06.02.129718 ◽

2020 ◽

Author(s):

Daniel L. Matera ◽

Katarina M. DiLillo ◽

Makenzee R. Smith ◽

Christopher D. Davidson ◽

Ritika Parikh ◽

...

Keyword(s):

Idiopathic Pulmonary Fibrosis ◽

Pulmonary Fibrosis ◽

Bioinformatics Analysis ◽

Critical Role ◽

Interstitial Tissue ◽

Myofibroblast Differentiation ◽

Fiber Density ◽

Established Relationship ◽

Metalloprotease Activity

AbstractFibrosis is often untreatable and is characterized by aberrant tissue scarring from activated myofibroblasts. Although the extracellular matrix becomes increasingly stiff and fibrous during disease progression, how these physical cues impact myofibroblast differentiation in 3D is poorly understood. Here we describe a multicomponent hydrogel that recapitulates the 3D fibrous structure hallmark to the interstitial tissue regions where idiopathic pulmonary fibrosis (IPF) initiates. In contrast to findings on 2D hydrogels, myofibroblast differentiation in 3D was inversely correlated with hydrogel stiffness, but positively correlated with matrix fiber density. Employing a multi-step bioinformatics analysis of IPF patient transcriptomes and in vitro pharmacologic screening, we identify matrix-metalloprotease activity to be essential for 3D but not 2D myofibroblast differentiation. Given our observation that compliant degradable 3D matrices amply support fibrogenesis, these studies demonstrate a departure from the established relationship between stiffness and myofibroblast differentiation in 2D, and provide a new 3D model for studying fibrosis.

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