Exploration of oxidative stress-related hub genes in idiopathic pulmonary fibrosis by integrating bioinformatics analysis

doi:10.21203/rs.3.rs-4820805/v1

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Exploration of oxidative stress-related hub genes in idiopathic pulmonary fibrosis by integrating bioinformatics analysis

https://doi.org/10.21203/rs.3.rs-4820805/v1

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Idiopathic pulmonary fibrosis (IPF) characterized by a poor prognosis is a chronic and fatal interstitial lung disease. Oxidative stress has great impacts on the initiation and development of IPF. The aim of the present study was to determine oxidative stress-related hub genes for the diagnosis and intervention of IPF. The gene expression profile of IPF (GSE10667, GSE32537, GSE110147, and GSE213001 datasets) were collected from the GEO database. The differentially expressed oxidative stress-related genes (DEOSRGs) were screened on the basis of the common DEGs, oxidative stress related genes from GeneCard database and module genes from WGCNA. Four hub DEOSRGs (ENC1, EPHA3, FMO1, and GPX8) were further identified using the LASSO analysis and SVM-RFE algorithms, and validated by external datasets (GSE24206 and GSE53845). The ROC analysis revealed that the four hub DEOSRGs had diagnostic values with excellent specificity and sensitivity. The CIBERSORT analysis revealed that T cells CD4 memory activated, T cells regulatory (Tregs) and Dendritic cells resting might be related to the progress of IPF. In conclusion, the present study shows that ENC1, EPHA3, FMO1, and GPX8 may be considered as novel diagnostic biomarkers and therapeutic targets for IPF.

Idiopathic pulmonary fibrosis

oxidative stress

immune cell infiltration

biomarkers

bioinformatics

Idiopathic pulmonary fibrosis (IPF), a common fatal lung disease of unknown etiology, is characterized by unusual extracellular matrix (ECM) storing in the lung parenchyma, and progressive scarring of lung tissue[1]. After diagnosis, patients with IPF have the poorest prognosis, with a median survival time of around 3 to 5 years[2]. Although two anti-fibrotic drugs (nintedanib and pirfenidone) have been used to treat patients with IPF, both cannot reverse pulmonary fibrosis, and have more adverse reactions[3–4]. Therefore, understanding the pathological mechanism of IPF, and finding therapeutic targets for therapy have important clinical significance.

Oxidative stress, the initiating factor of pulmonary fibrosis, has long been regarded as a vital factor in the pathogenesis of IPF[5]. It can result in alveolar epithelial cells (AECs) senescence[6], AECs epithelial-mesenchymal transition[7], AECs apoptosis[8], myofibroblast differentiation and excessive deposition of ECM in lung tissue[9], eventually lead to the occurrence of pulmonary fibrosis. Much evidence has demonstrated that oxidative damage is relevant to pulmonary fibrosis. Daniil et al.[10] found that the serum levels of oxidative stress dramatically increased in IPF patients, and were negatively relevant to forced vital capacity (FVC). Mansour et al.[11] found that antioxidant N-acetylcysteine (NAC) could inhibit the contents of malondialdehyde (MDA) and nitric oxide, and partially restored the levels of superoxide dismutase (SOD) and glutathione (GSH) in a pulmonary fibrosis rat model, which are relevant to the activation of SIRT1 and AMPK. Searching for reliable oxidative stress biomarkers would contribute to further understanding the molecular mechanisms of IPF, and provide a new strategy for antifibrotic therapy.

Therefore, in the current study, we conduct a systematic bioinformatic analysis to obtain differentially expressed oxidative stress-related genes (DEOSRGs) that are promising candidate targets for the early prevention and treatment of IPF.

2.1. Data Source and Processing

The GEO database was utilized to analyse the messenger RNA (mRNA) expression levels in the lung tissue of IPF patients. Subsequently, four IPF-related datasets, namely GSE10667, GSE32537, GSE110147, and GSE213001, were obtained from the GEO database. The platforms of datasets GSE10667, GSE213001, GSE32537, and GSE110147 were derived from GPL4133, GPL21290, and GPL6244, respectively. Figure 1 displays the detailed flow chart of this study.

2.2. Identification of common differentially expressed genes (DEGs)

With FDR < 0.05 and |log2 fold change (FC)| > 0.585, DEGs from GSE10667, GSE32537, GSE110147, and GSE213001 were obtained by utilizing “limma” package. Subsequently, the DEGs from the four mRNA datasets between IPF and control samples were intersected to obtain the common DEGs.

2.3. Enrichment analysis

Enrichment analyses of the common DEGs, including Gene Ontology (GO) and Kyoto Encyclopedia of Genes Genomes (KEGG) analyses, were implemented using DAVID database to assess the function of the common DEGs.

2.4. Weighted gene co-expression network analysis (WGCNA)

WGCNA, a common gene co-expression network screening technique, has been widely used to reveal co-expressed modules with crucial biological implication and screen gene networks relevant to disease[12]. In this study, the WGCNA was implemented using “WGCNA” package to obtain the modules correlated with IPF. Subsequently, we obtained the core modules by analyzing the correlation between the modules obtained and the clinical traits of IPF.

2.5. Screening and validation of hub DEOSRGs

We collected the oxidative stress (OS) genes by GeneCards database (https://www.genecards.org). The common DEGs, and the genes obtained from the WGCNA, and the OS genes intersected using the Venn diagrams, and the obtained genes were taken as DEOSRGs. Subsequently, the DEOSRGs were further screened using two machine-learning methods, least absolute shrinkage and selection operator (LASSO) algorithm and support vector machine recursive feature elimination (SVM-RFE) algorithm, to obtain hub DEOSRGs. The “glmnet” package and the “e1071” package in R software were utilized to conduct LASSO and SVM-RFE respectively. Finally, we validated the expression levels of the hub DEOSRGs in external datasets (GSE24206 and GSE53845).

2.6. Analysis of ROC Curves for hub DEOSRGs

The diagnostic significance of the hub DEOSRGs was determined through the utilization of “pROC” package. We used the external datasets (GSE24206 and GSE53845) to test the diagnostic significance of the hub DEOSRGs, and those with an area under the curve (AUC) > 0.8 were taken as potential diagnostic biomarkers of IPF.

2.7. Immune cell infiltration analysis

The “CIBERSORT” package was utilized to determine the level of immune cell infiltration in each lung tissue sample. The “corrplot” package was utilized to evaluate the correlation between immune cells. Violin plot, which was mainly utilized to analyse the variation in the infiltration expression of the immune cells among the IPF and control groups, was drawn using the “vioplot” package. Principal component analysis (PCA) was implemented to cluster the IPF and control lung tissue samples. Finally, the association between differential infiltrating immune cells and hub DEOSRGs was analysed using the Spearman correlation analysis.

3.1. Identification of DEGs

We extracted the DEGs between IPF patients and healthy control from four GEO datasets (GSE10667, GSE32537, GSE110147, and GSE213001) using the “limma” package with FDR < 0.05 and |log2FC| > 0.585. As a result, we obtained 849, 682, 4195, and 1762 upregulated DEGs in GSE10667, GSE32537, GSE110147, and GSE213001, respectively. In addition, we detected 920, 538, 3627, and 1542 downregulated DEGs in GSE10667, GSE32537, GSE110147, and GSE213001, respectively (Fig. 2A-D). Finally, 134 common upregulated DEGs and 104 common downregulated DEGs were identified in the four datasets on the basis of the Venn diagram (Fig. 2E-F).

3.2. Enrichment analysis

GO analysis was conducted to analyse the function of the 238 common DEGs. As illustrated in Fig. 3A, the common DEGs were principally enriched in extracellular matrix organization, cell adhesion and collagen fibril organization in the biological process (BP) analysis. In the cellular component (CC) analysis, the common DEGs were principally enriched in extracellular matrix, extracellular region and collagen trimer. In the molecular function (MF) analysis, the common DEGs were principally enriched in extracellular matrix structural constituent, collagen binding and fibronectin binding. KEGG analysis depicted that the common DEGs mainly enriched in the ECM-receptor interaction, Protein digestion and absorption, PI3K-Akt signaling pathway and Cell adhesion molecules (Fig. 3B).

3.3. Coexpression module construction and identification of DEOSRGs

The co-expression network was obtained through WGCNA to further identify genes strongly related to IPF. A total of 13 modules were screened using WGCNA (Fig. 4A-B). In particular, the yellow module, with a scale of 351 genes, had strong positive correlations with IPF (Fig. 4C). We took the intersection of common DEGs and oxidative stress related genes from GeneCard database and yellow module genes to obtain 6 DEOSRGs (ENC1, FMO1, EPHA3, FBN1, GPX8, and IGF1) (Fig. 4D).

3.4. Identification and validation of the hub DEOSRGs

These 6 DEOSRGs were analysed by the utilization of the LASSO analysis and SVM-RFE algorithms to identify the hub genes (Fig. 5A-B). Then, a total of 4 hub DEOSRGs (ENC1, EPHA3, FMO1, and GPX8) were obtained from the intersection of these two algorithms (Fig. 5C). Subsequently, two external datasets (GSE24206 and GSE53845) were utilized to compare the expression levels of the 4 hub DEOSRGs between the IPF and control samples. The results showed high expression levels of ENC1, EPHA3, FMO1, and GPX8 in the lung tissue samples from the patients with IPF (Fig. 5D-K).

3.5. Validation of biomarkers for IPF

ROC curve analysis was employed to assess the diagnostic efficacy of ENC1, FMO1, EPHA3, and GPX8 in the two external datasets (GSE24206 and GSE53845). The AUC values of the hub DEOSRGs (ENC1, EPHA3, FMO1 and GPX8) were 0.980(95% CI 0.912–1.000), 0.902(95% CI 0.676–1.000), 0.863(95% CI 0.559–1.000) and 0.941(95% CI 0.794–1.000) in the GSE24206 dataset, respectively (Figure S1A-D). In the GSE53845 dataset, the AUC values of ENC1 were 0.944(95% CI 0.844–1.000), the AUC values of EPHA3 were 0.969(95% CI 0.903–1.000), the AUC values of FMO1 were 0.941(95% CI 0.866–0.991), and the AUC values of GPX8 were 0.850(95% CI 0.662–0.981) (Figure S1E-H). These results indicated that the 4 hub DEOSRGs had diagnostic values with excellent specificity and sensitivity.

3.6. Immune infiltration analysis

We explored the relevant proportions of 22 immune cells in the IPF and control samples in the merged dataset by the utilization of the CIBERSORT algorithm (Fig. 6A). The correlation analysis of the 22 immune cell types revealed that Mast cells resting was positively correlated with NK cells activated (r = 0.48), Monocytes was positively correlated with NK cells resting (r = 0.4), T cells CD8 was negatively correlated with T cells CD4 memory resting (r=-0.54), Plasma cells was negatively correlated with Monocytes (r=-0.46) (Fig. 6B). Compared with the control, the levels of T cells CD4 naive, NK cells resting, Monocytes, Macrophages M2, Dendritic cells activated, and Neutrophils in the IPF group were relatively low, while the levels of B cells memory, Plasma cells, T cells CD4 memory activated, T cells regulatory (Tregs), T cells gamma delta, Dendritic cells resting, and Mast cells resting in the IPF group were relatively high (Fig. 6C). The PCA results showed a difference in immune infiltration status between the IPF and control groups (Fig. 6D).

The correlations between the hub DEOSRGs (ENC1, EPHA3, FMO1, and GPX8) and differential immune cells were shown in Fig. 7. Figure S2A showed that positively correlation between ENC1 and T cells CD4 memory activated (R = 0.33, p = 3.3e − 10), while negatively correlation between ENC1 and Neutrophils (R=-0.51, p < 2.2e − 16). In addition, EPHA3 and FMO1 positively correlated with T cells CD4 memory activated (R = 0.35, p = 1.7e − 11 and R = 0.41, p = 1e − 15, respectively) but negatively correlated with monocytes (R=-0.43, p < 2.2e − 16 and R=-0.38, p = 1.8e − 13, respectively (Figure S2B-C). GPX8 positively correlated with Plasma cells (R = 0.39, p = 3.9e − 14), but negatively correlated with Monocytes (R=-0.44, p < 2.2e − 16) (Figure S2D).

IPF is a chronic progressive lung disease that affects 3 million individuals worldwide[13]. Oxidative stress, caused by the imbalance between the oxidants and antioxidants, and immune infiltration are important factors in the occurrence and progression of IPF[14–15]. N-acetylcysteine (NAC), which is a representative of antioxidant, could slow the deterioration of the vital capacity and diffusing capacity of the lung for carbon monoxide (DLCO) in patients with IPF[16]. Furthermore, a study found that NAC supplementation could delay the progression of pulmonary fibrosis by scavenging reactive oxygen species (ROS)[17]. Hence, antioxidant therapy is a crucial treatment option for IPF.

The aim of this study was to identify the hub DEOSRGs of IPF. Firstly, 4 mRNA datasets (GSE10667, GSE32537, GSE110147, and GSE213001) were analyzed, and a total of 238 common DEGs were found in the IPF samples, including 134 common upregulated genes and 104 common downregulated genes. The KEGG analysis results showed that these common DEGs principally involved in ECM-receptor interaction, Protein digestion and absorption, PI3K-Akt signaling pathway and Cell adhesion molecules. Next, we identified 4 hub DEOSRGs (ENC1, EPHA3, FMO1, and GPX8) by the intersection of the common DEGs, oxidative stress related genes from GeneCard database and module genes from WGCNA, and the utilization of the LASSO analysis and SVM-RFE algorithms.

ENC1, a negative regulator of nuclear factor erythroid 2-related factor 2 (Nrf2), is principally produced in the nervous system[18–19]. ENC1 expression was upregulated in endometrial cancer (EC) tissues or EC cell lines, and might be connected with immune infiltration in EC[20]. In addition, ENC1 might contribute to enhancing radio-resistance in breast carcinoma cells by regulating the Hippo/YAP1/TAZ pathway and the expression levels of Gli1, CTGF and FGF1[21]. In this study, we revealed that ENC1 was highly expressed in the lung tissues of IPF patients, and had a diagnostic accuracy value based on GSE24206 and GSE53845 datasets. The AUC was 0.980(95% CI 0.912–1.000) for GSE24206 and 0.944(95% CI 0.844–1.000) for GSE53845.

EPHA3, which belongs to the Eph family of receptor tyrosine kinases, is highly expressed in numerous tumors[22]. For instance, EPHA3, which was dramatically upregulated in the prostate cancer samples, might be involved in the development of prostate cancer[23]. Toyama et al.[24] found that the epidermal growth factor could promote EPHA3 production, and lead to the formation of glioblastoma cell aggregates. Xi et al.[25] revealed that EPHA3 was highly expressed in gastric cancer, and was dramatically related to the Tumor-Node-Metastasis (TNM) stage and poor prognosis of gastric cancer. In our study, we found that the EPHA3 expression level was higher in IPF patients, and had excellent diagnostic efficacy, with an AUC of 0.902(95% CI, 0.676 − 1.000) and 0.969(95% CI 0.903–1.000) for IPF in the GSE24206 and GSE53845 datasets, respectively.

FMO1, a member of the flavin-containing monooxygenases (FMOs) gene family, is a drug-oxygenating enzymes in humans[26]. Gong et al.[27] demonstrated that increased FMO1 expression level was observed in patients with gastric cancer, and typically connected with disease-free-survival (DFS), overall survival (OS), and immune score of gastric cancer. Our study suggested that FMO1 was highly expressed in the lung tissues of IPF patients, and had a high diagnostic value in the two external datasets (GSE24206 and GSE53845).

GPX8, a member of the glutathione peroxidase (GPX) family, can regulate cell proliferation, cell migration, tumorigenesis development, and oxidative stress. Yin et al.[28] revealed that GPX8 was highly expressed in both esophageal squamous cell carcinoma (ESCC) cell lines and tumor tissues, and could promote the proliferation of ESCC cells by regulating the IRE1/JNK pathway. Zhang et al.[29] suggested that GPX8 could promote the migration and invasion of lung cancer cell lines. In addition, a study found that GPX8 knockout could delay the initiation of breast cancer and inhibit its growth rate in mice[30]. However, another study found that GPX8, a regulator of redox homoeostasis, could inhibit the activation of endoplasmic reticulum (ER) stress and cell apoptosis induced by oxidative stress[31]. Our study showed that GPX8 expression level increased in patients with IPF, and that GPX8 might be a diagnostic marker of IPF. Therefore, more experiments are required to confirm the role of GPX8 in IPF.

In our research, “CIBERSORT” package was utilized to explore the level of immune cell infiltration in the lung tissue of IPF patients. Our results indicated that an increased permeability of B cells memory, Plasma cells, T cells CD4 memory activated, T cells regulatory (Tregs), T cells gamma delta, Dendritic cells resting, and Mast cells resting might be connected with the pathogenesis of IPF. Previous research has revealed that innate and adaptive immune mechanisms are strongly connected with the pathogenesis of IPF[32]. Lymphocyte aggregates, which was mainly consisted of T lymphocytes and B lymphocytes, were abundant in the lungs of IPF patients[33]. In the sheep model of bleomycin-induced pulmonary fibrosis, the levels of T-cell and B-cell infiltration were markedly increased[34]. A previous study showed that mast cells were dramatically elevated in the lungs of IPF patients, and positively correlated with the number of fibroblast foci[35]. In vitro, mast cells could promote fibroblast proliferation and activation[35]. Moreover, our study revealed that ENC1, EPHA3, and FMO1 were positively correlated with T cells CD4 memory activated, that GPX8 was positively correlated with Plasma cells. We also found that EPHA3, FMO1, and GPX8 were negatively correlated with Monocytes, and that ENC1 was negatively correlated with Neutrophils.

Our study has several limitations. We identified 4 hub DEOSRGs (ENC1, EPHA3, FMO1, and GPX8) on the basis of the public database, and verified their expression levels in patients with IPF. But further animal and cell experiments are needed to elucidate the roles of ENC1, EPHA3, FMO1, and GPX8 in the pathogenesis of IPF. In addition, clinical trials with larger sample sizes are needed to examine the relationships of ENC1, EPHA3, FMO1, and GPX8 with IPF.

In conclusion, by combining a bioinformatics analysis and two machine learning algorithms, we revealed that ENC1, EPHA3, FMO1, and GPX8 might be considered novel targets for the diagnosis and treatment of IPF.

Funding

This research was funded by the Nature Science Foundation of Hubei province (No: 2023AFB249).

Author Contribution

J.H. , L.S. and D.W. wrote the main manuscript text, J.H. and M.Y. prepared figures . All authors reviewed the manuscript.

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No competing interests reported.

FigureS1.tif
Figure S1. Diagnostic value evaluation. A–D: ROC curves of ENC1, EPHA3, FMO1, and GPX8 in the GSE24206 dataset. E–H: ROC curves of ENC1, EPHA3, FMO1, and GPX8 in the GSE53845 dataset.
FigureS2.tif
Figure S2. Correlation between the hub DEOSRGs and some immune cells. A: Correlation of ENC1 withT cells CD4 memory activated and Neutrophils. B: Correlation of EPHA3 with T cells CD4 memory activated and Monocytes. C: Correlation of FMO1 with T cells CD4 memory activated and Monocytes. D: Correlation of GPX8 with Plasma cells and Monocytes .

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Exploration of oxidative stress-related hub genes in idiopathic pulmonary fibrosis by integrating bioinformatics analysis

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