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Article

Study on Potential Differentially Expressed Genes in Idiopathic Pulmonary Fibrosis by Bioinformatics and Next-Generation Sequencing Data Analysis

by
Muttanagouda Giriyappagoudar
1,
Basavaraj Vastrad
2,
Rajeshwari Horakeri
3 and
Chanabasayya Vastrad
4,*
1
Department of Radiation Oncology, Karnataka Institute of Medical Sciences (KIMS), Hubballi 580022, Karnataka, India
2
Department of Pharmaceutical Chemistry, K.L.E. Socitey’s College of Pharmacy, Gadag 582101, Karnataka, India
3
Department of Computer Science, Govt First Grade College, Hubballi 580032, Karnataka, India
4
Biostatistics and Bioinformatics, Chanabasava Nilaya, Bharthinagar, Dharwad 580001, Karnataka, India
*
Author to whom correspondence should be addressed.
Biomedicines 2023, 11(12), 3109; https://doi.org/10.3390/biomedicines11123109
Submission received: 23 September 2023 / Revised: 31 October 2023 / Accepted: 2 November 2023 / Published: 21 November 2023
(This article belongs to the Special Issue Biomarkers for Idiopathic Pulmonary Fibrosis)
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Abstract

:
Idiopathic pulmonary fibrosis (IPF) is a chronic progressive lung disease with reduced quality of life and earlier mortality, but its pathogenesis and key genes are still unclear. In this investigation, bioinformatics was used to deeply analyze the pathogenesis of IPF and related key genes, so as to investigate the potential molecular pathogenesis of IPF and provide guidance for clinical treatment. Next-generation sequencing dataset GSE213001 was obtained from Gene Expression Omnibus (GEO), and the differentially expressed genes (DEGs) were identified between IPF and normal control group. The DEGs between IPF and normal control group were screened with the DESeq2 package of R language. The Gene Ontology (GO) and REACTOME pathway enrichment analyses of the DEGs were performed. Using the g:Profiler, the function and pathway enrichment analyses of DEGs were performed. Then, a protein–protein interaction (PPI) network was constructed via the Integrated Interactions Database (IID) database. Cytoscape with Network Analyzer was used to identify the hub genes. miRNet and NetworkAnalyst databaseswereused to construct the targeted microRNAs (miRNAs), transcription factors (TFs), and small drug molecules. Finally, receiver operating characteristic (ROC) curve analysis was used to validate the hub genes. A total of 958 DEGs were screened out in this study, including 479 up regulated genes and 479 down regulated genes. Most of the DEGs were significantly enriched in response to stimulus, GPCR ligand binding, microtubule-based process, and defective GALNT3 causes HFTC. In combination with the results of the PPI network, miRNA-hub gene regulatory network and TF-hub gene regulatory network, hub genes including LRRK2, BMI1, EBP, MNDA, KBTBD7, KRT15, OTX1, TEKT4, SPAG8, and EFHC2 were selected. Cyclothiazide and rotigotinethe are predicted small drug molecules for IPF treatment. Our findings will contribute to identification of potential biomarkers and novel strategies for the treatment of IPF, and provide a novel strategy for clinical therapy.

1. Introduction

Lung fibrosis is a progressive, chronic, and irreversible fibrosing interstitial lung disease; it affects 2–9 people per 1,00,000 worldwide [1]. It is also known as idiopathic pulmonary fibrosis (IPF) [2].It is characterized by clinical symptoms of cough and dyspnea, declining pulmonary function with impaired gas exchange, and progressive lung scarring [3]. Debate on the good strategy for IPF management continues despite great advancement in treating IPF in recent decades. Extensive research has shown current therapeutic approaches in IPF included anti-inflammatory drugs, cytotoxic and immunosuppressive agents, immunomodulators, antifibrotic agents, and antioxidants targeting several crucial signaling pathways that predominantly regulate the inflammation.Genetic, aging, and environmental factors are thought to be the contributing factors to IPF [4]. However, IPF might also can be caused by many unknown causes, such as pulmonary hypertension [5], lung cancer [6], diabetes mellitus [7], dermatomyositis [8],polymyositis [9], systemic sclerosis [10],mixed connective tissue disease [11], systemic lupus erythematosus [12], rheumatoid arthritis [13],sarcoidosis [14], scleroderma [15], pneumonia [16], heart failure [17], obesity [18], viral respiratory diseases [19], gastroesophageal reflux disease [20], chronic obstructive pulmonary disease [21], and airway inflammation [22], which cannot be well solved by current drug treatment and IPF is still a complicated incurable pulmonary disease. Thus, it is necessary for us to utilize bioinformatics and next-generation sequencing (NGS) technology to explore the molecular pathogenesis or potential treatments of IPF.
The recent bioinformatics and NGS data analysis of specimens from sufferers and normal individuals enable us to investigate numerous diseases at diverse levels from somatic mutations and copy number variations to genomic expressions at the transcriptomic level [23,24]. Defining the molecular targets for diagnosis and re-examination is crucial for therapeutic action and prognostic outcome of IPF patients. Many researchers are committed to exploring new biomarkers for IPF. For example, studies have found that serum levels of p53 [25], TINF2 [26], ELMOD2 [27], TERT [28], and ABCA3 [29] are altered among IPF patients. Several investigations have described that significantsignaling pathways in IPF were identified such as TGF-β signaling pathway [30], Smad and STAT3 signaling pathways [31], p38 MAPK signaling pathway [32], Wnt/β-Catenin signaling pathway [33], and JAK-STAT signaling pathway [34]. Despite the increase in different potential biomarkers and pathways in IPF, such efforts have not yet yielded satisfactory results.In this regard, it is necessary to address the association of genes and signaling pathways in candidate genomes with IPF development.
Omics data areroutinely utilized to discover and validate new disease biomarkers.Potential and novel diagnostic biomarkers and therapeutic targets of IPF have been proposed in such integrative bioinformatics studies based on the identification of differentially expressed genes (DEGs). Here, NGS datasets (GSE213001), which includes gene expression data from IPF and normal control samples, were obtained from the Gene Expression Omnibus (GEO) [https://www.ncbi.nlm.nih.gov/geo/] (accessed on 23 July 2023) [35] database. Non-biased bioinformatics analyses, including identification of DEGs, gene ontology (GO) term enrichment analysis, REACTOME pathway enrichment analysis, protein–protein interaction (PPI) network analysis, module analysis, miRNA-hub gene regulatory network, TF-hub gene regulatory network, and protein–drug interaction network analysis were conducted, and the findings were further validated by receiver operating characteristic (ROC) curve analysis. The investigation probably revealed the pathogenic mechanism and potential therapeutic target of IPF.

2. Materials and Methods

2.1. Next-Generation Sequencing Data Source

NGS data of human mRNA regardingIPF research (GSE213001) were obtained from the GEO database. There were 180 samples in GSE213001, including 98 IPF samples and 41 normal control samples without IPF. All samples were detected through the Illumina HiSeq 3000 (Homo sapiens) platform. The detail pipeline of this study was showed in Figure 1.

2.2. Identification of DEGs

The DESeq2 package [36] of R language was utilized to screen DEGs. The false discovery rate (FDR) of the Benjamini–Hochberg (BH) method was applied to adjust p-values for multiple comparisons [37]. The significant differentially expressed cut-off was set as |logFC|  > 0.512 for up regulated genes and |logFC|  < −0.831 for down regulated genes, and adjusted p <  0.05. The volcano map and heatmap of the DEGs were respectively generated using the ggplot2 and gplot packages in R software (Version. 3.4.1 abd Version 3.1.3)

2.3. GO and Pathway Enrichment Analyses of DEGs

GO and REACTOME pathway enrichment analyses of DEGs were performed via g:Profiler (http://biit.cs.ut.ee/gprofiler/) (Accessed on 2 August 2023) [38]. The GO enrichment analysis (http://www.geneontology.org) (Accessed on 2 August 2023) [39] consists of biological processes (BP), cellular components (CC), and molecular functions (MF), which can be used to clarify the potential biological functions of the enriched genes. Pathway enrichment analysis can be used to identify the main biochemical metabolic pathways and signal transduction pathways involved in enriched genes. REACTOME (https://reactome.org/) (Accessed on 2 August 2023) [40] is a pathway database resource for understanding high-level biological functions and utilities. Gene count > 2 and p < 0.05 were set as the threshold.

2.4. Construction of the PPI Network and Module Analysis

To further analyze the impacts of DEGs on IPF, the PPI network was constructed among various DEGs. Also, the online software Integrated Interactions Database (IID) (http://iid.ophid.utoronto.ca/search_by_proteins/) (accessed on 26 July 2023) [41] was used to analyze the interactions of proteins encoded by DEGs. Then, the Cytoscape software (V3.10.0; http://cytoscape.org/) (accessed on 31 July 2023) [42] was utilized to visualize the PPI network. Hub genes were identified using Network Analyzer, a plug-in of Cytoscape software. Finally, the degree [43], betweenness [44], stress [45], and closeness [46] of each hub genes was obtained by analyzing the topological structure of the PPI network. Significant modules in the PPI network were identified by PEWCC [47], another plug-in of Cytoscape software.

2.5. Construction of the miRNA-Hub Gene Regulatory Network

MicroRNAs (miRNAs) can control gene expression by promoting or inhibiting mRNA degradation and translation. We therefore investigate miRNAs involved in regulatory mechanism and development process in IPF. The hub genes in PPI were selected as the promising targets for searching miRNA through the miRNet database (https://www.mirnet.ca/) (accessed on 4 August 2023), which is an experimentally validated miRNA–hub gene interactions database [48]. This database contains miRNA-hub gene regulatory network data from 14 disparate sources including TarBase, miRTarBase, miRecords, miRanda (S mansoni only), miR2Disease, HMDD, PhenomiR, SM2miR, PharmacomiR, EpimiR, starBase, TransmiR, ADmiRE, and TAM 2. The intersection of miRNAs and hub genes in IPF was used to construct the miRNAs–hub genes regulated network. The identified miRNA -hub gene regulatory network was visualized using the Cytoscape software [42].

2.6. Construction of the TF-Hub Gene Regulatory Network

Transcription factors (TFs) can control gene expression by promoting or inhibiting translation. We therefore investigate TFs involved in regulatory mechanism and development process in IPF. The hub genes in PPI were selected as the promising targets for searching TF through the NetworkAnalyst database (https://www.networkanalyst.ca/) (Accessed on 4 August 2023) [49]. This database contains TF–hub gene regulatory network data from JASPAR. The results of this process were arranged such that each entry was a specific TF–hub gene interaction associated with its source link. The intersection of TFs and hub genes in IPF was used to construct the TFs–hub genes regulated network. The identified TF–hub gene regulatory network was visualized using the Cytoscape software [42].

2.7. Construction of the Protein–Drug Interaction Network

In this investigation, prediction of protein–drug interactions or small molecules identification is one of the key parts. The DEGs were selected as the promising targets for searching small drug molecules through the NetworkAnalyst database (https://www.networkanalyst.ca/) (Accessed date 16 October 2023) [49]. This database contains protein–drug interaction network data from DrugBank. The results of this process were arranged such that each entry was a specific protein–drug interaction associated with its source link. The intersection of drug and DEGs in IPF was used to construct the protein–drug interaction network. The identified protein–drug interaction network was visualized using the Cytoscape software [42].

2.8. Receiver Operating Characteristic Curve (ROC) Analysis

ROC curve analyses to determine the specificity, sensitivity, likelihood ratios, positive predictive values, and negative predictive values for all possible thresholds of the ROC curve were performed using the R packages “pROC” [50]. The receiver operator characteristic curves were plotted and area under curve (AUC) was determined independently to assess the conduct of each model. The diagnostic values of the genes were predicted based on the ROC curve analysis. AUC  >  0.8 marked that the model had a good fitting effect.

3. Results

3.1. Identification of DEGs

The NGS dataset GSE213001 was downloaded from the GEO database, and total of 958 DEGs were screened between IPF and normal control groups with |logFC|  > 0.512 for up regulated genes, |logFC|  < −0.831 for down regulated genes and adjusted p  <  0.05, including 479 up regulated DEGs and 479 down regulated DEGs (Supplementary Table S1 and Figure 2). The heatmap of the DEGs has been shown in Figure 3.

3.2. GO and Pathway Enrichment Analyses of DEGs

Functional enrichment analyses of the GO terms and REACTOME pathway were performed for both up regulated and down regulated DEGs. To gain insight into the BP, CC, and MF of the DEGs products, we performed a GO enrichment analysis (Supplementary Table S2). In the BP group, the up regulated genes were mainly clustered in response to stimulus and biological regulation, and the down regulated genes were mainly clustered in microtubule-based process and plasma membrane bounded cell projection organization. For the CC group, the up regulated genes were primarily clustered in cell periphery and membrane. The down regulated genes were primarily clustered in cell projection and cytoplasm. The up regulated genes in the MF group were mostly clustered in signaling receptor binding and molecular transducer activity, and the down regulated genes were mostly clustered in tubulin binding and calcium ion binding. The top significantly enriched REACTOME pathways for the DEGs were also displayed by the g:Profiler online software (https://biit.cs.ut.ee/gprofiler/gost, accessed on 23 July 2023) and are presented in Supplementary Table S3. The up regulated genes were associated with GPCR ligand binding and class A/1 (Rhodopsin-like receptors), while the down regulated genes were involved in defective GALNT3 causes HFTC and Lewis blood group biosynthesis.

3.3. Construction of the PPI Network and Module Analysis

To investigate the molecular mechanism of IPF from a systematic perspective, a PPI network was constructed to explore the relationship between proteins. The PPI network was constructed by IID for DEGs. There were 5557 nodes and 9632 edges in the visualization network using the Cyctoscape (Figure 4). The genes with high node degree, betweenness, stress, and closeness are considered as hub genes, and include LRRK2, BMI1, EBP, MNDA, KBTBD7, KRT15, OTX1, TEKT4, SPAG8, and EFHC2, and are listed in Supplementary Table S4. According to the node degree of importance, we chose two significant modules from the PPI network complex for further analysis using Cytotype MCODE. Functional enrichment analysis showed that module 1 consisted of 14 nodes and 26 edges (Figure 5A), which are mainly associated with response to stimulus, metabolism of lipids and biological regulation, and that module 2 consisted of 9 nodes and 17 edges (Figure 5B), which are mainly associated with cytoplasm and cell projection.

3.4. Construction of the miRNA-Hub Gene Regulatory Network

To explore the interactions between IPF related hub genes and miRNA, the miRNA-hub gene regulatory network containing 2299 nodes and 10,240 edges was constructed (Figure 6). Of all the nodes, 2023 nodes were miRNAs, while the other 276 nodes were hub genes. The 217 miRNAs (ex: hsa-mir-6830-5p) interacted with PARD6B; 156 miRNAs (ex: hsa-mir-362-3p) interacted with NEK7; 115 miRNAs (ex: hsa-mir-15b) interacted with BMI1; 115 miRNAs (ex: hsa-mir-766-5p) interacted with CAV1; 80 miRNAs (ex: hsa-mir-8057) interacted with TRIB3; 42 miRNAs (ex: hsa-mir-302a-3p) interacted with GPR156; 39 miRNAs (ex: hsa-mir-19b-3p) interacted with PITX1; 27 miRNAs (ex: hsa-mir-4524a-3p) interacted with TRIM29; 24 miRNAs (ex: hsa-mir-1537-5p) interacted with OTX1; 11 miRNAs (ex: hsa-mir-941) interacted with CCDC146 (Supplementary Table S5). Therefore, we speculate that miRNAs might function importantly in the molecular mechanism of IPF.

3.5. Construction of the TF-Hub Gene Regulatory Network

To explore the interactions between IPF related hub genes and TF, the TF-hub gene regulatory network containing 358 nodes and 2169 edges was constructed (Figure 7). Of all the nodes, 79 nodes were TFs, while the other 279 nodes were hub genes. The 16 TFs (ex: NFYA) interacted with CAV1; 11 TFs (ex: SRF) interacted with TTN; 11 TFs (ex: TFAP2A) interacted with MAPT; 10 TFs (ex: EN1) interacted with TRIB3; 9 TFs (ex: ELK4) interacted with NEK7; 12 TFs (ex: JUN) interacted with IQUB; 9 TFs (ex: STAT1) interacted with MORN3; 9 TFs (ex: MEF2A) interacted with OTX1; 9 TFs (ex: TP53) interacted with CCDC146; 8 TFs (ex: NFKB1) interacted with EFHC2 (Supplementary Table S5). Therefore, we speculate that TFs might function importantly in the molecular mechanism of IPF.

3.6. Construction of the Protein–Drug Interaction Network

To explore the interactions between IPF related DEGs genes and drugs, the protein–drug interaction network containing 445 nodes and 671 edges was constructed (Figure 8). The 95 drug molecules (ex: Cyclothiazide) interacted with CA2; 89 drug molecules (ex: Tetryzoline) interacted with ADRA1A; 86 drug molecules(ex: Fesoterodine) interacted with CHRM1; 72 drug molecules(ex: Triflupromazine) interacted with CHRM2; 67 drugmolecules (ex: Methacholine) interacted with CHRM3; 26 drug molecules(ex: Rotigotine) interacted with DRD5; 21 drug molecules (ex: Hexobarbital) interacted with GRIK2; 15 drug molecules (ex: Atomoxetine) interacted with GRIN3B; 12 drug molecules (ex: Methylethylamine) interacted with MB; 1 drug molecule (ex: L-Glutamic Acid) interacted with LGSN (Supplementary Table S6).

3.7. Receiver Operating Characteristic Curve (ROC) Analysis

To identify new potential biomarkers for IPF, ROC curves of data derived from IPF and normal controls samples wereanalyzed using the R package. ROC curves were generated, and the area under the curves was used to compare the different hub genes. The results revealed hub genes with predicted AUC > 0.8, namely, LRRK2, BMI1, EBP, MNDA, KBTBD7, KRT15, OTX1, TEKT4, SPAG8, and EFHC2 (Figure 9). This analysis demonstrated that the ten selected hub genes had a diagnostic role in IPF.

4. Discussion

Despite advances in adjunctive pharmacotherapy of IPF, it is still the leading threat to human health in lung diseases [51]. Thus, successful screening techniques and accurate diagnosis remain the great challenges for decreasing the incidence of IPF. A bioinformatics and NGS data analysis is an ideal way to comprehensively investigate IPF.By performing DEGs analysis, 479 up regulated and 479 down regulated DEGs were successfully identified (|logFC|  > 0.512 for up regulated genes, |logFC|  < −0.831 for down regulated genes and adjust p-value < 0.05), respectively. It should be noted that there were some DEGs that were not shared among IPF networks, but have been proven to play indispensable roles in IPF in recent years. Involvement of highly significant DEGs including SLCO1A2 [52], OLFM4 [53], RTKN2 [54], CYP1A1 [55], and MUC5AC [56] plays a key role in rheumatoid arthritis development.Highly significant DEGs including OLFM4 [57], RTKN2 [58], CYP1A1 [59], MUC5AC [60], CYP2A6 [61], PCK1 [62], and PITX1 [63] have been reported to encourage the development of lung cancer. Recent studies have demonstrated that the OLFM4 [64] is associated with obesity. Highly significant DEGs including OLFM4 [65] and AGTR2 [66] are important in the development of viral respiratory diseases. Highly significant DEGs including CYP1A1 [67] and AGTR2 [68] might be associated with systemic sclerosis. The abnormal expression of CYP1A1 [67] might be related to the progression of systemic lupus erythematosus. The abnormal expression of CYP1A1 [69] and MUC5AC [70] contributes to the progression of pneumonia. Studies had shown that CYP1A1 [71] and AGTR2 [72] are master regulators that are activated in heart failure. Highly significant DEGs including CYP1A1 [73], HHIP (hedgehog interacting protein) [74], MUC5AC [75], and CYP2A6 [76] might be related to the pathophysiology of chronic obstructive pulmonary disease. Previous studies have reported that the CYP1A1 [77], MUC5AC [78], and ATP12A [79] expressions arerelated to the patients with airway inflammation. Studies have found that HHIP (hedgehog interacting protein) [80], MUC5AC [81], CYP2A6 [82], and PCK1 [83] can promote obesity. Highly significant DEGs including AGTR2 [68], MUC5AC [70], and ATP12A [84] are potential targets for IPF therapy. CYP1A1 [85], HHIP (hedgehog interacting protein) [86], AGTR2 [87], CYP2A6 [88], and PCK1 [83] are involved in growth and development of diabetes mellitus. Result suggests that these significant DEGs play a key role in the progression of IPF.
GO and REACTOME enrichment analyses were used to explore the molecular mechanisms of the enriched genes involved in the occurrence and development of IPF. Signaling pathways including GPCR ligand binding [89], neutrophil degranulation [90], immune system [91], metabolism of lipids [92], and signal transduction [93] play an important role in the IPF. Enriched genes including MAP3K15 [94], PRTN3 [95], CX3CR1 [96], AGRP(agouti related neuropeptide) [97], MPO (myeloperoxidase) [98], CD5L [99], S100A8 [100], NPR3 [101], VEGFD (vascular endothelial growth factor D) [102], CXCL11 [103], IL1A [104], CBS (cystathionine beta-synthase) [105], WNT7A [106], SCD (stearoyl-CoA desaturase) [107], LRP2 [108], SLC6A4 [109], BDNF (brain derived neurotrophic factor) [110], CXCL10 [111], ANGPTL7 [112], S100A9 [113], NPY1R [114], IL1B [115], GPIHBP1 [116], CYP1B1 [117], CD36 [118], MACROD2 [119], TRIB3 [120], SPX (spexin hormone) [121], PCSK9 [122], GPD1 [123], CDH13 [124], FFAR4 [125], FGF2 [126], FASN (fatty acid synthase) [127], DGAT2 [128], DACH1 [129], PNPLA3 [130], FGF9 [131], SLC7A11 [132], CLIC5 [133], VIP (vasoactive intestinal peptide) [134], SMAD6 [135], BMPR2 [136], APOA1 [137], INSIG1 [138], TLR3 [139], NLRP12 [140], ADRB1 [141], TLR8 [142], GATA3 [143], CCR2 [144], TLR7 [145], CCRL2 [146], BMPER (BMP binding endothelial regulator) [147], CAV1 [148], TFPI (tissue factor pathway inhibitor) [149], FADS1 [150], SUCNR1 [151], CADM2 [152], SLC19A3 [153], SGCG (sarcoglycan gamma) [154], ADH1B [155], NEGR1 [156], HSD17B12 [157], OXTR (oxytocin receptor) [158] and ANKK1 [159] play a key role in obesity. Enriched genes including MAP3K15 [94], CX3CR1 [160], S100A12 [161], PF4 [162], FFAR2 [163], MPO (myeloperoxidase) [98], HMGCS2 [164], F11 [165], S100A8 [100], GRIA1 [166], NPR3 [167], CXCL11 [103], CBS (cystathionine beta-synthase) [105], WNT7A [106], AQP4 [168], SCD (stearoyl-CoA desaturase) [169], SLC6A4 [170], CXCL10 [171], S100A9 [172], NPY1R [173], IL1B [174], CXCR1 [175], CXCR2 [176], GPIHBP1 [177], WNT3A [178], APOH (apolipoprotein H) [179], CHRM3 [180], CD36 [181], TRIB3 [182], PCSK9 [183], ACVR1C [184], GPD1 [123], FFAR4 [185], GPX3 [186], FGF2 [187], FASN (fatty acid synthase) [188], DGAT2 [189], DACH1 [190], PNPLA3 [191], FGF9 [192], SLC7A11 [193], VIP (vasoactive intestinal peptide) [194], KL (klotho) [195], UBE2D1 [196], APOA1 [197], RASGRF1 [198], LRRK2 [199], TLR3 [200], OCLN (occludin) [201], SLC22A3 [202], LIFR (LIF receptor subunit alpha) [203], TLR8 [142], GATA3 [204], CCR2 [205], NEK7 [206], CD274 [207], TLR7 [208], CCRL2 [146], EFNB2 [209], CAV1 [210], TRPC3 [211], DLL4 [212], ANXA3 [213], TFPI (tissue factor pathway inhibitor) [214], FADS1 [215], GPER1 [216], SUCNR1 [217], CADM2 [218], SLC19A3 [219], ADH1B [220], NEGR1 [156], HSD17B12 [157], KIF6 [221], UCN3 [222], ANKK1 [223], AQP5 [224], and HCN4 [225] are a key regulators of diabetes mellitus. Enriched genes includingPRTN3 [226], CX3CR1 [227], S100A12 [228], CSF2 [229], FGG (fibrinogen gamma chain) [230], LHX9 [231], MPO (myeloperoxidase) [232], F11 [233], S100A8 [234], CXCL11 [235], BPI (bactericidal permeability increasing protein) [236], BDNF (brain derived neurotrophic factor) [237], CXCL10 [238], S100A9 [239], IL1B [240], CXCR1 [241], CXCR2 [242], CYP1B1 [243], EDNRB (endothelin receptor type B) [244], CEBPA (CCAAT enhancer binding protein alpha) [245], CDH13 [246], GPX3 [247], FGF2 [248], SHH (sonic hedgehog signaling molecule) [249], VIP (vasoactive intestinal peptide) [250], KL (klotho) [251], SMAD6 [252], BMPR2 [253], APOA1 [254], TLR3 [255], GATA3 [256], CCR2 [257], CAV1 [258], TRPC3 [259], EPAS1 [260], SIGLEC14 [261], MAPK15 [262], DNAH5 [263], and AQP5 [264] were altered expressed in chronic obstructive pulmonary disease. Enriched genes includingDEFA3 [265], CX3CR1 [266], S100A12 [161], TUBB1 [267], ANKRD1 [268], ADRA1A [269], FGG (fibrinogen gamma chain) [270], AGER (advanced glycosylation end-product specific receptor) [271], PF4 [272], FFAR2 [273], MPO (myeloperoxidase) [274], CD5L [275], HMGCS2 [164], RXFP1 [276], F11 [277], S100A8 [278], PGLYRP1 [279], VEGFD (vascular endothelial growth factor D) [280], CHRM2 [281], CBS (cystathionine beta-synthase) [282], BPI (bactericidal permeability increasing protein) [283], LRP2 [284], BDNF (brain derived neurotrophic factor) [285], GCOM1 [286], CXCL10 [287], ANGPTL7 [288], PRODH (proline dehydrogenase 1) [289], P2RY1 [290], LRRN4 [291], S100A9 [292] CXCR1 [293], CXCR2 [293], GPIHBP1 [294], TNNT1 [295], WNT3A [296], BMI1 [297], CYP1B1 [298], FCN3 [299], TTN (titin) [300], STC1 [301], CD36 [302], MYZAP (myocardial zonula adherens protein) [303], TRIB3 [304], GPR18 [305], TNNC1 [306], SPX (spexin hormone) [121], SYNPO2L [307],PCSK9 [308], GPD1 [309], FFAR4 [310], GPX3 [311], FGF2 [187], ACKR4 [312], NDUFC2 [313], KBTBD7 [314], SHH (sonic hedgehog signaling molecule) [315], DACH1 [316], PNPLA3 [317], FGF9 [192], SLC7A11 [193], SGPP1 [318], VIP (vasoactive intestinal peptide) [319], KCNJ2 [320], KL (klotho) [321], SMAD6 [135], BMPR2 [322], APOA1 [323], CALCRL (calcitonin receptor like receptor) [324], INSIG1 [325], RASGRF1 [198],LRRK2 [326], TLR3 [327], ADRB1 [328], SLC22A3 [329], CA2 [330], SNX10 [331], LIFR (LIF receptor subunit alpha) [332], TLR8 [333], CMPK2 [334], GATA3 [335], RSPO2 [336], CCR2 [205], NEK7 [337], TLR7 [338], BEX1 [339], EFNB2 [340], CAV1 [341], ARRB1 [342], TRPC3 [343], CR1 [344], PEG10 [345], DLL4 [346], MEFV (MEFV innate immuity regulator, pyrin) [347], TFPI (tissue factor pathway inhibitor) [348], EPAS1 [349], FADS1 [215], DKK2 [350], CACNA2D2 [351], DPP6 [352], KCNA4 [353], PCDH17 [354], SUSD2 [355], PHACTR2 [356], DNAH9 [357], DNAH11 [358], CFAP45 [359], DNAH5 [360], FOXJ1 [361], MNS1 [299], KIF6 [221], DRD5 [362], UCN3 [363], OXTR (oxytocin receptor) [364], ANKK1 [365], and HCN4 [366] were associated with heart failure. Researchdemonstrated that enriched genes including PI3 [367], CX3CR1 [368], S100A12 [369], MPO (myeloperoxidase) [370], CD5L [371], S100A8 [372], CXCL11 [373], BPI (bactericidal permeability increasing protein) [374], AQP4 [375], BDNF (brain derived neurotrophic factor) [376], CXCL10 [377], CCL8 [378], S100A9 [379], IL1B [240], CXCR1 [380], CXCR2 [381], ABCA3 [382], GPR18 [383], VIP (vasoactive intestinal peptide) [384], KL (klotho) [385], TLR3 [386], NLRP12 [387], GATA3 [388], CCR2 [389], TLR7 [390], CAV1 [391], CR1 [392], DLL4 [393], and AQP5 [394] might be potential therapeutic targets for airway inflammation. Enriched genes includingCX3CR1 [395], S100A12 [396], MPO (myeloperoxidase) [397], RXFP1 [398], S100A8 [399], CXCL11 [373], CBS (cystathionine beta-synthase) [400], WNT7A [401], BDNF (brain derived neurotrophic factor) [402], CXCL10 [403], CCL8 [404], FCGR3B [405], S100A9 [406], IL1B [407], CXCR2 [408], WNT3A [409], BMI1 [410], STC1 [411], ABCA3 [412], CD36 [413], TRIB3 [414], GPX3 [415],FGF2 [416], FASN (fatty acid synthase) [417], SHH (sonic hedgehog signaling molecule) [418], DACH1 [419], FGF9 [420], SLC7A11 [421], VIP (vasoactive intestinal peptide) [422], KL (klotho) [423], BMPR2 [424], APOA1 [425], LRRK2 [426], TLR3 [427], GATA3 [428], RSPO2 [429], CCR2 [430], NEK7 [431], BMPER (BMP binding endothelial regulator) [432], CAV1 [433], CR1 [434], TFPI (tissue factor pathway inhibitor) [435], AP1S2 [436], FOXJ1 [437], AQP5 [438], MUC16 [439], and MUC4 [440] have been found in a IPF. Enriched genes including CX3CR1 [441], S100A12 [442], PF4 [443], MPO (myeloperoxidase) [444], WNT7A [445], SLC6A4 [446], BDNF (brain derived neurotrophic factor) [447], CXCL10 [448], NEK7 [449], CYP1B1 [450], ABCA3 [451], TRIB3 [452], PCSK9 [453], FGF2 [454], ACKR4 [455], FASN (fatty acid synthase) [456], VIP (vasoactive intestinal peptide) [457], KL (klotho) [458], BMPR2 [459], APOA1 [323], TLR3 [460], CCR2 [461], TLR7 [462], CAV1 [463], WWC2 [464], TFPI (tissue factor pathway inhibitor) [465], EPAS1 [466], and CCDC40 [467] were identified to be associated with pulmonary hypertension. A previous study reported that the enriched genes includingCX3CR1 [468], CSF2 [469], CLDN18 [470], TRIM58 [471], PF4 [472], FFAR2 [473], MPO (myeloperoxidase) [474], CD5L [475], SH3GL2 [476], ITGA2B [477], S100A8 [478], VEGFD (vascular endothelial growth factor D) [479], CXCL11 [480], IL1A [481], WNT7A [482], SSTR1 [483], AQP4 [484], SCD (stearoyl-CoA desaturase) [485], SLC6A4 [486], BDNF (brain derived neurotrophic factor) [487], CXCL10 [488], ODAM (odontogenic, ameloblast associated) [489], CASP5 [490], CCL8 [491], TMEM100 [492], S100A9 [493], IL1B [494], CXCR1 [495], CXCR2 [496], WNT3A [497], BMI1 [498], CYP1B1 [499], FCN3 [500], TTN (titin) [501], SHISA3 [502], AZGP1 [503], ABCA3 [504], CD36 [505], EDNRB (endothelin receptor type B) [506], BTNL9 [507], CEBPA (CCAAT enhancer binding protein alpha) [508], TRIB3 [509], TNNC1 [510], PCSK9 [511], P2RY13 [512], KITLG (KIT ligand) [513], CDH13 [514], GPX3 [515], FGF2 [416], FUT7 [516], FASN (fatty acid synthase) [517], NKD1 [518], FOXD1 [519], SLC1A1 [520], SHH (sonic hedgehog signaling molecule) [521], DACH1 [522], FGF9 [523], SLC7A11 [524], CLIC5 [525], MGAT3 [526], HSPA6 [527], TSPAN12 [528], SCAI (suppressor of cancer cell invasion) [529], VIP (vasoactive intestinal peptide) [530], SH3GL3 [531], KCNJ2 [532], KL (klotho) [533], UBE2D1 [534], SMAD6 [535], BMPR2 [536], APOA1 [537], TGFBR3 [538], RASGRF1 [539], LRRK2 [540], ATP8A2 [541], TLR3 [542], OCLN (occludin) [543], EMP2 [544], MNDA (myeloid cell nuclear differentiation antigen) [545], TLR8 [546], GATA3 [547], RSPO2 [548], CCR2 [549], EPB41L5 [550], CD274 [551], DDIAS (DNA damage induced apoptosis suppressor) [552], TLR7 [553], CCRL2 [554], BMPER (BMP binding endothelial regulator) [555], DUSP26 [556], CCBE1 [557], FZD8 [558], CAV1 [559], ARRB1 [560], CR1 [561], WWC2 [562], DLL4 [563], ANXA3 [564], EPAS1 [565], FADS1 [566], DKK2 [567], GPER1 [568], CADM2 [569], PARD6B [570], CACNA2D2 [571], ATP8A1 [572], PDZD2 [573], STXBP6 [574], ADH1A [575], GCA (grancalcin) [576], SUSD2 [577], EDIL3 [578], PHACTR2 [579], DNAH10 [580], CCDC65 [581], SPAG6 [582], MAPK15 [583], ENKUR (enkurin, TRPC channel interacting protein) [584], DNAH5 [585], PIERCE1 [586], TPPP3 [587], TTC21A [588], DLEC1 [589], SRCIN1 [590], PROM1 [591], AQP5 [592], SYT13 [593], TTC21A [594], SPTBN2 [595], MUC13 [596], MUC16 [597], and MUC4 [598] have been shown to be biomarkers of lung cancer. The altered expression of enriched genes including CX3CR1 [599], S100A12 [600], PF4 [601], MPO (myeloperoxidase) [602], RXFP1 [603], S100A8 [604], VEGFD (vascular endothelial growth factor D) [605], CXCL11 [606], IL1A [607], BPI (bactericidal permeability increasing protein) [608], SLC6A4 [609], CXCL10 [610], FCGR3B [611], S100A9 [612], IL1B [613], CXCR2 [614], CTNND2 [615], CD36 [616], PCSK9 [617], FGF2 [618], SHH (sonic hedgehog signaling molecule) [619], KL (klotho) [620], BMPR2 [621], TLR8 [622], GATA3 [623], CCR2 [624], TLR7 [625], CAV1 [626], TFPI (tissue factor pathway inhibitor) [627], and SPAG17 [628] have been proposed as novel biomarkers for systemic sclerosis. A previous study found that enriched genes including CX3CR1 [629], S100A12 [630], MPO (myeloperoxidase) [631], CD5L [632], F11 [633], S100A8 [634], BPI (bactericidal permeability increasing protein) [635], AQP4 [636], MME (membrane metalloendopeptidase) [637], BDNF (brain derived neurotrophic factor) [638], CXCL10 [639], RNASE2 [640], FCGR3B [641], S100A9 [642], GPIHBP1 [643], AFF3 [644], APOH (apolipoprotein H) [645], FCN3 [646], PCSK9 [308], LILRA2 [647], APOA1 [648], LRRK2 [649], CD244 [650], TLR3 [651], TLR8 [652], GATA3 [653], CCR2 [654], IFIT3 [655], NEK7 [656], TLR7 [657], CAV1 [658], CR1 [659], TFPI (tissue factor pathway inhibitor) [660], GPER1 [661], SIGLEC14 [662], FOXJ1 [663], GABRP (gamma-aminobutyric acid type A receptor subunit pi) [664], and TSGA10 [665] play a certain role in systemic lupus erythematosus.Altered expression of enriched genes including CX3CR1 [666], S100A12 [667], CSF2 [668], MPO (myeloperoxidase) [669], CD5L [670], F11 [671], S100A8 [672], PGLYRP1 [673], VEGFD (vascular endothelial growth factor D) [674], CXCL11 [373], BPI (bactericidal permeability increasing protein) [675], CXCL10 [676], S100A9 [677], CXCR1 [678], CXCR2 [679], ABCA3 [680], CD36 [681], SHH (sonic hedgehog signaling molecule) [682], TLR3 [683], CLEC4D [684], CCR2 [685], NEK7 [686], TLR7 [687], CCRL2 [688], and CAV1 [689] are associated with pneumonia progression. Enriched genes including CX3CR1 [666], CD177 [690], PF4 [691], FFAR2 [692], MPO (myeloperoxidase) [693], F11 [694], S100A8 [695], VEGFD (vascular endothelial growth factor D) [674], IL1A [696], BPI (bactericidal permeability increasing protein) [697], AQP4 [698], BDNF (brain derived neurotrophic factor) [699], CXCL10 [700], RNASE2 [701], FCGR3B [702], S100A9 [703], IL1B [704], CXCR2 [705], GPIHBP1 [294], CD36 [706], TRIB3 [707], PCSK9 [708], FGF2 [709], FASN (fatty acid synthase) [710], PNPLA3 [711], HSPA6 [712], VIP (vasoactive intestinal peptide) [713], TLR3 [683], ADRB1 [328], SPOCK2 [714], TLR8 [715], CCR2 [716], IFIT3 [717], NEK7 [718], TLR7 [687], EFNB2 [719], CAV1 [720], CR1 [721], and AQP5 [722] are positively correlated with the severity of viral respiratory diseases, suggesting theirpotential as a biomarkers for viral respiratory diseases. Previous study confirmed that enriched genes including CX3CR1 [723], S100A12 [724], CD177 [725], PF4 [726], MPO (myeloperoxidase) [727], CD5L [728], F11 [729], S100A8 [730], PGLYRP1 [731], GPR15 [732], BPI (bactericidal permeability increasing protein) [733], AQP4 [734], BDNF (brain derived neurotrophic factor) [735], CXCL10 [736], FCGR3B [737], S100A9 [738], IL1B [739], CXCR1 [740], CXCR2 [741], AFF3 [742], WNT3A [743], FCN3 [744], AZGP1 [745], CD36 [746], PCSK9 [747], GPX3 [748], FGF2 [749], SHH (sonic hedgehog signaling molecule) [750], SLC7A11 [751], VIP (vasoactive intestinal peptide) [752], KL (klotho) [753], APOA1 [754], RASGRF1 [755], CD244 [756], TLR3 [757], NLRP12 [758], SNX10 [759], TLR8 [760], GATA3 [761], CCR2 [762], TLR7 [763], CCRL2 [764], EFNB2 [765], FZD8 [766], CAV1 [767], CR1 [768], MEFV (MEFV innate immunity regulator, pyrin) [769], SUCNR1 [770], GCA (grancalcin) [771] and, FOXJ1 [663] were observed in rheumatoid arthritis. Enriched genes including S100A12 [772], MPO (myeloperoxidase) [773], S100A8 [774], CXCL10 [775], S100A9 [774], CD244 [775], CD244 [776],and TLR7 [777] have been linked to dermatomyositis. MPO (myeloperoxidase) [778] participated in the regulation of mixed connective tissue disease progression. Enriched genes including MPO (myeloperoxidase) [779], CXCL11 [780], IL1A [781], CXCL10 [782], FCGR3B [783], IL1B [784], TTN (titin) [300], BATF2 [785], VIP (vasoactive intestinal peptide) [786], TLR3 [787], CCR2 [788], TLR7 [789], and CR1 [790] wereclosely related to sarcoidosis. A study indicates that enriched genes including MPO (myeloperoxidase) [791], RXFP1 [792], CXCL11 [793], CTNND2 [615], BMPR2 [794], TLR3 [795], CCR2 [796], and CAV1 [797] are altered expressed in scleroderma. Enriched genes including CXCL11 [798] and CD244 [776] are considered potential biomarkers for polymyositis. Study demonstrated that enriched genes including IL1B [799], CXCR1 [800], FFAR4 [801], VIP (vasoactive intestinal peptide) [802], and GATA3 [803] have been identified as a key candidate genes in patients with gastroesophageal reflux disease. Therefore, it is necessary to perform GO term and pathway enrichment analysis in order to understand the interactions between DEGs and the associated biological processes. In this investigation, we identified some enriched genes for which the functions in IPF have not been completely characterized, suggesting their potential as biomarkers for this disease., There may be a relationship between these enriched genes in other diseases, includingdiabetes mellitus, obesity, chronic obstructive pulmonary disease, heart failure, airway inflammation, pulmonary hypertension, lung cancer, systemic sclerosis, systemic lupus erythematosus, pneumonia, viral respiratory diseases, rheumatoid arthritis, mixed connective tissue disease, sarcoidosis, polymyositis, and gastroesophageal reflux disease.
Hub genes in the molecular pathogenesis of IPF were identified by using the IID software of Cytoscape. Studies have shown that hub genes including LRRK2 [199] and FFAR2 [163] have been reported to be correlated with prognosis in a diabetes mellitus. Hub genes including LRRK2 [326], BMI1 [297], KBTBD7 [314], and FFAR2 [273] were associated with the risk of heart failure. Hub genes including LRRK2 [426] and BMI1 [410] are highly associated with IPF. Hub genes including LRRK2 [540], BMI1 [398], MNDA (myeloid cell nuclear differentiation antigen) [545], OTX1 [804], FFAR2 [473], and PITX1 [63] play a significant role in lung cancer progression. Hub gene LRRK2 [649] was identified as being associated with increased risk of systemic lupus erythematosus. A study has shown that hub gene FFAR2 [692] was significantly associated with viral respiratory diseases. There is no research showing that novel hub genes including EBP (EBP cholestenol delta-isomerase), KRT15, TEKT4, SPAG8, EFHC2, TMEM97, and NHLRC4 are related to IPF. This finding is consistent with our results.
miRNA-hub gene regulatory network and TF-hub gene regulatory network can be regarded as key to the understanding of pathogenesis of IPF and might also lead to new therapeutic approaches. Studies have shown that biomarkers including PARD6B [570], BMI1 [498], CAV1 [559], TRIB3 [509], TTN (titin) [501], PITX1 [63],TRIM29 [805], OTX1 [804], NFYA (nuclear transcription factor Y subunit alpha) [806], TFAP2A [807], JUN (Jun proto-oncogene, AP-1 transcription factor subunit) [808], STAT1 [809], TP53 [810], and NFKB1 [811] might act as bifunctional mediators to lung cancer. Studies have shown that biomarkers including NEK7 [206], CAV1 [210], TRIB3 [182], STAT1 [812], TP53 [813], and NFKB1 [814] are closely related to the development of diabetes mellitus.Some papers reported that NEK7 [337], BMI1 [297], CAV1 [341], TRIB3 [304], TTN (titin) [300],hsa-mir-19b-3p [815], hsa-mir-941 [816], NFYA (nuclear transcription factor Y subunit alpha) [817], STAT1 [818], MEF2A [819], TP53 [820], and NFKB1 [821] have been revealed to be associated with heart failure. Previous studies have demonstrated that biomarkers including NEK7 [431], BMI1 [410], CAV1 [433], TRIB3 [414], SRF (serum response factor) [822], STAT1 [823], and TP53 [824] are believed to be related to the occurrence of IPF. Biomarkers including NEK7 [656], CAV1 [658], TSGA10 [665], STAT1 [825], TP53 [826], and NFKB1 [827] are reported to be associated with systemic lupus erythematosus. Biomarkers including NEK7 [686] and CAV1 [689] were associated with the risk of pneumonia. Biomarkers includingNEK7 [718], CAV1 [720], TRIB3 [707], has-mir-8057 [828], hsa-mir-1537-5p [829], STAT1 [830], and TP53 [831] are highly associated with viral respiratory diseases.Biomarkers including NEK7 [449], TRIB3 [452], CAV1 [463],NFYA (nuclear transcription factor Y subunit alpha) [817], STAT1 [832], and TP53 [833] played an important role in the pulmonary hypertension. Biomarkers including CAV1 [148], TRIB3 [120], STAT1 [834], TP53 [835], and NFKB1 [836] have been reported in obesity. Biomarkers including CAV1 [258], hsa-mir-19b-3p [837], SRF (serum response factor) [838], and TP53 [839] play an important role in the regulation of chronic obstructive pulmonary disease. Biomarkers including CAV1 [391], STAT1 [840], and NFKB1 [841] play a major regulatory role in the development of airway inflammation. CAV1 [626], TFAP2A [842],TP53 [843], and NFKB1 [844] are the most specific biomarkers for the detection systemic sclerosis. Biomarkers including CAV1 [767], hsa-mir-19b-3p [845], JUN (Jun proto-oncogene, AP-1 transcription factor subunit) [846], STAT1 [847], TP53 [848], and NFKB1 [849] were largely detected in rheumatoid arthritis. CAV1 [797] could be a potential biomarker for scleroderma. Biomarkers including TTN (titin) [300] and STAT1 [850] are widely involved in sarcoidosis. Biomarker STAT1 [851] was implicated in the pathology ofdermatomyositis. MAPT, GPR156, CCDC146, IQUB, MORN3, hsa-mir-6830-5p, hsa-mir-362-3p, hsa-mir-15b, hsa-mir-766-5p, hsa-mir-302a-3p, hsa-mir-4524a-3p, EN1, and ELK4 are all new biomarkers for the development of IPF and its complications. Our study showed that hub genes, miRNA, and TFs regulated in patients, although further research is needed to explore the underlying molecular mechanism.
To date, there is no effective drug for the treatment of IPF. Few investigations have indicated that some drugs, including pirfenidone and nintedanib, can improve the outcomes of IPF [852]. Herein, we predicted a set of drugs that could target the predicted protein–drug interaction network by using NetworkAnalyst database. Cyclothiazide, tetryzoline, fesoterodine, triflupromazine, methacholine, rotigotine, hexobarbital, atomoxetine, methylethylamine, and L-glutamic acid were the drugs with the highest number of target genes. The predicted genes might be efficient in the treatment of IPF, which needs further experimental screening and validation.
In conclusion, in the present study, we conducted a thorough bioinformatics analysis of DEGs by GSE213001 NGS data screening and identified several genes implicated in the development and progression of IPF. A total of 958 DEGs were identified, of which LRRK2, BMI1, EBP, MNDA, KBTBD7, KRT15, OTX1, TEKT4, SPAG8, and EFHC2 are probable hub genes of IPF. This investigation reveals a series of valuable genes for further investigation into the non-invasive diagnosis and targeted therapy of IPF. However, bioinformatics analyses merely indicate a general direction for further investigation. To confirm the functions of hub genes in IPF, molecular biology experiments are required.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/biomedicines11123109/s1, Table S1 The statistical metrics for key differentially expressed genes (DEGs). Table S2 The enriched GO terms of the up and down regulated differentially expressed genes. Table S3 The enriched pathway terms of the up and down regulated differentially expressed. Table S4 Topology table for up and down regulated genes. Table S5 Candidate miRNA and TFs targeted by the hub genes. Table S6 Candidate drugs targeted by the hub genes.

Author Contributions

M.G.—Acquisition of resources and investigation; B.V.—Writing original draft, and review and editing; R.H.—Formal analysis and validation; C.V.—Software and investigation. All authors have read and agreed to the published version of the manuscript.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Institutional Review Board Statement

Not applicable.
Informed ConsentStatement: No informed consent because this study does not contain human or animal participants.

Data Availability Statement

The datasets supporting the conclusions of this article are available in the GEO (Gene Expression Omnibus) (https://www.ncbi.nlm.nih.gov/geo/, accessed on 1 November 2023) repository. [(GSE213001) https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE213001, accessed on 1 November 2023].

Acknowledgments

We thank Katherine Monaghan, CSL Limited, Research and Development, Melbourne, Victoria, Australia, very much, the author who deposited their NGS dataset GSE213001 into the public GEO database.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

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Figure 1. The overall design of the study. DEGs, differentially expressed genes; GO, Gene Ontology; PPI, protein–protein interaction; TF, transcription factor.
Figure 1. The overall design of the study. DEGs, differentially expressed genes; GO, Gene Ontology; PPI, protein–protein interaction; TF, transcription factor.
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Figure 2. Volcano plot of DEGs between IPF samples and normal control samples. Green dot represented up regulated significant genes and red dot represented down regulated significant genes.
Figure 2. Volcano plot of DEGs between IPF samples and normal control samples. Green dot represented up regulated significant genes and red dot represented down regulated significant genes.
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Figure 3. Heatmap of clustering analysis for IPF-related differentially-expressed genes. Legend on the top left indicate log fold change ingenes. (A1–A98 = IPF samples; B1–B 41 = Normal control samples).
Figure 3. Heatmap of clustering analysis for IPF-related differentially-expressed genes. Legend on the top left indicate log fold change ingenes. (A1–A98 = IPF samples; B1–B 41 = Normal control samples).
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Figure 4. PPI network of DEGs. The PPI network contains 5557 nodes and 9632 edges; green color circle represents up regulated DEGs, and red color circle represents down regulated DEGs.
Figure 4. PPI network of DEGs. The PPI network contains 5557 nodes and 9632 edges; green color circle represents up regulated DEGs, and red color circle represents down regulated DEGs.
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Figure 5. Modular analysis of DEGs. (A) Module 1 contains 14 nodes and 26 edges. (B) Module 2 contains 9 nodes and 17 edges. The color of each node represents DEGs (green represents up regulated DEGs, and red represents down regulated DEGs).
Figure 5. Modular analysis of DEGs. (A) Module 1 contains 14 nodes and 26 edges. (B) Module 2 contains 9 nodes and 17 edges. The color of each node represents DEGs (green represents up regulated DEGs, and red represents down regulated DEGs).
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Figure 6. Interaction network between hub genes and targeted miRNAs. Hub genes are presented in green color (up regulated genes) and red color (down regulated genes) circles, whereas small miRNAs are shown in brown color diamond.
Figure 6. Interaction network between hub genes and targeted miRNAs. Hub genes are presented in green color (up regulated genes) and red color (down regulated genes) circles, whereas small miRNAs are shown in brown color diamond.
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Figure 7. Interaction network between hub genes and targeted TFs. Hub genes are presented in green color (up regulated genes) and red color (down regulated genes) circles, whereas TFs are shown in olive color triangle.
Figure 7. Interaction network between hub genes and targeted TFs. Hub genes are presented in green color (up regulated genes) and red color (down regulated genes) circles, whereas TFs are shown in olive color triangle.
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Figure 8. Interaction network between DEGs genes and small drug molecules. Hub genes are presented in green color (up regulated genes) and red color (down regulated genes) circles, whereas small drug molecules are shown in brown circles.
Figure 8. Interaction network between DEGs genes and small drug molecules. Hub genes are presented in green color (up regulated genes) and red color (down regulated genes) circles, whereas small drug molecules are shown in brown circles.
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Figure 9. ROC curve analyses of hub genes. (A) LRRK2, (B) BMI1, (C) EBP, (D) MNDA, (E) KBTBD7, (F) KRT15, (G) OTX1, (H) TEKT4, (I) SPAG8, (J) EFHC2.
Figure 9. ROC curve analyses of hub genes. (A) LRRK2, (B) BMI1, (C) EBP, (D) MNDA, (E) KBTBD7, (F) KRT15, (G) OTX1, (H) TEKT4, (I) SPAG8, (J) EFHC2.
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MDPI and ACS Style

Giriyappagoudar, M.; Vastrad, B.; Horakeri, R.; Vastrad, C. Study on Potential Differentially Expressed Genes in Idiopathic Pulmonary Fibrosis by Bioinformatics and Next-Generation Sequencing Data Analysis. Biomedicines 2023, 11, 3109. https://doi.org/10.3390/biomedicines11123109

AMA Style

Giriyappagoudar M, Vastrad B, Horakeri R, Vastrad C. Study on Potential Differentially Expressed Genes in Idiopathic Pulmonary Fibrosis by Bioinformatics and Next-Generation Sequencing Data Analysis. Biomedicines. 2023; 11(12):3109. https://doi.org/10.3390/biomedicines11123109

Chicago/Turabian Style

Giriyappagoudar, Muttanagouda, Basavaraj Vastrad, Rajeshwari Horakeri, and Chanabasayya Vastrad. 2023. "Study on Potential Differentially Expressed Genes in Idiopathic Pulmonary Fibrosis by Bioinformatics and Next-Generation Sequencing Data Analysis" Biomedicines 11, no. 12: 3109. https://doi.org/10.3390/biomedicines11123109

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