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. 2023 Dec 1;102(48):e36343. doi: 10.1097/MD.0000000000036343

Bioinformatics analysis of ceRNA network of autophagy-related genes in pediatric asthma

Hao Zhu a, Jiao Shi b, Wen Li a,*
PMCID: PMC10695615  PMID: 38050261

Abstract

The molecular underpinnings of pediatric asthma present avenues for targeted therapies. A deeper exploration into the significance of differentially expressed autophagy-related genes (DE-ARGs) and their interactions with the long noncoding RNA (lncRNA)–microRNA (miRNA)–mRNA network may offer insights into the pathogenesis of pediatric asthma. DE-ARGs were retrieved from the Gene Expression Omnibus and the Human Autophagy Database. These DE-ARGs were subjected to comprehensive analyses, including Gene Ontology, Kyoto Encyclopedia of Genes and Genomes pathway, Gene Set Enrichment Analysis, and protein–protein interaction networks. The identified DE-ARGs were further verified for core gene expression. The miRDB and ENCORI databases were used for inverse miRNA predictions. Furthermore, miRNA–lncRNA interactions were predicted using LncBase and ENCORI platforms. Following the exclusion of lncRNAs exclusively localized in the nucleus and extracellular space, a competitive endogenous RNA (ceRNA) network was established and subsequently subjected to detailed analysis. The mRNA expression patterns in the ceRNA network were validated using quantitative real-time PCR. In total, 31 DE-ARGs were obtained, of which 29 were up-regulated and 2 were down-regulated. Notably, the autophagy, regulation of apoptotic signaling pathways, interferon-α/β signaling, interferon γ signaling, autophagy-animal, and apoptosis pathways were predominantly enriched in pediatric asthma. Five hub genes (VEGFA, CFLAR, RELA, FAS, and ATF6) were further analyzed using the Gene Expression Omnibus dataset to verify their expression patterns and diagnostic efficacy. Four hub genes (VEGFA, CFLAR, RELA, and FAS) were obtained. Finally, a ceRNA network of 4 mRNAs (VEGFA, CFLAR, RELA, and FAS), 3 miRNAs (hsa-miR-320b, hsa-miR-22-3p, and hsa-miR-625-5p), and 35 lncRNAs was constructed by integrating data from literature review and analyzing the predicted miRNAs and lncRNAs. Moreover, the quantitative real-time PCR data revealed a pronounced upregulation of Fas cell surface death receptor. The identification of 4 DE-ARGs, especially Fas cell surface death receptor, has shed light on their potential pivotal role in the pathogenesis of pediatric asthma. The established ceRNA network provides novel insights into the autophagy mechanism in asthma and suggests promising avenues for the development of potential therapeutic strategies.

Keywords: asthma, autophagy, bioinformatics, ceRNA network

1. Introduction

Asthma, a common chronic disease in children, primarily presents with symptoms such as cough, wheezing, chest constriction, and dyspnea.[1] The prevalence of asthma has witnessed a notable increase over the past 3 decades, particularly among children in low- and middle-income countries.[2] This increase has placed substantial burdens on the lives of children, healthcare facilities, and economies. Currently, asthma remains an incurable condition, underscoring the need for further exploration of the molecular mechanisms underlying asthma to find novel strategies for prevention and treatment.

Autophagy is an evolutionarily highly conserved biological process (BP) crucial for maintaining intracellular homeostasis.[3] Under the regulation of autophagy-related genes (ARGs), the cellular bilayer membrane structures encapsulate targeted substances for degradation, resulting in the formation of autophagosomes. Subsequently, autophagosomes fuse with lysosomes to form autolysosomes, enabling cellular metabolism through the actions of hydrolytic enzymes.[4] Autophagy has been associated with inflammatory diseases, cardiovascular diseases, neurodegenerative diseases, and cancers.[5] Ahmad et al[6] highlighted the pivotal role of ARG ATG5 in pediatric asthma. Furthermore, microRNA (miRNA, miR)-30a was identified to target ATG5, thereby inhibiting autophagy, which subsequently leads to a reduction in airway fibrosis in patients with asthma.[7] Thus, autophagy emerged as a novel therapeutic target for the prevention and treatment of asthma.

In recent years, the integration of high-throughput sequencing with bioinformatics has become increasingly prevalent in identifying potential disease targets. The introduction of competitive endogenous RNA (ceRNA) networks has further broadened our understanding of the mechanism of RNA interaction. Specifically, miRNAs or circular RNAs (circRNAs) can bind to target mRNAs, inhibiting their translation or promoting their degradation. In contrast, lncRNAs or circRNAs can competitively bind to miRNAs to regulate gene expression.[8] Given these interactions, the ceRNA network offers a pioneering avenue for investigating the underlying mechanisms of asthma.

In this study, we identified differentially expressed ARGs (DE-ARGs) associated with asthma and subsequently pinpointed the hub genes. The diagnostic efficacy and the expression patterns of the core genes were further verified using the dataset analysis and quantitative real-time PCR (qRT-PCR). Additionally, ceRNA networks were further constructed based on the predicted miRNAs, lncRNAs, and mRNAs. This study may provide a theoretical basis for understanding the molecular mechanism of pediatric asthma.

2. Materials and methods

2.1. Data retrieval

Data were retrieved from the Gene Expression Omnibus (https://www.ncbi.nlm.nih.gov/geo/). The microarray dataset was downloaded from the mentioned database,[9] and the pediatric asthma-related datasets GSE30326,[10] GSE120172, and GSE103166[11] were downloaded based on the screening keywords “asthma” and “children.” In addition, a total of 222 ARGs were downloaded from the Human Autophagy Database (http://www.autophagy.lu/) (Table S1, Supplemental Digital Content, http://links.lww.com/MD/K872).

2.2. Identification of DE-ARGs

The expression matrix files and platform annotation files for each microarray dataset were retrieved from the Gene Expression Omnibus database. Subsequently, microarray probe names were converted to gene symbols. Probes corresponding to multiple gene symbols were removed, and the average expression value for gene symbols associated with multiple probes was calculated. Moreover, any terms corresponding to a gene name were removed from the analysis. Differentially expressed genes (DEGs) were obtained from the GSE30326 dataset using the limma package[12] (version 3.52.4) of R software (version 4.2.1) with the criteria of |logFC| >1 and P < .05. The resulting DE-ARGs associated with pediatric asthma were visualized with heatmap and volcano plots using the ComplexHeatmap package[13] (version 2.13.4) and ggplot2 package (version 3.3.6) of R software. The DEGs and ARGs were subsequently intersected, and the overlap was depicted using the Venn diagram.

2.2.1. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis of DE-ARGs

Large-scale functional enrichment studies are commonly conducted using GO[14] analysis, which assesses the BP, molecular function (MF), and cellular component (CC). The KEGG[15] database is a widely used database resource that provides comprehensive information on genomes, biological pathways, diseases, and drugs. In this study, the DE-ARGs were subjected to GO annotation and KEGG pathway enrichment analyses. The clusterProfiler package[16] (version 4.4.4) of R software was utilized for these analyses, while visualization was achieved using the ggplot2 package (version 3.3.6) in R software. A false discovery rate (FDR) cutoff value < 0.05 was used to determine statistical significance.

2.3. Gene set enrichment analysis (GSEA)

GSEA[17] is a computational method designed to determine whether a specific set of genes shows statistically significant differences between 2 biological conditions. This method is commonly used to estimate changes in pathways and activities of BPs in gene expression dataset samples. In this study, GSEA was conducted based on the gene expression profiling of the GSE30326 dataset related to pediatric asthma. Relevant data were downloaded from the MSigDB database using the “c2.cp.v7.2.symbols.gmt” gene sets. These sets were subsequently analyzed using the clusterProfiler package (version 4.4.4) and visualized using the ggplot2 package (version 3.3.6). An enrichment was deemed statistically significant if the normalized enrichment score was | >1, the FDR was <0.25, and the adjusted P-value was <.05.

2.4. Protein–protein interaction (PPI) network construction and module analysis

The DE-ARGs were input into the STRING database (https://cn.string-db.org/, version 1.1.5)[18] to construct a PPI network. A threshold combined score > 0.4 was set. Subsequently, the results from the PPI analysis were visualized using Cytoscape software (version 3.7.2).[19] These interaction results were derived from experimental data, text mining of PubMed abstracts, data amalgamation from various databases, and predictions made via bioinformatics methods. In the PPI network, the most closely connected gene sets were analyzed using the MCODE plug-in. Four topological analysis methods,[20] including maximal clique centrality (MCC), density of maximum neighborhood component, degree, and maximum neighborhood component as part of the cytoHubba plug-in, were employed to identify the top 10 genes exhibiting the highest connectivity. Genes that were identified across all 4 methods were recognized as hub genes.

2.5. Hub genes validation and receiver operating characteristic (ROC) curve analysis

The expression of the identified hub genes was corroborated using the GSE103166 dataset. To assess the diagnostic accuracy of each hub gene, ROC curves were generated using the pROC package[21] (version 1.18.0) of R software. Subsequently, the area under the curve (AUC) was computed for each hub gene.

2.6. mRNA-miRNA network construction

Based on the criteria of |logFC| >1 and P < .05, differentially expressed miRNAs were acquired from the GSE120172 dataset using the limma package (version 3.52.4). Subsequent visualization of these results was achieved through heatmaps and volcano plots using the ComplexHeatmap package (version 2.13.4) and ggplot2 package (version 3.3.6).

The MiRDB (https://www.mirdb.org/)[22] and ENCORI (https://starbase.sysu.edu.cn/)[23] databases were used for reverse prediction of miRNAs corresponding to the validated hub genes. The intersection between these predicted miRNAs and the differentially expressed miRNAs from the GSE120172 dataset, as indicated in both databases, was depicted using a Venn plot.

2.7. Construction of the ceRNA networks

Based on the aforementioned miRNAs, we explored miRNA-lncRNA associations in the LncBase database (https://diana.e-ce.uth.gr/lncbasev3/, Version 3.0)[24] and cross-referenced them with the ENCORI database. To bolster the reliability of our predictions, we retained only the lncRNAs predicted in both databases. Given that ceRNA networks predominantly function in the cytoplasm, we used the RNALocate database (http://www.rna-society.org/rnalocate/, version 2.0)[25] to determine the subcellular localization of lncRNAs, and excluded any lncRNAs localized only in the nucleus or extracellular space. Finally, we constructed ceRNA networks that integrated mRNAs, miRNAs, and lncRNAs, which were then graphically represented using Cytoscape software.

2.8. Ethics approval and consent to participate

The study was carried out in accordance with the guidelines of the Declarations of Helsinki. The studies involving human participants were reviewed and approved by the Ethics Committee of Xianning Central Hospital (K[2023]-005). The legal guardians of the participants provided their written informed consent to participate in this study.

2.9. Samples collection and qRT-PCR

From January 2023 to June 2023, we collected 20 whole blood samples from the Xianning Central Hospital in Hubei, China. This collection comprised 10 samples from pediatric asthma patients and ten from healthy controls, all aged between 6 and 10 years. Inclusion criteria: (1) meet the diagnostic criteria of bronchial asthma in children of the Respiratory Group of Chinese Pediatric Society in 2016.[26] (2) Children aged 6 to 14 years, (3) children with acute attack of asthma. (4) Children with good medical compliance, (5) children without other underlying diseases. Exclusion criteria: (1) children with asthma in remission, (2) children with heart disease, (3) children with blood system diseases, (4) children with mental disorders, (5) poor compliance, difficult to cooperate with children. Each participant provided 3 to 5 mL of whole blood, collected in tubes containing EDTA, which then underwent white blood cell enrichment. Total RNA was extracted from the leukocytes of asthma-afflicted children and healthy controls. Reverse transcription was performed using the Servicebio® RT First Strand cDNA Synthesis Kit (Servicebio, Wuhan, China). The qPCR was performed using 2 × SYBR Green qPCR Master Mix (None ROX) (Servicebio, Wuhan, China) following the manufacturer’s instructions. The qPCR thermocycling conditions were set to begin with an initial activation at 95 °C for 30 seconds, followed by 40 cycles, each comprising stages of 95 °C for 15 seconds and 2 subsequent phases at 60 °C for 30 seconds each. GAPDH was used as the internal reference for data normalization. Relative expression was calculated using the 2−ΔΔCt method. Primers are listed in Table S2, Supplemental Digital Content, http://links.lww.com/MD/K873.

2.10. Statistical analysis

All data calculations and statistical analyses were performed using R software (version 4.2.1). The Kolmogorov–Smirnov test was used for the normal distribution of data. The statistical significance of normally distributed variables was estimated with the independent Student t test for difference comparisons between 2 groups of continuous variables. Conversely, differences between non-normally distributed variables were analyzed using the Mann–Whitney U test. Statistical significance was defined as P < .05.

3. Results

3.1. Identification of genes associated with autophagy in asthma

Initially, we delineated our study via a flow chart (Fig. 1). Subsequently, we conducted a differential expression analysis on the GSE30326 dataset, comparing the asthma group with the control group, using the limma package in R software. This analysis identified 936 DEGs (|log FC| > 1, P < .05). Of these, 845 genes were up-regulated, and 91 were down-regulated, as illustrated in the volcano plot (Fig. 2A). The resulting DEGs were then intersected with 222 ARGs, yielding 31 DE-ARGs (Table S3, Supplemental Digital Content, http://links.lww.com/MD/K874), Visual representations of these findings were created using a heatmap and a Venn diagram (Fig. 2B and C).

Figure 1.

Figure 1.

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Flowchart of the study.

Figure 2.

Figure 2.

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Differential analysis of the GSE30326 dataset and Venn diagram of autophagy-related genes (ARGs) in pediatric asthma, along with GO and KEGG enrichment analysis. (A) Volcano plot of the the GSE30326 dataset; (B) heatmap of differentially expressed ARGs (DE-ARGs) in the GSE30326 dataset; (C) Venn diagram of differentially expressed genes and ARGs in the GSE30326 dataset; (D) biological process (BP) in the GO enrichment analysis; (E) cellular component (CC) in the GO enrichment analysis; (F) molecular function (MF) in the GO enrichment analysis; (G) KEGG pathway enrichment analysis; (H) circular diagram depicting the GO and KEGG enrichment of ARGs associated with asthma. GO = Gene Ontology; KEGG = Kyoto Encyclopedia of Genes and Genomes.

3.2. GO and KEGG enrichment analysis of DE-ARGs

The identified DE-ARGs were subjected to GO and KEGG enrichment analyses. The primary enrichments of BPs were found in autophagy mechanisms, autophagy, macroautophagy, regulation of apoptotic signaling pathways, and cellular response to external stimuli. CCs were mainly enriched in the endoplasmic reticulum, autophagosome membrane, pigment granules, and melanosomes. In terms of MFs, significant enrichments were associated with ubiquitin ligase binding, enhancer binding, activating transcription factor binding, and cysteine-type endopeptidase activity. The KEGG enrichment analysis revealed involvement in various signaling pathways, such as human papillomavirus infection, Kaposi sarcoma-associated herpes virus infection, autophagy-animal, human cytomegalovirus infection, Epstein-Barr virus infection, hepatitis C, influenza A, and apoptosis. These results are presented in bubble and chord plots (Fig. 2D–H).

3.3. GSEA results

GSEA was conducted to elucidate the differences in BPs between groups based on the GSE30326 dataset. The GSEA enrichment plot revealed that the genes from GSE30326 were mainly enriched in the interferon-α/β signaling, interferon signaling, interferon γ signaling, immune response to tuberculosis, lysosomes, and other pathways (Fig. 3).

Figure 3.

Figure 3.

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GSEA of the GSE30326 dataset. (A) Enrichment of gene sets related to REACTOME_INTERFERON_ALPHA_BETA_SIGNALING; (B) enrichment of gene sets related to REACTOME_INTERFERON_SIGNALING; C. Enrichment of gene sets related to REACTOME_INTERFERON_GAMMA_SIGNALING; (D) enrichment of gene sets related to WP_THE_HUMAN_IMMUNE_RESPONSE_TO_TUBERCULOSIS; (E) enrichment of gene sets related to KEGG_LYSOSOME; (F) ridge plot visualization of GSEA results for the GSE30326 dataset.

3.4. PPI network construction and module analysis

Subsequent to the removal of network interruptions, the PPI network comprised 30 nodes and 62 edges. The visualization of 31 DE-ARGs was accomplished through Cytoscape software (version 3.7.2) (Fig. 4A). Two clusters of gene sets were identified from the entire network using the MCODE plug-in (Fig. 4B and C). The top 10 genes with the highest degree of connectivity were then selected using 4 topological analysis methods (MCC, density of maximum neighborhood component, degree, and maximum neighborhood component) in the cytoHubba plug-in. Following the intersection selection, 5 up-regulated hub genes were obtained (Figure. 5), including VEGFA, CFLAR, RELA, FAS, and ATF6.

Figure 4.

Figure 4.

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Construction of protein–protein interaction (PPI) network construction and analysis of key clusters. (A) The whole PPI network with all autophagy-related genes; (B) PPI network of Cluster 1; (C) PPI network of Cluster 2; the size and color intensity of nodes in the networks are determined by their degree, with larger nodes and bluer colors indicating higher degrees. Similarly, the thickness and color intensity of edges in the networks are determined by the combined_score, with thicker and bluer edges representing higher combined_scores.

Figure 5.

Figure 5.

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Identification of hub genes. (A) Degree; (B) MCC; (C) DMNC; (D) MNC; (E) Venn diagram of 5 hub genes screened based on the 4 algorithms.

3.5. Hub genes validation and ROC curve analysis

To assess the reliability of core gene expression, we used the GSE103166 dataset to validate the up-regulation and statistical significance of the aforementioned 4 genes (VEGFA, CFLAR, RELA, and FAS) in asthma (Figure 6A–D), Conversely, ATF6 (Fig. 6E) was found to be downregulated and not statistically significant in asthma. Further evaluation of the sensitivity and specificity of these core genes was conducted through ROC curve analysis using the GSE103166 dataset. The results indicated that vascular endothelial growth factor A (VEGFA) (AUC = 0.712), CFLAR (AUC = 0.724), RELA (AUC = 0.729), and Fas cell surface death receptor (FAS) (AUC = 0.736) (Fig. 6F–H, 6J) exhibited a favorable diagnostic performance, whereas ATF6 (AUC = 0.548) (Fig. 6K) demonstrated lower effectiveness in the diagnosis of pediatric asthma. Finally, ATF6, which displayed poor diagnostic performance and lacked statistical insignificance, was removed from consideration. This removal led to the selection of 4 core genes, namely VEGFA, CFLAR, RELA, and FAS, for the prediction of associated miRNAs.

Figure 6.

Figure 6.

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Verification and ROC curve analysis of the hub genes using the GSE103166 dataset. Panels A, B, C, D, and E display the relative expression levels of the 5 mRNAs. *P < .05; ns: not significant; Panels F, G, H, J, and K present ROC curve analyses of the 5 mRNAs.

3.6. mRNA-miRNA network construction

The application of the R software limma package to perform differential expression analysis between the asthma group and the control group in the GSE1020172 dataset resulted in the identification of 25 differentially expressed miRNAs (|logFC| >1, P < .05). Among these, 19 were up-regulated, and 6 were downregulated (Fig. 7A and B).

Figure 7.

Figure 7.

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Construction of the mRNA-miRNA network. (A) Display of the volcano plot based on the GSE120172 dataset; (B) heatmap illustrating the differentially expressed genes in the GSE120172 dataset; (C) Venn diagram depicting the predicted miRNAs from the 2 databases; (D) Venn diagram depicting the predicted miRNAs and differentially expressed genes in the GSE120172 dataset.

We used the miRDB and the ENCORI databases for the reverse prediction of miRNAs associated with the 4 hub genes. Subsequently, we conducted an intersection analysis of differentially expressed miRNAs from the 2 databases, co-predicted miRNAs, and the GSE120172 dataset, which was visualized through a Venn diagram (Fig. 7C and D). This analysis led to the identification of 5 miRNAs (hsa-miR-625-5p, hsa-miR-320b, hsa-miR-22-3p, hsa-miR-200b-3p, and hsa-miR-206). Among these, hsa-miR-625-5p, hsa-miR-320b, and hsa-miR-22-3p were down-regulated, and hsa-miR-200b-3p and hsa-miR-206 were up-regulated. According to the ceRNA hypothesis, we selected 3 downregulated miRNAs (hsa-miR-625-5p, hsa-miR-320b, and hsa-miR-22-3p) for the construction of the ceRNA network.

3.7. Construction of the ceRNA network

We predicted lncRNAs using the 3 miRNAs described above, resulting in the identification of 45 lncRNAs through a combination of the LncBase and ENCORI databases (Table S4, Supplemental Digital Content, http://links.lww.com/MD/K875). Considering that endogenous competition for lncRNAs primarily occurs in the cytoplasm, we excluded 10 lncRNAs that were predominantly localized in the nucleus or extracellular space. Consequently, we retained a total of 35 lncRNAs for further analysis. Subsequently, we constructed a ceRNA network consisting of 4 mRNAs, 3 miRNAs, and 35 lncRNAs (Fig. 8A). To identify the top-ranking genes in the ceRNA network, we applied the MCC algorithm from the cytoHubba plug-in (Fig. 8B). The top 10 genes included hsa-miR-320b, hsa-miR-22-3p, hsa-miR-625-5p, FTX, MALAT1, PAX8-AS1, FAS, MEG3, DHRS4-AS1, and NEAT1.

Figure 8.

Figure 8.

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Construction of the ceRNA network. (A) Representation of the ceRNA network, with blue triangles indicating 4 mRNAs, yellow circles representing 3 miRNAs, and green diamonds reflecting 35 lncRNAs; (B) identification of the top 10 ceRNAs ranked using the MCC algorithm. In this representation, mRNAs, miRNAs, and lncRNAs are denoted by triangles, ellipses, and diamonds, respectively. The color intensity reflects the score, with red indicating higher scores and yellow indicating lower scores.

3.8. Validation by qRT-PCR

We conducted qRT-PCR validation for FAS, CFLAR, VEGFA, and RELA. The results demonstrated a significantly elevated expression level of FAS in pediatric asthma compared to healthy controls (Fig. 9). However, no significant differences were observed in the expression levels of the remaining 3 mRNAs between the 2 groups.

Figure 9.

Figure 9.

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Validation of the differential expression of potential diagnostic markers, including FAS, CFLAR, VEGFA, and RELA, via qRT-PCR. *P < .05, **P < .01; ns: not significant.

4. Discussion

Asthma is a common chronic respiratory disease in children and severely impacts their well-being.[27] Asthma attacks are associated with many factors, such as viral infections,[28] environmental factors,[29] ethnicity,[30] parental education, genetic factors, and maternal smoking history during pregnancy.[31] The interplay between genetic and environmental factors is recognized as the primary etiological basis of asthma.[32,33] Consequently, delving into the genetic underpinnings of asthma holds paramount importance for understanding its pathogenesis.

Autophagy has emerged as a significant research focus in the context of asthma. For instance, Lou et al[34] identified miR-192-5p as a potential regulator capable of mitigating airway remodeling and autophagy in asthma by targeting ATG7. Notably, cells with depleted ARGs, such as ATG5 or ATG14, showed impaired respiratory mucus secretion in the context of interleukin 13 (IL-13)-mediated type II inflammatory asthma.[35] These findings suggest that autophagy may exert a detrimental influence in asthma.

With the rapid development of microarray sequencing technology, bioinformatics has become increasingly crucial to identify disease biomarkers. For instance, He et al[36] uncovered the association of 4 miRNAs (hsa-miR-106a-5p, hsa-miR-18a-5p, hsa-miR-144-3p, and hsa-miR-375) with pediatric asthma. Lin et al,[37] through the analysis of 3 datasets (GSE45111, GSE41863, and GSE137268), identified 2 hub genes (CXCR1/2 and S100A12) that may be associated with neutrophilic asthma. Additionally, Ma et al[38] pinpointed a panel of lncRNAs that may serve as biomarkers for the diagnosis or prognosis of asthma. Among these, lncRNA PVT1 was noted for its significant role in mitigating airway remodeling in asthma[39] and preventing asthma deterioration. However, there remains a scarcity of bioinformatics analyses examining the interplay between asthma and autophagy.

In this study, we identified a total of 31 DE-ARGs based on an mRNA microarray dataset and the human autophagy database. Among these DE-ARGs, 29 were up-regulated, while 2 were down-regulated. To investigate the functional interaction among these DE-ARGs, we used GO analysis, KEGG pathway analysis, and GSEA and established PPI networks. GO analysis showed that these DE-ARGs were predominantly associated with BPs, such as autophagy and apoptosis. Additionally, they were linked to CCs, such as endoplasmic reticulum and autophagosome membrane, and MFs, such as ubiquitin ligase binding, enhancer binding, and activating transcription factor binding. The KEGG analysis further highlighted significant signaling pathways, such as viral infection, autophagy, and apoptosis. GSEA was highly enriched in interferon-α/β signaling, interferon γ signaling, and lysosomes. Viral infections have been known to suppress interferon α production by plasma cell dendritic stem cells,[40] which may further lead to increased susceptibility to asthma exacerbation. Furthermore, children with allergic asthma tend to produce lower levels of virus-induced interferon α compared to healthy children.[41] Interferon γ,[42] on the other hand, plays a role in strengthening lysosomal activity for effective clearance.

Following the construction of the PPI network, we obtained 5 up-regulated hub genes (VEGFA, CFLAR, RELA, FAS, and ATF6). Further validation showed that 4 of these genes exhibited high AUC values in ROC curve analysis. As a result, VEGFA, CFLAR, RELA, and FAS were identified as potential hub genes involved in autophagy in asthma.

VEGF is a key regulator of angiogenesis, and within this family, VEGFA is a prominent member. In the context of asthma, VEGFA levels are elevated in affected children, and this elevation has been linked to airway hyperresponsiveness and airway remodeling.[43] Notably, inhaled corticosteroid has demonstrated effectiveness in reducing the expression of VEGFA in sputum.[44] Multiple studies have suggested that VEGFA may be a potential biomarker of asthma.[45,46] The CASP8 and FADD-like apoptosis regulator (CFLAR) inhibit apoptosis by preventing the activation of CASP8. CFLAR has been shown to coordinate 3 intracellular processes: autophagy, apoptosis, and necroptosis, to obtain the most favorable cellular outcomes.[47] Wu et al[48] showed that CFLAR could block apoptosis and promote the development of Th2 cytokine-induced allergy in asthmatic mice. Additionally, CFLAR has been implicated in the context of other diseases, such as lung cancer[49] and nonalcoholic steatohepatitis.[50] The RELA proto-oncogene (RELA) is involved in the chronic inflammatory process associated with steroid-dependent asthma.[51] The research of Michela et al[52] found significant differential expression of the RELA gene in asthmatic mice compared to control mice, suggesting its potential as a biomarker for asthma. RELA typically forms a complex with the NF-κB protein and p50. NF-κB protein is a widespread transcription factor that regulates the expression of numerous pro-inflammatory genes, indicating its involvement in the inflammatory responses observed in asthma. Huang et al[53] demonstrated the significant anti-inflammatory and anti-airway hyperresponsiveness effects of icariin C in asthmatic mice. These effects were achieved through the negative regulation of the NF-κB pathway. FAS is a receptor signaling component of apoptosis and induces apoptosis upon binding to its ligand (Fas L). A Japanese study[54] showed a significant increase in serum FAS levels during allergic asthma attacks. Yao et al[55] revealed the potential therapeutic effects of low doses of interferon γ in a mouse model of asthma, and it could reduce respiratory inflammation by promoting Fas/FasL-induced apoptosis of CD4+ T cells. Another study[56] showed that T lymphocytes from children with allergic asthma were resistant to Fas-mediated apoptosis, which may contribute to the development of allergic asthma.

MiR-22-3p may be involved in the inflammatory response in asthmatic children by targeting the CCL2 gen.[57] It may also assume a role in dust mite-induced pediatric asthma by regulating the CBL gene and reducing the levels of interferon γ and tumor necrosis factor-α. In addition, miR-22-3p and miR-625-5p are involved in down-regulating the target gene ESR1, leading to the inhibition of the NF-κB pathway and a reduction in the secretion of IL-12 cytokine in pediatric asthma.[58] This finding suggests a potential protective effect on asthma by preventing further tissue remodeling. MiR-625-5p may suppress airway inflammation in bronchial epithelial cells by targeting the AKT2 gene.[59] However, the role of miR-320b in asthma is less clear, as it has been primarily studied as a diagnostic marker in squamous cell lung cancer[60] and for its potential anticancer effects in breast cancer through the inhibition of tumor angiogenesis.[61] The down-regulation of miR-22-3p and miR-625-5p in pediatric asthma, as observed in our study, is consistent with previous research findings.

Subsequently, our predictive analyses identified 45 lncRNAs, and we excluded 10 lncRNAs localized in the nucleus or extracellular space. Based on these lncRNAs, we constructed a ceRNA network consisting of 4 mRNAs, 4 miRNAs, and 35 lncRNAs, culminating in the identification of the top 10 ceRNAs. An extensive literature search was conducted for predicted lncRNAs, focusing specifically on those previously reported to be up-regulated in asthma. For instance, Shen et al[62] highlighted the robust expression of lncRNA FTX transcript (FTX) in asthma, which was linked to its role in promoting the proliferation and migration of airway smooth muscle cells, ultimately contributing to airway remodeling. Additionally, the involvement of lncRNA metastasis-associated lung adenocarcinoma transcript 1 (MALAT1) in asthma emerged as a critical aspect, as it was found to exert a substantial influence on the proliferation and migration of airway smooth muscle cells in this context.[63] Furthermore, Liang et al[64] provided evidence of elevated MALAT1 expression in asthma, potentially instigating airway inflammation by modulating the Th1/Th2 balance. In accordance with the hypothesis of ceRNA, we suggested that the regulatory pathways involving FTX/MALAT1-hsa-miR-625-5p/has-miR-22-3p-FAS may play critical roles in ceRNA networks associated with asthma. Our study is poised to contribute valuable insights into unraveling the molecular underpinnings of the asthma pathogenesis.

Our study had some limitations that warrant consideration. Firstly, the relatively small sample size of this study may introduce potential bias. In future research endeavors, we intend to enhance our recruitment efforts and expand the sample size to bolster the overall credibility and robustness of the study. Secondly, due to the limited availability of suitable datasets pertaining to lncRNA expression in pediatric asthma, we encountered challenges in determining whether the predicted lncRNAs were upregulated or downregulated in this context. Consequently, our experiment featured a small sample size, and we did not conduct comprehensive validation of the expression levels of miRNAs and lncRNAs. Finally, Some experiments such as RNA binding protein immunoprecipitation,[65,66] RNA sequencing,[67] and cell function[68,69] experiments should be used to further explore the relationship between ceRNA networks.

5. Conclusion

In the present study, we utilized bioinformatics methods to construct a ceRNA network consisting of 4 mRNAs, 3 miRNAs, and 35 lncRNAs. This network was further validated by qRT-PCR and prediction results. Notably, our findings suggest that the FTX/MALAT1-hsa-miR-625-5p/has-miR-22-3p-FAS pathway may assume an important role in the pathogenesis of asthma. Nonetheless, it is imperative to acknowledge that further investigations are necessary to provide deeper insights into these mechanisms.

Acknowledgments

We thank Bullet Edits Limited for the linguistic editing and proofreading of the manuscript.

Author contributions

Conceptualization: Hao Zhu, Wen Li.

Data curation: Hao Zhu.

Formal analysis: Hao Zhu.

Funding acquisition: Wen Li.

Investigation: Hao Zhu, Jiao Shi.

Methodology: Hao Zhu, Wen Li.

Project administration: Jiao Shi, Wen Li.

Resources: Jiao Shi, Wen Li.

Software: Hao Zhu, Jiao Shi.

Supervision: Jiao Shi, Wen Li.

Validation: Hao Zhu, Jiao Shi, Wen Li.

Visualization: Hao Zhu.

Writing – original draft: Hao Zhu, Jiao Shi.

Writing – review & editing: Hao Zhu, Wen Li.

Supplementary Material

medi-102-e36343-s001.docx (19.2KB, docx)
medi-102-e36343-s003.docx (16.6KB, docx)
medi-102-e36343-s004.docx (15.7KB, docx)

Abbreviations:

ARGs
autophagy-related genes
AUC
area under the curve
BP
biological process
CC
cellular component
ceRNA
competitive endogenous RNA
CFLAR
CASP8 and FADD-like apoptosis regulator
DE-ARGs
differentially expressed autophagy-related genes
DEGs
differentially expressed genes
FAS
Fas cell surface death receptor
FTX
FTX transcript
GO
Gene Ontology
GSEA
gene set enrichment analysis
KEGG
Kyoto Encyclopedia of Genes and Genomes
MALAT1
metastasis associated lung adenocarcinoma transcript 1
MCC
Maximal Clique Centrality
MF
molecular function
PPI
protein–protein interaction
qRT-PCR
quantitative real-time PCR
RELA
RELA proto-oncogene
ROC
receiver operating characteristic
VEGFA
vascular endothelial growth factor A

HZ and JS contributed equally to this work.

The authors have no conflicts of interest to disclose.

This study was supported by the Natural Science Foundation of Xianning Municipal (2022ZRKX072).

Supplemental Digital Content is available for this article.

The datasets generated during and/or analyzed during the current study are publicly available.

How to cite this article: Zhu H, Shi J, Li W. Bioinformatics analysis of ceRNA network of autophagy-related genes in pediatric asthma. Medicine 2023;102:48(e36343).

Contributor Information

Hao Zhu, Email: 2021710968@yangtzeu.edu.cn.

Jiao Shi, Email: shijiao880715@163.com.

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