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Identification of mitochondria-related biomarkers in childhood allergic asthma

BMC Medical Genomics volume 17, Article number: 141 (2024) Cite this article

Abstract

Background

The mechanism of mitochondria-related genes (MRGs) in childhood allergic asthma (CAS) was unclear. The aim of this study was to find new biomarkers related to MRGs in CAS.

Methods

This research utilized two CAS-related datasets (GSE40888 and GSE40732) and extracted 40 MRGs from the MitoCarta3.0 Database. Initially, differential expression analysis was performed on CAS and control samples in the GSE40888 dataset to obtain the differentially expressed genes (DEGs). Differentially expressed MRGs (DE-MRGs) were obtained by overlapping the DEGs and MRGs. Protein protein interactions (PPI) network of DE-MRGs was created and the top 10 genes in the degree ranking of Maximal Clique Centrality (MCC) algorithm were defined as feature genes. Hub genes were obtained from the intersection genes from the Least absolute shrinkage and selection operator (LASSO) and EXtreme Gradient Boosting (XGBoost) algorithms. Additionally, the expression validation was conducted, functional enrichment analysis, immune infiltration analysis were finished, and transcription factors (TFs)-miRNA-mRNA regulatory network was constructed.

Results

A total of 1505 DEGs were obtained from the GSE40888, and 44 DE-MRGs were obtained. A PPI network based on these 44 DE-MRGs was created and revealed strong interactions between ADCK5 and MFN1, BNIP3 and NBR1. Four hub genes (NDUFAF7, MTIF3, MRPS26, and NDUFAF1) were obtained by taking the intersection of genes from the LASSO and XGBoost algorithms based on 10 signature genes which obtained from PPI. In addition, hub genes-based alignment diagram showed good diagnostic performance. The results of Gene Set Enrichment Analysis (GSEA) suggested that hub genes were closely related to mismatch repair. The B cells naive cells were significantly expressed between CAS and control groups, and MTIF3 was most strongly negatively correlated with B cells naive. In addition, the expression of MTIF3 and MRPS26 may have influenced the inflammatory response in CAS patients by affecting mitochondria-related functions. The quantitative real-time polymerase chain reaction (qRT‒PCR) results showed that four hub genes were all down-regulated in the CAS samples.

Conclusion

NDUFAF7, MTIF3, MRPS26, and NDUFAF1 were identified as an MRGs-related biomarkers in CAS, which provides some reference for further research on CAS.

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Introduction

Asthma is a chronic airway disease characterized by eosinophilic inflammation of the airways, increased airway hyperresponsiveness (AHR), transformation of goblet cells, structural changes in the airways, elevated levels of Immunoglobulin E (IgE) levels, and reversible obstruction of airflow during exhalation [1]. Many environmental stimuli and genetic factors are known to contribute to the development of asthma [2, 3]. As a prevalent disease, asthma imposes a significant economic burden on society [4], patients, and their families. The diagnosis of childhood allergic asthma (CAS) primarily relies on patient’s medical history, clinical symptoms, and immunological examination, which has certain limitations [5] In recent decades, there has been a global increase in the incidence and mortality rate of asthma worldwide [6]. While currently available anti-asthma drugs are effective in controlling the clinical symptoms of asthma [7], they do not stop the natural progression of the disease. Therefore, it is necessary to carefully study the factors related to the progression of asthma in order to develop new therapeutic drugs for the treatment of CAS. About 50% of preschoolers experience wheezing at least once [8], but only a small number of children who wheeze go on to develop asthma. Currently, there is no “gold standard” for diagnosing CAS in children under the age of six. Therefore, the early detection of children who are at a high risk for asthma in CAS is critical. Notably, the early screening and effective prevention of high-risk CAS from a bioinformatics perspective will be of great significance for the occurrence and development of CAS. Given the extensive development of asthma research, asthma is now considered to be a complex and heterogeneous disease linked to mitochondrial genes [9, 10].

Mitochondrion (MT) is a well-known double-membrane organelle that is the power source for cells to produce energy in the form of ATP through oxidative phosphorylation (OXPHOS) in the electron transport chain [11]. Any change in the state within the mitochondria disrupts the OXPHOS mechanism, resulting in the accumulation and production of ROS [12], which in turn leads to mitochondrial dysfunction, forming a vicious cycle of cell damage and death [13]. Many studies [14, 15] have shown that MT play a crucial role in providing intermediates of oxidative metabolism through arginine/ornithine and may have an important influence on the signal transduction of airway inflammation. In addition, polymorphisms or haplotype differences in the mitochondrial genome have been found to be associated with asthma in humans [16]. The mitochondrial genome encodes the core catalytic peptides that form the main proteins in the electron transport chain [9]. Some studies [9, 17] have reported a link between allergic diseases and mitochondrial function. For example, MT provide intermediates for oxidative metabolism through arginine/ornithine Furthermore, they may have an important influence on signal transduction of airway inflammation. The pathophysiology of asthma is closely related to mitochondrial dysfunction [18], which leads to decreased ATP synthase activity, increased oxidative stress, apoptosis induction and abnormal calcium homeostasis [19,20,21]. Given the increasing incidence of CAS over the years, there is limited data on the role of mitochondria in mediating or regulating the effects of airway inflammation. No reports of a joint study between mitochondria-related genes (MRGs) and CAS have been found. Therefore, targeting mitochondrial dysfunction in the treatment of such diseases is currently promising and can be pursued as a research direction.

In this study, the public data of GEO database GSE40888 dataset was used to further screen the candidate characteristic genes for MRGs in CAS. We employed difference analysis, the lasso algorithm, and XGBoost algorithm. The genes obtained by the two algorithms were crossed to obtain four key genes: NDUFAF7, MTIF3, MRPS26, and NDUFAF1. We further explored four key genes, including expression correlation analysis and GSEA analysis, immune infiltration analysis, and inflammatory index analysis. In particular, the expression of MTIF3 gene affected mitochondria-related functions, which negatively affected the immune system response to asthma, mainly in the abundance of B cell naive cells. It also positively affected the inflammatory response in asthma patients, suggesting that it may be able to reduce inflammation. It has influenced the onset and progression of CAS and could potentially be a novel approach to diagnosing and treating CAS.

Materials and methods

Data source

Two CAS-related datasets (GSE40888 and GSE40732) were obtained from the Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/gds). GSE40888 includes expression data from 41 blood samples of CAS and 86 blood samples of control samples. GSE40732 includes expression data from 97 blood samples of CAS and 97 blood samples of control samples. Additionally, 1136 MRGs were retrieved from the MitoCarta3.0 Database [22].

Differential expression analysis

Differential expression analysis was performed between CAS and control samples using the limma R package [23] in the GSE40888 dataset to screen differentially expressed genes (DEGs) by setting |logFC| > 0.5 and P.adj < 0.05. And The Benjamini-Hochberg method was used to control for false discoveries generated when conducting multiple hypothesis tests. To better visualize the differences in gene expression among the two groups, the R packages ggpubr and ggplot2 [24] were used to plot volcano and heat maps of DEGs respectively. Differentially expressed MRGs (DE-MRGs) were obtained by overlapping the DEGs and MRGs. In addition, Gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) enrichment analysis of DE-MRGs were completed using the clusterProfiler package [25] to explore the functions of it.

Construction of protein protein interactions (PPI) network and acquisition of feature genes

To explore whether interactions existed among the DE-MRGs, a protein protein interactions (PPI) network was created using search tool for recurring instances of neighbouring genes (STRING, https://string-db.org). The CytoHubba plug-in in Cytoscape software was used to perform Maximal Clique Centrality (MCC) algorithms to obtain the degree ranking of each node in the above PPI network. The top 10 genes in the degree ranking were defined as feature genes.

Acquisition of hub genes and construction of alignment diagram

To obtain valuable diagnostic genes, least absolute shrinkage and selection operator (LASSO) and EXtreme Gradient Boosting (XGBoost) based on feature genes were performed. The LASSO was completed by R package glmnet [26]. The XGBoost was completed by R package xgboost. The intersection of the diagnostic genes obtained by the two algorithms was defined as hub genes. Next, analysis of the expression trend of hub genes in CAS and control samples from GSE40888 and GSE40732 were finished. Moreover, to explore the potential mechanism of hub genes, Gene Set Enrichment Analysis (GSEA) for hub genes was conducted with ClusterProfiler package [25] in GSE40888.

Alignment diagram of hub genes were constructed in GSE40888 using the rms package [27] in R. The predictive power of the alignment diagram was assessed using calibration curves and decision curves. The closer the area under curve (AUC) of the receiver operating characteristic (ROC) curve was to 1, the more accurate of the diagnosis of the alignment diagram was. To further demonstrate the potential that hub genes can be used as biomarkers, we plotted the ROC curves of hub genes using pROC at GSE40888.

Immuno-microenvironmental analysis and inflammatory microenvironmental analysis

The immune abundance of 22 immune cells in the CAS and control samples of the GSE40888 was calculated using the CIBERSORT algorithm [28]. After finding differentially expressed (DE)-immune cells between CAS and control samples, spearman correlations among the hub genes and between hub genes and DE-immune cells were calculated. A total of 200 inflammation-related genes (IRGs) were obtained from the HALLMARK_INFLAMMATORY_RESPONSE pass-through in the Molecular Signatures Database (MSigDB). The single sample gene set enrichment analysis (ssGSEA) of IRGs in all samples of the GSE127487 was conducted using the gsva [23] package in R to get the score of IRGs. Finally, the correlation between IRGs scores and hub genes was calculated.

Construction of TF-miRNA-mRNA regulatory networks

To understand the regulatory role of hub genes in the pathogenesis of CAS, potential target miRNAs of hub genes were predicted by the miRNet database (https://www.mirnet.ca/miRNet/home.xhtml), the NetworkAnalyst database (https://www.networkanalyst.ca) was used to predict the transcription factors (TFs) that could interact with hub genes. Finally, based on the above miRNAs and TFs, the TF-miRNA-mRNA network was mapped using Cytoscape software.

Quantitative real-time polymerase chain reaction (qRT‒PCR)

To further explore the role of hub genes in CAS, the expression of hub genes was validated by qRT-PCR. Totally 10 clinical blood samples in this experiment were obtained from normal and CAS patients in the Second People’s Hospital of Hefei including 5 normal and 5 CAS samples. Normal sample: Age range: 2–10 years old; no history of respiratory disease; no history of allergic disease; no infectious disease has occurred in the past 6 months.Sample of children with allergic asthma: Age range: 2–10 years old; the patient is clinically diagnosed with allergic asthma and meets one of the following criteria: (a) meets the World Health Organization (WHO) diagnostic criteria for asthma; (b) Have the characteristic clinical symptoms of asthma, such as wheezing, shortness of breath, chest tightness, etc. (c) Positive test results for allergen-specific IgE antibodies; (d) Response to bronchodilation test. Total RNA was prepared from blood samples using TRIZol reagent. Reverse transcription was performed using the SureScript-First-strand-cDNA-synthesis-kit to obtain cDNAs. The qRT-PCR was conducted as follows: totally 40 cycles, 95 °C for 1 min, 95 °C for 20 s, 55 °C for 20 s, and 72 °C for 30 s. GAPDH was used as the internal reference gene of hub genes. The qPCR primers were shown in Table 1. The relative expression levels of hub genes were calculated by 2−ΔΔCt method.

Table 1 The information of primer sequences in quantitative real-time polymerase chain reaction (qRT-PCR)
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Statistical analysis

Limma was used to identify DEGs. Venn was used for multiple gene sets to take intersections. LASSO and XGBoost were used to screen for hub genes. The ssGSEA for calculation of proportions of immune cells. GSEA was used to analyze the biological pathways in hub genes that may be involved. Statistical analysis was carried out through R software (https://www.r-project.org/). Differences between groups were analyzed via the Wilcox test. P < 0.05 represented a significant difference.

Results

DE-MRGs were associated with mitochondrial transcription

A total of 1505 DEGs were obtained from the GSE40888, and 877 DEGs were upregulated and 628 DEGs were downregulated in CAS (Fig. 1A-B). A total of 44 DE-MRGs were obtained by overlapping the 1505 DEGs and 1136 MRGs (Fig. 1C). Functional enrichment analysis revealed that DE-MRGs were associated with mitochondrial gene expression, mitochondrial RNA metabolic process, and mitochondrial transcription in GO entries (Fig. 1D), and enriched in propanoate metabolism, ferroptosis, and pyruvate metabolism in KEGG entries (Fig. 1E).

Fig. 1
figure 1

Identification of differentially expressed mitochondria-related genes (DE-MRGs). (A) The heatmap of differentially expressed genes (DEGs) from the GSE40888 in childhood allergic asthma (CAS). (B) The volcano plot of DEGs from the GSE40888 in CAS. (C) The Venn plot of DE-MRGs between DEGs and MRGs. (D) Gene ontology (GO) enrichment analysis of DE-MRGs (top: bubble char; bottom: circle chart). (E) Kyoto encyclopedia of genes and genomes (KEGG) enrichment analysis of DE-MRGs (left: bubble char; right: circle chart)

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Four genes (NDUFAF7, MTIF3, MRPS26 and NDUFAF1) were identified as hub genes

To explore whether interactions existed between the 44 DE-MRGs, a PPI network includes 35 nodes and 60 edges was created (Fig. 2A, Confidence = 0.4), there were strong interactions between ADCK5 and MFN1, BNIP3 and NBR1. The top 10 genes (MTIF3, MRPS18C, MRPL44, MRPS26, MRPS31, SSBP1, NDUFAF7, NDUFA8, COX6C, and NDUFAF1) in the degree ranking of MCC algorithms were defined as feature genes (Fig. 2B). The results of the LASSO regression analysis suggested that when λ = 0.033, six model genes with regression coefficients that are not penalized to 0 are obtained after tenfold cross-validation (Fig. 2C-D). Five model genes were obtained from the XGBoost model, and histograms of gene coefficients were plotted with larger Gini coefficients indicating the greater importance of the gene (Fig. 2E). Four hub genes (NDUFAF7, MTIF3, MRPS26, and NDUFAF1) were obtained by taking the intersection of the model genes of the above two models (Fig. 2F).

Fig. 2
figure 2

Screening of hub genes. (A) Construction of protein-protein interactions (PPI) network. (B) Top ten essential DE-MRGs ranked by maximal clique centrality (MCC) scores. (C) Least absolute shrinkage and selection operator (LASSO) regression analysis for screening the risk model genes. (D) The variation characteristics of the coefficient of variables. (E) Top five model genes selected using EXtreme Gradient Boosting (XGBoost) and the corresponding variable importance score. (F) The Venn plot of hub genes by taking the intersection of the XGBoost and LASSO algorithms

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Hub genes were closely related to pyrimidine metabolism

The results of GSEA suggested that the MRPS26 was enriched in pyrimidine metabolism, mismatch repair, and homologous recombination in KEGG terms. MTIF3 was enriched in mismatch repair, butanoate metabolism, and lysosome in KEGG terms. NDUFAF7 was enriched in glycolysis gluconeogenesis, pyrimidine metabolism, and mismatch repair in KEGG terms. NDUFAF1 was enriched in spliceosome, mismatch repair, and pyruvate metabolism in KEGG terms (Fig. 3A). In addition, the expression validation results of the hub genes suggested that NDUFAF7 was down-regulated in CAS samples and showed consistent trends in the GSE40888 dataset and GSE40732 dataset, with other genes to be verified in further experiments. (Fig. 3B, P < 0.05). To further explore the correlation between hub genes and pyrimidine metabolism, we calculated the correlation between hub genes and β-Ureidopropionase (UPB1), Dihydropyrimidine Dehydrogenase (DPYD), and Cytidine Deaminase (CDA), and the results showed that MTIF3 had a strong positive correlation with DPYD (Fig. 3C).

Fig. 3
figure 3

Functionality of four hub genes. (A) Gene set enrichment analysis (GSEA) of four hub genes. (B) The box plot of expression of four hub genes between CAS and control groups in GSE40888 dataset and GSE40732 dataset. (C) Correlation analysis of hub genes with pyrimidine metabolism, red represents positive correlation and blue represents negative correlation. * p < 0.05, ** p < 0.01, *** p < 0.001, **** p < 0.0001, ns, no significance

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Hub gene-based alignment diagram showed good diagnostic performance

Based on the results of the logistic regression model, an alignment diagram was constructed, and the score of each sample was calculated by the alignment diagram, the higher the total score of the patient, the higher the likelihood that the patient will develop CAS disease (Fig. 4A). The calibration curve and decision curve suggested that the ROC of the model was 0.840, indicating that the alignment diagram possesses good diagnostic efficacy (Fig. 4B-D). By plotting the ROC curves, we found that the AUC of hub genes in the GSE40888 dataset were all greater than 0.6, indicating that hub genes have the potential to be used as CAS biomarkers (Fig. 4E).

Fig. 4
figure 4

A nomogram prediction of hub genes in CAS samples. (A) Hub genes nomogram. (B–D) Calibration curves (left). Receiver operating characteristic (ROC) curves (middle) and decision curve analysis (DCA) (right) for the diagnostic nomogram. (E) ROC analysis of hub genes in GSE40888 dataset

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MTIF3 was strongly negatively correlated with B cells naive

Immune cells with a cell abundance of 0 in more than 75% of the samples were filtered out, leaving 15 immune cells, and significant differences in the expression of B cells naive were observed between CAS and control groups (Fig. 5A). The results of the correlation analysis between hub genes and DE-immune cells suggested that MTIF3 is the most strongly negatively correlated with B cells naive (Fig. 5B, Cor=-0.5, P < 0.0001). The results of the correlation analysis among hub genes suggested significant correlations between NDUFAF7 and MRPS26 (Cor = 0.38, P < 0.001), NDUFAF7 and NDUFAF1 (Cor = 0.72, P < 0.001), and MRPS26 and NDUFAF1 (Cor = 0.50, P < 0.0001) (Fig. 5C). The results of the correlation analysis between Hub genes and the score of IRGs suggested that MTIF3 correlated significantly positively with IRGs score (Cor = 0.47) and MRPS26 correlated significantly negatively with IRGs score (Cor=-0.32) (Fig. 5D). A total of 25 TFs and 55 miRNAs that may interact with hub genes were obtained, and a TF-miRNA-mRNA network was constructed (Fig. 5E). There were some relationships existed such as NDUFAF1 acting on hsa-mir-499a-3p by influencing E2F1.

Fig. 5
figure 5

Immune correlation analysis. (A) Box plot for the expression levels of different cell types and the heat maps of the proportion of different immune cells in CAS and control groups. (B) The correlation analysis between hub genes and DE-immune cells. (C) The correlation analysis among hub genes. (D) The correlation analysis between hub genes and the score of inflammation-related genes (IRGs). (E) Construction of transcription factors (TFs) -miRNA-mRNA network. Green indicates hub genes, pink indicates miRNAs, yellow indicates TFs. * p < 0.05, ** p < 0.01, *** p < 0.001, **** p < 0.0001, ns, no significance

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Hub genes were confirmed by qRT-PCR

The qRT-PCR results showed that the expression levels of four genes (NDUFAF1, MTIF3, MRPS26 and NDUFAF7) in the CAS group were significantly lower compared with the normal group (Fig. 6).

Fig. 6
figure 6

Validation of four hub genes (NDUFAF7, MTIF3, MRPS26, and NDUFAF1) expression by quantitative real-time polymerase chain reaction (qRT-PCR) in control and CAS blood samples. * p < 0.05, ** p < 0.01, *** p < 0.001

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Discussion

At present, there have been few studies on asthma and mitochondria-related genes. Therefore, the aim of our research is of great significance to as we explore mitochondrial related biomarkers in CAS. In this study, we investigated the hub genes (NDUFAF7, MTIF3, MRPS26, NDUFAF1) that can be used for accurate diagnosis of CAS. Significantly, we detected the expression of each hub gene and obtained the corresponding score. The incidence of CAS could be determined based on the total score.

Over-expression of the target protein amyloid precursor protein (APP) gene caused by exon variants of NDUFAF7 is associated with the risk of Alzheimer’s disease [29]. Mammalian mitochondrial ribosomal protein MRPS26 expression levels are correlated with tumor purity in non-small cell lung cancer (NSCLC). MRPS26 has been observed to participate in mitochondrial activity of muscle stem cells [30]. MTIF3 encodes 29 kDa proteins that promote the formation of initiation complexes on mitochondrial 55 S ribosomes and play an active role in translation initiation. The MTIF3 gene may influence Parkinson’s disease by causing mitochondrial dysfunction [31]. In our study, in the GSE40888 dataset, the expression of MTIF3 in the CAS sample was lower than that in the normal sample. This suggested that, similar to Parkinson’s disease, MTIF3 may trigger CAS by causing mitochondrial dysfunction. In addition, we found that MTIF3 was negatively correlated with infantile B cells, suggesting that there could be an imbalance in the immune system. This negative correlation may lead to an abnormal response to the immune system, affecting the immune response and autoimmune response. Especially in CAS, it may lead to an abnormal response to the sensitivities. The negative correlation between MTIF3 and the immature B cells may further aggravate the abnormal immune response, increase the inflammatory and bronchial contraction induced by the system, and promote the development of allergic asthma in children. Previous studies have shown that cell B cells play a key role in allergic diseases [32], while the decline of MTIF3 levels may affect the function and differentiation of juvenile b cells, further exacerbating the immune disorder caused by the process. A Study [33] has demonstrated that NDUFAF1 is an crucial mitochondrial protein involved in complex I assembly and stabilization, with important functions in electron transport and proton pumping. Expression levels of NDUFAF1 in two central nodes involved in epidermal growth factor (EGF) and Olfactomedin 4 (OLFM4) have been associated with acute kidney injury and septic shock [34]. It is suggested that NDUFAF1 is involved in the cellular inflammatory response pathway.

Some scholars have found that [14] MT function also has a positive effect on the inflammatory response of asthma patients, suggesting that inflammatory response may affect the occurrence and progression of asthma. Wu et al. [35] found that an iron chelator could relieve allergic airway inflammation. CAMKK2 and CISD1 are key suppressors of iron-induced cell death in asthma. CAMKK2 is down-regulated in patients with asthma. Interestingly, the negative correlation between CISD1 gene expression and Tregs suggests a potential role of CISD1 in regulating immune cell infiltration [36]. Tregs are viable targets of airway allergic inflammatory responses and play an integral role in maintaining immune tolerance in asthma [37]. By up regulating immunosuppressive molecules and suppressor genes, the Tregs portion of CD4 T cells prevents the development of pro-inflammatory activity and reduces inflammation [38, 39]. In our study, an analysis to examine the correlation between the hub gene and immune cells. The results revealed that the immune system’s response to asthma is negatively affected, mainly due to the high the abundance of B-cell naive cells. MTIF3 had the strongest negative correlation with B cell naive. The activation of B cells in the airway during the inflammatory response is quite different from the classical B cell activation observed in secondary lymphoid organs, and has been demonstrated [40]. In addition, the expression of the MTIF3 and MRPS26 genes negatively affects the immune system’s response to asthma, impacting mitochondrial-related functions.

Lin et al. [41] showed that CAMKK2 participated in mitochondrial autophagy and promoted iron death. Autophagy may play a key role in chronic airway inflammation [42]. Increased autophagy plays an important role in airway remodeling, extracellular matrix deposition and fibrosis in asthma [43]. In addition, genetic mutations in autophagy genes were associated with asthma [44]. Particulate matter 2.5 (PM2.5) has been reported [45] to drive mitochondrial autophagy and induce cell cycle arrest and senescence. Our KEGG enrichment results indicate that differentially expressed mitochondria-related genes are associated with thermogenesis, mitochondrial phagocytosis - animal, diabetic cardiomyopathy, citric acid cycle (TCA cycle), propionic acid metabolism, Parkinson’s disease, oxidative phosphorylation, nonalcoholic fatty liver, valine, leucine, and isoleucine degradation. Additionally, these genes are correlated with chemo-9 - Carcinogenic effects - reactive oxygen species and other pathways. GSEA enrichment analysis based on KEGG gene set showed that the enrichment results of four hub genes were enriched in a total of one cell cycle pathway. Four hub genes regulate the occurrence of autophagy through some mitochondrial signaling pathways, resulting in cell cycle arrest and cell senescence, which contribute to the development of asthma phenotype. This is in line with some studies suggesting that cell senescence in the lung may be an important risk factor for asthma [46, 47]. And mitochondrial dysfunction has been shown to drive premature cellular aging, consistent with its impact on airway diseases [48, 49].

According to our research, we found a group of 25 TFs and 55 miRNA that interact with the hub gene, and built a TF-miRNA-mRNA network. In this network, we found that NDUFAF1 was used to affect E2F1 in the case of hsa-mir-499a-3p. These findings reveal the complex interactions between TFs and miRNA in regulating the network, providing important clues to the pathogenesis of CAS. These TFs and miRNA may have an impact on the development of CAS by regulating the hub genes and its relevant signaling pathways, affecting the process of inflammatory regulation, airway reconstruction and immune response. In addition, we also found that the hsa-mir-1-3p played a major role in the joint control hub gene. It is reported that the hsa-mir-1-3p has been found to be involved in the control of multiple diseases, such as hepatocellular carcinoma, alzheimer’s disease and type 2 diabetes, etc [50,51,52]. However, it is not clear how the specific mechanism in CAS is unclear, and further research will help clarify the exact role of these regulatory factors in the mechanism of asthma pathogenesis and provide theoretical basis for future development of more effective treatment strategies. At the same time, the TF-miRNA-mRNA network we established provides potential biomarkers and therapeutic targets for molecular diagnosis and targeted therapy for related diseases. In general, our study provides a new perspective and thought for the pathogenesis of CAS, and lays the foundation for future clinical transformation and personalized treatment.

This study is similar to previous research protocols, but is different in that it is a biomarker related to mitochondrial function in CAS, simple and straightforward. The results of this study were obtained from a dataset of blood samples. The chronic inflammation, vasoconstriction, and hyperresponsiveness of CAS often lead to systemic inflammatory changes. Therefore, it is possible to collect blood samples of CAS. Screening out dysregulated mitochondria could be a promising way to prevent or stop the development of these chronic lung diseases. The clinical application of the results of bioinformatics analysis requires more sample data support and further clinical experiments. It also shows that we will continue to pay attention to the role of these genes. However, this study has some limitations, especially the small sample size, which may impact the reliability and statistical significance of the results. Furthermore, due to the limitations of the sample type in the dataset, this study was unable to accurately determine the specific cell type associated with the observed differential expression. Nevertheless, this study offers a novel reference for CAS treatment.

Conclusion

In this work, the study identified four biomarkers (NDUFAF7, MTIF3, MRPS26, and NDUFAF1) related to mitochondrial function in CAS using bioinformatics analysis and conducted correlation analysis to explore the mechanism of action, which provides some reference for further research on CAS.

Data availability

These databases, namely the GEO database (https://www.ncbi.nlm.nih.gov/gds) [GSE40888 and GSE40732], MiRNet database (https://www.mirnet.ca/miRNet/home.xhtml), and NetworkAnalyst database (https://www.networkanalyst.ca), are utilized in current research for data analysis.

Abbreviations

MRGs:

Mitochondria-Related Genes

CAS:

Childhood Allergic Asthma

DEGs:

Differentially Expressed Genes

DE-MRGs:

Differentially Expressed Mrgs

PPI:

Protein-Protein Interactions

MCC:

Maximal Clique Centrality

LASSO:

Least Absolute Shrinkage And Selection Operator

XGBoost:

EXtreme Gradient Boosting

TFs:

Transcription Factors

GSEA:

Gene Set Enrichment Analysis

AHR:

Airway Hyperresponsiveness

IgE:

Immunoglobulin E

MT:

Mitochondrion

OXPHOS:

Oxidative Phosphorylation

ROS:

Reactive Oxygen Species

GEO:

Gene Expression Omnibus

GO:

Gene Ontology

KEGG:

Kyoto Encyclopedia of Genes And Genomes

AUC:

Area Under Curve

ROC:

Receiver Operating Characteristic

MSigDB:

Molecular Signatures Database

IRGs:

Inflammation-Related Genes

ssGSEA:

Single Sample Gene Set Enrichment Analysis

APP:

Amyloid Precursor Protein

NSCLC:

Non-Small Cell Lung Cancer

EGF:

Epidermal Growth Factor

OLFM4:

Olfactomedin 4

PM2.5:

Particulate Matter 2.5

TCA cycle:

Tricarboxylic Acid Cycle

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Acknowledgements

The authors state that the study was conducted in the absence of any business or financial relationships with potential conflicts of interest. All statements expressed in this article are those of the authors only and do not necessarily represent the statements of their affiliated organizations or publishers, editors and reviewers. The experimental data in this paper may have errors due to the small sample size, and the later research can obtain more informative data under the research of multi-center big data.

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This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

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Authors and Affiliations

  1. Department of Pediatrics, The Second People’s Hospital of Hefei, Hefei, Anhui, China

    Wei Zhao, Hongjuan Fang, Tao Wang & Chao Yao

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  1. Wei Zhao
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  2. Hongjuan Fang
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  3. Tao Wang
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Contributions

ZW is mainly responsible for specimen collection and article writing, CY is responsible for case screening, FHJ is responsible for data analysis, and WT is responsible for determining specific gene expression levels. All authors read and approved the final manuscript.

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Correspondence to Wei Zhao.

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This study was approved by the Medical Ethics Committee of the Second People’s Hospital of Hefei (Ethics approval number: 2023-Research-126). All experiments were conducted in accordance with the relevant guidelines and regulations of the Ethics Committee of the Second People’s Hospital of Hefei, and informed consent of the subject’s guardian was obtained.(Figures S1, S2, S3, S4 and S5).

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Zhao, W., Fang, H., Wang, T. et al. Identification of mitochondria-related biomarkers in childhood allergic asthma. BMC Med Genomics 17, 141 (2024). https://doi.org/10.1186/s12920-024-01901-y

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Keywords

BMC Medical Genomics

ISSN: 1755-8794

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