Abstract Recent empirical investigations reinforce the understanding of a profound interconnection between metabolic functions and Obstructive Sleep Apnea-hypopnea Syndrome (OSAHS). This study identifies distinctive miRNA signatures in OSAHS with Metabolic Syndrome (Mets) patients from healthy subjects, that could serve as diagnostic biomarkers or describe differential molecular mechanisms with potential therapeutic implications. In this study, OSAHS with MetS patients showed significantly higher Apnea Hyponea Index(AHI), but lower oxygen desaturation index(ODI 4/h) and minimum pulse oxygen saturation(SpO[2]). A total of 33 differentially expressed miRNAs by Limma method, and 31 differentially expressed miRNAs by DEseq2 method were screened. In addition, GO enrichment analysis of target genes associated with differentially expressed miRNAs revealed significant enrichment in metabolic processes, suggesting that the differential expression of OSAHS-induced miRNAs may contribute to the progression of metabolic disorders through the regulation of metabolic pathways. Furthermore, KEGG pathway enrichment analysis revealed significant enrichment in the p53 signaling pathway and several other pathways. Notably, the Wnt signaling pathway, PI3K-Akt signaling pathway, cAMP signaling pathway, and AMPK signaling pathway are implicated in the metabolic processes of glucose dysregulation and lipid homeostasis, as well as the pathogenesis of hypertension associated with OSAHS. We identified IKBKB, PIK3R1, and MAP2K1 as the target genes most associated with Mets pathogenesis in OSAHS, regulated by miR-503-5p, miR-497-5p, and miR-497-5p, respectively. Additionally, the target genes of differentially expressed miRNAs between Tibetan OSAHS patients with MetS and healthy individuals are regulated by transcription factors such as NR2C1, STAT3, STAT5a, HIF1a, ETV4, NANOG, RELA, SP1, E2F1, NFKB1, AR, and MYC. In conlusion, we found differentially expressed miRNAs in Tibetan OSAHS patients with Metabolic Syndrome for the first time. Enrichment analysis results suggest that differentially expressed miRNAs may involved in the development of OSAHS-related metabolic disorders by regulating metabolic pathways. We also revealed that IKBKB, PIK3R1, and MAP2K1 are mostly associated with metabolic disorder in OSAHS, and miR-503-5p and miR-497-5p may regulate the development of MetS associated with OSAHS by modulating IKBKB, PIK3R1, and MAP2K1. Keywords: OSAHS, Metabolic syndrome (MetS), miRNAs profiling Subject terms: Metabolic disorders, Genetics Introduction Sleep apnea, particularly Obstructive Sleep Apnea-hypopnea Syndrome (OSAHS), is a multifaceted and prevalent disease, characterized by a myriad of symptoms and comorbidities, including Type 2 Diabetes Mellitus (T2DM), Metabolic Syndrome (MetS), cardiovascular disorders, respiratory issues like asthma, idiopathic pulmonary fibrosis^[32]1–[33]4. Over the last decade, substantial advancements have been made in our understanding of OSAHS’s prevalence, diagnostic techniques, and therapeutic interventions^[34]5. OSAHS is now widely acknowledged as a major public health concern, imposing substantial financial burdens on global healthcare systems.microRNAs (miRNAs), post-transcriptional regulators, play a crucial role in lung development, maturation, and the maintenance of lung function^[35]6,[36]7, Their dysregulation may stem from either direct or indirect effects of airway disease progression. In recent years, a growing body of research has highlighted the involvement of miRNAs in the etiology of numerous human diseases, particularly in the context of respiratory disorders^[37]8,[38]9. These findings suggest the pivotal role of miRNAs in the pathogenesis of conditions like OSAHS, given their participation in the underlying physiopathological mechanisms. It has already been reported that differentially expressed miRNAs between healthy, OSAHS, and OSAHS with arterial hypertension patients^[39]10–[40]12. However, there are no relevant reports on miRNA profile between healthy individuals and OSAHS with Mets patients. So we determined to study the differential expressed miRNAs in the process of OSAHS with Mets in middle altitude of China. Sujects and methods Study patients characteristics The present study was approved by the Ethics Committee of Qinghai Provincial People’s Hospital (Approval NO. 2018-54), and performed in accordance with relevant guidelines/regulations and the Declaration of Helsinki^[41]8. The patients of this study and/or their guardians were informed and signed an informed consent form. 5 OSAHS with metabolic syndrome (MetS) patients and 4 Tibetan healthy subjects were selected as the control group from January 2018 to January 2021, who signed an informed, written consent form. All OSAHS Patients diagnosed by Polysomnography (PSG). Patients suffering from other respiratory diseases, or combined with allergic and autoimmune diseases, tumors and other serious systemic serious primary diseases were excluded from this study. Plasma was isolated and frozen at − 80 ◦C. miRNA sequencing Four Tibetan healthy subjects and five Tibetan OSAHS patients with MetS were selected for high-throughput sequencing of miRNAs. Total plasma RNA was extracted with Trizol (Tiangen, Beijing) and assessed with Agilent 2100 BioAnalyzer (Agilent Technologies, Santa Clara, CA, USA) and Qubit Fluorometer (Invitrogen). Total RNA per sample was used as input material for the small RNA library. Illumina Hiseq 2500 platform was used to sequence the library preparations and 50 bp single-end reads were generated. Bioinformatic analysis of differentially expressed miRNAs To identify the biological processes of differentially expressed miRNAs, genomic enrichment analysis was employed to investigate their regulated target genes. Target genes of differentially expressed miRNAs were predicted based on Targetscan and miRDB database (Score value > 0.95) by miRWalk2.0, and the target genes predicted by both databases were used for subsequent analysis. Subsequently, we employed GO analysis (Gene Ontology) to analyze the functional characteristics of these genes, while Kyoto Encyclopedia of Genes and Genomes(KEGG) pathways analysis was employed to identify potential signaling pathways associated with the target genes^[42]13. GO enrichment analysis was performed using KOBAS^[43]14, with a significance threshold of p-value < 0.01, and the top 20 enriched GO terms were visualized. KEGG analysis was conducted using DAVID^[44]15, and the classification stringency was set to high, resulting in the identification of annotation clusters. miRNAs-mRNA interaction network was visualized by Cytoscape. Summary of enrichment analysis in TRRUST was performd by Metascape platform([45]https://metascape.org/gp/index.html) to analysis the Transcription factor-target interaction^[46]16. Statistical analysis All values are presented as the mean ± SD. SPSS 15.0 software was used for statistical analysis. After quantile normalization and quality control, statistical significance of the differentially expressed miRNAs was assessed by unpaired t-test using a p-value cut-off of 0.05 and a fold-change 2.0. P < 0.05 was considered differences significant. Results Patient characteristics Five Tibetan OSAHS patients with MetS and four Tibetan healthy people were included in this study. Characterization of the demographic, clinical and functional features of the entire population are shown in Tables [47]1 and [48]2. Briefly, there was no statistical significance in age and gender. Compared to healthy individuals, patients with OSAHS and MetS exhibited a higher Body Mass Index (BMI), although the difference was not statistically significant. Table 1. Characteristics of discovery cohort subjects. Control group OSAHS with MetS group P-value Age(years) 47.75 ± 2.87 49.20 ± 14.20 0.848 Gender (ratio/%) 0.343 Male 2(63.3%) 4(66.7%) Female 2(36.7%) 1(33.3%) BMI 27.44 ± 1.98 35.92 ± 11.84 0.205 [49]Open in a new tab Table 2. Distribution of sleep quality related indicators of tibetan OSAHS with MetS patients. Control group OSAHS with MetS group P-value AHI 3.53 ± 0.75 55.48 ± 13.27 0.001 Epworth score 11 (7, 16) 12 (8, 16) 0.863 Monitoring duration (min) 515.8 ± 26.6 517.0 ± 61.1 0.969 Total sleep time (min) 384.9 ± 73.3 343.1 ± 94.5 0.493 Sleep efficiency (%) 74.6 ± 13.5 66.6 ± 17.0 0.470 Sleep latency (min) 25.9 ± 2.0 28.6 ± 18.3 0.778 Proportion of NREM (%) 92.9 ± 6.4 93.6 ± 5.9 0.859 Proportion of REM (%) 7.2 ± 6.4 6.4 ± 5.9 0.859 Proportion of N3 (%) 45.2 ± 40.9 40.4 ± 47.6 0.879 ODI 4/h 10.3 ± 2.2 56.1 ± 27.2 0.019 Average SpO[2] 91.0 ± 1.4 78.0 ± 11.1 0.056 Minimum SpO[2] 80.0 ± 2.4 57.6 ± 13.5 0.019 Proportion of time when SpO[2] ≤ 90% during night sleep 80.0 ± 2.4 80.0 ± 2.4 0.095 Proportion of time when SpO[2] ≤ 85% during night sleep 80.0 ± 2.4 80.0 ± 2.4 0.051 Proportion of time when SpO[2] ≤ 80% during night sleep 80.0 ± 2.4 80.0 ± 2.4 0.080 [50]Open in a new tab Significant values are in [bold]. There were no significant differences in Epworth score, monitoring duration, tlatal sleep time, sleep efficiency, sleep latency, proportion of NREM/REM/N3 between Control group and OSAHS with MetS patients. Moreover, OSAHS patients with MetS showed significantly higher AHI, but lower oxygen desaturation index(ODI 4/h) and minimum pulse oxygen saturation(SpO[2]). Although the average SpO[2], the Proportion of time when SpO[2] ≤ 90%, SpO[2] ≤ 85%, SpO[2] ≤ 80% during night sleep in OSAHS with MetS patients were lower than control healthy individuals, that is not significant. Difference of circulating miRNA expression between tibetan healthy individuals and tibetan OASHS with MetS patients The difference of circulating miRNA expression between the Tibetan healthy individuals and OSAHS with MetS patients were measured in the study. Differentially expressed miRNAs were analyzed by Limma and DEseq2 method. As showed in Fig. [51]1A, there were 33 differentially expressed miRNAs by Limma method, and 31 differentially expressed miRNAs by DEseq2 method. A total of 46 differentially expressed miRNAs were screened by FC ≥ 2, and P value < 0.05. Results showed that 21 miRNAs were downregulated, and 25 miRNAs were upregulated (Fig. [52]1B and C; Table [53]3). Fig. 1. [54]Fig. 1 [55]Open in a new tab miRNA profiling of Tibetan-con vs. Tibetan-OSAHS with MetS groups. (A) Venn digram of differentially expressed miRNAs between tibetan OSAHS with MetS patients and healthy subjects by 2 method. (B) Comparison of cluster data between ibetan OSAHS with MetS patients and healthy subjects. (C) Volcano plot of differential miRNAs of Tibetan OSAHS with MetS patients and healthy subjects. The green dots on the left of the graph show downregulated miRNAs with log2 (Fold change, FC) ≤ 1, and the red dots on the right of graph show upregulated miRNAs with log2FC ≥ 1. Table 4. KEGG pathway of differentially expressed miRNAs’ target gene. Annotation cluster 1 Enrichment score: 2.86 Count P Value KEGG pathway p53 signaling pathway 7 2.70E−05 KEGG pathway Cell cycle 8 1.10E−04 KEGG pathway Measles 7 1.20E−03 KEGG pathway Hippo signaling pathway 6 1.10E−02 KEGG pathway Wnt signaling pathway 4 1.30E−01 Annotation cluster 2 Enrichment score: 1.65 Count P Value KEGG pathway Pancreatic cancer 6 2.80E−04 KEGG pathway Prolactin signaling pathway 6 4.20E−04 KEGG pathway Focal adhesion 9 4.80E−04 KEGG pathway Ras signaling pathway 9 8.80E−04 KEGG pathway PI3K-Akt signaling pathway 11 9.20E−04 KEGG pathway MicroRNAs in cancer 10 9.50E−04 KEGG pathway Hepatitis B 7 1.80E−03 KEGG pathway HTLV-I infection 9 1.90E−03 KEGG pathway Melanoma 5 3.70E−03 KEGG pathway Chronic myeloid leukemia 5 3.90E−03 KEGG pathway Osteoclast differentiation 6 6.30E−03 KEGG pathway FoxO signaling pathway 6 6.90E−03 KEGG pathway Small cell lung cancer 5 7.00E−03 KEGG pathway Prostate cancer 5 7.90E−03 KEGG pathway Rap1 signaling pathway 7 1.10E−02 KEGG pathway Regulation of actin cytoskeleton 7 1.10E−02 KEGG pathway Acute myeloid leukemia 4 1.40E−02 KEGG pathway Non-small cell lung cancer 4 1.40E−02 KEGG pathway Glioma 4 2.00E−02 KEGG pathway Renal cell carcinoma 4 2.10E−02 KEGG pathway Pathways in cancer 9 2.40E−02 KEGG pathway Proteoglycans in cancer 6 3.40E−02 KEGG pathway Type II diabetes mellitus 3 6.90E−02 KEGG pathway Influenza A 5 7.10E−02 KEGG pathway TNF signaling pathway 4 7.20E−02 KEGG pathway Endometrial cancer 3 8.00E−02 KEGG pathway Thyroid hormone signaling pathway 4 8.50E−02 KEGG pathway Chemokine signaling pathway 5 8.60E−02 KEGG pathway Neurotrophin signaling pathway 4 9.30E−02 KEGG pathway cAMP signaling pathway 5 1.00E−01 KEGG pathway Colorectal cancer 3 1.10E−01 KEGG pathway Insulin signaling pathway 4 1.30E−01 KEGG pathway B cell receptor signaling pathway 3 1.30E−01 KEGG pathway Signaling pathways regulating pluripotency of stem cells 4 1.30E−01 KEGG pathway Estrogen signaling pathway 3 2.20E−01 KEGG pathway T cell receptor signaling pathway 3 2.30E−01 KEGG pathway Toll-like receptor signaling pathway 3 2.50E−01 KEGG pathway Insulin resistance 3 2.50E−01 KEGG pathway Cholinergic synapse 3 2.60E−01 KEGG pathway AMPK signaling pathway 3 3.00E−01 KEGG pathway Oxytocin signaling pathway 3 3.90E−01 KEGG pathway Non-alcoholic fatty liver disease (NAFLD) 3 4.00E−01 [56]Open in a new tab Table 3. Differentially expressed circulating miRNAs between tibetan OSHAS with Mets patients and healthy individuals. miRNA ID log2(FC) P Value hsa-miR-3127-5p 13.79 0.037 hsa-miR-337-5p 13.68 0.048 hsa-miR-181d-5p 13.49 0.026 hsa-let-7a-3p 13.48 0.017 hsa-miR-10527-5p 13.36 0.038 hsa-miR-4433b-3p 7.96 0.009 hsa-miR-1469 7.53 0.019 hsa-miR-331-3p 7.33 0.020 hsa-miR-4467 7.27 0.022 hsa-miR-1303 7.13 0.025 hsa-miR-3940-3p 6.85 0.016 hsa-miR-378a-5p 6.83 0.039 hsa-miR-6786-3p 6.77 0.049 hsa-miR-184 6.09 0.001 hsa-miR-1247-5p 5.95 0.016 hsa-miR-502-3p 5.73 0.020 hsa-miR-1294 4.39 0.003 hsa-miR-503-5p 3.96 0.002 hsa-miR-320e 3.39 0.040 hsa-miR-4732-3p 2.84 0.001 hsa-miR-133a-3p 2.75 0.046 hsa-miR-505-5p 2.04 0.036 hsa-miR-140-5p 1.94 0.038 hsa-miR-106b-3p 1.71 0.026 hsa-miR-10a-5p 1.14 0.030 hsa-miR-122-5p − 1.16 0.020 hsa-miR-320b − 1.58 0.024 hsa-miR-193a-5p − 1.60 0.025 hsa-miR-320c − 1.75 0.020 hsa-miR-193b-5p − 1.75 0.023 hsa-miR-574-3p − 1.87 0.038 hsa-miR-185-3p − 1.94 0.012 hsa-miR-483-5p − 2.02 0.014 hsa-miR-1246 − 2.38 0.005 hsa-miR-100-5p − 3.05 0.035 hsa-miR-671-5p − 3.06 0.010 hsa-miR-3195 − 3.77 0.036 hsa-miR-378c − 5.20 0.007 hsa-miR-296-3p − 6.11 0.019 hsa-miR-6766-3p − 6.96 0.037 hsa-miR-3691-5p − 7.62 0.030 hsa-miR-6754-5p − 8.95 0.012 hsa-miR-376b-3p − 9.08 0.011 hsa-miR-6877-5p − 9.17 0.001 hsa-miR-497-5p − 13.46 0.033 hsa-miR-6772-3p − 14.61 0.011 [57]Open in a new tab Enrichment analysis of predicted target genes of differentially expressed miRNAs Gene Ontology (GO) enrichment analysis of target genes revealed an impact on several biological processes, including the regulation of primary metabolic processes, macromolecular metabolic processes, nitrogen compound metabolic processes, and cellular protein modification processes (Fig. [58]2A). KEGG pathway enrichment analysis (Table [59]4; Fig. [60]2C) highlighted the p53 signaling pathway, Cell cycle, Hippo signaling pathway, and several other pathways (Fig. [61]2C). Notably, of the KEGG pathways, 14 were associated with Diabetes, including the p53 signaling pathway, Hippo signaling pathway, Wnt signaling pathway, and Prolactin signaling pathway. Additionally, 10 pathways were related to Hypertension, such as the Wnt signaling pathway, Prolactin signaling pathway, Ras signaling pathway, and PI3K-Akt signaling pathway. Importantly, the Wnt signaling pathway, PI3K-Akt signaling pathway, cAMP signaling pathway, and AMPK signaling pathway are implicated in both glucose dysregulation and lipid homeostasis metabolic processes, as well as in the pathogenesis of hypertension associated with OSAHS. IKBKB, PIK3R1, and MAP2K1 were identified as key target genes in the pathogenesis of Mets in OSAHS, regulated by miR-503-5p, miR-497-5p, and miR-497-5p, respectively. Furthermore, transcription factors such as NR2C1, STAT3, STAT5a, and HIF1a were found to regulate the target genes of differentially expressed miRNAs between Tibetan OSAHS patients with MetS and healthy individuals (Fig. [62]2B) . The mRNA-mRNA interaction network visualization was visualized in Fig. [63]3. Fig. 2. [64]Fig. 2 [65]Open in a new tab Enrichment analysis of predicted target genes of differentially expressed miRNAs. (A)The GO enrichment analysis. (B) Transcription Factor, regulated miRNAs. (C) KEGG pathway enrichment analysis. Fig. 3. [66]Fig. 3 [67]Open in a new tab mRNAs-mRNA interaction network was visualized by Cytoscape. Discussion This is the first study to investigate a specific differentially expressed miRNA profile between Tibetan healthy people and Tibetan OSAHS with MetS patients. The present study aimed to identify the involvement of miRNAs in the pathophysiology of OSAHS, and to explore their possible effects on Tibetan OSAHS who developed to MetS. MetS is a condition characterized by a combination of risk factors, including abdominal obesity, elevated blood pressure (BP), increased fasting blood glucose (FBG), elevated triglycerides (TGs), and decreased levels of high-density lipoprotein (HDL). The relationship between OSAHS and MetS is unequivocal. Sleep apnea results in intermittent hypoxia (IH) and sleep fragmentation leading to and exacerbating obesity, MetS, type 2 Diabetes and non-alcoholic fatty liver disease^[68]17,[69]18. Therefore, the fragmentation of sleep and intermittent hypoxia may contribute to the development of metabolic syndrome. It’s reported that Tibetan OSAHS patients showed lower MSaO[2] and LSaO[2] than Han group in the circumstances of similar AHI, and the differences partially remained after adjusting for BMI. Our results showed that patients with OSAHS and MetS exhibited a significantly higher AHI, but lower oxygen desaturation index (ODI 4/h) and minimum pulse oxygen saturation (SpO[2]) than healthy individuals. Moreover, the average SpO[2], the Proportion of time when SpO[2] ≤ 90%, SpO[2] ≤ 85%, SpO[2] ≤ 80% during night sleep in OSAHS with MetS patients were lower than control healthy individuals, but that is not significant. In our study, there were 46 differentially expressed miRNAs between Tibetan healthy individuals and Tibetan OSAHS with MetS patients. Of them, 21 miRNAs were downregulated, and 25 miRNAs were upregulated. Among the differentially expressed miRNAs, miR-3127-5p, miR-337-5p, miR-181d-5p, let-7a-3p, and miR-105 were the top five upregulated miRNAs, while miR-6772-3p, miR-497-5p, miR-6877-5p, miR-376b-3p, and miR-6754-5p were the top five downregulated miRNAs in patients with OSAHS and MetS. It has been reported that metabolic diseases are prevalent in patients with OSAHS, including those who are lean. Additionally, OSAHS-induced intermittent hypoxia (IH) and fragmented sleep (SF) can result in decreased insulin sensitivity, sympathetic excitation, and systemic inflammation, which ultimately contribute to metabolic consequences^[70]19. In the present study, Gene Ontology (GO) enrichment analysis of target genes associated with differentially expressed miRNAs revealed significant enrichment in metabolic processes. These processes included primary metabolic processes, regulation of macromolecule metabolic processes, regulation of metabolic processes, regulation of nitrogen compound metabolic processes, macromolecule metabolic processes, and cellular protein modification processes. Therefore, OSAHS-induced IH and SF may play a role in the progression of metabolic disorders by regulating metabolic pathways. In addition, KEGG pathway enrichment analysis showed that the target genes are enriched in several KEGG pathways, including the p53 signaling pathway, Hippo signaling pathway, and other several pathway. Of these pathway, Type II diabetes mellitus, Insulin signaling pathway, Hippo signaling pathway^[71]20, Wnt signaling pathway^[72]21, Prolactin signaling pathway^[73]22, PI3K-Akt signaling pathway^[74]23, cAMP signaling pathway^[75]24, Estrogen signaling pathway^[76]25, and AMPK signaling pathway^[77]26 involve in the metabolic process of glucose dysregulation, which may be associated with OSAHS-induced Diabetes. Wnt signaling pathway^[78]27, Ras signaling pathway^[79]28, Prolactin signaling pathway^[80]29,] PI3K-Akt signaling pathway^[81]30, Rap1 signaling pathway^[82]31, cAMP signaling pathway^[83]32, Estrogen signaling pathway^[84]33, Toll-like receptor signaling pathway^[85]34, and AMPK signaling pathway^[86]35 play an important role in regulating lipid homeostasis. p53 signaling pathway^[87]36, Hippo signaling pathway^[88]37, Wnt signaling pathway^[89]38, Ras signaling pathway^[90]39, PI3K-Akt signaling pathway^[91]40, Rap1 signaling pathway^[92]41, cAMP signaling pathway^[93]42, Toll-like receptor signaling pathway^[94]43, AMPK signaling pathway^[95]44 may be involved in the pathogenesis of OSAHS-related hypertension. Notably, the Wnt signaling pathway, PI3K-Akt signaling pathway, cAMP signaling pathway, and AMPK signaling pathway are implicated in the metabolic processes of glucose dysregulation and lipid homeostasis, as well as the pathogenesis of hypertension associated with OSAHS. In this study, we identified IKBKB, PIK3R1, and MAP2K1 as the target genes most closely associated with the pathogenesis of metabolic syndrome in patients with OSAHS, regulated by miR-503-5p, miR-497-5p, and miR-497-5p, respectively. Through a literature review, we found that miR-503-5p exhibits regulatory functions in diabetes and its associated complications^[96]45,[97]46. Additionally, previous studies have established a strong link between miR-497-5p, the second most downregulated miRNA in our analysis, and lipid metabolism^[98]47, as well as various diabetes-related conditions, including gestational diabetes mellitus, diabetic cardiomyopathy, and diabetic nephropathy^[99]48,[100]49. This will allow us to more definitively elucidate the regulatory roles of miR-503-5p and miR-497-5p in the pathogenesis of OSAHS-related metabolic syndrome via their influence on IKBKB, PIK3R1, and MAP2K1.Therefore, this study speculates that miR-503-5p and miR-497-5p may primarily regulate the development of MetS associated with OSAHS by modulating IKBKB, PIK3R1, and MAP2K1. Conclusion The present study is the first to show significant differential expressed miRNAs between Tibetan healthy subjects and Tibetan OSAHS with MetS patients. In addition, these differential expressed miRNAs between healthy individuals and OSAHS with Mets pateints mainly enriched in metabolic process. We also revealed that IKBKB, PIK3R1, and MAP2K1 are mostly associated with metabolic pathogenesis in OSAHS, and miR-503-5p and miR-497-5p may regulate the development of MetS associated with OSAHS by modulating IKBKB, PIK3R1, and MAP2K1. Author contributions Xue-feng Shi designed the study and wrote the manuscript. Xiang He, Ze-rui Sun collected cases and analyzed the data. Jie Duo and Hao Yang revised the manuscript. All authors read and approved the final manuscrip. Funding The study was funded by Qinghai Science and Technology Department (No.2024-ZJ-934), Health commission of Qinghai Province(No. 2018-wjzdx-09), Kunlun Elite of Qinghai Province High-End Innovation and Entrepreneurship leading Talents (NO.2022 and 2023), and Qinghai Clinical Research Center for Respiratory Diseases (NO. 2019-SF-L4). Data availability The data that support the findings of this study are openly available in Gene Expression Omnibus ([101]GSE274563). Declarations Competing interests The authors declare no competing interests. Ethics approval and consent to participate The study was approved by the Ethics Committee of Qinghai Provincial People’s Hospital (Approval NO. 2018-54), and performed in accordance with relevant guidelines/regulations and the Declaration of Helsinki. The patients of this study and/or their guardians were informed and signed an informed consent form. Footnotes Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Contributor Information Jie Duo, Email: qhjieduo@163.com. Hao Yang, Email: 1029558414@qq.com. References