Comprehensive analysis of diabetic nephropathy expression profile based on weighted gene co-expression network analysis algorithm

Identification and Validation of Novel Metastasis-Related Immune Gene Signature in Breast Cancer

Background

Distant metastasis remains the leading cause of death among patients with breast cancer (BRCA). The process of cancer metastasis involves multiple mechanisms, including compromised immune system. However, not all genes involved in immune function have been comprehensively identified.

Methods

Firstly 1623 BRCA samples, including transcriptome sequencing and clinical information, were acquired from Gene Expression Omnibus (GSE102818, GSE45255, GSE86166) and The Cancer Genome Atlas-BRCA (TCGA-BRCA) dataset. Subsequently, weighted gene co-expression network analysis (WGCNA) was performed using the GSE102818 dataset to identify the most relevant module to the metastasis of BRCA. Besides, ConsensusClusterPlus was applied to divide TCGA-BRCA patients into two subgroups (G1 and G2). In the meantime, the least absolute shrinkage and selection operator (LASSO) regression analysis was used to construct a metastasis-related immune genes (MRIGs)_score to predict the metastasis and progression of cancer. Importantly, the expression of vital genes was validated through reverse transcription quantitative polymerase chain reaction (RT-qPCR) and immunohistochemistry (IHC).

Results

The expression pattern of 76 MRIGs screened by WGCNA divided TCGA-BRCA patients into two subgroups (G1 and G2), and the prognosis of G1 group was worse. Also, G1 exhibited a higher mRNA expression level based on stemness index score and Tumor Immune Dysfunction and Exclusion score. In addition, higher MRIGs_score represented the higher probability of progression in BRCA patients. It was worth mentioning that the patients in the G1 group had a high MRIGs_score than those in the G2 group. Importantly, the results of RT-qPCR and IHC demonstrated that fasciculation and elongation protein zeta 1 (FEZ1) and insulin-like growth factor 2 receptor (IGF2R) were risk factors, while interleukin (IL)-1 receptor antagonist (IL1RN) was a protective factor.

Conclusion

Our study revealed a prognostic model composed of eight immune related genes that could predict the metastasis and progression of BRCA. Higher score represented higher metastasis probability. Besides, the consistency of key genes in BRCA tissue and bioinformatics analysis results from mRNA and protein levels was verified.

Long noncoding RNA: An emerging diagnostic and therapeutic target in kidney diseases

Long noncoding RNAs (lncRNAs) have critical roles in the development of many diseases including kidney disease. An increasing number of studies have shown that lncRNAs are involved in kidney development and that their dysregulation can result in distinct disease processes, including acute kidney injury, chronic kidney disease, and renal cell carcinoma. Understanding the roles of lncRNAs in kidney disease may provide new diagnostic and therapeutic opportunities in the clinic. This review provides an overview of lncRNA characteristics, and biological function and discusses specific studies that provide insight into the function and potential application of lncRNAs in kidney disease treatment.

Metabolome panels as potential noninvasive biomarkers for primary glomerulonephritis sub-types: meta-analysis of profiling metabolomics studies

Primary glomerulonephritis diseases (PGDs) are known as the top causes of chronic kidney disease worldwide. Renal biopsy, an invasive method, is the main approach to diagnose PGDs. Studying the metabolome profiles of kidney diseases is an inclusive approach to identify the disease's underlying pathways and discover novel non-invasive biomarkers. So far, different experiments have explored the metabolome profiles in different PGDs, but the inconsistencies might hinder their clinical translations. The main goal of this meta-analysis study was to achieve consensus panels of dysregulated metabolites in PGD sub-types. The PGDs-related metabolome profiles from urine samples in humans were selected in a comprehensive search. Amanida package in R software was utilized for performing the meta-analysis. Through sub-type analyses, the consensus list of metabolites in each category was obtained. To identify the most affected pathways, functional enrichment analysis was performed. Also, a gene-metabolite network was constructed to identify the key metabolites and their connected proteins. After a vigorous search, among the 11 selected studies (15 metabolite profiles), 270 dysregulated metabolites were recognized in urine of 1154 PGDs and control samples. Through sub-type analyses by Amanida package, the consensus list of metabolites in each category was obtained. Top dysregulated metabolites (vote score of ≥ 4 or ≤ - 4) in PGDs urines were selected as main panel of meta-metabolites including glucose, leucine, choline, betaine, dimethylamine, fumaric acid, citric acid, 3-hydroxyisovaleric acid, pyruvic acid, isobutyric acid, and hippuric acid. The enrichment analyses results revealed the involvement of different biological pathways such as the TCA cycle and amino acid metabolisms in the pathogenesis of PGDs. The constructed metabolite-gene interaction network revealed the high centralities of several metabolites, including pyruvic acid, leucine, and choline. The identified metabolite panels could shed a light on the underlying pathological pathways and be considered as non-invasive biomarkers for the diagnosis of PGD sub-types.

The potential of cardiac biomarkers, NT-ProBNP and troponin T, in predicting the progression of nephropathy in diabetic patients: A meta-analysis of prospective cohort studies

AIMS

A meta-analysis was done to investigate the association of two cardiac biomarkers of N-terminal prohormone of B-type natriuretic peptide (NT-proBNP) and circulating troponin T (TnT) with the progression of diabetic nephropathy (DN).

METHODS

A thorough search of the PubMed, Scopus, and Web of Science databases was done until June 2022. The outcome (progression of DN) was described as either of the followings: a) eGFR decline, b) albuminuria, c) end-stage renal disease, or d) mortality. A pooled analysis of eligible studies was performed using random-effect models to compensate for the differences in measurement standards between the studies. We further carried out subgroup analyses to examine our results' robustness and find the source of heterogeneity. A sensitivity analysis was performed to assess the influence of individual studies on the pooled result and the funnel plot and Egger's test were used to assess publication bias.

RESULTS

For NT-proBNP, 8741 participants from 14 prospective cohorts, and for TnT, 7292 participants from 9 prospective cohorts were included in the meta-analysis. Higher NT-proBNP levels in diabetic patients were associated with a higher probability of DN progression (relative risk [RR]: 1.67, 95% confidence interval [CI]: 1.44 to 1.92). Likewise, elevated levels of TnT were associated with an increased likelihood of DN (RR: 1.57, 95% CI: 1.34 to 1.83). The predictive power of both biomarkers for DN remained significant when the subgroup analyses were performed. The risk estimates were sensitive to none of the studies. The funnel plot and Egger's tests indicated publication bias for both biomarkers. Hence, trim and fill analysis was performed to compensate for this putative bias and the results remained significant both for NT-proBNP (RR: 1.50, 95% CI: 1.31 to 1.79) and TnT (RR: 1.35, 95% CI 1.15 to 1.60).

CONCLUSIONS

The increased blood levels of TnT and NT-proBNP can be considered as predictors of DN progression in diabetic individuals. PROSPERO registration code: CRD42022350491.

Identification of key genes and biological regulatory mechanisms in diabetic nephropathy: Meta-analysis of gene expression datasets

BACKGROUND

Diabetic nephropathy (DN) which refers to the cases with biopsy proven kidney lesions, is one of the main complications of diabetes all around the world; however, the underlying biological changes causing DN remain to be understood. Studying the alterations in gene expression profiles could give a holistic view of the molecular pathogenicity of DN and aid to discover key molecules as potential therapeutic targets. Here, we performed a meta-analysis study that included microarray gene expression profiles coming from glomerular samples of DN patients in order to acquire a list of consensus Differentially Expressed Genes (meta-DEGs) correlated with DN.

METHODS

After quality control and normalization steps, five gene expression datasets (GES1009, GSE30528, GSE47183, GSE104948, and GSE93804) were entered into the meta-analysis. The meta-analysis was performed by random effect size method and the meta-DEGs were put through network analysis and different pathway enrichment analyses steps. MiRTarBase and TRRUST databases were utilized to predict the meta-DEGs related miRNAs and transcription factors. A co-regulatory network including DEGs, transcription factors and miRNAs was constructed by Cytoscape, and top molecules were identified based on centrality scores in the network.

RESULTS

The identified meta-DEGs were 1364 DEGs including 665 downregulated and 669 upregulated DEGs. The results of pathway enrichment analysis showed, "immune system", "extracellular matrix organization", "hemostasis", "signal transduction", and "platelet activation" to be the top enriched terms with involvement of the meta-DEGs. After construction of the multilayer regulatory network, several top DEGs (TP53, MYC, BTG2, VEGFA, PTEN, etc.), as well as top miRNAs (miR-335, miR-16, miR-17, miR-20a, and miR-93), and transcription factors (SP1, STAT3, NF-KB1, RELA, E2F1), were introduced as potential therapeutic targets in DN. Among the regulatory molecules, miR-335-5p and SP1 were the most interactive miRNA and transcription factor molecules with the highest degree scores in the constructed network.

CONCLUSION

By performing a meta-analysis of available DN-related transcriptomics datasets, we reached a consensus list of DEGs for this complicated disorder. Further enrichment and network analyses steps revealed the involved pathways in the DN pathogenesis and marked the most potential therapeutic targets in this disease.

Interaction of Biochemical Processes between Chronic Obstructive Pulmonary Disease (COPD), Pulmonary Arterial Hypertension (PAH), and Coronavirus Disease 2019 (COVID-19)

Both pulmonary arterial hypertension (PAH) and chronic obstructive pulmonary disease (COPD) are risk factors for coronavirus disease 2019 (COVID-19). Patients with lung injury and altered pulmonary vascular anatomy or function are more susceptible to infections. The purpose of the study is to ascertain whether individuals with COPD or PAH are affected synergistically by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Data sources for the construction of a protein-protein interaction (PPI) network and the identification of differentially expressed genes (DEGs) included three RNA-seq datasets from the GEO database (GSE147507, GSE106986, and GSE15197). Then, relationships between miRNAs, common DEGs, and transcription factor (TF) genes were discovered. Functional analysis using Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and other databases, as well as the forecasting of antiviral medications for COPD and PAH patients infected with SARS-CoV-2, were also performed. Eleven common DEGs were found in the three datasets, and their biological functions were primarily enriched in the control of protein modification processes, particularly phosphorylation. Growth factor receptor binding reflects molecular function. KEGG analysis indicated that co-DEGs mainly activate Ras, and PI3K-Akt signaling pathways and act on focal adhesions. NFKB1 interacted with HSA-miR-942 in the TF-miRNA-DEGs synergistic regulatory network. Acetaminophen is considered an effective drug candidate. There are some connections between COPD and PAH and the development of COVID-19. This research could aid in developing COVID-19 vaccines and medication candidates that would work well as COVID-19 therapies.

Differential Methylation of Telomere-Related Genes Is Associated with Kidney Disease in Individuals with Type 1 Diabetes

Diabetic kidney disease (DKD) represents a major global health problem. Accelerated ageing is a key feature of DKD and, therefore, characteristics of accelerated ageing may provide useful biomarkers or therapeutic targets. Harnessing multi-omics, features affecting telomere biology and any associated methylome dysregulation in DKD were explored. Genotype data for nuclear genome polymorphisms in telomere-related genes were extracted from genome-wide case-control association data (n = 823 DKD/903 controls; n = 247 end-stage kidney disease (ESKD)/1479 controls). Telomere length was established using quantitative polymerase chain reaction. Quantitative methylation values for 1091 CpG sites in telomere-related genes were extracted from epigenome-wide case-control association data (n = 150 DKD/100 controls). Telomere length was significantly shorter in older age groups (p = 7.6 × 10^-6). Telomere length was also significantly reduced (p = 6.6 × 10^-5) in DKD versus control individuals, with significance remaining after covariate adjustment (p = 0.028). DKD and ESKD were nominally associated with telomere-related genetic variation, with Mendelian randomisation highlighting no significant association between genetically predicted telomere length and kidney disease. A total of 496 CpG sites in 212 genes reached epigenome-wide significance (p ≤ 10^-8) for DKD association, and 412 CpG sites in 193 genes for ESKD. Functional prediction revealed differentially methylated genes were enriched for Wnt signalling involvement. Harnessing previously published RNA-sequencing datasets, potential targets where epigenetic dysregulation may result in altered gene expression were revealed, useful as potential diagnostic and therapeutic targets for intervention.

Identification of a novel immune landscape signature as effective diagnostic markers related to immune cell infiltration in diabetic nephropathy

Background

The study aimed to identify core biomarkers related to diagnosis and immune microenvironment regulation and explore the immune molecular mechanism of diabetic nephropathy (DN) through bioinformatics analysis.

Methods

GSE30529, GSE99325, and GSE104954 were merged with removing batch effects, and different expression genes (DEGs) were screened at a criterion |log2FC| >0.5 and adjusted P <0.05. KEGG, GO, and GSEA analyses were performed. Hub genes were screened by conducting PPI networks and calculating node genes using five algorithms with CytoHubba, followed by LASSO and ROC analysis to accurately identify diagnostic biomarkers. In addition, two different GEO datasets, GSE175759 and GSE47184, and an experiment cohort with 30 controls and 40 DN patients detected by IHC, were used to validate the biomarkers. Moreover, ssGSEA was performed to analyze the immune microenvironment in DN. Wilcoxon test and LASSO regression were used to determine the core immune signatures. The correlation between biomarkers and crucial immune signatures was calculated by Spearman analysis. Finally, cMap was used to explore potential drugs treating renal tubule injury in DN patients.

Results

A total of 509 DEGs, including 338 upregulated and 171 downregulated genes, were screened out. “chemokine signaling pathway” and “cell adhesion molecules” were enriched in both GSEA and KEGG analysis. CCR2, CX3CR1, and SELP, especially for the combination model of the three genes, were identified as core biomarkers with high diagnostic capabilities with striking AUC, sensitivity, and specificity in both merged and validated datasets and IHC validation. Immune infiltration analysis showed a notable infiltration advantage for APC co-stimulation, CD8+ T cells, checkpoint, cytolytic activity, macrophages, MHC class I, and parainflammation in the DN group. In addition, the correlation analysis showed that CCR2, CX3CR1, and SELP were strongly and positively correlated with checkpoint, cytolytic activity, macrophages, MHC class I, and parainflammation in the DN group. Finally, dilazep was screened out as an underlying compound for DN analyzed by CMap.

Conclusions

CCR2, CX3CR1, and SELP are underlying diagnostic biomarkers for DN, especially in their combination. APC co-stimulation, CD8+ T cells, checkpoint, cytolytic activity, macrophages, MHC class I, and parainflammation may participate in the occurrence and development of DN. At last, dilazep may be a promising drug for treating DN.

Broadening horizons in mechanisms, management, and treatment of diabetic kidney disease

Diabetic kidney disease (DKD) is the first cause of end-stage kidney disease in patients with diabetes and its prevalence is increasing worldwide. It encompasses histological alterations that mainly affect the glomerular filtration unit, which include thickening of the basement membrane, mesangial cell proliferation, endothelial alteration, and podocyte injury. These morphological abnormalities further result in a persistent increase of urinary albumin-to-creatinine ratio and in a reduction of the estimated glomerular filtration rate. Several molecular and cellular mechanisms have been recognized, up to date, as major players in mediating such clinical and histological features and many more are being under investigation. This review summarizes the most recent advances in understanding cell death mechanisms, intracellular signaling pathways and molecular effectors that play a role in the onset and progression of diabetic kidney damage. Some of those molecular and cellular mechanisms have been already successfully targeted in preclinical models of DKD and, in some cases, strategies have been tested in clinical trials. Finally, this report sheds light on the relevance of novel pathways that may become therapeutic targets for future applications in DKD.

Study on fatty acid binding protein in lipid metabolism of livestock and poultry

Fatty acid binding proteins (FABPs) are key proteins in lipid transport, and 12 family members have been documented in the literature. In recent years, new insights have been gained into the structure and function of FABPs, which are important regulators of lipid metabolic processes in the body and play a central role in coordinating lipid transport and metabolism in various tissues and organs across species. This paper provides a brief overview of the structure and biological functions of FABPs and reviews related studies on lipid metabolism in livestock and poultry to lay the foundation for research on the mechanism underlying the regulatory effect of FABPs on lipid metabolism in livestock and poultry and for the genetic improvement of livestock and poultry.

Clear Cell Renal Cell Carcinoma: A Comprehensive in silico Study in Searching for Therapeutic Targets

INTRODUCTION

Clear cell renal cell carcinoma (ccRCC) is recognized as one of the leading causes of illness and death worldwide. Understanding the molecular mechanisms in ccRCC pathogenesis is crucial for discovering novel therapeutic targets and developing efficient drugs. With the application of a comprehensive in silico analysis of the ccRCC-related array sets, the main objective of this study was to discover the top molecules and pathways in the pathogenesis of this cancer.

METHODS

ccRCC microarray datasets were downloaded from the Gene Expression Omnibus database, and after quality checking, normalization, and analysis using the Limma algorithm, differentially expressed genes (DEGs) were identified, considering the adjusted p value <0.049. The intensity values of the identified DEGs were introduced to the Weighted Gene Co-Expression Network Analysis (WGCNA) algorithm to construct co-expression modules. Functional enrichment analyses were performed using the DEGs in the disease-correlated module, and hub genes were identified among the top genes in a protein-protein interaction network and the disease most correlated module. The expression analysis of hub genes was done by utilizing GEPIA, and the GSCA server was used to compare the expression patterns of hub genes in ccRCC and other cancers. DGIdb database was utilized to identify the hub gene-related drugs.

RESULTS

Three datasets, including GSE11151, GSE12606, and GSE36897, were retrieved, merged, normalized, and analyzed. Using WGCNA, the DEGs were clustered into eight different modules. Translocation of ZAP-70 to immunological synapse, endosomal/vacuolar pathway, cell surface interactions at the vascular wall, and immune-related pathways were the topmost enriched terms for the ccRCC-correlated DEGs. Twelve genes including PTPRC, ITGAM, TLR2, CD86, PLEK, TYROBP, ITGB2, RAC2, CSF1R, CCR5, CCL5, and LCP2 were introduced as hub genes. All the 12 hub genes were upregulated in ccRCC samples and showed a positive correlation with the infiltration of different immune cells. According to the DGIdb database, 127 drugs, including tyrosine kinase inhibitors, glucocorticoids, and chemotaxis targeting molecules, were identified to interact with the hub genes.

CONCLUSION

By utilizing an integrative bioinformatics approach, this experiment shed light on the underlying pathways in the pathogenesis of ccRCC and introduced several potential therapeutic targets for repurposing or developing novel drugs for an efficient treatment of this cancer. Our next step would be to assess the gene expression profiles of the identified hubs in different cell populations in the tumor microenvironment.

An Integrative in silico Study to Discover Key Drivers in Pathogenicity of Focal and Segmental Glomerulosclerosis

BACKGROUND

Focal and segmental glomerulosclerosis (FSGS) is a clinical-pathologic condition marked by segmental and localized glomerular damages. Despite investigations, the molecular mechanisms behind FSGS development remain to be more clarified. By a comprehensive analysis of an FSGS-related array set, the aim of this study was to unravel the top pathways and molecules involved in the pathogenesis of this disorder.

METHODS

FSGS-related microarray dataset (GSE129973) from the Gene Expression Omnibus database was quality checked, analyzed, and its differentially expressed genes (DEGs) (log2 fold change > 1) were used for the construction of a protein-protein interaction (PPI) network (STRING). The degree of centrality was considered to select the hub molecules in the network. The weighted gene co-expression network analysis (WGCNA) was utilized to construct co-expression modules. Hub molecules were selected based on module membership and gene significance values in the disease's most correlated module. After spotting the key molecules considering both strategies, their expression pattern was checked in other FSGS microarray datasets. Gene ontology and Reactome pathway enrichment analyses were performed on the DEGs of the related module.

RESULTS

After quality checking, normalization, and analysis of the dataset, 5,296 significant DEGs, including 2,469 upregulated and 2,827 downregulated DEGs were identified. The WGCNA algorithm clustered the DEGs into nine independent co-expression modules. The disease most correlated module (black module) was recognized and considered for further enrichment analysis. The immune system, cell cycle, and vesicle-mediated transports were among the top enriched terms for the identified module's DEGs. The immune system, cell cycle, and vesicle-mediated transports were among the top enriched terms for the black module's DEGs. The key molecules (BMP-2 and COL4A1) were identified as common hub molecules extracted from the two methods of PPI and the co-expressed networks. The two identified key molecules were validated in other FSGS datasets, where a similar pattern of expression was observed for both the genes.

CONCLUSIONS

Two hub molecules (BMP-2 and COL4A) and some pathways (vesicle-mediated transport) were recognized as potential players in the pathogenesis of FSGS.

Proteomic Analysis of Mouse Kidney Tissue Associates Peroxisomal Dysfunction with Early Diabetic Kidney Disease

Background: The absence of efficient inhibitors for diabetic kidney disease (DKD) progression reflects the gaps in our understanding of DKD molecular pathogenesis. Methods: A comprehensive proteomic analysis was performed on the glomeruli and kidney cortex of diabetic mice with the subsequent validation of findings in human biopsies and omics datasets, aiming to better understand the underlying molecular biology of early DKD development and progression. Results: LC–MS/MS was employed to analyze the kidney proteome of 2 DKD models: Ins2Akita (early and late DKD) and db/db mice (late DKD). The abundance of detected proteins was defined. Pathway analysis of differentially expressed proteins in the early and late DKD versus the respective controls predicted dysregulation in DKD hallmarks (peroxisomal lipid metabolism and β-oxidation), supporting the functional relevance of the findings. Comparing the observed protein changes in early and late DKD, the consistent upregulation of 21 and downregulation of 18 proteins was detected. Among these were downregulated peroxisomal and upregulated mitochondrial proteins. Tissue sections from 16 DKD patients were analyzed by IHC confirming our results. Conclusion: Our study shows an extensive differential expression of peroxisomal proteins in the early stages of DKD that persists regardless of the disease severity, providing new perspectives and potential markers of diabetic kidney dysfunction.

Upregulation of T Cell Receptor Signaling Pathway Components in Gestational Diabetes Mellitus Patients: Joint Analysis of mRNA and circRNA Expression Profiles

OBJECTIVE

Gestational diabetes mellitus (GDM) is one of the most common complications of pregnancy, and its pathogenesis is still unclear. Studies have shown that circular RNAs (circRNAs) can regulate blood glucose levels by targeting mRNAs, but the role of circRNAs in GDM is still unknown. Therefore, a joint microarray analysis of circRNAs and their target mRNAs in GDM patients and healthy pregnant women was carried out.

METHODS

In this study, microarray analyses of mRNA and circRNA in 6 GDM patients and 6 healthy controls were conducted to identify the differentially expressed mRNA and circRNA in GDM patients, and some of the discovered mRNAs and circRNAs were further validated in additional 56 samples by quantitative realtime PCR (qRT-PCR) and droplet digital PCR (ddPCR).

RESULTS

Gene ontology and pathway analyses showed that the differentially expressed genes were significantly enriched in T cell immune-related pathways. Cross matching of the differentially expressed mRNAs and circRNAs in the top 10 KEGG pathways identified 4 genes (CBLB, ITPR3, NFKBIA, and ICAM1) and 4 corresponding circRNAs (circ-CBLB, circ-ITPR3, circ-NFKBIA, and circ-ICAM1), and these candidates were subsequently verified in larger samples. These differentially expressed circRNAs and their linear transcript mRNAs were all related to the T cell receptor signaling pathway, and PCR results confirmed the initial microarray results. Moreover, circRNA/miRNA/mRNA interactions and circRNA-binding proteins were predicted, and circ-CBLB, circ-ITPR3, and circ-ICAM1 may serve as GDM-related miRNA sponges and regulate the expression of CBLB, ITPR3, NFKBIA, and ICAM1 in cellular immune pathways.

CONCLUSION

Upregulation of T cell receptor signaling pathway components may represent the major pathological mechanism underlying GDM, thus providing a potential approach for the prevention and treatment of GDM.

Metabolic Variation Dictates Cardiac Pathogenesis in Patients With Tetralogy of Fallot

BACKGROUND

Herein, we aimed to analyze cardiac metabolic reprogramming in patients with tetralogy of Fallot (ToF).

METHODS

Cardiac metabolic reprogramming was analyzed through comprehensive bioinformatics analysis, which included gene set enrichment, gene set variation, and consensus clustering analyses, so as to assess changes in metabolic pathways. In addition, full-spectrum metabolomics analysis was performed using right atrial biopsy samples obtained from patients with ToF and atrial septal defect (ASD) before cardiopulmonary bypass; ultrahigh performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) was used to construct a metabolic map of cardiac metabolic reprogramming in cyanotic congenital heart disease.

RESULTS

The metabolic maps of carbohydrate metabolic process and heme metabolism were significantly activated, while bile acid metabolism, lipid droplet, and lipid binding were primarily restrained in ToF samples as compared with that in ASD samples. The reprogramming of butanoate metabolism was identified basing on the UPLC-MS/MS detection and analysis in myocardial hypoxia damage in cyanotic heart disease. Finally, the butanoate metabolism-related hub regulators ALDH5A1 and EHHADH were identified and they were significantly downregulated in ToF samples.

CONCLUSIONS

The metabolic network of butanoate metabolism involved ALDH5A1 and EHHADH, which could contribute to myocardial tissue damage in cyanotic congenital heart of ToF. Our results provide further insights into the mechanisms underlying metabolic reprogramming in cyanotic congenital heart disease and could lead to the identification of potential therapeutic targets.