Dataset Information

Identification and experimental validation of mitochondria-related genes biomarkers associated with immune infiltration for sepsis.

ABSTRACT:

Background

Sepsis remains a complex condition with incomplete understanding of its pathogenesis. Further research is needed to identify prognostic factors, risk stratification tools, and effective diagnostic and therapeutic targets.

Methods

Three GEO datasets (GSE54514, GSE65682, and GSE95233) were used to explore the potential role of mitochondria-related genes (MiRGs) in sepsis. WGCNA and two machine learning algorithms (RF and LASSO) were used to identify the feature of MiRGs. Consensus clustering was subsequently carried out to determine the molecular subtypes for sepsis. CIBERSORT algorithm was conducted to assess the immune cell infiltration of samples. A nomogram was also established to evaluate the diagnostic ability of feature biomarkers via "rms" package.

Results

Three different expressed MiRGs (DE-MiRGs) were identified as sepsis biomarkers. A significant difference in the immune microenvironment landscape was observed between healthy controls and sepsis patients. Among the DE-MiRGs, NDUFB3 was selected to be a potential therapeutic target and its significant elevated expression level was confirmed in sepsis using in vitro experiments and confocal microscopy, indicating its significant contribution to the mitochondrial quality imbalance in the LPS-simulated sepsis model.

Conclusion

By digging the role of these pivotal genes in immune cell infiltration, we gained a better understanding of the molecular immune mechanism in sepsis and identified potential intervention and treatment strategies.

SUBMITTER: Shu Q

PROVIDER: S-EPMC10203506 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature

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Publications

Identification and experimental validation of mitochondria-related genes biomarkers associated with immune infiltration for sepsis.

Shu Qi Q She Han H Chen Xi X Zhong Like L Zhu Junfeng J Fang Luo L

Frontiers in immunology 20230509

<h4>Background</h4>Sepsis remains a complex condition with incomplete understanding of its pathogenesis. Further research is needed to identify prognostic factors, risk stratification tools, and effective diagnostic and therapeutic targets.<h4>Methods</h4>Three GEO datasets (GSE54514, GSE65682, and GSE95233) were used to explore the potential role of mitochondria-related genes (MiRGs) in sepsis. WGCNA and two machine learning algorithms (RF and LASSO) were used to identify the feature of MiRGs. ...[more]

PMID: 37228596

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Project description:BackgroundCardiomyocyte death is an important pathophysiological basis for ischemic cardiomyopathy (ICM). Many studies have suggested that ferroptosis is a key link in the development of ICM. We performed bioinformatics analysis and experiment validation to explore the potential ferroptosis-related genes and immune infiltration of ICM.MethodsWe downloaded the datasets of ICM from the Gene Expression Omnibus database and analyzed the ferroptosis-related differentially expressed genes (DEGs). Gene Ontology, Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis, and protein-protein interaction network were performed to analyze ferroptosis-related DEGs. Gene Set Enrichment Analysis was used to evaluate the gene enrichment signaling pathway of ferroptosis-related genes in ICM. Then, we explored the immune landscape of patients with ICM. Finally, the RNA expression of the top five ferroptosis-related DEGs was validated in blood samples from patients with ICM and healthy controls using qRT-PCR.ResultsOverall, 42 ferroptosis-related DEGs (17 upregulated and 25 downregulated genes) were identified. Functional enrichment analysis indicated several enriched terms related to ferroptosis and the immune pathway. Immunological analysis suggested that the immune microenvironment in patients with ICM is altered. The immune checkpoint-related genes (PDCD1LG2, LAG3, and TIGIT) were overexpressed in ICM. The qRT-PCR results showed that the expression levels of IL6, JUN, STAT3, and ATM in patients with ICM and healthy controls were consistent with the bioinformatics analysis results from the mRNA microarray.ConclusionOur study showed significant differences in ferroptosis-related genes and functional pathway between ICM patients and healthy controls. We also provided insight into the landscape of immune cells and the expression of immune checkpoints in patients with ICM. This study provides a new road for future investigation of the pathogenesis and treatment of ICM.

Project description:Background: DNA methylation is an important form of epigenetic regulation and is closely related to atherosclerosis (AS). The purpose of this study was to identify DNA methylation-related biomarkers and explore the immune-infiltrate characteristics of AS based on methylation data. Methods: DNA methylation data of 15 atherosclerotic and paired healthy tissues were obtained from Gene Expression Omnibus database. Differential methylation positions (DMPs) and differential methylation regions (DMRs) were screened by the ChAMP R package. The methylation levels of DMPs located on CpG islands of gene promoter regions were averaged. The limma R package was used to screen differentially methylated genes in the CpG islands of the promoter regions. The diagnostic values of the methylation levels were evaluated using the pROC R package. The EpiDISH algorithm was applied to quantify the infiltration levels of seven types of immune cells. Subsequently, three pairs of clinical specimens of coronary atherosclerosis with Stary's pathological stage III were collected, and the methylation levels were detected by the methylation-specific PCR (MS-PCR) assay. Western blot was performed to detect the protein expression levels of monocyte markers. Results: A total of 110, 695 DMPs, and 918 DMRs were screened in the whole genome. Also, six genes with significant methylation differences in the CpG islands of the promoter regions were identified, including 49 DMPs. In total, three genes (GRIK2, HOXA2, and HOXA3) had delta beta greater than 0.2. The infiltration level of monocytes was significantly upregulated in AS tissues. MS-PCR assay confirmed the methylation status of the aforementioned three genes in AS samples. The Western blot results showed that the expression levels of the monocyte marker CD14 and M1-type macrophage marker CD86 were significantly increased in AS while M2-type macrophage marker protein CD206 was significantly decreased. Conclusion: This study identified potential DNA methylation-related biomarkers and revealed the role of monocytes in early AS.

Project description:ObjectiveSepsis is linked to high morbidity and mortality rates. Consequently, early diagnosis is crucial for proper treatment, reducing hospitalization, and mortality rates. Additionally, over one-fifth of sepsis patients still face a risk of death. Hence, early diagnosis, and effective treatment play pivotal roles in enhancing the prognosis of patients with sepsis.MethodThe study analyzed whole blood data obtained from patients with sepsis and control samples sourced from three datasets (GSE57065, GSE69528, and GSE28750). Commonly dysregulated immune-related genes (IRGs) among these three datasets were identified. The differential characteristics of these common IRGs in the sepsis and control samples were assessed using the REO-based algorithm. Based on these differential characteristics, samples from eight Gene Expression Omnibus (GEO) databases (GSE57065, GSE69528, GSE28750, GSE65682, GSE69063, GSE95233, GSE131761, and GSE154918), along with three ArrayExpress databases (E-MTAB-4421, E-MTAB-4451, and E-MTAB-7581), were categorized and scored. The effectiveness of these differential characteristics in distinguishing sepsis samples from control samples was evaluated using the AUC value derived from the receiver operating characteristic curve (ROC) curve. Furthermore, the expression of IRGs was validated in peripheral blood samples obtained from patients with sepsis through qRT-PCR.ResultsAmong the three training datasets, a total of 84 common dysregulated immune-related genes (IRGs) were identified. Utilizing a within-sample relative expression ordering (REOs)-based algorithm to analyze these common IRGs, differential characteristics were observed in three reverse stable pairs (ELANE-RORA, IL18RAP-CD247, and IL1R1-CD28). In the eight GEO datasets, the expression of ELANE, IL18RAP, and IL1R1 demonstrated significant upregulation, while RORA, CD247, and CD28 expression exhibited notable downregulation during sepsis. These three pairs of immune-related marker genes displayed accuracies of 95.89% and 97.99% in distinguishing sepsis samples among the eight GEO datasets and the three independent ArrayExpress datasets, respectively. The area under the receiver operating characteristic curve ranged from 0.81 to 1.0. Additionally, among these three immune-related marker gene pairs, mRNA expression levels of ELANE and IL1R1 were upregulated, whereas the levels of CD247 and CD28 mRNA were downregulated in blood samples from patients with sepsis compared to normal controls.ConclusionThese three immune-related marker gene pairs exhibit high predictive performance for blood samples from patients with sepsis. They hold potential as valuable auxiliary clinical blood screening tools for sepsis.

Project description:Background: Prior research has demonstrated that programmed cell death (PCD) and mitochondria assume pivotal roles in controlling cellular metabolism and maintaining bone cell equilibrium. Nonetheless, the comprehensive elucidation of their mode of operation in osteoporosis (OP) warrants further investigation. Therefore, this study aimed at analyzing the role of genes associated with PCD (PCD-RGs) and mitochondria (mortality factor-related genes; MRGs) in OP. Methods: Differentially expressed genes (DEGs) were identified by subjecting the GSE56815 dataset obtained from the Gene Expression Omnibus database to differential expression analysis and comparing OP patients with healthy individuals. The genes of interest were ascertained through the intersection of DEGs, MRGs, and PCD-RGs; these genes were filtered using machine learning methodologies to discover potential biomarkers. The prospective biomarkers displaying uniform patterns and statistically meaningful variances were identified by evaluating their levels in the GSE56815 dataset and conducting quantitative real-time polymerase chain reaction-based assessments. Moreover, the functional mechanisms of these biomarkers were further delineated by constructing a nomogram, which conducted gene set enrichment analysis, explored immune infiltration, generated regulatory networks, predicted drug responses, and performed molecular docking analyses. Results: Eighteen candidate genes were documented contingent upon the intersection between 2,354 DEGs, 1,136 MRGs, and 1,548 PCD-RGs. The biomarkers DAP3, BIK, and ACAA2 were upregulated in OP and were linked to oxidative phosphorylation. Furthermore, the predictive ability of the nomogram designed based on the OP biomarkers exhibited a certain degree of accuracy. Correlation analysis revealed a strong positive correlation between CD56dim natural killer cells and ACAA2 and a significant negative correlation between central memory CD4+ T cells and DAP3. DAP3, BIK, and ACAA2 were regulated by multiple factors; specifically, SETDB1 and ZNF281 modulated ACAA2 and DAP3, whereas TP63 and TFAP2C governed DAP3 and BIK. Additionally, a stable binding force was observed between the drugs (estradiol, valproic acid, and CGP52608) and the biomarkers. Conclusion: This investigation evidenced that the biomarkers DAP3, BIK, and ACAA2 are associated with PCD and mitochondria in OP, potentially facilitate the diagnosis of OP in clinical settings.

Project description:BackgroundOwing to the complex pathophysiological features and heterogeneity of sepsis, current diagnostic methods are not sufficiently precise or timely, causing a delay in treatment. It has been suggested that mitochondrial dysfunction plays a critical role in sepsis. However, the role and mechanism of mitochondria-related genes in the diagnostic and immune microenvironment of sepsis have not been sufficiently investigated.MethodsMitochondria-related differentially expressed genes (DEGs) were identified between human sepsis and normal samples from GSE65682 dataset. Least absolute shrinkage and selection operator (LASSO) regression and the Support Vector Machine (SVM) analyses were carried out to locate potential diagnostic biomarkers. Gene ontology and gene set enrichment analyses were conducted to identify the key signaling pathways associated with these biomarker genes. Furthermore, correlation of these genes with the proportion of infiltrating immune cells was estimated using CIBERSORT. The expression and diagnostic value of the diagnostic genes were evaluated using GSE9960 and GSE134347 datasets and septic patients. Furthermore, we established an in vitro sepsis model using lipopolysaccharide (1 µg/mL)-stimulated CP-M191 cells. Mitochondrial morphology and function were evaluated in PBMCs from septic patients and CP-M191 cells, respectively.ResultsIn this study, 647 mitochondrion-related DEGs were obtained. Machine learning confirmed six critical mitochondrion-related DEGs, including PID1, CS, CYP1B1, FLVCR1, IFIT2, and MAPK14. We then developed a diagnostic model using the six genes, and receiver operating characteristic (ROC) curves indicated that the novel diagnostic model based on the above six critical genes screened sepsis samples from normal samples with area under the curve (AUC) = 1.000, which was further demonstrated in the GSE9960 and GSE134347 datasets and our cohort. Importantly, we also found that the expression of these genes was associated with different kinds of immune cells. In addition, mitochondrial dysfunction was mainly manifested by the promotion of mitochondrial fragmentation (p<0.05), impaired mitochondrial respiration (p<0.05), decreased mitochondrial membrane potential (p<0.05), and increased reactive oxygen species (ROS) generation (p<0.05) in human sepsis and LPS-simulated in vitro sepsis models.ConclusionWe constructed a novel diagnostic model containing six MRGs, which has the potential to be an innovative tool for the early diagnosis of sepsis.

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Project description:Thyroid associated ophthalmopathy (TAO) is an orbital autoimmune inflammatory disease that is commonly associated with thyroid dysfunction. Although the etiology of TAO is unclear, ROS accumulation and oxidative stress have been closely linked to the pathogenesis of TAO. Ferroptosis is an iron-dependent programmed cell death characterized by intracellular labile iron levels, excessive accumulation of reactive oxygen species (ROS) and lipid peroxidation. Currently, there are few reports regarding the role of ferroptosis in TAO. This article aimed to identify ferroptosis-related genes (FRGs) with diagnostic and therapeutic potential in TAO and explore their relationship with immune cells and lncRNAs. GSE58331 was downloaded from Gene Expression Omnibus (GEO) database. A total of 162 DEGs were identified between 27 TAO samples and 22 health samples from GSE58331, among which six FRGs (CYBB, CTSB, SLC38A1, TLR4, PEX3, and ABCC1) were obtained. The AUC of SLC38A1, TLR4, PEX3 in lacrimal gland tissues was greater than 80 which suggested high diagnostic value in TAO. The result of immune cell infiltrate analysis indicated increased infiltration of monocytes (p < 0.001), macrophages M0(p = 0.039), mast cells activated (p = 0.008), and neutrophils (p = 0.045) in orbital tissues from TAO patients. Meanwhile, mast cells resting (p = 0.043) and macrophages M2 (p = 0.02) showed reduced infiltration in TAO samples. There were no gender differences in immune cell infiltration in the TAO patients. Two differentially expressed lncRNAs, LINC01140 and ZFHX4-AS1, in TAO groups were identified as ferroptosis-related lncRNAs. CYBB-LINC01140-TLR4, CYBB- LINC01140- SLC38A1, TLR4- LINC01140- SLC38A1, and CTSB- ZFHX4-AS1- CYBB may be potential RNA regulatory pathways in TAO. Targeted drugs and transcription factors for differential expressed FRGs were also screened out in our study. In vitro, experiments revealed that CTSB, PEX3, ABCC1 and ZFHX4-AS1(lncRNA) were differentially expressed in orbital fibroblasts (OFs) between TAO groups and healthy controls at the transcriptional level.

Project description:Background: The precise diagnostic and prognostic biological markers were needed in immunotherapy for sepsis. Considering the role of necroptosis and immune cell infiltration in sepsis, differentially expressed necroptosis-related genes (DE-NRGs) were identified, and the relationship between DE-NRGs and the immune microenvironment in sepsis was analyzed. Methods: Machine learning algorithms were applied for screening hub genes related to necroptosis in the training cohort. CIBERSORT algorithms were employed for immune infiltration landscape analysis. Then, the diagnostic value of these hub genes was verified by the receiver operating characteristic (ROC) curve and nomogram. In addition, consensus clustering was applied to divide the septic patients into different subgroups, and quantitative real-time PCR was used to detect the mRNA levels of the hub genes between septic patients (SP) (n = 30) and healthy controls (HC) (n = 15). Finally, a multivariate prediction model based on heart rate, temperature, white blood count and 4 hub genes was established. Results: A total of 47 DE-NRGs were identified between SP and HC and 4 hub genes (BACH2, GATA3, LEF1, and BCL2) relevant to necroptosis were screened out via multiple machine learning algorithms. The high diagnostic value of these hub genes was validated by the ROC curve and Nomogram model. Besides, the immune scores, correlation analysis and immune cell infiltrations suggested an immunosuppressive microenvironment in sepsis. Septic patients were divided into 2 clusters based on the expressions of hub genes using consensus clustering, and the immune microenvironment landscapes and immune function between the 2 clusters were significantly different. The mRNA levels of the 4 hub genes significantly decreased in SP as compared with HC. The area under the curve (AUC) was better in the multivariate prediction model than in other indicators. Conclusion: This study indicated that these necroptosis hub genes might have great potential in prognosis prediction and personalized immunotherapy for sepsis.

Dataset Information

Identification and experimental validation of mitochondria-related genes biomarkers associated with immune infiltration for sepsis.

Background

Methods

Results

Conclusion

Publications

Identification and experimental validation of mitochondria-related genes biomarkers associated with immune infiltration for sepsis.

Similar Datasets

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