Identification of immune-related endoplasmic reticulum stress genes in sepsis using bioinformatics and machine learning

Identification of endoplasmic reticulum stress-related genes as prognostic markers in colon cancer

Endoplasmic reticulum stress (ERS) has been implicated in the pathogenesis of various cancers, including colon cancer, by regulating tumor cell survival, growth, and immune response. However, the specific genes involved in ERS that could serve as prognostic markers in colon cancer remain underexplored. This study aims to identify and validate endoplasmic reticulum stress related genes (ERSRGs) in colon cancer that correlate with patient prognosis, thereby enhancing the understanding of ERS in oncological outcomes and potential therapeutic targeting. We utilized bioinformatics analyses to identify ERSRGs from publicly available colon cancer datasets. Differential expression analysis and survival analysis were performed to assess the prognostic significance of these genes. Validation was conducted through quantitative real-time PCR (RT-qPCR) on selected colon cancer cell lines. Our study identified nine ERS related genes (ASNS, ATF4, ATF6B, BOK, CLU, DDIT3, MANF, SLC39A14, TRAF2) involved in critical pathways including IL-12, PI3K-AKT, IL-7, and IL-23 signaling, and linked to 1-, 3-, and 5-year survival of patients with colon cancer. A multivariate Cox model based on these ERS related genes demonstrated significant prognostic power. Further, TRAF2 strong correlated with immune cells infiltration, suggesting its potential roles in modulating immune responses in the tumor microenvironment. The RT-qPCR validation confirmed the differential expression of these genes in human colon cancer cell lines versus human normal colonic epithelial cell line. The identified ERSRGs could serve as valuable prognostic markers and may offer new insights into the therapeutic targeting of ERS in colon cancer.

Identification immune-related hub genes in diagnosing atherosclerosis with ischemic stroke through comprehensive bioinformatics analysis and machine learning

Background

Atheroma plaques are major etiological factors in the pathogenesis of ischemic stroke (IS). Emerging evidence highlights the critical involvement of the immune microenvironment and dysregulated inflammatory responses throughout IS progression. Consequently, therapeutic strategies targeting specific immune-related markers or signaling pathways within this microenvironment hold significant promise for IS management.

Methods

We integrated Weighted Gene Co-expression Network Analysis (WGCNA), CIBERSORT, and machine learning (LASSO/Random Forest) to identify disease-associated modules and hub genes. Immune infiltration analysis evaluated hub gene-immune cell correlations, while protein-protein interaction (PPI) and ROC curve analyses assessed diagnostic performance.

Results

Comprehensive bioinformatics analysis identified three hub genes-OAS2, TMEM106A, and ABCB1-with high prognostic value for ischemic stroke. Immune infiltration profiling revealed significant correlations between these genes and distinct immune cell populations, underscoring their roles in modulating the immune microenvironment. The diagnostic performance of the gene panel was robust, achieving an area under the curve (AUC) was calculated as 0.9404 (p < 0.0001; 95% CI: 0.887-0.9939) for atherosclerotic plaques, demonstrating superior accuracy compared to conventional biomarkers.

Conclusion

By integrating machine learning with multi-omics bioinformatics, we established a novel three-gene signature (OAS2, TMEM106A, ABCB1) for precise diagnosis of atherosclerosis and ischemic stroke. These genes exhibit dual diagnostic utility and may influence disease progression through immune cell modulation. Our findings provide a foundation for developing targeted therapies and biomarker-driven clinical tools.

Identification and validation of m6A RNA methylation and ferroptosis-related biomarkers in sepsis: transcriptome combined with single-cell RNA sequencing

Background

Sepsis, a systemic inflammatory response syndrome triggered by infection, is associated with high mortality rates and an increasing global incidence. While N 6-methyladenosine (m6A) RNA methylation and ferroptosis are implicated in inflammatory diseases, their specific genes and mechanisms in sepsis remain unclear.

Methods

Transcriptomic datasets of sepsis, along with m6A-related genes (m6A-RGs) and ferroptosis-related genes (FRGs), were sourced from public databases. Differentially expressed genes (DEGs) were identified between the sepsis and control groups, and m6A-RGs were analyzed through weighted gene co-expression network analysis (WGCNA) to uncover m6A module genes. These were then intersected with DEGs and FRGs to identify candidate genes. Biomarkers were identified using two machine learning methods, receiver operating characteristic (ROC) curves, and expression validation, followed by the development of a nomogram. Further in-depth analyses of the biomarkers were performed, including functional enrichment, immune infiltration, drug prediction, and molecular docking. Single-cell analysis was conducted to identify distinct cell clusters and evaluate biomarker expression at the single-cell level. Finally, reverse transcription-quantitative PCR (RT-qPCR) was employed to validate biomarker expression in clinical samples.

Results

DPP4 and TXN were identified as key biomarkers, showing higher expression in control and sepsis samples, respectively. The nomogram incorporating these biomarkers demonstrated strong diagnostic potential. Enrichment analysis highlighted their involvement in spliceosome function and antigen processing and presentation. Differential analysis of immune cell types revealed significant correlations between biomarkers and immune cells, such as macrophages and activated dendritic cells. Drug predictions identified gambogenic acid and valacyclovir as potential treatments, which were successfully docked with the biomarkers. Single-cell analysis revealed that the biomarkers were predominantly expressed in CD4+ memory cells, and CD16+ and CD14+ monocytes. The expression of DPP4 was further validated in clinical samples.

Conclusions

DPP4 and TXN were validated as biomarkers for sepsis, with insights into immune infiltration and therapeutic potential at the single-cell level, offering novel perspectives for sepsis treatment.

Utilizing integrated bioinformatics and machine learning approaches to elucidate biomarkers linking sepsis to purine metabolism-associated genes

Sepsis, characterized as a systemic inflammatory response triggered by pathogen invasion, represents a continuum that may progress from mild systemic infection to severe sepsis, potentially culminating in septic shock and multiple organ dysfunction syndrome. A pivotal element in the pathogenesis and progression of sepsis involves the significant disruption of oncological metabolic networks, where cells within the pathological milieu exhibit metabolic functions that diverge from their healthy counterparts. Among these, purine metabolism plays a crucial role in nucleic acid synthesis. However, the contribution of Purine Metabolism Genes (PMGs) to the defense mechanisms against sepsis remains inadequately explored. Leveraging bioinformatics, this study aimed to identify and substantiate potential PMGs implicated in sepsis. The approach encompassed a differential expression analysis across a pool of 75 candidate PMGs. Gene Set Enrichment Analysis (GSEA) and Gene Set Variation Analysis (GSVA) were employed to assess the biological significance and pathways associated with these genes. Additionally, Lasso regression and Support Vector Machine-Recursive Feature Elimination (SVM-RFE) methodologies were implemented to identify key hub genes and evaluate the diagnostic potential of nine selected PMGs in sepsis identification. The study also examined the correlation between these hub PMGs and related genes, with validation conducted through expression level analysis using the GSE13904 and GSE65682 datasets. The study identified twelve PMGs correlated with sepsis, namely AK9, ENTPD3, NUDT16, GMPR2, PKM, RRM2B, POLR2J, POLE3, ADCY3, ADCY4, ADSSL1, and AMPD1. Functional analysis revealed their involvement in critical processes such as purine nucleotide and ribose phosphate metabolism. The diagnostic capability of these PMGs to effectively differentiate sepsis cases underscored their potential as biomarkers. This research elucidates twelve PMGs associated with sepsis, providing valuable insights into novel biomarkers for this condition and facilitating the monitoring of its progression. These findings highlight the significance of purine metabolism in sepsis pathogenesis and open avenues for further investigation into therapeutic targets.

Identification of the Shared Gene Signatures of HCK, NOG, RNF125 and Biological Mechanism in Pediatric Acute Lymphoblastic Leukaemia and Pediatric Sepsis

The shared mechanisms between pediatric acute lymphoblastic leukaemia (ALL) and pediatric sepsis are currently unclear. This study was aimed to explore the shared key genes of pediatric ALL and pediatric sepsis. The datasets involved were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) between disease and control samples in GSE13904 and GSE79533 were intersected. The least absolute shrinkage and selection operator (LASSO) and the boruta analyses were performed in GSE13904 and GSE79533 separately based on shared DEGs, and shared key genes were obtained by taking the intersection of sepsis-related key genes and ALL-related key genes. Three shared key genes (HCK, NOG, RNF125) were obtained, that have a good diagnostic value for both sepsis and ALL. The correlation between shared key genes and differentially expressed immune cells was higher in GSE13904 and conversely, the correlation of which was lower in GSE79533. Suggesting that the sharing key genes had a different impact on the immune environment in pediatric ALL and pediatric sepsis. We make the case that this study provides a new perspective to study the relationship between pediatric ALL and pediatric sepsis.

Unraveling the copper-death connection: Decoding COVID-19's immune landscape through advanced bioinformatics and machine learning approaches

This study aims to analyze Coronavirus Disease 2019 (COVID-19)-associated copper-death genes using the Gene Expression Omnibus (GEO) dataset and machine learning, exploring their immune microenvironment correlation and underlying mechanisms. Utilizing GEO, we analyzed the GSE217948 dataset with control samples. Differential expression analysis identified 16 differentially expressed copper-death genes, and Cell type Identification By Estimating Relative Subsets Of RNA Transcripts (CIBERSORT) quantified immune cell infiltration. Gene classification yielded two copper-death clusters, with Weighted Gene Co-expression Network Analysis (WGCNA) identifying key module genes. Machine learning models (random forest, Support Vector Machine (SVM), Generalized Linear Model (GLM), eXtreme Gradient Boosting (XGBoost)) selected 6 feature genes validated by the GSE213313 dataset. Ferredoxin 1 (FDX1) emerged as the top gene, corroborated by Area Under the Curve (AUC) analysis. Gene Set Enrichment Analysis (GSEA) and Gene Set Variation Analysis (GSVA) revealed enriched pathways in T cell receptor, natural killer cytotoxicity, and Peroxisome Proliferator-Activated Receptor (PPAR). We uncovered differentially expressed copper-death genes and immune infiltration differences, notably CD8 T cells and M0 macrophages. Clustering identified modules with potential implications for COVID-19. Machine learning models effectively predicted COVID-19 risk, with FDX1's pivotal role validated. FDX1's high expression was associated with immune pathways, suggesting its role in COVID-19 pathogenesis. This comprehensive approach elucidated COVID-19-related copper-death genes, their immune context, and risk prediction potential. FDX1's connection to immune pathways offers insights into COVID-19 mechanisms and therapy.

Role of immune-related endoplasmic reticulum stress genes in sepsis-induced cardiomyopathy: Novel insights from bioinformatics analysis

BACKGROUND

The current study aims to elucidate the key molecular mechanisms linked to endoplasmic reticulum stress (ERS) in the pathogenesis of sepsis-induced cardiomyopathy (SIC) and offer innovative therapeutic targets for SIC.

METHODS

The study downloaded dataset GSE79962 from the Gene Expression Omnibus database and acquired the ERS-related gene set from GeneCards. It utilized weighted gene co-expression network analysis (WGCNA) and conducted differential expression analysis to identify key modules and genes associated with SIC. The SIC hub genes were determined by the intersection of WGCNA-based hubs, DEGs, and ERS-related genes, followed by protein-protein interaction (PPI) network construction. Enrichment analyses, encompassing GO, KEGG, GSEA, and GSVA, were performed to elucidate potential biological pathways. The CIBERSORT algorithm was employed to analyze immune infiltration patterns. Diagnostic and prognostic models were developed to assess the clinical significance of hub genes in SIC. Additionally, in vivo experiments were conducted to validate the expression of hub genes.

RESULTS

Differential analysis revealed 1031 differentially expressed genes (DEGs), while WGCNA identified a hub module with 1327 key genes. Subsequently, 13 hub genes were pinpointed by intersecting with ERS-related genes. NOX4, PDHB, SCP2, ACTC1, DLAT, EDN1, and NSDHL emerged as hub ERS-related genes through the protein-protein interaction network, with their diagnostic values confirmed via ROC curves. Diagnostic models incorporating five genes (NOX4, PDHB, ACTC1, DLAT, NSDHL) were validated using the LASSO algorithm, highlighting only the prognostic significance of serum PDHB levels in predicting the survival of septic patients. Additionally, decreased PDHB mRNA and protein expression levels were observed in the cardiac tissue of septic mice compared to control mice.

CONCLUSIONS

This study elucidated the interplay between metabolism and the immune microenvironment in SIC, providing fresh perspectives on the investigation of potential SIC pathogenesis. PDHB emerged as a significant biomarker of SIC, with implications on its progression through the regulation of ERS and metabolism.

Recent nanoengineered therapeutic advancements in sepsis management

Sepsis (defined as sepsis 3.0) is a life-threatening organ dysfunction caused by a dysregulated host response to a variety of pathogenic microorganisms. Characterized by high morbidity and mortality, sepsis has become a global public health problem. However, there is a lack of appropriate diagnostic and therapeutic strategies for sepsis and current management rely on the limited treatment strategies. Recently, nanomedicines targeting and controlling the release of bio-active agents have shown excellent potency in sepsis management, with improved therapeutic efficacy and reduced adverse effects. In this review, we have summarized the advantages of nanomaterials. Also, the preparation and efficacy of the main categories of anti-sepsis nanomedicines applied in sepsis management are described in detail, including antibiotic-coated nanomaterials, antimicrobial peptides-coated nanomaterials, biomimetic nanomaterials, nanomaterials targeting macrophages and natural products loaded nanomaterials. These advances in nanomedicines establish the huge potential for nanomaterials-based sepsis management, especially in the improved pharmaceutical and pharmacological properties, enhanced therapeutic efficacy, controllable drug-targeting and reduced side effects. To further facilitate clinical translation of anti-sepsis nanomedicines, we propose that the issues involving safety, regulatory laws and cost-effectiveness should receive much more attention in the future.

A lncRNA signature associated with endoplasmic reticulum stress supports prognostication and prediction of drug resistance in acute myelogenous leukemia

BACKGROUND

Acute myelogenous leukemia (AML) is a type of blood cancer that is characterized by the accumulation of young and undeveloped myeloid cells in the bone marrow. It is considered a heterogeneous disease due to its diverse nature. Endoplasmic reticulum (ER) stress has emerged as a critical regulator of tumor development and drug resistance in various cancers. Long non-coding RNAs (lncRNAs) have been found to play a role in the development and prognosis of AML. Nonetheless, there is still limited understanding regarding the involvement of ER stress-related lncRNAs in AML prognosis and their predictive ability for drug resistance. The objective of this study was to examine the potential prognostic and predictive significance of an ER stress-related lncRNA signature in patients diagnosed with AML.

METHODS

Based on the bulk RNA sequence data, we constructed an ER stress-related lncRNA signature using least absolute shrinkage and selection operator (LASSO) and multivariate logistic regression analysis. We established nomograms and calibration curves to assess the clinical value of the signature by analyzing overall survival (OS) rates between different risk groups. We also conducted tumor mutation burden (TMB) analysis, predicted immune responses, performed functional and biological enrichment analysis, and evaluated drug sensitivity to investigate the impact of the prognostic signature. Additionally, we built a consensus cluster to explore the need for personalized immunotherapy approaches in treating patients with AML.

RESULTS

A prognostic signature was constructed using 227 ER stress-related lncRNAs that showed differential expression. Patients in the high-risk category demonstrated decreased OS rates in comparison to individuals in the low-risk category. The findings from the nomogram and receiver operating characteristic (ROC) curve analysis suggest a notable disparity in age between the different categories. Among the group at high risk, we noticed a considerably greater TMB in comparison to the low-risk group. Furthermore, individuals with both an elevated risk score and high TMB demonstrated the most unfavorable survival rates. Significant differences were observed in the immune responses between the groups classified as high- and low-risk. We then systematically evaluated three different clusters to assess immune responses and drug responses. Through analyzing the association between the risk score and various medications, we have discovered 18 potential drug contenders capable of effectively addressing AML. Furthermore, we conducted pathway analyses to determine the targeted pathways of these drugs.

CONCLUSIONS

Our data serve as a valuable resource for decoding the immune responses, somatic mutational landscape, drug resistance, and potential biological functions in AML patients. Additionally, our findings offer valuable insights into the association between related lncRNAs and the immune microenvironment of AML. It provides us with promising insights that can help in the development of precise therapeutic strategies.

Identification of KCNQ1 as a diagnostic biomarker related to endoplasmic reticulum stress for intervertebral disc degeneration based on machine learning and experimental evidence

Intervertebral disc degeneration (IDD) is a primary cause of low back pain and disability. Cellular senescence and apoptosis due to endoplasmic reticulum stress (ERS) are key in IDD pathology. Identifying biomarkers linked to ERS in IDD is crucial for diagnosis and treatment. We utilized machine learning on gene expression profiles from the Gene Expression Omnibus database to discover biomarkers associated with ERS in IDD. Gene set enrichment analysis (GSEA) and single-sample GSEA were applied to evaluate the immunological features and biological functions of these biomarkers. The expression of KCNQ1 was experimentally validated. Machine learning identified KCNQ1 as a diagnostic biomarker for ERS in IDD, confirmed by Western blotting. GSEA indicated that KCNQ1 influences IDD primarily through the Notch signaling pathway and by regulating macrophage and monocyte infiltration. KCNQ1, identified as an ERS-associated biomarker in IDD, impacts the Notch signaling pathway and immune cell infiltration, suggesting its potential as a therapeutic target for IDD. Further validation through prospective studies and additional experimental methods is necessary to elucidate the role of KCNQ1 in IDD comprehensively.

IL7R, GZMA and CD8A serve as potential molecular biomarkers for sepsis based on bioinformatics analysis

Purpose

Sepsis is an unusual systemic reaction to what is sometimes an otherwise ordinary infection, and it probably represents a pattern of response by the immune system to injury. However, the relationship between biomarkers and sepsis remains unclear. This study aimed to find potential molecular biomarkers, which could do some help to patients with sepsis.

Methods

The sepsis dataset GSE28750, GSE57065 was downloaded from the GEO database, and ten patients with or without sepsis from our hospital were admitted for RNA-seq and the differentially expressed genes (DEGs) were screened. The Metascape database was used for functional enrichment analysis and was used to found the differential gene list. Protein-protein interaction network was used and further analyzed by using Cytoscape and STRING. Logistic regression and Correlation analysis were used to find the potential molecular biomarkers.

Results

Taking the intersection of the three datasets yielded 287 differential genes. The enrichment results included Neutrophil degranulation, leukocyte activation, immune effectors process, positive regulation of immune response, regulation of leukocyte activation. The top 10 key genes of PPI connectivity were screened using cytoHubba plugin, which were KLRK1, KLRB1, IL7R, GZMA, CD27, PRF1, CD8A, CD2, IL2RB, and GZMB. All of the hub genes are higher expressed in health group of different databases. Logistic regression showed that IL7R, GZMA and CD8A proteins were analyzed and all of them were statistically significant. Correlation analysis showed that there was a statistically significant correlation between IL7R, GZMA and CD8A.

Conclusion

KLRK1, KLRB1, IL7R, GZMA, CD27, PRF1, CD8A, CD2, IL2RB, GZMB are key genes in sepsis, which associated with the development of sepsis. However, IL7R, GZMA and CD8A may serve as the attractively potential molecular biomarkers for sepsis.

Utilizing integrated bioinformatics and machine learning approaches to elucidate biomarkers linking sepsis to fatty acid metabolism-associated genes

Sepsis, characterized as a systemic inflammatory response triggered by the invasion of pathogens, represents a continuum that may escalate from mild systemic infection to severe sepsis, potentially resulting in septic shock and multiple organ dysfunction syndrome. Advancements in lipidomics and metabolomics have unveiled the complex role of fatty acid metabolism (FAM) in both healthy and pathological states. Leveraging bioinformatics, this investigation aimed to identify and substantiate potential FAM-related genes (FAMGs) implicated in sepsis. The approach encompassed a differential expression analysis across a pool of 36 candidate FAMGs. GSEA and GSVA were employed to assess the biological significance and pathways associated with these genes. Furthermore, Lasso regression and SVM-RFE methodologies were implemented to determine key hub genes and assess the diagnostic prowess of nine selected FAMGs in sepsis identification. The study also investigated the correlation between these hub FAMGs. Validation was conducted through expression-level analysis using the GSE13904 and GSE65682 datasets. The study identified 13 sepsis-associated FAMGs, including ABCD2, ACSL3, ACSM1, ACSS1, ACSS2, ACOX1, ALDH9A1, ACACA, ACACB, FASN, OLAH, PPT1, and ELOVL4. As demonstrated by functional enrichment analysis results, these genes played key roles in several critical biological pathways, such as the Peroxisome, PPAR signaling pathway, and Insulin signaling pathway, all of which are intricately linked to metabolic regulation and inflammatory responses. The diagnostic potential of these FAMGs was further highlighted. In short, the expression patterns of these FAMGs c effectively distinguished sepsis cases from non-septic controls, which suggested that they may be promising biomarkers for early sepsis detection. This discovery not only enhanced our understanding of the molecular mechanisms underpinning sepsis but also paved the way for developing novel diagnostic tools and therapeutic strategies targeting metabolic dysregulation in septic patients. This research sheds light on 13 FAMGs associated with sepsis, providing valuable insights into novel biomarkers for this condition and facilitating the monitoring of its progression. These findings underscore the significance of purine metabolism in sepsis pathogenesis and open avenues for further investigation into therapeutic targets.

Single-cell RNA sequencing and transcriptomic analysis reveal key genes and regulatory mechanisms in sepsis

The pathogenesis of sepsis, with a high mortality rate and often poor prognosis, has not been fully elucidated. Therefore, an in-depth study on the pathogenesis of sepsis at the molecular level is essential to identify key sepsis-related genes. The aim of this study was to explore the key genes and potential molecular mechanisms of sepsis using a bioinformatics approach. In addition, key genes with miRNA network correlation analysis and immune infiltration correlation analysis were investigated. The scRNA dataset (GSE167363) and RNA-seq dataset (GSE65682, GSE134347) from GEO database were used for screening out differentially expressed genes using single-cell sequencing and transcriptome sequencing. The analysis of immune infiltration was evaluated by the CIBERSORT method. Key genes and possible mechanisms were identified by WGCNA analysis, GSVA analysis, GSEA enrichment analysis and regulatory network analysis, and miRNA networks associated with key genes were constructed. Nine key genes associated with the development of sepsis, namely IL7R, CD3D, IL32, GPR183, HLA-DPB1, CD81, PEBP1, NCL, and ETS1 were screened, and the specific signaling mechanisms associated with the key genes causing sepsis were predicted. Immune profiling showed immune heterogeneity between control and sepsis samples. A regulatory network of 82 miRNAs, 266 pairs of mRNA-miRNA relationship pairs was also constructed. These nine key genes have the potential to become biomarkers for the diagnosis of sepsis and provide new targets and research directions for the treatment of sepsis.

Alpha-1 antitrypsin targeted neutrophil elastase protects against sepsis-induced inflammation and coagulation in mice via inhibiting neutrophil extracellular trap formation

AIMS

Sepsis pathophysiology is complex and identifying effective treatments for sepsis remains challenging. The study aims to identify effective drugs and targets for sepsis through transcriptomic analysis of sepsis patients, repositioning analysis of compounds, and validation by animal models.

MAIN METHODS

GSE185263 obtained from the GEO database that includes gene expression profiles of 44 healthy controls and 348 sepsis patients categorized by severity. Bioinformatic algorithms revealed the molecular, function, and immune characteristics of the sepsis, and constructed sepsis-related protein-protein interaction networks. Subsequently, Random Walk with Restart analysis was applied to identify candidate drugs for sepsis, which were tested in animal models for survival, inflammation, coagulation, and multi-organ damage.

KEY FINDINGS

Our analysis found 1862 genes linked to sepsis development, enriched in functions like neutrophil extracellular trap formation (NETs) and complement/coagulation cascades. With disease progression, immune activation-associated cells were inhibited, while immune suppression-associated cells were activated. Next, the drug repositioning method identified candidate drugs, such as alpha-1 antitrypsin, that may play a therapeutic role by targeting neutrophil elastase (NE) to inhibit NETs. Animal experiments proved that alpha-1 antitrypsin treatment can improve the survival rate, reduce sepsis score, reduce the levels of inflammation markers in serum, and alleviate muti-organ morphological damage in mice with sepsis. The further results showed that α-1 antitrypsin can inhibit the NETs by suppressing the NE for the treatment of sepsis.

SIGNIFICANCE

Alpha-1 antitrypsin acted on the NE to inhibit NETs thereby protecting mice from sepsis-induced inflammation and coagulation.

Elucidating common pathogenic transcriptional networks in infective endocarditis and sepsis: integrated insights from biomarker discovery and single-cell RNA sequencing

Background

Infective Endocarditis (IE) and Sepsis are two closely related infectious diseases, yet their shared pathogenic mechanisms at the transcriptional level remain unclear. This research gap poses a barrier to the development of refined therapeutic strategies and drug innovation.

Methods

This study employed a collaborative approach using both microarray data and single-cell RNA sequencing (scRNA-seq) data to identify biomarkers for IE and Sepsis. It also offered an in-depth analysis of the roles and regulatory patterns of immune cells in these diseases.

Results

We successfully identified four key biomarkers correlated with IE and Sepsis, namely CD177, IRAK3, RNASE2, and S100A12. Further investigation revealed the central role of Th1 cells, B cells, T cells, and IL-10, among other immune cells and cytokines, in the pathogenesis of these conditions. Notably, the small molecule drug Matrine exhibited potential therapeutic effects by targeting IL-10. Additionally, we discovered two Sepsis subgroups with distinct inflammatory responses and therapeutic strategies, where CD177 demonstrated significant classification value. The reliability of CD177 as a biomarker was further validated through qRT-PCR experiments.

Conclusion

This research not only paves the way for early diagnosis and treatment of IE and Sepsis but also underscores the importance of identifying shared pathogenic mechanisms and novel therapeutic targets at the transcriptional level. Despite limitations in data volume and experimental validation, these preliminary findings add new perspectives to our understanding of these complex diseases.

Uncovering hub genes in sepsis through bioinformatics analysis

In-depth studies on the mechanisms of pathogenesis of sepsis and diagnostic biomarkers in the early stages may be the key to developing individualized and effective treatment strategies. This study aimed to identify sepsis-related hub genes and evaluate their diagnostic reliability. The gene expression profiles of GSE4607 and GSE131761 were obtained from the Gene Expression Omnibus. Differentially co-expressed genes between the sepsis and control groups were screened. Single-sample gene set enrichment analysis and gene set variation analysis were performed to investigate the biological functions of the hub genes. A receiver operating characteristic curve was used to evaluate diagnostic value. Datasets GSE154918 and GSE185263 were used as external validation datasets to verify the reliability of the hub genes. Four differentially co-expressed genes, FAM89A, FFAR3, G0S2, and FGF13, were extracted using a weighted gene co-expression network analysis and differential gene expression analysis methods. These 4 genes were upregulated in the sepsis group and were distinct from those in the controls. Moreover, the receiver operating characteristic curves of the 4 genes exhibited considerable diagnostic value in discriminating septic blood samples from those of the non-septic control group. The reliability and consistency of these 4 genes were externally validated. Single-sample gene set enrichment analysis and gene set variation analysis analyses indicated that the 4 hub genes were significantly correlated with the regulation of immunity and metabolism in sepsis. The identified FAM89A, FFAR3, G0S2, and FGF13 genes may help elucidate the molecular mechanisms underlying sepsis and drive the introduction of new biomarkers to advance diagnosis and treatment.

Predicting the prognosis in patients with sepsis by an endoplasmic reticulum stress gene signature

BACKGROUND

Prognostic stratification of patients with sepsis is important for the development of individualized treatment strategies. Endoplasmic reticulum stress (ERS) plays a key role in sepsis. This study aimed to identify a set of genes related to ER stress to construct a predictive model for the prognosis of sepsis.

METHODS

The transcriptomic and clinical data of 479 sepsis patients were obtained from GSE65682 and divided into a training set (n=288) and a validation set (n=191) at a ratio of 3:2. The external test set was GSE95233 (n=51). LASSO and Cox regression analyses were performed to establish a signature to predict the prognosis of patients with sepsis. Moreover, we developed a nomogram that included the risk signature and clinical features to predict survival probability.

RESULTS

A prognostic signature was constructed with ten endoplasmic reticulum related genes (ADRB2, DHCR7, GABARAPL2, MAOA, MPO, PDZD8, QDPR, SCAP, TFRC, and TLR4) in the training set, which significantly divided patients with sepsis into high- and low-risk groups in terms of survival. This signature was validated using validation and external test sets. A nomogram based on the risk signature was constructed to quantitatively predict the prognosis of patients with sepsis.

CONCLUSIONS

We constructed an ERS signature as a novel prognostic marker for predicting survival in sepsis patients, which could be used to develop novel biomarkers for the diagnosis, treatment, and prognosis of sepsis and to provide new ideas and prospects for future clinical research.

Bioinformatics-led discovery of ferroptosis-associated diagnostic biomarkers and molecule subtypes for tuberculosis patients

BACKGROUND

Ferroptosis is closely associated with the pathophysiological processes of many diseases, such as infection, and is characterized by the accumulation of excess lipid peroxides on the cell membranes. However, studies on the ferroptosis-related diagnostic markers in tuberculosis (TB) is still lacking. Our study aimed to explore the role of ferroptosis-related biomarkers and molecular subtypes in TB.

METHODS

GSE83456 dataset was applied to identify ferroptosis-related genes (FRGs) associated with TB, and GSE42826, GSE28623, and GSE34608 datasets for external validation of core biomarkers. Core FRGs were identified using weighted gene co-expression network analysis (WGCNA). Subsequently, two ferroptosis-related subtypes were constructed based on ferroptosis score, and differently expressed analysis, GSEA, GSEA, immune cell infiltration analysis between the two subtypes were performed.Affiliations: Please check and confirm that the authors and their respective affiliations have been correctly identified and amend if necessary.correctly RESULTS: A total of 22 FRGs were identified, of which three genes (CHMP5, SAT1, ZFP36) were identified as diagnostic biomarkers that were enriched in pathways related to immune-inflammatory response. In addition, TB patients were divided into high- and low-ferroptosis subtypes (HF and LF) based on ferroptosis score. HF patients had activated immune- and inflammation-related pathways and higher immune cell infiltration levels than LF patients.

CONCLUSION

Three potential diagnostic biomarkers and two ferroptosis-related subtypes were identified in TB patients, which would help to understand the pathogenesis of TB.Author names: Kindly check and confirm the process of the author names [2,4]correctly.

IL-1β-pretreated bone mesenchymal stem cell-derived exosomes alleviate septic endoplasmic reticulum stress via regulating SIRT1/ERK pathway

BACKGROUND

Endoplasmic reticulum (ER) plays a crucial role in the development of organ injury caused by sepsis. Therefore, it is highly important to devise strategies that specially target ER stress for the treatment of sepsis. Previous research has shown that priming chemokines can enhance the therapeutic effects of mesenchymal stem cells (MSCs). In this study, we aimed to investigate the function and mechanism of exosomes derived from MSCs that were pretreated with IL-1β (IB-exos) in the context of septic ER stress.

METHODS

Mouse bone MSCs were preconditioned with or without IL-1β and the supernatant was used for exosome extraction. In vitro sepsis cell mode was induced by treating HUVECs with LPS, while in vivo sepsis model was established through cecal ligation and puncture (CLP) operation in mice. Cell viability, apoptosis, motility, and tube formation were assessed using the EDU proliferation assay, flow cytometry analysis, migration assay, and tube formation assay, respectively. The molecular mechanism was investigated using ELISA, qRT-PCR, Western blot, and immunofluorescence staining.

RESULTS

Pretreatment with IL-1β enhanced the positive impact of MSC-exos on the viability, apoptosis, motility, and tube formation ability of HUVECs. The administration of LPS or CLP increased ER stress response, but this effect was blocked by the treatment of IB-exos. Additionally, IB-exos reversed the inhibitory effects of LPS or CLP on the expression levels of SIRT1 and ERK phosphorylation. Knockdown of SIRT1 counteracted the effects of IB-exos on HUVEC cellular function and ER stress. In a mouse model, the injection of IB-exos mitigated sepsis-induced lung injury by inhibiting ER stress response through the activation of SIRT1.

CONCLUSION

IB-exos have been found to alleviate sepsis-induced lung injury via inhibiting ER stress through the SIRT1/ERK pathway. These findings indicated that IB-exos could potentially be used as a strategy to mitigate lung injury caused by sepsis.

Classification of subtypes and identification of dysregulated genes in sepsis

Background

Sepsis is a clinical syndrome with high mortality. Subtype identification in sepsis is meaningful for improving the diagnosis and treatment of patients. The purpose of this research was to identify subtypes of sepsis using RNA-seq datasets and further explore key genes that were deregulated during the development of sepsis.

Methods

The datasets GSE95233 and GSE13904 were obtained from the Gene Expression Omnibus database. Differential analysis of the gene expression matrix was performed between sepsis patients and healthy controls. Intersection analysis of differentially expressed genes was applied to identify common differentially expressed genes for enrichment analysis and gene set variation analysis. Obvious differential pathways between sepsis patients and healthy controls were identified, as were developmental stages during sepsis. Then, key dysregulated genes were revealed by short time-series analysis and the least absolute shrinkage and selection operator model. In addition, the MCPcounter package was used to assess infiltrating immunocytes. Finally, the dysregulated genes identified were verified using 69 clinical samples.

Results

A total of 898 common differentially expressed genes were obtained, which were chiefly related to increased metabolic responses and decreased immune responses. The two differential pathways (angiogenesis and myc targets v2) were screened on the basis of gene set variation analysis scores. Four subgroups were identified according to median expression of angiogenesis and myc target v2 genes: normal, myc target v2, mixed-quiescent, and angiogenesis. The genes CHPT1, CPEB4, DNAJC3, MAFG, NARF, SNX3, S100A9, S100A12, and METTL9 were recognized as being progressively dysregulated in sepsis. Furthermore, most types of immune cells showed low infiltration in sepsis patients and had a significant correlation with the key genes. Importantly, all nine key genes were highly expressed in sepsis patients.

Conclusion

This study revealed novel insight into sepsis subtypes and identified nine dysregulated genes associated with immune status in the development of sepsis. This study provides potential molecular targets for the diagnosis and treatment of sepsis.