Identification of featured necroptosis-related genes and imbalanced immune infiltration in sepsis via machine learning

EXPLORING THE PROGNOSTIC NECROPTOSIS-RELATED GENES AND UNDERLYING MECHANISM IN SEPSIS USING BIOINFORMATICS

ABSTRACT

Sepsis is a life-threatening disease due to a dysregulated host response to infection, with an unknown regulatory mechanism for prognostic necroptosis-related genes (NRGs). Using GEO datasets GSE65682 and GSE134347, we identified six NRG biomarkers ( ATRX , TSC1 , CD40 , BACH2 , BCL2 , and LEF1 ) with survival and diagnostic significance through Kaplan-Meier (KM) and receiver operating characteristic (ROC) analyses. Afterward, the ingenuity pathway analysis (IPA) highlighted enrichment in hepatic fibrosis pathways and BEX2 protein. Moreover, we examined their regulatory targets and functional links with necroptotic signaling molecules via miRDB, TargetScan, Network analyst, and GeneMANIA. The molecular regulatory network displayed that hsa-miR-5195-3p and hsa-miR-145-5p regulated ATRX, BACH2, and CD40, while YY1 showed strong connectivity, concurrently controlling LEF1, ATRX, BCL2, BACH2, and CD40. CD40 exhibited similar expression patterns to RIPK3 and MLKL, and LEF1 was functionally associated with MLKL. Additionally, DrugBank analysis identified paclitaxel, docetaxel, and rasagiline as potential BCL2-targeting sepsis treatments. Finally, real-time quantitative PCR confirmed ATRX, TSC1, and LEF1 downregulation in sepsis samples, contrasting CD40's increased expression in CTL samples. In conclusion, ATRX , TSC1 , CD40 , BACH2 , BCL2 , and LEF1 may be critical regulatory targets of necroptosis in sepsis, providing a basis for further necroptosis-related studies in sepsis.

Cuproptosis-Related Genes as Prognostic Biomarkers for Sepsis: Insights into Immune Function and Personalized Immunotherapy

Background

This study aimed to discover diagnostic and prognostic biomarkers for sepsis immunotherapy through analyzing the novel cellular death process, cuproptosis.

Methods

We used transcriptome data from sepsis patients to identify key cuproptosis-related genes (CuRGs). We created a predictive model and used the CIBERSORT algorithm to observe the link between these genes and the septic immune microenvironment. We segregated sepsis patients into three subgroups, comparing immune function, immune cell infiltration, and differential analysis. Single-cell sequencing and real-time quantitative PCR were used to view the regulatory effect of CuRGs on the immune microenvironment and compare the mRNA levels of these genes in sepsis patients and healthy controls. We established a sepsis forecast model adapted to heart rate, body temperature, white blood cell count, and cuproptosis key genes. This was followed by a drug sensitivity analysis of cuproptosis key genes.

Results

Our results filtered three key genes (LIAS, PDHB, PDHA1) that impact sepsis prognosis. We noticed that the high-risk group had poorer immune cell function and lesser immune cell infiltration. We also discovered a significant connection between CuRGs and immune cell infiltration in sepsis. Through consensus clustering, sepsis patients were classified into three subgroups. The best immune functionality and prognosis was observed in subgroup B. Single-cell sequencing exposed that the key genes manage the immune microenvironment by affecting T cell activation. The qPCR results highlighted substantial mRNA level reduction of the three key genes in the SP compared to the HC. The prediction model, which combines CuRGs and traditional diagnostic indicators, performed better in accuracy than the other markers. The drug sensitivity analysis listed bisphenol A as highly sensitive to all the key genes.

Conclusion

Our study suggests these CuRGs may offer substantial potential for sepsis prognosis prediction and personalized immunotherapy.

Identification of apoptosis-immune-related gene signature and construction of diagnostic model for sepsis based on single-cell sequencing and bulk transcriptome analysis

Introduction

Sepsis leads to multi-organ dysfunction due to disorders of the host response to infections, which makes diagnosis and prognosis challenging. Apoptosis, a classic programmed cell death, contributes to the pathogenesis of various diseases. However, there is much uncertainty about its mechanism in sepsis.

Methods

Three sepsis gene expression profiles (GSE65682, GSE13904, and GSE26378) were downloaded from the Gene Expression Omnibus database. Apoptosis-related genes were obtained from the Kyoto Encyclopedia of Genes and Genomes Pathway database. We utilized LASSO regression and SVM-RFE algorithms to identify characteristic genes associated with sepsis. CIBERSORT and single cell sequencing analysis were employed to explore the potential relationship between hub genes and immune cell infiltration. The diagnostic capability of hub genes was validated across multiple external datasets. Subsequently, the animal sepsis model was established to assess the expression levels of hub genes in distinct target organs through RT-qPCR and Immunohistochemistry analysis.

Results

We identified 11 apoptosis-related genes as characteristic diagnostic markers for sepsis: CASP8, VDAC2, CHMP1A, CHMP5, FASLG, IFNAR1, JAK1, JAK3, STAT4, IRF9, and BCL2. Subsequently, a prognostic model was constructed using LASSO regression with BCL2, FASLG, IRF9 and JAK3 identified as hub genes. Apoptosis-related genes were closely associated with the immune response during the sepsis process. Furthermore, in the validation datasets, aside from IRF9, other hub genes demonstrated similar expression patterns and diagnostic abilities as observed in GSE65682 dataset. In the mouse model, the expression differences of hub genes between sepsis and control group revealed the potential impacts on sepsis-induced organ injury.

Conclusion

The current findings indicated the participant of apoptosis in sepsis, and apoptosis-related differentially expressed genes could be used for diagnosis biomarkers. BCL2, FASLG, IRF9 and JAK3 might be key regulatory genes affecting apoptosis in sepsis. Our findings provided a novel aspect for further exploration of the pathological mechanisms in sepsis.

Prognostic gene landscapes and therapeutic insights in sepsis-induced coagulopathy

BACKGROUND

Sepsis is a common and critical condition encountered in clinical practice that can lead to multi-organ dysfunction. Sepsis-induced coagulopathy (SIC) significantly affects patient outcomes. However, the precise mechanisms remain unclear, making the identification of effective prognostic and therapeutic targets imperative.

METHODS

The analysis of transcriptome data from the whole blood of sepsis patients, facilitated the identification of key genes implicated in coagulation. Then we developed a prognostic model and a nomogram to predict patient survival. Consensus clustering classified sepsis patients into three subgroups for comparative analysis of immune function and immune cell infiltration. Single-cell sequencing elucidated alterations in intercellular communication between platelets and immune cells in sepsis, as well as the role of the coagulation-related gene FYN. Real-time quantitative PCR determined the mRNA levels of critical coagulation genes in septic rats' blood. Finally, administration of a FYN agonist to septic rats was observed for its effects on coagulation functions and survival.

RESULTS

This study identified four pivotal genes-CFD, FYN, ITGAM, and VSIG4-as significant predictors of survival in patients with sepsis. Among them, CFD, FYN, and ITGAM were underexpressed, while VSIG4 was upregulated in patients with sepsis. Moreover, a nomogram that incorporates the coagulation-related genes (CoRGs) risk score with clinical features of patients accurately predicted survival probabilities. Subgroup analysis of CoRGs expression delineated three molecular sepsis subtypes, each with distinct prognoses and immune profiles. Single-cell sequencing shed light on heightened communication between platelets and monocytes, T cells, and plasmacytoid dendritic cells, alongside reduced interactions with neutrophils in sepsis. The collagen signaling pathway was found to be essential in this dynamic. FYN may affect platelet function by modulating factors such as ELF1, PTCRA, and RASGRP2. The administration of the FYN agonist can effectively improve coagulation dysfunction and survival in septic rats.

CONCLUSIONS

The research identifies CoRGs as crucial prognostic markers for sepsis, highlighting the FYN gene's central role in coagulation disorders associated with the condition and suggesting novel therapeutic intervention strategies.

Development of macrophage-associated genes prognostic signature predicts clinical outcome and immune infiltration for sepsis

Sepsis is a major global health problem, causing a significant burden of disease and death worldwide. Risk stratification of sepsis patients, identification of severe patients and timely initiation of treatment can effectively improve the prognosis of sepsis patients. We procured gene expression datasets for sepsis (GSE54514, GSE65682, GSE95233) from the Gene Expression Omnibus and performed normalization to mitigate batch effects. Subsequently, we applied weighted gene co-expression network analysis to categorize genes into modules that exhibit correlation with macrophage activity. To pinpoint macrophage-associated genes (MAAGs), we executed differential expression analysis and single sample gene set enrichment analysis. We then established a prognostic model derived from four MAAGs that were significantly differentially expressed. Functional enrichment analysis and immune infiltration assessments were instrumental in deciphering the biological mechanisms involved. Furthermore, we employed principal component analysis and conducted survival outcome analyses to delineate molecular subgroups within sepsis. Four novel MAAGs-CD160, CX3CR1, DENND2D, and FAM43A-were validated and used to create a prognostic model. Subgroup classification revealed distinct molecular profiles and a correlation with 28-day survival outcomes. The MAAGs risk score was developed through univariate Cox, LASSO, and multivariate Cox analyses to predict patient prognosis. Validation of the risk score upheld its prognostic significance. Functional enrichment implicated ribonucleoprotein complex biogenesis, mitochondrial matrix, and transcription coregulator activity in sepsis, with an immune infiltration analysis indicating an association between MAAGs risk score and immune cell populations. The four MAAGs exhibited strong diagnostic capabilities for sepsis. The research successfully developed a MAAG-based prognostic model for sepsis, demonstrating that such genes can significantly stratify risk and reflect immune status. Although in-depth mechanistic studies are needed, these findings propose novel targets for therapy and provide a foundation for future precise clinical sepsis management.

Construction and validation of a mitochondria-associated genes prognostic signature and immune microenvironment characteristic of sepsis

BACKGROUND

Sepsis is a common critical condition seen in clinical settings, with mitochondrial dysfunction playing an important role in the progression of sepsis. However, a mitochondrial prognosis model related to sepsis has not been established yet, and the relationship between the sepsis immune microenvironment and mitochondria remains unclear.

METHODS

Sepsis prognostic mitochondria-associated genes (MiAGs) were screened by univariate Cox, multivariate Cox, and LASSO analysis from the GEO dataset. Consensus Cluster was used to analyze MiAGs-based molecular subtypes for sepsis. The ESTIMATE and ssGSEA algorithms were used to analyze the situation of sepsis immune cell infiltration and its relation to MiAGs. Further, MiAGs score was calculated to construct a sepsis prognosis risk model to predict the prognosis of sepsis patients. Clinical blood samples were used to investigate the expression level of selected MiAGs in sepsis. Single-cell sequencing analysis, mitochondrial membrane potential (MMP), reactive oxygen species (ROS), and ATP detection were used to verify the influence of MiAGs on mitochondrial dysfunction in sepsis.

RESULTS

A total of 5 MiAGs of sepsis were screened. Based on MiAGs, sepsis MiAGs subtypes were analyzed, where Cluster A had a better prognosis than Cluster B, and there were significant differences in immune infiltration between the two clusters. The sepsis mitochondrial prognosis model suggested that the high MiAG score group had a shorter survival time compared to the low MiAG score group. Significant differences were also observed in the immune microenvironment between the high and low MiAG score groups. Prognostic analysis and the Nomogram indicated that the MiAG score is an independent prognostic factor in sepsis. Single-cell sequencing analysis exhibited the possible influence of MiAGs on the mitochondrial function of monocytes. Finally, the downregulation of the COX7B could effectively improve mitochondrial function in the LPS-stimulated sepsis model.

CONCLUSION

Our findings suggest that MiAGs can be used to predict the prognosis of sepsis and that regulating the mitochondrial prognostic gene COX7B can effectively improve the mitochondrial function of immune cells in sepsis.

Analysis of lactate metabolism-related genes and their association with immune infiltration in septic shock via bioinformatics method

Background: Lactate, as an essential clinical evaluation index of septic shock, is crucial in the incidence and progression of septic shock. This study aims to investigate the differential expression, regulatory relationship, clinical diagnostic efficacy, and immune infiltration of lactate metabolism-related genes (LMGs) in septic shock. Methods: Two sepsis shock datasets (GSE26440 and GSE131761) were screened from the GEO database, and the common differentially expressed genes (DEGs) of the two datasets were screened out. LMGs were selected from the GeneCards database, and lactate metabolism-related DEGs (LMDEGs) were determined by integrating DEGs and LMGs. Protein-protein interaction networks, mRNA-miRNA, mRNA-RBP, and mRNA-TF interaction networks were constructed using STRING, miRDB, ENCORI, and CHIPBase databases, respectively. Receiver operating characteristic (ROC) curves were constructed for each of the LMDEGs to evaluate the diagnostic efficacy of the expression changes in relation to septic shock. Finally, immune infiltration analysis was performed using ssGSEA and CIBERSORT. Results: This study identified 10 LMDEGs, including LDHB, STAT3, LDHA, GSR, FOXM1, PDP1, GCDH, GCKR, ABCC1, and CDKN3. Enrichment analysis revealed that DEGs were significantly enriched in pathways such as pyruvate metabolism, hypoxia pathway, and immune-inflammatory pathways. PPI networks based on LMDEGs, as well as 148 pairs of mRNA-miRNA interactions, 243 pairs of mRNA-RBP interactions, and 119 pairs of mRNA-TF interactions were established. ROC curves of eight LMDEGs (LDHA, GSR, STAT3, CDKN3, FOXM1, GCKR, PDP1, and LDHB) with consistent expression patterns in two datasets had an area under the curve (AUC) ranging from 0.662 to 0.889. The results of ssGSEA and CIBERSORT both showed significant differences in the infiltration of various immune cells, including CD8 T cells, T regulatory cells, and natural killer cells, and LMDEGs such as STAT3, LDHB, LDHA, PDP1, GSR, FOXM1, and CDKN3 were significantly associated with various immune cells. Conclusion: The LMDEGs are significantly associated with the immune-inflammatory response in septic shock and have a certain diagnostic accuracy for septic shock.