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Exploring Ferroptosis-Associated Gene Signatures as Diagnostic and Therapeutic Targets for Sepsis-Induced
Haobin Huang1, Chenbo Ge1, Yawei Dai1
1Cardiovascular Surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, CHN.
Insights
Sepsis-induced cardiomyopathy involves ferroptosis, a cell death process. This study identified key ferroptosis genes as potential biomarkers for sepsis-induced cardiomyopathy, offering new diagnostic and therapeutic avenues.
Area of Science:
- Cardiovascular Biology
- Molecular Medicine
- Cell Death Mechanisms
Background:
- Sepsis-induced cardiomyopathy (SICM) is a critical complication of sepsis with high mortality.
- The molecular mechanisms of SICM are not fully understood, especially the role of ferroptosis.
Purpose of the Study:
- To investigate the role of ferroptosis in SICM.
- To identify potential biomarkers and therapeutic targets for SICM.
Main Methods:
- Analyzed cardiac gene expression data from SICM patients and controls.
- Identified differentially expressed ferroptosis-related genes (DE-FRGs) using differential expression analysis.
- Applied machine learning (LASSO, SVM-RFE) for biomarker discovery and GSEA for pathway analysis.
Main Results:
- Identified 145 DE-FRGs, implicating lipid metabolism and insulin resistance pathways.
- Pinpointed five key DE-FRGs (NCOA4, GABARAPL1, GJA1, CISD1, CP) with strong predictive potential for SICM.
- Revealed complex post-transcriptional regulatory networks involving these key genes.
Conclusions:
- Ferroptosis plays a significant role in the pathophysiology of SICM.
- Identified novel DE-FRGs as potential diagnostic biomarkers and therapeutic targets for SICM.
- Advanced understanding of SICM molecular mechanisms to guide future treatment strategies.
Background:
Sepsis-induced cardiomyopathy (SICM) is a severe complication of sepsis associated with high mortality rates. Despite its significance, the molecular mechanisms underlying SICM remain poorly understood, particularly the role of ferroptosis - a form of iron-dependent programmed cell death.
Methodology:
This study analyzed the GSE79962 dataset from the Gene Expression Omnibus, containing cardiac gene expression profiles from SICM patients and controls. A list of ferroptosis-related genes (FRGs) was retrieved from the FerrDb. We used the limma package in R for differential expression analysis, setting an adjusted P-value cutoff of <0.05 and a log2-fold change threshold of ±1 to identify differentially expressed ferroptosis-related genes (DE-FRGs). We applied machine learning algorithms for biomarker identification, including least absolute shrinkage and selection operator (LASSO) logistic regression and support vector machine with recursive feature elimination (SVM-RFE), implemented via the glmnet and e1071 packages in R, respectively. Gene set enrichment analysis (GSEA) was conducted using the GSEA package to investigate the biological pathways related to key DE-FRGs.
Results:
After differential expression analysis, we identified 145 DE-FRGs. Functional enrichment analyses underscored the involvement of these genes in critical biological processes and pathways, such as lipid metabolism and insulin resistance. Machine learning approaches pinpointed five key DE-FRGs (NCOA4, GABARAPL1, GJA1, CISD1, CP), with strong predictive potential for SICM. Further analyses, including the construction of a ceRNA network, revealed intricate post-transcriptional regulatory mechanisms that may influence the expression of these key genes.
Conclusions:
Our findings highlight the central role of ferroptosis in SICM and identify potential biomarkers and therapeutic targets that could help refine diagnostic and treatment strategies. This study advances our understanding of the molecular underpinnings of SICM and sets the stage for future research aimed at mitigating this severe sepsis complication.
