Identification of candidate macrophage-ferroptosis crosstalk genes associated with immune infiltration in myocardial infarction: A bioinformatics analysis

Ferroptosis Signatures in Diabetic Cardiomyopathy: Multi-Omics Discovery and Validation of ACOT1 and TXNIP

Background: Diabetic cardiomyopathy (DCM) is a serious cardiovascular complication specific to diabetes mellitus, with rising global prevalence. Ferroptosis, an iron-dependent form of regulated cell death driven by lethal lipid peroxidation, has been implicated in the pathogenesis of DCM. However, the key regulatory genes remain poorly characterized. This study aimed to identify and validate ferroptosis-related signature genes in DCM. Methods: Three murine transcriptomic datasets (GSE123975, GSE155377, and GSE210611) were retrieved from GEO and merged after batch correction. Differentially expressed genes were intersected with weighted gene co-expression network analysis disease-associated module genes and FerrDb ferroptosis annotations to define the ferroptosis-related differentially expressed gene candidate pool. LASSO regression and random forest selection then prioritized hub genes, defined operationally as candidates consistently prioritized by both machine-learning algorithms rather than by network-topological centrality. Classification performance was evaluated by ROC analysis and validated in two independent cohorts (GSE161931 and GSE274500). mMCPcounter estimated immune and stromal infiltration. ScRNA-seq (GSE290095) and spatial transcriptomic (GSE290094) profiling characterized cellular distribution, predicted cardiomyocyte network perturbations and tissue-level expression patterns. High-fat diet/streptozotocin (HFD/STZ)-induced DCM rat models provided experimental validation. Results: Acot1 and Txnip were identified as hub genes, with strong discriminatory performance in the discovery cohort (AUC = 1.000 and 0.988; in-sample estimates, n = 26) and independent external validation (AUC = 0.951 and 0.988). Immune profiling linked both genes inversely with vessel scores, and Txnip was also linked with eosinophils. Single-cell analysis localized Acot1 enrichment to cardiomyocytes and endothelial cells, while Txnip was broadly expressed across multiple cell types, with elevated levels in DCM. In silico knockout analysis predicted distinct cardiomyocyte network perturbation profiles for Acot1 and Txnip, and spatial transcriptomics revealed modest but disease-specific spatial associations between hub gene expression and ferroptosis driver scores (Acot1: rho = 0.123; Txnip: rho = 0.154). Both genes were significantly upregulated at mRNA and protein levels in HFD/STZ-induced DCM rats, with concurrent GPX4 depletion, ACSL4 accumulation, and FTH1 reduction consistent with ferroptosis activation. Conclusions: This study identifies Acot1 and Txnip as ferroptosis-related molecular signatures in DCM and provides multistep prioritization and validation spanning bulk transcriptomics, single-cell and spatial transcriptomics, and in vivo experimental verification, offering potential targets for ferroptosis-targeted therapeutic intervention.

High FIBP expression is associated with a ferroptosis-resistant transcriptional phenotype and adverse survival in acute myeloid leukemia: An integrative TCGA-LAML analysis

Background: FIBP has been linked to adverse acute myeloid leukemia (AML) features, but whether its expression identifies a ferroptosis-related biological state is unknown. We evaluated the association of FIBP with iron handling, lipid-peroxidation defense, immune signatures, and survival while explicitly separating association from causal mechanism. Methods: The KEGG ferroptosis pathway was compared with the STRING first-shell FIBP network. Legacy TCGA-LAML RNA-sequencing data were analyzed in 173 samples after log2(RSEM+1) transformation. Eleven prespecified genes covering iron homeostasis, antioxidant defense, lipid remodeling, and mitochondrial function were compared across median-defined FIBP groups and correlated with continuous FIBP expression. Marker-derived regulatory T-cell (Treg) and M2-like macrophage scores were examined exploratorily. A ferroptosis sensitivity score was defined as mean Z(TFRC, SLC11A2, ACSL4, LPCAT3) minus mean Z(FTH1, GPX4, AIFM2, DHODH). Overall survival was analyzed in 149 matched patients using Kaplan-Meier estimates, log-rank tests, 3-year restricted mean survival time (RMST), continuous-variable Cox models with proportional-hazards diagnostics, bootstrap-corrected C-index, and time-dependent area under the curve (AUC). Results: The strict STRING and KEGG sets had no direct gene overlap. Four target genes were higher in FIBP-high AML after transcriptome-wide false-discovery correction: FTH1, GPX4, AIFM2/FSP1, and LPCAT3. FIBP correlated positively with GPX4 (rho=0.619), FTH1 (rho=0.512), LPCAT3 (rho=0.502), and AIFM2 (rho=0.285), and negatively with SLC11A2 (rho=-0.220) and ACSL4 (rho=-0.196). The global FIBP-M2 association was nominal (rho=0.157; P=0.039) but did not remain significant after correction across the two immune scores (q=0.077); the Treg association was not reproduced. Survival differed among low-FIBP, high-FIBP/high-score, and high-FIBP/low-score groups (log-rank P<0.001). Three-year RMST was 729, 492, and 448 days, respectively; the two high-FIBP groups did not differ (RMST difference -44 days; bootstrap 95% CI, -236 to 137). Continuous FIBP remained associated with mortality after adjustment for the score and age (hazard ratio per SD, 1.420; 95% CI, 1.119-1.803; P=0.004), whereas the score was not independently associated (P=0.657). Adding the score did not improve the FIBP-only C-index (0.637 vs 0.636; delta 0.0008, bootstrap 95% CI, -0.0470 to 0.0014). Conclusions: High FIBP expression is associated with an iron-buffering and antioxidant transcriptional phenotype and adverse survival in AML, but the current data do not demonstrate direct FIBP control of ferroptosis or independent prognostic value of the proposed sensitivity score. GPX4-, FSP1-, and ferritin-centered defenses are testable biological hypotheses rather than established therapeutic indications.

Immune–Inflammatory Hub Genes Intersecting with a Ferroptosis-Associated Gene Set in Active Tuberculosis: A Multi-Dataset Bioinformatics Study

The progression of tuberculosis (TB) from a latent infection to an active disease involves intricate modifications in host immune, inflammatory, oxidative, and metabolic pathways. Ferroptosis represents a distinct form of regulated cell death that requires iron and is associated with excessive lipid peroxidation and has been associated with tissue damage in TB; however, its connection with host transcriptional changes during active TB is not fully understood. This study sought to identify and externally validate immune–inflammatory hub genes among differentially expressed genes (DEGs) in active TB that overlap with a ferroptosis-associated gene set derived from FerrDb, employing an integrated transcriptomic and systems-biology methodology. Differential expression analysis of GSE37250 revealed 1015 DEGs in active TB compared to latent TB, comprising 585 upregulated and 430 downregulated genes, and 93 DEGs in active TB compared to healthy controls, including 65 upregulated and 28 downregulated genes. Intersection analysis identified 94 DEGs common to the active TB versus latent TB comparison and the ferroptosis-associated gene set, and eight DEGs common to the active tuberculosis versus healthy-control comparison and the same gene set, with no genes shared across all three sets. Functional enrichment of the 94 intersection genes underscored immune response, defense response, stress response, Toll-like receptor signaling, NOD-like receptor signaling, IL-17 signaling, TNF signaling, glutathione metabolism, neutrophil degranulation, cytokine signaling, and antimicrobial metal sequestration. Protein–protein interaction analysis followed by cytoHubba prioritization identified 10 hub genes: IL1B, TLR4, CXCL10, MMP9, CYBB, MPO, CD36, LCN2, S100A8, and LTF. Subsequent to outcome-independent probe selection, external validation in GSE28623 demonstrated significant positive differential expression of LCN2, S100A8, and LTF, while GSE62525 showed significant positive differential expression of IL1B, TLR4, MMP9, MPO, LCN2, and LTF. LCN2 and LTF were significantly upregulated in both validation datasets, indicating the strongest cross-dataset reproducibility. These results identify an immune–inflammatory transcriptional network intersecting with ferroptosis-associated genes in active TB. Notably, the transcriptomic findings do not confirm ferroptotic cell death but suggest candidate genes and biological processes for future experimental exploration.

WGCNA-based identification and experimental validation of TOP2A as a key regulator of ferroptosis in sepsis-associated acute respiratory distress syndrome

CCL4 as a Potential Immune-Metabolic Biomarker in Acute Myocardial Infarction via Machine Learning and Single-Cell Transcriptomics.

Acute myocardial infarction (AMI) is a leading cause of morbidity and mortality worldwide, highlighting the need for novel complementary biomarkers. By integrating bulk and single-cell transcriptomic data with machine learning approaches, calmodulin-related genes associated with AMI and explored their immune-metabolic features were identified. Differential expression and weighted co-expression analyses revealed 60 calmodulin-related genes, from which six key genes (SOCS3, GBP4, ST14, KPNA5, STAB1 and CCL4) were screened using multiple machine learning algorithms and validated in independent datasets. Functional analyses indicated enrichment in immune, inflammatory, and metabolic pathways. Immune infiltration and single-cell transcriptomics showed cell-type-specific expression patterns, with CCL4 predominantly expressed in T and NK cells and markedly reduced in AMI samples. qPCR validation confirmed significant expression changes for four of the six genes in the local cohort. Drug-gene interaction and docking analyses suggested candidate compounds for further investigation. Collectively, the findings suggest that CCL4 may serve as a potential diagnostic biomarker for AMI, and the observed immune-metabolic associations provide a basis for future mechanistic and translational studies.

Identifying the Oxidative Stress-related Hub Genes in Dilated Cardiomyopathy by Bioinformatics Analysis.

INTRODUCTION Characterized as an idiopathic primary myocardial disorder, dilated cardiomyopathy (DCM) predominantly affects children and elderly adults. However, a lack of specific clinical symptoms and reliable biomarkers impedes timely diagnosis and rational clinical management of DCM. METHODS Whole-genome expression profiles (GSE120895, GSE9800) were retrieved from the Gene Expression Omnibus database via the GEOquery R package. A series of bioinformatic methods were employed, including DEG, GSVA, WGCNA, GO/KEGG enrichment, PPI, and immune infiltration analysis. Key biomarkers were validated by qRT-PCR in a Doxorubicin (DOX)-induced DCM model. RESULTS In total, 629 differentially expressed genes (DEGs) were screened out between DCM and control groups. Combined analysis of DEGs and WGCNA outputs identified 13 hub genes overlapping with oxidative stress-associated gene modules. Receiver Operating Characteristic (ROC) curve analyses confirmed that these hub genes exhibit favorable diagnostic efficiency for DCM. Functional enrichment results showed that these genes are mainly enriched in transmembrane transport and nucleotide metabolism pathways. Immune infiltration analysis indicated significantly elevated infiltration levels of five immune cell subsets in DCM myocardial tissues. In vivo experiments verified the significant upregulation of five core hub genes in DOX-induced DCM mice. By screening hub genes with diagnostic potency based on public transcriptome datasets and validating their expression alterations in a DOX-induced DCM animal model, this study provides partial experimental evidence to support the above bioinformatic outcomes. DISCUSSION Through integrative analysis of multiple datasets and molecular biology validation, this study provides robust evidence supporting the involvement of these hub genes in DCM pathogenesis. Although validation was limited to a single murine model, the findings lay the groundwork for future mechanistic studies and clinical exploration of these candidate genes. CONCLUSION Hub genes including MVP, WISP1, FCN1, AMPD3, RARRES1, FTL, and KRT14 possess promising auxiliary diagnostic potential, which provides novel clues for subsequent clinical evaluation research on DCM.