Identification of energy metabolism-related biomarkers for risk prediction of heart failure patients using random forest algorithm

ALOX15-Mediated Neuron Ferroptosis Was Involved in Diabetic Peripheral Neuropathic Pain

BACKGROUND

Diabetic peripheral neuropathic pain (DPNP) is one of the most common complications in diabetic patients. Current treatment strategies primarily focus on blood glucose control and pain relief, but they often yield limited effects. Ferroptosis, a regulated form of cell death driven by lipid peroxidation and iron imbalance, plays a crucial role in various diseases, including neuropathic pain.

METHODS

In this study, we employed a combined bioinformatics and machine learning approach to identify genes most strongly associated with DPNP and ferroptosis. Subsequently, we established a DPNP mouse model via streptozotocin (STZ) injection and a high-glucose-induced SH-SY5Y cell injury model. ALOX15 was knocked down in the in vitro model using siRNA transfection.

RESULTS

Bioinformatics analysis identified ALOX15 as a hub gene linking DPNP and ferroptosis. In both in vivo and in vitro DPNP models, ALOX15 expression was significantly upregulated and correlated with ferroptosis biomarkers. Knockdown of ALOX15 in the cellular model mitigated high-glucose-induced ferroptosis, reduced lipid peroxidation and free iron ion accumulation, and restored cell viability.

CONCLUSION

In conclusion, ALOX15 contributes to the onset and progression of DPNP by promoting ferroptosis, and its knockdown effectively suppresses ferroptosis, providing a novel target and strategy for DPNP treatment.

Metabolomics reveals the mechanism of Zhilong Huoxue Tongyu capsule in the treatment of heart failure

Energy deprivation in cardiomyocytes is a pivotal factor in the progression of heart failure (HF). Zhilong Huoxue Tongyu capsule (ZL) is a widely used Chinese patent medicine that has been employed in the treatment of various cardiovascular diseases. However, its effects on HF and its impact on cardiac metabolism remain to be elucidated. This study aims to validate the therapeutic effects of ZL on heart failure and analyze its influence on human cardiac metabolism through clinical trials and untargeted metabolomics research. A cohort of 80 HF patients was enrolled, all of whom received conventional treatment (CT) in conjunction with ZL. Primary therapeutic endpoints included left ventricular ejection fraction, brain natriuretic peptide levels, 6-min walking distance, the Minnesota Living with Heart Failure Questionnaire score, and traditional Chinese medicine (TCM) syndrome scores. Ultra-high performance liquid chromatography-tandem mass spectrometry was utilized to identify key compounds, core targets, and pathways implicated in the anti-HF effects of CT combined with ZL. Seventy-six subjects completed the clinical study. Post-treatment, significant improvements were observed in heart function, exercise endurance, quality of life, and TCM syndrome scores. Untargeted metabolomics screening identified 57 differential metabolites in the serum of subjects pre- and post-treatment, including PC 20:2_20:2 and cyclic acid, among others. Of these, 25 metabolites were upregulated, while 32 were downregulated. Pathway analysis indicated that these differential metabolites were involved in riboflavin metabolism, the citrate cycle, alanine, aspartate and glutamate metabolism, arginine biosynthesis, butanoate metabolism, lipoic acid metabolism, and fatty acid biosynthesis. The combination of CT and ZL for HF treatment exhibits promising clinical efficacy, potentially mediated through the optimization of cardiac energy metabolism.

Identification of Key lncRNAs in Gout Under Copper Death and Iron Death Mechanisms: A Study Based on ceRNA Network Analysis and Random Forest Algorithm

This study focused on identifying potential key lncRNAs associated with gout under the mechanisms of copper death and iron death through ceRNA network analysis and Random Forest (RF) algorithm, which aimed to provide new insights into the molecular mechanisms of gout, and potential molecular targets for future therapeutic strategies of gout. Initially, we conducted an in-depth bioinformatics analysis of gout microarray chips to screen the key cuproptosis-related genes (CRGs) and key ferroptosis-related genes (FRGs). Using these data, we constructed a key ceRNA network for gout. Finally, key lncRNAs associated with gout were identified through the RF algorithm combined with ROC curves, and validated using the Comparative Toxicogenomics Database (CTD). We successfully identified NLRP3, LIPT1, and DBT as key CRGs associated with gout, and G6PD, PRKAA1, LIG3, PHF21A, KLF2, PGRMC1, JUN, PANX2, and AR as key FRGs associated with gout. The key ceRNA network identified four downregulated key lncRNAs (SEPSECS-AS1, LINC01054, REV3L-IT1, and ZNF883) along with three downregulated mRNAs (DBT, AR, and PRKAA1) based on the ceRNA theory. According to CTD validation inference scores and biological functions of target mRNAs, we identified a potential gout-associated lncRNA ZNF883/hsa-miR-539-5p/PRKAA1 regulatory axis. This study identified the key lncRNA ZNF883 in the context of copper death and iron death mechanisms related to gout for the first time through the application of ceRNA network analysis and the RF algorithm, thereby filling a research gap in this field and providing new insights into the molecular mechanisms of gout. We further found that lncRNA ZNF883 might function in gout patients by regulating PRKAA1, the mechanism of which was potentially related to uric acid reabsorption in the proximal renal tubules and inflammation regulation. The proposed lncRNA ZNF883/hsa-miR-539-5p/PRKAA1 regulatory axis might represent a potential RNA regulatory pathway for controlling the progression of gout disease. This discovery offered new molecular targets for the treatment of gout, and had significant implications for future therapeutic strategies in managing the gout.

Identification and functional analysis of energy metabolism and pyroptosis-related genes in diabetic nephropathy

Background

Energy metabolism and pyroptosis are integral to the pathogenesis of diabetic nephropathy (DN). However, the precise roles of energy metabolism and pyroptosis in DN development remain unclear. This study aims to elucidate the roles of energy metabolism- and pyroptosis-related differentially expressed genes (EMAPRDEGs) in DN development.

Methods

EMAPRDEGs were identified by querying the GeneCards and Gene Expression Omnibus (GEO) databases. Subsequent analyses included Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment, Gene Set Enrichment Analysis (GSEA), and Protein-Protein Interaction (PPI) network analysis. Additionally, mRNA-miRNA, mRNA-drug, and mRNA-transcription factor (TF) interaction networks were constructed. Differential expression and receiver operating characteristic (ROC) curve analyses were performed to evaluate the diagnostic potential of EMAPRDEGs. Immune cell infiltration in DN was assessed using the ssGSEA algorithm, and the expression levels of EMAPRDEGs in DN tissues were validated by quantitative real-time PCR (qRT-PCR).

Results

Thirteen EMAPRDEGs were identified, with GO and KEGG analyses indicating their involvement in energy metabolism pathways. GSEA revealed significant enrichment of these genes in biological pathways associated with diabetic nephropathy. PPI network analysis highlighted the central role of these genes within the relevant pathways. Predictive modeling demonstrated interactions between EMAPRDEGs, 69 miRNAs, and 117 TFs. Immune infiltration analysis showed substantial alterations in immune cell populations, with ADH1B and PC showing a significant correlation with natural killer cells and memory B cells. ROC curve analysis confirmed the diagnostic potential of EMAPRDEGs for diabetic nephropathy. qRT-PCR validated the expression patterns of CASP1, IL-18, PDK4, and FBP1, which were consistent with the bioinformatics predictions.

Conclusion

Bioinformatics analysis identified 13 candidate EMAPRDEGs, among which CASP1, IL-18, PDK4, and FBP1 emerge as potential biomarkers for diabetic nephropathy.

Identification of transcription factor-lipid droplet-related gene biomarkers for the prognosis of post-acute myocardial infarction-induced heart failure

Introduction

Patients with acute myocardial infarction (AMI) are at high risk of progressing to heart failure (HF). Recent research has shown that lipid droplet-related genes (LDRGs) play a crucial role in myocardial metabolism following MI, thereby influencing the progression to HF.

Methods

Weighted gene co-expression network analysis (WGCNA) and differential expression gene analysis were used to screen a transcriptome dataset of whole blood cells from AMI patients with (AMI HF, n = 16) and without progression (AMI no-HF, n = 16). Functional enrichment analysis were performed to observe the involved function. Machine learning methods were used to screen the genes related to prognosis. Transcriptional factors (TF) were predicted by using relevant databases. ROC curves were drawn to evaluate the TF-LDRG pair in predicting HF in the validation dataset (n = 16) and the clinical trial (n = 13).

Results

The 235 identified genes were primarily involved in pathways related to fatty acid and energy metabolism. 22 genes were screened out that they were strongly associated with prognosis. 35 corresponding transcription factors were predicted. The TF-LDRG pair, ABHD5-ARID3a, was demonstrated good predictive accuracy.

Discussion

Our findings suggest that ABHD5-ARID3a have significant potential as predictive biomarkers for heart failure post-AMI which also provides a foundation for further exploration into the molecular mechanisms underlying the progression from AMI to HF.

LncRNA DANCR suppresses acute myocardial infarction in mice via mediating p-RXRA/TRAF2/NIK/IKK/NF-κB signaling pathway

OBJECTIVES

This study aimed to investigate the role of LncRNA differentiation antagonizing non-protein coding RNA (DANCR) in acute myocardial infarction (AMI).

METHODS

A mouse model of AMI was established, and the cardiac contractile function was detected. Next, cardiomyocytes treated with oxygen-glucose deprivation (OGD) were used for gain- and loss-function experiments in vitro. RIP analysis was used to verify the binding of DANCR and Retinoid X receptor alpha (RXRA), and Co-IP assay was used to measure the binding of phosphorylated RXRA to TNF receptor associated factor 2 (TRAF2).

RESULTS

The expression of DANCR in myocardial tissues of AMI mice were significantly downregulated. Overexpression of DANCR decreased myocardial infarct size and enhanced cardiac contractile function in AMI mice. Moreover, overexpression of DANCR promoted proliferation and inhibited apoptosis in OGD-induced cardiomyocytes. Mechanism studies demonstrated that DANCR interacted with RXRA and promoted glycogen synthase kinase 3beta (GSK3β)-mediated phosphorylation of RXRA, and phosphorylated RXRA interacted with TRAF2 protein to downregulate TRAF2 protein level. Bexarotene (Bex), an activator of RXRA, inhibited TRAF2 protein expression, while RXRA overexpression had no effect on TRAF2 protein expression. Bex treatment or silencing TRAF2 promoted proliferation and inhibited apoptosis in cardiomyocytes. In addition, silencing DANCR inhibited cardiomyocyte proliferation and induced apoptosis by activating the NIK/IKK/NF-κB pathway, while B022, an inhibitor of NIK, counteracted the effects of DANCR silencing on cardiomyocytes.

CONCLUSIONS

Studies demonstrated that DANCR suppressed AMI in mice by mediating p-RXRA/TRAF2/NIK/IKK/NF-κB pathway.

The transcription factor PPARA mediates SIRT1 regulation of NCOR1 to protect damaged heart cells

Background

Heart failure (HF) is a clinical syndrome with a high risk. Our previous research showed a regulatory relationship between Sirtuin 1 (SIRT1), peroxisome proliferator-activated receptor α (PPARA) and nuclear receptor co-repressor 1 (NCOR1). This study aimed to investigate the regulatory mechanism of SIRT1/PPARA/NCOR1 axis in HF.

Methods

HF models in vitro were established by doxorubicin (DOX)-induced AC16 and human cardiac microvascular endothelial cell (HCMEC) lines. The contents of atrial natriuretic peptide (ANP), brain natriuretic peptide (BNP), interleukin-1β (IL-1β), and IL-18 were detected using enzyme-linked immunosorbent assay. Then, we assessed the levels of reactive oxygen species (ROS), malondialdehyde (MDA), superoxide dismutase (SOD) and adenosine triphosphate (ATP). Moreover, the relationship between SIRT1 and PPARA was detected using the co-immunoprecipitation (Co-IP) analysis. The connection between PPARA and NCOR1 was analyzed using chromatin immunoprecipitation (ChIP).

Results

Overexpression of SIRT1 or PPARA could reduce apoptosis in DOX-induced AC16 and HCMEC cells, the levels of IL-1β, IL-18, ANP, BNP, ROS and MDA, while increasing the levels of SOD and ATP. In addition, overexpression of PPARA could increase the viability of DOX-induced cells and the levels of myosin heavy chain 6 (Myh6) and Myh7. Co-IP showed that SIRT1 interacted with PPARA. Silencing PPARA could reverse the effect of SIRT1 overexpression on DOX-induced AC16 and HCMEC cells. ChIP assay demonstrated that PPARA could bind to the promoter region of NCOR1. Silencing NCOR1 could reverse the effect of PPARA overexpression on DOX-induced AC16 and HCMEC cells.

Conclusions

This study revealed that PPARA could mediate SIRT1 to promote NCOR1 expression and thus protect damaged heart cells. The finding provided an important reference for the treatment of HF.

Computational algorithm based on health and lifestyle traits to categorize lifemetabotypes in the NUTRiMDEA cohort

Classifying individuals based on metabotypes and lifestyle phenotypes using exploratory factor analyses, cluster definition, and machine-learning algorithms is promising for precision chronic disease prevention and management. This study analyzed data from the NUTRiMDEA online cohort (baseline: n = 17332 and 62 questions) to develop a clustering tool based on 32 accessible questions using machine-learning strategies. Participants ranged from 18 to over 70 years old, with 64.1% female and 35.5% male. Five clusters were identified, combining metabolic, lifestyle, and personal data: Cluster 1 ("Westernized Millennial", n = 967) included healthy young individuals with fair lifestyle habits; Cluster 2 ("Healthy", n = 10616) consisted of healthy adults; Cluster 3 ("Mediterranean Young Adult", n = 2013) represented healthy young adults with a healthy lifestyle and showed the highest adherence to the Mediterranean diet; Cluster 4 ("Pre-morbid", n = 600) was characterized by healthy adults with declined mood; Cluster 5 ("Pro-morbid", n = 312) comprised older individuals (47% >55 years) with poorer lifestyle habits, worse health, and a lower health-related quality of life. A computational algorithm was elicited, which allowed quick cluster assignment based on responses ("lifemetabotypes"). This machine-learning approach facilitates personalized interventions and precision lifestyle recommendations, supporting online methods for targeted health maintenance and chronic disease prevention.

Identification of Potential Neddylation-related Key Genes in Ischemic Stroke based on Machine Learning Methods

Ischemic stroke (IS) is a complex neurological disease that can lead to severe disability or even death. Understanding the molecular mechanisms involved in the occurrence and progression of IS is of great significance for developing effective treatment strategies. In this context, the role of neddylation refers to the potential impact of neddylation on various cellular processes, which may contribute to the pathogenesis and outcome of IS. First, differential analysis was conducted on the GSE16561 dataset from the GEO database to identify 350 differentially expressed genes (DEGs) between the IS and Control groups. By intersecting the differential genes with neddylation-related genes, 11 neddylation-related DEGs were obtained. Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) analyses showed that the DEGs were mainly enriched in hematopoietic cell lineage and neutrophil degranulation, while the neddylation-related DEGs were mainly enriched in apoptosis and post-translational protein modification. Further Lasso-Cox and random forest analyses were performed on the 11 neddylation-related DEGs, identifying key genes SRPK1, BIRC2, and KLHL3. Additionally, validation of the key genes was carried out using the GSE58294 dataset and clinical patients. Finally, the correlation between the key genes and ferroptosis and cuproptosis was analyzed, and a ceRNA network was constructed. Our study helps to elucidate the complex role of neddylation in the mechanism of ischemic stroke, providing potential opportunities for the development of therapeutic interventions.

Exploring the mechanism of Tingli Pill in the treatment of HFpEF based on network pharmacology and molecular docking

To explore the mechanism of action of Tingli Pill (TLP) in the treatment of heart failure with preserved ejection fraction (HFpEF) by using network pharmacology and molecular docking technology. The active components and targets of TLP were screened using the TCMSP and UniProt databases. HFpEF-related targets were identified using the OMIM and GeneCards databases. Drug-disease intersection targets were obtained via Venny 2.1.0, as well as establishing the "component-target" network and screening out the core active components. Construct a protein-protein interaction network of intersecting targets using the STRING database as well as Cytoscape software and filter the core targets. Gene ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analysis of core targets were performed using the Metascape database. The core active components of TLP for HFpEF were quercetin, kaempferol, β-sitosterol, isorhamnetin and hederagenin. The core targets of TLP for HFpEF were JUN, MAPK1, TP53, AKT1, RELA, TNF, MAPK14, and IL16. Gene ontology enrichment analysis obtained 1528 biological processes, 85 cell components, and 140 molecular functions. The Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis yielded 1940 signaling pathways, mainly involved in lipid and atherosclerosis, regulation of apoptotic signaling pathway, PI3K-Akt signaling pathway, HIF-1 signaling pathway, oxidative stress, TNF signaling pathway, and IL-17 signaling pathway. TLP has the characteristics of multi-component, multi-target, and multi-pathway in the treatment of HFpEF. This study lays the foundation for revealing the pharmacodynamic substances and mechanism of TLP in the treatment of HFpEF.

Machine learning-based analysis of risk factors for chronic total occlusion in an Asian population

OBJECTIVES

Chronic total occlusion (CTO) is a form of coronary artery disease (CAD) requiring percutaneous coronary intervention. There has been minimal research regarding CTO-specific risk factors and predictive models. We developed machine learning predictive models based on clinical characteristics to identify patients with CTO before coronary angiography.

METHODS

Data from 1473 patients with CAD, including 317 patients with and 1156 patients without CTO, were retrospectively analyzed. Partial least squares discriminant analysis (PLS-DA), random forest (RF), and support vector machine (SVM) models were used to identify CTO-specific risk factors and predict CTO development. Receiver operating characteristic (ROC) curve analysis was performed for model validation.

RESULTS

For CTO prediction, the PLS-DA model included 10 variables; the ROC value was 0.706. The RF model included 42 variables; the ROC value was 0.702. The SVM model included 20 variables; the ROC value was 0.696. DeLong's test showed no difference among the three models. Four variables were present in all models: sex, neutrophil percentage, creatinine, and brain natriuretic peptide (BNP).

CONCLUSIONS

Validation of machine learning prediction models for CTO revealed that the PLS-DA model had the best prediction performance. Sex, neutrophil percentage, creatinine, and BNP may be important risk factors for CTO development.

A novel FCTF evaluation and prediction model for food efficacy based on association rule mining

Introduction

Food-components-target-function (FCTF) is an evaluation and prediction model based on association rule mining (ARM) and network interaction analysis, which is an innovative exploration of interdisciplinary integration in the food field.

Methods

Using the components as the basis, the targets and functions are comprehensively explored in various databases and platforms under the guidance of the ARM concept. The focused active components, key targets and preferred efficacy are then analyzed by different interaction calculations. The FCTF model is particularly suitable for preliminary studies of medicinal plants in remote and poor areas.

Results

The FCTF model of the local medicinal food Laoxianghuang focuses on the efficacy of digestive system cancers and neurological diseases, with key targets ACE, PTGS2, CYP2C19 and corresponding active components citronellal, trans-nerolidol, linalool, geraniol, α-terpineol, cadinene and α-pinene.

Discussion

Centuries of traditional experience point to the efficacy of Laoxianghuang in alleviating digestive disorders, and our established FCTF model of Laoxianghuang not only demonstrates this but also extends to its possible adjunctive efficacy in neurological diseases, which deserves later exploration. The FCTF model is based on the main line of components to target and efficacy and optimizes the research level from different dimensions and aspects of interaction analysis, hoping to make some contribution to the future development of the food discipline.