Heart failure is a global health problem and the number of sufferers is increasing as the population grows and ages. Existing diagnostic techniques for heart failure have various limitations in the clinical setting and there is a need to develop a new diagnostic model to complement the existing diagnostic methods. In recent years, with the development and improvement of gene sequencing technology, more genes associated with heart failure have been identified. We screened for differentially expressed genes in heart failure using available gene expression data from the Gene Expression Omnibus database and identified 6 important genes by a random forest classifier (ASPN, MXRA5, LUM, GLUL, CNN1, and SERPINA3). And we have successfully constructed a new heart failure diagnostic model using an artificial neural network and validated its diagnostic efficacy in a public dataset. We calculated heart failure-related differentially expressed genes and obtained 24 candidate genes by random forest classification, and selected the top 6 genes as important genes for subsequent analysis. The prediction weights of the genes of interest were determined by the neural network model and the model scores were evaluated in 2 independent sample datasets (GSE16499 and GSE57338 datasets). Since the weights of RNA-seq predictions for constructing neural network models were theoretically more suitable for disease classification of RNA-seq data, the GSE57338 dataset had the best performance in the validation results. The diagnostic model derived from our study can be of clinical value in determining the likelihood of HF occurring through cardiac biopsy. In the meantime, we need to further investigate the accuracy of the diagnostic model based on the results of our study.

To analyze the pharmacological mechanism of Epimedium in regulating heart failure (HF) based on the network pharmacology method, and to provide a reference for the clinical application of Epimedium in treating HF. Obtaining the main active ingredients and their targets of Epimedium through TCMSP (Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform) database. Access to major HF targets through Genecards, OMIM, PharmGKB, Therapeutic Target Database, Drug Bank database. Protein interaction analysis using String platform and construction of PPI network. Subsequently, Cytoscape software was used to construct the "Epimedium active ingredient-heart failure target" network. Finally, the molecular docking is verified through the Systems Dock Web Site. The core active ingredients of Epimedium to regulate HF are quercetin, luteolin, kaempferol, etc. The core targets are JUN, MYC, TP53, HIF1A, ESR1, RELA, MAPK1, etc. Molecular docking validation showed better binding activity of the major targets of HF to the core components of Epimedium. The biological pathways that Epimedium regulates HF mainly act on lipid and atherosclerotic pathways, PI3K-Akt signaling pathway, and chemoattractant-receptor activation. And its molecular functions are mainly DNA-binding transcription factor binding, RNA polymerase II-specific DNA-binding transcription factor binding, and neurotransmitter receptor activity. This study reveals the multi-component, multi-target and multi-pathway mechanism of action of Epimedium in regulating mental failure, and provides a basis for the clinical development and utilization of Epimedium to intervene in HF.

We know that cancer is rich in neutrophil extracellular traps (NETs) and NETs can promote breast cancer (BC) metastasis, but whether NETs-related genes are associated with the prognosis of BC patients is unclear. As part of this study, we used the TCGA database to obtain 1113 BC samples and 113 normal samples and screened for 102 differentially expressed genes associated with NETs. Following that, we modeled the prognostic risk for six genes (CYBA, RAC2, ITGAL, C3 down-regulated and VDAC1, SLC25A5 up-regulated) using multivariate Cox regression and LASSO regression analyses. In order to determine the risk groups for BC patients, we calculated a risk score and then classified the patients into high and low risk groups based on their median risk value. A significant difference in survival rates was found between high-risk and low-risk BC patients (p < 0.001), according to Kaplan-Meier survival analysis. The same conclusion was obtained for the dataset we obtained in the GEO database. An independent prognostic analysis of the constructed model revealed that the risk score correlated with BC survival independently of other clinical features. And the clinical correlation analysis showed that the change model correlated with the patient's age, gender, the stage of the tumor and the T-stage of the tumor. Furthermore, the risk values of our constructed Nomogram model were less than 0.01 in both univariate and multivariate, correlated with BC prognosis, and were independent of other clinical characteristics. According to the analysis of mutated genes in BC patients, the mutated genes in high and low risk BC patients were PIK3CA, TP53, TTN, CDH1, GATA3, MUC16, KMT2C, MAP3K1, HMCN1, RYR2, FLG, USH2A, SYNE2, ZFHX5 and PTEN. A comparison of immune cell differences between high and low risk groups revealed relatively lower levels of infiltrating immune cells in the high risk group. It is concluded that BC patients' prognosis can be independently predicted by risk profiles derived from the NET-related gene expression.