Identification of crucial genes related to heart failure based on GEO database.

BACKGROUND: The molecular biological mechanisms underlying heart failure (HF) remain poorly understood. Therefore, it is imperative to use innovative approaches, such as high-throughput sequencing and artificial intelligence, to investigate the pathogenesis, diagnosis, and potential treatment of HF. METHODS: First, we initially screened Two data sets (GSE3586 and GSE5406) from the GEO database containing HF and control samples from the GEO database to establish the Train group, and selected another dataset (GSE57345) to construct the Test group for verification. Next, we identified the genes with significantly different expression levels in patients with or without HF and performed functional and pathway enrichment analyses. HF-specific genes were identified, and an artificial neural network was constructed by Random Forest. The ROC curve was used to evaluate the accuracy and reliability of the constructed model in the Train and Test groups. Finally, immune cell infiltration was analyzed to determine the role of the inflammatory response and the immunological microenvironment in the pathogenesis of HF. RESULTS: In the Train group, 153 significant differentially expressed genes (DEGs) associated with HF were found to be abnormal, including 81 down-regulated genes and 72 up-regulated genes. GO and KEGG enrichment analyses revealed that the down-regulated genes were primarily enriched in organic anion transport, neutrophil activation, and the PI3K-Akt signaling pathway. The upregulated genes were mainly enriched in neutrophil activation and the calcium signaling. DEGs were identified using Random Forest, and finally, 16 HF-specific genes were obtained. In the ROC validation and evaluation, the area under the curve (AUC) of the Train and Test groups were 0.996 and 0.863, respectively. CONCLUSIONS: Our research revealed the potential functions and pathways implicated in the progression of HF, and designed an RNA diagnostic model for HF tissues using machine learning and artificial neural networks. Sensitivity, specificity, and stability were confirmed by ROC curves in the two different cohorts.

Clinical Value and Mechanism of Long Non-Coding RNA UCA1 in Acute Respiratory Distress Syndrome Induced by Cardiopulmonary Bypass.

AIM: Long non-coding RNA (lncRNA) can be used as a biological marker for the diagnosis and treatment of various diseases. The study aimed to detect changes in the expression of lncRNA for urothelial carcinoma associated 1 (UCA1) in patients with cardiopulmonary bypass (CPB)-induced acute respiratory distress syndrome (ARDS). Clinical values and cell function in ARDS were explored. METHOD: In total, 195 patients without CPB-induced ARDS were included in the control group, and 85 patients with ARDS were included in the ARDS group. Serum UCA1 levels were measured by quantitative real-time polymerase chain reaction. A549 was used for the cell experiments by establishing oxygen-glucose deprivation/reperfusion (OGD/R) cell models, and the cell viability and apoptosis were tested. The concentration of inflammatory factors was tested by an enzyme-linked immunosorbent assay. A luciferase reporting assay was applied for target gene analysis. RESULTS: Quantitative real-time polymerase chain reaction revealed a gradual increase in serum UCA1 in both control and ARDS cases, and patients with ARDS had higher levels of UCA1 than those in the control group. Serum UCA1 was positively correlated with serum tumour necrosis factor-α and interleukin-6 concentration in patients with ARDS. UCA1 had the ability to distinguish patients with ARDS from those without it. UCA1 inhibition protected against lung injury and inhibited cell inflammation in vitro. MicroRNA (miR-182-5p) was downregulated in OGD/R-induced cell models and sponged by UCA1. CONCLUSIONS: Elevated expression of UCA1 may be associated with the occurrence of ARDS after CPB surgery. The regulatory role of UCA1 in ARDS might be related to inflammation and downregulated miR-182-5p in alveolar epithelial cells.