Mitochondrial quality control in abdominal aortic aneurysm: From molecular mechanisms to therapeutic strategies.

Mitochondria are the energy supply sites of cells and are crucial for eukaryotic life. Mitochondrial dysfunction is involved in the pathogenesis of abdominal aortic aneurysm (AAA). Multiple mitochondrial quality control (MQC) mechanisms, including mitochondrial DNA repair, biogenesis, antioxidant defense, dynamics, and autophagy, play vital roles in maintaining mitochondrial homeostasis under physiological and pathological conditions. Abnormalities in these mechanisms may induce mitochondrial damage and dysfunction leading to cell death and tissue remodeling. Recently, many clues suggest that dysregulation of MQC is closely related to the pathogenesis of AAA. Therefore, specific interventions targeting MQC mechanisms to maintain and restore mitochondrial function have become promising therapeutic methods for the prevention and treatment of AAA.

Identification of the prognostic value of ferroptosis-related gene signature in breast cancer patients.

BACKGROUND: Breast cancer (BRCA) is a malignant tumor with high morbidity and mortality, which is a threat to women's health worldwide. Ferroptosis is closely related to the occurrence and development of breast cancer. Here, we aimed to establish a ferroptosis-related prognostic gene signature for predicting patients' survival. METHODS: Gene expression profile and corresponding clinical information of patients from The Cancer Genome Atlas (TCGA) database and Gene Expression Omnibus (GEO) database. The Least absolute shrinkage and selection operator (LASSO)-penalized Cox regression analysis model was utilized to construct a multigene signature. The Kaplan-Meier (K-M) and Receiver Operating Characteristic (ROC) curves were plotted to validate the predictive effect of the prognostic signature. Gene Ontology (GO) and Kyoto Encyclopedia of Genes, Genomes (KEGG) pathway and single-sample gene set enrichment analysis (ssGSEA) were performed for patients between the high-risk and low-risk groups divided by the median value of risk score. RESULTS: We constructed a prognostic signature consisted of nine ferroptosis-related genes (ALOX15, CISD1, CS, GCLC, GPX4, SLC7A11, EMC2, G6PD and ACSF2). The Kaplan-Meier curves validated the fine predictive accuracy of the prognostic signature (p < 0.001). The area under the curve (AUC) of the ROC curves manifested that the ferroptosis-related signature had moderate predictive power. GO and KEGG functional analysis revealed that immune-related responses were largely enriched, and immune cells, including activated dendritic cells (aDCs), dendritic cells (DCs), T-helper 1 (Th1), were higher in high-risk groups (p < 0.001). Oppositely, type I IFN response and type II IFN response were lower in high-risk groups (p < 0.001). CONCLUSION: Our study indicated that the ferroptosis-related prognostic signature gene could serve as a novel biomarker for predicting breast cancer patients' prognosis. Furthermore, we found that immunotherapy might play a vital role in therapeutic schedule based on the level and difference of immune-related cells and pathways in different risk groups for breast cancer patients.

Identification of key monocytes/macrophages related gene set of the early-stage abdominal aortic aneurysm by integrated bioinformatics analysis and experimental validation.

Objective: Abdominal aortic aneurysm (AAA) is a lethal peripheral vascular disease. Inflammatory immune cell infiltration is a central part of the pathogenesis of AAA. It's critical to investigate the molecular mechanisms underlying immune infiltration in early-stage AAA and look for a viable AAA marker. Methods: In this study, we download several mRNA expression datasets and scRNA-seq datasets of the early-stage AAA models from the NCBI-GEO database. mMCP-counter and CIBERSORT were used to assess immune infiltration in early-stage experimental AAA. The scRNA-seq datasets were then utilized to analyze AAA-related gene modules of monocytes/macrophages infiltrated into the early-stage AAA by Weighted Correlation Network analysis (WGCNA). After that, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment analysis for the module genes was performed by ClusterProfiler. The STRING database was used to create the protein-protein interaction (PPI) network. The Differentially Expressed Genes (DEGs) of the monocytes/macrophages were explored by Limma-Voom and the key gene set were identified. Then We further examined the expression of key genes in the human AAA dataset and built a logistic diagnostic model for distinguishing AAA patients and healthy people. Finally, real-time quantitative polymerase chain reaction (RT-qPCR) and Enzyme Linked Immunosorbent Assay (ELISA) were performed to validate the gene expression and serum protein level between the AAA and healthy donor samples in our cohort. Results: Monocytes/macrophages were identified as the major immune cells infiltrating the early-stage experimental AAA. After pseudocell construction of monocytes/macrophages from scRNA-seq datasets and WGCNA analysis, four gene modules from two datasets were identified positively related to AAA, mainly enriched in Myeloid Leukocyte Migration, Collagen-Containing Extracellular matrix, and PI3K-Akt signaling pathway by functional enrichment analysis. Thbs1, Clec4e, and Il1b were identified as key genes among the hub genes in the modules, and the high expression of Clec4e, Il1b, and Thbs1 was confirmed in the other datasets. Then, in human AAA transcriptome datasets, the high expression of CLEC4E, IL1B was confirmed and a logistic regression model based on the two gene expressions was built, with an AUC of 0.9 in the train set and 0.79 in the validated set. Additionally, in our cohort, we confirmed the increased serum protein levels of IL-1ß and CLEC4E in AAA patients as well as the increased expression of these two genes in AAA aorta samples. Conclusion: This study identified monocytes/macrophages as the main immune cells infiltrated into the early-stage AAA and constructed a logistic regression model based on monocytes/macrophages related gene set. This study could aid in the early diagnostic of AAA.

Cellular senescence and abdominal aortic aneurysm: From pathogenesis to therapeutics.

As China's population enters the aging stage, the threat of abdominal aortic aneurysm (AAA) mainly in elderly patients is becoming more and more serious. It is of great clinical significance to study the pathogenesis of AAA and explore potential therapeutic targets. The purpose of this paper is to analyze the pathogenesis of AAA from the perspective of cellular senescence: on the basis of clear evidence of cellular senescence in aneurysm wall, we actively elucidate specific molecular and regulatory pathways, and to explore the targeted drugs related to senescence and senescent cells eliminate measures, eventually improve the health of patients with AAA and prolong the life of human beings.

Through network pharmacology and molecular docking to explore the underlying mechanism of Artemisia annua L. treating in abdominal aortic aneurysm.

Background: Abdominal aortic aneurysm (AAA) is a degenerative disease that causes health problems in humans. However, there are no effective drugs for the treatment of AAA. Artemisia annua L. (A. annua) is a traditional herbal that has been widely used in cardiovascular disease. Based on network pharmacology and molecular docking technology, this study predicted the practical components and potential mechanisms of A. annua inhibiting the occurrence and development of AAA. Methods: The main active ingredients and targets of A. annua were screened through the TCMSP database; the GeneCards, OMIM, PharmGkb, and TTD databases were used to search for the targeted genes of AAA and map them to the targets of the active ingredients to obtain the active ingredient therapy of A. annua. The targets of AAA were to construct a protein interaction network through the STRING platform. R software was used to carry out the enrichment analysis of GO and KEGG for relevant targets, and Cytoscape was used to construct the active ingredient-target network prediction model of A. annua. Finally, AutoDock Vina was used to verify the results of the active ingredients and critical targets. Results: The main active ingredients obtained from A. annua for the treatment of AAA include quercetin, luteolin, kaempferol, isorhamnetin, and artemetin, as well as 117 effective targets, including RELA, MAPK14, CCND1, MAPK1, AKT1, MYC, MAPK8, TP53, ESR1, FOS, and JUN. The 11 targeted genes might play a key role in disease treatment. Enriched in 2115 GO biological processes, 159 molecular functions, 56 cellular components, and 156 KEGG pathways, inferred that its mechanism of action might be related to PI3K-Akt signaling pathway, fluid shear stress, atherosclerosis, and AGE-RAGE signaling pathway. Molecular docking results showed that the top five active components of A. annua had a good affinity for core disease targets and played a central role in treating AAA. The low binding energy molecular docking results provided valuable information for the development of drugs to treat AAA. Conclusion: Therefore, A. annua may have multiple components, multiple targets, and multiple signaling pathways to play a role in treating AAA. A. annua may have the potential to treat AAA.