A Single-Cell Atlas of the Atherosclerotic Plaque in the Femoral Artery and the Heterogeneity in Macrophage Subtypes between Carotid and Femoral Atherosclerosis.

Atherosclerosis of femoral arteries can cause the insufficient blood supply to the lower limbs and lead to gangrenous ulcers and other symptoms. Atherosclerosis and inflammatory factors are significantly different from other plaques. Therefore, it is crucial to observe the cellular composition of the femoral atherosclerotic plaque and identify plaque heterogeneity in other arteries. To this end, we performed single-cell sequencing of a human femoral artery plaque. We identified 14 cell types, including endothelial cells, smooth muscle cells, monocytes, three macrophages with four different subtypes of foam cells, three T cells, natural killer cells, and B cells. We then downloaded single-cell sequencing data of carotid atherosclerosis from GEO, which were compared with the one femoral sample. We identified similar cell types, but the femoral artery had significantly more nonspecific immune cells and fewer specific immune cells than the carotid artery. We further compared the differences in the proportion of inflammatory macrophages, and resident macrophages, and the proportion of inflammatory macrophages was greater within the carotid artery. Through comparing one femoral sequencing sample with carotid samples from public datasets, our study reveals the single-cell map of the femoral artery and the heterogeneity of carotid and femoral arteries at the cellular level, laying the foundation for mechanistic and pharmacological studies of the femoral artery.

A Cuproptosis-Related lncRNAs Signature Could Accurately Predict Prognosis in Patients with Clear Cell Renal Cell Carcinoma.

Background: Clear cell renal cell carcinoma (ccRCC) is the most common subtype of kidney cancers. As cuproptosis, a new cell death mechanism proposed recently, differs from all other known mechanisms regulating cell death, we aimed to create prognostic markers using cuproptosis-related long non-coding ribonucleic acids (RNAs; lncRNAs) and elucidate the molecular mechanism. Methods: Data from transcriptome RNA sequencing of ccRCC samples and the relevant clinical data were downloaded from The Cancer Genome Atlas, and Pearson's correlation analysis was implemented to obtain the cuproptosis-related lncRNAs. Then, univariate Cox, multivariate Cox, and Least Absolute Shrinkage and Selection Operator Cox analyses were performed to construct the risk signatures. The cuproptosis-related lncRNAs predictive signature was evaluated with receiver operating characteristic curves and subgroup analysis. Finally, Gene Set Enrichment Analysis (GSEA), single-sample GSEA (ssGSEA), tumor immune microenvironment (TIME), and immune checkpoints were performed to explore the relationship between immunity and patient prognosis. Results: Five cuproptosis-related lncRNAs, including FOXD2-AS1, LINC00460, AC091212.1, AC007365.1, and AC026401.3, were used to construct the signature. In the training and test sets, low-risk groups (as identified by a risk score lower than the median) demonstrated a better prognosis with an area under the curve for 1-, 3-, and 5-year survival being 0.793, 0.716, and 0.719, respectively. GSEA analysis suggested significant enrichment of the tricarboxylic acid cycle and metabolism-related pathways in the low-risk group. Besides, both ssGSEA and TIME suggested that the high-risk group exhibited more active immune infiltration. Conclusion: We proposed a cuproptosis-related lncRNAs signature, which had the potential for prognoses and prediction. Our findings might contribute to elucidating potential genomic biomarkers and targets for future therapies in the cuproptosis-related signaling pathways.