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Mitophagy, the selective degradation of mitochondria by autophagy, plays a crucial role in cancer progression and therapy response. This study aims to elucidate the role of mitophagy-related genes (MRGs) in cutaneous melanoma (CM) through single-cell RNA sequencing (scRNA-seq) and machine learning approaches, ultimately developing a predictive model for patient prognosis. The scRNA-seq data, bulk transcriptomic data, and clinical data of CM were obtained from publicly available databases. The single-sample gene set enrichment analysis (ssGSEA) and weighted gene co-expression network analysis (WGCNA) were used to identify gene modules associated with mitophagy phenotypes. A machine learning framework employing ten different algorithms was used to develop the prognostic model. Based on scRNA-seq data, we identified 16 distinct cell subpopulations in melanoma, and melanoma cells exhibited significantly higher mitophagy scores. The turquoise module identified via WGCNA showed the strongest correlation with mitophagy scores. A prognostic model incorporating seven genes was developed through machine learning algorithms, achieving an average C-index of 0.754 across training and validation cohorts. Functionally, low-risk patients were enriched in interferon-gamma response and inflammatory processes, whereas high-risk patients showed enrichment in glycolysis regulation and signaling pathways such as KRAS and Wnt/β-catenin. Notably, low-risk patients demonstrated enhanced immune infiltration and greater sensitivity to immunotherapy. RT-qPCR validated the expression level of 7 model genes in human melanoma cell lines and normal melanocyte cell lines. Our study provides a comprehensive understanding of MRGs in melanoma and presents a novel prognostic model. These findings enhance our understanding of the tumor microenvironment and may guide personalized treatment strategies for CM patients. [ABSTRACT FROM AUTHOR]

Radiation-induced skin toxicity, resulting from ionizing or nonionizing radiation, is a common skin disorder. However, the underlying relationship between skin microbiota and radiation-induced skin toxicity remains largely unexplored. Herein, we uncover the microbiota–skin interaction based on a genome-wide association study (GWAS) featuring 150 skin microbiota and three types of skin microenvironment. Summary datasets of human skin microbiota were extracted from the GWAS catalog database, and summary datasets of radiation-induced skin toxicity from the FinnGen biobank. Mendelian Randomization (MR) analysis was leveraged to sort out the causal link between skin microbiota and radiation-induced skin toxicity. We identified 33 causal connections between human skin microbiota and radiation-induced skin toxicity, including 19 positive and 14 negative causative directions. Among these potential associations, the genus Staphylococcus could serve as a common risk factor for radiation-induced skin toxicity, especially for radiodermatitis. And Streptococcus salivarius was identified as a potential protective factor against radiation-induced skin toxicity. Additional analysis indicated no pleiotropy, heterogeneity, or reverse causal relationship in the results. We comprehensively assessed potential associations of skin microbiota with radiation-induced skin toxicity and identified several suggestive links. Our results provide promising targets for the prevention and treatment of radiation-induced skin toxicity. [ABSTRACT FROM AUTHOR]

Within Anemarrhena asphodeloides Bunge (AAB), the pivotal bioactive constituents are identified as Anemarrhena asphodeloides Bunge total saponins (ABS). In traditional pharmacology, ABS has exhibited notable anti-inflammatory, hypoglycemic, and cardioprotective properties. Despite these observed effects, the specific protective mechanisms of ABS against metabolic diseases and improving the endocrine system remain largely uncharted. This work intends to shed light on the effects and intrinsic mechanisms of ABS on metabolic diseases. The characterization of ABS components was achieved through High-Performance Liquid Chromatography/Mass Spectrometry (HPLC/MS). To evaluate ABS's anti-inflammatory efficacy, mouse macrophages underwent analysis using the Griess method. Induced differentiation of mouse fibroblasts was assessed through Oil Red O staining. In an obesity model with C57BL/6 N mice, ABS administration prompted measurements of glucose and insulin tolerance. Western blot analysis quantified lipolysis and anti-inflammatory protein expression. Nile red staining gauged body fat content in C. elegans post-ABS treatment. The mechanism of ABS action was elucidated through mRNA sequencing, further validated using RNA interference technology, and nematode mutants. ABS showcased the ability to diminish Nitric Oxide (NO) production in inflammatory macrophages and shrink adipocyte lipid droplets. In mice experiments, ABS was effective in alleviating fat accumulation and affecting serum lipid metabolism in diabetic mice. It enhanced oral glucose tolerance and insulin tolerance while increasing lipolysis-associated protein expression. ABS notably reduced fat content in C. elegans. Mechanistically, ABS downregulated NOD-like receptor thermal protein domain associated protein 3 (NLRP3) and monoamine oxidase A (MAOA) expression while enhancing UGT, ilys-2 , and ilys-3. Lipolysis emerged as a pivotal pathway for ABS in the therapeutic intervention of metabolic diseases. Our investigation has revealed that ABS exert a role in combating metabolic diseases by enhancing the body's defense mechanisms. ABS activate the NLRP3-neurotransmitter-visceral adipose pathway in mice, thereby bolstering resistance and diminishing fat accumulation. In C. elegans , ABS downregulated the expression of MAOA, bolstered resistance, and augmented glucuronidase activity, consequently leading to a reduction in fat content. [Display omitted] [ABSTRACT FROM AUTHOR]