Results for “FDX1” | Search PubMed

PubMed2025

CYFIP1 coordinate with RNMT to induce osteosarcoma cuproptosis via AURKAIP1 m7G modification.

Osteosarcoma (OS) presents challenges due to its genomic instability and complexity, necessitating investigation into its oncogenesis and progression mechanisms. Recent studies have implicated m7G, a post-transcriptional modification, in the development of various cancers. However, research on m7G modification in OS remains limited. This study aimed to explore the impact of m7G modification in OS, focusing on the role and mechanism of CYFIP1, a member of m7G cap binding complexes. Our findings demonstrated prominent anti-OS effects of CYFIP1 in vitro and vivo. Mechanistically, CYFIP1 collaborated with RNMT to induce the m7G methylation of AURKAIP1 mRNA, which resulted in the stability and the increasing translation of AURKAIP1 mRNA. AURKAIP1, a kind of mitochondrial small ribosomal subunit protein, exhibited increased expression, leading to the dysregulation of mitochondrial translation. This, in turn, caused an increase in the expression of FDX1, eventually triggering cuproptosis in OS cells and repressing OS occurrence and progression. In summary, our study identified the CYFIP1/RNMT/AURKAIP1/FDX1 axis as a potential therapeutic target for OS. These insights contribute to OS research and may guide the development of novel treatments for this challenging disease.

Humans↗

PubMed2026

Analysis of end-stage renal disease mediated by cuproptosis-related genes.

OBJECTIVE: The complex pathophysiological mechanism of end-stage renal disease (ESRD) has not been fully understood. Cuproptosis is a newly discovered type of programmed cell death. Therefore, this study attempts to clarify the relationship between cuproptosis-related genes (CRGs) and the phenotype of ESRD. MATERIALS AND METHODS: The National Center for Biological Information Gene Expression Omnibus database was applied to obtain the GSE37171 dataset comprising whole-genome microarray analysis of peripheral blood samples. A 3 : 1 case-control design was employed with 75 ESRD patients and 20 healthy controls who were frequency-matched for age, sex, and ethnicity. Based on differentially expressed genes (DEGs) and genes related to cuproptosis, CRGs were identified. Thereafter, we explored two different subpopulations based on the cuproptosis gene and analyzed their expression and immune infiltration. Genes specific to the CRG cluster were identified through the weighted gene co-expression network analysis algorithm, and the best prediction model was determined and verified by four machine learning methods. RESULTS: The study identified 14 differentially expressed CRGs, among which ATP7B, SLC31A1, LIAS, LIPT1, DLD, MTF1, CDKN2A, DBT, and DLST had relatively high expression levels in the ESRD samples. Compared with the control group, expression levels of FDX1, DLAT, PDHA1, PDHB, and GLS were significantly lower in the ESRD group, and CRGs played a key role in the regulation of immune infiltration in ESRD. Two cuproptosis-related molecular clusters were identified in the ESRD samples. Cluster2 was more correlated with the immune infiltration of ESRD. By analyzing the intersection points between CRG cluster and key genes of ESRD, a total of 888 specific DEGs were identified. Functional differences related to specific DEGs were further explored using gene set variation analysis. Five significant genes (SMC5, USP47, USP53, AGA, and DMXL1) were identified by the support vector machine model as key predictors for ESRD disease risk, achieving an area under the curve (AUC) of 1.00 in internal validation. However, external validation in independent cohorts is required prior to clinical application. Individual gene analysis showed an AUC > 0.81 in discriminating ESRD patients from healthy controls, and the expression of all 5 genes in ESRD patients was significantly lower than in the control group. CONCLUSION: This study clarified the relationship between CRGs and the phenotype of ESRD, analyzed their specific roles in the immune microenvironment, and obtained a predictive model, providing new insights for the study of its potential therapeutic targets.

Humans↗