Identification of MTFR1 as a Novel Prognostic Biomarker and Putative Oncogene for Breast Cancer: A Multi-Omics Analysis and in Vitro Experimental Validation
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引用次数: 0
Abstract
Mitochondrial Fission Regulator 1 (MTFR1) plays a critical regulatory role in various malignancies; however, its specific function and clinical significance in breast cancer (BRCA) remain unexplored. This study systematically characterised MTFR1 by integrating multi-omics data, spatial transcriptomics, and single-cell sequencing, followed by in vitro experimental validation. MTFR1 was significantly overexpressed in BRCA tissues and predominantly localised to malignant cells. Functionally, MTFR1 was closely associated with cell cycle progression; in vitro knockdown significantly suppressed cell proliferation and induced cell cycle arrest. Clinically, high MTFR1 expression served as an independent predictor of poor prognosis and was linked to chemotherapy resistance. Cross-cancer immunotherapy analyses revealed that MTFR1 exhibited improved predictive performance for anti-PD-1 response during the on-treatment phase compared with pretreatment settings. This study elucidates the multidimensional role of MTFR1 as a putative oncogene in BRCA, highlighting it as a promising prognostic biomarker and therapeutic target.
期刊介绍:
IET Systems Biology covers intra- and inter-cellular dynamics, using systems- and signal-oriented approaches. Papers that analyse genomic data in order to identify variables and basic relationships between them are considered if the results provide a basis for mathematical modelling and simulation of cellular dynamics. Manuscripts on molecular and cell biological studies are encouraged if the aim is a systems approach to dynamic interactions within and between cells.
The scope includes the following topics:
Genomics, transcriptomics, proteomics, metabolomics, cells, tissue and the physiome; molecular and cellular interaction, gene, cell and protein function; networks and pathways; metabolism and cell signalling; dynamics, regulation and control; systems, signals, and information; experimental data analysis; mathematical modelling, simulation and theoretical analysis; biological modelling, simulation, prediction and control; methodologies, databases, tools and algorithms for modelling and simulation; modelling, analysis and control of biological networks; synthetic biology and bioengineering based on systems biology.