Year: 2025
Type: article
Abstract: Adaptive mitochondrial modules: Going with the flow of cancer-specific metabolic rewiring B iological systems are intrinsically complex, dynamic, and deeply interconnected.To maintain cellular homeostasis, they rely on multilayered regulatory networks that combine structural redundancy with exceptional adaptive flexibility [1,2].This adaptability may allow cancer cells to emerge as reorganized-yet still coordinated-deviations from the original regulatory architecture.Even in the disease state, internal logic and systemic coordination may persist through altered but non-random arrangements of regulatory configurations [3].Understanding these transformations is particularly challenging due to the high-dimensional, non-linear, and interdependent nature of molecular interactions.Numerous molecular components operate simultaneously and influence one another in non-linear ways, making it difficult to isolate individual Objectives: Mitochondrial gene networks constitute a fundamental subsystem of cellular homeostasis, integrating bioenergetic, metabolic, and signaling functions.In cancer, the rewiring of these networks represents a critical mechanism of metabolic adaptation, enabling tumor cells to sustain growth and survival under diverse microenvironmental constraints.To systematically characterize these alterations, we analyzed transcriptomic data from The Cancer Genome Atlas (TCGA) with a specific focus on mitochondrial genes, aiming to uncover cancer-type-specific patterns of differential expression and their potential biological implications.Methods: Transcriptomic data from The Cancer Genome Atlas (TCGA) were analysed to identify differential expression patterns in mitochondrial genes.Weighted Gene Co-expression Network Analysis (WGCNA) was applied to detect coexpressed gene modules.The biological relevance of these modules was assessed through functional enrichment analysis and survival modelling using Cox regression and Kaplan-Meier estimations.Dimensionality reduction techniques including PCA and UMAP were used to evaluate module-driven clustering patterns across cancer types.Results: Seven mitochondrial gene modules were identified, six of which demonstrated significant associations with specific cancer types.Modules ME2, ME4, ME5, ME6, and ME7 were associated with improved overall survival, while ME3 correlated with poorer prognosis.Functional enrichment analyses revealed distinct mitochondrial processes including oxidative phosphorylation, apoptosis, fatty acid β-oxidation, and ketone body metabolism.Dimensionality reduction analyses supported the presence of module-specific expression patterns with cancer-type-dependent clustering.Conclusion: The observed cancer-type-specific expression and prognostic associations of mitochondrial gene networks reflect their central involvement in the metabolic flexibility of tumors.By underscoring the clinical and biological significance of mitochondrial subsystems, these findings suggest that they may serve not only as prognostic markers but also as promising targets for therapeutic modulation.
Author: Mehmet Taha Yildiz
Language: English
Subfield: Cancer Research
Domain: Life Sciences
Open Access status: diamond
OpenAlex ID: W4415438608