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Cancer metabolism is characterized by significant heterogeneity, presenting challenges for treatment efficacy and patient outcomes. Understanding this heterogeneity and its regulatory mechanisms at single-cell resolution is crucial for developing personalized therapeutic strategies. In this study, we employed a single-cell network approach to characterize malignant heterogeneity in gynecologic and breast cancers, focusing on the transcriptional regulatory mechanisms driving metabolic alterations. By leveraging single-cell RNA sequencing (scRNA-seq) data, we assessed the metabolic pathway activities and inferred cancer-specific protein-protein interactomes (PPI) and gene regulatory networks (GRNs). We explored the crosstalk between these networks to identify key alterations in metabolic regulation. Clustering cells by metabolic pathways revealed tumor heterogeneity across cancers, highlighting variations in oxidative phosphorylation, glycolysis, cholesterol, fatty acid, hormone, amino acid, and redox metabolism. Our analysis identified metabolic modules associated with these pathways, along with their key transcriptional regulators. Notably, transcription factors related to ER stress, immune response, and cell proliferation, along with hypoxia-inducible factor and sterol regulatory element-binding proteins were found to drive metabolic reprogramming. These findings provide new insights into the complex interplay between metabolic rewiring and transcriptional regulation in gynecologic and breast cancers, offering potential avenues for targeted therapeutic strategies in precision oncology. Furthermore, this pipeline for dissecting coregulatory metabolic networks can be broadly applied to decipher metabolic regulation in any disease at single-cell resolution.
Cancer metabolism is characterized by significant heterogeneity, presenting challenges for treatment efficacy and patient outcomes. Understanding this heterogeneity and its regulatory mechanisms at single-cell resolution is crucial for developing personalized therapeutic strategies. In this study, we employed a single-cell network approach to characterize malignant heterogeneity in gynecologic and breast cancers, focusing on the transcriptional regulatory mechanisms driving metabolic alterations. By leveraging single-cell RNA sequencing (scRNA-seq) data, we assessed the metabolic pathway activities and inferred cancer-specific protein-protein interactomes (PPI) and gene regulatory networks (GRNs). We explored the crosstalk between these networks to identify key alterations in metabolic regulation. Clustering cells by metabolic pathways revealed tumor heterogeneity across cancers, highlighting variations in oxidative phosphorylation, glycolysis, cholesterol, fatty acid, hormone, amino acid, and redox metabolism. Our analysis identified metabolic modules associated with these pathways, along with their key transcriptional regulators. Notably, transcription factors related to ER stress, immune response, and cell proliferation, along with hypoxia-inducible factor and sterol regulatory element-binding proteins were found to drive metabolic reprogramming. These findings provide new insights into the complex interplay between metabolic rewiring and transcriptional regulation in gynecologic and breast cancers, offering potential avenues for targeted therapeutic strategies in precision oncology. Furthermore, this pipeline for dissecting coregulatory metabolic networks can be broadly applied to decipher metabolic regulation in any disease at single-cell resolution.
Despite the high incidence and disability rates of delayed encephalopathy after acute carbon monoxide poisoning (DEACMP), its pathogenesis remains enigmatic, and specific predictive markers are lacking. This study aimed to elucidate the molecular underpinnings and identify predictive biomarkers of DEACMP through multi-omics and single-nucleus RNA sequencing (snRNA-seq). We collected clinical data and blood samples from 105 participants, including healthy controls (HCs), acute carbon monoxide poisoning patients (ACOP), and those receiving comprehensive treatment for ACOP (ACOP-CT). Untargeted metabolomics sequencing was employed to profile serum metabolites across these groups. Additionally, individuals from the HCs, ACOP, non-delayed encephalopathy after ACOP (DEACMP-N), and DEACMP groups (n = 3 each) were randomly selected for transcriptome sequencing to identify potential predictive targets and pivotal signaling pathways associated with DEACMP. Furthermore, we established severe DEACMP and Control Sprague-Dawley rat models and assessed neurocognitive function using the Morris water maze on the 28th day. Subsequently, three rats from the Control, DEACMP, and DEACMP + Dexamethasone + Selenomethionine groups were selected for snRNA-seq to analyze hippocampal single-cell transcriptional profiles. Immunofluorescence multiplexing was then performed to validate the identified predictive targets. Our analysis of clinical data from 105 participants highlights the pivotal role of inflammation in influencing the prognosis of carbon monoxide poisoning. Metabolomics analysis identified 19 metabolites that significantly differed between the DEACMP-N and DEACMP groups compared to the ACOP-CT follow-up results. Transcriptomics analysis of 12 participants indicated that DEACMP is primarily associated with six signaling pathways, including lysosome and tuberculosis. Given that microglia are central nervous system immune effectors, our snRNA-seq analysis revealed altered genes expression and signaling pathways in microglia during DEACMP, with KEGG analysis highlighting phagosome, neutrophil extracellular trap formation, lysosome, and tuberculosis as the predominant pathways. Differential gene analysis from transcriptome and snRNA-seq identified 28 genes differentially expressed in DEACMP. The STRING database and immunomultiplexing confirmed the pivotal role of the IFNGR1/STAT1/CTSS axis in DEACMP. This study provides a comprehensive overview of serum metabolite expression, differential genes expression, and signaling pathways in DEACMP patients, offering a robust theoretical foundation for understanding the pathogenesis for DEACMP.
The field of personalized medicine is undergoing a transformative shift through the integration of multi-omics data, which mainly encompasses genomics, transcriptomics, proteomics, and metabolomics. This synergy allows for a comprehensive understanding of individual health by analyzing genetic, molecular, and biochemical profiles. The generation and integration of multi-omics data enable more precise and tailored therapeutic strategies, improving the efficacy of treatments and reducing adverse effects. However, several challenges hinder the full realization of personalized medicine. Key hurdles include the complexity of data integration across different omics layers, the need for advanced computational tools, and the high cost of comprehensive data generation. Additionally, issues related to data privacy, standardization, and the need for robust validation in diverse populations remain significant obstacles. Looking ahead, the future of personalized medicine promises advancements in technology and methodologies that will address these challenges. Emerging innovations in data analytics, machine learning, and high-throughput sequencing are expected to enhance the integration of multi-omics data, making personalized medicine more accessible and effective. Collaborative efforts among researchers, clinicians, and industry stakeholders are crucial to overcoming these hurdles and fully harnessing the potential of multi-omics for individualized healthcare.
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