Patient-specific analysis of co-expression to measure biological network rewiring in individuals.
Lanying WeiYucui XinMengchen PuYingsheng John ZhangPublished in: Life science alliance (2023)
To effectively understand the underlying mechanisms of disease and inform the development of personalized therapies, it is critical to harness the power of differential co-expression (DCE) network analysis. Despite the promise of DCE network analysis in precision medicine, current approaches have a major limitation: they measure an average differential network across multiple samples, which means the specific etiology of individual patients is often overlooked. To address this, we present Cosinet, a DCE-based single-sample network rewiring degree quantification tool. By analyzing two breast cancer datasets, we demonstrate that Cosinet can identify important differences in gene co-expression patterns between individual patients and generate scores for each individual that are significantly associated with overall survival, recurrence-free interval, and other clinical outcomes, even after adjusting for risk factors such as age, tumor size, HER2 status, and PAM50 subtypes. Cosinet represents a remarkable development toward unlocking the potential of DCE analysis in the context of precision medicine.
Keyphrases
- network analysis
- end stage renal disease
- poor prognosis
- ejection fraction
- risk factors
- newly diagnosed
- chronic kidney disease
- peritoneal dialysis
- prognostic factors
- binding protein
- magnetic resonance imaging
- machine learning
- young adults
- risk assessment
- gene expression
- single cell
- big data
- climate change
- magnetic resonance
- copy number
- data analysis
- artificial intelligence
- childhood cancer