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bioRxiv · 10.1101/2023.08.23.554466

Patient-Specific Analysis of Co-expression Networks for Predicting Clinical Outcomes in Breast Cancer

Wei, L.·Xin, Y.·Pu, m.·zhang, y.
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Abstract

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 network analysis. Despite the promise of differential co-expression 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 differential co-expression-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 differential co-expression analysis in the context of precision medicine.

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Wei, L., Xin, Y., Pu, m., zhang, y.. 2023-08-24. Patient-Specific Analysis of Co-expression Networks for Predicting Clinical Outcomes in Breast Cancer. https://doi.org/10.1101/2023.08.23.554466

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