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J Cell Mol Med. 2018 Sep;22(9):4304-4316. doi: 10.1111/jcmm.13720. Epub 2018 Jul 4.

Inference of patient-specific subpathway activities reveals a functional signature associated with the prognosis of patients with breast cancer.

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College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
College of Basic Medical Science, Heilongjiang University of Chinese Medicine, Harbin, China.
School of Medical Informatics, Daqing Campus, Harbin Medical University, Harbin, China.


Breast cancer is one of the most deadly forms of cancer in women worldwide. Better prediction of breast cancer prognosis is essential for more personalized treatment. In this study, we aimed to infer patient-specific subpathway activities to reveal a functional signature associated with the prognosis of patients with breast cancer. We integrated pathway structure with gene expression data to construct patient-specific subpathway activity profiles using a greedy search algorithm. A four-subpathway prognostic signature was developed in the training set using a random forest supervised classification algorithm and a prognostic score model with the activity profiles. According to the signature, patients were classified into high-risk and low-risk groups with significantly different overall survival in the training set (median survival of 65 vs 106 months, P = 1.82e-13) and test set (median survival of 75 vs 101 months, P = 4.17e-5). Our signature was then applied to five independent breast cancer data sets and showed similar prognostic values, confirming the accuracy and robustness of the subpathway signature. Stratified analysis suggested that the four-subpathway signature had prognostic value within subtypes of breast cancer. Our results suggest that the four-subpathway signature may be a useful biomarker for breast cancer prognosis.


pathway structure; patient-specific; prognostic signature; subpathway activity

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