Identification of the associated expression patterns as potential predictive markers for cancer prognosis

Dysregulated gene expression can develop as a consequence of uncontrolled alterations of tumor cells. Analysis of these abnormal alterations will improve our understanding of the tumor development and reveal the corresponding clinical associations. It is well known that multiple genetic abnormalities could be observed in the same tumor, however, the interactions between those abnormal events are rarely analyzed. To address this problem, we constructed a novel gene expression correlation network by integrating the transcriptomes of 5,001 cancer patients from 22 cancer types. We investigated how the change of associated expression pattern (AEP), which describe certain associations between gene expression, could affect the cancer patient’s prognosis. Consequently, we identified an AEP composed of mitosis-related gene expressions, which is significantly correlated with overall survival in most cancer types. In particular, the AEPs could present the association between gene expressions and show distinct effects on prognosis prediction for cancer patients, suggesting that AEP analysis is indispensable to uncover the complex interactions of abnormal gene expressions in tumor development.