Although chronic inflammation increases many cancers' risk, how inflammation facilitates cancer development is still not well studied. Recognizing whether and when inflamed tissues transition to cancerous tissues is of utmost importance. To unbiasedly infer molecular events, immune cell types, and secreted factors contributing to the inflammation-to-cancer (I2C) transition, we develop a computational package called "SwitchDetector" based on liver, gastric, and colon cancer I2C data. Using it, we identify angiogenesis associated with a common critical transition stage for multiple I2C events. Furthermore, we infer infiltrated immune cell type composition and their secreted or suppressed extracellular proteins to predict expression of important transition stage genes. This identifies extracellular proteins that may serve as early-detection biomarkers for pre-cancer and early-cancer stages. They alone or together with I2C hallmark angiogenesis genes are significantly related to cancer prognosis and can predict immune therapy response. The SwitchDetector and I2C database are publicly available at www.inflammation2cancer.org.
Keywords: biomarker; cancer; cancer survival; cancer transition; immune therapy; inflammation; microenvironment; network analysis; secreted factor.
Copyright © 2019 The Authors. Published by Elsevier Inc. All rights reserved.