Key Laboratory of Computational Biology, CAS Center for Excellence in Molecular Cell Science, Collaborative Innovation Center for Genetics and Developmental Biology, Chinese Academy of Sciences-Max Planck Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, 320 Yue Yang Road, Shanghai 200031, China; University of Chinese Academy of Sciences, Beijing 100049, China.
Key Laboratory of Computational Biology, CAS Center for Excellence in Molecular Cell Science, Collaborative Innovation Center for Genetics and Developmental Biology, Chinese Academy of Sciences-Max Planck Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, 320 Yue Yang Road, Shanghai 200031, China.
Cell Rep. 2019 Feb 12;26(7):1965-1977.e4. doi: 10.1016/j.celrep.2019.01.080.
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.
虽然慢性炎症会增加许多癌症的风险，但炎症如何促进癌症的发展仍未得到充分研究。识别炎症组织何时转变为癌组织至关重要。为了公正地推断参与炎症向癌症（I2C）转变的分子事件、免疫细胞类型和分泌因子，我们基于肝、胃和结肠癌症 I2C 数据开发了一个名为“SwitchDetector”的计算包。使用它，我们确定了与多种 I2C 事件的共同关键转变阶段相关的血管生成。此外，我们推断浸润免疫细胞类型组成及其分泌或抑制的细胞外蛋白，以预测重要转变阶段基因的表达。这确定了可能作为癌前和早期癌症阶段的早期检测生物标志物的细胞外蛋白。它们单独或与 I2C 特征性血管生成基因一起，与癌症预后显著相关，并可预测免疫治疗反应。SwitchDetector 和 I2C 数据库可在 www.inflammation2cancer.org 上公开获取。
