Inflammatory microenvironments are known to contribute to different stages of cancer development, including carcinogenesis, cancer development, and metastasis. Similarly, chronic and non-resolving inflammation, now recognized as a hallmark of cancer, can increase the risk of many cancers and facilitate cancer progression.

However, the molecular mechanisms of how inflammation facilitates cancer development are still not well studied. In particular, systems level analyses to unbiasedly uncover mechanisms and biomarkers for early detection of the transition from inflammation to cancer are still lagging behind.

Recently, a team led by Prof. Jing-Dong Jackie HAN from CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health of Chinese Academy of Sciences developed a computational network analysis package called “SwitchDetector”. The study was published in Cell Reports.

Using it, they integrated the expression profiles of normal tissues and tissues with inflammation, using samples from liver, esophageal and colon cancers, and found that angiogenesis is a common event during the inflammation-to-cancer (I2C) transition in multiple cancers.

Together with protein-protein interactomes and gene expression signatures of different immune cells, scientists identified interface genes between normal, inflammation and cancer backgrounds, and inferred regulatory immune cell types and their activated or repressed secreted factors and cytokine receptors (SFCRs) at the I2C transition.

They also identified a transition stage between advanced inflammation and early cancerous transformation stages that not only governs the transformation process, but provides pre-cancer cellular and molecular markers, and prognosis and immune therapy response predictors.

To facilitate the usage of the computational program and database, both SwitchDetector package and I2C database were made freely available at www.inflammation2cancer.org.

This work was supported by grants from National Natural Science Foundation of China, Ministry of Science and Technology, and Chinese Academy of Sciences.

Schematic Illustration of Inflammation-to-cancer Transition Stage Detection (Image by Prof. HAN’s Team)