The data on cancer stem cell surface molecular markers of 27 most common cancer diseases were analyzed using natural language processing and data mining techniques. As a source, 8933 full-text open-access English-language scientific articles available on the Internet were used. Text mining was based on searching for three entities within one sentence, namely a tumor name, a phrase "cancer stem cells" or its synonym, and a name of differentiation cluster molecule. As a result, a list of surface molecular markers was formed that included markers most frequently mentioned in the context of certain tumor diseases and used in studies of human and animal tumor cells. Based on similarity of the associated markers, the tumors were divided into five groups.
Keywords: cancer stem cells; data mining; information extraction; natural language processing; surface molecular markers.