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Background: Renal cell carcinoma (RCC) is the most common kidney cancer, accounting for approximately 80-90% of all primary kidney cancer. Treatment for patients with advanced RCC remains unsatisfactory. Rare cancer stem cells (CSCs) are proposed to be responsible for failure of current treatment. Methods: OncoLnc was used as a tool for interactively exploring survival correlations. Gene manipulation and expression analysis were carried out using siRNA, RT-PCR and Western blotting. Wound healing and invasion assays were used for phenotypical characterization. Aldefluor assay and FACS sorting Sphere culture were used to determine the "stemness" of CSCs. Co-Immunoprecipitation (Co-IP) was used to examine the interaction between OCT4 and CBFA2T2. Student's t-test and Chi square test was used to analyze statistical significance. Results: CBFA2T2 expression can significantly predict the survival of RCC patients. Knocking-down of CBFA2T2 can inhibit cell migration and invasion in RCC cells in vitro, and reduce ALDHhigh CSCs populations. CBFA2T2 expression is necessary for sphere-forming ability and cancer stem cells marker expression in RCC cell lines. Conclusions: Our data suggest that CBFA2T2 expression correlates with aggressive characteristics of RCC and CBFA2T2 is required for maintenance of "stemness" through regulation of stem cells factors, thereby highlighting CBFA2T2 as a potential therapeutic target for RCC treatment. [ABSTRACT FROM AUTHOR]

Background: Biomedical studies need assistance from automated tools and easily accessible data to address the problem of the rapidly accumulating literature. Text-mining tools and curated databases have been developed to address such needs and they can be applied to improve the understanding of molecular pathogenesis of complex diseases like thyroid cancer. Results: We have developed a system, PWTEES, which extracts pathway interactions from the literature utilizing an existing event extraction tool (TEES) and pathway named entity recognition (PathNER). We then applied the system on a thyroid cancer corpus and systematically extracted molecular interactions involving either genes or pathways. With the extracted information, we constructed a molecular interaction network taking genes and pathways as nodes. Using curated pathway information and network topological analyses, we highlight key genes and pathways involved in thyroid carcinogenesis. Conclusions: Mining events involving genes and pathways from the literature and integrating curated pathway knowledge can help improve the understanding of molecular interactions of complex diseases. The system developed for this study can be applied in studies other than thyroid cancer. The source code is freely available online at https://github.com/chengkun-wu/PWTEES. [ABSTRACT FROM AUTHOR]