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OSpaad: An online tool to perform survival analysis by integrating gene expression profiling and long-term follow-up data of 1319 pancreatic carcinoma patients.
Zhang, Guosen; Wang, Qiang; Yang, Mengsi; Yao, Xitong; Qi, Xinlei; An, Yang; Dong, Huan; Zhang, Lu; Zhu, Wan; Li, Yongqiang; Guo, Xiangqian.
Afiliación
  • Zhang G; Department of Predictive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, School of Software, School of Basic Medical Sciences, Henan University, Kaifeng, China.
  • Wang Q; Department of Predictive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, School of Software, School of Basic Medical Sciences, Henan University, Kaifeng, China.
  • Yang M; Department of Predictive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, School of Software, School of Basic Medical Sciences, Henan University, Kaifeng, China.
  • Yao X; Department of Predictive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, School of Software, School of Basic Medical Sciences, Henan University, Kaifeng, China.
  • Qi X; Department of Predictive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, School of Software, School of Basic Medical Sciences, Henan University, Kaifeng, China.
  • An Y; Department of Predictive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, School of Software, School of Basic Medical Sciences, Henan University, Kaifeng, China.
  • Dong H; Department of Predictive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, School of Software, School of Basic Medical Sciences, Henan University, Kaifeng, China.
  • Zhang L; Department of Predictive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, School of Software, School of Basic Medical Sciences, Henan University, Kaifeng, China.
  • Zhu W; Department of Anesthesia, Stanford University, Stanford, California.
  • Li Y; Department of Predictive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, School of Software, School of Basic Medical Sciences, Henan University, Kaifeng, China.
  • Guo X; Department of Predictive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, School of Software, School of Basic Medical Sciences, Henan University, Kaifeng, China.
Mol Carcinog ; 59(3): 304-310, 2020 03.
Article en En | MEDLINE | ID: mdl-31912599
ABSTRACT
Pancreatic carcinoma (PC) is a type of highly lethal malignant tumor that has unfavorable outcomes. One major challenge in improving clinical outcomes is to identify novel biomarkers for prognosis. In this study, we developed an online consensus survival tool for pancreatic adenocarcinoma (OSpaad), which allows researchers and clinicians to analyze the prognostic value of selected genes in PC. OSpaad contains 1319 unique PC cases that have both gene expression data and correspondent clinical data from seven individual cohorts and provides four survival terms including overall survival, disease-specific survival, disease-free interval, progression-free interval for prognosis evaluation. To meet the different research needs, OSpaad allows users to limit survival analysis in subgroups by selecting different terms of clinical confounding factors such as TNM stage, sex, smoking time, lymph invasion, and race. Moreover, we showed that 97% (116 out of 120) previously reported prognostic biomarkers, including ERBB2, TP53, EGFR and so forth, were validated and confirmed their prognostic significance in OSpaad, demonstrating the well performance of survival analysis in OSpaad. OSpaad is a user-friendly online tool with a straightforward interface allowing clinicians and basic research scientists with even a limited bioinformatics background to easily screen and evaluate the prognostic value of genes in a large PC cohort. This online tool can be accessed at http//bioinfo.henu.edu.cn/PAAD/PAADList.jsp.
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Texto completo: 1 Bases de datos: MEDLINE Asunto principal: Neoplasias Pancreáticas / Programas Informáticos Tipo de estudio: Observational_studies / Prognostic_studies / Risk_factors_studies Límite: Humans Idioma: En Revista: Mol Carcinog Asunto de la revista: BIOLOGIA MOLECULAR / NEOPLASIAS Año: 2020 Tipo del documento: Article País de afiliación: China
Texto completo
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Texto completo: 1 Bases de datos: MEDLINE Asunto principal: Neoplasias Pancreáticas / Programas Informáticos Tipo de estudio: Observational_studies / Prognostic_studies / Risk_factors_studies Límite: Humans Idioma: En Revista: Mol Carcinog Asunto de la revista: BIOLOGIA MOLECULAR / NEOPLASIAS Año: 2020 Tipo del documento: Article País de afiliación: China