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Identification of MSX1 and DCLK1 as mRNA Biomarkers for Colorectal Cancer Detection Through DNA Methylation Information.
Sun, Ai-Jun; Gao, Hai-Bo; Liu, Gao; Ge, Heng-Fa; Ke, Zun-Ping; Li, Sen.
Afiliación
  • Sun AJ; Department of General Surgery, The Affiliated Huai'an Hospital of Xuzhou Medical University and The Second People's Hospital of Huai'an, Huai'an, China.
  • Gao HB; Department of General Surgery, Huai'an Tumor Hospital, Huai'an, China.
  • Liu G; Department of Gastrointestinal Surgery, Enshi Clinical College of Wuhan University, Central Hospital of Enshi Autonomous Prefecture, Enshi, Hubei, China.
  • Ge HF; Department of Intestinal Surgery, Huai'an First People's Hospital, Nanjing Medical University, Huai'an, China.
  • Ke ZP; Department of Cardiology, The Fifth People's Hospital of Shanghai, Fudan University, Shanghai, China.
  • Li S; Department of Spinal Surgery, Affiliated Traditional Chinese Medicine Hospital, Southwest Medical University, Luzhou, China.
J Cell Physiol ; 232(7): 1879-1884, 2017 Jul.
Article en En | MEDLINE | ID: mdl-27966796
Colorectal cancer is the second most deadly malignancy in the United States. However, the currently screening options had their limitation. Novel biomarkers for colorectal cancer detections are necessary to reduce the mortality. The clinical information, mRNA expression levels and DNA methylation information of colorectal cancer were downloaded from TCGA. The patients were separated into training group and testing group based on their platforms for DNA methylation. Beta values of DNA methylation from tumor tissues and normal tissues were utilized to figure out the position that were differentially methylated. The expression levels of mRNA of thirteen genes, whose CpG islands were differentially methylated, were extracted from the RNA-Seq results from TCGA. The probabilities whether the mRNA was differentially expressed between tumor and normal samples were calculated using Student's t-test. Logistic regression and decision tree were built for cancer detection and their performances were evaluated by the area under the curve (AUC). Twenty-four genomic locations were differentially methylated, which could be mapped to eleven genes. Nine out of eleven genes had differentially expressed mRNA levels, which were used to build the model for cancer detection. The final detection models consisting of mRNA expression levels of these nine genes had great performances on both training group and testing group. The model that constructed in this study suggested MSX1 and DCLK1 might be used in colorectal cancer detection or as target of cancer therapies. J. Cell. Physiol. 232: 1879-1884, 2017. © 2016 Wiley Periodicals, Inc.
Asunto(s)

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Neoplasias Colorrectales / Biomarcadores de Tumor / Proteínas Serina-Treonina Quinasas / Metilación de ADN / Péptidos y Proteínas de Señalización Intracelular / Factor de Transcripción MSX1 Tipo de estudio: Diagnostic_studies / Prognostic_studies Límite: Humans Idioma: En Revista: J Cell Physiol Año: 2017 Tipo del documento: Article País de afiliación: China Pais de publicación: Estados Unidos

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Neoplasias Colorrectales / Biomarcadores de Tumor / Proteínas Serina-Treonina Quinasas / Metilación de ADN / Péptidos y Proteínas de Señalización Intracelular / Factor de Transcripción MSX1 Tipo de estudio: Diagnostic_studies / Prognostic_studies Límite: Humans Idioma: En Revista: J Cell Physiol Año: 2017 Tipo del documento: Article País de afiliación: China Pais de publicación: Estados Unidos