E-GEOD-44001 - Genetic profiling to predict recurrence of early cervical cancer

Released on 1 May 2013, last updated on 2 June 2014
Homo sapiens
Samples (300)
Array (1)
Protocols (5)
We compared the prediction powers for disease recurrence between gene set prognostic model and clinical prognostic model developed in a single large population to see whether genetic quantitative approach will have significant prognostic role in early cervical cancer patients who underwent radical hysterectomy with or without adjuvant therapies. Gene set model to predict disease free survival of early cervical cancer was developed using DASL assay dataset from the cohort of early cervical cancer patients who were treated with radical surgery with or without adjuvant therapies at the Samsung Medical Center of Sungkyunkwan University School of Medicine in Seoul, Korea, between January 2002 and September 2008. Clinical prediction model was also developed in the same cohort and the ability of predicting recurrence from each model was compared. Adequate DASL assay profiles were obtained in 300 patients and we selected 12 genes for the gene set model. When the proportions of patients were categorized as having a low or high risk by the prognostic scores using these genes from LOOCV procedure, the Kaplan-Meier curve showed significant different recurrence rate between two groups. Clinical model was developed using FIGO stage as well as post-surgical pathological findings.
Experiment type
transcription profiling by array 
Investigation descriptionE-GEOD-44001.idf.txt
Sample and data relationshipE-GEOD-44001.sdrf.txt
Processed data (1)E-GEOD-44001.processed.1.zip
Additional data (1)E-GEOD-44001.additional.1.zip
Array designA-GEOD-14951.adf.txt