OBJECTIVE: We examined the whole genome expression profile in advanced colorectal cancer (ACC) patients who had received FOLFOX4 chemotherapy to establish a genetic biomarker model predicting chemotherapy sensitivity. METHODS:Eligible ACC patients were divided into two groups, based on postchemotherapy evaluation results: specifically, the sensitive group (experimental group) and the resistant group (control group). The genome expression profiles of colorectal cancer tissues were examined using DNA microarray analysis, and differential gene expression was identified using a significance analysis of the microarray. The probe signal log ratios were used to produce the area-under-the-curve, sensitivity, and specificity for candidate genes. Genes exhibiting differential expression and significant predictive power were used to simulate a genetic model for estimating chemotherapy sensitivity. RESULTS: Totally, 30 ACC patients were eligible for the study, 13 assigned to the experimental group and 17 to the control group. In total, 30 genes showing significant differential expression were identified. Seven candidate genes (NKX2-3, FXYD6, TGFB1I1, ACTG2, ANPEP, HOXB8, and KLK11), which exhibited positive or negative correlations, were incorporated into a genetic model, with an overall accurate predication rate of 93.3%. CONCLUSIONS: The predictive model involving the seven genes listed had high accuracy in estimating chemotherapy sensitivity to the FOLFOX4 regimen.
RCT Entities:
OBJECTIVE: We examined the whole genome expression profile in advanced colorectal cancer (ACC) patients who had received FOLFOX4 chemotherapy to establish a genetic biomarker model predicting chemotherapy sensitivity. METHODS: Eligible ACC patients were divided into two groups, based on postchemotherapy evaluation results: specifically, the sensitive group (experimental group) and the resistant group (control group). The genome expression profiles of colorectal cancer tissues were examined using DNA microarray analysis, and differential gene expression was identified using a significance analysis of the microarray. The probe signal log ratios were used to produce the area-under-the-curve, sensitivity, and specificity for candidate genes. Genes exhibiting differential expression and significant predictive power were used to simulate a genetic model for estimating chemotherapy sensitivity. RESULTS: Totally, 30 ACC patients were eligible for the study, 13 assigned to the experimental group and 17 to the control group. In total, 30 genes showing significant differential expression were identified. Seven candidate genes (NKX2-3, FXYD6, TGFB1I1, ACTG2, ANPEP, HOXB8, and KLK11), which exhibited positive or negative correlations, were incorporated into a genetic model, with an overall accurate predication rate of 93.3%. CONCLUSIONS: The predictive model involving the seven genes listed had high accuracy in estimating chemotherapy sensitivity to the FOLFOX4 regimen.
Authors: B Z Vider; A Zimber; E Chastre; C Gespach; M Halperin; P Mashiah; A Yaniv; A Gazit Journal: Biochem Biophys Res Commun Date: 2000-06-07 Impact factor: 3.575
Authors: Howard L McLeod; Daniel J Sargent; Sharon Marsh; Erin M Green; Cristi R King; Charles S Fuchs; Ramesh K Ramanathan; Stephen K Williamson; Brian P Findlay; Stephen N Thibodeau; Axel Grothey; Roscoe F Morton; Richard M Goldberg Journal: J Clin Oncol Date: 2010-06-07 Impact factor: 44.544
Authors: Stefan Madajewicz; David M Waterhouse; Paul S Ritch; M Qaseem Khan; Donald J Higby; Cynthia G Leichman; Sandeep K Malik; Patricia Hentschel; John F Gill; Luping Zhao; Steven J Nicol Journal: Invest New Drugs Date: 2010-12-01 Impact factor: 3.850
Authors: S Rivetti; M Lauriola; M Voltattorni; M Bianchini; D Martini; C Ceccarelli; A Palmieri; G Mattei; M Franchi; G Ugolini; G Rosati; I Montroni; M Taffurelli; Rossella Solmi Journal: Int J Immunopathol Pharmacol Date: 2011 Jul-Sep Impact factor: 3.219
Authors: S E T Larkin; S Holmes; I A Cree; T Walker; V Basketter; B Bickers; S Harris; S D Garbis; P A Townsend; C Aukim-Hastie Journal: Br J Cancer Date: 2011-11-10 Impact factor: 7.640
Authors: Xiaocong Geng; Yueyang Liu; Tobias Dreyer; Holger Bronger; Enken Drecoll; Viktor Magdolen; Julia Dorn Journal: Am J Cancer Res Date: 2018-09-01 Impact factor: 6.166