BACKGROUND: Colorectal cancer patients with lymph node metastases (stage III) show poorer prognosis than those without. Predicting development of recurrence may guide the need for intensive follow-up and/or adjuvant chemotherapy in such patients. The authors' objective was to identify a set of discriminating genes that could predict recurrence in stage III colorectal cancer. METHODS: Thirty-six stage III colorectal cancer patients were studied. Tumor samples were obtained from surgically resected specimens. Thirteen patients developed recurrence, whereas 23 patients did not. Gene expression profiles were determined using human HG-U133 Plus 2.0 Gene Chip (Affymetrix, Santa Clara, Calif). RESULTS: The authors identified 45 discriminating genes between patients with and without recurrence. By using this gene set, they established a new model to predict recurrence with an accuracy of 90.9%. The discriminating genes included calcineurin-binding protein 1 (CABIN1), whose expression differed remarkably between patients with and without recurrence (P=.0073). The authors further examined the DNA copy number of CABIN1 and were able to show a significant relation with recurrence (P<.012). Patients having CABIN1 gene loss demonstrated a higher risk of recurrence (odds ratio, 18.8). DNA copy number of CABIN1 alone could predict recurrence with an accuracy of 80.0%. CONCLUSIONS: The results of the current study demonstrated that gene expression profiling is useful in predicting recurrence in stage III colorectal cancer. The authors identified CABIN1 among discriminating genes that may play a key role in the development of recurrence. These results may help to establish an individualized therapy for stage III colorectal cancer. Copyright (c) 2009 American Cancer Society.
BACKGROUND:Colorectal cancerpatients with lymph node metastases (stage III) show poorer prognosis than those without. Predicting development of recurrence may guide the need for intensive follow-up and/or adjuvant chemotherapy in such patients. The authors' objective was to identify a set of discriminating genes that could predict recurrence in stage III colorectal cancer. METHODS: Thirty-six stage III colorectal cancerpatients were studied. Tumor samples were obtained from surgically resected specimens. Thirteen patients developed recurrence, whereas 23 patients did not. Gene expression profiles were determined using human HG-U133 Plus 2.0 Gene Chip (Affymetrix, Santa Clara, Calif). RESULTS: The authors identified 45 discriminating genes between patients with and without recurrence. By using this gene set, they established a new model to predict recurrence with an accuracy of 90.9%. The discriminating genes included calcineurin-binding protein 1 (CABIN1), whose expression differed remarkably between patients with and without recurrence (P=.0073). The authors further examined the DNA copy number of CABIN1 and were able to show a significant relation with recurrence (P<.012). Patients having CABIN1 gene loss demonstrated a higher risk of recurrence (odds ratio, 18.8). DNA copy number of CABIN1 alone could predict recurrence with an accuracy of 80.0%. CONCLUSIONS: The results of the current study demonstrated that gene expression profiling is useful in predicting recurrence in stage III colorectal cancer. The authors identified CABIN1 among discriminating genes that may play a key role in the development of recurrence. These results may help to establish an individualized therapy for stage III colorectal cancer. Copyright (c) 2009 American Cancer Society.
Authors: Abdul K Siraj; Tariq Masoodi; Rong Bu; Shaham Beg; Saif S Al-Sobhi; Fouad Al-Dayel; Mohammed Al-Dawish; Fowzan S Alkuraya; Khawla S Al-Kuraya Journal: Am J Hum Genet Date: 2016-05-26 Impact factor: 11.025
Authors: Yoshifumi Baba; Katsuhiko Nosho; Kaori Shima; Marika Hayashi; Jeffrey A Meyerhardt; Andrew T Chan; Edward Giovannucci; Charles S Fuchs; Shuji Ogino Journal: Cancer Date: 2010-11-08 Impact factor: 6.860
Authors: James C Lee; Paul A Lyons; Eoin F McKinney; John M Sowerby; Edward J Carr; Francesca Bredin; Hannah M Rickman; Huzefa Ratlamwala; Alexander Hatton; Tim F Rayner; Miles Parkes; Kenneth G C Smith Journal: J Clin Invest Date: 2011-09-26 Impact factor: 14.808
Authors: Rebeca Sanz-Pamplona; Antoni Berenguer; David Cordero; Samantha Riccadonna; Xavier Solé; Marta Crous-Bou; Elisabet Guinó; Xavier Sanjuan; Sebastiano Biondo; Antonio Soriano; Giuseppe Jurman; Gabriel Capella; Cesare Furlanello; Victor Moreno Journal: PLoS One Date: 2012-11-07 Impact factor: 3.240