Zhang Song-Nan, Sun Hong-Hua, Jin Yong-Min, Piao Long-Zhen, Jin De-Hao, Lin Zhen-Hua, Shen Xiong-Hu
Department of Oncology, Affiliated Hospital of Yanbian University, Yanji, China.
Cancer Genet. 2012 Apr;205(4):147-55. doi: 10.1016/j.cancergen.2012.01.003.
The identification of molecular markers for diagnosis, treatment, and prognosis is a significant issue in the management of patients with gastric cancer. We compared the expression profiles of 23 gastric cancers and 22 normal gastric tissues using cDNA microarrays. We divided the samples into two sets, 11 pairs as a training set and 12 unpaired gastric cancer and 11 unpaired normal gastric tissues as a test set. We selected significant genes in the training set and validated the significance of the genes in the test set. We obtained 238 classifier genes that showed a maximum cross-validation probability and clear hierarchical clustering pattern in the training set, and showed excellent class prediction probability in the independent test set. The classifier genes consisted of known genes related to the biological features of cancer and 28% unknown genes. We obtained genome-wide molecular signatures of gastric cancer, which provides preliminary exploration data for the pathophysiology of gastric cancer.
鉴定用于胃癌诊断、治疗及预后的分子标志物是胃癌患者管理中的一个重要问题。我们使用cDNA微阵列比较了23例胃癌组织和22例正常胃组织的表达谱。我们将样本分为两组，11对作为训练集，12例未配对的胃癌组织和11例未配对的正常胃组织作为测试集。我们在训练集中筛选出显著基因，并在测试集中验证这些基因的显著性。我们获得了238个分类基因，这些基因在训练集中显示出最大交叉验证概率和清晰的层次聚类模式，并且在独立测试集中显示出优异的分类预测概率。这些分类基因包括与癌症生物学特征相关的已知基因以及28%的未知基因。我们获得了胃癌的全基因组分子特征，为胃癌的病理生理学提供了初步探索数据。
