预测微阵列分析法评估慢性乙型肝炎肝脏硬化组织发生癌变的基因谱变化

Predication analysis of microarray data to determine altered gene profiles in liver carcinoma related to HBV- related cirrhosis

目的采用基因芯片的显著微阵列分析法（SAM）和预测微阵列分析法（PAM）了解慢性乙型肝炎肝硬化发生肝癌的基因表达谱的改变情况，以筛选肝硬化癌变的风险基因。方法采用人Affymetrix基因芯片技术，检测15例乙型肝炎肝硬化癌变患者的癌组织和癌旁硬化组织的基因表达谱。采用SAM和PAM软件筛选肝脏硬化组织和癌组织中表达差异明显的显著性基因和风险基因。实时定量PCR验证11个风险基因的mRNA在肝组织中的表达情况。两组数据间比较采用f检验，三组以上数据间比较采用单因素方差分析。结果芯片软件筛选出两组间差异2倍以上且P＜0．0l的差异基因共497个。SAM分析得出162个显著性基因，其中18个显著性基因呈上调表达，144个显著性基因呈下调表达，平均显著性差异值的差异倍数是-1．46～1．28。PAM分析能有效分类两组的最少风险基因数为22个（阈值=5．5），交叉验证样本被分类的准确性达80％以上。22个基因的信号值在肝癌组和肝硬化组比较，在肝癌组4个风险基因表达上调，18个风险基因表达下调，组间差异倍数在2．038～7．897（P值均〈0．01）。实时定量PCR验证11个风险基因，肝癌组的mRNA（1．21±0．45）和mRNA的相对表达水平（1．11±0．16）较肝硬化组（1．26±0．44）显著下调护=0．002），而mRNA的相对表达水平显著上调（1．66±0．09与1．37±0．04，P=0．004）。结论乙型肝炎肝硬化发生肝癌会产生数百个基因表达的变化，PAM筛选出的3个风险预测基因叉头样转录因子P1、丝氨酸肽酶抑制因子Kazal型1和钾离子通道相关基因J16有望用于诊断和预测该病。

Objective To investigate whether gene expression profiles can be used to determine risk genes and predict I-IBV-related cirrhosis progression to liver carcinoma using Significance Analysis of Microarray （SAM） and Prediction Analysis ofMicroarray （PAM） methods. Methods The Affymetrix GeneChip was used to establish the gene expression profiles of liver tissues from 15 patients with chronic hepatitis B and cirrhosis or hepatocellular carcinoma （HCC）. Differentially expressed genes （fold-change 〉2; P value 〈0.01） were selected by GeneSpring GX software. Risk genes related to cirrhosis and liver carcinoma were generated by SAM and PAM methods. Real-time PCR was used to verify the expression of risk genes in the liver tissues. Results Samples were clustered into the cirrhosis subgroup （a = 15） or the HCC subgroup （11 = 15）. A total of 497 differentially expressed genes were identified, SAM identified 162 significant genes, including 18 up-regulatedgenes and 144 down-regulated genes （fold-change： -1.46 to 1.28）. PAM identified 22 genes with a “poor risk signature” （defined with a threshold of 5.5）, which were associated with classifying cirrhosis and liver carcinoma; of these risk genes, 4 were down-regulated and 18 were up-regulated in the HCC group compared to the cirrhosis group （fold-change： 2.038 to 7.897, P value 〈0.01）. The correction of classification was more than 80%. FOXP1, SPINK1 and KCNJ16 were verified by real-time PCR as differently expressed in the two subgroups （P value = 0.011, 0.002 and 0.004, respectively）. Conclusion The altered gene profiles of carcinogenesis in HBV- related cirrhosis involves hundreds of genes. The combination of three “poor risk gene” may represent potential targets for diagnosis and prediction of liver carcinoma progression.