Predicting Ulcerative Colitis-Associated Colorectal Cancer Using Reverse-Transcription Polymerase Chain Reaction Analysis
Predicting Ulcerative Colitis-Associated Colorectal Cancer Using Reverse-Transcription Polymerase Chain Reaction Analysis
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DOI：
10.1016/j.clcc.2011.03.011
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发表时间：
2011-06-01
影响因子：
3.4
通讯作者：
Nagawa, Hirokazu
中科院分区：
文献类型：
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作者：
Watanabe, Toshiaki;Kobunai, Takashi;Nagawa, Hirokazu
背景资料：广泛的遗传改变不仅存在于溃疡性结肠炎（UC）相关的肿瘤性病变中，也存在于邻近的正常结肠粘膜中。这表明，非肿瘤性粘膜的遗传变化可能是预测UC相关癌（UC-Ca）发展的有效标志物。本研究的目的是建立一个预测模型的基础上，通过逆转录聚合酶链反应（RT-PCR）分析在非肿瘤性直肠粘膜的基因表达水平的UC-Ca的发展。患者和方法：对53例UC患者进行了检查，其中10例有UC-Ca，43例没有（UC-NonCa）。除了先前在我们的微阵列研究中显示出预测UC-Ca发展的40个基因和转录物之外，还选择了149个新基因用于低密度阵列（LDA）分析，这些新基因被报道在致癌作用中是重要的。采用RT-PCR方法检测非肿瘤性直肠粘膜中189个基因的表达。结果如下：我们鉴定了20个在UC-Ca和UC-NonCa患者中表现出差异表达的基因，包括癌症相关基因，如CYP 27 B1、RUNX 3、SAMSN 1、EDIL 3、NOL 3、CXCL 9、ITGB 2和林恩。使用这20个基因，我们能够建立一个预测模型，区分有和没有UC-Ca的患者，准确率高达83%，阴性预测值为100%。结论：该预测模型表明，有可能确定UC患者患癌症的高风险。这些结果对提高结肠镜监测的有效性具有重要意义，并为未来研究UC相关癌症的分子机制提供了方向。
Background: Widespread genetic alterations are present not only in ulcerative colitis (UC)-associated neoplastic lesions but also in the adjacent normal colonic mucosa. This suggests that genetic changes in nonneoplastic mucosa might be effective markers for predicting the development of UC-associated cancer (UC-Ca). This study aimed to build a predictive model for the development of UC-Ca based on gene expression levels measured by reverse-transcription polymerase chain reaction (RT-PCR) analysis in nonneoplastic rectal mucosa. Patients and Methods: Fifty-three UC patients were examined, of which 10 had UC-Ca and 43 did not (UC-NonCa). In addition to the 40 genes and transcripts previously shown to be predictive for developing UC-Ca in our microarray studies, 149 new genes, reported to be important in carcinogenesis, were selected for low density array (LDA) analysis. The expression of a total of 189 genes was examined by RT-PCR in nonneoplastic rectal mucosa. Results: We identified 20 genes showing differential expression in UC-Ca and UC-NonCa patients, including cancer-related genes such as CYP27B1, RUNX3, SAMSN1, EDIL3, NOL3, CXCL9, ITGB2, and LYN. Using these 20 genes, we were able to build a predictive model that distinguished patients with and without UC-Ca with a high accuracy rate of 83% and a negative predictive value of 100%. Conclusion: This predictive model suggests that it is possible to identify UC patients at a high risk of developing cancer. These results have important implications for improving the efficacy of surveillance by colonoscopy and suggest directions for future research into the molecular mechanisms of UC-associated cancer.