Early detection of colorectal cancer based on circular DNA and common clinical detection indicators.
Early detection of colorectal cancer based on circular DNA and common clinical detection indicators.
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DOI：
10.4240/wjgs.v14.i8.833
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发表时间：
2022-08-27
影响因子：
2
通讯作者：
Xiang, Guo-An
中科院分区：
文献类型：
--
作者：
Li, Jian;Jiang, Tao;Ren, Zeng-Ci;Wang, Zhen-Lei;Zhang, Peng-Jun;Xiang, Guo-An
结直肠癌(CRC)是全球第三大常见癌症，也是世界第二大癌症死亡原因，约占所有癌症死亡的9%。临床上迫切需要对结直肠癌进行早期检测。建立早期发现结直肠癌的多参数诊断模型。共59例大肠息肉(CRP)组，101例结直肠癌患者(早期38例，晚期63例)用于造模。此外，30个CRP组和62例结直肠癌患者(30例早期结直肠癌和32例晚期结直肠癌)分别被纳入验证模型。51临床常用检测指标和前期筛选的4个染色体外环状DNA标记NDUFB7、CAMK1D、PIK3CD和PSEN2。采用二元Logistic回归分析、判别分析、分类树和神经网络四种多参数联合分析方法建立多参数联合诊断模型。选择包括癌胚抗原、缺血修饰白蛋白、唾液酸、PIK3CD和脂蛋白a的神经网络作为区分C反应蛋白和结直肠癌的最佳多参数组合辅助诊断模型，当区分59例C反应蛋白和101例结直肠癌时，其总体准确率为90.8%，曲线下面积为0.959(0.934，0.985)，敏感性和特异性分别为91.5%和82.2%。经验证，根据30例C反应蛋白和62例结直肠癌患者的AUC为0.965(0.930~1.000)，其敏感度和特异度分别为66.1%和70.0%。根据30例C-反应蛋白和32例早期结直肠癌患者的AUC值为0.960(0.916~1.000)，灵敏度和特异度分别为87.5%和90.0%；以30例C反应蛋白和30例晚期结直肠癌患者为例，AUC值为0.970(0.936~1.000)，灵敏度和特异度分别为96.7%和86.7%。建立了包括CEA、IMA、SA、PIK3CD和LPA在内的多参数神经网络诊断模型，用于大肠癌的早期诊断，与传统的CEA相比，有明显的改善。
Colorectal cancer (CRC) is the third most common cancer worldwide, and it is the second leading cause of death from cancer in the world, accounting for approximately 9% of all cancer deaths. Early detection of CRC is urgently needed in clinical practice. To build a multi-parameter diagnostic model for early detection of CRC. Total 59 colorectal polyps (CRP) groups, and 101 CRC patients (38 early-stage CRC and 63 advanced CRC) for model establishment. In addition, 30 CRP groups, and 62 CRC patients (30 early-stage CRC and 32 advanced CRC) were separately included to validate the model. 51 commonly used clinical detection indicators and the 4 extrachromosomal circular DNA markers NDUFB7, CAMK1D, PIK3CD and PSEN2 that we screened earlier. Four multi-parameter joint analysis methods: binary logistic regression analysis, discriminant analysis, classification tree and neural network to establish a multi-parameter joint diagnosis model. Neural network included carcinoembryonic antigen (CEA), ischemia-modified albumin (IMA), sialic acid (SA), PIK3CD and lipoprotein a (LPa) was chosen as the optimal multi-parameter combined auxiliary diagnosis model to distinguish CRP and CRC group, when it differentiated 59 CRP and 101 CRC, its overall accuracy was 90.8%, its area under the curve (AUC) was 0.959 (0.934, 0.985), and the sensitivity and specificity were 91.5% and 82.2%, respectively. After validation, when distinguishing based on 30 CRP and 62 CRC patients, the AUC was 0.965 (0.930-1.000), and its sensitivity and specificity were 66.1% and 70.0%. When distinguishing based on 30 CRP and 32 early-stage CRC patients, the AUC was 0.960 (0.916-1.000), with a sensitivity and specificity of 87.5% and 90.0%, distinguishing based on 30 CRP and 30 advanced CRC patients, the AUC was 0.970 (0.936-1.000), with a sensitivity and specificity of 96.7% and 86.7%. We built a multi-parameter neural network diagnostic model included CEA, IMA, SA, PIK3CD and LPa for early detection of CRC, compared to the conventional CEA, it showed significant improvement.
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