Superficial modification based 5hmC (5-hydroxymethylcytosine) polymolecular marker and CRC (colorectal cancer) early diagnosis model
A colorectal cancer, diagnostic model technology, applied in the field of clinical biomedicine, to achieve the effect of convenient detection, strong stability, and improved compliance
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Embodiment 1
[0059] Embodiment 1 Construction of colorectal cancer early diagnosis model
[0060] 1. Screening of genetic markers
[0061] The present invention enrolls 100 patients with stage I-III colorectal cancer and 100 healthy people with normal colonoscopic examination. With informed consent, 10ml of peripheral venous blood was collected for testing. The detection process cooperates with Shanghai Yibien Gene Technology Co., Ltd., and adopts the 5-hmC sequencing (Nano-hmC-Seal) technology with low input DNA. The detection process is as follows: figure 1 shown. The 5-hmC expression level of more than 20,000 gene loci in the whole genome was detected. Such as figure 2 As shown in (left), there is no significant difference in PCI dimensionality reduction in the whole genome 5-hmC expression between the two groups. Then, we used the Deseq method to compare the differences in 5-hmC expression at each gene locus between the two groups, and selected the top 80 genes with the largest di...
Embodiment 2
[0077] Embodiment 2 Verification of early diagnosis model of colorectal cancer
[0078] Internal validation of the constructed diagnostic model. Using the model constructed in Example 1 to score 200 samples, the cutoff value is 0.5, and the result is greater than or equal to 0.5 to be diagnosed as colorectal cancer, otherwise it is considered a healthy person, and the judgment result is as follows Figure 4 (left) shown. Among 100 colorectal cancer patients, 92 cases were diagnosed as colorectal cancer, and 8 cases were misjudged; among 100 healthy people, 90 cases were judged as non-colorectal cancer, and 10 cases were misjudged. The sensitivity and specificity of internal validation were 92% and 90%. The area under the AUC curve of the internal validation is 0.9488, and the sensitivity and specificity values are 89.4% and 93.4% respectively, see Figure 4 (right).
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