Lung cancer has a high mortality rate. Promoting early diagnosis and screening of lung cancer is the most effective way to enhance the survival rate of lung cancer patients. Through computer technology, a comprehensive evaluation of genetic testing results and basic clinical information of lung cancer patients could effectively diagnose early lung cancer and indicate cancer risks. This study retrospectively collected 70 pairs of lung cancer tissue samples and normal human tissue samples. The methylation frequencies of 6 genes (FHIT, p16, MGMT, RASSF1A, APC, DAPK) in lung cancer patients, the basic clinical information, and tumor marker levels of these patients were analyzed. Then, the python package “sklearn” was employed to build a support vector machine (SVM) classifier which performed 10-fold cross-validation to construct diagnostic models that could identify lung cancer risk of suspected cases. Receiver operation characteristic (ROC) curves were drawn, and the performance of the combined diagnostic model based on several factors (clinical information, tumor marker level, and methylation frequency of 6 genes in blood) was shown to be better than that of models with only one pathological feature. The AUC value of the combined model was 0.963, and the sensitivity, specificity, and accuracy were 0.900, 0.971, and 0.936, respectively. The above results revealed that the diagnostic model based on these features was highly reliable, which could screen and diagnose suspected early lung cancer patients, contributing to increasing diagnosis rate and survival rate of lung cancer patients.

中文翻译：

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基于血液中 6 基因甲基化频率、临床特征和血清肿瘤标志物的肺癌诊断模型的构建和验证

肺癌的死亡率很高。促进肺癌的早期诊断和筛查是提高肺癌患者生存率的最有效途径。通过计算机技术，综合评估肺癌患者的基因检测结果和临床基础信息，可以有效诊断早期肺癌，提示癌症风险。本研究回顾性收集了 70 对肺癌组织样本和正常人体组织样本。分析肺癌患者6个基因（FHIT、p16、MGMT、RASSF1A、APC、DAPK）的甲基化频率、患者的基本临床信息和肿瘤标志物水平。然后，使用 python 包“sklearn”构建支持向量机 (SVM) 分类器，该分类器执行 10 倍交叉验证以构建可识别疑似病例肺癌风险的诊断模型。绘制接受者操作特征（ROC）曲线，基​​于多个因素（临床信息、肿瘤标志物水平、血液中6个基因的甲基化频率）的组合诊断模型的性能显示优于仅具有一种病理特征。组合模型的AUC值为0.963，敏感性、特异性和准确度分别为0.900、0.971和0.936。以上结果表明，基于这些特征的诊断模型是高度可靠的，可以筛查和诊断疑似早期肺癌患者，