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<p>Screening is the key to improving the five-year survival rate of lung cancer patients. We aimed to investigate the risk factors for lung cancer and to achieve screening and early warning of individuals at high risk of lung cancer in the coke oven worker population. According to the inclusion and exclusion criteria, 185 lung cancer patients and 163 normal controls were included. To screen for biomarkers, we established a mouse lung cancer model and a cellular malignant transformation model. Proteomics was utilised to screen for differentially expressed proteins. Data mining techniques were applied to combine candidate molecular markers of lung cancer with traditional tumor markers to construct lung cancer screening models. The population of coke oven workers was screened for individuals at high risk of lung cancer. CDH1, CLEC3B, CLU, sCD146 and VIM were investigated as candidate markers. Among all the screening models, support vector machine and C5.0 models performed better based on eight protein markers and epidemiological information. The models were applied to predict individuals at high risk of lung cancer among coke oven workers, and a total of 13 high-risk individuals were screened. Dysregulated expression of CDH1, CLU, CLEC3B, sCD146 and VIM was associated with lung cancer. Support vector machine and C5.0 models proved to be more effective and could be applied to screen high-risk individuals among coke oven workers.</p>

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Data mining-based lung cancer diagnostic models and high-risk warning for occupational group

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月31日

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  • Yaru Chai,
  • Huijie Yuan,
  • Shuyin Duan,
  • Penghui Ji,
  • Ziqi Wang,
  • Lijun Miao,
  • Hong Liu,
  • Lihua Ding,
  • Yongjun Wu,
  • Sitian He

摘要

Screening is the key to improving the five-year survival rate of lung cancer patients. We aimed to investigate the risk factors for lung cancer and to achieve screening and early warning of individuals at high risk of lung cancer in the coke oven worker population. According to the inclusion and exclusion criteria, 185 lung cancer patients and 163 normal controls were included. To screen for biomarkers, we established a mouse lung cancer model and a cellular malignant transformation model. Proteomics was utilised to screen for differentially expressed proteins. Data mining techniques were applied to combine candidate molecular markers of lung cancer with traditional tumor markers to construct lung cancer screening models. The population of coke oven workers was screened for individuals at high risk of lung cancer. CDH1, CLEC3B, CLU, sCD146 and VIM were investigated as candidate markers. Among all the screening models, support vector machine and C5.0 models performed better based on eight protein markers and epidemiological information. The models were applied to predict individuals at high risk of lung cancer among coke oven workers, and a total of 13 high-risk individuals were screened. Dysregulated expression of CDH1, CLU, CLEC3B, sCD146 and VIM was associated with lung cancer. Support vector machine and C5.0 models proved to be more effective and could be applied to screen high-risk individuals among coke oven workers.