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

Figures

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  Fig. 1. Flowchart of experimental design.

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  Fig. 2. Flow chart of data mining. Note: The Date.sav node was the input data source; the Type node described the type of variable; the Feature Selection node can filter the variables and rank the importance of the variables; the Partition node can set the partition ratio of the data, and the study divided the samples into training set and testing set at a ratio of 3:1, and the number of random seeds was set to 1,111,111 (For statistical analyses in the model-building process, normality was tested using the Shapiro–Wilk test (n ≤ 50) or Kolmogorov–Smirnov test (n > 50), and homogeneity of variance was assessed via Levene’s test, as described in Section "Statistical methods").

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  Fig. 3. Screening candidate protein markers in vivo and in vitro models of lung cancer. (a) Volcano plots of the differentially expressed proteins in cellular experiments. (b) Volcano plots of the differentially expressed proteins in animal experiments. (c) Hub proteins for cellular experiment. (d) Hub proteins for animal experiment.

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  Fig. 4. Comparison of plasma expression levels of 8 proteins in LC, CO and NC. Note: *P < 0.05; **P < 0.01; ***P < 0.001; ****P < 0.0001; ns, the difference is not statistically significant.

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  Fig. 5. ROC curves of the classification results of the prediction set by the four models. (a) Based on 3 traditional lung cancer markers (CEA, CYFRA21-1, NSE). (b) Based on 5 candidate molecular markers (CDH1, CLEC3B, CLU, sCD146 and VIM). (c) Based on 8 protein molecules (CEA, CYFRA21-1, NSE, CDH1, CLEC3B, CLU, sCD146 and VIM). (d) Based on 8 protein molecules and epidemiological information (CEA, CYFRA21-1, NSE, CDH1, CLEC3B, CLU, sCD146, VIM, History of smoking and History of alcohol consumption).