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Construction and assessment of the prognostic risk model

LY Leilei Yang
CF Chengfeng Fang
RZ Ruili Zhang
SZ Shenkang Zhou
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The prognostic OS-related genes were selected with univariate Cox regression and screened for the construction of the prognostic risk model by the LASSO algorithm using the glmnet R package (4.1.7). The risk score of each CRC patient was calculated as previously documented [11]. Risk Score =∑inXi×Yi (X indicates correlation coefficient between genes and survival, Y indicates expression level of genes). CRC patients were separated into two (high/low) risk groups with the median risk score as the cutoff value. The differences in patient prognosis between the high and low-risk groups were assessed with Kaplan–Meier analysis using log-rank statistical methods. ROC curves and DCA curves were generated to assess the sensitivity and accuracy of the risk model. The time-dependent receiver operating characteristic (ROC) curves (1-, 3-, and 5-year) were generated using the survminer and timeROC package in R software. P < 0.05 was used as the threshold value.

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