Using LASSO logistic regression to adaptively perform variable selection, the expression of 68 cancer-related proteins were considered as potential predictors, with the optimal model combining the expression values of CA125 with three additional proteins (HE4, ITGAV, and SEZ6L), such that the predicted ovarian cancer risk score is equal to:(1)expit(−3.43+0.959× CA125+0.380× HE4+−0.946× ITGAV +−0.964× SEZ6L)where expit(x) = ex/(1 + ex). This evidence concerns the gene MUC16 and ovarian carcinoma.