Establishment and Validation of a Gene Signature-Based Prognostic Model to Improve Survival Prediction in Adrenocortical Carcinoma Patients

Figure 1

Selection of robust biomarkers to establish a survival-related gene signature: (a) a total of 985 DEGs were identified in ACC compared to normal tissues; (b and c) 35 promising candidates were filtered out using univariate Cox regression analysis; (d) cross-validation was applied to prevent overfitting, and the optimal λ value of 0.2967 with log(λ) = −1.215 was selected; (e) MKI67, TIGD1, and SGK1 finally remained with their nonzero LASSO coefficients; (f) distribution of LASSO coefficients of the gene signature; and (g) hierarchical clustering analysis showed normal tissues were characterized by lower expression levels of MKI67 and TIGD1 and by higher SGK1 expression levels compared to ACC.

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