Identification of Four Novel Prognostic Biomarkers and Construction of Two Nomograms in Adrenocortical Carcinoma: A Multi-Omics Data Study via Bioinformatics and Machine Learning Methods - Yi, Xiaochun; Wan, Yueming; Cao, Weiwei; Peng, Keliang; Li, Xin; Liao, Wangchun (Frontiers in Molecular Biosciences, 2022)
Background: Adrenocortical carcinoma (ACC) is an orphan tumor which have bad prognoses. Therefore, it is of urgent need for us to find out candidate prognostic biomarkers and provide clinicians with an accurate method for survival prediction of ACC in this study via bioinformatics and machine learning methods. Methods: Eight different methods including (differentially-expressed gene (DEG) analysis, weighted correlation network analysis (WGCNA), Protein-Protein Interaction (PPI) network construction, survival analysis, expression level comparison, receiver operating characteristic (ROC) analysis, and decision curve analysis (DCA)) were used to identify potential prognostic biomarkers for ACC via seven independent datasets. Linear discriminant analysis (LDA), K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and time-dependent ROC were performed to further identify meaningful prognostic biomarkers (MPBs). Cox regression analyses were performed to screen factors for nomogram construction. Results: We identified 9 hub genes correlated to prognosis of patients with ACC. Furthermore, 4 MPBs (ASPM, BIRC5, CCNB2, and CDK1) with high accuracy of survival prediction were screened out, which were enriched in cell cycle. We also found that mutations and CNVs of these MPBs were associated with OS of ACC patients. Moreover, MPBs expressions were associated with immune infiltration level. Two nomograms (OS-nomogram and DFS-nomogram) were established, which could provide clinicians an accurate, quick, and visualized method for survival prediction. Conclusions: Four novel MPBs were identified and two nomograms were constructed, which might break through in treatment and prognosis prediction of patients with ACC.