Background: Adrenocortical carcinoma (ACC) is a rare malignancy with poor prognosis. Thus, we aimed to establish a gene signature to predict the prognosis for ACC. Methods: Firstly, “WGCNA” package was used to construct a co-expression network and screen key module. Secondly, survival associated genes were identified by performing survival analysis. Thirdly, regression models were constructed by using the Ridge, ELASTIC-NET, and LASSO methods. Time-dependent ROC analysis, Cox regression analysis, GSEA, DCA and nomogram were performed to validate the model. Fourthly, mutations and CNVs of genes in the model were explored. Finally, LDA, KNN, SVM, PPI network and meta-analysis were used screened and validated meaningful prognostic biomarkers.Results: Two key modules were selected and 93 survival associated genes were identified. Furthermore, 11 models were constructed and two models were further selected, which were validated in each dataset (training set, internal validation set, GSE19750, and GSE76021). Model 2 was further identified as the best model (training set: survival analysis: p < 0.0001; AUC: 0.92 at 1 year, 0.91 at 3 years and 0.95 at 5 years). In genes in the best model, MKI67 was altered most (12%). Six hub genes were further analyzed by constructing a PPI network and validated by meta-analysis. Conclusion: In summary, we constructed and validated a prognostic multi-gene model and six powerful prognostic biomarkers, which might be useful instruments for predicting the prognosis of ACC patients.
Background: Clear cell renal cell carcinoma (ccRCC) occupied most of renal cell carcinoma (RCC), which associated with poor prognosis. The purpose of this study is to screen novel and prognostic biomarkers for patients with ccRCC. Methods and Results: Firstly, Gene Expression Omnibus database was used to collect microarray data for weighted gene co-expression network construction. Gene modules related to prognosis which interest us most were picked out. 90 hub genes were further chosen in the key modules, two of which including GNRH1 and LTB4R were screened and validated as immune-related prognostic biomarkers. Several methods including survival analysis, spearman correlation analysis, HPA, One-way ANOVA and ROC analysis were used for the validation of immune-related prognostic biomarkers. We further explored the relationship between immune-related prognostic biomarker expressions and immunocytes. Finally, gene set enrichment analysis (GSEA) demonstrated that the two immune-related prognostic biomarkers were significantly correlated with cell cycle. Conclusions: Generally speaking, the present study has identified two novel prognostic biomarkers for patients with ccRCC, which showed strong correlation with prognosis of patients with ccRCC, could further be used as potential prognostic biomarkers in ccRCC.
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