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. 2020 Jul 30;11:857. doi: 10.3389/fgene.2020.00857

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Copyright © 2020 Gan, Luo, Dai, Lin, Liu, Zhang and Li.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

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Diagnostic performance of the DP.eHCC model selected by elastic net and RRA. (A) Heatmap showing the top 27 up-regulated genes and top 81 down-regulated genes in the training datasets (logFC > 1, adjusted p < 0.05). Each row represents one gene and each column indicates one dataset. Red indicates up-regulation and light blue represents down-regulation. DEGs: differentially expressed genes; RRA: robust rank aggregation. (B) As illustrated in the Venn Diagram, nine robust DEGs (AFM, AKR1C3, CYP1A2, CYP2E1, GPC3, HAMP, HBB, MT1G, and SPINK1) were identified by the intersection genes from the RRA (blue) and elastic net (yellow) analyses. (C) Receiver operating characteristic (ROC) curve analyses of the DP.eHCC model and its gene members for early HCC diagnosis. When compared with each gene member of the DP.eHCC model, the prediction efficiency of the DP.eHCC model was shown to be significantly enhanced (AUC = 0.956, p < 0.001).