Fig 1.
Venn diagrams represent the overlaps of DEGs between three datasets of RCC subtypes and pathway enrichment analysis.
(A) Venn diagrams showing overlap of upregulated genes between ccRCC (green), pRCC (blue) and chRCC (red) dataset. (B and C) The bubble plots of the top 10 pathway enrichment for DEGs, B is the top 10 pathway enrichment of ccRCC, and C is the top 10 pathway enrichment of chRCC.
Table 1.
The top ten genes were selected based on FDR and |log2FC|.
Table 2.
The performance of different feature sets in the feature selection process by using the Decision Tree Classifier.
Fig 2.
The ROC curve and AUC score of six predictive models for determining ccRCC and non-ccRCC based on transcriptomic datasets.
(A-F) The ROC curves of LR, DT, RF, KNN, SVM and ANN, respectively. The x-axis represents a false positive rate, and the y-axis represents a true positive rate. Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), K-Nearest Neighbors (KNN), Support Vector Machine (SVM) and Artificial Neural Network (ANN).
Table 3.
The performance of six classifier models to distinguish between ccRCC and non-ccRCC on a test set based on transcriptomic datasets.
Fig 3.
The box plot diagrams represent the correlations between gene expression distribution of NDUFA4L2 and DAT with RCC groupings.
(A) The box plot of NDUFA4L2 expression between ccRCC and non-ccRCC grouping. (B) The box plot of DAT expression between ccRCC and non-ccRCC grouping. The x-axis represents the groups of RCC, and the y-axis represents the gene expression values based on H-score.
Fig 4.
Expression of NDUFA4L2 on tissues in ccRCC and non-ccRCC group.
(A-C) These demonstrated ccRCC group, (D-F) pRCC subtypes, (G-I) chRCC subtypes, and (J-L) rRCC subtypes (the examples of cases with histo- and immunologically suspected translocation-type RCC). The H&E staining of each histologic type including ccRCC (A), pRCC (D), chRCC (G), and rRCC (J) subtypes. The representative of low NDUFA4L2 expression in each subtype was demonstrated (B, E, H and K). The high expression of NDUFA4L2 in each subtype was shown (C, F, I and L). clear cell renal cell carcinoma (ccRCC), papillary renal cell carcinoma (pRCC), chromophobe renal cell carcinoma (chRCC) and rare subtypes renal cell carcinoma (rRCC). (All figures were taken at 20X).
Fig 5.
Expression of DAT on tissues in ccRCC and non-ccRCC group.
(A-C) These demonstrated ccRCC group, (D-F) pRCC subtypes, (G-I) chRCC subtypes and (J-M) rRCC subtypes (J and K demonstrated that cases with histo- and immunologically suspected translocation-type RCC. L and M represented cases with histo- and immunologically suspected acquired cystic disease associated renal cell carcinoma). The H&E staining of each histologic type including ccRCC (A), pRCC (D), chRCC(G) and rRCC (J). The low expression of DAT was shown in ccRCC (B), pRCC (E), chRCC (H) and rRCC (M). The high signal of DAT staining in each subtype was shown in ccRCC (C), pRCC (F), chRCC (I) and rRCC (K). clear cell renal cell carcinoma (ccRCC), papillary renal cell carcinoma (pRCC), chromophobe renal cell carcinoma (chRCC) and rare subtypes renal cell carcinoma (rRCC). (All figures were taken at 20X).
Table 4.
The Correlation of two gene expressions with RCC groups by IHC technique.
Table 5.
The correlation of NDUFA4L2 expression and clinicopathological features.
Table 6.
The correlation of DAT expression and clinicopathological features.
Fig 6.
The ROC cure and AUC score of four predictive models on IHC dataset.
(A-D) The ROC curves of LR, DT, RF and GB, respectively. The x-axis represents a false positive rate, and the y-axis represents a true positive rate.
Table 7.
The performance of two markers was evaluated on four classification models for separating ccRCC from non-ccRCC based on the H-score (immunohistochemistry score).