Figures 3(a) and 3(b) show the decision tree models trained on the whole dataset of GSE20347 using RUVBL1 and CNIH, respectively.
The performances using both genes of RUVBL1 and CNIH are 88.24%, 94.12%, and 91.18% for sensitivity, specificity, and accuracy, respectively.
The decision tree models trained on the 34 profiles using RUVBL1 and both genes of RUVBL1 and CNIH are shown in Figures 4(a) and 4(b), respectively.
Two genes of RUVBL1 and CNIH were discovered with a high LOOCV accuracy of 99.06% in a published dataset GSE23400 (available at GEO database) consisting of 53 pairs of ESCC and normal tissues.
An LOOCV accuracy of 91.18% obtained by using RUVBL1 and CNIH shows their potential as biomarkers for ESCC.
The relationship between the two newly identified biomarkers of RUVBL1 and CNIH genes and ESCC has not been reported.
CNIH is involved in the selective transport and maturation of TGF-alpha family proteins.