Skip to main content

Table 2 Cociter analysis of top 30 prostate cancer driver genes identified by our method

From: Identifying driver genes involving gene dysregulated expression, tissue-specific expression and gene-gene network

GenesCancerProstateDriveris driverDyTidriverDiffusionDriverNetDawnRankMuf maxMuf sum
TP53677229811011111384
CTNNB1251717044122221409
ASH1L401031703NANA65378
SPOP43244141721316983
ATM1377615051311123614
PTEN3047642641670094NA3937
TTN1002071724221422
FOXA118269100817533710
KMT2D25220985554NANANA
PIK3CA11993454110710NA28236
DYNC1H191201166195121972
CDH124000121511NA755349296
BRAF217533126113326633634834
AKT12152317231142023NA5233
FAT3111015192675NANA
LRP47020161440NANA1426541
GRIN2B13020177433NA22090
KMT2C232401861327NANANA
NCOR11092731195977584160
HSPA89691020108NA43867
OBSCN7000211714168408124
GRIN2A501022285928537473
PCDHA12100023145327119732465
MED12194402437616215731784
STAT318241472702516155588
PCDH182110261656936626239
CDH2350102745797NA29563
SPTA1301028171916922115
UFL1701029NANANA12381265
SP139338313089NA865
  1. The second to the fourth column show the co-appeared times of top 30 identified genes with ‘driver’,‘prostate’ and ‘cancer’ (from the left to the right). Is_driver indicates whether the given gene is a driver or not in benchmark dataset. The left columns represent the ranking positions of identified genes in Dytidriver, Diffusion, DriverNet, DawnRank, Muf_max, Muf_sum respectively