Correlation between RPPA expression and clinical features
Prostate Adenocarcinoma (Primary solid tumor)
21 August 2015  |  analyses__2015_08_21
Maintainer Information
Citation Information
doi:10.7908/C17D2TDF
Maintained by Juok Cho (Broad Institute)
Cite as Broad Institute TCGA Genome Data Analysis Center (2015): Correlation between RPPA expression and clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C17D2TDF
Overview
Introduction

This pipeline uses various statistical tests to identify RPPAs whose expression levels correlated to selected clinical features.

Summary

Testing the association between 195 genes and 11 clinical features across 352 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 9 clinical features related to at least one genes.

  • 30 genes correlated to 'YEARS_TO_BIRTH'.

    • RB1|RB_PS807_S811 ,  MAP2K1|MEK1 ,  JUN|C-JUN_PS73 ,  ANXA1|ANNEXIN-1 ,  MAPK1 MAPK3|MAPK_PT202_Y204 ,  ...

  • 30 genes correlated to 'PATHOLOGY_T_STAGE'.

    • EIF4G1|EIF4G ,  XRCC5|KU80 ,  PRKCA |PKC-ALPHA ,  SQSTM1|P62-LCK-LIGAND ,  PRKCA |PKC-ALPHA_PS657 ,  ...

  • 30 genes correlated to 'PATHOLOGY_N_STAGE'.

    • SQSTM1|P62-LCK-LIGAND ,  NF2|NF2 ,  ITGA2|CD49B ,  TFRC|TFRC ,  CDH3|P-CADHERIN ,  ...

  • 30 genes correlated to 'RADIATION_THERAPY'.

    • ASNS|ASNS ,  COL6A1|COLLAGEN_VI ,  NF2|NF2 ,  CAV1|CAVEOLIN-1 ,  MAPK14|P38 ,  ...

  • 12 genes correlated to 'HISTOLOGICAL_TYPE'.

    • BAD|BAD_PS112 ,  MAPK14|P38_PT180_Y182 ,  SRC|SRC_PY527 ,  MAPK14|P38 ,  RAD51|RAD51 ,  ...

  • 30 genes correlated to 'RESIDUAL_TUMOR'.

    • CCNB1|CYCLIN_B1 ,  ASNS|ASNS ,  MAP2K1|MEK1_PS217_S221 ,  TFRC|TFRC ,  SQSTM1|P62-LCK-LIGAND ,  ...

  • 30 genes correlated to 'NUMBER_OF_LYMPH_NODES'.

    • SQSTM1|P62-LCK-LIGAND ,  NF2|NF2 ,  ITGA2|CD49B ,  TFRC|TFRC ,  CDH3|P-CADHERIN ,  ...

  • 30 genes correlated to 'GLEASON_SCORE'.

    • EIF4G1|EIF4G ,  BCL2L11|BIM ,  GAB2|GAB2 ,  SQSTM1|P62-LCK-LIGAND ,  CAV1|CAVEOLIN-1 ,  ...

  • 30 genes correlated to 'PSA_VALUE'.

    • BRCA2|BRCA2 ,  ACVRL1|ACVRL1 ,  MYH9|MYOSIN-IIA_PS1943 ,  PRKCD|PKC-DELTA_PS664 ,  SYK|SYK ,  ...

  • No genes correlated to 'DAYS_TO_DEATH_OR_LAST_FUP', and 'RACE'.

Results
Overview of the results

Complete statistical result table is provided in Supplement Table 1

Table 1.  Get Full Table This table shows the clinical features, statistical methods used, and the number of genes that are significantly associated with each clinical feature at P value < 0.05 and Q value < 0.3.

Clinical feature Statistical test Significant genes Associated with                 Associated with
DAYS_TO_DEATH_OR_LAST_FUP Cox regression test   N=0        
YEARS_TO_BIRTH Spearman correlation test N=30 older N=16 younger N=14
PATHOLOGY_T_STAGE Spearman correlation test N=30 higher stage N=16 lower stage N=14
PATHOLOGY_N_STAGE Wilcoxon test N=30 n1 N=30 n0 N=0
RADIATION_THERAPY Wilcoxon test N=30 yes N=30 no N=0
HISTOLOGICAL_TYPE Wilcoxon test N=12 prostate adenocarcinoma acinar type N=12 prostate adenocarcinoma other subtype N=0
RESIDUAL_TUMOR Kruskal-Wallis test N=30        
NUMBER_OF_LYMPH_NODES Spearman correlation test N=30 higher number_of_lymph_nodes N=12 lower number_of_lymph_nodes N=18
GLEASON_SCORE Spearman correlation test N=30 higher score N=17 lower score N=13
PSA_VALUE Spearman correlation test N=30 higher psa_value N=11 lower psa_value N=19
RACE Kruskal-Wallis test   N=0        
Clinical variable #1: 'DAYS_TO_DEATH_OR_LAST_FUP'

No gene related to 'DAYS_TO_DEATH_OR_LAST_FUP'.

Table S1.  Basic characteristics of clinical feature: 'DAYS_TO_DEATH_OR_LAST_FUP'

DAYS_TO_DEATH_OR_LAST_FUP Duration (Months) 0.7-165.2 (median=31)
  censored N = 344
  death N = 7
     
  Significant markers N = 0
Clinical variable #2: 'YEARS_TO_BIRTH'

30 genes related to 'YEARS_TO_BIRTH'.

Table S2.  Basic characteristics of clinical feature: 'YEARS_TO_BIRTH'

YEARS_TO_BIRTH Mean (SD) 61.44 (6.6)
  Significant markers N = 30
  pos. correlated 16
  neg. correlated 14
List of top 10 genes differentially expressed by 'YEARS_TO_BIRTH'

Table S3.  Get Full Table List of top 10 genes significantly correlated to 'YEARS_TO_BIRTH' by Spearman correlation test

SpearmanCorr corrP Q
RB1|RB_PS807_S811 0.2558 1.277e-06 0.000249
MAP2K1|MEK1 -0.2273 1.804e-05 0.00176
JUN|C-JUN_PS73 0.2068 9.934e-05 0.00591
ANXA1|ANNEXIN-1 -0.2043 0.0001213 0.00591
MAPK1 MAPK3|MAPK_PT202_Y204 0.2007 0.0001598 0.00623
MAPK8|JNK_PT183_PY185 0.1931 0.0002858 0.00823
EGFR|EGFR_PY1068 0.1921 0.0003065 0.00823
EIF4EBP1|4E-BP1_PT37_T46 0.1908 0.0003375 0.00823
GSK3A GSK3B|GSK3-ALPHA-BETA -0.1882 0.0004093 0.00842
FOXM1|FOXM1 0.1873 0.0004343 0.00842
Clinical variable #3: 'PATHOLOGY_T_STAGE'

30 genes related to 'PATHOLOGY_T_STAGE'.

Table S4.  Basic characteristics of clinical feature: 'PATHOLOGY_T_STAGE'

PATHOLOGY_T_STAGE Mean (SD) 2.71 (0.51)
  N
  T2 111
  T3 227
  T4 10
     
  Significant markers N = 30
  pos. correlated 16
  neg. correlated 14
List of top 10 genes differentially expressed by 'PATHOLOGY_T_STAGE'

Table S5.  Get Full Table List of top 10 genes significantly correlated to 'PATHOLOGY_T_STAGE' by Spearman correlation test

SpearmanCorr corrP Q
EIF4G1|EIF4G 0.2847 6.495e-08 1.27e-05
XRCC5|KU80 0.2744 1.994e-07 1.94e-05
PRKCA |PKC-ALPHA -0.2542 1.554e-06 9.95e-05
SQSTM1|P62-LCK-LIGAND 0.2514 2.041e-06 9.95e-05
PRKCA |PKC-ALPHA_PS657 -0.2464 3.285e-06 0.000128
DVL3|DVL3 0.2423 4.846e-06 0.000158
GAB2|GAB2 0.2346 9.76e-06 0.000272
PKC|PKC-PAN_BETAII_PS660 -0.2287 1.64e-05 0.000359
CCNB1|CYCLIN_B1 0.2286 1.655e-05 0.000359
ASNS|ASNS 0.2259 2.108e-05 0.000411
Clinical variable #4: 'PATHOLOGY_N_STAGE'

30 genes related to 'PATHOLOGY_N_STAGE'.

Table S6.  Basic characteristics of clinical feature: 'PATHOLOGY_N_STAGE'

PATHOLOGY_N_STAGE Labels N
  N0 246
  N1 63
     
  Significant markers N = 30
  Higher in N1 30
  Higher in N0 0
List of top 10 genes differentially expressed by 'PATHOLOGY_N_STAGE'

Table S7.  Get Full Table List of top 10 genes differentially expressed by 'PATHOLOGY_N_STAGE'

W(pos if higher in 'N1') wilcoxontestP Q AUC
SQSTM1|P62-LCK-LIGAND 10492 1.462e-05 0.00285 0.677
NF2|NF2 5348 0.0001484 0.0145 0.6549
ITGA2|CD49B 5443 0.0002688 0.0175 0.6488
TFRC|TFRC 9915 0.0006207 0.0303 0.6398
CDH3|P-CADHERIN 5668 0.001009 0.0393 0.6343
PGR|PR 5839 0.002546 0.0827 0.6232
ASNS|ASNS 9609.5 0.003287 0.0916 0.62
RAF1|C-RAF_PS338 5916 0.003778 0.0921 0.6183
EGFR|EGFR_PY1173 6012.5 0.006077 0.132 0.612
SYK|SYK 9456 0.006997 0.136 0.6101
Clinical variable #5: 'RADIATION_THERAPY'

30 genes related to 'RADIATION_THERAPY'.

Table S8.  Basic characteristics of clinical feature: 'RADIATION_THERAPY'

RADIATION_THERAPY Labels N
  NO 278
  YES 40
     
  Significant markers N = 30
  Higher in YES 30
  Higher in NO 0
List of top 10 genes differentially expressed by 'RADIATION_THERAPY'

Table S9.  Get Full Table List of top 10 genes differentially expressed by 'RADIATION_THERAPY'

W(pos if higher in 'YES') wilcoxontestP Q AUC
ASNS|ASNS 7629 0.0001421 0.0277 0.6861
COL6A1|COLLAGEN_VI 3716 0.0006972 0.0469 0.6658
NF2|NF2 3781 0.001071 0.0469 0.66
CAV1|CAVEOLIN-1 3795 0.001173 0.0469 0.6587
MAPK14|P38 2944 0.001203 0.0469 0.6906
PGR|PR 3858 0.001751 0.05 0.6531
PRKCA |PKC-ALPHA 3862 0.001795 0.05 0.6527
PRKCD|PKC-DELTA_PS664 3892 0.002162 0.0527 0.65
RICTOR|RICTOR 3912 0.002444 0.053 0.6482
TFRC|TFRC 7187 0.002776 0.0541 0.6463
Clinical variable #6: 'HISTOLOGICAL_TYPE'

12 genes related to 'HISTOLOGICAL_TYPE'.

Table S10.  Basic characteristics of clinical feature: 'HISTOLOGICAL_TYPE'

HISTOLOGICAL_TYPE Labels N
  PROSTATE ADENOCARCINOMA OTHER SUBTYPE 11
  PROSTATE ADENOCARCINOMA ACINAR TYPE 341
     
  Significant markers N = 12
  Higher in PROSTATE ADENOCARCINOMA ACINAR TYPE 12
  Higher in PROSTATE ADENOCARCINOMA OTHER SUBTYPE 0
List of top 10 genes differentially expressed by 'HISTOLOGICAL_TYPE'

Clinical variable #7: 'RESIDUAL_TUMOR'

30 genes related to 'RESIDUAL_TUMOR'.

Table S12.  Basic characteristics of clinical feature: 'RESIDUAL_TUMOR'

RESIDUAL_TUMOR Labels N
  R0 220
  R1 108
  R2 3
  RX 10
     
  Significant markers N = 30
List of top 10 genes differentially expressed by 'RESIDUAL_TUMOR'

Table S13.  Get Full Table List of top 10 genes differentially expressed by 'RESIDUAL_TUMOR'

kruskal_wallis_P Q
CCNB1|CYCLIN_B1 3.548e-05 0.00692
ASNS|ASNS 9.075e-05 0.00885
MAP2K1|MEK1_PS217_S221 0.0002141 0.0125
TFRC|TFRC 0.000256 0.0125
SQSTM1|P62-LCK-LIGAND 0.000558 0.0159
PRKCA |PKC-ALPHA_PS657 0.0005613 0.0159
PRKCA |PKC-ALPHA 0.0005718 0.0159
FOXM1|FOXM1 0.0007133 0.0174
CCNE1|CYCLIN_E1 0.0008662 0.0188
RICTOR|RICTOR_PT1135 0.00116 0.0226
Clinical variable #8: 'NUMBER_OF_LYMPH_NODES'

30 genes related to 'NUMBER_OF_LYMPH_NODES'.

Table S14.  Basic characteristics of clinical feature: 'NUMBER_OF_LYMPH_NODES'

NUMBER_OF_LYMPH_NODES Mean (SD) 0.51 (1.5)
  Significant markers N = 30
  pos. correlated 12
  neg. correlated 18
List of top 10 genes differentially expressed by 'NUMBER_OF_LYMPH_NODES'

Table S15.  Get Full Table List of top 10 genes significantly correlated to 'NUMBER_OF_LYMPH_NODES' by Spearman correlation test

SpearmanCorr corrP Q
SQSTM1|P62-LCK-LIGAND 0.2568 6.662e-06 0.0013
NF2|NF2 -0.2326 4.75e-05 0.00463
ITGA2|CD49B -0.2128 0.0002052 0.0126
TFRC|TFRC 0.2095 0.0002583 0.0126
CDH3|P-CADHERIN -0.2011 0.0004587 0.0179
PGR|PR -0.193 0.0007799 0.0253
RAF1|C-RAF_PS338 -0.1802 0.001725 0.0481
ASNS|ASNS 0.1739 0.002501 0.061
EGFR|EGFR_PY1173 -0.1696 0.003215 0.0697
SYK|SYK 0.1597 0.005565 0.0888
Clinical variable #9: 'GLEASON_SCORE'

30 genes related to 'GLEASON_SCORE'.

Table S16.  Basic characteristics of clinical feature: 'GLEASON_SCORE'

GLEASON_SCORE Mean (SD) 7.64 (1)
  Score N
  6 30
  7 174
  8 41
  9 105
  10 2
     
  Significant markers N = 30
  pos. correlated 17
  neg. correlated 13
List of top 10 genes differentially expressed by 'GLEASON_SCORE'

Table S17.  Get Full Table List of top 10 genes significantly correlated to 'GLEASON_SCORE' by Spearman correlation test

SpearmanCorr corrP Q
EIF4G1|EIF4G 0.4011 4.904e-15 9.56e-13
BCL2L11|BIM 0.382 1.129e-13 1.1e-11
GAB2|GAB2 0.3779 2.145e-13 1.39e-11
SQSTM1|P62-LCK-LIGAND 0.3401 5.59e-11 2.73e-09
CAV1|CAVEOLIN-1 -0.328 2.856e-10 1.11e-08
XRCC5|KU80 0.3248 4.291e-10 1.39e-08
PGR|PR -0.3173 1.132e-09 3.15e-08
ACVRL1|ACVRL1 -0.3149 1.535e-09 3.74e-08
NF2|NF2 -0.3126 2.03e-09 4.23e-08
CCNB1|CYCLIN_B1 0.3116 2.312e-09 4.23e-08
Clinical variable #10: 'PSA_VALUE'

30 genes related to 'PSA_VALUE'.

Table S18.  Basic characteristics of clinical feature: 'PSA_VALUE'

PSA_VALUE Mean (SD) 1.29 (4.7)
  Significant markers N = 30
  pos. correlated 11
  neg. correlated 19
List of top 10 genes differentially expressed by 'PSA_VALUE'

Table S19.  Get Full Table List of top 10 genes significantly correlated to 'PSA_VALUE' by Spearman correlation test

SpearmanCorr corrP Q
BRCA2|BRCA2 -0.2453 9.665e-06 0.00188
ACVRL1|ACVRL1 -0.2115 0.0001443 0.0141
MYH9|MYOSIN-IIA_PS1943 0.1876 0.0007716 0.0502
PRKCD|PKC-DELTA_PS664 -0.1776 0.001474 0.07
SYK|SYK 0.1744 0.001796 0.07
ERBB3|HER3_PY1289 -0.1703 0.002308 0.07
PRDX1|PRDX1 -0.1686 0.002562 0.07
RB1|RB_PS807_S811 0.1659 0.003012 0.07
PRKCA |PKC-ALPHA -0.1647 0.003229 0.07
PRKCA |PKC-ALPHA_PS657 -0.1603 0.004155 0.0709
Clinical variable #11: 'RACE'

No gene related to 'RACE'.

Table S20.  Basic characteristics of clinical feature: 'RACE'

RACE Labels N
  ASIAN 2
  BLACK OR AFRICAN AMERICAN 6
  WHITE 121
     
  Significant markers N = 0
Methods & Data
Input

  • Expresson data file = PRAD-TP.rppa.txt

  • Clinical data file = PRAD-TP.merged_data.txt

  • Number of patients = 352

  • Number of genes = 195

  • Number of clinical features = 11

Selected clinical features

  • Further details on clinical features selected for this analysis, please find a documentation on selected CDEs (Clinical Data Elements). The first column of the file is a formula to convert values and the second column is a clinical parameter name.

  • Survival time data

    • Survival time data is a combined value of days_to_death and days_to_last_followup. For each patient, it creates a combined value 'days_to_death_or_last_fup' using conversion process below.

      • if 'vital_status'==1(dead), 'days_to_last_followup' is always NA. Thus, uses 'days_to_death' value for 'days_to_death_or_fup'

      • if 'vital_status'==0(alive),

        • if 'days_to_death'==NA & 'days_to_last_followup'!=NA, uses 'days_to_last_followup' value for 'days_to_death_or_fup'

        • if 'days_to_death'!=NA, excludes this case in survival analysis and report the case.

      • if 'vital_status'==NA,excludes this case in survival analysis and report the case.

    • cf. In certain diesase types such as SKCM, days_to_death parameter is replaced with time_from_specimen_dx or time_from_specimen_procurement_to_death .

  • This analysis excluded clinical variables that has only NA values.

Survival analysis

For survival clinical features, Wald's test in univariate Cox regression analysis with proportional hazards model (Andersen and Gill 1982) was used to estimate the P values using the 'coxph' function in R. Kaplan-Meier survival curves were plot using the four quartile subgroups of patients based on expression levels

Correlation analysis

For continuous numerical clinical features, Spearman's rank correlation coefficients (Spearman 1904) and two-tailed P values were estimated using 'cor.test' function in R

Wilcoxon rank sum test (Mann-Whitney U test)

For two groups (mutant or wild-type) of continuous type of clinical data, wilcoxon rank sum test (Mann and Whitney, 1947) was applied to compare their mean difference using 'wilcox.test(continuous.clinical ~ as.factor(group), exact=FALSE)' function in R. This test is equivalent to the Mann-Whitney test.

Q value calculation

For multiple hypothesis correction, Q value is the False Discovery Rate (FDR) analogue of the P value (Benjamini and Hochberg 1995), defined as the minimum FDR at which the test may be called significant. We used the 'Benjamini and Hochberg' method of 'p.adjust' function in R to convert P values into Q values.

Download Results

In addition to the links below, the full results of the analysis summarized in this report can also be downloaded programmatically using firehose_get, or interactively from either the Broad GDAC website or TCGA Data Coordination Center Portal.

References
[1] Andersen and Gill, Cox's regression model for counting processes, a large sample study, Annals of Statistics 10(4):1100-1120 (1982)
[2] Spearman, C, The proof and measurement of association between two things, Amer. J. Psychol 15:72-101 (1904)
[3] Mann and Whitney, On a Test of Whether one of Two Random Variables is Stochastically Larger than the Other, Annals of Mathematical Statistics 18 (1), 50-60 (1947)
[4] Benjamini and Hochberg, Controlling the false discovery rate: a practical and powerful approach to multiple testing, Journal of the Royal Statistical Society Series B 59:289-300 (1995)
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