Correlation between RPPA expression and clinical features
Kidney Renal Clear Cell Carcinoma (Primary solid tumor)
28 January 2016  |  analyses__2016_01_28
Maintainer Information
Citation Information
Maintained by Juok Cho (Broad Institute)
Cite as Broad Institute TCGA Genome Data Analysis Center (2016): Correlation between RPPA expression and clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1J67G9K
Overview
Introduction

This pipeline uses various statistical tests to identify RPPAs whose expression levels correlated to selected clinical features. The input file "KIRC-TP.rppa.txt" is generated in the pipeline RPPA_AnnotateWithGene in the stddata run.

Summary

Testing the association between 217 genes and 12 clinical features across 478 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 8 clinical features related to at least one genes.

  • 30 genes correlated to 'DAYS_TO_DEATH_OR_LAST_FUP'.

    • GAB2|GAB2 ,  AR|AR ,  ERBB3|HER3 ,  SHC1|SHC_PY317 ,  ERRFI1|MIG-6 ,  ...

  • 30 genes correlated to 'YEARS_TO_BIRTH'.

    • IGFBP2|IGFBP2 ,  TGM2|TRANSGLUTAMINASE ,  MYH11|MYH11 ,  MTCO2|MITOCHONDRIA ,  EGFR|EGFR ,  ...

  • 30 genes correlated to 'PATHOLOGIC_STAGE'.

    • ERBB3|HER3_PY1289 ,  BAD|BAD_PS112 ,  PECAM1|CD31 ,  SRC|SRC_PY527 ,  TFRC|TFRC ,  ...

  • 30 genes correlated to 'PATHOLOGY_T_STAGE'.

    • BAD|BAD_PS112 ,  ERBB3|HER3_PY1289 ,  TFRC|TFRC ,  EEF2|EEF2 ,  CDH3|P-CADHERIN ,  ...

  • 30 genes correlated to 'PATHOLOGY_N_STAGE'.

    • SERPINE1|PAI-1 ,  FOXO3|FOXO3A_PS318_S321 ,  ERBB3|HER3 ,  BCL2L1|BCL-XL ,  EGFR|EGFR_PY1068 ,  ...

  • 30 genes correlated to 'PATHOLOGY_M_STAGE'.

    • ERBB3|HER3_PY1289 ,  PECAM1|CD31 ,  CCNB1|CYCLIN_B1 ,  SHC1|SHC_PY317 ,  TP53BP1|53BP1 ,  ...

  • 28 genes correlated to 'GENDER'.

    • CLDN7|CLAUDIN-7 ,  SRC|SRC ,  SERPINE1|PAI-1 ,  ASNS|ASNS ,  DIABLO|SMAC ,  ...

  • 22 genes correlated to 'RACE'.

    • BRCA2|BRCA2 ,  FOXO3|FOXO3A ,  RAB25|RAB25 ,  CHEK1|CHK1 ,  CLDN7|CLAUDIN-7 ,  ...

  • No genes correlated to 'KARNOFSKY_PERFORMANCE_SCORE', 'NUMBER_PACK_YEARS_SMOKED', 'YEAR_OF_TOBACCO_SMOKING_ONSET', and 'ETHNICITY'.

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=30   N=NA   N=NA
YEARS_TO_BIRTH Spearman correlation test N=30 older N=18 younger N=12
PATHOLOGIC_STAGE Kruskal-Wallis test N=30        
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
PATHOLOGY_M_STAGE Wilcoxon test N=30 class1 N=30 class0 N=0
GENDER Wilcoxon test N=28 male N=28 female N=0
KARNOFSKY_PERFORMANCE_SCORE Spearman correlation test   N=0        
NUMBER_PACK_YEARS_SMOKED Spearman correlation test   N=0        
YEAR_OF_TOBACCO_SMOKING_ONSET Spearman correlation test   N=0        
RACE Kruskal-Wallis test N=22        
ETHNICITY Wilcoxon test   N=0        
Clinical variable #1: 'DAYS_TO_DEATH_OR_LAST_FUP'

30 genes 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) 0.1-149.2 (median=38.6)
  censored N = 311
  death N = 166
     
  Significant markers N = 30
  associated with shorter survival NA
  associated with longer survival NA
List of top 10 genes differentially expressed by 'DAYS_TO_DEATH_OR_LAST_FUP'

Table S2.  Get Full Table List of top 10 genes significantly associated with 'Time to Death' by Cox regression test. For the survival curves, it compared quantile intervals at c(0, 0.25, 0.50, 0.75, 1) and did not try survival analysis if there is only one interval.

logrank_P Q C_index
GAB2|GAB2 7.95e-11 1.7e-08 0.356
AR|AR 2.14e-10 2.3e-08 0.354
ERBB3|HER3 6.66e-09 4.8e-07 0.365
SHC1|SHC_PY317 6.15e-08 3.3e-06 0.363
ERRFI1|MIG-6 1.15e-07 5e-06 0.363
CDH3|P-CADHERIN 4.26e-07 1.5e-05 0.614
CDKN1A|P21 6.63e-07 2.1e-05 0.622
CTNNB1|BETA-CATENIN 3.28e-06 8.9e-05 0.37
ACACA|ACC1 5.17e-06 0.00012 0.627
MAPK1 MAPK3|MAPK_PT202_Y204 8.59e-06 0.00019 0.354
Clinical variable #2: 'YEARS_TO_BIRTH'

30 genes related to 'YEARS_TO_BIRTH'.

Table S3.  Basic characteristics of clinical feature: 'YEARS_TO_BIRTH'

YEARS_TO_BIRTH Mean (SD) 60.43 (12)
  Significant markers N = 30
  pos. correlated 18
  neg. correlated 12
List of top 10 genes differentially expressed by 'YEARS_TO_BIRTH'

Table S4.  Get Full Table List of top 10 genes significantly correlated to 'YEARS_TO_BIRTH' by Spearman correlation test

SpearmanCorr corrP Q
IGFBP2|IGFBP2 0.2188 1.407e-06 0.000305
TGM2|TRANSGLUTAMINASE 0.2023 8.458e-06 0.000918
MYH11|MYH11 -0.1758 0.0001138 0.00823
MTCO2|MITOCHONDRIA 0.1711 0.0002528 0.0137
EGFR|EGFR 0.1639 0.0003242 0.0141
ERRFI1|MIG-6 -0.1533 0.0007787 0.0282
SQSTM1|P62-LCK-LIGAND 0.1494 0.001062 0.0329
PYGB|PYGB 0.1494 0.001424 0.0386
EIF4EBP1|4E-BP1_PT70 0.1416 0.001932 0.0466
MS4A1|CD20 -0.138 0.002531 0.0505
Clinical variable #3: 'PATHOLOGIC_STAGE'

30 genes related to 'PATHOLOGIC_STAGE'.

Table S5.  Basic characteristics of clinical feature: 'PATHOLOGIC_STAGE'

PATHOLOGIC_STAGE Labels N
  STAGE I 234
  STAGE II 48
  STAGE III 113
  STAGE IV 81
     
  Significant markers N = 30
List of top 10 genes differentially expressed by 'PATHOLOGIC_STAGE'

Table S6.  Get Full Table List of top 10 genes differentially expressed by 'PATHOLOGIC_STAGE'

kruskal_wallis_P Q
ERBB3|HER3_PY1289 1.762e-12 3.82e-10
BAD|BAD_PS112 1.105e-11 1.2e-09
PECAM1|CD31 1.247e-10 9.02e-09
SRC|SRC_PY527 2.451e-09 1.33e-07
TFRC|TFRC 5.09e-09 1.87e-07
CDH3|P-CADHERIN 5.166e-09 1.87e-07
SHC1|SHC_PY317 6.788e-09 2.1e-07
EEF2|EEF2 1.181e-08 3.2e-07
MAPK1 MAPK3|MAPK_PT202_Y204 3.453e-08 8.33e-07
RAF1|C-RAF_PS338 5.746e-08 1.25e-06
Clinical variable #4: 'PATHOLOGY_T_STAGE'

30 genes related to 'PATHOLOGY_T_STAGE'.

Table S7.  Basic characteristics of clinical feature: 'PATHOLOGY_T_STAGE'

PATHOLOGY_T_STAGE Mean (SD) 1.9 (0.97)
  N
  T1 240
  T2 59
  T3 168
  T4 11
     
  Significant markers N = 30
  pos. correlated 16
  neg. correlated 14
List of top 10 genes differentially expressed by 'PATHOLOGY_T_STAGE'

Table S8.  Get Full Table List of top 10 genes significantly correlated to 'PATHOLOGY_T_STAGE' by Spearman correlation test

SpearmanCorr corrP Q
BAD|BAD_PS112 0.3175 1.174e-12 2.55e-10
ERBB3|HER3_PY1289 -0.3089 5.023e-12 5.45e-10
TFRC|TFRC 0.3028 1.367e-11 9.89e-10
EEF2|EEF2 0.2898 1.063e-10 5.77e-09
CDH3|P-CADHERIN 0.2827 3.097e-10 1.29e-08
SRC|SRC_PY527 -0.2818 3.557e-10 1.29e-08
PECAM1|CD31 -0.2791 5.323e-10 1.65e-08
ACACA|ACC1 0.2699 2.02e-09 5.48e-08
SHC1|SHC_PY317 -0.2631 5.184e-09 1.25e-07
ACACA ACACB|ACC_PS79 0.2556 1.44e-08 3.01e-07
Clinical variable #5: 'PATHOLOGY_N_STAGE'

30 genes related to 'PATHOLOGY_N_STAGE'.

Table S9.  Basic characteristics of clinical feature: 'PATHOLOGY_N_STAGE'

PATHOLOGY_N_STAGE Labels N
  N0 214
  N1 15
     
  Significant markers N = 30
  Higher in N1 30
  Higher in N0 0
List of top 10 genes differentially expressed by 'PATHOLOGY_N_STAGE'

Table S10.  Get Full Table List of top 10 genes differentially expressed by 'PATHOLOGY_N_STAGE'

W(pos if higher in 'N1') wilcoxontestP Q AUC
SERPINE1|PAI-1 2663 2.014e-05 0.00437 0.8296
FOXO3|FOXO3A_PS318_S321 2575 9.283e-05 0.00998 0.8022
ERBB3|HER3 659 0.0001379 0.00998 0.7947
BCL2L1|BCL-XL 2508 0.0002742 0.0149 0.7813
EGFR|EGFR_PY1068 717 0.0003462 0.015 0.7766
CCNB1|CYCLIN_B1 2476 0.000449 0.0162 0.7713
PEA15|PEA15 2457 0.0005972 0.0177 0.7654
TFRC|TFRC 2445 0.0007131 0.0177 0.7617
TSC2|TUBERIN 767 0.0007343 0.0177 0.7611
ESR1|ER-ALPHA 775 0.0008252 0.0179 0.7586
Clinical variable #6: 'PATHOLOGY_M_STAGE'

30 genes related to 'PATHOLOGY_M_STAGE'.

Table S11.  Basic characteristics of clinical feature: 'PATHOLOGY_M_STAGE'

PATHOLOGY_M_STAGE Labels N
  class0 381
  class1 76
     
  Significant markers N = 30
  Higher in class1 30
  Higher in class0 0
List of top 10 genes differentially expressed by 'PATHOLOGY_M_STAGE'

Table S12.  Get Full Table List of top 10 genes differentially expressed by 'PATHOLOGY_M_STAGE'

W(pos if higher in 'class1') wilcoxontestP Q AUC
ERBB3|HER3_PY1289 8748 5.034e-08 1.09e-05 0.6979
PECAM1|CD31 9139 3.811e-07 3.03e-05 0.6844
CCNB1|CYCLIN_B1 19798 4.19e-07 3.03e-05 0.6837
SHC1|SHC_PY317 9378 1.229e-06 5.52e-05 0.6761
TP53BP1|53BP1 19571 1.271e-06 5.52e-05 0.6759
PIK3R1 PIK3R2|PI3K-P85 19449 2.266e-06 8.2e-05 0.6717
SRC|SRC_PY527 9744 6.71e-06 0.000208 0.6635
MAPK1 MAPK3|MAPK_PT202_Y204 9781 7.915e-06 0.000215 0.6622
TFRC|TFRC 19133 9.532e-06 0.00023 0.6608
EEF2|EEF2 19026 1.52e-05 0.00033 0.6571
Clinical variable #7: 'GENDER'

28 genes related to 'GENDER'.

Table S13.  Basic characteristics of clinical feature: 'GENDER'

GENDER Labels N
  FEMALE 162
  MALE 316
     
  Significant markers N = 28
  Higher in MALE 28
  Higher in FEMALE 0
List of top 10 genes differentially expressed by 'GENDER'

Table S14.  Get Full Table List of top 10 genes differentially expressed by 'GENDER'. 2 significant gene(s) located in sex chromosomes is(are) filtered out.

W(pos if higher in 'MALE') wilcoxontestP Q AUC
CLDN7|CLAUDIN-7 31619 2.519e-05 0.00273 0.6177
SRC|SRC 20096 0.0001195 0.00864 0.6074
SERPINE1|PAI-1 30704 0.0003529 0.0191 0.5998
ASNS|ASNS 30422 0.0007363 0.032 0.5943
DIABLO|SMAC 30274 0.001067 0.0386 0.5914
MAPK14|P38_MAPK 21010 0.001338 0.0401 0.5896
PRKCA |PKC-ALPHA 30120 0.001554 0.0401 0.5884
NDRG1|NDRG1_PT346 21138 0.001819 0.0401 0.5871
NOTCH1|NOTCH1 21145 0.00185 0.0401 0.5869
EIF4EBP1|4E-BP1_PS65 21439 0.003641 0.0673 0.5812
Clinical variable #8: 'KARNOFSKY_PERFORMANCE_SCORE'

No gene related to 'KARNOFSKY_PERFORMANCE_SCORE'.

Table S15.  Basic characteristics of clinical feature: 'KARNOFSKY_PERFORMANCE_SCORE'

KARNOFSKY_PERFORMANCE_SCORE Mean (SD) 88.54 (21)
  Significant markers N = 0
Clinical variable #9: 'NUMBER_PACK_YEARS_SMOKED'

No gene related to 'NUMBER_PACK_YEARS_SMOKED'.

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

NUMBER_PACK_YEARS_SMOKED Mean (SD) 27.81 (14)
  Significant markers N = 0
Clinical variable #10: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

No gene related to 'YEAR_OF_TOBACCO_SMOKING_ONSET'.

Table S17.  Basic characteristics of clinical feature: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

YEAR_OF_TOBACCO_SMOKING_ONSET Mean (SD) 1979.29 (15)
  Significant markers N = 0
Clinical variable #11: 'RACE'

22 genes related to 'RACE'.

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

RACE Labels N
  ASIAN 8
  BLACK OR AFRICAN AMERICAN 44
  WHITE 420
     
  Significant markers N = 22
List of top 10 genes differentially expressed by 'RACE'

Table S19.  Get Full Table List of top 10 genes differentially expressed by 'RACE'

kruskal_wallis_P Q
BRCA2|BRCA2 0.0001485 0.0238
FOXO3|FOXO3A 0.0002193 0.0238
RAB25|RAB25 0.0003803 0.0264
CHEK1|CHK1 0.0004865 0.0264
CLDN7|CLAUDIN-7 0.0008648 0.0375
CHEK1|CHK1_PS345 0.001934 0.07
MAPK1|ERK2 0.004142 0.121
IRS1|IRS1 0.004473 0.121
MAPK14|P38_MAPK 0.006274 0.151
ESR1|ER-ALPHA_PS118 0.00698 0.151
Clinical variable #12: 'ETHNICITY'

No gene related to 'ETHNICITY'.

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

ETHNICITY Labels N
  HISPANIC OR LATINO 21
  NOT HISPANIC OR LATINO 315
     
  Significant markers N = 0
Methods & Data
Input
  • Expresson data file = KIRC-TP.rppa.txt

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

  • Number of patients = 478

  • Number of genes = 217

  • Number of clinical features = 12

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, logrank test in univariate Cox regression analysis with proportional hazards model (Andersen and Gill 1982) was used to estimate the P values comparing quantile intervals using the 'coxph' function in R. Kaplan-Meier survival curves were plotted using quantile intervals at c(0, 0.25, 0.50, 0.75, 1). If there is only one interval group, it will not try survival analysis.

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)