Correlation between RPPA expression and clinical features
Kidney Renal Clear Cell Carcinoma (Primary solid tumor)
02 April 2015  |  analyses__2015_04_02
Maintainer Information
Citation Information
doi:10.7908/C1VH5MVJ
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/C1VH5MVJ
Overview
Introduction

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

Summary

Testing the association between 155 genes and 10 clinical features across 454 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'.

    • SRC|SRC_PY527-R-V ,  VASP|VASP-R-C ,  CDKN1A|P21-R-C ,  GAB2|GAB2-R-V ,  ACACA|ACC1-R-C ,  ...

  • 29 genes correlated to 'YEARS_TO_BIRTH'.

    • IGFBP2|IGFBP2-R-V ,  ERRFI1|MIG-6-M-V ,  MRE11A|MRE11-R-C ,  GAB2|GAB2-R-V ,  ACACA ACACB|ACC_PS79-R-V ,  ...

  • 30 genes correlated to 'NEOPLASM_DISEASESTAGE'.

    • ERBB3|HER3_PY1298-R-C ,  CDH3|P-CADHERIN-R-C ,  PECAM1|CD31-M-V ,  SRC|SRC_PY527-R-V ,  SHC1|SHC_PY317-R-NA ,  ...

  • 30 genes correlated to 'PATHOLOGY_T_STAGE'.

    • ERBB3|HER3_PY1298-R-C ,  CDH3|P-CADHERIN-R-C ,  SRC|SRC_PY527-R-V ,  EEF2|EEF2-R-V ,  ACACA|ACC1-R-C ,  ...

  • 30 genes correlated to 'PATHOLOGY_N_STAGE'.

    • CCNB1|CYCLIN_B1-R-V ,  FOXO3|FOXO3A_PS318_S321-R-C ,  BCL2L1|BCL-XL-R-V ,  EGFR|EGFR_PY1068-R-V ,  ERBB3|HER3-R-V ,  ...

  • 30 genes correlated to 'PATHOLOGY_M_STAGE'.

    • ERBB3|HER3_PY1298-R-C ,  CCNB1|CYCLIN_B1-R-V ,  PIK3R1|PI3K-P85-R-V ,  TP53BP1|53BP1-R-C ,  SHC1|SHC_PY317-R-NA ,  ...

  • 28 genes correlated to 'GENDER'.

    • CLDN7|CLAUDIN-7-R-V ,  SRC|SRC-M-V ,  PTK2|FAK-R-C ,  PRKCA |PKC-ALPHA-M-V ,  MAPK14|P38_MAPK-R-C ,  ...

  • 15 genes correlated to 'RACE'.

    • CLDN7|CLAUDIN-7-R-V ,  STMN1|STATHMIN-R-V ,  ERRFI1|MIG-6-M-V ,  MAPK1|ERK2-R-NA ,  GAB2|GAB2-R-V ,  ...

  • No genes correlated to 'KARNOFSKY_PERFORMANCE_SCORE', 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 shorter survival N=14 longer survival N=16
YEARS_TO_BIRTH Spearman correlation test N=29 older N=15 younger N=14
NEOPLASM_DISEASESTAGE Kruskal-Wallis test N=30        
PATHOLOGY_T_STAGE Spearman correlation test N=30 higher stage N=14 lower stage N=16
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        
RACE Kruskal-Wallis test N=15        
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) 0.1-120.6 (median=37.1)
  censored N = 295
  death N = 158
     
  Significant markers N = 30
  associated with shorter survival 14
  associated with longer survival 16
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

HazardRatio Wald_P Q C_index
SRC|SRC_PY527-R-V 0.54 7.762e-12 1.2e-09 0.369
VASP|VASP-R-C 4.1 1.751e-11 1.4e-09 0.634
CDKN1A|P21-R-C 6.1 6.409e-11 3.3e-09 0.624
GAB2|GAB2-R-V 0.5 1.686e-10 6.5e-09 0.353
ACACA|ACC1-R-C 2.6 5.852e-10 1.8e-08 0.63
MAPK1 MAPK3|MAPK_PT202_Y204-R-V 0.6 1.262e-09 2.9e-08 0.352
PRKAA1|AMPK_PT172-R-V 0.46 1.302e-09 2.9e-08 0.372
CTNNA1|ALPHA-CATENIN-M-V 0.2 1.504e-09 2.9e-08 0.361
CCNB1|CYCLIN_B1-R-V 1.75 2.039e-09 3.5e-08 0.581
AR|AR-R-V 0.33 2.715e-09 4.2e-08 0.352
Clinical variable #2: 'YEARS_TO_BIRTH'

29 genes related to 'YEARS_TO_BIRTH'.

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

YEARS_TO_BIRTH Mean (SD) 60.42 (12)
  Significant markers N = 29
  pos. correlated 15
  neg. correlated 14
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-R-V 0.2268 1.08e-06 0.000167
ERRFI1|MIG-6-M-V -0.1526 0.00112 0.0868
MRE11A|MRE11-R-C -0.1367 0.003553 0.115
GAB2|GAB2-R-V -0.1355 0.003866 0.115
ACACA ACACB|ACC_PS79-R-V 0.1329 0.004614 0.115
MS4A1|CD20-R-C -0.1282 0.006307 0.115
KIT|C-KIT-R-V -0.1245 0.007995 0.115
EGFR|EGFR-R-C 0.1234 0.008553 0.115
CHEK2|CHK2-M-C 0.1228 0.008899 0.115
NOTCH3|NOTCH3-R-C -0.1228 0.008914 0.115
Clinical variable #3: 'NEOPLASM_DISEASESTAGE'

30 genes related to 'NEOPLASM_DISEASESTAGE'.

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

NEOPLASM_DISEASESTAGE Labels N
  STAGE I 219
  STAGE II 44
  STAGE III 115
  STAGE IV 76
     
  Significant markers N = 30
List of top 10 genes differentially expressed by 'NEOPLASM_DISEASESTAGE'

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

kruskal_wallis_P Q
ERBB3|HER3_PY1298-R-C 5.02e-12 7.78e-10
CDH3|P-CADHERIN-R-C 3.79e-11 2.94e-09
PECAM1|CD31-M-V 1.357e-09 6.65e-08
SRC|SRC_PY527-R-V 1.717e-09 6.65e-08
SHC1|SHC_PY317-R-NA 4.923e-09 1.53e-07
EEF2|EEF2-R-V 4.929e-08 1.17e-06
CTNNA1|ALPHA-CATENIN-M-V 5.266e-08 1.17e-06
PIK3R1|PI3K-P85-R-V 6.551e-08 1.27e-06
ACACA ACACB|ACC_PS79-R-V 1.58e-07 2.72e-06
CCNB1|CYCLIN_B1-R-V 1.836e-07 2.85e-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) 1.92 (0.97)
  N
  T1 224
  T2 54
  T3 165
  T4 11
     
  Significant markers N = 30
  pos. correlated 14
  neg. correlated 16
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
ERBB3|HER3_PY1298-R-C -0.3175 4.306e-12 6.67e-10
CDH3|P-CADHERIN-R-C 0.3132 8.676e-12 6.72e-10
SRC|SRC_PY527-R-V -0.2907 2.722e-10 1.41e-08
EEF2|EEF2-R-V 0.2871 4.586e-10 1.78e-08
ACACA|ACC1-R-C 0.276 2.209e-09 6.85e-08
SHC1|SHC_PY317-R-NA -0.2739 2.974e-09 7.68e-08
CTNNA1|ALPHA-CATENIN-M-V -0.2683 6.35e-09 1.41e-07
ACACA ACACB|ACC_PS79-R-V 0.2659 8.744e-09 1.69e-07
PECAM1|CD31-M-V -0.2648 1.003e-08 1.73e-07
YBX1|YB-1_PS102-R-V 0.2628 1.313e-08 2.04e-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 208
  N1 16
     
  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
CCNB1|CYCLIN_B1-R-V 2604 0.0001692 0.0114 0.7825
FOXO3|FOXO3A_PS318_S321-R-C 2576 0.0002633 0.0114 0.774
BCL2L1|BCL-XL-R-V 2574 0.0002717 0.0114 0.7734
EGFR|EGFR_PY1068-R-V 759 0.0002936 0.0114 0.7719
ERBB3|HER3-R-V 780 0.000405 0.0126 0.7656
ESR1|ER-ALPHA-R-V 830 0.0008479 0.0219 0.7506
TSC2|TUBERIN-R-C 865 0.001391 0.0277 0.7401
RAD50|RAD50-M-C 867 0.00143 0.0277 0.7395
PEA15|PEA-15-R-V 2431 0.002152 0.0371 0.7305
VASP|VASP-R-C 2415 0.002661 0.0412 0.7257
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 379
  class1 75
     
  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_PY1298-R-C 8673.5 9.559e-08 1.48e-05 0.6949
CCNB1|CYCLIN_B1-R-V 19538 2.909e-07 2.18e-05 0.6874
PIK3R1|PI3K-P85-R-V 19465 4.216e-07 2.18e-05 0.6848
TP53BP1|53BP1-R-C 19367.5 6.869e-07 2.66e-05 0.6814
SHC1|SHC_PY317-R-NA 9307.5 2.31e-06 7.16e-05 0.6726
PECAM1|CD31-M-V 9406 3.668e-06 9.48e-05 0.6691
PEA15|PEA-15-R-V 18939 5.307e-06 0.000118 0.6663
YBX1|YB-1-R-V 18893.5 6.53e-06 0.000127 0.6647
MAPK1 MAPK3|MAPK_PT202_Y204-R-V 9640 1.063e-05 0.000183 0.6609
CDH3|P-CADHERIN-R-C 18713 1.46e-05 0.000203 0.6583
Clinical variable #7: 'GENDER'

28 genes related to 'GENDER'.

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

GENDER Labels N
  FEMALE 151
  MALE 303
     
  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-R-V 28686 1.032e-05 0.00136 0.627
SRC|SRC-M-V 17796.5 0.000115 0.00594 0.611
PTK2|FAK-R-C 17894 0.0001553 0.00602 0.6089
PRKCA |PKC-ALPHA-M-V 27186 0.00107 0.0332 0.5942
MAPK14|P38_MAPK-R-C 18679.5 0.001442 0.0373 0.5917
PRKCA |PKC-ALPHA_PS657-R-V 26965 0.001911 0.0423 0.5894
EEF2|EEF2-R-V 26838 0.002636 0.0482 0.5866
INPP4B|INPP4B-G-C 26814 0.002798 0.0482 0.5861
CCNB1|CYCLIN_B1-R-V 26684 0.003848 0.0549 0.5832
PEA15|PEA-15-R-V 19075 0.003904 0.0549 0.5831
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) 93.53 (7.7)
  Score N
  70 1
  80 3
  90 13
  100 17
     
  Significant markers N = 0
Clinical variable #9: 'RACE'

15 genes related to 'RACE'.

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

RACE Labels N
  ASIAN 8
  BLACK OR AFRICAN AMERICAN 20
  WHITE 420
     
  Significant markers N = 15
List of top 10 genes differentially expressed by 'RACE'

Table S17.  Get Full Table List of top 10 genes differentially expressed by 'RACE'

kruskal_wallis_P Q
CLDN7|CLAUDIN-7-R-V 0.001081 0.116
STMN1|STATHMIN-R-V 0.001878 0.116
ERRFI1|MIG-6-M-V 0.002247 0.116
MAPK1|ERK2-R-NA 0.00328 0.127
GAB2|GAB2-R-V 0.004525 0.132
CHEK1|CHK1_PS345-R-C 0.005104 0.132
CHEK1|CHK1-R-C 0.008268 0.183
RAD50|RAD50-M-C 0.01058 0.204
EIF4E|EIF4E-R-V 0.01411 0.204
XRCC1|XRCC1-R-C 0.01483 0.204
Clinical variable #10: 'ETHNICITY'

No gene related to 'ETHNICITY'.

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

ETHNICITY Labels N
  HISPANIC OR LATINO 19
  NOT HISPANIC OR LATINO 295
     
  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 = 454

  • Number of genes = 155

  • Number of clinical features = 10

Selected clinical features

  • For clinical features selected for this analysis and their value conozzle.versions, please find a documentation on selected CDEs .

  • 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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