Correlation between RPPA expression and clinical features
Kidney Renal Clear Cell Carcinoma (Primary solid tumor)
17 October 2014  |  analyses__2014_10_17
Maintainer Information
Citation Information
doi:10.7908/C1QV3KDR
Maintained by Juok Cho (Broad Institute)
Cite as Broad Institute TCGA Genome Data Analysis Center (2014): Correlation between RPPA expression and clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1QV3KDR
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.

  • 57 genes correlated to 'Time to Death'.

    • SRC|SRC_PY527-R-V ,  VASP|VASP-R-C ,  CDKN1A|P21-R-C ,  GAB2|GAB2-R-V ,  MAPK1 MAPK3|MAPK_PT202_Y204-R-V ,  ...

  • 2 genes correlated to 'AGE'.

    • IGFBP2|IGFBP2-R-V ,  ERRFI1|MIG-6-M-V

  • 41 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 ,  ...

  • 49 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 ,  ...

  • 8 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 ,  ...

  • 33 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 ,  ...

  • 6 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 ,  ...

  • 2 genes correlated to 'RACE'.

    • CLDN7|CLAUDIN-7-R-V ,  STMN1|STATHMIN-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
Time to Death Cox regression test N=57 shorter survival N=31 longer survival N=26
AGE Spearman correlation test N=2 older N=1 younger N=1
NEOPLASM DISEASESTAGE Kruskal-Wallis test N=41        
PATHOLOGY T STAGE Spearman correlation test N=49 higher stage N=24 lower stage N=25
PATHOLOGY N STAGE Wilcoxon test N=8 class1 N=8 class0 N=0
PATHOLOGY M STAGE Wilcoxon test N=33 m1 N=33 m0 N=0
GENDER Wilcoxon test N=6 male N=6 female N=0
KARNOFSKY PERFORMANCE SCORE Spearman correlation test   N=0        
RACE Kruskal-Wallis test N=2        
ETHNICITY Wilcoxon test   N=0        
Clinical variable #1: 'Time to Death'

57 genes related to 'Time to Death'.

Table S1.  Basic characteristics of clinical feature: 'Time to Death'

Time to Death Duration (Months) 0.1-120.6 (median=37)
  censored N = 296
  death N = 158
     
  Significant markers N = 57
  associated with shorter survival 31
  associated with longer survival 26
List of top 10 genes differentially expressed by 'Time to Death'

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 8.279e-12 1.3e-09 0.371
VASP|VASP-R-C 4.2 1.489e-11 2.3e-09 0.633
CDKN1A|P21-R-C 6.1 6.687e-11 1e-08 0.623
GAB2|GAB2-R-V 0.5 1.952e-10 3e-08 0.354
MAPK1 MAPK3|MAPK_PT202_Y204-R-V 0.6 1.104e-09 1.7e-07 0.352
ACACA|ACC1-R-C 2.5 1.191e-09 1.8e-07 0.625
PRKAA1|AMPK_PT172-R-V 0.46 1.191e-09 1.8e-07 0.372
CTNNA1|ALPHA-CATENIN-M-V 0.2 1.658e-09 2.5e-07 0.362
CCNB1|CYCLIN_B1-R-V 1.76 1.807e-09 2.7e-07 0.58
AR|AR-R-V 0.32 2.143e-09 3.1e-07 0.352
Clinical variable #2: 'AGE'

2 genes related to 'AGE'.

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

AGE Mean (SD) 60.42 (12)
  Significant markers N = 2
  pos. correlated 1
  neg. correlated 1
List of 2 genes differentially expressed by 'AGE'

Table S4.  Get Full Table List of 2 genes significantly correlated to 'AGE' 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 0.173
Clinical variable #3: 'NEOPLASM.DISEASESTAGE'

41 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 = 41
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'

ANOVA_P Q
ERBB3|HER3_PY1298-R-C 5.02e-12 7.78e-10
CDH3|P-CADHERIN-R-C 3.79e-11 5.84e-09
PECAM1|CD31-M-V 1.357e-09 2.08e-07
SRC|SRC_PY527-R-V 1.717e-09 2.61e-07
SHC1|SHC_PY317-R-NA 4.923e-09 7.43e-07
EEF2|EEF2-R-V 4.929e-08 7.39e-06
CTNNA1|ALPHA-CATENIN-M-V 5.266e-08 7.85e-06
PIK3R1|PI3K-P85-R-V 6.551e-08 9.7e-06
ACACA ACACB|ACC_PS79-R-V 1.58e-07 2.32e-05
CCNB1|CYCLIN_B1-R-V 1.836e-07 2.68e-05
Clinical variable #4: 'PATHOLOGY.T.STAGE'

49 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
  1 224
  2 54
  3 165
  4 11
     
  Significant markers N = 49
  pos. correlated 24
  neg. correlated 25
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 1.34e-09
SRC|SRC_PY527-R-V -0.2907 2.722e-10 4.16e-08
EEF2|EEF2-R-V 0.2871 4.586e-10 6.97e-08
ACACA|ACC1-R-C 0.276 2.209e-09 3.34e-07
SHC1|SHC_PY317-R-NA -0.2739 2.974e-09 4.46e-07
CTNNA1|ALPHA-CATENIN-M-V -0.2683 6.35e-09 9.46e-07
ACACA ACACB|ACC_PS79-R-V 0.2659 8.744e-09 1.29e-06
PECAM1|CD31-M-V -0.2648 1.003e-08 1.47e-06
YBX1|YB-1_PS102-R-V 0.2628 1.313e-08 1.92e-06
Clinical variable #5: 'PATHOLOGY.N.STAGE'

8 genes related to 'PATHOLOGY.N.STAGE'.

Table S9.  Basic characteristics of clinical feature: 'PATHOLOGY.N.STAGE'

PATHOLOGY.N.STAGE Labels N
  class0 208
  class1 16
     
  Significant markers N = 8
  Higher in class1 8
  Higher in class0 0
List of 8 genes differentially expressed by 'PATHOLOGY.N.STAGE'

Table S10.  Get Full Table List of 8 genes differentially expressed by 'PATHOLOGY.N.STAGE'

W(pos if higher in 'class1') wilcoxontestP Q AUC
CCNB1|CYCLIN_B1-R-V 2604 0.0001692 0.0262 0.7825
FOXO3|FOXO3A_PS318_S321-R-C 2576 0.0002633 0.0406 0.774
BCL2L1|BCL-XL-R-V 2574 0.0002717 0.0416 0.7734
EGFR|EGFR_PY1068-R-V 759 0.0002936 0.0446 0.7719
ERBB3|HER3-R-V 780 0.000405 0.0611 0.7656
ESR1|ER-ALPHA-R-V 830 0.0008479 0.127 0.7506
TSC2|TUBERIN-R-C 865 0.001391 0.207 0.7401
RAD50|RAD50-M-C 867 0.00143 0.212 0.7395
Clinical variable #6: 'PATHOLOGY.M.STAGE'

33 genes related to 'PATHOLOGY.M.STAGE'.

Table S11.  Basic characteristics of clinical feature: 'PATHOLOGY.M.STAGE'

PATHOLOGY.M.STAGE Labels N
  M0 379
  M1 75
     
  Significant markers N = 33
  Higher in M1 33
  Higher in M0 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 'M1') 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 4.48e-05 0.6874
PIK3R1|PI3K-P85-R-V 19465 4.216e-07 6.45e-05 0.6848
TP53BP1|53BP1-R-C 19367.5 6.869e-07 0.000104 0.6814
SHC1|SHC_PY317-R-NA 9307.5 2.31e-06 0.000349 0.6726
PECAM1|CD31-M-V 9406 3.668e-06 0.00055 0.6691
PEA15|PEA-15-R-V 18939 5.307e-06 0.000791 0.6663
YBX1|YB-1-R-V 18893.5 6.53e-06 0.000966 0.6647
MAPK1 MAPK3|MAPK_PT202_Y204-R-V 9640 1.063e-05 0.00156 0.6609
CDH3|P-CADHERIN-R-C 18713 1.46e-05 0.00213 0.6583
Clinical variable #7: 'GENDER'

6 genes related to 'GENDER'.

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

GENDER Labels N
  FEMALE 151
  MALE 303
     
  Significant markers N = 6
  Higher in MALE 6
  Higher in FEMALE 0
List of 6 genes differentially expressed by 'GENDER'

Table S14.  Get Full Table List of 6 genes differentially expressed by 'GENDER'. 1 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 0.0016 0.627
SRC|SRC-M-V 17796.5 0.000115 0.0176 0.611
PTK2|FAK-R-C 17894 0.0001553 0.0236 0.6089
PRKCA |PKC-ALPHA-M-V 27186 0.00107 0.162 0.5942
MAPK14|P38_MAPK-R-C 18679.5 0.001442 0.216 0.5917
PRKCA |PKC-ALPHA_PS657-R-V 26965 0.001911 0.285 0.5894
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'

2 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 = 2
List of 2 genes differentially expressed by 'RACE'

Table S17.  Get Full Table List of 2 genes differentially expressed by 'RACE'

ANOVA_P Q
CLDN7|CLAUDIN-7-R-V 0.001081 0.168
STMN1|STATHMIN-R-V 0.001878 0.289
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

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

ANOVA analysis

For multi-class clinical features (ordinal or nominal), one-way analysis of variance (Howell 2002) was applied to compare the log2-expression levels between different clinical classes using 'anova' function in R

Student's t-test analysis

For two-class clinical features, two-tailed Student's t test with unequal variance (Lehmann and Romano 2005) was applied to compare the log2-expression levels between the two clinical classes using 't.test' function in R

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] Howell, D, Statistical Methods for Psychology. (5th ed.), Duxbury Press:324-5 (2002)
[4] Lehmann and Romano, Testing Statistical Hypotheses (3E ed.), New York: Springer. ISBN 0387988645 (2005)
[5] 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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