Correlation between RPPA expression and clinical features
Prostate Adenocarcinoma (Primary solid tumor)
16 April 2014  |  analyses__2014_04_16
Maintainer Information
Citation Information
doi:10.7908/C1154FQD
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/C1154FQD
Overview
Introduction

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

Summary

Testing the association between 189 genes and 13 clinical features across 146 samples, statistically thresholded by Q value < 0.05, 7 clinical features related to at least one genes.

  • 1 gene correlated to 'AGE'.

    • EIF4EBP1|4E-BP1_PT37_T46-R-V

  • 4 genes correlated to 'PATHOLOGY.T.STAGE'.

    • EIF4G1|EIF4G-R-C ,  DVL3|DVL3-R-V ,  EEF2K|EEF2K-R-V ,  RBM15|RBM15-R-V

  • 1 gene correlated to 'GLEASON_SCORE_COMBINED'.

    • BCL2L11|BIM-R-V

  • 4 genes correlated to 'GLEASON_SCORE_PRIMARY'.

    • CCNB1|CYCLIN_B1-R-V ,  ERRFI1|MIG-6-M-V ,  MAPK14|P38_PT180_Y182-R-V ,  EIF4G1|EIF4G-R-C

  • 5 genes correlated to 'GLEASON_SCORE'.

    • BCL2L11|BIM-R-V ,  EIF4G1|EIF4G-R-C ,  CCNB1|CYCLIN_B1-R-V ,  GAB2|GAB2-R-V ,  TFRC|TFRC-R-V

  • 2 genes correlated to 'PSA_RESULT_PREOP'.

    • EEF2|EEF2-R-C ,  RAF1|C-RAF-R-V

  • 21 genes correlated to 'DAYS_TO_PREOP_PSA'.

    • EGFR|EGFR_PY1173-R-V ,  RPS6|S6_PS235_S236-R-V ,  RPS6|S6_PS240_S244-R-V ,  TSC2|TUBERIN_PT1462-R-V ,  SRC|SRC_PY527-R-V ,  ...

  • No genes correlated to 'PATHOLOGY.N.STAGE', 'COMPLETENESS.OF.RESECTION', 'NUMBER.OF.LYMPH.NODES', 'GLEASON_SCORE_SECONDARY', 'PSA_VALUE', and 'DAYS_TO_PSA'.

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 Q value < 0.05.

Clinical feature Statistical test Significant genes Associated with                 Associated with
AGE Spearman correlation test N=1 older N=1 younger N=0
PATHOLOGY T STAGE Spearman correlation test N=4 higher stage N=4 lower stage N=0
PATHOLOGY N STAGE t test   N=0        
COMPLETENESS OF RESECTION ANOVA test   N=0        
NUMBER OF LYMPH NODES Spearman correlation test   N=0        
GLEASON_SCORE_COMBINED Spearman correlation test N=1 higher score N=1 lower score N=0
GLEASON_SCORE_PRIMARY Spearman correlation test N=4 higher score N=3 lower score N=1
GLEASON_SCORE_SECONDARY Spearman correlation test   N=0        
GLEASON_SCORE Spearman correlation test N=5 higher score N=5 lower score N=0
PSA_RESULT_PREOP Spearman correlation test N=2 higher psa_result_preop N=2 lower psa_result_preop N=0
DAYS_TO_PREOP_PSA Spearman correlation test N=21 higher days_to_preop_psa N=15 lower days_to_preop_psa N=6
PSA_VALUE Spearman correlation test   N=0        
DAYS_TO_PSA Spearman correlation test   N=0        
Clinical variable #1: 'AGE'

One gene related to 'AGE'.

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

AGE Mean (SD) 60.3 (7.1)
  Significant markers N = 1
  pos. correlated 1
  neg. correlated 0
List of one gene significantly correlated to 'AGE' by Spearman correlation test

Table S2.  Get Full Table List of one gene significantly correlated to 'AGE' by Spearman correlation test

SpearmanCorr corrP Q
EIF4EBP1|4E-BP1_PT37_T46-R-V 0.3124 0.0001303 0.0246

Figure S1.  Get High-res Image As an example, this figure shows the association of EIF4EBP1|4E-BP1_PT37_T46-R-V to 'AGE'. P value = 0.00013 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #2: 'PATHOLOGY.T.STAGE'

4 genes related to 'PATHOLOGY.T.STAGE'.

Table S3.  Basic characteristics of clinical feature: 'PATHOLOGY.T.STAGE'

PATHOLOGY.T.STAGE Mean (SD) 2.66 (0.54)
  N
  2 55
  3 85
  4 5
     
  Significant markers N = 4
  pos. correlated 4
  neg. correlated 0
List of 4 genes significantly correlated to 'PATHOLOGY.T.STAGE' by Spearman correlation test

Table S4.  Get Full Table List of 4 genes significantly correlated to 'PATHOLOGY.T.STAGE' by Spearman correlation test

SpearmanCorr corrP Q
EIF4G1|EIF4G-R-C 0.3552 1.166e-05 0.0022
DVL3|DVL3-R-V 0.3385 3.127e-05 0.00588
EEF2K|EEF2K-R-V 0.3057 0.0001844 0.0345
RBM15|RBM15-R-V 0.3003 0.0002425 0.0451

Figure S2.  Get High-res Image As an example, this figure shows the association of EIF4G1|EIF4G-R-C to 'PATHOLOGY.T.STAGE'. P value = 1.17e-05 with Spearman correlation analysis.

Clinical variable #3: 'PATHOLOGY.N.STAGE'

No gene related to 'PATHOLOGY.N.STAGE'.

Table S5.  Basic characteristics of clinical feature: 'PATHOLOGY.N.STAGE'

PATHOLOGY.N.STAGE Labels N
  class0 116
  class1 14
     
  Significant markers N = 0
Clinical variable #4: 'COMPLETENESS.OF.RESECTION'

No gene related to 'COMPLETENESS.OF.RESECTION'.

Table S6.  Basic characteristics of clinical feature: 'COMPLETENESS.OF.RESECTION'

COMPLETENESS.OF.RESECTION Labels N
  R0 106
  R1 29
  RX 3
     
  Significant markers N = 0
Clinical variable #5: 'NUMBER.OF.LYMPH.NODES'

No gene related to 'NUMBER.OF.LYMPH.NODES'.

Table S7.  Basic characteristics of clinical feature: 'NUMBER.OF.LYMPH.NODES'

NUMBER.OF.LYMPH.NODES Mean (SD) 0.2 (0.74)
  Value N
  0 116
  1 9
  2 1
  3 3
  6 1
     
  Significant markers N = 0
Clinical variable #6: 'GLEASON_SCORE_COMBINED'

One gene related to 'GLEASON_SCORE_COMBINED'.

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

GLEASON_SCORE_COMBINED Mean (SD) 7.32 (0.82)
  Significant markers N = 1
  pos. correlated 1
  neg. correlated 0
List of one gene significantly correlated to 'GLEASON_SCORE_COMBINED' by Spearman correlation test

Table S9.  Get Full Table List of one gene significantly correlated to 'GLEASON_SCORE_COMBINED' by Spearman correlation test

SpearmanCorr corrP Q
BCL2L11|BIM-R-V 0.368 4.896e-06 0.000925

Figure S3.  Get High-res Image As an example, this figure shows the association of BCL2L11|BIM-R-V to 'GLEASON_SCORE_COMBINED'. P value = 4.9e-06 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #7: 'GLEASON_SCORE_PRIMARY'

4 genes related to 'GLEASON_SCORE_PRIMARY'.

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

GLEASON_SCORE_PRIMARY Mean (SD) 3.53 (0.59)
  Score N
  2 1
  3 73
  4 66
  5 6
     
  Significant markers N = 4
  pos. correlated 3
  neg. correlated 1
List of 4 genes significantly correlated to 'GLEASON_SCORE_PRIMARY' by Spearman correlation test

Table S11.  Get Full Table List of 4 genes significantly correlated to 'GLEASON_SCORE_PRIMARY' by Spearman correlation test

SpearmanCorr corrP Q
CCNB1|CYCLIN_B1-R-V 0.3384 2.937e-05 0.00555
ERRFI1|MIG-6-M-V 0.3014 0.0002186 0.0411
MAPK14|P38_PT180_Y182-R-V -0.2995 0.0002401 0.0449
EIF4G1|EIF4G-R-C 0.2982 0.000257 0.0478

Figure S4.  Get High-res Image As an example, this figure shows the association of CCNB1|CYCLIN_B1-R-V to 'GLEASON_SCORE_PRIMARY'. P value = 2.94e-05 with Spearman correlation analysis.

Clinical variable #8: 'GLEASON_SCORE_SECONDARY'

No gene related to 'GLEASON_SCORE_SECONDARY'.

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

GLEASON_SCORE_SECONDARY Mean (SD) 3.79 (0.64)
  Score N
  3 48
  4 80
  5 18
     
  Significant markers N = 0
Clinical variable #9: 'GLEASON_SCORE'

5 genes related to 'GLEASON_SCORE'.

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

GLEASON_SCORE Mean (SD) 7.38 (0.86)
  Score N
  6 9
  7 97
  8 17
  9 21
  10 2
     
  Significant markers N = 5
  pos. correlated 5
  neg. correlated 0
List of 5 genes significantly correlated to 'GLEASON_SCORE' by Spearman correlation test

Table S14.  Get Full Table List of 5 genes significantly correlated to 'GLEASON_SCORE' by Spearman correlation test

SpearmanCorr corrP Q
BCL2L11|BIM-R-V 0.3931 9.207e-07 0.000174
EIF4G1|EIF4G-R-C 0.3239 6.666e-05 0.0125
CCNB1|CYCLIN_B1-R-V 0.3171 9.624e-05 0.018
GAB2|GAB2-R-V 0.3029 0.0002018 0.0375
TFRC|TFRC-R-V 0.3005 0.0002285 0.0423

Figure S5.  Get High-res Image As an example, this figure shows the association of BCL2L11|BIM-R-V to 'GLEASON_SCORE'. P value = 9.21e-07 with Spearman correlation analysis.

Clinical variable #10: 'PSA_RESULT_PREOP'

2 genes related to 'PSA_RESULT_PREOP'.

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

PSA_RESULT_PREOP Mean (SD) 10.82 (10)
  Significant markers N = 2
  pos. correlated 2
  neg. correlated 0
List of 2 genes significantly correlated to 'PSA_RESULT_PREOP' by Spearman correlation test

Table S16.  Get Full Table List of 2 genes significantly correlated to 'PSA_RESULT_PREOP' by Spearman correlation test

SpearmanCorr corrP Q
EEF2|EEF2-R-C 0.3526 1.46e-05 0.00276
RAF1|C-RAF-R-V 0.3063 0.0001882 0.0354

Figure S6.  Get High-res Image As an example, this figure shows the association of EEF2|EEF2-R-C to 'PSA_RESULT_PREOP'. P value = 1.46e-05 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #11: 'DAYS_TO_PREOP_PSA'

21 genes related to 'DAYS_TO_PREOP_PSA'.

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

DAYS_TO_PREOP_PSA Mean (SD) -2.37 (56)
  Significant markers N = 21
  pos. correlated 15
  neg. correlated 6
List of top 10 genes significantly correlated to 'DAYS_TO_PREOP_PSA' by Spearman correlation test

Table S18.  Get Full Table List of top 10 genes significantly correlated to 'DAYS_TO_PREOP_PSA' by Spearman correlation test

SpearmanCorr corrP Q
EGFR|EGFR_PY1173-R-V -0.4432 3.707e-08 7.01e-06
RPS6|S6_PS235_S236-R-V 0.4259 1.404e-07 2.64e-05
RPS6|S6_PS240_S244-R-V 0.4086 4.917e-07 9.2e-05
TSC2|TUBERIN_PT1462-R-V 0.3934 1.396e-06 0.00026
SRC|SRC_PY527-R-V 0.3855 2.347e-06 0.000434
EIF4EBP1|4E-BP1_PT37_T46-R-V 0.3851 2.415e-06 0.000444
RB1|RB_PS807_S811-R-V 0.3786 3.667e-06 0.000671
MAPK1 MAPK3|MAPK_PT202_Y204-R-V 0.3627 9.843e-06 0.00179
MAPK1|ERK2-R-E -0.3627 9.867e-06 0.00179
RPS6KA1|P90RSK_PT359_S363-R-C 0.3474 2.434e-05 0.00438

Figure S7.  Get High-res Image As an example, this figure shows the association of EGFR|EGFR_PY1173-R-V to 'DAYS_TO_PREOP_PSA'. P value = 3.71e-08 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #12: 'PSA_VALUE'

No gene related to 'PSA_VALUE'.

Table S19.  Basic characteristics of clinical feature: 'PSA_VALUE'

PSA_VALUE Mean (SD) 1.5 (4.6)
  Significant markers N = 0
Clinical variable #13: 'DAYS_TO_PSA'

No gene related to 'DAYS_TO_PSA'.

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

DAYS_TO_PSA Mean (SD) 554.97 (490)
  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 = 146

  • Number of genes = 189

  • Number of clinical features = 13

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

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

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

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] Spearman, C, The proof and measurement of association between two things, Amer. J. Psychol 15:72-101 (1904)
[2] Lehmann and Romano, Testing Statistical Hypotheses (3E ed.), New York: Springer. ISBN 0387988645 (2005)
[3] Howell, D, Statistical Methods for Psychology. (5th ed.), Duxbury Press:324-5 (2002)
[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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