Correlation between RPPA expression and clinical features
Bladder Urothelial Carcinoma (Primary solid tumor)
02 April 2015  |  analyses__2015_04_02
Maintainer Information
Citation Information
doi:10.7908/C1D799DQ
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/C1D799DQ
Overview
Introduction

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

Summary

Testing the association between 179 genes and 12 clinical features across 127 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 7 clinical features related to at least one genes.

  • 1 gene correlated to 'NEOPLASM_DISEASESTAGE'.

    • SRC|SRC_PY416

  • 20 genes correlated to 'PATHOLOGY_N_STAGE'.

    • RPS6|S6_PS240_S244 ,  RPS6|S6_PS235_S236 ,  YBX1|YB-1_PS102 ,  RPS6KA1|P90RSK ,  PEA15|PEA15 ,  ...

  • 6 genes correlated to 'GENDER'.

    • MYH11|MYH11 ,  BAX|BAX ,  CAV1|CAVEOLIN-1 ,  SRC|SRC ,  YWHAE|14-3-3_EPSILON ,  ...

  • 2 genes correlated to 'KARNOFSKY_PERFORMANCE_SCORE'.

    • NFKB1|NF-KB-P65_PS536 ,  VHL|VHL

  • 8 genes correlated to 'NUMBER_PACK_YEARS_SMOKED'.

    • PDK1|PDK1_PS241 ,  SFRS1|SF2 ,  EIF4E|EIF4E ,  TSC1|TSC1 ,  PDK1|PDK1 ,  ...

  • 10 genes correlated to 'NUMBER_OF_LYMPH_NODES'.

    • RPS6|S6_PS240_S244 ,  RPS6|S6_PS235_S236 ,  YBX1|YB-1_PS102 ,  RAB25|RAB25 ,  BAK1|BAK ,  ...

  • 30 genes correlated to 'RACE'.

    • YBX1|YB-1 ,  FN1|FIBRONECTIN ,  CDH1|E-CADHERIN ,  ACVRL1|ACVRL1 ,  CHEK1|CHK1 ,  ...

  • No genes correlated to 'DAYS_TO_DEATH_OR_LAST_FUP', 'YEARS_TO_BIRTH', 'PATHOLOGY_T_STAGE', 'PATHOLOGY_M_STAGE', 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=0        
YEARS_TO_BIRTH Spearman correlation test   N=0        
NEOPLASM_DISEASESTAGE Kruskal-Wallis test N=1        
PATHOLOGY_T_STAGE Spearman correlation test   N=0        
PATHOLOGY_N_STAGE Spearman correlation test N=20 higher stage N=6 lower stage N=14
PATHOLOGY_M_STAGE Wilcoxon test   N=0        
GENDER Wilcoxon test N=6 male N=6 female N=0
KARNOFSKY_PERFORMANCE_SCORE Spearman correlation test N=2 higher score N=0 lower score N=2
NUMBER_PACK_YEARS_SMOKED Spearman correlation test N=8 higher number_pack_years_smoked N=5 lower number_pack_years_smoked N=3
NUMBER_OF_LYMPH_NODES Spearman correlation test N=10 higher number_of_lymph_nodes N=3 lower number_of_lymph_nodes N=7
RACE Kruskal-Wallis test N=30        
ETHNICITY Wilcoxon 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) 0.1-140.8 (median=16.8)
  censored N = 66
  death N = 60
     
  Significant markers N = 0
Clinical variable #2: 'YEARS_TO_BIRTH'

No gene related to 'YEARS_TO_BIRTH'.

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

YEARS_TO_BIRTH Mean (SD) 67.15 (10)
  Significant markers N = 0
Clinical variable #3: 'NEOPLASM_DISEASESTAGE'

One gene related to 'NEOPLASM_DISEASESTAGE'.

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

NEOPLASM_DISEASESTAGE Labels N
  STAGE I 1
  STAGE II 35
  STAGE III 43
  STAGE IV 44
     
  Significant markers N = 1
List of one gene differentially expressed by 'NEOPLASM_DISEASESTAGE'

Table S4.  Get Full Table List of one gene differentially expressed by 'NEOPLASM_DISEASESTAGE'

kruskal_wallis_P Q
SRC|SRC_PY416 0.000263 0.0471
Clinical variable #4: 'PATHOLOGY_T_STAGE'

No gene related to 'PATHOLOGY_T_STAGE'.

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

PATHOLOGY_T_STAGE Mean (SD) 2.93 (0.67)
  N
  T1 1
  T2 26
  T3 63
  T4 20
     
  Significant markers N = 0
Clinical variable #5: 'PATHOLOGY_N_STAGE'

20 genes related to 'PATHOLOGY_N_STAGE'.

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

PATHOLOGY_N_STAGE Mean (SD) 0.72 (0.99)
  N
  N0 71
  N1 13
  N2 26
  N3 6
     
  Significant markers N = 20
  pos. correlated 6
  neg. correlated 14
List of top 10 genes differentially expressed by 'PATHOLOGY_N_STAGE'

Table S7.  Get Full Table List of top 10 genes significantly correlated to 'PATHOLOGY_N_STAGE' by Spearman correlation test

SpearmanCorr corrP Q
RPS6|S6_PS240_S244 -0.3197 0.0004692 0.084
RPS6|S6_PS235_S236 -0.296 0.001253 0.112
YBX1|YB-1_PS102 -0.2825 0.002127 0.127
RPS6KA1|P90RSK -0.2608 0.004691 0.155
PEA15|PEA15 0.2576 0.00525 0.155
XRCC1|XRCC1 0.2484 0.007186 0.155
RPS6KA1|P90RSK_PT359_S363 -0.2479 0.007305 0.155
RAB25|RAB25 0.2455 0.007899 0.155
BAK1|BAK -0.2442 0.008244 0.155
EGFR|EGFR_PY1173 -0.2428 0.008642 0.155
Clinical variable #6: 'PATHOLOGY_M_STAGE'

No gene related to 'PATHOLOGY_M_STAGE'.

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

PATHOLOGY_M_STAGE Labels N
  class0 73
  class1 5
     
  Significant markers N = 0
Clinical variable #7: 'GENDER'

6 genes related to 'GENDER'.

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

GENDER Labels N
  FEMALE 33
  MALE 94
     
  Significant markers N = 6
  Higher in MALE 6
  Higher in FEMALE 0
List of 6 genes differentially expressed by 'GENDER'

Table S10.  Get Full Table List of 6 genes differentially expressed by 'GENDER'. 0 significant gene(s) located in sex chromosomes is(are) filtered out.

W(pos if higher in 'MALE') wilcoxontestP Q AUC
MYH11|MYH11 901 0.0003561 0.0637 0.7095
BAX|BAX 953 0.001021 0.0914 0.6928
CAV1|CAVEOLIN-1 1017 0.003358 0.168 0.6721
SRC|SRC 2063 0.004924 0.168 0.6651
YWHAE|14-3-3_EPSILON 2056 0.005545 0.168 0.6628
ANXA7 |ANNEXIN_VII 2055 0.00564 0.168 0.6625
Clinical variable #8: 'KARNOFSKY_PERFORMANCE_SCORE'

2 genes related to 'KARNOFSKY_PERFORMANCE_SCORE'.

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

KARNOFSKY_PERFORMANCE_SCORE Mean (SD) 79.14 (16)
  Significant markers N = 2
  pos. correlated 0
  neg. correlated 2
List of 2 genes differentially expressed by 'KARNOFSKY_PERFORMANCE_SCORE'

Table S12.  Get Full Table List of 2 genes significantly correlated to 'KARNOFSKY_PERFORMANCE_SCORE' by Spearman correlation test

SpearmanCorr corrP Q
NFKB1|NF-KB-P65_PS536 -0.5612 0.0004533 0.0811
VHL|VHL -0.4954 0.002477 0.222
Clinical variable #9: 'NUMBER_PACK_YEARS_SMOKED'

8 genes related to 'NUMBER_PACK_YEARS_SMOKED'.

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

NUMBER_PACK_YEARS_SMOKED Mean (SD) 36.76 (24)
  Significant markers N = 8
  pos. correlated 5
  neg. correlated 3
List of 8 genes differentially expressed by 'NUMBER_PACK_YEARS_SMOKED'

Table S14.  Get Full Table List of 8 genes significantly correlated to 'NUMBER_PACK_YEARS_SMOKED' by Spearman correlation test

SpearmanCorr corrP Q
PDK1|PDK1_PS241 0.3634 0.0009949 0.178
SFRS1|SF2 -0.3308 0.0029 0.213
EIF4E|EIF4E 0.3103 0.00538 0.213
TSC1|TSC1 0.3067 0.005971 0.213
PDK1|PDK1 0.3032 0.006609 0.213
IDH3A|IDH3A -0.3005 0.007128 0.213
RPS6KB1|P70S6K_PT389 -0.2841 0.01116 0.26
PIK3R1|PI3K-P85 0.2827 0.0116 0.26
Clinical variable #10: 'NUMBER_OF_LYMPH_NODES'

10 genes related to 'NUMBER_OF_LYMPH_NODES'.

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

NUMBER_OF_LYMPH_NODES Mean (SD) 1.82 (3.8)
  Significant markers N = 10
  pos. correlated 3
  neg. correlated 7
List of 10 genes differentially expressed by 'NUMBER_OF_LYMPH_NODES'

Table S16.  Get Full Table List of 10 genes significantly correlated to 'NUMBER_OF_LYMPH_NODES' by Spearman correlation test

SpearmanCorr corrP Q
RPS6|S6_PS240_S244 -0.4032 7.394e-05 0.0132
RPS6|S6_PS235_S236 -0.3817 0.0001893 0.0169
YBX1|YB-1_PS102 -0.3449 0.0008129 0.0479
RAB25|RAB25 0.3375 0.00107 0.0479
BAK1|BAK -0.3297 0.001417 0.0507
INPP4B|INPP4B 0.2955 0.004455 0.133
ERBB2|HER2 0.277 0.007857 0.201
EGFR|EGFR_PY1173 -0.2719 0.009119 0.202
RPS6|S6 -0.2681 0.01018 0.202
RPS6KA1|P90RSK -0.2504 0.01665 0.298
Clinical variable #11: 'RACE'

30 genes related to 'RACE'.

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

RACE Labels N
  ASIAN 8
  BLACK OR AFRICAN AMERICAN 9
  WHITE 103
     
  Significant markers N = 30
List of top 10 genes differentially expressed by 'RACE'

Table S18.  Get Full Table List of top 10 genes differentially expressed by 'RACE'

kruskal_wallis_P Q
YBX1|YB-1 7.935e-05 0.0142
FN1|FIBRONECTIN 0.0005645 0.0505
CDH1|E-CADHERIN 0.001171 0.0698
ACVRL1|ACVRL1 0.003106 0.0897
CHEK1|CHK1 0.003505 0.0897
SRC|SRC 0.003524 0.0897
PDCD4|PDCD4 0.003732 0.0897
STMN1|STATHMIN 0.00401 0.0897
CAV1|CAVEOLIN-1 0.005415 0.108
RAB25|RAB25 0.006319 0.112
Clinical variable #12: 'ETHNICITY'

No gene related to 'ETHNICITY'.

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

ETHNICITY Labels N
  HISPANIC OR LATINO 3
  NOT HISPANIC OR LATINO 114
     
  Significant markers N = 0
Methods & Data
Input

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

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

  • Number of patients = 127

  • Number of genes = 179

  • Number of clinical features = 12

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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