Correlation between RPPA expression and clinical features
Adrenocortical Carcinoma (Primary solid tumor)
02 April 2015  |  analyses__2015_04_02
Maintainer Information
Citation Information
doi:10.7908/C1ZC81V6
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/C1ZC81V6
Overview
Introduction

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

Summary

Testing the association between 192 genes and 7 clinical features across 46 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 5 clinical features related to at least one genes.

  • 30 genes correlated to 'DAYS_TO_DEATH_OR_LAST_FUP'.

    • TFRC|TFRC-R-V ,  CDKN1B|P27_PT157-R-C ,  FN1|FIBRONECTIN-R-V ,  CHEK2|CHK2-M-E ,  CCNB1|CYCLIN_B1-R-V ,  ...

  • 2 genes correlated to 'YEARS_TO_BIRTH'.

    • BECN1|BECLIN-G-C ,  WWTR1|TAZ-R-V

  • 30 genes correlated to 'NEOPLASM_DISEASESTAGE'.

    • MAPK1|ERK2-R-E ,  FN1|FIBRONECTIN-R-V ,  CCNB1|CYCLIN_B1-R-V ,  YAP1|YAP_PS127-R-E ,  FOXO3|FOXO3A-R-C ,  ...

  • 30 genes correlated to 'PATHOLOGY_T_STAGE'.

    • CCNB1|CYCLIN_B1-R-V ,  YAP1|YAP_PS127-R-E ,  TFRC|TFRC-R-V ,  GSK3A GSK3B|GSK3-ALPHA-BETA-M-V ,  MAPK1|ERK2-R-E ,  ...

  • 13 genes correlated to 'GENDER'.

    • CHEK1|CHK1_PS345-R-C ,  MAPK1|ERK2-R-E ,  ADAR|ADAR1-M-V ,  FOXO3|FOXO3A-R-C ,  PRKCD|PKC-DELTA_PS664-R-V ,  ...

  • No genes correlated to 'PATHOLOGY_N_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=30 shorter survival N=15 longer survival N=15
YEARS_TO_BIRTH Spearman correlation test N=2 older N=1 younger N=1
NEOPLASM_DISEASESTAGE Kruskal-Wallis test N=30        
PATHOLOGY_T_STAGE Spearman correlation test N=30 higher stage N=15 lower stage N=15
PATHOLOGY_N_STAGE Wilcoxon test   N=0        
GENDER Wilcoxon test N=13 male N=13 female N=0
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) 4.1-153.6 (median=39.5)
  censored N = 33
  death N = 12
     
  Significant markers N = 30
  associated with shorter survival 15
  associated with longer survival 15
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
TFRC|TFRC-R-V 2.9 4.738e-05 0.0091 0.861
CDKN1B|P27_PT157-R-C 0 0.0003277 0.028 0.231
FN1|FIBRONECTIN-R-V 3 0.000441 0.028 0.823
CHEK2|CHK2-M-E 121 0.001098 0.037 0.686
CCNB1|CYCLIN_B1-R-V 2 0.001126 0.037 0.847
MAPK1|ERK2-R-E 5.8 0.001143 0.037 0.772
WWTR1|TAZ-R-V 111 0.001832 0.04 0.748
PRDX1|PRDX1-R-V 0.03 0.001891 0.04 0.239
INPP4B|INPP4B-R-V 0 0.001958 0.04 0.169
YAP1|YAP-R-E 39 0.002179 0.04 0.743
Clinical variable #2: 'YEARS_TO_BIRTH'

2 genes related to 'YEARS_TO_BIRTH'.

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

YEARS_TO_BIRTH Mean (SD) 47.17 (14)
  Significant markers N = 2
  pos. correlated 1
  neg. correlated 1
List of 2 genes differentially expressed by 'YEARS_TO_BIRTH'

Table S4.  Get Full Table List of 2 genes significantly correlated to 'YEARS_TO_BIRTH' by Spearman correlation test

SpearmanCorr corrP Q
BECN1|BECLIN-G-C -0.4663 0.001089 0.159
WWTR1|TAZ-R-V 0.451 0.00166 0.159
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 2
  STAGE II 26
  STAGE III 10
  STAGE IV 8
     
  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
MAPK1|ERK2-R-E 0.003051 0.263
FN1|FIBRONECTIN-R-V 0.003294 0.263
CCNB1|CYCLIN_B1-R-V 0.004113 0.263
YAP1|YAP_PS127-R-E 0.007744 0.285
FOXO3|FOXO3A-R-C 0.008405 0.285
TFRC|TFRC-R-V 0.01144 0.285
RAD50|RAD50-M-V 0.01372 0.285
PRKCA |PKC-ALPHA_PS657-R-C 0.014 0.285
CDKN1B|P27_PT157-R-C 0.01486 0.285
PRKCA |PKC-ALPHA-M-V 0.01547 0.285
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) 2.41 (0.8)
  N
  T1 2
  T2 30
  T3 7
  T4 7
     
  Significant markers N = 30
  pos. correlated 15
  neg. correlated 15
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
CCNB1|CYCLIN_B1-R-V 0.5436 9.468e-05 0.0182
YAP1|YAP_PS127-R-E 0.4972 0.0004394 0.0422
TFRC|TFRC-R-V 0.4673 0.001059 0.0678
GSK3A GSK3B|GSK3-ALPHA-BETA-M-V 0.4559 0.001453 0.068
MAPK1|ERK2-R-E 0.4485 0.001771 0.068
FASN|FASN-R-V 0.4306 0.002812 0.09
FOXO3|FOXO3A-R-C -0.4148 0.004151 0.113
ESR1|ER-ALPHA-R-V -0.4077 0.004919 0.113
TSC1|TSC1-R-C 0.3966 0.006357 0.113
RAB25|RAB25-R-V -0.3935 0.006824 0.113
Clinical variable #5: 'PATHOLOGY_N_STAGE'

No gene related to 'PATHOLOGY_N_STAGE'.

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

PATHOLOGY_N_STAGE Labels N
  N0 40
  N1 6
     
  Significant markers N = 0
Clinical variable #6: 'GENDER'

13 genes related to 'GENDER'.

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

GENDER Labels N
  FEMALE 28
  MALE 18
     
  Significant markers N = 13
  Higher in MALE 13
  Higher in FEMALE 0
List of top 10 genes differentially expressed by 'GENDER'

Table S11.  Get Full Table List of top 10 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
CHEK1|CHK1_PS345-R-C 386 0.002658 0.263 0.7659
MAPK1|ERK2-R-E 123 0.003825 0.263 0.756
ADAR|ADAR1-M-V 124 0.004109 0.263 0.754
FOXO3|FOXO3A-R-C 372 0.007153 0.293 0.7381
PRKCD|PKC-DELTA_PS664-R-V 365 0.01134 0.293 0.7242
RPS6KB1|P70S6K_PT389-R-V 365 0.01134 0.293 0.7242
CAV1|CAVEOLIN-1-R-V 146 0.01757 0.293 0.7103
RAD50|RAD50-M-V 146 0.01757 0.293 0.7103
TP53|P53-R-E 358 0.01757 0.293 0.7103
RPS6KA1|P90RSK-R-C 146 0.01757 0.293 0.7103
Clinical variable #7: 'ETHNICITY'

No gene related to 'ETHNICITY'.

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

ETHNICITY Labels N
  HISPANIC OR LATINO 6
  NOT HISPANIC OR LATINO 17
     
  Significant markers N = 0
Methods & Data
Input

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

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

  • Number of patients = 46

  • Number of genes = 192

  • Number of clinical features = 7

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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