Correlation between RPPA expression and clinical features
Esophageal Carcinoma (Primary solid tumor)
02 April 2015  |  analyses__2015_04_02
Maintainer Information
Citation Information
doi:10.7908/C19022SR
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/C19022SR
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 9 clinical features across 116 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 5 clinical features related to at least one genes.

  • 1 gene correlated to 'DAYS_TO_DEATH_OR_LAST_FUP'.

    • SCD1|SCD1-M-V

  • 30 genes correlated to 'YEARS_TO_BIRTH'.

    • C12ORF5|TIGAR-R-V ,  NOTCH1|NOTCH1-R-V ,  SRC|SRC_PY527-R-V ,  RAB25|RAB25-R-V ,  SRC|SRC-M-V ,  ...

  • 27 genes correlated to 'PATHOLOGY_T_STAGE'.

    • RPS6|S6_PS240_S244-R-V ,  CLDN7|CLAUDIN-7-R-V ,  KIT|C-KIT-R-V ,  RB1|RB_PS807_S811-R-V ,  CHEK2|CHK2-M-E ,  ...

  • 18 genes correlated to 'PATHOLOGY_N_STAGE'.

    • EEF2|EEF2-R-C ,  RB1|RB_PS807_S811-R-V ,  NDRG1|NDRG1_PT346-R-V ,  RPS6KB1|P70S6K_PT389-R-V ,  CHEK1|CHK1_PS345-R-C ,  ...

  • 30 genes correlated to 'RACE'.

    • PIK3CA |PI3K-P110-ALPHA-R-C ,  SRC|SRC_PY527-R-V ,  C12ORF5|TIGAR-R-V ,  CDKN1A|P21-R-V ,  CCNE2|CYCLIN_E2-R-C ,  ...

  • No genes correlated to 'NEOPLASM_DISEASESTAGE', 'PATHOLOGY_M_STAGE', 'GENDER', and 'NUMBER_PACK_YEARS_SMOKED'.

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=1 shorter survival N=1 longer survival N=0
YEARS_TO_BIRTH Spearman correlation test N=30 older N=17 younger N=13
NEOPLASM_DISEASESTAGE Kruskal-Wallis test   N=0        
PATHOLOGY_T_STAGE Spearman correlation test N=27 higher stage N=14 lower stage N=13
PATHOLOGY_N_STAGE Spearman correlation test N=18 higher stage N=9 lower stage N=9
PATHOLOGY_M_STAGE Wilcoxon test   N=0        
GENDER Wilcoxon test   N=0        
NUMBER_PACK_YEARS_SMOKED Spearman correlation test   N=0        
RACE Wilcoxon test N=30 white N=30 asian N=0
Clinical variable #1: 'DAYS_TO_DEATH_OR_LAST_FUP'

One 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) 0.1-122.1 (median=12.7)
  censored N = 76
  death N = 39
     
  Significant markers N = 1
  associated with shorter survival 1
  associated with longer survival 0
List of one gene differentially expressed by 'DAYS_TO_DEATH_OR_LAST_FUP'

Table S2.  Get Full Table List of one gene significantly associated with 'Time to Death' by Cox regression test

HazardRatio Wald_P Q C_index
SCD1|SCD1-M-V 5.6 0.0009821 0.19 0.512
Clinical variable #2: 'YEARS_TO_BIRTH'

30 genes related to 'YEARS_TO_BIRTH'.

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

YEARS_TO_BIRTH Mean (SD) 62.86 (12)
  Significant markers N = 30
  pos. correlated 17
  neg. correlated 13
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
C12ORF5|TIGAR-R-V 0.4268 1.778e-06 0.000341
NOTCH1|NOTCH1-R-V -0.389 1.594e-05 0.00128
SRC|SRC_PY527-R-V 0.3849 1.992e-05 0.00128
RAB25|RAB25-R-V 0.3434 0.0001604 0.0077
SRC|SRC-M-V 0.3384 0.0002029 0.00779
PRKCD|PKC-DELTA_PS664-R-V 0.2937 0.001378 0.0441
BIRC2 |CIAP-R-V 0.2852 0.001913 0.0525
GAPDH|GAPDH-M-C -0.2767 0.002636 0.0633
SRC|SRC_PY416-R-C 0.2712 0.003229 0.0636
PKC|PKC-PAN_BETAII_PS660-R-V 0.2705 0.003312 0.0636
Clinical variable #3: 'NEOPLASM_DISEASESTAGE'

No gene related to 'NEOPLASM_DISEASESTAGE'.

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

NEOPLASM_DISEASESTAGE Labels N
  STAGE I 6
  STAGE IA 3
  STAGE IB 5
  STAGE II 1
  STAGE IIA 34
  STAGE IIB 22
  STAGE III 17
  STAGE IIIA 10
  STAGE IIIB 8
  STAGE IIIC 2
  STAGE IV 3
  STAGE IVA 1
     
  Significant markers N = 0
Clinical variable #4: 'PATHOLOGY_T_STAGE'

27 genes related to 'PATHOLOGY_T_STAGE'.

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

PATHOLOGY_T_STAGE Mean (SD) 2.46 (0.78)
  N
  T1 18
  T2 28
  T3 67
  T4 2
     
  Significant markers N = 27
  pos. correlated 14
  neg. correlated 13
List of top 10 genes differentially expressed by 'PATHOLOGY_T_STAGE'

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

SpearmanCorr corrP Q
RPS6|S6_PS240_S244-R-V -0.302 0.001037 0.121
CLDN7|CLAUDIN-7-R-V -0.2971 0.001262 0.121
KIT|C-KIT-R-V 0.2797 0.002469 0.158
RB1|RB_PS807_S811-R-V -0.26 0.005012 0.179
CHEK2|CHK2-M-E -0.252 0.006587 0.179
RPTOR|RAPTOR-R-V 0.248 0.00754 0.179
SRC|SRC_PY527-R-V -0.2465 0.007918 0.179
TGM2|TRANSGLUTAMINASE-M-V 0.2429 0.008906 0.179
SHC1|SHC_PY317-R-E -0.2422 0.009096 0.179
RPS6|S6_PS235_S236-R-V -0.2402 0.009722 0.179
Clinical variable #5: 'PATHOLOGY_N_STAGE'

18 genes related to 'PATHOLOGY_N_STAGE'.

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

PATHOLOGY_N_STAGE Mean (SD) 0.64 (0.74)
  N
  N0 56
  N1 46
  N2 9
  N3 3
     
  Significant markers N = 18
  pos. correlated 9
  neg. correlated 9
List of top 10 genes differentially expressed by 'PATHOLOGY_N_STAGE'

Table S9.  Get Full Table List of top 10 genes significantly correlated to 'PATHOLOGY_N_STAGE' by Spearman correlation test

SpearmanCorr corrP Q
EEF2|EEF2-R-C -0.306 0.0009293 0.106
RB1|RB_PS807_S811-R-V -0.2904 0.001726 0.106
NDRG1|NDRG1_PT346-R-V -0.2872 0.00195 0.106
RPS6KB1|P70S6K_PT389-R-V 0.2837 0.002218 0.106
CHEK1|CHK1_PS345-R-C -0.2542 0.006347 0.235
EGFR|EGFR-R-V -0.2498 0.007353 0.235
BAD|BAD_PS112-R-V -0.2311 0.01338 0.253
PIK3CA |PI3K-P110-ALPHA-R-C -0.2295 0.01404 0.253
EIF4EBP1|4E-BP1-R-V -0.2283 0.01457 0.253
BECN1|BECLIN-G-C 0.2271 0.0151 0.253
Clinical variable #6: 'PATHOLOGY_M_STAGE'

No gene related to 'PATHOLOGY_M_STAGE'.

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

PATHOLOGY_M_STAGE Labels N
  class0 97
  class1 4
     
  Significant markers N = 0
Clinical variable #7: 'GENDER'

No gene related to 'GENDER'.

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

GENDER Labels N
  FEMALE 15
  MALE 101
     
  Significant markers N = 0
Clinical variable #8: 'NUMBER_PACK_YEARS_SMOKED'

No gene related to 'NUMBER_PACK_YEARS_SMOKED'.

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

NUMBER_PACK_YEARS_SMOKED Mean (SD) 37.31 (21)
  Significant markers N = 0
Clinical variable #9: 'RACE'

30 genes related to 'RACE'.

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

RACE Labels N
  ASIAN 38
  WHITE 70
     
  Significant markers N = 30
  Higher in WHITE 30
  Higher in ASIAN 0
List of top 10 genes differentially expressed by 'RACE'

Table S14.  Get Full Table List of top 10 genes differentially expressed by 'RACE'

W(pos if higher in 'WHITE') wilcoxontestP Q AUC
PIK3CA |PI3K-P110-ALPHA-R-C 453 1.712e-08 3.29e-06 0.8297
SRC|SRC_PY527-R-V 2180 4.626e-08 4.44e-06 0.8195
C12ORF5|TIGAR-R-V 2013 1.13e-05 0.000723 0.7568
CDKN1A|P21-R-V 1983 2.696e-05 0.00129 0.7455
CCNE2|CYCLIN_E2-R-C 700 5.126e-05 0.00173 0.7368
FRAP1|MTOR_PS2448-R-C 1958 5.416e-05 0.00173 0.7361
G6PD|G6PD-M-V 708 6.379e-05 0.00175 0.7338
DVL3|DVL3-R-V 731 0.0001179 0.00272 0.7252
EIF4G1|EIF4G-R-C 734 0.0001276 0.00272 0.7241
SRC|SRC-M-V 1896 0.0002747 0.00474 0.7128
Methods & Data
Input

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

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

  • Number of patients = 116

  • Number of genes = 192

  • Number of clinical features = 9

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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