Correlation between mRNA expression and clinical features
Breast Invasive Carcinoma (Primary solid tumor)
28 January 2016  |  analyses__2016_01_28
Maintainer Information
Citation Information
doi:10.7908/C1C828NT
Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/Harvard Medical School)
Cite as Broad Institute TCGA Genome Data Analysis Center (2016): Correlation between mRNA expression and clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1C828NT
Overview
Introduction

This pipeline uses various statistical tests to identify mRNAs whose log2 expression levels correlated to selected clinical features. The input file "BRCA-TP.medianexp.txt" is generated in the pipeline mRNA_Preprocess_Median in the stddata run.

Summary

Testing the association between 17814 genes and 12 clinical features across 526 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 10 clinical features related to at least one genes.

  • 2 genes correlated to 'DAYS_TO_DEATH_OR_LAST_FUP'.

    • POLG2 ,  PTGES3

  • 30 genes correlated to 'YEARS_TO_BIRTH'.

    • ESR1 ,  CNTNAP3 ,  KRT17 ,  C20ORF42 ,  MAGED4B ,  ...

  • 30 genes correlated to 'PATHOLOGIC_STAGE'.

    • RWDD3 ,  RPL11 ,  RANBP2 ,  OR4D1 ,  CHST5 ,  ...

  • 30 genes correlated to 'PATHOLOGY_T_STAGE'.

    • RBBP5 ,  PPP2R5D ,  TMEM81 ,  CCNF ,  ZBED5 ,  ...

  • 30 genes correlated to 'PATHOLOGY_N_STAGE'.

    • RWDD3 ,  RPL5 ,  HIST1H2AG ,  PBX1 ,  LRP6 ,  ...

  • 1 gene correlated to 'PATHOLOGY_M_STAGE'.

    • OR4C13

  • 30 genes correlated to 'RADIATION_THERAPY'.

    • SRGN ,  CD300A ,  CTSS ,  LAPTM5 ,  NMI ,  ...

  • 30 genes correlated to 'HISTOLOGICAL_TYPE'.

    • CDH1 ,  ADRM1 ,  C10ORF56 ,  GLTSCR2 ,  MFAP4 ,  ...

  • 30 genes correlated to 'NUMBER_OF_LYMPH_NODES'.

    • CSDE1 ,  RWDD3 ,  BCAR1 ,  STS ,  ACY1L2 ,  ...

  • 30 genes correlated to 'RACE'.

    • PSPH ,  CRYBB2 ,  IL27 ,  PRSS36 ,  RAI16 ,  ...

  • No genes correlated to 'GENDER', 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=2   N=NA   N=NA
YEARS_TO_BIRTH Spearman correlation test N=30 older N=6 younger N=24
PATHOLOGIC_STAGE Kruskal-Wallis test N=30        
PATHOLOGY_T_STAGE Spearman correlation test N=30 higher stage N=21 lower stage N=9
PATHOLOGY_N_STAGE Spearman correlation test N=30 higher stage N=15 lower stage N=15
PATHOLOGY_M_STAGE Wilcoxon test N=1 class1 N=1 class0 N=0
GENDER Wilcoxon test   N=0        
RADIATION_THERAPY Wilcoxon test N=30 yes N=30 no N=0
HISTOLOGICAL_TYPE Kruskal-Wallis test N=30        
NUMBER_OF_LYMPH_NODES Spearman correlation test N=30 higher number_of_lymph_nodes N=16 lower number_of_lymph_nodes N=14
RACE Kruskal-Wallis test N=30        
ETHNICITY Wilcoxon test   N=0        
Clinical variable #1: 'DAYS_TO_DEATH_OR_LAST_FUP'

2 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) 0.2-282.9 (median=33.8)
  censored N = 442
  death N = 83
     
  Significant markers N = 2
  associated with shorter survival NA
  associated with longer survival NA
List of 2 genes differentially expressed by 'DAYS_TO_DEATH_OR_LAST_FUP'

Table S2.  Get Full Table List of 2 genes significantly associated with 'Time to Death' by Cox regression test. For the survival curves, it compared quantile intervals at c(0, 0.25, 0.50, 0.75, 1) and did not try survival analysis if there is only one interval.

logrank_P Q C_index
POLG2 2.3e-05 0.26 0.584
PTGES3 2.95e-05 0.26 0.604
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) 58.07 (13)
  Significant markers N = 30
  pos. correlated 6
  neg. correlated 24
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
ESR1 0.4219 7.358e-24 1.31e-19
CNTNAP3 -0.3026 1.779e-12 1.58e-08
KRT17 -0.2961 5.531e-12 2.65e-08
C20ORF42 -0.2957 5.952e-12 2.65e-08
MAGED4B -0.2924 1.032e-11 3.61e-08
FOXD2 0.2914 1.228e-11 3.61e-08
KLK6 -0.2903 1.487e-11 3.61e-08
SOSTDC1 -0.2891 1.81e-11 3.61e-08
SYT8 -0.289 1.83e-11 3.61e-08
PPP1R14C -0.288 2.17e-11 3.61e-08
Clinical variable #3: 'PATHOLOGIC_STAGE'

30 genes related to 'PATHOLOGIC_STAGE'.

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

PATHOLOGIC_STAGE Labels N
  STAGE I 51
  STAGE IA 35
  STAGE IB 3
  STAGE II 1
  STAGE IIA 182
  STAGE IIB 113
  STAGE IIIA 78
  STAGE IIIB 13
  STAGE IIIC 19
  STAGE IV 13
  STAGE X 13
     
  Significant markers N = 30
List of top 10 genes differentially expressed by 'PATHOLOGIC_STAGE'

Table S6.  Get Full Table List of top 10 genes differentially expressed by 'PATHOLOGIC_STAGE'

kruskal_wallis_P Q
RWDD3 7.851e-07 0.0105
RPL11 1.455e-06 0.0105
RANBP2 2.777e-06 0.0105
OR4D1 2.925e-06 0.0105
CHST5 2.949e-06 0.0105
LOC51252 5.615e-06 0.0167
OR51L1 1.419e-05 0.0341
DHPS 1.532e-05 0.0341
MIER1 1.929e-05 0.0382
DDX20 2.403e-05 0.039
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) 1.94 (0.72)
  N
  T1 132
  T2 312
  T3 60
  T4 20
     
  Significant markers N = 30
  pos. correlated 21
  neg. correlated 9
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
RBBP5 0.27 3.336e-10 5.94e-06
PPP2R5D 0.2321 7.662e-08 0.000682
TMEM81 0.228 1.326e-07 0.000787
CCNF 0.2211 3.164e-07 0.00116
ZBED5 -0.2209 3.243e-07 0.00116
TBRG4 0.2193 4.005e-07 0.00117
TMEM63B 0.2181 4.603e-07 0.00117
SEPT10 -0.2163 5.77e-07 0.00121
RWDD3 -0.2158 6.136e-07 0.00121
ZC3H12D 0.205 2.232e-06 0.00392
Clinical variable #5: 'PATHOLOGY_N_STAGE'

30 genes related to 'PATHOLOGY_N_STAGE'.

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

PATHOLOGY_N_STAGE Mean (SD) 0.73 (0.88)
  N
  N0 255
  N1 171
  N2 60
  N3 29
     
  Significant markers N = 30
  pos. correlated 15
  neg. correlated 15
List of top 10 genes differentially expressed by 'PATHOLOGY_N_STAGE'

Table S10.  Get Full Table List of top 10 genes significantly correlated to 'PATHOLOGY_N_STAGE' by Spearman correlation test

SpearmanCorr corrP Q
RWDD3 -0.2534 5.457e-09 9.72e-05
RPL5 -0.2305 1.233e-07 0.0011
HIST1H2AG 0.2258 2.226e-07 0.00132
PBX1 0.2213 3.932e-07 0.00175
LRP6 -0.217 6.626e-07 0.00236
SRD5A2L 0.2132 1.042e-06 0.003
CALM1 0.212 1.212e-06 0.003
C11ORF80 0.211 1.362e-06 0.003
RBBP8 -0.2092 1.684e-06 0.003
USP33 -0.2092 1.685e-06 0.003
Clinical variable #6: 'PATHOLOGY_M_STAGE'

One gene related to 'PATHOLOGY_M_STAGE'.

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

PATHOLOGY_M_STAGE Labels N
  class0 495
  class1 15
     
  Significant markers N = 1
  Higher in class1 1
  Higher in class0 0
List of one gene differentially expressed by 'PATHOLOGY_M_STAGE'

Table S12.  Get Full Table List of one gene differentially expressed by 'PATHOLOGY_M_STAGE'

W(pos if higher in 'class1') wilcoxontestP Q AUC
OR4C13 1239.5 1.12e-05 0.2 0.8327
Clinical variable #7: 'GENDER'

No gene related to 'GENDER'.

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

GENDER Labels N
  FEMALE 520
  MALE 6
     
  Significant markers N = 0
Clinical variable #8: 'RADIATION_THERAPY'

30 genes related to 'RADIATION_THERAPY'.

Table S14.  Basic characteristics of clinical feature: 'RADIATION_THERAPY'

RADIATION_THERAPY Labels N
  NO 208
  YES 264
     
  Significant markers N = 30
  Higher in YES 30
  Higher in NO 0
List of top 10 genes differentially expressed by 'RADIATION_THERAPY'

Table S15.  Get Full Table List of top 10 genes differentially expressed by 'RADIATION_THERAPY'

W(pos if higher in 'YES') wilcoxontestP Q AUC
SRGN 34202.5 4.532e-06 0.0667 0.6229
CD300A 33928 1.089e-05 0.0667 0.6179
CTSS 33844.5 1.412e-05 0.0667 0.6163
LAPTM5 33774.5 1.751e-05 0.0667 0.6151
NMI 33646 2.587e-05 0.0667 0.6127
C1ORF162 33645.5 2.591e-05 0.0667 0.6127
ALOX5AP 33605.5 2.921e-05 0.0667 0.612
CD300C 33597 2.996e-05 0.0667 0.6118
AQP9 33441 4.747e-05 0.0913 0.609
CD53 33364 5.934e-05 0.0913 0.6076
Clinical variable #9: 'HISTOLOGICAL_TYPE'

30 genes related to 'HISTOLOGICAL_TYPE'.

Table S16.  Basic characteristics of clinical feature: 'HISTOLOGICAL_TYPE'

HISTOLOGICAL_TYPE Labels N
  INFILTRATING DUCTAL CARCINOMA 446
  INFILTRATING LOBULAR CARCINOMA 43
  MEDULLARY CARCINOMA 1
  MIXED HISTOLOGY (PLEASE SPECIFY) 12
  MUCINOUS CARCINOMA 2
  OTHER, SPECIFY 21
     
  Significant markers N = 30
List of top 10 genes differentially expressed by 'HISTOLOGICAL_TYPE'

Table S17.  Get Full Table List of top 10 genes differentially expressed by 'HISTOLOGICAL_TYPE'

kruskal_wallis_P Q
CDH1 2.08e-18 3.71e-14
ADRM1 4.855e-11 4.31e-07
C10ORF56 7.262e-11 4.31e-07
GLTSCR2 1.566e-10 6.97e-07
MFAP4 2.465e-10 8.78e-07
ALG3 7.32e-10 1.7e-06
C1QTNF7 7.361e-10 1.7e-06
PPIL1 7.64e-10 1.7e-06
RBMS3 1.104e-09 2.07e-06
IGF1 1.205e-09 2.07e-06
Clinical variable #10: 'NUMBER_OF_LYMPH_NODES'

30 genes related to 'NUMBER_OF_LYMPH_NODES'.

Table S18.  Basic characteristics of clinical feature: 'NUMBER_OF_LYMPH_NODES'

NUMBER_OF_LYMPH_NODES Mean (SD) 1.81 (3.5)
  Significant markers N = 30
  pos. correlated 16
  neg. correlated 14
List of top 10 genes differentially expressed by 'NUMBER_OF_LYMPH_NODES'

Table S19.  Get Full Table List of top 10 genes significantly correlated to 'NUMBER_OF_LYMPH_NODES' by Spearman correlation test

SpearmanCorr corrP Q
CSDE1 -0.2383 1.127e-06 0.0172
RWDD3 -0.2325 2.065e-06 0.0172
BCAR1 0.2292 2.897e-06 0.0172
STS 0.223 5.397e-06 0.024
ACY1L2 -0.2175 9.267e-06 0.0271
DDX20 -0.2169 9.824e-06 0.0271
HIST1H2AG 0.2139 1.317e-05 0.0271
ARRDC1 0.2137 1.343e-05 0.0271
NFATC1 -0.2124 1.521e-05 0.0271
SH3GL1 0.2117 1.62e-05 0.0271
Clinical variable #11: 'RACE'

30 genes related to 'RACE'.

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

RACE Labels N
  AMERICAN INDIAN OR ALASKA NATIVE 1
  ASIAN 34
  BLACK OR AFRICAN AMERICAN 40
  WHITE 361
     
  Significant markers N = 30
List of top 10 genes differentially expressed by 'RACE'

Table S21.  Get Full Table List of top 10 genes differentially expressed by 'RACE'

kruskal_wallis_P Q
PSPH 2.725e-16 4.85e-12
CRYBB2 1.187e-13 1.06e-09
IL27 8.988e-11 4.51e-07
PRSS36 1.012e-10 4.51e-07
RAI16 1.656e-10 5.9e-07
DPF2 1.832e-09 5.44e-06
UTS2 2.16e-09 5.5e-06
CRCT1 1.266e-08 2.58e-05
MAB21L2 1.302e-08 2.58e-05
TNMD 2.08e-08 3.7e-05
Clinical variable #12: 'ETHNICITY'

No gene related to 'ETHNICITY'.

Table S22.  Basic characteristics of clinical feature: 'ETHNICITY'

ETHNICITY Labels N
  HISPANIC OR LATINO 7
  NOT HISPANIC OR LATINO 372
     
  Significant markers N = 0
Methods & Data
Input

  • Expresson data file = BRCA-TP.medianexp.txt

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

  • Number of patients = 526

  • Number of genes = 17814

  • Number of clinical features = 12

Selected clinical features

  • Further details on clinical features selected for this analysis, please find a documentation on selected CDEs (Clinical Data Elements). The first column of the file is a formula to convert values and the second column is a clinical parameter name.

  • 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, logrank test in univariate Cox regression analysis with proportional hazards model (Andersen and Gill 1982) was used to estimate the P values comparing quantile intervals using the 'coxph' function in R. Kaplan-Meier survival curves were plotted using quantile intervals at c(0, 0.25, 0.50, 0.75, 1). If there is only one interval group, it will not try survival analysis.

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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