Correlation between mRNAseq expression and clinical features
Prostate Adenocarcinoma (Primary solid tumor)
28 January 2016  |  analyses__2016_01_28
Maintainer Information
Citation Information
doi:10.7908/C13R0SC1
Maintained by Juok Cho (Broad Institute)
Cite as Broad Institute TCGA Genome Data Analysis Center (2016): Correlation between mRNAseq expression and clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C13R0SC1
Overview
Introduction

This pipeline uses various statistical tests to identify mRNAs whose log2 expression levels correlated to selected clinical features. The input file "PRAD-TP.uncv2.mRNAseq_RSEM_normalized_log2.txt" is generated in the pipeline mRNAseq_Preprocess in the stddata run.

Summary

Testing the association between 18274 genes and 11 clinical features across 497 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 8 clinical features related to at least one genes.

  • 30 genes correlated to 'YEARS_TO_BIRTH'.

    • ADAP2|55803 ,  ARFGAP3|26286 ,  FCGR1A|2209 ,  TDP2|51567 ,  ELMO2|63916 ,  ...

  • 30 genes correlated to 'PATHOLOGY_T_STAGE'.

    • ASPN|54829 ,  KCNK6|9424 ,  EPHX2|2053 ,  CHRNA2|1135 ,  NAGLU|4669 ,  ...

  • 30 genes correlated to 'PATHOLOGY_N_STAGE'.

    • SPAG1|6674 ,  EDN3|1908 ,  RNF19A|25897 ,  SLC43A1|8501 ,  FAM107A|11170 ,  ...

  • 30 genes correlated to 'RADIATION_THERAPY'.

    • PHYHIPL|84457 ,  DPT|1805 ,  GNG4|2786 ,  IGSF1|3547 ,  STXBP6|29091 ,  ...

  • 30 genes correlated to 'RESIDUAL_TUMOR'.

    • GEMIN4|50628 ,  KIAA0319L|79932 ,  CYP20A1|57404 ,  C15ORF58|390637 ,  C20ORF20|55257 ,  ...

  • 30 genes correlated to 'NUMBER_OF_LYMPH_NODES'.

    • SPAG1|6674 ,  EDN3|1908 ,  RNF19A|25897 ,  ABCC5|10057 ,  MBD4|8930 ,  ...

  • 30 genes correlated to 'GLEASON_SCORE'.

    • C15ORF42|90381 ,  FAM72B|653820 ,  FAM72D|728833 ,  TROAP|10024 ,  PRC1|9055 ,  ...

  • 30 genes correlated to 'PSA_VALUE'.

    • PRR11|55771 ,  TLR8|51311 ,  RPS27L|51065 ,  TRIM59|286827 ,  CYBB|1536 ,  ...

  • No genes correlated to 'DAYS_TO_DEATH_OR_LAST_FUP', 'HISTOLOGICAL_TYPE', and 'RACE'.

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=30 older N=11 younger N=19
PATHOLOGY_T_STAGE Spearman correlation test N=30 higher stage N=20 lower stage N=10
PATHOLOGY_N_STAGE Wilcoxon test N=30 n1 N=30 n0 N=0
RADIATION_THERAPY Wilcoxon test N=30 yes N=30 no N=0
HISTOLOGICAL_TYPE Wilcoxon test   N=0        
RESIDUAL_TUMOR Kruskal-Wallis test N=30        
NUMBER_OF_LYMPH_NODES Spearman correlation test N=30 higher number_of_lymph_nodes N=14 lower number_of_lymph_nodes N=16
GLEASON_SCORE Spearman correlation test N=30 higher score N=26 lower score N=4
PSA_VALUE Spearman correlation test N=30 higher psa_value N=23 lower psa_value N=7
RACE Kruskal-Wallis 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) 0.8-165.2 (median=30.5)
  censored N = 486
  death N = 10
     
  Significant markers N = 0
Clinical variable #2: 'YEARS_TO_BIRTH'

30 genes related to 'YEARS_TO_BIRTH'.

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

YEARS_TO_BIRTH Mean (SD) 61.04 (6.8)
  Significant markers N = 30
  pos. correlated 11
  neg. correlated 19
List of top 10 genes differentially expressed by 'YEARS_TO_BIRTH'

Table S3.  Get Full Table List of top 10 genes significantly correlated to 'YEARS_TO_BIRTH' by Spearman correlation test

SpearmanCorr corrP Q
ADAP2|55803 0.3036 8.065e-12 1.47e-07
ARFGAP3|26286 -0.2432 5.636e-08 0.000491
FCGR1A|2209 0.2407 8.063e-08 0.000491
TDP2|51567 -0.23 2.949e-07 0.000942
ELMO2|63916 -0.2299 2.988e-07 0.000942
KCNS1|3787 -0.2353 3.507e-07 0.000942
MNX1|3110 0.2297 3.608e-07 0.000942
HLX|3142 0.2268 4.326e-07 0.000988
ACSL3|2181 -0.2249 5.478e-07 0.00111
FBXL16|146330 0.2226 7.154e-07 0.00131
Clinical variable #3: 'PATHOLOGY_T_STAGE'

30 genes related to 'PATHOLOGY_T_STAGE'.

Table S4.  Basic characteristics of clinical feature: 'PATHOLOGY_T_STAGE'

PATHOLOGY_T_STAGE Mean (SD) 2.64 (0.52)
  N
  T2 187
  T3 293
  T4 10
     
  Significant markers N = 30
  pos. correlated 20
  neg. correlated 10
List of top 10 genes differentially expressed by 'PATHOLOGY_T_STAGE'

Table S5.  Get Full Table List of top 10 genes significantly correlated to 'PATHOLOGY_T_STAGE' by Spearman correlation test

SpearmanCorr corrP Q
ASPN|54829 0.3894 3.405e-19 6.22e-15
KCNK6|9424 -0.3841 1.131e-18 1.03e-14
EPHX2|2053 -0.3798 2.916e-18 1.78e-14
CHRNA2|1135 -0.3732 1.227e-17 5.6e-14
NAGLU|4669 -0.3653 6.494e-17 2.13e-13
FAM72A|729533 0.3755 6.979e-17 2.13e-13
CIT|11113 0.3633 9.789e-17 2.56e-13
COL10A1|1300 0.3609 1.866e-16 4.26e-13
CTHRC1|115908 0.359 2.356e-16 4.78e-13
CHRM1|1128 -0.3562 4.227e-16 7.72e-13
Clinical variable #4: 'PATHOLOGY_N_STAGE'

30 genes related to 'PATHOLOGY_N_STAGE'.

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

PATHOLOGY_N_STAGE Labels N
  N0 345
  N1 79
     
  Significant markers N = 30
  Higher in N1 30
  Higher in N0 0
List of top 10 genes differentially expressed by 'PATHOLOGY_N_STAGE'

Table S7.  Get Full Table List of top 10 genes differentially expressed by 'PATHOLOGY_N_STAGE'

W(pos if higher in 'N1') wilcoxontestP Q AUC
SPAG1|6674 20181 2.561e-11 4.68e-07 0.7405
EDN3|1908 7015 3.777e-09 3.45e-05 0.7187
RNF19A|25897 19244 1.09e-08 5.59e-05 0.7061
SLC43A1|8501 8070 1.549e-08 5.59e-05 0.7039
FAM107A|11170 8115 2.02e-08 5.59e-05 0.7023
CECR6|27439 8123 2.118e-08 5.59e-05 0.702
GNE|10020 8125 2.143e-08 5.59e-05 0.7019
PROK1|84432 8172 3.266e-08 7.46e-05 0.6993
MBD4|8930 18960 5.73e-08 0.00011 0.6957
ABCC5|10057 18952 5.997e-08 0.00011 0.6954
Clinical variable #5: 'RADIATION_THERAPY'

30 genes related to 'RADIATION_THERAPY'.

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

RADIATION_THERAPY Labels N
  NO 394
  YES 59
     
  Significant markers N = 30
  Higher in YES 30
  Higher in NO 0
List of top 10 genes differentially expressed by 'RADIATION_THERAPY'

Table S9.  Get Full Table List of top 10 genes differentially expressed by 'RADIATION_THERAPY'

W(pos if higher in 'YES') wilcoxontestP Q AUC
PHYHIPL|84457 6711 1.63e-07 0.00298 0.7113
DPT|1805 6969 6.972e-07 0.00637 0.7002
GNG4|2786 7116 1.545e-06 0.00656 0.6939
IGSF1|3547 7030 1.977e-06 0.00656 0.6921
STXBP6|29091 7174 2.1e-06 0.00656 0.6914
KIAA0319L|79932 7191 2.297e-06 0.00656 0.6907
CD300LG|146894 6278 2.512e-06 0.00656 0.6964
YIPF1|54432 7273 3.518e-06 0.00741 0.6871
PCOTH|542767 7301 4.063e-06 0.00741 0.6859
TAF4|6874 15939 4.19e-06 0.00741 0.6857
Clinical variable #6: 'HISTOLOGICAL_TYPE'

No gene related to 'HISTOLOGICAL_TYPE'.

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

HISTOLOGICAL_TYPE Labels N
  PROSTATE ADENOCARCINOMA ACINAR TYPE 482
  PROSTATE ADENOCARCINOMA, OTHER SUBTYPE 15
     
  Significant markers N = 0
Clinical variable #7: 'RESIDUAL_TUMOR'

30 genes related to 'RESIDUAL_TUMOR'.

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

RESIDUAL_TUMOR Labels N
  R0 315
  R1 147
  R2 5
  RX 15
     
  Significant markers N = 30
List of top 10 genes differentially expressed by 'RESIDUAL_TUMOR'

Table S12.  Get Full Table List of top 10 genes differentially expressed by 'RESIDUAL_TUMOR'

kruskal_wallis_P Q
GEMIN4|50628 8.825e-09 8.19e-05
KIAA0319L|79932 8.962e-09 8.19e-05
CYP20A1|57404 1.912e-08 0.000116
C15ORF58|390637 4.135e-08 0.000182
C20ORF20|55257 5.531e-08 0.000182
VPS36|51028 6.258e-08 0.000182
CNIH2|254263 6.974e-08 0.000182
ZNRF3|84133 1.188e-07 0.000228
ITGB1BP1|9270 1.26e-07 0.000228
ZBED1|9189 1.381e-07 0.000228
Clinical variable #8: 'NUMBER_OF_LYMPH_NODES'

30 genes related to 'NUMBER_OF_LYMPH_NODES'.

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

NUMBER_OF_LYMPH_NODES Mean (SD) 0.45 (1.4)
  Significant markers N = 30
  pos. correlated 14
  neg. correlated 16
List of top 10 genes differentially expressed by 'NUMBER_OF_LYMPH_NODES'

Table S14.  Get Full Table List of top 10 genes significantly correlated to 'NUMBER_OF_LYMPH_NODES' by Spearman correlation test

SpearmanCorr corrP Q
SPAG1|6674 0.3413 1.548e-12 2.83e-08
EDN3|1908 -0.3176 1.173e-10 1.07e-06
RNF19A|25897 0.3034 4.338e-10 2.64e-06
ABCC5|10057 0.3012 5.873e-10 2.68e-06
MBD4|8930 0.2934 1.678e-09 5.16e-06
FAM107A|11170 -0.2933 1.695e-09 5.16e-06
CECR6|27439 -0.2909 2.321e-09 6.06e-06
PROK1|84432 -0.2899 2.773e-09 6.18e-06
CHRNA2|1135 -0.2889 3.042e-09 6.18e-06
PEBP4|157310 -0.2876 3.747e-09 6.85e-06
Clinical variable #9: 'GLEASON_SCORE'

30 genes related to 'GLEASON_SCORE'.

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

GLEASON_SCORE Mean (SD) 7.61 (1)
  Score N
  6 45
  7 247
  8 64
  9 137
  10 4
     
  Significant markers N = 30
  pos. correlated 26
  neg. correlated 4
List of top 10 genes differentially expressed by 'GLEASON_SCORE'

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

SpearmanCorr corrP Q
C15ORF42|90381 0.4899 2.292e-31 2.6e-27
FAM72B|653820 0.4892 2.843e-31 2.6e-27
FAM72D|728833 0.4705 9.573e-29 5.83e-25
TROAP|10024 0.4686 1.712e-28 7.82e-25
PRC1|9055 0.4624 1.061e-27 3.88e-24
SPAG5|10615 0.4615 1.396e-27 4.25e-24
FAM72A|729533 0.4708 3.4e-27 8.88e-24
CBX2|84733 0.4574 4.531e-27 1.04e-23
KIF23|9493 0.4568 5.444e-27 1.11e-23
NUF2|83540 0.4559 7.11e-27 1.3e-23
Clinical variable #10: 'PSA_VALUE'

30 genes related to 'PSA_VALUE'.

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

PSA_VALUE Mean (SD) 1.75 (16)
  Significant markers N = 30
  pos. correlated 23
  neg. correlated 7
List of top 10 genes differentially expressed by 'PSA_VALUE'

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

SpearmanCorr corrP Q
PRR11|55771 0.268 3.053e-08 0.000299
TLR8|51311 0.2592 4.019e-08 0.000299
RPS27L|51065 -0.2564 4.915e-08 0.000299
TRIM59|286827 0.2516 8.813e-08 0.000375
CYBB|1536 0.2498 1.095e-07 0.000375
PLXNC1|10154 0.2484 1.528e-07 0.000375
ARHGEF3|50650 0.2466 1.61e-07 0.000375
NCOA3|8202 0.2451 1.916e-07 0.000375
CD84|8832 0.2443 2.245e-07 0.000375
DYRK2|8445 0.2434 2.341e-07 0.000375
Clinical variable #11: 'RACE'

No gene related to 'RACE'.

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

RACE Labels N
  ASIAN 2
  BLACK OR AFRICAN AMERICAN 7
  WHITE 147
     
  Significant markers N = 0
Methods & Data
Input

  • Expresson data file = PRAD-TP.uncv2.mRNAseq_RSEM_normalized_log2.txt

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

  • Number of patients = 497

  • Number of genes = 18274

  • Number of clinical features = 11

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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