Correlation between mRNAseq expression and clinical features
Testicular Germ Cell Tumors (Primary solid tumor)
28 January 2016  |  analyses__2016_01_28
Maintainer Information
Citation Information
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/C1XS5TVD
Overview
Introduction

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

Summary

Testing the association between 19071 genes and 10 clinical features across 134 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 4 clinical features related to at least one genes.

  • 30 genes correlated to 'YEARS_TO_BIRTH'.

    • LDLR|3949 ,  ANAPC7|51434 ,  LRRC41|10489 ,  PNKP|11284 ,  TXLNA|200081 ,  ...

  • 30 genes correlated to 'PATHOLOGIC_STAGE'.

    • C1ORF125|126859 ,  CATSPERG|57828 ,  AGAP6|414189 ,  CHMP2B|25978 ,  ZNF565|147929 ,  ...

  • 30 genes correlated to 'PATHOLOGY_T_STAGE'.

    • ZNF181|339318 ,  NCRNA00219|114915 ,  ZNF333|84449 ,  LOC388955|388955 ,  ZNF264|9422 ,  ...

  • 30 genes correlated to 'RADIATION_THERAPY'.

    • GRIK2|2898 ,  HSD17B12|51144 ,  COL21A1|81578 ,  AMOT|154796 ,  VLDLR|7436 ,  ...

  • No genes correlated to 'DAYS_TO_DEATH_OR_LAST_FUP', 'PATHOLOGY_N_STAGE', 'PATHOLOGY_M_STAGE', 'KARNOFSKY_PERFORMANCE_SCORE', 'RACE', 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=30 older N=14 younger N=16
PATHOLOGIC_STAGE Kruskal-Wallis test N=30        
PATHOLOGY_T_STAGE Spearman correlation test N=30 higher stage N=15 lower stage N=15
PATHOLOGY_N_STAGE Spearman correlation test   N=0        
PATHOLOGY_M_STAGE Wilcoxon test   N=0        
RADIATION_THERAPY Wilcoxon test N=30 yes N=30 no N=0
KARNOFSKY_PERFORMANCE_SCORE Spearman correlation test   N=0        
RACE Kruskal-Wallis test   N=0        
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) 0.1-244.5 (median=41.4)
  censored N = 129
  death N = 4
     
  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) 31.99 (9.3)
  Significant markers N = 30
  pos. correlated 14
  neg. correlated 16
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
LDLR|3949 -0.3827 5.041e-06 0.029
ANAPC7|51434 0.3783 6.581e-06 0.029
LRRC41|10489 -0.375 8.025e-06 0.029
PNKP|11284 0.3738 8.638e-06 0.029
TXLNA|200081 -0.3738 8.64e-06 0.029
THAP8|199745 0.3659 1.373e-05 0.029
KIAA1191|57179 -0.3659 1.379e-05 0.029
NPTXR|23467 -0.3627 1.652e-05 0.029
ZSCAN21|7589 0.3606 1.87e-05 0.029
C14ORF169|79697 0.358 2.173e-05 0.029
Clinical variable #3: 'PATHOLOGIC_STAGE'

30 genes related to 'PATHOLOGIC_STAGE'.

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

PATHOLOGIC_STAGE Labels N
  IS 46
  STAGE I 18
  STAGE IA 26
  STAGE IB 11
  STAGE II 4
  STAGE IIA 6
  STAGE IIB 1
  STAGE IIC 1
  STAGE III 2
  STAGE IIIA 1
  STAGE IIIB 6
  STAGE IIIC 5
     
  Significant markers N = 30
List of top 10 genes differentially expressed by 'PATHOLOGIC_STAGE'

Table S5.  Get Full Table List of top 10 genes differentially expressed by 'PATHOLOGIC_STAGE'

kruskal_wallis_P Q
C1ORF125|126859 1.151e-06 0.022
CATSPERG|57828 7.344e-06 0.07
AGAP6|414189 1.679e-05 0.0897
CHMP2B|25978 4.088e-05 0.0897
ZNF565|147929 4.958e-05 0.0897
SYCP2|10388 6.552e-05 0.0897
MTCH1|23787 6.563e-05 0.0897
STRADA|92335 6.932e-05 0.0897
SH2D4B|387694 8.535e-05 0.0897
ARHGEF1|9138 8.79e-05 0.0897
Clinical variable #4: 'PATHOLOGY_T_STAGE'

30 genes related to 'PATHOLOGY_T_STAGE'.

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

PATHOLOGY_T_STAGE Mean (SD) 1.47 (0.58)
  N
  T1 76
  T2 51
  T3 6
     
  Significant markers N = 30
  pos. correlated 15
  neg. correlated 15
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
ZNF181|339318 -0.3862 4.395e-06 0.0412
NCRNA00219|114915 0.3736 9.43e-06 0.0412
ZNF333|84449 -0.3696 1.197e-05 0.0412
LOC388955|388955 0.3691 1.23e-05 0.0412
ZNF264|9422 -0.3633 1.722e-05 0.0412
POR|5447 0.3631 1.739e-05 0.0412
NAP1L5|266812 -0.363 1.75e-05 0.0412
C3ORF64|285203 -0.3595 2.139e-05 0.0412
CELF2|10659 -0.3594 2.152e-05 0.0412
FOXR1|283150 0.3763 2.471e-05 0.0412
Clinical variable #5: 'PATHOLOGY_N_STAGE'

No gene related to 'PATHOLOGY_N_STAGE'.

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

PATHOLOGY_N_STAGE Mean (SD) 0.25 (0.51)
  N
  N0 46
  N1 11
  N2 2
     
  Significant markers N = 0
Clinical variable #6: 'PATHOLOGY_M_STAGE'

No gene related to 'PATHOLOGY_M_STAGE'.

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

PATHOLOGY_M_STAGE Labels N
  class0 115
  class1 4
     
  Significant markers N = 0
Clinical variable #7: 'RADIATION_THERAPY'

30 genes related to 'RADIATION_THERAPY'.

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

RADIATION_THERAPY Labels N
  NO 111
  YES 21
     
  Significant markers N = 30
  Higher in YES 30
  Higher in NO 0
List of top 10 genes differentially expressed by 'RADIATION_THERAPY'

Table S11.  Get Full Table List of top 10 genes differentially expressed by 'RADIATION_THERAPY'

W(pos if higher in 'YES') wilcoxontestP Q AUC
GRIK2|2898 260 2.813e-08 0.000224 0.8843
HSD17B12|51144 275 3.075e-08 0.000224 0.882
COL21A1|81578 277 3.695e-08 0.000224 0.8801
AMOT|154796 287 4.696e-08 0.000224 0.8769
VLDLR|7436 296 6.428e-08 0.000245 0.873
CDH6|1004 302 7.911e-08 0.000251 0.8704
RHBDL3|162494 309 1.006e-07 0.000261 0.8674
DUSP8|1850 311.5 1.096e-07 0.000261 0.8664
CD38|952 2002 1.981e-07 0.00042 0.8589
KCNMB4|27345 335 2.42e-07 0.000452 0.8563
Clinical variable #8: 'KARNOFSKY_PERFORMANCE_SCORE'

No gene related to 'KARNOFSKY_PERFORMANCE_SCORE'.

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

KARNOFSKY_PERFORMANCE_SCORE Mean (SD) 95 (5.9)
  Score N
  80 5
  90 41
  100 56
     
  Significant markers N = 0
Clinical variable #9: 'RACE'

No gene related to 'RACE'.

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

RACE Labels N
  ASIAN 4
  BLACK OR AFRICAN AMERICAN 6
  WHITE 119
     
  Significant markers N = 0
Clinical variable #10: 'ETHNICITY'

No gene related to 'ETHNICITY'.

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

ETHNICITY Labels N
  HISPANIC OR LATINO 12
  NOT HISPANIC OR LATINO 111
     
  Significant markers N = 0
Methods & Data
Input
  • Expresson data file = TGCT-TP.uncv2.mRNAseq_RSEM_normalized_log2.txt

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

  • Number of patients = 134

  • Number of genes = 19071

  • Number of clinical features = 10

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)