Correlation between mRNAseq expression and clinical features
Brain Lower Grade Glioma (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/C1WW7H2V
Overview
Introduction

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

Summary

Testing the association between 18334 genes and 8 clinical features across 515 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 'DAYS_TO_DEATH_OR_LAST_FUP'.

    • ABI1|10006 ,  ACTR1A|10121 ,  ARL3|403 ,  ASB13|79754 ,  B3GALNT2|148789 ,  ...

  • 30 genes correlated to 'YEARS_TO_BIRTH'.

    • SYT6|148281 ,  ABI1|10006 ,  PRSS35|167681 ,  CTBP2|1488 ,  CNTN3|5067 ,  ...

  • 4 genes correlated to 'GENDER'.

    • NCRNA00183|554203 ,  CYORF15A|246126 ,  HDHD1A|8226 ,  CYORF15B|84663

  • 30 genes correlated to 'RADIATION_THERAPY'.

    • STK40|83931 ,  TUB|7275 ,  C8ORF51|78998 ,  FAM155A|728215 ,  TGIF1|7050 ,  ...

  • 30 genes correlated to 'KARNOFSKY_PERFORMANCE_SCORE'.

    • ZBTB47|92999 ,  TTC15|51112 ,  MTSS1L|92154 ,  EEF1A1|1915 ,  RPL3|6122 ,  ...

  • 30 genes correlated to 'HISTOLOGICAL_TYPE'.

    • AK2|204 ,  TXNDC12|51060 ,  TRAPPC3|27095 ,  STK40|83931 ,  HDAC1|3065 ,  ...

  • 30 genes correlated to 'RACE'.

    • ARMC10|83787 ,  C21ORF56|84221 ,  ULK4|54986 ,  KIAA1908|114796 ,  PPIL3|53938 ,  ...

  • 30 genes correlated to 'ETHNICITY'.

    • MUC20|200958 ,  DKFZP434J0226|93429 ,  LOC90834|90834 ,  GK5|256356 ,  GOLGA6L10|647042 ,  ...

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   N=NA   N=NA
YEARS_TO_BIRTH Spearman correlation test N=30 older N=10 younger N=20
GENDER Wilcoxon test N=4 male N=4 female N=0
RADIATION_THERAPY Wilcoxon test N=30 yes N=30 no N=0
KARNOFSKY_PERFORMANCE_SCORE Spearman correlation test N=30 higher score N=20 lower score N=10
HISTOLOGICAL_TYPE Kruskal-Wallis test N=30        
RACE Kruskal-Wallis test N=30        
ETHNICITY Wilcoxon test N=30 not hispanic or latino N=30 hispanic or latino 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) 0-211.2 (median=22.3)
  censored N = 388
  death N = 126
     
  Significant markers N = 30
  associated with shorter survival NA
  associated with longer survival NA
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. 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
ABI1|10006 0 0 0.23
ACTR1A|10121 0 0 0.285
ARL3|403 0 0 0.206
ASB13|79754 0 0 0.229
B3GALNT2|148789 0 0 0.744
C10ORF76|79591 0 0 0.315
C13ORF18|80183 0 0 0.694
C20ORF72|92667 0 0 0.769
CCDC46|201134 0 0 0.784
CD101|9398 0 0 0.793
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) 42.93 (13)
  Significant markers N = 30
  pos. correlated 10
  neg. correlated 20
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
SYT6|148281 -0.4443 2.792e-26 5.12e-22
ABI1|10006 -0.4021 2.146e-21 1.97e-17
PRSS35|167681 -0.3999 3.676e-21 2.25e-17
CTBP2|1488 -0.3816 2.93e-19 1.14e-15
CNTN3|5067 -0.3816 3.121e-19 1.14e-15
NOL3|8996 0.3787 5.632e-19 1.72e-15
TCTA|6988 0.3779 6.738e-19 1.76e-15
IL17RC|84818 0.3743 1.528e-18 3.5e-15
GLUD1|2746 -0.3738 1.729e-18 3.52e-15
GLUD2|2747 -0.372 2.556e-18 4.69e-15
Clinical variable #3: 'GENDER'

4 genes related to 'GENDER'.

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

GENDER Labels N
  FEMALE 230
  MALE 285
     
  Significant markers N = 4
  Higher in MALE 4
  Higher in FEMALE 0
List of 4 genes differentially expressed by 'GENDER'

Table S6.  Get Full Table List of 4 genes differentially expressed by 'GENDER'. 26 significant gene(s) located in sex chromosomes is(are) filtered out.

W(pos if higher in 'MALE') wilcoxontestP Q AUC
NCRNA00183|554203 7144 1.281e-52 2.61e-49 0.891
CYORF15A|246126 14808 2.36e-30 2.55e-27 0.9992
HDHD1A|8226 13708 6.873e-30 7e-27 0.7909
CYORF15B|84663 11101 5.372e-24 4.92e-21 0.9987
Clinical variable #4: 'RADIATION_THERAPY'

30 genes related to 'RADIATION_THERAPY'.

Table S7.  Basic characteristics of clinical feature: 'RADIATION_THERAPY'

RADIATION_THERAPY Labels N
  NO 186
  YES 296
     
  Significant markers N = 30
  Higher in YES 30
  Higher in NO 0
List of top 10 genes differentially expressed by 'RADIATION_THERAPY'

Table S8.  Get Full Table List of top 10 genes differentially expressed by 'RADIATION_THERAPY'

W(pos if higher in 'YES') wilcoxontestP Q AUC
STK40|83931 38554 1.297e-13 2.38e-09 0.7003
TUB|7275 16757 4.648e-13 4.26e-09 0.6956
C8ORF51|78998 38065 8.068e-13 4.77e-09 0.6937
FAM155A|728215 16957 1.24e-12 4.77e-09 0.692
TGIF1|7050 38089 1.302e-12 4.77e-09 0.6918
ANG|283 37781.5 2.331e-12 7.12e-09 0.6899
GABBR1|2550 17285 5.964e-12 1.28e-08 0.686
OSR2|116039 25262 6.28e-12 1.28e-08 0.7095
DDOST|1650 37760 6.281e-12 1.28e-08 0.6858
IFNGR2|3460 37705 8.134e-12 1.49e-08 0.6848
Clinical variable #5: 'KARNOFSKY_PERFORMANCE_SCORE'

30 genes related to 'KARNOFSKY_PERFORMANCE_SCORE'.

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

KARNOFSKY_PERFORMANCE_SCORE Mean (SD) 86.64 (13)
  Significant markers N = 30
  pos. correlated 20
  neg. correlated 10
List of top 10 genes differentially expressed by 'KARNOFSKY_PERFORMANCE_SCORE'

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

SpearmanCorr corrP Q
ZBTB47|92999 0.3548 1.544e-10 2.83e-06
TTC15|51112 0.3095 3.075e-08 0.000282
MTSS1L|92154 0.3012 7.386e-08 0.000449
EEF1A1|1915 0.2985 9.787e-08 0.000449
RPL3|6122 0.2936 1.617e-07 0.000582
RPS6|6194 0.2916 1.981e-07 0.000582
EEF2|1938 0.2903 2.25e-07 0.000582
C7ORF42|55069 -0.2874 3.008e-07 0.000582
TTC26|79989 -0.2871 3.079e-07 0.000582
UBXN1|51035 0.2868 3.172e-07 0.000582
Clinical variable #6: 'HISTOLOGICAL_TYPE'

30 genes related to 'HISTOLOGICAL_TYPE'.

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

HISTOLOGICAL_TYPE Labels N
  ASTROCYTOMA 194
  OLIGOASTROCYTOMA 130
  OLIGODENDROGLIOMA 191
     
  Significant markers N = 30
List of top 10 genes differentially expressed by 'HISTOLOGICAL_TYPE'

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

kruskal_wallis_P Q
AK2|204 1.628e-39 2.99e-35
TXNDC12|51060 1.604e-36 1.47e-32
TRAPPC3|27095 2.737e-36 1.51e-32
STK40|83931 3.297e-36 1.51e-32
HDAC1|3065 6.53e-36 2.39e-32
TXLNA|200081 2.82e-35 8.62e-32
WDR77|79084 3.353e-35 8.78e-32
FAM155A|728215 5.307e-35 1.09e-31
ASAP3|55616 5.346e-35 1.09e-31
CHGB|1114 1.061e-34 1.94e-31
Clinical variable #7: 'RACE'

30 genes related to 'RACE'.

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

RACE Labels N
  AMERICAN INDIAN OR ALASKA NATIVE 1
  ASIAN 8
  BLACK OR AFRICAN AMERICAN 21
  WHITE 475
     
  Significant markers N = 30
List of top 10 genes differentially expressed by 'RACE'

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

kruskal_wallis_P Q
ARMC10|83787 2.429e-07 0.00445
C21ORF56|84221 7.42e-07 0.0068
ULK4|54986 3.918e-06 0.0238
KIAA1908|114796 5.184e-06 0.0238
PPIL3|53938 8.098e-06 0.0289
SEC1|653677 9.456e-06 0.0289
POLR2J2|246721 2.673e-05 0.07
SERHL|94009 4.235e-05 0.094
DHRS4L2|317749 5.125e-05 0.094
LRRIQ3|127255 5.154e-05 0.094
Clinical variable #8: 'ETHNICITY'

30 genes related to 'ETHNICITY'.

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

ETHNICITY Labels N
  HISPANIC OR LATINO 32
  NOT HISPANIC OR LATINO 449
     
  Significant markers N = 30
  Higher in NOT HISPANIC OR LATINO 30
  Higher in HISPANIC OR LATINO 0
Methods & Data
Input
  • Expresson data file = LGG-TP.uncv2.mRNAseq_RSEM_normalized_log2.txt

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

  • Number of patients = 515

  • Number of genes = 18334

  • Number of clinical features = 8

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)