Correlation between mRNAseq expression and clinical features
Thymoma (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/C1BK1BVR
Overview
Introduction

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

Summary

Testing the association between 18301 genes and 8 clinical features across 120 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 5 clinical features related to at least one genes.

  • 30 genes correlated to 'DAYS_TO_DEATH_OR_LAST_FUP'.

    • PHKA2|5256 ,  DHX38|9785 ,  SUV39H2|79723 ,  SMARCC1|6599 ,  HIST1H4I|8294 ,  ...

  • 30 genes correlated to 'YEARS_TO_BIRTH'.

    • MPO|4353 ,  NDOR1|27158 ,  IMPA2|3613 ,  METT11D1|64745 ,  SMU1|55234 ,  ...

  • 4 genes correlated to 'GENDER'.

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

  • 30 genes correlated to 'RADIATION_THERAPY'.

    • LOC100134259|100134259 ,  C1ORF170|84808 ,  CA4|762 ,  ZNF521|25925 ,  OLIG1|116448 ,  ...

  • 30 genes correlated to 'HISTOLOGICAL_TYPE'.

    • SLAMF1|6504 ,  LEF1|51176 ,  LOC400027|400027 ,  CYP2U1|113612 ,  GRAP2|9402 ,  ...

  • No genes correlated to 'TUMOR_TISSUE_SITE', '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=30   N=NA   N=NA
YEARS_TO_BIRTH Spearman correlation test N=30 older N=8 younger N=22
TUMOR_TISSUE_SITE Wilcoxon test   N=0        
GENDER Wilcoxon test N=4 male N=4 female N=0
RADIATION_THERAPY Wilcoxon test N=30 yes N=30 no N=0
HISTOLOGICAL_TYPE Kruskal-Wallis test N=30        
RACE Kruskal-Wallis test   N=0        
ETHNICITY Wilcoxon test   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) 0.5-150.4 (median=39.2)
  censored N = 111
  death N = 8
     
  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
PHKA2|5256 1.03e-06 0.0099 0.084
DHX38|9785 1.08e-06 0.0099 0.118
SUV39H2|79723 4.49e-06 0.023 0.118
SMARCC1|6599 4.94e-06 0.023 0.127
HIST1H4I|8294 6.44e-06 0.024 0.122
ACSL5|51703 1.23e-05 0.038 0.901
AGER|177 2.15e-05 0.056 0.149
C18ORF19|125228 2.69e-05 0.056 0.084
DHX16|8449 2.74e-05 0.056 0.229
IL15|3600 3.1e-05 0.057 0.844
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) 57.87 (13)
  Significant markers N = 30
  pos. correlated 8
  neg. correlated 22
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
MPO|4353 -0.5287 1.251e-09 2.29e-05
NDOR1|27158 -0.4536 2.21e-07 0.00202
IMPA2|3613 -0.433 8.709e-07 0.00227
METT11D1|64745 -0.4324 9.064e-07 0.00227
SMU1|55234 -0.4292 1.111e-06 0.00227
AMDHD1|144193 -0.4311 1.222e-06 0.00227
SNCAIP|9627 0.4257 1.392e-06 0.00227
TRIM14|9830 -0.4254 1.42e-06 0.00227
LINS1|55180 -0.4247 1.482e-06 0.00227
TOR2A|27433 -0.4234 1.607e-06 0.00227
Clinical variable #3: 'TUMOR_TISSUE_SITE'

No gene related to 'TUMOR_TISSUE_SITE'.

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

TUMOR_TISSUE_SITE Labels N
  ANTERIOR MEDIASTINUM 27
  THYMUS 93
     
  Significant markers N = 0
Clinical variable #4: 'GENDER'

4 genes related to 'GENDER'.

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

GENDER Labels N
  FEMALE 57
  MALE 63
     
  Significant markers N = 4
  Higher in MALE 4
  Higher in FEMALE 0
List of 4 genes differentially expressed by 'GENDER'

Table S7.  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 330 1.373e-14 2.79e-11 0.9081
CYORF15B|84663 1197 4.96e-11 6.05e-08 1
CYORF15A|246126 1071 3.129e-10 3.58e-07 1
HDHD1A|8226 609 4.585e-10 4.94e-07 0.8304
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 78
  YES 42
     
  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
LOC100134259|100134259 814 8.102e-06 0.0412 0.7483
C1ORF170|84808 838 1.088e-05 0.0412 0.7442
CA4|762 616 1.432e-05 0.0412 0.7589
ZNF521|25925 861 1.934e-05 0.0412 0.7372
OLIG1|116448 1670 1.976e-05 0.0412 0.7605
MYH7|4625 587 1.998e-05 0.0412 0.7569
SCN4A|6329 839 2.166e-05 0.0412 0.7376
NMUR1|10316 845 2.514e-05 0.0412 0.7358
PRUNE2|158471 872 2.533e-05 0.0412 0.7338
PMP22|5376 873 2.595e-05 0.0412 0.7335
Clinical variable #6: 'HISTOLOGICAL_TYPE'

30 genes related to 'HISTOLOGICAL_TYPE'.

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

HISTOLOGICAL_TYPE Labels N
  THYMOMA; TYPE A 17
  THYMOMA; TYPE AB 35
  THYMOMA; TYPE B1 15
  THYMOMA; TYPE B2 31
  THYMOMA; TYPE B3 11
  THYMOMA; TYPE C 11
     
  Significant markers N = 30
List of top 10 genes differentially expressed by 'HISTOLOGICAL_TYPE'

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

kruskal_wallis_P Q
SLAMF1|6504 8.428e-15 5.54e-11
LEF1|51176 8.917e-15 5.54e-11
LOC400027|400027 1.206e-14 5.54e-11
CYP2U1|113612 1.579e-14 5.54e-11
GRAP2|9402 1.684e-14 5.54e-11
MTA3|57504 1.817e-14 5.54e-11
SLA|6503 2.484e-14 5.81e-11
PAFAH2|5051 2.54e-14 5.81e-11
FBXL12|54850 3.202e-14 6.5e-11
IKZF1|10320 3.793e-14 6.5e-11
Clinical variable #7: 'RACE'

No gene related to 'RACE'.

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

RACE Labels N
  ASIAN 12
  BLACK OR AFRICAN AMERICAN 6
  WHITE 100
     
  Significant markers N = 0
Clinical variable #8: 'ETHNICITY'

No gene related to 'ETHNICITY'.

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

ETHNICITY Labels N
  HISPANIC OR LATINO 10
  NOT HISPANIC OR LATINO 96
     
  Significant markers N = 0
Methods & Data
Input
  • Expresson data file = THYM-TP.uncv2.mRNAseq_RSEM_normalized_log2.txt

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

  • Number of patients = 120

  • Number of genes = 18301

  • 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)