Correlation between mRNAseq expression and clinical features
Pancreatic Adenocarcinoma (Primary solid tumor)
28 January 2016  |  analyses__2016_01_28
Maintainer Information
Citation Information
doi:10.7908/C19P313N
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/C19P313N
Overview
Introduction

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

Summary

Testing the association between 18475 genes and 15 clinical features across 178 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 7 clinical features related to at least one genes.

  • 30 genes correlated to 'DAYS_TO_DEATH_OR_LAST_FUP'.

    • TRIM67|440730 ,  ARNTL2|56938 ,  MET|4233 ,  THSD1P1|374500 ,  ZNF488|118738 ,  ...

  • 30 genes correlated to 'YEARS_TO_BIRTH'.

    • ABI2|10152 ,  MOSC1|64757 ,  MRPL1|65008 ,  UQCRC2|7385 ,  PSMB8|5696 ,  ...

  • 30 genes correlated to 'PATHOLOGIC_STAGE'.

    • TMEM17|200728 ,  METTL10|399818 ,  C10ORF88|80007 ,  HAR1B|768097 ,  NUDT9|53343 ,  ...

  • 30 genes correlated to 'PATHOLOGY_T_STAGE'.

    • STK33|65975 ,  CHST10|9486 ,  DUSP15|128853 ,  ABCA11P|79963 ,  CDK20|23552 ,  ...

  • 5 genes correlated to 'GENDER'.

    • NCRNA00183|554203 ,  HDHD1A|8226 ,  CYORF15B|84663 ,  CA5BP|340591 ,  CYORF15A|246126

  • 30 genes correlated to 'HISTOLOGICAL_TYPE'.

    • EIF2S1|1965 ,  SDHD|6392 ,  CHST11|50515 ,  SEZ6L2|26470 ,  GPR132|29933 ,  ...

  • 1 gene correlated to 'NUMBER_PACK_YEARS_SMOKED'.

    • HIST2H2AB|317772

  • No genes correlated to 'PATHOLOGY_N_STAGE', 'PATHOLOGY_M_STAGE', 'RADIATION_THERAPY', 'YEAR_OF_TOBACCO_SMOKING_ONSET', 'RESIDUAL_TUMOR', 'NUMBER_OF_LYMPH_NODES', '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=17 younger N=13
PATHOLOGIC_STAGE Kruskal-Wallis test N=30        
PATHOLOGY_T_STAGE Spearman correlation test N=30 higher stage N=0 lower stage N=30
PATHOLOGY_N_STAGE Wilcoxon test   N=0        
PATHOLOGY_M_STAGE Wilcoxon test   N=0        
GENDER Wilcoxon test N=5 male N=5 female N=0
RADIATION_THERAPY Wilcoxon test   N=0        
HISTOLOGICAL_TYPE Kruskal-Wallis test N=30        
NUMBER_PACK_YEARS_SMOKED Spearman correlation test N=1 higher number_pack_years_smoked N=1 lower number_pack_years_smoked N=0
YEAR_OF_TOBACCO_SMOKING_ONSET Spearman correlation test   N=0        
RESIDUAL_TUMOR Kruskal-Wallis test   N=0        
NUMBER_OF_LYMPH_NODES 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'

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) 0.1-90.1 (median=15.3)
  censored N = 84
  death N = 93
     
  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
TRIM67|440730 3.73e-10 6.9e-06 0.339
ARNTL2|56938 8.76e-08 0.00081 0.669
MET|4233 1.02e-06 0.0063 0.674
THSD1P1|374500 2.22e-06 0.0083 0.354
ZNF488|118738 2.25e-06 0.0083 0.643
BACH1|571 7.14e-06 0.016 0.577
PTPRT|11122 8e-06 0.016 0.363
MICAL1|64780 8.08e-06 0.016 0.347
USP20|10868 8.83e-06 0.016 0.348
EPS8|2059 9.42e-06 0.016 0.665
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) 64.58 (11)
  Significant markers N = 30
  pos. correlated 17
  neg. correlated 13
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
ABI2|10152 -0.3812 1.526e-07 0.00156
MOSC1|64757 0.3799 1.692e-07 0.00156
MRPL1|65008 0.3354 4.736e-06 0.0292
UQCRC2|7385 0.3225 1.135e-05 0.0474
PSMB8|5696 0.3195 1.377e-05 0.0474
CHAF1A|10036 0.3172 1.606e-05 0.0474
RRS1|23212 0.3139 1.978e-05 0.0474
BCORL1|63035 -0.3134 2.053e-05 0.0474
NAB2|4665 -0.3104 2.48e-05 0.0498
CACNG4|27092 -0.3071 3.046e-05 0.0498
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 1
  STAGE IA 5
  STAGE IB 15
  STAGE IIA 28
  STAGE IIB 119
  STAGE III 4
  STAGE IV 4
     
  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
TMEM17|200728 8.326e-05 0.265
METTL10|399818 8.366e-05 0.265
C10ORF88|80007 8.392e-05 0.265
HAR1B|768097 8.903e-05 0.265
NUDT9|53343 0.0001063 0.265
CHST10|9486 0.0001303 0.265
ZG16B|124220 0.0001656 0.265
FGFBP3|143282 0.0001793 0.265
MRFAP1L1|114932 0.0001923 0.265
SLC25A17|10478 0.0002143 0.265
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) 2.8 (0.52)
  N
  T1 7
  T2 24
  T3 142
  T4 3
     
  Significant markers N = 30
  pos. correlated 0
  neg. correlated 30
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
STK33|65975 -0.3573 1.206e-06 0.0223
CHST10|9486 -0.3253 1.054e-05 0.0353
DUSP15|128853 -0.3246 1.107e-05 0.0353
ABCA11P|79963 -0.3233 1.209e-05 0.0353
CDK20|23552 -0.3222 1.296e-05 0.0353
TMEM232|642987 -0.3222 1.369e-05 0.0353
TRAM1L1|133022 -0.3187 1.631e-05 0.0353
GPR27|2850 -0.3227 2.11e-05 0.0353
BEX1|55859 -0.3142 2.172e-05 0.0353
EFCAB2|84288 -0.3134 2.288e-05 0.0353
Clinical variable #5: 'PATHOLOGY_N_STAGE'

No gene related to 'PATHOLOGY_N_STAGE'.

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

PATHOLOGY_N_STAGE Labels N
  N0 49
  N1 124
     
  Significant markers N = 0
Clinical variable #6: 'PATHOLOGY_M_STAGE'

No gene related to 'PATHOLOGY_M_STAGE'.

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

PATHOLOGY_M_STAGE Labels N
  class0 80
  class1 4
     
  Significant markers N = 0
Clinical variable #7: 'GENDER'

5 genes related to 'GENDER'.

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

GENDER Labels N
  FEMALE 80
  MALE 98
     
  Significant markers N = 5
  Higher in MALE 5
  Higher in FEMALE 0
List of 5 genes differentially expressed by 'GENDER'

Table S12.  Get Full Table List of 5 genes differentially expressed by 'GENDER'. 25 significant gene(s) located in sex chromosomes is(are) filtered out.

W(pos if higher in 'MALE') wilcoxontestP Q AUC
NCRNA00183|554203 1235 4.16e-15 6.41e-12 0.8425
HDHD1A|8226 1612 1.503e-11 1.63e-08 0.7944
CYORF15B|84663 1660 7.377e-11 6.49e-08 0.9964
CA5BP|340591 1936 6.626e-09 4.71e-06 0.7531
CYORF15A|246126 1254 1.571e-08 1.07e-05 0.9843
Clinical variable #8: 'RADIATION_THERAPY'

No gene related to 'RADIATION_THERAPY'.

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

RADIATION_THERAPY Labels N
  NO 119
  YES 44
     
  Significant markers N = 0
Clinical variable #9: 'HISTOLOGICAL_TYPE'

30 genes related to 'HISTOLOGICAL_TYPE'.

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

HISTOLOGICAL_TYPE Labels N
  PANCREAS-ADENOCARCINOMA DUCTAL TYPE 147
  PANCREAS-ADENOCARCINOMA-OTHER SUBTYPE 25
  PANCREAS-COLLOID (MUCINOUS NON-CYSTIC) CARCINOMA 4
  PANCREAS-UNDIFFERENTIATED CARCINOMA 1
     
  Significant markers N = 30
List of top 10 genes differentially expressed by 'HISTOLOGICAL_TYPE'

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

kruskal_wallis_P Q
EIF2S1|1965 2.431e-06 0.0191
SDHD|6392 4.599e-06 0.0191
CHST11|50515 7.973e-06 0.0191
SEZ6L2|26470 8.906e-06 0.0191
GPR132|29933 9.215e-06 0.0191
DNAH6|1768 9.58e-06 0.0191
EPHA10|284656 1.089e-05 0.0191
SLC38A5|92745 1.167e-05 0.0191
C9ORF7|11094 1.232e-05 0.0191
WNT11|7481 1.247e-05 0.0191
Clinical variable #10: 'NUMBER_PACK_YEARS_SMOKED'

One gene related to 'NUMBER_PACK_YEARS_SMOKED'.

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

NUMBER_PACK_YEARS_SMOKED Mean (SD) 26.84 (18)
  Significant markers N = 1
  pos. correlated 1
  neg. correlated 0
List of one gene differentially expressed by 'NUMBER_PACK_YEARS_SMOKED'

Table S17.  Get Full Table List of one gene significantly correlated to 'NUMBER_PACK_YEARS_SMOKED' by Spearman correlation test

SpearmanCorr corrP Q
HIST2H2AB|317772 0.7953 1.51e-07 0.00279
Clinical variable #11: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

No gene related to 'YEAR_OF_TOBACCO_SMOKING_ONSET'.

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

YEAR_OF_TOBACCO_SMOKING_ONSET Mean (SD) 1970.74 (13)
  Significant markers N = 0
Clinical variable #12: 'RESIDUAL_TUMOR'

No gene related to 'RESIDUAL_TUMOR'.

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

RESIDUAL_TUMOR Labels N
  R0 106
  R1 53
  R2 5
  RX 4
     
  Significant markers N = 0
Clinical variable #13: 'NUMBER_OF_LYMPH_NODES'

No gene related to 'NUMBER_OF_LYMPH_NODES'.

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

NUMBER_OF_LYMPH_NODES Mean (SD) 2.97 (3.4)
  Significant markers N = 0
Clinical variable #14: 'RACE'

No gene related to 'RACE'.

Table S21.  Basic characteristics of clinical feature: 'RACE'

RACE Labels N
  ASIAN 11
  BLACK OR AFRICAN AMERICAN 6
  WHITE 157
     
  Significant markers N = 0
Clinical variable #15: 'ETHNICITY'

No gene related to 'ETHNICITY'.

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

ETHNICITY Labels N
  HISPANIC OR LATINO 5
  NOT HISPANIC OR LATINO 131
     
  Significant markers N = 0
Methods & Data
Input

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

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

  • Number of patients = 178

  • Number of genes = 18475

  • Number of clinical features = 15

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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