Correlation between mRNA expression and clinical features
Colon Adenocarcinoma (Primary solid tumor)
02 April 2015  |  analyses__2015_04_02
Maintainer Information
Citation Information
doi:10.7908/C1J38RJC
Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/Harvard Medical School)
Cite as Broad Institute TCGA Genome Data Analysis Center (2015): Correlation between mRNA expression and clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1J38RJC
Overview
Introduction

This pipeline uses various statistical tests to identify mRNAs whose log2 expression levels correlated to selected clinical features.

Summary

Testing the association between 17814 genes and 10 clinical features across 153 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 'YEARS_TO_BIRTH'.

    • ANAPC1 ,  PRSS12 ,  HSPH1 ,  HOXD8 ,  RIC8B ,  ...

  • 1 gene correlated to 'PATHOLOGY_T_STAGE'.

    • ALG8

  • 30 genes correlated to 'PATHOLOGY_N_STAGE'.

    • PPP1R14A ,  PDK4 ,  THEX1 ,  SCUBE2 ,  ENOSF1 ,  ...

  • 30 genes correlated to 'PATHOLOGY_M_STAGE'.

    • CYB5D1 ,  TK1 ,  GUF1 ,  C9ORF116 ,  ZSCAN5 ,  ...

  • 6 genes correlated to 'GENDER'.

    • JARID1D ,  CYORF15A ,  CYORF15B ,  UTX ,  JARID1C ,  ...

  • 30 genes correlated to 'HISTOLOGICAL_TYPE'.

    • AGR2 ,  SPDEF ,  ALOX5 ,  AQP3 ,  SERF2 ,  ...

  • 30 genes correlated to 'NUMBER_OF_LYMPH_NODES'.

    • PDK4 ,  PPP1R14A ,  THEX1 ,  CXCL3 ,  CXCL2 ,  ...

  • No genes correlated to 'DAYS_TO_DEATH_OR_LAST_FUP', 'NEOPLASM_DISEASESTAGE', and 'COMPLETENESS_OF_RESECTION'.

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=18 younger N=12
NEOPLASM_DISEASESTAGE Kruskal-Wallis test   N=0        
PATHOLOGY_T_STAGE Spearman correlation test N=1 higher stage N=0 lower stage N=1
PATHOLOGY_N_STAGE Spearman correlation test N=30 higher stage N=19 lower stage N=11
PATHOLOGY_M_STAGE Wilcoxon test N=30 class1 N=30 class0 N=0
GENDER Wilcoxon test N=6 male N=6 female N=0
HISTOLOGICAL_TYPE Wilcoxon test N=30 colon mucinous adenocarcinoma N=30 colon adenocarcinoma N=0
COMPLETENESS_OF_RESECTION Kruskal-Wallis test   N=0        
NUMBER_OF_LYMPH_NODES Spearman correlation test N=30 higher number_of_lymph_nodes N=18 lower number_of_lymph_nodes N=12
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) 0-54 (median=23)
  censored N = 122
  death N = 30
     
  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) 70.78 (12)
  Significant markers N = 30
  pos. correlated 18
  neg. correlated 12
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
ANAPC1 -0.388 7.19e-07 0.0128
PRSS12 0.3583 5.428e-06 0.0483
HSPH1 -0.3504 8.966e-06 0.0532
HOXD8 0.3307 2.977e-05 0.119
RIC8B 0.3288 3.329e-05 0.119
SLC7A6 -0.3256 4.005e-05 0.119
DDX27 -0.3205 5.357e-05 0.136
FLJ22222 0.3133 8.033e-05 0.169
KIAA1729 -0.3118 8.718e-05 0.169
CEACAM20 0.3089 0.0001022 0.169
Clinical variable #3: 'NEOPLASM_DISEASESTAGE'

No gene related to 'NEOPLASM_DISEASESTAGE'.

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

NEOPLASM_DISEASESTAGE Labels N
  STAGE I 29
  STAGE II 12
  STAGE IIA 45
  STAGE IIB 5
  STAGE III 8
  STAGE IIIA 3
  STAGE IIIB 12
  STAGE IIIC 16
  STAGE IV 21
  STAGE IVA 1
     
  Significant markers N = 0
Clinical variable #4: 'PATHOLOGY_T_STAGE'

One gene related to 'PATHOLOGY_T_STAGE'.

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

PATHOLOGY_T_STAGE Mean (SD) 2.84 (0.62)
  N
  T1 4
  T2 31
  T3 103
  T4 15
     
  Significant markers N = 1
  pos. correlated 0
  neg. correlated 1
List of one gene differentially expressed by 'PATHOLOGY_T_STAGE'

Table S6.  Get Full Table List of one gene significantly correlated to 'PATHOLOGY_T_STAGE' by Spearman correlation test

SpearmanCorr corrP Q
ALG8 -0.3416 1.547e-05 0.276
Clinical variable #5: 'PATHOLOGY_N_STAGE'

30 genes related to 'PATHOLOGY_N_STAGE'.

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

PATHOLOGY_N_STAGE Mean (SD) 0.59 (0.81)
  N
  N0 94
  N1 28
  N2 31
     
  Significant markers N = 30
  pos. correlated 19
  neg. correlated 11
List of top 10 genes differentially expressed by 'PATHOLOGY_N_STAGE'

Table S8.  Get Full Table List of top 10 genes significantly correlated to 'PATHOLOGY_N_STAGE' by Spearman correlation test

SpearmanCorr corrP Q
PPP1R14A 0.3353 2.273e-05 0.206
PDK4 0.3322 2.719e-05 0.206
THEX1 -0.3239 4.411e-05 0.206
SCUBE2 0.3176 6.336e-05 0.206
ENOSF1 -0.3131 8.107e-05 0.206
RGS5 0.3076 0.0001099 0.206
KIAA1826 0.3064 0.0001172 0.206
FLJ22222 -0.3043 0.0001311 0.206
CXCL3 -0.302 0.0001486 0.206
C20ORF195 0.3018 0.0001497 0.206
Clinical variable #6: 'PATHOLOGY_M_STAGE'

30 genes related to 'PATHOLOGY_M_STAGE'.

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

PATHOLOGY_M_STAGE Labels N
  class0 129
  class1 22
     
  Significant markers N = 30
  Higher in class1 30
  Higher in class0 0
List of top 10 genes differentially expressed by 'PATHOLOGY_M_STAGE'

Table S10.  Get Full Table List of top 10 genes differentially expressed by 'PATHOLOGY_M_STAGE'

W(pos if higher in 'class1') wilcoxontestP Q AUC
CYB5D1 593 1.337e-05 0.238 0.7911
TK1 649 4.938e-05 0.252 0.7713
GUF1 689 0.0001193 0.252 0.7572
C9ORF116 695 0.0001357 0.252 0.7551
ZSCAN5 699.5 0.0001493 0.252 0.7535
DEDD 2137 0.0001541 0.252 0.753
LAP3 701 0.0001541 0.252 0.753
NPAS2 2132 0.0001713 0.252 0.7512
COBLL1 2130 0.0001787 0.252 0.7505
TEKT4 2128 0.0001864 0.252 0.7498
Clinical variable #7: 'GENDER'

6 genes related to 'GENDER'.

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

GENDER Labels N
  FEMALE 75
  MALE 78
     
  Significant markers N = 6
  Higher in MALE 6
  Higher in FEMALE 0
List of 6 genes differentially expressed by 'GENDER'

Table S12.  Get Full Table List of 6 genes differentially expressed by 'GENDER'. 24 significant gene(s) located in sex chromosomes is(are) filtered out.

W(pos if higher in 'MALE') wilcoxontestP Q AUC
JARID1D 5721 1.93e-24 3.44e-20 0.9779
CYORF15A 5589 2.456e-22 7.29e-19 0.9554
CYORF15B 5535 1.67e-21 3.31e-18 0.9462
UTX 1479 1.323e-07 0.000139 0.7472
JARID1C 1574.5 8.348e-07 0.000826 0.7309
DDX43 4064 3.251e-05 0.02 0.6947
Clinical variable #8: 'HISTOLOGICAL_TYPE'

30 genes related to 'HISTOLOGICAL_TYPE'.

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

HISTOLOGICAL_TYPE Labels N
  COLON ADENOCARCINOMA 129
  COLON MUCINOUS ADENOCARCINOMA 22
     
  Significant markers N = 30
  Higher in COLON MUCINOUS ADENOCARCINOMA 30
  Higher in COLON ADENOCARCINOMA 0
List of top 10 genes differentially expressed by 'HISTOLOGICAL_TYPE'

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

W(pos if higher in 'COLON MUCINOUS ADENOCARCINOMA') wilcoxontestP Q AUC
AGR2 2468 3.201e-08 0.000517 0.8696
SPDEF 2448 5.809e-08 0.000517 0.8626
ALOX5 2411 1.7e-07 0.000802 0.8495
AQP3 2409 1.8e-07 0.000802 0.8488
SERF2 2398 2.458e-07 0.000876 0.845
CREB3L1 2388.5 3.208e-07 0.00094 0.8416
C20ORF177 458 4.064e-07 0.00094 0.8386
SLC19A3 464 4.796e-07 0.00094 0.8365
PTGER2 2370 5.353e-07 0.00094 0.8351
SLC39A9 2361 6.844e-07 0.00094 0.8319
Clinical variable #9: 'COMPLETENESS_OF_RESECTION'

No gene related to 'COMPLETENESS_OF_RESECTION'.

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

COMPLETENESS_OF_RESECTION Labels N
  R0 128
  R1 1
  R2 19
     
  Significant markers N = 0
Clinical variable #10: 'NUMBER_OF_LYMPH_NODES'

30 genes related to 'NUMBER_OF_LYMPH_NODES'.

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

NUMBER_OF_LYMPH_NODES Mean (SD) 2.14 (4.5)
  Significant markers N = 30
  pos. correlated 18
  neg. correlated 12
List of top 10 genes differentially expressed by 'NUMBER_OF_LYMPH_NODES'

Table S17.  Get Full Table List of top 10 genes significantly correlated to 'NUMBER_OF_LYMPH_NODES' by Spearman correlation test

SpearmanCorr corrP Q
PDK4 0.3417 1.648e-05 0.153
PPP1R14A 0.341 1.718e-05 0.153
THEX1 -0.326 4.156e-05 0.161
CXCL3 -0.321 5.537e-05 0.161
CXCL2 -0.314 8.167e-05 0.161
SCUBE2 0.3113 9.478e-05 0.161
NFKBIZ -0.309 0.0001076 0.161
FLJ22222 -0.3089 0.0001077 0.161
KIAA1826 0.3084 0.0001108 0.161
AQP1 0.3073 0.0001177 0.161
Methods & Data
Input

  • Expresson data file = COAD-TP.medianexp.txt

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

  • Number of patients = 153

  • Number of genes = 17814

  • Number of clinical features = 10

Selected clinical features

  • For clinical features selected for this analysis and their value conozzle.versions, please find a documentation on selected CDEs .

  • 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, Wald's test in univariate Cox regression analysis with proportional hazards model (Andersen and Gill 1982) was used to estimate the P values using the 'coxph' function in R. Kaplan-Meier survival curves were plot using the four quartile subgroups of patients based on expression levels

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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