Correlation between gene methylation status and clinical features
Prostate Adenocarcinoma (Primary solid tumor)
16 April 2014  |  analyses__2014_04_16
Maintainer Information
Citation Information
doi:10.7908/C1DB80H0
Maintained by Juok Cho (Broad Institute)
Cite as Broad Institute TCGA Genome Data Analysis Center (2014): Correlation between gene methylation status and clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1DB80H0
Overview
Introduction

This pipeline uses various statistical tests to identify genes whose promoter methylation levels correlated to selected clinical features.

Summary

Testing the association between 20049 genes and 13 clinical features across 206 samples, statistically thresholded by Q value < 0.05, 9 clinical features related to at least one genes.

  • 9 genes correlated to 'AGE'.

    • NOVA1 ,  FAM7A1 ,  FAM7A2 ,  AVPR1A ,  PYDC1 ,  ...

  • 8 genes correlated to 'PATHOLOGY.T.STAGE'.

    • TACC2 ,  KBTBD11 ,  EMX1 ,  NPAS4 ,  SYTL1 ,  ...

  • 14 genes correlated to 'PATHOLOGY.N.STAGE'.

    • DYRK2 ,  DLEU2__3 ,  RRM2 ,  KCNJ2 ,  PDE3B ,  ...

  • 3 genes correlated to 'COMPLETENESS.OF.RESECTION'.

    • CNO ,  CTNNA3__1 ,  LRRTM3__1

  • 18 genes correlated to 'GLEASON_SCORE_COMBINED'.

    • LIX1L ,  FAM13C ,  PKD1L3 ,  OR51A7 ,  RRM2 ,  ...

  • 76 genes correlated to 'GLEASON_SCORE_PRIMARY'.

    • RRM2 ,  PGLYRP4 ,  TP53TG3B ,  C21ORF15 ,  C9ORF109 ,  ...

  • 19 genes correlated to 'GLEASON_SCORE'.

    • FAM13C ,  PKD1L3 ,  LIX1L ,  DLEU2__3 ,  RRM2 ,  ...

  • 13 genes correlated to 'PSA_RESULT_PREOP'.

    • FCRL2 ,  FCRL3 ,  FCRL4 ,  PGLYRP4 ,  S100A7 ,  ...

  • 505 genes correlated to 'DAYS_TO_PREOP_PSA'.

    • TRIO ,  THEG ,  CLCA4 ,  KIAA1211 ,  ZBTB5 ,  ...

  • No genes correlated to 'NUMBER.OF.LYMPH.NODES', 'GLEASON_SCORE_SECONDARY', 'PSA_VALUE', and 'DAYS_TO_PSA'.

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 Q value < 0.05.

Clinical feature Statistical test Significant genes Associated with                 Associated with
AGE Spearman correlation test N=9 older N=9 younger N=0
PATHOLOGY T STAGE Spearman correlation test N=8 higher stage N=8 lower stage N=0
PATHOLOGY N STAGE t test N=14 class1 N=0 class0 N=14
COMPLETENESS OF RESECTION ANOVA test N=3        
NUMBER OF LYMPH NODES Spearman correlation test   N=0        
GLEASON_SCORE_COMBINED Spearman correlation test N=18 higher score N=12 lower score N=6
GLEASON_SCORE_PRIMARY Spearman correlation test N=76 higher score N=28 lower score N=48
GLEASON_SCORE_SECONDARY Spearman correlation test   N=0        
GLEASON_SCORE Spearman correlation test N=19 higher score N=5 lower score N=14
PSA_RESULT_PREOP Spearman correlation test N=13 higher psa_result_preop N=2 lower psa_result_preop N=11
DAYS_TO_PREOP_PSA Spearman correlation test N=505 higher days_to_preop_psa N=236 lower days_to_preop_psa N=269
PSA_VALUE Spearman correlation test   N=0        
DAYS_TO_PSA Spearman correlation test   N=0        
Clinical variable #1: 'AGE'

9 genes related to 'AGE'.

Table S1.  Basic characteristics of clinical feature: 'AGE'

AGE Mean (SD) 60.28 (6.9)
  Significant markers N = 9
  pos. correlated 9
  neg. correlated 0
List of 9 genes significantly correlated to 'AGE' by Spearman correlation test

Table S2.  Get Full Table List of 9 genes significantly correlated to 'AGE' by Spearman correlation test

SpearmanCorr corrP Q
NOVA1 0.35 2.871e-07 0.00576
FAM7A1 0.344 4.711e-07 0.00944
FAM7A2 0.344 4.711e-07 0.00944
AVPR1A 0.3426 5.292e-07 0.0106
PYDC1 0.3359 9.02e-07 0.0181
TRIM72 0.3359 9.02e-07 0.0181
C17ORF104 0.3341 1.043e-06 0.0209
NRN1 0.3315 1.275e-06 0.0255
TEKT3 0.3237 2.335e-06 0.0468

Figure S1.  Get High-res Image As an example, this figure shows the association of NOVA1 to 'AGE'. P value = 2.87e-07 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #2: 'PATHOLOGY.T.STAGE'

8 genes related to 'PATHOLOGY.T.STAGE'.

Table S3.  Basic characteristics of clinical feature: 'PATHOLOGY.T.STAGE'

PATHOLOGY.T.STAGE Mean (SD) 2.57 (0.54)
  N
  2 92
  3 107
  4 5
     
  Significant markers N = 8
  pos. correlated 8
  neg. correlated 0
List of 8 genes significantly correlated to 'PATHOLOGY.T.STAGE' by Spearman correlation test

Table S4.  Get Full Table List of 8 genes significantly correlated to 'PATHOLOGY.T.STAGE' by Spearman correlation test

SpearmanCorr corrP Q
TACC2 0.3678 6.233e-08 0.00125
KBTBD11 0.3559 1.747e-07 0.0035
EMX1 0.342 5.535e-07 0.0111
NPAS4 0.3329 1.146e-06 0.023
SYTL1 0.3323 1.201e-06 0.0241
ABCC10 0.3318 1.243e-06 0.0249
TRPM6 0.3281 1.656e-06 0.0332
CLIC1 0.3259 1.968e-06 0.0394

Figure S2.  Get High-res Image As an example, this figure shows the association of TACC2 to 'PATHOLOGY.T.STAGE'. P value = 6.23e-08 with Spearman correlation analysis.

Clinical variable #3: 'PATHOLOGY.N.STAGE'

14 genes related to 'PATHOLOGY.N.STAGE'.

Table S5.  Basic characteristics of clinical feature: 'PATHOLOGY.N.STAGE'

PATHOLOGY.N.STAGE Labels N
  class0 156
  class1 17
     
  Significant markers N = 14
  Higher in class1 0
  Higher in class0 14
List of top 10 genes differentially expressed by 'PATHOLOGY.N.STAGE'

Table S6.  Get Full Table List of top 10 genes differentially expressed by 'PATHOLOGY.N.STAGE'

T(pos if higher in 'class1') ttestP Q AUC
DYRK2 -7.37 1.747e-10 3.5e-06 0.7436
DLEU2__3 -6.86 2.563e-09 5.14e-05 0.8322
RRM2 -6.32 5.999e-08 0.0012 0.7768
KCNJ2 -5.63 9.018e-08 0.00181 0.7017
PDE3B -5.82 2.328e-07 0.00467 0.7345
NME1-NME2__2 -6.24 4.151e-07 0.00832 0.7689
NME2__1 -6.24 4.151e-07 0.00832 0.7689
CCNE2 -5.6 5.399e-07 0.0108 0.7613
GABRG1 -5.24 7.78e-07 0.0156 0.6473
NTAN1 -5.23 9.113e-07 0.0183 0.6874

Figure S3.  Get High-res Image As an example, this figure shows the association of DYRK2 to 'PATHOLOGY.N.STAGE'. P value = 1.75e-10 with T-test analysis.

Clinical variable #4: 'COMPLETENESS.OF.RESECTION'

3 genes related to 'COMPLETENESS.OF.RESECTION'.

Table S7.  Basic characteristics of clinical feature: 'COMPLETENESS.OF.RESECTION'

COMPLETENESS.OF.RESECTION Labels N
  R0 153
  R1 38
  RX 5
     
  Significant markers N = 3
List of 3 genes differentially expressed by 'COMPLETENESS.OF.RESECTION'

Table S8.  Get Full Table List of 3 genes differentially expressed by 'COMPLETENESS.OF.RESECTION'

ANOVA_P Q
CNO 5.406e-09 0.000108
CTNNA3__1 1.305e-08 0.000262
LRRTM3__1 1.305e-08 0.000262

Figure S4.  Get High-res Image As an example, this figure shows the association of CNO to 'COMPLETENESS.OF.RESECTION'. P value = 5.41e-09 with ANOVA analysis.

Clinical variable #5: 'NUMBER.OF.LYMPH.NODES'

No gene related to 'NUMBER.OF.LYMPH.NODES'.

Table S9.  Basic characteristics of clinical feature: 'NUMBER.OF.LYMPH.NODES'

NUMBER.OF.LYMPH.NODES Mean (SD) 0.18 (0.67)
  Value N
  0 154
  1 11
  2 2
  3 3
  6 1
     
  Significant markers N = 0
Clinical variable #6: 'GLEASON_SCORE_COMBINED'

18 genes related to 'GLEASON_SCORE_COMBINED'.

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

GLEASON_SCORE_COMBINED Mean (SD) 7.23 (0.77)
  Significant markers N = 18
  pos. correlated 12
  neg. correlated 6
List of top 10 genes significantly correlated to 'GLEASON_SCORE_COMBINED' by Spearman correlation test

Table S11.  Get Full Table List of top 10 genes significantly correlated to 'GLEASON_SCORE_COMBINED' by Spearman correlation test

SpearmanCorr corrP Q
LIX1L 0.4127 7.111e-10 1.43e-05
FAM13C 0.4113 8.181e-10 1.64e-05
PKD1L3 0.3844 1.173e-08 0.000235
OR51A7 -0.3625 8.606e-08 0.00173
RRM2 -0.3602 1.054e-07 0.00211
DLEU2__3 -0.351 2.308e-07 0.00463
FAM55B 0.3453 3.729e-07 0.00747
C21ORF15 -0.3386 6.443e-07 0.0129
MMP20 0.3371 7.264e-07 0.0146
LDLRAD3 0.3349 8.648e-07 0.0173

Figure S5.  Get High-res Image As an example, this figure shows the association of LIX1L to 'GLEASON_SCORE_COMBINED'. P value = 7.11e-10 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #7: 'GLEASON_SCORE_PRIMARY'

76 genes related to 'GLEASON_SCORE_PRIMARY'.

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

GLEASON_SCORE_PRIMARY Mean (SD) 3.47 (0.57)
  Score N
  2 1
  3 115
  4 83
  5 7
     
  Significant markers N = 76
  pos. correlated 28
  neg. correlated 48
List of top 10 genes significantly correlated to 'GLEASON_SCORE_PRIMARY' by Spearman correlation test

Table S13.  Get Full Table List of top 10 genes significantly correlated to 'GLEASON_SCORE_PRIMARY' by Spearman correlation test

SpearmanCorr corrP Q
RRM2 -0.4225 2.529e-10 5.07e-06
PGLYRP4 -0.3829 1.346e-08 0.00027
TP53TG3B -0.377 2.323e-08 0.000466
C21ORF15 -0.3748 2.855e-08 0.000572
C9ORF109 -0.371 4.038e-08 0.000809
C9ORF110 -0.371 4.038e-08 0.000809
C1ORF173 -0.3706 4.193e-08 0.00084
MYO3A -0.3699 4.458e-08 0.000893
MEF2D 0.3686 4.992e-08 0.001
CYCS -0.3682 5.17e-08 0.00104

Figure S6.  Get High-res Image As an example, this figure shows the association of RRM2 to 'GLEASON_SCORE_PRIMARY'. P value = 2.53e-10 with Spearman correlation analysis.

Clinical variable #8: 'GLEASON_SCORE_SECONDARY'

No gene related to 'GLEASON_SCORE_SECONDARY'.

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

GLEASON_SCORE_SECONDARY Mean (SD) 3.77 (0.61)
  Score N
  3 68
  4 118
  5 20
     
  Significant markers N = 0
Clinical variable #9: 'GLEASON_SCORE'

19 genes related to 'GLEASON_SCORE'.

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

GLEASON_SCORE Mean (SD) 7.28 (0.8)
  Score N
  6 16
  7 143
  8 22
  9 23
  10 2
     
  Significant markers N = 19
  pos. correlated 5
  neg. correlated 14
List of top 10 genes significantly correlated to 'GLEASON_SCORE' by Spearman correlation test

Table S16.  Get Full Table List of top 10 genes significantly correlated to 'GLEASON_SCORE' by Spearman correlation test

SpearmanCorr corrP Q
FAM13C 0.4063 1.361e-09 2.73e-05
PKD1L3 0.3995 2.715e-09 5.44e-05
LIX1L 0.3789 1.954e-08 0.000392
DLEU2__3 -0.3621 8.895e-08 0.00178
RRM2 -0.3581 1.258e-07 0.00252
OR51A7 -0.3574 1.336e-07 0.00268
SLC5A11 -0.353 1.941e-07 0.00389
DHX33 0.3515 2.207e-07 0.00442
C21ORF15 -0.3461 3.484e-07 0.00698
RBM15 -0.3453 3.708e-07 0.00743

Figure S7.  Get High-res Image As an example, this figure shows the association of FAM13C to 'GLEASON_SCORE'. P value = 1.36e-09 with Spearman correlation analysis.

Clinical variable #10: 'PSA_RESULT_PREOP'

13 genes related to 'PSA_RESULT_PREOP'.

Table S17.  Basic characteristics of clinical feature: 'PSA_RESULT_PREOP'

PSA_RESULT_PREOP Mean (SD) 10.26 (10)
  Significant markers N = 13
  pos. correlated 2
  neg. correlated 11
List of top 10 genes significantly correlated to 'PSA_RESULT_PREOP' by Spearman correlation test

Table S18.  Get Full Table List of top 10 genes significantly correlated to 'PSA_RESULT_PREOP' by Spearman correlation test

SpearmanCorr corrP Q
FCRL2 -0.4237 2.718e-10 5.45e-06
FCRL3 -0.3572 1.572e-07 0.00315
FCRL4 -0.3494 3.015e-07 0.00604
PGLYRP4 -0.3362 8.852e-07 0.0177
S100A7 -0.3359 9.023e-07 0.0181
SPDYE7P -0.3309 1.334e-06 0.0267
ATP8B3 0.3308 1.345e-06 0.027
YIPF7 -0.3308 1.348e-06 0.027
C6 -0.3263 1.911e-06 0.0383
BICC1 -0.3231 2.43e-06 0.0487

Figure S8.  Get High-res Image As an example, this figure shows the association of FCRL2 to 'PSA_RESULT_PREOP'. P value = 2.72e-10 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #11: 'DAYS_TO_PREOP_PSA'

505 genes related to 'DAYS_TO_PREOP_PSA'.

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

DAYS_TO_PREOP_PSA Mean (SD) -3.45 (96)
  Significant markers N = 505
  pos. correlated 236
  neg. correlated 269
List of top 10 genes significantly correlated to 'DAYS_TO_PREOP_PSA' by Spearman correlation test

Table S20.  Get Full Table List of top 10 genes significantly correlated to 'DAYS_TO_PREOP_PSA' by Spearman correlation test

SpearmanCorr corrP Q
TRIO -0.4743 1.297e-12 2.6e-08
THEG -0.4636 4.72e-12 9.46e-08
CLCA4 -0.46 7.276e-12 1.46e-07
KIAA1211 -0.4552 1.267e-11 2.54e-07
ZBTB5 -0.449 2.574e-11 5.16e-07
FIP1L1 -0.4484 2.777e-11 5.57e-07
GNAS 0.4465 3.424e-11 6.86e-07
UBE3C -0.4454 3.879e-11 7.77e-07
GRAMD1C -0.4437 4.706e-11 9.43e-07
C1ORF25 -0.4395 7.464e-11 1.5e-06

Figure S9.  Get High-res Image As an example, this figure shows the association of TRIO to 'DAYS_TO_PREOP_PSA'. P value = 1.3e-12 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #12: 'PSA_VALUE'

No gene related to 'PSA_VALUE'.

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

PSA_VALUE Mean (SD) 1.35 (4.3)
  Significant markers N = 0
Clinical variable #13: 'DAYS_TO_PSA'

No gene related to 'DAYS_TO_PSA'.

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

DAYS_TO_PSA Mean (SD) 574.9 (550)
  Significant markers N = 0
Methods & Data
Input

  • Expresson data file = PRAD-TP.meth.by_min_clin_corr.data.txt

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

  • Number of patients = 206

  • Number of genes = 20049

  • Number of clinical features = 13

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

Student's t-test analysis

For two-class clinical features, two-tailed Student's t test with unequal variance (Lehmann and Romano 2005) was applied to compare the log2-expression levels between the two clinical classes using 't.test' function in R

ANOVA analysis

For multi-class clinical features (ordinal or nominal), one-way analysis of variance (Howell 2002) was applied to compare the log2-expression levels between different clinical classes using 'anova' function in R

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] Spearman, C, The proof and measurement of association between two things, Amer. J. Psychol 15:72-101 (1904)
[2] Lehmann and Romano, Testing Statistical Hypotheses (3E ed.), New York: Springer. ISBN 0387988645 (2005)
[3] Howell, D, Statistical Methods for Psychology. (5th ed.), Duxbury Press:324-5 (2002)
[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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