Correlation between gene methylation status and clinical features
Thyroid Adenocarcinoma (Primary solid tumor)
16 April 2014  |  analyses__2014_04_16
Maintainer Information
Citation Information
doi:10.7908/C16W98RG
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/C16W98RG
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 19707 genes and 15 clinical features across 486 samples, statistically thresholded by Q value < 0.05, 12 clinical features related to at least one genes.

  • 387 genes correlated to 'AGE'.

    • MGA ,  NHLRC1 ,  INA ,  RANBP17 ,  SYNGR3 ,  ...

  • 435 genes correlated to 'NEOPLASM.DISEASESTAGE'.

    • OTOS ,  C1ORF91__1 ,  EIF3I__1 ,  ACTA1 ,  C10ORF137 ,  ...

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

    • TBKBP1 ,  GJD3 ,  IFT140 ,  TMEM204 ,  PDGFB ,  ...

  • 1348 genes correlated to 'PATHOLOGY.N.STAGE'.

    • PON2 ,  MACF1 ,  BMP1 ,  CPNE1__1 ,  RBM12__1 ,  ...

  • 23 genes correlated to 'PATHOLOGY.M.STAGE'.

    • C1ORF91__1 ,  EIF3I__1 ,  LCA5 ,  KCTD12 ,  GPI ,  ...

  • 27 genes correlated to 'GENDER'.

    • ALG11__2 ,  UTP14C__1 ,  ETF1 ,  KIF4B ,  MTFR1 ,  ...

  • 2246 genes correlated to 'HISTOLOGICAL.TYPE'.

    • PON2 ,  EMP1 ,  SLC6A12 ,  LEPR ,  LEPROT ,  ...

  • 49 genes correlated to 'RADIATIONS.RADIATION.REGIMENINDICATION'.

    • SIK1 ,  SNX31 ,  STX17 ,  TAF7 ,  KIAA1143__1 ,  ...

  • 56 genes correlated to 'RADIATIONEXPOSURE'.

    • CHEK2 ,  HSCB ,  MGC21881 ,  ATF7IP ,  CASP12 ,  ...

  • 368 genes correlated to 'EXTRATHYROIDAL.EXTENSION'.

    • OTOS ,  DEFB131 ,  TBX19 ,  SLC16A3 ,  PA2G4P4 ,  ...

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

    • CAPN5__1 ,  OMP ,  ANKRD34B ,  ASTN2 ,  HS3ST2 ,  ...

  • 1081 genes correlated to 'NUMBER.OF.LYMPH.NODES'.

    • FUT2 ,  TMEM173 ,  MET ,  TAGLN2 ,  MACF1 ,  ...

  • No genes correlated to 'Time to Death', 'MULTIFOCALITY', and 'TUMOR.SIZE'.

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
Time to Death Cox regression test   N=0        
AGE Spearman correlation test N=387 older N=386 younger N=1
NEOPLASM DISEASESTAGE ANOVA test N=435        
PATHOLOGY T STAGE Spearman correlation test N=164 higher stage N=75 lower stage N=89
PATHOLOGY N STAGE t test N=1348 class1 N=133 class0 N=1215
PATHOLOGY M STAGE ANOVA test N=23        
GENDER t test N=27 male N=16 female N=11
HISTOLOGICAL TYPE ANOVA test N=2246        
RADIATIONS RADIATION REGIMENINDICATION t test N=49 yes N=25 no N=24
RADIATIONEXPOSURE t test N=56 yes N=24 no N=32
EXTRATHYROIDAL EXTENSION ANOVA test N=368        
COMPLETENESS OF RESECTION ANOVA test N=13        
NUMBER OF LYMPH NODES Spearman correlation test N=1081 higher number.of.lymph.nodes N=25 lower number.of.lymph.nodes N=1056
MULTIFOCALITY t test   N=0        
TUMOR SIZE Spearman correlation test   N=0        
Clinical variable #1: 'Time to Death'

No gene related to 'Time to Death'.

Table S1.  Basic characteristics of clinical feature: 'Time to Death'

Time to Death Duration (Months) 0-158.8 (median=15.5)
  censored N = 468
  death N = 14
     
  Significant markers N = 0
Clinical variable #2: 'AGE'

387 genes related to 'AGE'.

Table S2.  Basic characteristics of clinical feature: 'AGE'

AGE Mean (SD) 47.18 (16)
  Significant markers N = 387
  pos. correlated 386
  neg. correlated 1
List of top 10 genes significantly correlated to 'AGE' by Spearman correlation test

Table S3.  Get Full Table List of top 10 genes significantly correlated to 'AGE' by Spearman correlation test

SpearmanCorr corrP Q
MGA 0.5294 1.865e-36 3.67e-32
NHLRC1 0.4888 1.469e-30 2.89e-26
INA 0.4797 2.428e-29 4.79e-25
RANBP17 0.4603 7.483e-27 1.47e-22
SYNGR3 0.4584 1.263e-26 2.49e-22
C1ORF59 0.4566 2.128e-26 4.19e-22
ACN9 0.4494 1.563e-25 3.08e-21
OTUD7A 0.4333 1.152e-23 2.27e-19
ZNF518B 0.4292 3.313e-23 6.53e-19
NTNG2 0.4281 4.487e-23 8.84e-19

Figure S1.  Get High-res Image As an example, this figure shows the association of MGA to 'AGE'. P value = 1.86e-36 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #3: 'NEOPLASM.DISEASESTAGE'

435 genes related to 'NEOPLASM.DISEASESTAGE'.

Table S4.  Basic characteristics of clinical feature: 'NEOPLASM.DISEASESTAGE'

NEOPLASM.DISEASESTAGE Labels N
  STAGE I 275
  STAGE II 51
  STAGE III 106
  STAGE IV 2
  STAGE IVA 44
  STAGE IVC 6
     
  Significant markers N = 435
List of top 10 genes differentially expressed by 'NEOPLASM.DISEASESTAGE'

Table S5.  Get Full Table List of top 10 genes differentially expressed by 'NEOPLASM.DISEASESTAGE'

ANOVA_P Q
OTOS 9.445e-30 1.86e-25
C1ORF91__1 1.451e-27 2.86e-23
EIF3I__1 1.451e-27 2.86e-23
ACTA1 4.04e-27 7.96e-23
C10ORF137 4.64e-23 9.14e-19
RABL2A 4.265e-16 8.4e-12
RPL23AP7 4.265e-16 8.4e-12
SFRP1 7.754e-15 1.53e-10
CHAT 1.036e-14 2.04e-10
SLC18A3 1.036e-14 2.04e-10

Figure S2.  Get High-res Image As an example, this figure shows the association of OTOS to 'NEOPLASM.DISEASESTAGE'. P value = 9.44e-30 with ANOVA analysis.

Clinical variable #4: 'PATHOLOGY.T.STAGE'

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

Table S6.  Basic characteristics of clinical feature: 'PATHOLOGY.T.STAGE'

PATHOLOGY.T.STAGE Mean (SD) 2.13 (0.88)
  N
  1 140
  2 162
  3 161
  4 21
     
  Significant markers N = 164
  pos. correlated 75
  neg. correlated 89
List of top 10 genes significantly correlated to 'PATHOLOGY.T.STAGE' by Spearman correlation test

Table S7.  Get Full Table List of top 10 genes significantly correlated to 'PATHOLOGY.T.STAGE' by Spearman correlation test

SpearmanCorr corrP Q
TBKBP1 0.3208 4.753e-13 9.37e-09
GJD3 0.3047 7.423e-12 1.46e-07
IFT140 0.3046 7.508e-12 1.48e-07
TMEM204 0.3046 7.508e-12 1.48e-07
PDGFB 0.2993 1.785e-11 3.52e-07
CKMT2 0.2807 3.232e-10 6.37e-06
RNU5D__1 0.2807 3.232e-10 6.37e-06
RNU5E__1 0.2807 3.232e-10 6.37e-06
PSD3 -0.2748 7.785e-10 1.53e-05
FLJ42875 0.2723 1.129e-09 2.22e-05

Figure S3.  Get High-res Image As an example, this figure shows the association of TBKBP1 to 'PATHOLOGY.T.STAGE'. P value = 4.75e-13 with Spearman correlation analysis.

Clinical variable #5: 'PATHOLOGY.N.STAGE'

1348 genes related to 'PATHOLOGY.N.STAGE'.

Table S8.  Basic characteristics of clinical feature: 'PATHOLOGY.N.STAGE'

PATHOLOGY.N.STAGE Labels N
  class0 221
  class1 218
     
  Significant markers N = 1348
  Higher in class1 133
  Higher in class0 1215
List of top 10 genes differentially expressed by 'PATHOLOGY.N.STAGE'

Table S9.  Get Full Table List of top 10 genes differentially expressed by 'PATHOLOGY.N.STAGE'

T(pos if higher in 'class1') ttestP Q AUC
PON2 -10.02 2.889e-21 5.69e-17 0.7379
MACF1 -10.02 2.952e-21 5.82e-17 0.737
BMP1 -9.99 2.995e-21 5.9e-17 0.7403
CPNE1__1 -9.97 3.759e-21 7.41e-17 0.7409
RBM12__1 -9.97 3.759e-21 7.41e-17 0.7409
DAGLA -9.9 6.451e-21 1.27e-16 0.7407
XDH -9.66 5.277e-20 1.04e-15 0.7277
EPHA4 -9.64 7.133e-20 1.41e-15 0.725
ADAMTS17 -9.51 1.776e-19 3.5e-15 0.7272
C8ORF73 -9.51 2.468e-19 4.86e-15 0.7174

Figure S4.  Get High-res Image As an example, this figure shows the association of PON2 to 'PATHOLOGY.N.STAGE'. P value = 2.89e-21 with T-test analysis.

Clinical variable #6: 'PATHOLOGY.M.STAGE'

23 genes related to 'PATHOLOGY.M.STAGE'.

Table S10.  Basic characteristics of clinical feature: 'PATHOLOGY.M.STAGE'

PATHOLOGY.M.STAGE Labels N
  M0 266
  M1 9
  MX 210
     
  Significant markers N = 23
List of top 10 genes differentially expressed by 'PATHOLOGY.M.STAGE'

Table S11.  Get Full Table List of top 10 genes differentially expressed by 'PATHOLOGY.M.STAGE'

ANOVA_P Q
C1ORF91__1 4.51e-17 8.89e-13
EIF3I__1 4.51e-17 8.89e-13
LCA5 9.413e-11 1.85e-06
KCTD12 5.552e-10 1.09e-05
GPI 8.546e-10 1.68e-05
C17ORF65__1 1.112e-09 2.19e-05
TMUB2__1 1.112e-09 2.19e-05
RPS10 4.853e-08 0.000956
EIF1B 6.839e-08 0.00135
CAPN5__1 1.136e-07 0.00224

Figure S5.  Get High-res Image As an example, this figure shows the association of C1ORF91__1 to 'PATHOLOGY.M.STAGE'. P value = 4.51e-17 with ANOVA analysis.

Clinical variable #7: 'GENDER'

27 genes related to 'GENDER'.

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

GENDER Labels N
  FEMALE 356
  MALE 130
     
  Significant markers N = 27
  Higher in MALE 16
  Higher in FEMALE 11
List of top 10 genes differentially expressed by 'GENDER'

Table S13.  Get Full Table List of top 10 genes differentially expressed by 'GENDER'

T(pos if higher in 'MALE') ttestP Q AUC
ALG11__2 42.43 1.951e-157 3.84e-153 0.9954
UTP14C__1 42.43 1.951e-157 3.84e-153 0.9954
ETF1 31.96 2.428e-82 4.79e-78 0.987
KIF4B -18.36 2.921e-49 5.76e-45 0.9048
MTFR1 13.69 2.003e-31 3.95e-27 0.8356
FAM35A -11.14 1.143e-24 2.25e-20 0.8409
GLUD1__1 -11.14 1.143e-24 2.25e-20 0.8409
WBP11P1 9.89 4.868e-19 9.59e-15 0.806
ANKRD20A4 8.82 9.759e-17 1.92e-12 0.7414
CCDC121__1 7.31 3.726e-12 7.34e-08 0.7069

Figure S6.  Get High-res Image As an example, this figure shows the association of ALG11__2 to 'GENDER'. P value = 1.95e-157 with T-test analysis.

Clinical variable #8: 'HISTOLOGICAL.TYPE'

2246 genes related to 'HISTOLOGICAL.TYPE'.

Table S14.  Basic characteristics of clinical feature: 'HISTOLOGICAL.TYPE'

HISTOLOGICAL.TYPE Labels N
  OTHER SPECIFY 9
  THYROID PAPILLARY CARCINOMA - CLASSICAL/USUAL 343
  THYROID PAPILLARY CARCINOMA - FOLLICULAR (>= 99% FOLLICULAR PATTERNED) 99
  THYROID PAPILLARY CARCINOMA - TALL CELL (>= 50% TALL CELL FEATURES) 35
     
  Significant markers N = 2246
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'

ANOVA_P Q
PON2 1.255e-41 2.47e-37
EMP1 1.217e-40 2.4e-36
SLC6A12 1.663e-39 3.28e-35
LEPR 2.188e-39 4.31e-35
LEPROT 2.188e-39 4.31e-35
LAMP3 4.732e-39 9.32e-35
C8ORF73 4.798e-39 9.45e-35
LOC100126784 7.34e-38 1.45e-33
NAV2__1 7.34e-38 1.45e-33
LIMK1 3.775e-37 7.44e-33

Figure S7.  Get High-res Image As an example, this figure shows the association of PON2 to 'HISTOLOGICAL.TYPE'. P value = 1.25e-41 with ANOVA analysis.

Clinical variable #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

49 genes related to 'RADIATIONS.RADIATION.REGIMENINDICATION'.

Table S16.  Basic characteristics of clinical feature: 'RADIATIONS.RADIATION.REGIMENINDICATION'

RADIATIONS.RADIATION.REGIMENINDICATION Labels N
  NO 14
  YES 472
     
  Significant markers N = 49
  Higher in YES 25
  Higher in NO 24
List of top 10 genes differentially expressed by 'RADIATIONS.RADIATION.REGIMENINDICATION'

Table S17.  Get Full Table List of top 10 genes differentially expressed by 'RADIATIONS.RADIATION.REGIMENINDICATION'

T(pos if higher in 'YES') ttestP Q AUC
SIK1 9.63 1.575e-19 3.1e-15 0.6945
SNX31 8.39 1.159e-15 2.28e-11 0.7016
STX17 9.31 1.173e-13 2.31e-09 0.6311
TAF7 7.92 1.676e-13 3.3e-09 0.7027
KIAA1143__1 8.88 4.093e-13 8.06e-09 0.7412
KIF15__1 8.88 4.093e-13 8.06e-09 0.7412
MYOM2 8.17 2.024e-12 3.99e-08 0.533
HORMAD1 -8.6 2.972e-11 5.85e-07 0.7214
CBLN1 7.26 1.65e-10 3.25e-06 0.7312
DCHS2 6.54 1.702e-10 3.35e-06 0.6538

Figure S8.  Get High-res Image As an example, this figure shows the association of SIK1 to 'RADIATIONS.RADIATION.REGIMENINDICATION'. P value = 1.58e-19 with T-test analysis.

Clinical variable #10: 'RADIATIONEXPOSURE'

56 genes related to 'RADIATIONEXPOSURE'.

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

RADIATIONEXPOSURE Labels N
  NO 409
  YES 17
     
  Significant markers N = 56
  Higher in YES 24
  Higher in NO 32
List of top 10 genes differentially expressed by 'RADIATIONEXPOSURE'

Table S19.  Get Full Table List of top 10 genes differentially expressed by 'RADIATIONEXPOSURE'

T(pos if higher in 'YES') ttestP Q AUC
CHEK2 -7.35 2.56e-12 5.04e-08 0.6039
HSCB -7.35 2.56e-12 5.04e-08 0.6039
MGC21881 -7.35 7.199e-12 1.42e-07 0.5872
ATF7IP -7.01 2.085e-11 4.11e-07 0.5882
CASP12 -7.65 3.025e-11 5.96e-07 0.6074
TMEM63C -7.44 7.548e-11 1.49e-06 0.626
HESX1 7.12 2.118e-10 4.17e-06 0.6666
POLR2F 7.38 4.348e-10 8.57e-06 0.6739
PHKB 6.61 5.981e-10 1.18e-05 0.7194
FRG1B -7.56 4.059e-09 7.99e-05 0.7277

Figure S9.  Get High-res Image As an example, this figure shows the association of CHEK2 to 'RADIATIONEXPOSURE'. P value = 2.56e-12 with T-test analysis.

Clinical variable #11: 'EXTRATHYROIDAL.EXTENSION'

368 genes related to 'EXTRATHYROIDAL.EXTENSION'.

Table S20.  Basic characteristics of clinical feature: 'EXTRATHYROIDAL.EXTENSION'

EXTRATHYROIDAL.EXTENSION Labels N
  MINIMAL (T3) 127
  MODERATE/ADVANCED (T4A) 16
  NONE 325
  VERY ADVANCED (T4B) 1
     
  Significant markers N = 368
List of top 10 genes differentially expressed by 'EXTRATHYROIDAL.EXTENSION'

Table S21.  Get Full Table List of top 10 genes differentially expressed by 'EXTRATHYROIDAL.EXTENSION'

ANOVA_P Q
OTOS 3.851e-72 7.59e-68
DEFB131 2.052e-32 4.04e-28
TBX19 1.24e-23 2.44e-19
SLC16A3 8.746e-20 1.72e-15
PA2G4P4 2.77e-18 5.46e-14
SEC22C 4.951e-18 9.75e-14
TRAM1L1 1.56e-17 3.07e-13
NPTN 2.333e-17 4.6e-13
LOC400804 5.782e-17 1.14e-12
BTBD12 1.737e-16 3.42e-12

Figure S10.  Get High-res Image As an example, this figure shows the association of OTOS to 'EXTRATHYROIDAL.EXTENSION'. P value = 3.85e-72 with ANOVA analysis.

Clinical variable #12: 'COMPLETENESS.OF.RESECTION'

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

Table S22.  Basic characteristics of clinical feature: 'COMPLETENESS.OF.RESECTION'

COMPLETENESS.OF.RESECTION Labels N
  R0 373
  R1 49
  R2 4
  RX 29
     
  Significant markers N = 13
List of top 10 genes differentially expressed by 'COMPLETENESS.OF.RESECTION'

Table S23.  Get Full Table List of top 10 genes differentially expressed by 'COMPLETENESS.OF.RESECTION'

ANOVA_P Q
CAPN5__1 6.397e-15 1.26e-10
OMP 6.397e-15 1.26e-10
ANKRD34B 9.737e-13 1.92e-08
ASTN2 3.115e-12 6.14e-08
HS3ST2 1.983e-11 3.91e-07
CYTSA 6.197e-11 1.22e-06
KCNJ14 1.988e-10 3.92e-06
CCDC147 1.009e-09 1.99e-05
C17ORF108 5.959e-09 0.000117
MYL2 1.933e-08 0.000381

Figure S11.  Get High-res Image As an example, this figure shows the association of CAPN5__1 to 'COMPLETENESS.OF.RESECTION'. P value = 6.4e-15 with ANOVA analysis.

Clinical variable #13: 'NUMBER.OF.LYMPH.NODES'

1081 genes related to 'NUMBER.OF.LYMPH.NODES'.

Table S24.  Basic characteristics of clinical feature: 'NUMBER.OF.LYMPH.NODES'

NUMBER.OF.LYMPH.NODES Mean (SD) 3.54 (6.2)
  Significant markers N = 1081
  pos. correlated 25
  neg. correlated 1056
List of top 10 genes significantly correlated to 'NUMBER.OF.LYMPH.NODES' by Spearman correlation test

Table S25.  Get Full Table List of top 10 genes significantly correlated to 'NUMBER.OF.LYMPH.NODES' by Spearman correlation test

SpearmanCorr corrP Q
FUT2 -0.4377 1.692e-19 3.33e-15
TMEM173 -0.4314 6.349e-19 1.25e-14
MET -0.4291 1.005e-18 1.98e-14
TAGLN2 -0.4214 4.789e-18 9.44e-14
MACF1 -0.4206 5.627e-18 1.11e-13
CAPN2 -0.4147 1.772e-17 3.49e-13
CPNE1__1 -0.4146 1.817e-17 3.58e-13
RBM12__1 -0.4146 1.817e-17 3.58e-13
HDAC9 -0.4137 2.162e-17 4.26e-13
SMURF1 -0.4101 4.324e-17 8.52e-13

Figure S12.  Get High-res Image As an example, this figure shows the association of FUT2 to 'NUMBER.OF.LYMPH.NODES'. P value = 1.69e-19 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #14: 'MULTIFOCALITY'

No gene related to 'MULTIFOCALITY'.

Table S26.  Basic characteristics of clinical feature: 'MULTIFOCALITY'

MULTIFOCALITY Labels N
  MULTIFOCAL 220
  UNIFOCAL 256
     
  Significant markers N = 0
Clinical variable #15: 'TUMOR.SIZE'

No gene related to 'TUMOR.SIZE'.

Table S27.  Basic characteristics of clinical feature: 'TUMOR.SIZE'

TUMOR.SIZE Mean (SD) 2.95 (1.6)
  Significant markers N = 0
Methods & Data
Input

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

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

  • Number of patients = 486

  • Number of genes = 19707

  • Number of clinical features = 15

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

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

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

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] Howell, D, Statistical Methods for Psychology. (5th ed.), Duxbury Press:324-5 (2002)
[4] Lehmann and Romano, Testing Statistical Hypotheses (3E ed.), New York: Springer. ISBN 0387988645 (2005)
[5] 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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