Correlation between gene methylation status and clinical features
Breast Invasive Carcinoma (Primary solid tumor)
15 January 2014  |  analyses__2014_01_15
Maintainer Information
Citation Information
doi:10.7908/C1TH8K33
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/C1TH8K33
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 19956 genes and 10 clinical features across 652 samples, statistically thresholded by Q value < 0.05, 8 clinical features related to at least one genes.

  • 369 genes correlated to 'AGE'.

    • KIAA1143 ,  KIF15 ,  C1ORF103 ,  LGALS8 ,  MEX3C ,  ...

  • 37 genes correlated to 'NEOPLASM.DISEASESTAGE'.

    • ZNF384 ,  WDR74 ,  RHBDL3 ,  FASTKD3 ,  MTRR__1 ,  ...

  • 1 gene correlated to 'PATHOLOGY.N.STAGE'.

    • TCP11L1

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

    • RHBDL3 ,  FASTKD3 ,  MTRR__1 ,  MMAB ,  MVK ,  ...

  • 199 genes correlated to 'GENDER'.

    • ALDOC ,  CRIP1 ,  DNAJC15 ,  NMNAT3 ,  EML1 ,  ...

  • 1188 genes correlated to 'HISTOLOGICAL.TYPE'.

    • CLUAP1 ,  RNF25 ,  STK36 ,  PRKAR2A ,  FAR1 ,  ...

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

    • PRIM1 ,  MAEA ,  C16ORF58 ,  HMG20B ,  ZNF639 ,  ...

  • 1 gene correlated to 'NUMBER.OF.LYMPH.NODES'.

    • TCP11L1

  • No genes correlated to 'Time to Death', and 'PATHOLOGY.T.STAGE'.

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=369 older N=302 younger N=67
NEOPLASM DISEASESTAGE ANOVA test N=37        
PATHOLOGY T STAGE Spearman correlation test   N=0        
PATHOLOGY N STAGE Spearman correlation test N=1 higher stage N=1 lower stage N=0
PATHOLOGY M STAGE ANOVA test N=78        
GENDER t test N=199 male N=51 female N=148
HISTOLOGICAL TYPE ANOVA test N=1188        
RADIATIONS RADIATION REGIMENINDICATION t test N=69 yes N=24 no N=45
NUMBER OF LYMPH NODES Spearman correlation test N=1 higher number.of.lymph.nodes N=1 lower number.of.lymph.nodes 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) 0-234.2 (median=21.2)
  censored N = 572
  death N = 72
     
  Significant markers N = 0
Clinical variable #2: 'AGE'

369 genes related to 'AGE'.

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

AGE Mean (SD) 57.97 (13)
  Significant markers N = 369
  pos. correlated 302
  neg. correlated 67
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
KIAA1143 0.3278 1.011e-17 2.02e-13
KIF15 0.3278 1.011e-17 2.02e-13
C1ORF103 0.3195 7.228e-17 1.44e-12
LGALS8 -0.3012 4.433e-15 8.85e-11
MEX3C 0.2976 9.74e-15 1.94e-10
C20ORF199 0.2939 2.145e-14 4.28e-10
SNORD12 0.2939 2.145e-14 4.28e-10
EGR2 0.2876 7.981e-14 1.59e-09
RPS3 0.2854 1.243e-13 2.48e-09
SNORD15B 0.2854 1.243e-13 2.48e-09

Figure S1.  Get High-res Image As an example, this figure shows the association of KIAA1143 to 'AGE'. P value = 1.01e-17 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #3: 'NEOPLASM.DISEASESTAGE'

37 genes related to 'NEOPLASM.DISEASESTAGE'.

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

NEOPLASM.DISEASESTAGE Labels N
  STAGE I 49
  STAGE IA 54
  STAGE IB 5
  STAGE II 8
  STAGE IIA 206
  STAGE IIB 152
  STAGE III 2
  STAGE IIIA 106
  STAGE IIIB 17
  STAGE IIIC 41
  STAGE IV 6
  STAGE X 5
     
  Significant markers N = 37
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
ZNF384 8.712e-21 1.74e-16
WDR74 3.411e-20 6.81e-16
RHBDL3 6.98e-20 1.39e-15
FASTKD3 2.176e-19 4.34e-15
MTRR__1 2.176e-19 4.34e-15
SIL1 1.952e-18 3.9e-14
ATP5J 1.301e-17 2.6e-13
GABPA 1.301e-17 2.6e-13
MMAB 3.314e-17 6.61e-13
MVK 3.314e-17 6.61e-13

Figure S2.  Get High-res Image As an example, this figure shows the association of ZNF384 to 'NEOPLASM.DISEASESTAGE'. P value = 8.71e-21 with ANOVA analysis.

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

No gene related to 'PATHOLOGY.T.STAGE'.

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

PATHOLOGY.T.STAGE Mean (SD) 1.93 (0.72)
  N
  1 173
  2 372
  3 85
  4 20
     
  Significant markers N = 0
Clinical variable #5: 'PATHOLOGY.N.STAGE'

One gene related to 'PATHOLOGY.N.STAGE'.

Table S7.  Basic characteristics of clinical feature: 'PATHOLOGY.N.STAGE'

PATHOLOGY.N.STAGE Mean (SD) 0.82 (0.9)
  N
  0 285
  1 231
  2 84
  3 44
     
  Significant markers N = 1
  pos. correlated 1
  neg. correlated 0
List of one gene significantly correlated to 'PATHOLOGY.N.STAGE' by Spearman correlation test

Table S8.  Get Full Table List of one gene significantly correlated to 'PATHOLOGY.N.STAGE' by Spearman correlation test

SpearmanCorr corrP Q
TCP11L1 0.2105 6.926e-08 0.00138

Figure S3.  Get High-res Image As an example, this figure shows the association of TCP11L1 to 'PATHOLOGY.N.STAGE'. P value = 6.93e-08 with Spearman correlation analysis.

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

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

Table S9.  Basic characteristics of clinical feature: 'PATHOLOGY.M.STAGE'

PATHOLOGY.M.STAGE Labels N
  CM0 (I+) 1
  M0 541
  M1 6
  MX 104
     
  Significant markers N = 78
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'

ANOVA_P Q
RHBDL3 5.037e-25 1.01e-20
FASTKD3 7.523e-25 1.5e-20
MTRR__1 7.523e-25 1.5e-20
MMAB 3.071e-22 6.13e-18
MVK 3.071e-22 6.13e-18
NHEDC1 2.143e-20 4.27e-16
TSTD1 2.864e-12 5.71e-08
USF1 2.864e-12 5.71e-08
SAG 3.379e-12 6.74e-08
TRIM21 7.379e-10 1.47e-05

Figure S4.  Get High-res Image As an example, this figure shows the association of RHBDL3 to 'PATHOLOGY.M.STAGE'. P value = 5.04e-25 with ANOVA analysis.

Clinical variable #7: 'GENDER'

199 genes related to 'GENDER'.

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

GENDER Labels N
  FEMALE 645
  MALE 7
     
  Significant markers N = 199
  Higher in MALE 51
  Higher in FEMALE 148
List of top 10 genes differentially expressed by 'GENDER'

Table S12.  Get Full Table List of top 10 genes differentially expressed by 'GENDER'

T(pos if higher in 'MALE') ttestP Q AUC
ALDOC -28.87 3.557e-116 7.1e-112 0.8609
CRIP1 -19.62 1.409e-67 2.81e-63 0.868
DNAJC15 -14.92 4.847e-42 9.67e-38 0.7132
NMNAT3 -14.72 6.077e-42 1.21e-37 0.6921
EML1 -13 2.669e-34 5.32e-30 0.6228
ADCY5 14.11 1.445e-31 2.88e-27 0.732
HSPC157 -14.43 1.324e-30 2.64e-26 0.614
RND2 -13.81 2.13e-30 4.25e-26 0.7555
SLCO4C1 -12.77 7.131e-30 1.42e-25 0.7176
ACADS -11.73 1.125e-28 2.24e-24 0.6146

Figure S5.  Get High-res Image As an example, this figure shows the association of ALDOC to 'GENDER'. P value = 3.56e-116 with T-test analysis.

Clinical variable #8: 'HISTOLOGICAL.TYPE'

1188 genes related to 'HISTOLOGICAL.TYPE'.

Table S13.  Basic characteristics of clinical feature: 'HISTOLOGICAL.TYPE'

HISTOLOGICAL.TYPE Labels N
  INFILTRATING CARCINOMA NOS 1
  INFILTRATING DUCTAL CARCINOMA 447
  INFILTRATING LOBULAR CARCINOMA 131
  MEDULLARY CARCINOMA 5
  MIXED HISTOLOGY (PLEASE SPECIFY) 23
  MUCINOUS CARCINOMA 12
  OTHER SPECIFY 32
     
  Significant markers N = 1188
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'

ANOVA_P Q
CLUAP1 0 0
RNF25 1.796e-267 3.58e-263
STK36 1.796e-267 3.58e-263
PRKAR2A 2.129e-209 4.25e-205
FAR1 6.754e-111 1.35e-106
RNF26 1.077e-83 2.15e-79
PDK2 6.592e-83 1.32e-78
AP1M2 1.219e-79 2.43e-75
CYB5D1__1 3.878e-72 7.74e-68
LSMD1__1 3.878e-72 7.74e-68

Figure S6.  Get High-res Image As an example, this figure shows the association of CLUAP1 to 'HISTOLOGICAL.TYPE'. P value = 0 with ANOVA analysis.

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

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

Table S15.  Basic characteristics of clinical feature: 'RADIATIONS.RADIATION.REGIMENINDICATION'

RADIATIONS.RADIATION.REGIMENINDICATION Labels N
  NO 214
  YES 438
     
  Significant markers N = 69
  Higher in YES 24
  Higher in NO 45
List of top 10 genes differentially expressed by 'RADIATIONS.RADIATION.REGIMENINDICATION'

Table S16.  Get Full Table List of top 10 genes differentially expressed by 'RADIATIONS.RADIATION.REGIMENINDICATION'

T(pos if higher in 'YES') ttestP Q AUC
PRIM1 7.52 1.945e-13 3.88e-09 0.6512
MAEA -6.84 2.169e-11 4.33e-07 0.6422
C16ORF58 -6.55 1.625e-10 3.24e-06 0.6528
HMG20B -6.4 3.287e-10 6.56e-06 0.6358
ZNF639 6.31 5.625e-10 1.12e-05 0.6374
METAP2 6.31 5.953e-10 1.19e-05 0.6472
SRRM5 -6.07 2.568e-09 5.12e-05 0.6287
ZNF428 -6.07 2.568e-09 5.12e-05 0.6287
TSTD1 -6.04 2.978e-09 5.94e-05 0.6257
USF1 -6.04 2.978e-09 5.94e-05 0.6257

Figure S7.  Get High-res Image As an example, this figure shows the association of PRIM1 to 'RADIATIONS.RADIATION.REGIMENINDICATION'. P value = 1.94e-13 with T-test analysis.

Clinical variable #10: 'NUMBER.OF.LYMPH.NODES'

One gene related to 'NUMBER.OF.LYMPH.NODES'.

Table S17.  Basic characteristics of clinical feature: 'NUMBER.OF.LYMPH.NODES'

NUMBER.OF.LYMPH.NODES Mean (SD) 2.59 (4.8)
  Significant markers N = 1
  pos. correlated 1
  neg. correlated 0
List of one gene significantly correlated to 'NUMBER.OF.LYMPH.NODES' by Spearman correlation test

Table S18.  Get Full Table List of one gene significantly correlated to 'NUMBER.OF.LYMPH.NODES' by Spearman correlation test

SpearmanCorr corrP Q
TCP11L1 0.2373 4.131e-09 8.24e-05

Figure S8.  Get High-res Image As an example, this figure shows the association of TCP11L1 to 'NUMBER.OF.LYMPH.NODES'. P value = 4.13e-09 with Spearman correlation analysis. The straight line presents the best linear regression.

Methods & Data
Input

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

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

  • Number of patients = 652

  • Number of genes = 19956

  • Number of clinical features = 10

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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