Correlation between gene methylation status and clinical features
Breast Invasive Carcinoma (Primary solid tumor)
21 April 2013  |  analyses__2013_04_21
Maintainer Information
Citation Information
doi:10.7908/C1H12ZXH
Maintained by Juok Cho (Broad Institute)
Cite as Broad Institute TCGA Genome Data Analysis Center (2013): Breast Invasive Carcinoma (Primary solid tumor cohort) - 21 April 2013: Correlation between gene methylation status and clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1H12ZXH
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 17320 genes and 8 clinical features across 530 samples, statistically thresholded by Q value < 0.05, 7 clinical features related to at least one genes.

  • 1 gene correlated to 'Time to Death'.

    • CDC73

  • 139 genes correlated to 'AGE'.

    • KIF15 ,  MEX3C ,  EGR2 ,  LGALS8 ,  RPL13A ,  ...

  • 186 genes correlated to 'GENDER'.

    • ALDOC ,  ZNF486 ,  CRIP1 ,  DNAJC15 ,  NMNAT3 ,  ...

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

    • CCDC86 ,  NDUFB4 ,  TUBA4B ,  HS1BP3 ,  MAP3K10 ,  ...

  • 6 genes correlated to 'DISTANT.METASTASIS'.

    • NHEDC1 ,  IL10RB ,  C9ORF153 ,  TINF2 ,  SNX29 ,  ...

  • 39 genes correlated to 'LYMPH.NODE.METASTASIS'.

    • SS18L1 ,  ZNF235 ,  MANBAL ,  HCRTR2 ,  TMEM33 ,  ...

  • 31 genes correlated to 'NEOPLASM.DISEASESTAGE'.

    • IDH3B ,  DRG2 ,  ATP5J ,  HIST1H4C ,  WDR74 ,  ...

  • No genes correlated to 'NUMBER.OF.LYMPH.NODES'

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=1 shorter survival N=0 longer survival N=1
AGE Spearman correlation test N=139 older N=126 younger N=13
GENDER t test N=186 male N=41 female N=145
RADIATIONS RADIATION REGIMENINDICATION t test N=161 yes N=139 no N=22
DISTANT METASTASIS ANOVA test N=6        
LYMPH NODE METASTASIS ANOVA test N=39        
NUMBER OF LYMPH NODES Spearman correlation test   N=0        
NEOPLASM DISEASESTAGE ANOVA test N=31        
Clinical variable #1: 'Time to Death'

One gene related to 'Time to Death'.

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

Time to Death Duration (Months) 0-223.4 (median=17.9)
  censored N = 444
  death N = 58
     
  Significant markers N = 1
  associated with shorter survival 0
  associated with longer survival 1
List of one gene significantly associated with 'Time to Death' by Cox regression test

Table S2.  Get Full Table List of one gene significantly associated with 'Time to Death' by Cox regression test

HazardRatio Wald_P Q C_index
CDC73 0 1.388e-06 0.024 0.355

Figure S1.  Get High-res Image As an example, this figure shows the association of CDC73 to 'Time to Death'. four curves present the cumulative survival rates of 4 quartile subsets of patients. P value = 1.39e-06 with univariate Cox regression analysis using continuous log-2 expression values.

Clinical variable #2: 'AGE'

139 genes related to 'AGE'.

Table S3.  Basic characteristics of clinical feature: 'AGE'

AGE Mean (SD) 57.6 (13)
  Significant markers N = 139
  pos. correlated 126
  neg. correlated 13
List of top 10 genes significantly correlated to 'AGE' by Spearman correlation test

Table S4.  Get Full Table List of top 10 genes significantly correlated to 'AGE' by Spearman correlation test

SpearmanCorr corrP Q
KIF15 0.3146 1.281e-13 2.22e-09
MEX3C 0.2866 1.839e-11 3.19e-07
EGR2 0.2857 2.157e-11 3.74e-07
LGALS8 -0.2825 3.667e-11 6.35e-07
RPL13A 0.281 4.709e-11 8.15e-07
C10ORF35 0.2783 7.276e-11 1.26e-06
FASN 0.2741 1.43e-10 2.48e-06
RPL27A 0.2672 4.222e-10 7.31e-06
RPL7A 0.2661 5.029e-10 8.71e-06
EIF4A1 0.2625 8.783e-10 1.52e-05

Figure S2.  Get High-res Image As an example, this figure shows the association of KIF15 to 'AGE'. P value = 1.28e-13 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #3: 'GENDER'

186 genes related to 'GENDER'.

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

GENDER Labels N
  FEMALE 524
  MALE 6
     
  Significant markers N = 186
  Higher in MALE 41
  Higher in FEMALE 145
List of top 10 genes differentially expressed by 'GENDER'

Table S6.  Get Full Table List of top 10 genes differentially expressed by 'GENDER'

T(pos if higher in 'MALE') ttestP Q AUC
ALDOC -25.69 1.408e-93 2.44e-89 0.8677
ZNF486 -18.27 5.628e-58 9.75e-54 0.8165
CRIP1 -16.92 1.283e-51 2.22e-47 0.8728
DNAJC15 -13.77 1.089e-35 1.89e-31 0.7325
NMNAT3 -13.22 4.298e-34 7.44e-30 0.6905
LOC400043 -13.13 3.755e-31 6.5e-27 0.6023
RND2 -13.17 2.007e-28 3.48e-24 0.792
EML1 -11.43 5.825e-27 1.01e-22 0.6072
SPC25 -12.2 3.114e-26 5.39e-22 0.7516
HSPC157 -12.95 7.35e-25 1.27e-20 0.6307

Figure S3.  Get High-res Image As an example, this figure shows the association of ALDOC to 'GENDER'. P value = 1.41e-93 with T-test analysis.

Clinical variable #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

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

Table S7.  Basic characteristics of clinical feature: 'RADIATIONS.RADIATION.REGIMENINDICATION'

RADIATIONS.RADIATION.REGIMENINDICATION Labels N
  NO 141
  YES 389
     
  Significant markers N = 161
  Higher in YES 139
  Higher in NO 22
List of top 10 genes differentially expressed by 'RADIATIONS.RADIATION.REGIMENINDICATION'

Table S8.  Get Full Table List of top 10 genes differentially expressed by 'RADIATIONS.RADIATION.REGIMENINDICATION'

T(pos if higher in 'YES') ttestP Q AUC
CCDC86 6.9 3.016e-11 5.22e-07 0.6798
NDUFB4 6.53 2.722e-10 4.71e-06 0.6671
TUBA4B 6.4 4.162e-10 7.21e-06 0.6405
HS1BP3 6.25 1.179e-09 2.04e-05 0.6597
MAP3K10 6.24 1.408e-09 2.44e-05 0.6496
PTRH1 6.12 1.798e-09 3.11e-05 0.6147
TICAM1 6.16 2.361e-09 4.09e-05 0.661
DDX54 6.12 2.798e-09 4.84e-05 0.6528
RASL11A 6.06 2.853e-09 4.94e-05 0.6322
CCDC85B 6.08 3.693e-09 6.39e-05 0.661

Figure S4.  Get High-res Image As an example, this figure shows the association of CCDC86 to 'RADIATIONS.RADIATION.REGIMENINDICATION'. P value = 3.02e-11 with T-test analysis.

Clinical variable #5: 'DISTANT.METASTASIS'

6 genes related to 'DISTANT.METASTASIS'.

Table S9.  Basic characteristics of clinical feature: 'DISTANT.METASTASIS'

DISTANT.METASTASIS Labels N
  CM0 (I+) 1
  M0 418
  M1 3
  MX 62
     
  Significant markers N = 6
List of 6 genes differentially expressed by 'DISTANT.METASTASIS'

Table S10.  Get Full Table List of 6 genes differentially expressed by 'DISTANT.METASTASIS'

ANOVA_P Q
NHEDC1 1.503e-28 2.6e-24
IL10RB 7.967e-12 1.38e-07
C9ORF153 1.715e-08 0.000297
TINF2 3.257e-07 0.00564
SNX29 5.012e-07 0.00868
DNAJB7 2.47e-06 0.0428

Figure S5.  Get High-res Image As an example, this figure shows the association of NHEDC1 to 'DISTANT.METASTASIS'. P value = 1.5e-28 with ANOVA analysis.

Clinical variable #6: 'LYMPH.NODE.METASTASIS'

39 genes related to 'LYMPH.NODE.METASTASIS'.

Table S11.  Basic characteristics of clinical feature: 'LYMPH.NODE.METASTASIS'

LYMPH.NODE.METASTASIS Labels N
  N0 139
  N0 (I+) 11
  N0 (I-) 60
  N0 (MOL+) 1
  N1 61
  N1A 76
  N1B 22
  N1C 2
  N1MI 13
  N2 33
  N2A 35
  N3 8
  N3A 16
  N3B 1
  NX 6
     
  Significant markers N = 39
List of top 10 genes differentially expressed by 'LYMPH.NODE.METASTASIS'

Table S12.  Get Full Table List of top 10 genes differentially expressed by 'LYMPH.NODE.METASTASIS'

ANOVA_P Q
SS18L1 1.514e-67 2.62e-63
ZNF235 1.125e-47 1.95e-43
MANBAL 1.686e-31 2.92e-27
HCRTR2 7.914e-23 1.37e-18
TMEM33 4.957e-19 8.58e-15
ZNF33A 3.578e-17 6.2e-13
SCRN2 1.593e-14 2.76e-10
KCNQ5 6.81e-13 1.18e-08
CLPP 4.655e-11 8.06e-07
TMEM208 1.445e-10 2.5e-06

Figure S6.  Get High-res Image As an example, this figure shows the association of SS18L1 to 'LYMPH.NODE.METASTASIS'. P value = 1.51e-67 with ANOVA analysis.

Clinical variable #7: 'NUMBER.OF.LYMPH.NODES'

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

Table S13.  Basic characteristics of clinical feature: 'NUMBER.OF.LYMPH.NODES'

NUMBER.OF.LYMPH.NODES Mean (SD) 2.41 (4.5)
  Significant markers N = 0
Clinical variable #8: 'NEOPLASM.DISEASESTAGE'

31 genes related to 'NEOPLASM.DISEASESTAGE'.

Table S14.  Basic characteristics of clinical feature: 'NEOPLASM.DISEASESTAGE'

NEOPLASM.DISEASESTAGE Labels N
  STAGE I 43
  STAGE IA 34
  STAGE IB 2
  STAGE II 8
  STAGE IIA 160
  STAGE IIB 114
  STAGE III 2
  STAGE IIIA 78
  STAGE IIIB 12
  STAGE IIIC 23
  STAGE IV 3
  STAGE X 4
     
  Significant markers N = 31
List of top 10 genes differentially expressed by 'NEOPLASM.DISEASESTAGE'

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

ANOVA_P Q
IDH3B 3.242e-59 5.62e-55
DRG2 4.677e-27 8.1e-23
ATP5J 5.262e-17 9.11e-13
HIST1H4C 1.805e-16 3.13e-12
WDR74 7.159e-16 1.24e-11
LNP1 3.022e-14 5.23e-10
TTC32 1.513e-13 2.62e-09
OPA1 2.176e-12 3.77e-08
POLE4 1.031e-11 1.78e-07
C17ORF75 3.753e-11 6.5e-07

Figure S7.  Get High-res Image As an example, this figure shows the association of IDH3B to 'NEOPLASM.DISEASESTAGE'. P value = 3.24e-59 with ANOVA analysis.

Methods & Data
Input

  • Expresson data file = BRCA-TP.meth.for_correlation.filtered_data.txt

  • Clinical data file = BRCA-TP.clin.merged.picked.txt

  • Number of patients = 530

  • Number of genes = 17320

  • Number of clinical features = 8

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

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

This is an experimental feature. The full results of the analysis summarized in this report can be downloaded from the TCGA Data Coordination Center.

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] Lehmann and Romano, Testing Statistical Hypotheses (3E ed.), New York: Springer. ISBN 0387988645 (2005)
[4] Howell, D, Statistical Methods for Psychology. (5th ed.), Duxbury Press:324-5 (2002)
[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)
Copyright © 2013 Broad Institute TCGA GDAC as part of the TCGA Research Network. All rights reserved.
Made with Nozzle