Correlation between gene methylation status and clinical features
Colon Adenocarcinoma (Primary solid tumor)
23 September 2013  |  analyses__2013_09_23
Maintainer Information
Citation Information
doi:10.7908/C1542KWF
Maintained by Juok Cho (Broad Institute)
Cite as Broad Institute TCGA Genome Data Analysis Center (2013): Correlation between gene methylation status and clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1542KWF
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 19884 genes and 11 clinical features across 266 samples, statistically thresholded by Q value < 0.05, 9 clinical features related to at least one genes.

  • 7 genes correlated to 'AGE'.

    • ELOVL2 ,  GDNF ,  KLF14 ,  C1QL1 ,  GPR1 ,  ...

  • 823 genes correlated to 'NEOPLASM.DISEASESTAGE'.

    • DEK ,  USP21 ,  ORC3L__1 ,  RARS2__1 ,  PPDPF ,  ...

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

    • UBE2L6 ,  CASP1__1 ,  APOL1 ,  IL12RB1 ,  SP140L ,  ...

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

    • FAM86B2 ,  JOSD2 ,  LYSMD2 ,  TMOD2 ,  DMKN ,  ...

  • 9 genes correlated to 'GENDER'.

    • GPX1 ,  KIF4B ,  POLDIP3 ,  RNU12 ,  PAFAH1B2 ,  ...

  • 7 genes correlated to 'HISTOLOGICAL.TYPE'.

    • AXIN2 ,  TMEM45B ,  GFOD2 ,  C11ORF9 ,  PRSS3 ,  ...

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

    • SUCLG2 ,  SLCO4C1 ,  RRN3P2 ,  ZNF665 ,  LASS4 ,  ...

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

    • TMEM115 ,  FUCA1 ,  ZNF140 ,  ASB7__1 ,  CCDC146__1 ,  ...

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

    • CASP1__1 ,  UBE2L6 ,  IL12RB1 ,  APOL1 ,  CSGALNACT1

  • 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=7 older N=7 younger N=0
NEOPLASM DISEASESTAGE ANOVA test N=823        
PATHOLOGY T STAGE Spearman correlation test   N=0        
PATHOLOGY N STAGE Spearman correlation test N=6 higher stage N=6 lower stage N=0
PATHOLOGY M STAGE ANOVA test N=265        
GENDER t test N=9 male N=2 female N=7
HISTOLOGICAL TYPE t test N=7 colon mucinous adenocarcinoma N=6 colon adenocarcinoma N=1
RADIATIONS RADIATION REGIMENINDICATION t test N=332 yes N=275 no N=57
COMPLETENESS OF RESECTION ANOVA test N=98        
NUMBER OF LYMPH NODES Spearman correlation test N=5 higher number.of.lymph.nodes N=5 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) 0.1-140.4 (median=13.1)
  censored N = 207
  death N = 49
     
  Significant markers N = 0
Clinical variable #2: 'AGE'

7 genes related to 'AGE'.

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

AGE Mean (SD) 65.06 (13)
  Significant markers N = 7
  pos. correlated 7
  neg. correlated 0
List of 7 genes significantly correlated to 'AGE' by Spearman correlation test

Table S3.  Get Full Table List of 7 genes significantly correlated to 'AGE' by Spearman correlation test

SpearmanCorr corrP Q
ELOVL2 0.4827 8.183e-17 1.63e-12
GDNF 0.3275 5.131e-08 0.00102
KLF14 0.3003 6.638e-07 0.0132
C1QL1 0.2996 7.074e-07 0.0141
GPR1 0.2942 1.137e-06 0.0226
PPM1E 0.2877 1.994e-06 0.0396
TAC1 0.2869 2.137e-06 0.0425

Figure S1.  Get High-res Image As an example, this figure shows the association of ELOVL2 to 'AGE'. P value = 8.18e-17 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #3: 'NEOPLASM.DISEASESTAGE'

823 genes related to 'NEOPLASM.DISEASESTAGE'.

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

NEOPLASM.DISEASESTAGE Labels N
  STAGE I 39
  STAGE IA 1
  STAGE II 19
  STAGE IIA 79
  STAGE IIB 5
  STAGE IIC 1
  STAGE III 8
  STAGE IIIA 10
  STAGE IIIB 40
  STAGE IIIC 20
  STAGE IV 19
  STAGE IVA 15
  STAGE IVB 1
     
  Significant markers N = 823
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
DEK 7.464e-223 1.48e-218
USP21 4.14e-184 8.23e-180
ORC3L__1 3.92e-172 7.79e-168
RARS2__1 3.92e-172 7.79e-168
PPDPF 6.882e-169 1.37e-164
LOC728723__1 1.221e-168 2.43e-164
ZBED3__1 1.221e-168 2.43e-164
FAM86B2 5.454e-158 1.08e-153
JOSD2 6.567e-157 1.31e-152
FBXW2 8.106e-154 1.61e-149

Figure S2.  Get High-res Image As an example, this figure shows the association of DEK to 'NEOPLASM.DISEASESTAGE'. P value = 7.46e-223 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) 2.91 (0.63)
  N
  0 1
  1 6
  2 40
  3 185
  4 32
     
  Significant markers N = 0
Clinical variable #5: 'PATHOLOGY.N.STAGE'

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

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

PATHOLOGY.N.STAGE Mean (SD) 0.56 (0.74)
  N
  0 156
  1 68
  2 40
     
  Significant markers N = 6
  pos. correlated 6
  neg. correlated 0
List of 6 genes significantly correlated to 'PATHOLOGY.N.STAGE' by Spearman correlation test

Table S8.  Get Full Table List of 6 genes significantly correlated to 'PATHOLOGY.N.STAGE' by Spearman correlation test

SpearmanCorr corrP Q
UBE2L6 0.3541 3.24e-09 6.44e-05
CASP1__1 0.3495 5.307e-09 0.000106
APOL1 0.3424 1.129e-08 0.000224
IL12RB1 0.3172 1.388e-07 0.00276
SP140L 0.2918 1.408e-06 0.028
CASP5 0.286 2.308e-06 0.0459

Figure S3.  Get High-res Image As an example, this figure shows the association of UBE2L6 to 'PATHOLOGY.N.STAGE'. P value = 3.24e-09 with Spearman correlation analysis.

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

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

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

PATHOLOGY.M.STAGE Labels N
  M0 187
  M1 27
  M1A 6
  M1B 1
  MX 39
     
  Significant markers N = 265
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
FAM86B2 1.099e-167 2.19e-163
JOSD2 8.597e-166 1.71e-161
LYSMD2 6.785e-98 1.35e-93
TMOD2 6.785e-98 1.35e-93
DMKN 5.655e-72 1.12e-67
KCNK4 5.01e-71 9.96e-67
FZD3 6.811e-54 1.35e-49
PDSS2 3.633e-53 7.22e-49
ENTPD8 3.898e-52 7.75e-48
CLTCL1 2.807e-51 5.58e-47

Figure S4.  Get High-res Image As an example, this figure shows the association of FAM86B2 to 'PATHOLOGY.M.STAGE'. P value = 1.1e-167 with ANOVA analysis.

Clinical variable #7: 'GENDER'

9 genes related to 'GENDER'.

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

GENDER Labels N
  FEMALE 119
  MALE 146
     
  Significant markers N = 9
  Higher in MALE 2
  Higher in FEMALE 7
List of 9 genes differentially expressed by 'GENDER'

Table S12.  Get Full Table List of 9 genes differentially expressed by 'GENDER'

T(pos if higher in 'MALE') ttestP Q AUC
GPX1 -10.9 8.395e-23 1.67e-18 0.836
KIF4B -9.56 8.95e-19 1.78e-14 0.8275
POLDIP3 -8.08 3.094e-14 6.15e-10 0.7873
RNU12 -8.08 3.094e-14 6.15e-10 0.7873
PAFAH1B2 -6.66 1.648e-10 3.28e-06 0.7221
UBAP2 -6.12 3.545e-09 7.05e-05 0.695
ZNF839 -5.71 3.114e-08 0.000619 0.6957
RIMBP3 5.55 7.274e-08 0.00145 0.6818
RUFY1 4.86 2.047e-06 0.0407 0.6587

Figure S5.  Get High-res Image As an example, this figure shows the association of GPX1 to 'GENDER'. P value = 8.39e-23 with T-test analysis.

Clinical variable #8: 'HISTOLOGICAL.TYPE'

7 genes related to 'HISTOLOGICAL.TYPE'.

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

HISTOLOGICAL.TYPE Labels N
  COLON ADENOCARCINOMA 232
  COLON MUCINOUS ADENOCARCINOMA 33
     
  Significant markers N = 7
  Higher in COLON MUCINOUS ADENOCARCINOMA 6
  Higher in COLON ADENOCARCINOMA 1
List of 7 genes differentially expressed by 'HISTOLOGICAL.TYPE'

Table S14.  Get Full Table List of 7 genes differentially expressed by 'HISTOLOGICAL.TYPE'

T(pos if higher in 'COLON MUCINOUS ADENOCARCINOMA') ttestP Q AUC
AXIN2 6.37 2.878e-08 0.000572 0.7304
TMEM45B 5.98 1.211e-07 0.00241 0.7236
GFOD2 5.66 8.118e-07 0.0161 0.749
C11ORF9 -5.45 1.427e-06 0.0284 0.7208
PRSS3 5.46 2.068e-06 0.0411 0.7546
SEPHS1 5.23 2.199e-06 0.0437 0.6903
RALGPS1__1 5.45 2.252e-06 0.0448 0.7666

Figure S6.  Get High-res Image As an example, this figure shows the association of AXIN2 to 'HISTOLOGICAL.TYPE'. P value = 2.88e-08 with T-test analysis.

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

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

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

RADIATIONS.RADIATION.REGIMENINDICATION Labels N
  NO 3
  YES 263
     
  Significant markers N = 332
  Higher in YES 275
  Higher in NO 57
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
SUCLG2 -19.66 1.797e-51 3.57e-47 0.9049
SLCO4C1 18.1 1.615e-47 3.21e-43 0.749
RRN3P2 15.85 3.405e-40 6.77e-36 0.7364
ZNF665 15.63 1.919e-39 3.82e-35 0.9062
LASS4 16.99 1.345e-38 2.67e-34 0.8986
OXNAD1 -16.67 1.662e-37 3.3e-33 0.8542
CYP2U1 14.89 1.652e-35 3.28e-31 0.7883
ELAVL2 16.07 2.175e-35 4.32e-31 0.7896
INHA__1 15.36 1.023e-34 2.03e-30 0.8112
OBSL1__1 15.36 1.023e-34 2.03e-30 0.8112

Figure S7.  Get High-res Image As an example, this figure shows the association of SUCLG2 to 'RADIATIONS.RADIATION.REGIMENINDICATION'. P value = 1.8e-51 with T-test analysis.

Clinical variable #10: 'COMPLETENESS.OF.RESECTION'

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

Table S17.  Basic characteristics of clinical feature: 'COMPLETENESS.OF.RESECTION'

COMPLETENESS.OF.RESECTION Labels N
  R0 164
  R1 2
  R2 4
  RX 23
     
  Significant markers N = 98
List of top 10 genes differentially expressed by 'COMPLETENESS.OF.RESECTION'

Table S18.  Get Full Table List of top 10 genes differentially expressed by 'COMPLETENESS.OF.RESECTION'

ANOVA_P Q
TMEM115 9.582e-24 1.91e-19
FUCA1 5.466e-18 1.09e-13
ZNF140 9.129e-18 1.82e-13
ASB7__1 1.614e-14 3.21e-10
CCDC146__1 2.849e-13 5.66e-09
EFR3A 3.927e-13 7.81e-09
FAM185A 1.587e-12 3.16e-08
CAB39L 2.71e-12 5.39e-08
SETDB2 2.71e-12 5.39e-08
C17ORF71 2.756e-12 5.48e-08

Figure S8.  Get High-res Image As an example, this figure shows the association of TMEM115 to 'COMPLETENESS.OF.RESECTION'. P value = 9.58e-24 with ANOVA analysis.

Clinical variable #11: 'NUMBER.OF.LYMPH.NODES'

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

Table S19.  Basic characteristics of clinical feature: 'NUMBER.OF.LYMPH.NODES'

NUMBER.OF.LYMPH.NODES Mean (SD) 1.94 (4.5)
  Significant markers N = 5
  pos. correlated 5
  neg. correlated 0
List of 5 genes significantly correlated to 'NUMBER.OF.LYMPH.NODES' by Spearman correlation test

Table S20.  Get Full Table List of 5 genes significantly correlated to 'NUMBER.OF.LYMPH.NODES' by Spearman correlation test

SpearmanCorr corrP Q
CASP1__1 0.3679 3.578e-09 7.11e-05
UBE2L6 0.3626 6.259e-09 0.000124
IL12RB1 0.3267 1.995e-07 0.00397
APOL1 0.3155 5.404e-07 0.0107
CSGALNACT1 0.3062 1.197e-06 0.0238

Figure S9.  Get High-res Image As an example, this figure shows the association of CASP1__1 to 'NUMBER.OF.LYMPH.NODES'. P value = 3.58e-09 with Spearman correlation analysis. The straight line presents the best linear regression.

Methods & Data
Input

  • Expresson data file = COAD-TP.meth.by_min_expr_corr.data.txt

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

  • Number of patients = 266

  • Number of genes = 19884

  • Number of clinical features = 11

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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