Correlation between gene methylation status and clinical features
Kidney Renal Papillary Cell Carcinoma (Primary solid tumor)
23 September 2013  |  analyses__2013_09_23
Maintainer Information
Citation Information
doi:10.7908/C14T6GPM
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/C14T6GPM
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 19964 genes and 9 clinical features across 110 samples, statistically thresholded by Q value < 0.05, 6 clinical features related to at least one genes.

  • 61 genes correlated to 'Time to Death'.

    • ADRA1A ,  PACSIN1 ,  KRTCAP2 ,  TRIM46 ,  C3ORF72 ,  ...

  • 1 gene correlated to 'AGE'.

    • WDR81__1

  • 157 genes correlated to 'NEOPLASM.DISEASESTAGE'.

    • C17ORF93 ,  PRAC ,  DLX6AS__1 ,  TMEM132B ,  PCDHA1__8 ,  ...

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

    • DLX6AS__1 ,  DLX6 ,  DLX6AS ,  OTP ,  CDO1 ,  ...

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

    • BAHD1 ,  LPPR2 ,  SCN4A ,  THAP2__1 ,  ZFC3H1__1 ,  ...

  • 8 genes correlated to 'GENDER'.

    • CCNYL1 ,  NARFL ,  PRKRIR ,  HNRNPD ,  FDPS ,  ...

  • No genes correlated to 'PATHOLOGY.N.STAGE', 'KARNOFSKY.PERFORMANCE.SCORE', and 'NUMBERPACKYEARSSMOKED'.

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=61 shorter survival N=59 longer survival N=2
AGE Spearman correlation test N=1 older N=1 younger N=0
NEOPLASM DISEASESTAGE ANOVA test N=157        
PATHOLOGY T STAGE Spearman correlation test N=92 higher stage N=82 lower stage N=10
PATHOLOGY N STAGE Spearman correlation test   N=0        
PATHOLOGY M STAGE ANOVA test N=159        
GENDER t test N=8 male N=5 female N=3
KARNOFSKY PERFORMANCE SCORE Spearman correlation test   N=0        
NUMBERPACKYEARSSMOKED Spearman correlation test   N=0        
Clinical variable #1: 'Time to Death'

61 genes related to 'Time to Death'.

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

Time to Death Duration (Months) 0-194.8 (median=13.7)
  censored N = 87
  death N = 13
     
  Significant markers N = 61
  associated with shorter survival 59
  associated with longer survival 2
List of top 10 genes significantly associated with 'Time to Death' by Cox regression test

Table S2.  Get Full Table List of top 10 genes significantly associated with 'Time to Death' by Cox regression test

HazardRatio Wald_P Q C_index
ADRA1A 7801 5.51e-09 0.00011 0.823
PACSIN1 411 4.248e-08 0.00085 0.821
KRTCAP2 10000001 4.924e-08 0.00098 0.618
TRIM46 10000001 4.924e-08 0.00098 0.618
C3ORF72 951 6.595e-08 0.0013 0.711
FOXL2 951 6.595e-08 0.0013 0.711
PLIN5 1501 7.621e-08 0.0015 0.848
NHLH2 1901 1.28e-07 0.0026 0.903
MT1G__1 2801 1.282e-07 0.0026 0.869
C10ORF67 4301 1.345e-07 0.0027 0.789

Figure S1.  Get High-res Image As an example, this figure shows the association of ADRA1A to 'Time to Death'. four curves present the cumulative survival rates of 4 quartile subsets of patients. P value = 5.51e-09 with univariate Cox regression analysis using continuous log-2 expression values.

Clinical variable #2: 'AGE'

One gene related to 'AGE'.

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

AGE Mean (SD) 59.98 (12)
  Significant markers N = 1
  pos. correlated 1
  neg. correlated 0
List of one gene significantly correlated to 'AGE' by Spearman correlation test

Table S4.  Get Full Table List of one gene significantly correlated to 'AGE' by Spearman correlation test

SpearmanCorr corrP Q
WDR81__1 0.4375 2.454e-06 0.049

Figure S2.  Get High-res Image As an example, this figure shows the association of WDR81__1 to 'AGE'. P value = 2.45e-06 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #3: 'NEOPLASM.DISEASESTAGE'

157 genes related to 'NEOPLASM.DISEASESTAGE'.

Table S5.  Basic characteristics of clinical feature: 'NEOPLASM.DISEASESTAGE'

NEOPLASM.DISEASESTAGE Labels N
  STAGE I 63
  STAGE II 7
  STAGE III 29
  STAGE IV 9
     
  Significant markers N = 157
List of top 10 genes differentially expressed by 'NEOPLASM.DISEASESTAGE'

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

ANOVA_P Q
C17ORF93 2.999e-11 5.99e-07
PRAC 2.999e-11 5.99e-07
DLX6AS__1 6.384e-11 1.27e-06
TMEM132B 3.12e-10 6.23e-06
PCDHA1__8 6.082e-10 1.21e-05
PCDHA10__5 6.082e-10 1.21e-05
PCDHA11__3 6.082e-10 1.21e-05
PCDHA12__3 6.082e-10 1.21e-05
PCDHA13__2 6.082e-10 1.21e-05
PCDHA2__8 6.082e-10 1.21e-05

Figure S3.  Get High-res Image As an example, this figure shows the association of C17ORF93 to 'NEOPLASM.DISEASESTAGE'. P value = 3e-11 with ANOVA analysis.

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

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

Table S7.  Basic characteristics of clinical feature: 'PATHOLOGY.T.STAGE'

PATHOLOGY.T.STAGE Mean (SD) 1.75 (0.94)
  N
  1 65
  2 9
  3 35
  4 1
     
  Significant markers N = 92
  pos. correlated 82
  neg. correlated 10
List of top 10 genes significantly correlated to 'PATHOLOGY.T.STAGE' by Spearman correlation test

Table S8.  Get Full Table List of top 10 genes significantly correlated to 'PATHOLOGY.T.STAGE' by Spearman correlation test

SpearmanCorr corrP Q
DLX6AS__1 0.58 3.138e-11 6.26e-07
DLX6 0.5683 9.469e-11 1.89e-06
DLX6AS 0.5683 9.469e-11 1.89e-06
OTP 0.539 1.242e-09 2.48e-05
CDO1 0.5316 2.295e-09 4.58e-05
C17ORF93 0.5265 3.455e-09 6.9e-05
PRAC 0.5265 3.455e-09 6.9e-05
GPR150 0.5264 3.501e-09 6.99e-05
TBX4 0.5237 4.342e-09 8.66e-05
LEFTY2 0.5233 4.487e-09 8.95e-05

Figure S4.  Get High-res Image As an example, this figure shows the association of DLX6AS__1 to 'PATHOLOGY.T.STAGE'. P value = 3.14e-11 with Spearman correlation analysis.

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

No gene related to 'PATHOLOGY.N.STAGE'.

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

PATHOLOGY.N.STAGE Mean (SD) 0.49 (0.68)
  N
  0 24
  1 11
  2 4
     
  Significant markers N = 0
Clinical variable #6: 'PATHOLOGY.M.STAGE'

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

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

PATHOLOGY.M.STAGE Labels N
  M0 47
  M1 5
  MX 55
     
  Significant markers N = 159
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
BAHD1 1e-12 2e-08
LPPR2 2.281e-11 4.55e-07
SCN4A 1.533e-10 3.06e-06
THAP2__1 1.751e-10 3.49e-06
ZFC3H1__1 1.751e-10 3.49e-06
PCDHA1__8 1.893e-10 3.78e-06
PCDHA10__5 1.893e-10 3.78e-06
PCDHA11__3 1.893e-10 3.78e-06
PCDHA12__3 1.893e-10 3.78e-06
PCDHA13__2 1.893e-10 3.78e-06

Figure S5.  Get High-res Image As an example, this figure shows the association of BAHD1 to 'PATHOLOGY.M.STAGE'. P value = 1e-12 with ANOVA analysis.

Clinical variable #7: 'GENDER'

8 genes related to 'GENDER'.

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

GENDER Labels N
  FEMALE 34
  MALE 76
     
  Significant markers N = 8
  Higher in MALE 5
  Higher in FEMALE 3
List of 8 genes differentially expressed by 'GENDER'

Table S13.  Get Full Table List of 8 genes differentially expressed by 'GENDER'

T(pos if higher in 'MALE') ttestP Q AUC
CCNYL1 -9.6 1.468e-12 2.93e-08 0.9211
NARFL -7.41 3.988e-11 7.96e-07 0.8231
PRKRIR 7.71 9.936e-10 1.98e-05 0.9172
HNRNPD 7.06 8.319e-09 0.000166 0.8599
FDPS 5.88 5.483e-07 0.0109 0.8897
RUSC1__1 5.88 5.483e-07 0.0109 0.8897
KIF4B -5.51 1.013e-06 0.0202 0.7988
WBP11P1 5.5 1.528e-06 0.0305 0.8216

Figure S6.  Get High-res Image As an example, this figure shows the association of CCNYL1 to 'GENDER'. P value = 1.47e-12 with T-test analysis.

Clinical variable #8: 'KARNOFSKY.PERFORMANCE.SCORE'

No gene related to 'KARNOFSKY.PERFORMANCE.SCORE'.

Table S14.  Basic characteristics of clinical feature: 'KARNOFSKY.PERFORMANCE.SCORE'

KARNOFSKY.PERFORMANCE.SCORE Mean (SD) 92 (13)
  Score N
  40 1
  90 10
  100 9
     
  Significant markers N = 0
Clinical variable #9: 'NUMBERPACKYEARSSMOKED'

No gene related to 'NUMBERPACKYEARSSMOKED'.

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

NUMBERPACKYEARSSMOKED Mean (SD) 13.75 (8.5)
  Value N
  5 1
  10 1
  15 1
  25 1
     
  Significant markers N = 0
Methods & Data
Input

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

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

  • Number of patients = 110

  • Number of genes = 19964

  • Number of clinical features = 9

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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