Uterine Corpus Endometrioid Carcinoma: Correlation between gene methylation status and clinical features
Maintained by Juok Cho (Broad Institute)
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 20238 genes and 5 clinical features across 313 samples, statistically thresholded by Q value < 0.05, 5 clinical features related to at least one genes.

  • 1 gene correlated to 'Time to Death'.

    • ACOT8

  • 138 genes correlated to 'AGE'.

    • ZNF574 ,  SLFN14 ,  MIR1307 ,  MIR1249 ,  FBXO43 ,  ...

  • 1556 genes correlated to 'HISTOLOGICAL.TYPE'.

    • UBE2T ,  SSTR1 ,  NPBWR2 ,  KCNA1 ,  CRYAB ,  ...

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

    • AP3M1 ,  PCYT1A ,  CCDC159 ,  PPP1R11 ,  RNMTL1 ,  ...

  • 6 genes correlated to 'NEOADJUVANT.THERAPY'.

    • SASS6 ,  LOC400927 ,  YIF1A ,  SOCS3 ,  GADD45G ,  ...

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=1 longer survival N=0
AGE Spearman correlation test N=138 older N=79 younger N=59
HISTOLOGICAL TYPE ANOVA test N=1556        
RADIATIONS RADIATION REGIMENINDICATION t test N=48 yes N=48 no N=0
NEOADJUVANT THERAPY t test N=6 yes N=6 no N=0
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) 0-187.1 (median=12.8)
  censored N = 279
  death N = 30
     
  Significant markers N = 1
  associated with shorter survival 1
  associated with longer survival 0
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
ACOT8 310000000000001 1.727e-06 0.035 0.69

Figure S1.  Get High-res Image As an example, this figure shows the association of ACOT8 to 'Time to Death'. four curves present the cumulative survival rates of 4 quartile subsets of patients. P value = 1.73e-06 with univariate Cox regression analysis using continuous log-2 expression values.

Clinical variable #2: 'AGE'

138 genes related to 'AGE'.

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

AGE Mean (SD) 63.49 (11)
  Significant markers N = 138
  pos. correlated 79
  neg. correlated 59
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
ZNF574 -0.3618 4.415e-11 8.93e-07
SLFN14 0.3577 7.515e-11 1.52e-06
MIR1307 -0.355 1.066e-10 2.16e-06
MIR1249 0.3432 4.738e-10 9.59e-06
FBXO43 -0.3397 7.27e-10 1.47e-05
CLDN16 -0.3344 1.373e-09 2.78e-05
FBXL5 -0.3316 1.918e-09 3.88e-05
POLR2J -0.3289 2.624e-09 5.31e-05
TREM1 0.3283 2.832e-09 5.73e-05
OTOR 0.3277 3.023e-09 6.11e-05

Figure S2.  Get High-res Image As an example, this figure shows the association of ZNF574 to 'AGE'. P value = 4.41e-11 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #3: 'HISTOLOGICAL.TYPE'

1556 genes related to 'HISTOLOGICAL.TYPE'.

Table S5.  Basic characteristics of clinical feature: 'HISTOLOGICAL.TYPE'

HISTOLOGICAL.TYPE Labels N
  ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA 229
  ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA (GRADE 1) 6
  ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA (GRADE 2) 1
  ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA (GRADE 3) 3
  MIXED SEROUS AND ENDOMETRIOID 16
  SEROUS ENDOMETRIAL ADENOCARCINOMA 58
     
  Significant markers N = 1556
List of top 10 genes differentially expressed by 'HISTOLOGICAL.TYPE'

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

ANOVA_P Q
UBE2T 4.831e-42 9.78e-38
SSTR1 7.116e-34 1.44e-29
NPBWR2 3.718e-33 7.52e-29
KCNA1 1.888e-31 3.82e-27
CRYAB 2.548e-31 5.15e-27
HSPB2 3.63e-31 7.34e-27
SFMBT1 8.92e-28 1.8e-23
GRIA4 1.274e-26 2.58e-22
IDO1 2.62e-26 5.3e-22
LOC400578 2.664e-25 5.39e-21

Figure S3.  Get High-res Image As an example, this figure shows the association of UBE2T to 'HISTOLOGICAL.TYPE'. P value = 4.83e-42 with ANOVA analysis.

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

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

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

RADIATIONS.RADIATION.REGIMENINDICATION Labels N
  NO 79
  YES 234
     
  Significant markers N = 48
  Higher in YES 48
  Higher in NO 0
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
AP3M1 6.09 8.254e-09 0.000167 0.7076
PCYT1A 5.93 1.842e-08 0.000373 0.6969
CCDC159 5.76 2.853e-08 0.000577 0.6667
PPP1R11 5.5 1.095e-07 0.00222 0.7095
RNMTL1 5.49 1.302e-07 0.00264 0.6808
YKT6 5.48 1.497e-07 0.00303 0.6868
SSRP1 5.45 1.61e-07 0.00326 0.6714
RFC2 5.53 1.698e-07 0.00344 0.6997
PLEKHA3 5.33 2.32e-07 0.00469 0.6712
NEK11 5.28 3.415e-07 0.00691 0.6956

Figure S4.  Get High-res Image As an example, this figure shows the association of AP3M1 to 'RADIATIONS.RADIATION.REGIMENINDICATION'. P value = 8.25e-09 with T-test analysis.

Clinical variable #5: 'NEOADJUVANT.THERAPY'

6 genes related to 'NEOADJUVANT.THERAPY'.

Table S9.  Basic characteristics of clinical feature: 'NEOADJUVANT.THERAPY'

NEOADJUVANT.THERAPY Labels N
  NO 70
  YES 243
     
  Significant markers N = 6
  Higher in YES 6
  Higher in NO 0
List of 6 genes differentially expressed by 'NEOADJUVANT.THERAPY'

Table S10.  Get Full Table List of 6 genes differentially expressed by 'NEOADJUVANT.THERAPY'

T(pos if higher in 'YES') ttestP Q AUC
SASS6 5.38 3.253e-07 0.00658 0.6848
LOC400927 5.29 3.46e-07 0.007 0.6628
YIF1A 5.24 5.093e-07 0.0103 0.6681
SOCS3 5.04 1.077e-06 0.0218 0.6466
GADD45G 4.94 2.065e-06 0.0418 0.668
CCRN4L 4.93 2.429e-06 0.0492 0.6751

Figure S5.  Get High-res Image As an example, this figure shows the association of SASS6 to 'NEOADJUVANT.THERAPY'. P value = 3.25e-07 with T-test analysis.

Methods & Data
Input

  • Expresson data file = UCEC.meth.for_correlation.filtered_data.txt

  • Clinical data file = UCEC.clin.merged.picked.txt

  • Number of patients = 313

  • Number of genes = 20238

  • Number of clinical features = 5

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

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] 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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