Correlation between gene methylation status and clinical features
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 20206 genes and 5 clinical features across 362 samples, statistically thresholded by Q value < 0.05, 4 clinical features related to at least one genes.

  • 196 genes correlated to 'AGE'.

    • ANGPT4 ,  TUFT1 ,  GPR158 ,  LOC100271836 ,  HDAC11 ,  ...

  • 2564 genes correlated to 'HISTOLOGICAL.TYPE'.

    • APBB1IP ,  SSTR1 ,  CRYAB ,  HSPB2 ,  CARD11 ,  ...

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

    • MAP2K2 ,  GPR89A ,  WBP4 ,  FZD8 ,  C22ORF13 ,  ...

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

    • MYRIP ,  TRIM37 ,  TNFRSF19 ,  BICC1 ,  TMEM145 ,  ...

  • No genes correlated to 'Time to Death'

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=196 older N=61 younger N=135
HISTOLOGICAL TYPE ANOVA test N=2564        
RADIATIONS RADIATION REGIMENINDICATION t test N=6 yes N=6 no N=0
COMPLETENESS OF RESECTION ANOVA test N=18        
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) 0-191.8 (median=16.8)
  censored N = 318
  death N = 40
     
  Significant markers N = 0
Clinical variable #2: 'AGE'

196 genes related to 'AGE'.

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

AGE Mean (SD) 63.92 (11)
  Significant markers N = 196
  pos. correlated 61
  neg. correlated 135
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
ANGPT4 0.3543 4.394e-12 8.88e-08
TUFT1 -0.3429 2.263e-11 4.57e-07
GPR158 -0.3417 2.708e-11 5.47e-07
LOC100271836 -0.3413 2.863e-11 5.78e-07
HDAC11 -0.3351 6.787e-11 1.37e-06
TBC1D23 -0.3329 9.103e-11 1.84e-06
GDNF -0.3299 1.381e-10 2.79e-06
SLFN14 0.3234 3.31e-10 6.69e-06
GPR142 0.3148 1.005e-09 2.03e-05
CACNB1 -0.314 1.123e-09 2.27e-05

Figure S1.  Get High-res Image As an example, this figure shows the association of ANGPT4 to 'AGE'. P value = 4.39e-12 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #3: 'HISTOLOGICAL.TYPE'

2564 genes related to 'HISTOLOGICAL.TYPE'.

Table S4.  Basic characteristics of clinical feature: 'HISTOLOGICAL.TYPE'

HISTOLOGICAL.TYPE Labels N
  ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA 267
  MIXED SEROUS AND ENDOMETRIOID 17
  SEROUS ENDOMETRIAL ADENOCARCINOMA 77
     
  Significant markers N = 2564
List of top 10 genes differentially expressed by 'HISTOLOGICAL.TYPE'

Table S5.  Get Full Table List of top 10 genes differentially expressed by 'HISTOLOGICAL.TYPE'

ANOVA_P Q
APBB1IP 1.405e-47 2.84e-43
SSTR1 3.603e-47 7.28e-43
CRYAB 1.414e-40 2.86e-36
HSPB2 1.414e-40 2.86e-36
CARD11 1.563e-40 3.16e-36
ADAMTS16 7.949e-40 1.61e-35
PRKCB 1.785e-38 3.61e-34
CGN 2.142e-38 4.33e-34
KCNA6 9.302e-38 1.88e-33
GRIK3 5.288e-37 1.07e-32

Figure S2.  Get High-res Image As an example, this figure shows the association of APBB1IP to 'HISTOLOGICAL.TYPE'. P value = 1.4e-47 with ANOVA analysis.

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

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

Table S6.  Basic characteristics of clinical feature: 'RADIATIONS.RADIATION.REGIMENINDICATION'

RADIATIONS.RADIATION.REGIMENINDICATION Labels N
  NO 83
  YES 279
     
  Significant markers N = 6
  Higher in YES 6
  Higher in NO 0
List of 6 genes differentially expressed by 'RADIATIONS.RADIATION.REGIMENINDICATION'

Table S7.  Get Full Table List of 6 genes differentially expressed by 'RADIATIONS.RADIATION.REGIMENINDICATION'

T(pos if higher in 'YES') ttestP Q AUC
MAP2K2 5.67 7.412e-08 0.0015 0.6952
GPR89A 5.33 3.242e-07 0.00655 0.7156
WBP4 5.23 3.969e-07 0.00802 0.6592
FZD8 4.91 1.931e-06 0.039 0.669
C22ORF13 4.87 2.4e-06 0.0485 0.6528
SNRPD3 4.87 2.4e-06 0.0485 0.6528

Figure S3.  Get High-res Image As an example, this figure shows the association of MAP2K2 to 'RADIATIONS.RADIATION.REGIMENINDICATION'. P value = 7.41e-08 with T-test analysis.

Clinical variable #5: 'COMPLETENESS.OF.RESECTION'

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

Table S8.  Basic characteristics of clinical feature: 'COMPLETENESS.OF.RESECTION'

COMPLETENESS.OF.RESECTION Labels N
  R0 239
  R1 18
  R2 12
  RX 25
     
  Significant markers N = 18
List of top 10 genes differentially expressed by 'COMPLETENESS.OF.RESECTION'

Table S9.  Get Full Table List of top 10 genes differentially expressed by 'COMPLETENESS.OF.RESECTION'

ANOVA_P Q
MYRIP 7.886e-12 1.59e-07
TRIM37 5.54e-11 1.12e-06
TNFRSF19 1.524e-09 3.08e-05
BICC1 1.665e-09 3.36e-05
TMEM145 4.332e-08 0.000875
CPPED1 5.198e-08 0.00105
FAM82A1 1.091e-07 0.0022
CEBPA 1.533e-07 0.0031
LOC80054 1.533e-07 0.0031
NDUFA12 2.533e-07 0.00512

Figure S4.  Get High-res Image As an example, this figure shows the association of MYRIP to 'COMPLETENESS.OF.RESECTION'. P value = 7.89e-12 with ANOVA analysis.

Methods & Data
Input

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

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

  • Number of patients = 362

  • Number of genes = 20206

  • 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.

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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