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 20386 genes and 7 clinical features across 380 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 4 clinical features related to at least one genes.

  • 513 genes correlated to 'AGE'.

    • ANGPT4 ,  ALS2CL ,  GPR158 ,  ITPK1 ,  NCRNA00203 ,  ...

  • 3991 genes correlated to 'HISTOLOGICAL.TYPE'.

    • APBB1IP ,  SSTR1 ,  RASGRF1 ,  ADAMTS16 ,  ITPK1 ,  ...

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

    • RMND5B ,  FIZ1 ,  ZNF524 ,  ZNF831 ,  MAP2K2 ,  ...

  • 30 genes correlated to 'RACE'.

    • C18ORF54 ,  LOC253039 ,  PSMD5 ,  ZNF525 ,  NCRNA00174 ,  ...

  • No genes correlated to 'Time to Death', 'COMPLETENESS.OF.RESECTION', and 'ETHNICITY'.

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 P value < 0.05 and Q value < 0.3.

Clinical feature Statistical test Significant genes Associated with                 Associated with
Time to Death Cox regression test   N=0        
AGE Spearman correlation test N=513 older N=167 younger N=346
HISTOLOGICAL TYPE Kruskal-Wallis test N=3991        
RADIATIONS RADIATION REGIMENINDICATION Wilcoxon test N=66 yes N=66 no N=0
COMPLETENESS OF RESECTION Kruskal-Wallis test   N=0        
RACE Kruskal-Wallis test N=30        
ETHNICITY Wilcoxon test   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) 0-191.8 (median=18)
  censored N = 333
  death N = 45
     
  Significant markers N = 0
Clinical variable #2: 'AGE'

513 genes related to 'AGE'.

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

AGE Mean (SD) 63.85 (11)
  Significant markers N = 513
  pos. correlated 167
  neg. correlated 346
List of top 10 genes differentially expressed by 'AGE'

Table S3.  Get Full Table List of top 10 genes significantly correlated to 'AGE' by Spearman correlation test

SpearmanCorr corrP Q
ANGPT4 0.352 1.712e-12 3.49e-08
ALS2CL -0.3468 3.785e-12 7.72e-08
GPR158 -0.3449 4.99e-12 1.02e-07
ITPK1 -0.3291 5.021e-11 1.02e-06
NCRNA00203 -0.3291 5.021e-11 1.02e-06
HDAC11 -0.3281 5.787e-11 1.18e-06
SLFN14 0.328 5.862e-11 1.19e-06
ADAMTS15 -0.3273 6.492e-11 1.32e-06
PLA2G15 -0.3226 1.257e-10 2.56e-06
EPB41L1 -0.3196 1.908e-10 3.89e-06
Clinical variable #3: 'HISTOLOGICAL.TYPE'

3991 genes related to 'HISTOLOGICAL.TYPE'.

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

HISTOLOGICAL.TYPE Labels N
  ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA 278
  MIXED SEROUS AND ENDOMETRIOID 19
  SEROUS ENDOMETRIAL ADENOCARCINOMA 83
     
  Significant markers N = 3991
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 5.856e-33 1.19e-28
SSTR1 1.774e-30 3.62e-26
RASGRF1 4.694e-30 9.57e-26
ADAMTS16 1.614e-29 3.29e-25
ITPK1 3.85e-29 7.85e-25
NCRNA00203 3.85e-29 7.85e-25
LRRC36__1 1.167e-28 2.38e-24
KCNA5 1.211e-28 2.47e-24
CREB5 1.279e-28 2.61e-24
ATP8A2 2.09e-28 4.26e-24
Clinical variable #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

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

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

RADIATIONS.RADIATION.REGIMENINDICATION Labels N
  NO 87
  YES 293
     
  Significant markers N = 66
  Higher in YES 66
  Higher in NO 0
List of top 10 genes differentially expressed by 'RADIATIONS.RADIATION.REGIMENINDICATION'

Table S7.  Get Full Table List of top 10 genes differentially expressed by 'RADIATIONS.RADIATION.REGIMENINDICATION'

W(pos if higher in 'YES') wilcoxontestP Q AUC
RMND5B 17999 5.25e-09 0.000107 0.7061
FIZ1 17946 7.464e-09 0.000152 0.704
ZNF524 17946 7.464e-09 0.000152 0.704
ZNF831 17853 1.373e-08 0.00028 0.7004
MAP2K2 17695 3.774e-08 0.000769 0.6942
C14ORF93 17678 4.2e-08 0.000856 0.6935
GIPR 17649 5.037e-08 0.00103 0.6924
SAP130 17624 5.887e-08 0.0012 0.6914
GOLGA3 17606 6.583e-08 0.00134 0.6907
TMEM125 17574 8.022e-08 0.00163 0.6894
Clinical variable #5: 'COMPLETENESS.OF.RESECTION'

No gene related to 'COMPLETENESS.OF.RESECTION'.

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

COMPLETENESS.OF.RESECTION Labels N
  R0 253
  R1 18
  R2 13
  RX 28
     
  Significant markers N = 0
Clinical variable #6: 'RACE'

30 genes related to 'RACE'.

Table S9.  Basic characteristics of clinical feature: 'RACE'

RACE Labels N
  AMERICAN INDIAN OR ALASKA NATIVE 2
  ASIAN 7
  BLACK OR AFRICAN AMERICAN 68
  NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER 7
  WHITE 275
     
  Significant markers N = 30
List of top 10 genes differentially expressed by 'RACE'

Table S10.  Get Full Table List of top 10 genes differentially expressed by 'RACE'

ANOVA_P Q
C18ORF54 3.28e-13 6.69e-09
LOC253039 1.223e-11 2.49e-07
PSMD5 1.223e-11 2.49e-07
ZNF525 2.738e-11 5.58e-07
NCRNA00174 3.436e-11 7e-07
GRAPL 2.638e-10 5.38e-06
GRAP 1.197e-09 2.44e-05
CS 1.262e-08 0.000257
CN5H6.4 1.764e-08 0.00036
GTSE1 1.764e-08 0.00036
Clinical variable #7: 'ETHNICITY'

No gene related to 'ETHNICITY'.

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

ETHNICITY Labels N
  HISPANIC OR LATINO 10
  NOT HISPANIC OR LATINO 275
     
  Significant markers N = 0
Methods & Data
Input

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

  • Clinical data file = UCEC-TP.merged_data.txt

  • Number of patients = 380

  • Number of genes = 20386

  • Number of clinical features = 7

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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