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 13026 genes and 4 clinical features across 262 samples, statistically thresholded by Q value < 0.05, 1 clinical feature related to at least one genes.

  • 20 genes correlated to 'AGE'.

    • KLK12 ,  PCDHGA3 ,  PCDHGA8 ,  PCDHGB3 ,  C16ORF45 ,  ...

  • No genes correlated to 'Time to Death', 'KARNOFSKY.PERFORMANCE.SCORE', and 'TUMOR.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=20 older N=6 younger N=14
KARNOFSKY PERFORMANCE SCORE Spearman correlation test   N=0        
TUMOR STAGE Spearman correlation 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) 0.3-180.2 (median=28)
  censored N = 113
  death N = 147
     
  Significant markers N = 0
Clinical variable #2: 'AGE'

20 genes related to 'AGE'.

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

AGE Mean (SD) 59.02 (11)
  Significant markers N = 20
  pos. correlated 6
  neg. correlated 14
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
KLK12 -0.3192 1.893e-07 0.00247
PCDHGA3 0.3179 2.149e-07 0.0028
PCDHGA8 0.3179 2.149e-07 0.0028
PCDHGB3 0.3179 2.149e-07 0.0028
C16ORF45 -0.3145 2.937e-07 0.00382
PCDHGB6 0.3142 3.002e-07 0.00391
PCDHGB7 0.3142 3.002e-07 0.00391
PDZK1 -0.3127 4.047e-07 0.00527
KCNIP1 -0.2999 1.073e-06 0.014
VWA5B1 -0.2956 1.547e-06 0.0201

Figure S1.  Get High-res Image As an example, this figure shows the association of KLK12 to 'AGE'. P value = 1.89e-07 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #3: 'KARNOFSKY.PERFORMANCE.SCORE'

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

Table S4.  Basic characteristics of clinical feature: 'KARNOFSKY.PERFORMANCE.SCORE'

KARNOFSKY.PERFORMANCE.SCORE Mean (SD) 74.29 (12)
  Score N
  60 5
  80 8
  100 1
     
  Significant markers N = 0
Clinical variable #4: 'TUMOR.STAGE'

No gene related to 'TUMOR.STAGE'.

Table S5.  Basic characteristics of clinical feature: 'TUMOR.STAGE'

TUMOR.STAGE Mean (SD) 3.06 (0.44)
  N
  Stage 2 18
  Stage 3 210
  Stage 4 33
     
  Significant markers N = 0
Methods & Data
Input

  • Expresson data file = OV-TP.meth.for_correlation.filtered_data.txt

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

  • Number of patients = 262

  • Number of genes = 13026

  • Number of clinical features = 4

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

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] 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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