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 19847 genes and 4 clinical features across 152 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 2 clinical features related to at least one genes.

  • 9 genes correlated to 'AGE'.

    • LOC153328 ,  CCL18 ,  IKZF1 ,  LAMB3 ,  ZIM2__1 ,  ...

  • 8 genes correlated to 'GENDER'.

    • ALG11__1 ,  UTP14C ,  FAM35A ,  GLUD1 ,  ASF1A ,  ...

  • No genes correlated to 'Time to Death', and 'RACE'.

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=9 older N=0 younger N=9
GENDER Wilcoxon test N=8 male N=8 female N=0
RACE Kruskal-Wallis 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) 0.1-175 (median=18.1)
  censored N = 101
  death N = 50
     
  Significant markers N = 0
Clinical variable #2: 'AGE'

9 genes related to 'AGE'.

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

AGE Mean (SD) 61.48 (14)
  Significant markers N = 9
  pos. correlated 0
  neg. correlated 9
List of 9 genes differentially expressed by 'AGE'

Table S3.  Get Full Table List of 9 genes significantly correlated to 'AGE' by Spearman correlation test

SpearmanCorr corrP Q
LOC153328 -0.3924 5.748e-07 0.0114
CCL18 -0.3894 7.119e-07 0.0141
IKZF1 -0.3667 3.377e-06 0.067
LAMB3 -0.3655 3.664e-06 0.0727
ZIM2__1 -0.3532 8.078e-06 0.16
PCK1 -0.3531 8.126e-06 0.161
SLAMF7 -0.3472 1.173e-05 0.233
HLA-DQA2 -0.3461 1.253e-05 0.249
DAP -0.3451 1.339e-05 0.266
Clinical variable #3: 'GENDER'

8 genes related to 'GENDER'.

Table S4.  Basic characteristics of clinical feature: 'GENDER'

GENDER Labels N
  FEMALE 87
  MALE 65
     
  Significant markers N = 8
  Higher in MALE 8
  Higher in FEMALE 0
List of 8 genes differentially expressed by 'GENDER'

Table S5.  Get Full Table List of 8 genes differentially expressed by 'GENDER'. 0 significant gene(s) located in sex chromosomes is(are) filtered out.

W(pos if higher in 'MALE') wilcoxontestP Q AUC
ALG11__1 5095 3.102e-17 6.16e-13 0.901
UTP14C 5095 3.102e-17 6.16e-13 0.901
FAM35A 1305 1.443e-08 0.000286 0.7692
GLUD1 1305 1.443e-08 0.000286 0.7692
ASF1A 4113 1.705e-06 0.0338 0.7273
TET2 4020 4.19e-06 0.0831 0.7191
TBX2 4044 5.938e-06 0.118 0.7151
ITM2C 4030 7.59e-06 0.151 0.7126
Clinical variable #4: 'RACE'

No gene related to 'RACE'.

Table S6.  Basic characteristics of clinical feature: 'RACE'

RACE Labels N
  ASIAN 5
  BLACK OR AFRICAN AMERICAN 10
  WHITE 111
     
  Significant markers N = 0
Methods & Data
Input

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

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

  • Number of patients = 152

  • Number of genes = 19847

  • 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

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

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

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] Lehmann and Romano, Testing Statistical Hypotheses (3E ed.), New York: Springer. ISBN 0387988645 (2005)
[4] Howell, D, Statistical Methods for Psychology. (5th ed.), Duxbury Press:324-5 (2002)
[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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