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 20103 genes and 6 clinical features across 233 samples, statistically thresholded by Q value < 0.05, 5 clinical features related to at least one genes.

  • 955 genes correlated to 'Time to Death'.

    • ATF3 ,  UMODL1 ,  HS3ST4 ,  ISM1 ,  HPD ,  ...

  • 307 genes correlated to 'AGE'.

    • TRIM58 ,  SHISA2 ,  SLC22A16 ,  LOC150786 ,  HOXD8 ,  ...

  • 15 genes correlated to 'GENDER'.

    • ALG11__1 ,  UTP14C ,  POLDIP3 ,  RNU12 ,  FAM35A ,  ...

  • 1151 genes correlated to 'HISTOLOGICAL.TYPE'.

    • CCDC88C ,  SLC2A4RG ,  MAPKAP1 ,  BVES ,  REST ,  ...

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

    • JAK2 ,  EFCAB7__2 ,  ITGB3BP__1 ,  ZMYM4 ,  HSPA13 ,  ...

  • No genes correlated to 'KARNOFSKY.PERFORMANCE.SCORE'

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=955 shorter survival N=125 longer survival N=830
AGE Spearman correlation test N=307 older N=181 younger N=126
GENDER t test N=15 male N=6 female N=9
KARNOFSKY PERFORMANCE SCORE Spearman correlation test   N=0        
HISTOLOGICAL TYPE ANOVA test N=1151        
RADIATIONS RADIATION REGIMENINDICATION t test N=16 yes N=10 no N=6
Clinical variable #1: 'Time to Death'

955 genes related to 'Time to Death'.

Table S1.  Basic characteristics of clinical feature: 'Time to Death'

Time to Death Duration (Months) 0-211.2 (median=14.4)
  censored N = 184
  death N = 49
     
  Significant markers N = 955
  associated with shorter survival 125
  associated with longer survival 830
List of top 10 genes significantly associated with 'Time to Death' by Cox regression test

Table S2.  Get Full Table List of top 10 genes significantly associated with 'Time to Death' by Cox regression test

HazardRatio Wald_P Q C_index
ATF3 0 4.896e-14 9.8e-10 0.238
UMODL1 0 2.389e-13 4.8e-09 0.266
HS3ST4 191 1.686e-12 3.4e-08 0.782
ISM1 6401 1.785e-12 3.6e-08 0.719
HPD 0 1.973e-12 4e-08 0.252
ZNF492 100 3.315e-12 6.7e-08 0.71
NID2 0 4.38e-12 8.8e-08 0.22
TLK1 0.01 5.349e-12 1.1e-07 0.252
CARD6 0.01 6.224e-12 1.3e-07 0.296
CD274 0.01 6.727e-12 1.4e-07 0.248

Figure S1.  Get High-res Image As an example, this figure shows the association of ATF3 to 'Time to Death'. four curves present the cumulative survival rates of 4 quartile subsets of patients. P value = 4.9e-14 with univariate Cox regression analysis using continuous log-2 expression values.

Clinical variable #2: 'AGE'

307 genes related to 'AGE'.

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

AGE Mean (SD) 42.81 (13)
  Significant markers N = 307
  pos. correlated 181
  neg. correlated 126
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
TRIM58 0.5442 2.3e-19 4.62e-15
SHISA2 0.5327 1.757e-18 3.53e-14
SLC22A16 0.5209 1.309e-17 2.63e-13
LOC150786 0.5163 2.814e-17 5.66e-13
HOXD8 0.5085 1.003e-16 2.02e-12
GALNT14 0.4993 4.301e-16 8.64e-12
ADAMTSL3 0.4915 1.418e-15 2.85e-11
HOXD11 0.4914 1.445e-15 2.9e-11
EPHA6 0.4906 1.635e-15 3.29e-11
PAX9 0.4803 7.484e-15 1.5e-10

Figure S2.  Get High-res Image As an example, this figure shows the association of TRIM58 to 'AGE'. P value = 2.3e-19 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #3: 'GENDER'

15 genes related to 'GENDER'.

Table S5.  Basic characteristics of clinical feature: 'GENDER'

GENDER Labels N
  FEMALE 106
  MALE 127
     
  Significant markers N = 15
  Higher in MALE 6
  Higher in FEMALE 9
List of top 10 genes differentially expressed by 'GENDER'

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

T(pos if higher in 'MALE') ttestP Q AUC
ALG11__1 24.37 3.497e-51 7.03e-47 0.9799
UTP14C 24.37 3.497e-51 7.03e-47 0.9799
POLDIP3 -16.76 9.925e-38 2e-33 0.9401
RNU12 -16.76 9.925e-38 2e-33 0.9401
FAM35A -11.6 1.109e-24 2.23e-20 0.8358
GLUD1 -11.6 1.109e-24 2.23e-20 0.8358
WBP11P1 9.97 1.897e-19 3.81e-15 0.8468
TFDP1 -8.76 5.124e-16 1.03e-11 0.882
KIF4B -7.81 4.566e-13 9.18e-09 0.7546
ZNF839 -6.94 4.68e-11 9.4e-07 0.7827

Figure S3.  Get High-res Image As an example, this figure shows the association of ALG11__1 to 'GENDER'. P value = 3.5e-51 with T-test analysis.

Clinical variable #4: 'KARNOFSKY.PERFORMANCE.SCORE'

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

Table S7.  Basic characteristics of clinical feature: 'KARNOFSKY.PERFORMANCE.SCORE'

KARNOFSKY.PERFORMANCE.SCORE Mean (SD) 88.17 (11)
  Significant markers N = 0
Clinical variable #5: 'HISTOLOGICAL.TYPE'

1151 genes related to 'HISTOLOGICAL.TYPE'.

Table S8.  Basic characteristics of clinical feature: 'HISTOLOGICAL.TYPE'

HISTOLOGICAL.TYPE Labels N
  ASTROCYTOMA 68
  OLIGOASTROCYTOMA 68
  OLIGODENDROGLIOMA 96
     
  Significant markers N = 1151
List of top 10 genes differentially expressed by 'HISTOLOGICAL.TYPE'

Table S9.  Get Full Table List of top 10 genes differentially expressed by 'HISTOLOGICAL.TYPE'

ANOVA_P Q
CCDC88C 7.965e-19 1.6e-14
SLC2A4RG 2.376e-17 4.78e-13
MAPKAP1 2.896e-17 5.82e-13
BVES 4.095e-17 8.23e-13
REST 5.869e-17 1.18e-12
CBX2 1.226e-16 2.46e-12
EMP1 4.039e-16 8.12e-12
TMEM51 4.235e-16 8.51e-12
NCKAP5 1.14e-15 2.29e-11
GLIS3 1.656e-15 3.33e-11

Figure S4.  Get High-res Image As an example, this figure shows the association of CCDC88C to 'HISTOLOGICAL.TYPE'. P value = 7.96e-19 with ANOVA analysis.

Clinical variable #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

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

Table S10.  Basic characteristics of clinical feature: 'RADIATIONS.RADIATION.REGIMENINDICATION'

RADIATIONS.RADIATION.REGIMENINDICATION Labels N
  NO 85
  YES 148
     
  Significant markers N = 16
  Higher in YES 10
  Higher in NO 6
List of top 10 genes differentially expressed by 'RADIATIONS.RADIATION.REGIMENINDICATION'

Table S11.  Get Full Table List of top 10 genes differentially expressed by 'RADIATIONS.RADIATION.REGIMENINDICATION'

T(pos if higher in 'YES') ttestP Q AUC
JAK2 -5.84 2.603e-08 0.000523 0.706
EFCAB7__2 5.69 3.908e-08 0.000786 0.7181
ITGB3BP__1 5.69 3.908e-08 0.000786 0.7181
ZMYM4 5.61 5.972e-08 0.0012 0.7077
HSPA13 5.52 9.131e-08 0.00184 0.6548
ZNF567 -5.3 4.462e-07 0.00897 0.6998
FLRT3 5.13 6.065e-07 0.0122 0.659
MACROD2 5.13 6.065e-07 0.0122 0.659
ELMOD3 -5.07 1.205e-06 0.0242 0.6869
RETSAT__1 -5.07 1.205e-06 0.0242 0.6869

Figure S5.  Get High-res Image As an example, this figure shows the association of JAK2 to 'RADIATIONS.RADIATION.REGIMENINDICATION'. P value = 2.6e-08 with T-test analysis.

Methods & Data
Input

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

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

  • Number of patients = 233

  • Number of genes = 20103

  • Number of clinical features = 6

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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