Brain Lower Grade Glioma: Correlation between gene methylation status and clinical features
Maintained by Juok Cho (Broad Institute)
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 20231 genes and 7 clinical features across 140 samples, statistically thresholded by Q value < 0.05, 4 clinical features related to at least one genes.

  • 730 genes correlated to 'Time to Death'.

    • HFE ,  PRKG2 ,  LOC254559 ,  MIR495 ,  TMPRSS13 ,  ...

  • 173 genes correlated to 'AGE'.

    • TBX20 ,  BARHL2 ,  TBX18 ,  POM121L2 ,  NPBWR1 ,  ...

  • 2 genes correlated to 'GENDER'.

    • KIF4B ,  MAZ

  • 176 genes correlated to 'HISTOLOGICAL.TYPE'.

    • BVES ,  MAPKAP1 ,  SLMO1 ,  GATA3 ,  TNIP1 ,  ...

  • No genes correlated to 'KARNOFSKY.PERFORMANCE.SCORE', 'RADIATIONS.RADIATION.REGIMENINDICATION', and 'NEOADJUVANT.THERAPY'.

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=730 shorter survival N=43 longer survival N=687
AGE Spearman correlation test N=173 older N=168 younger N=5
GENDER t test N=2 male N=1 female N=1
KARNOFSKY PERFORMANCE SCORE Spearman correlation test   N=0        
HISTOLOGICAL TYPE ANOVA test N=176        
RADIATIONS RADIATION REGIMENINDICATION t test   N=0        
NEOADJUVANT THERAPY t test   N=0        
Clinical variable #1: 'Time to Death'

730 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=17.8)
  censored N = 96
  death N = 44
     
  Significant markers N = 730
  associated with shorter survival 43
  associated with longer survival 687
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
HFE 0 3.838e-11 7.8e-07 0.267
PRKG2 0 6.584e-11 1.3e-06 0.286
LOC254559 281 1.542e-10 3.1e-06 0.742
MIR495 0 1.807e-10 3.7e-06 0.248
TMPRSS13 0 2.26e-10 4.6e-06 0.243
TUFT1 0.01 2.264e-10 4.6e-06 0.223
ZNF492 371 2.578e-10 5.2e-06 0.709
SPINK5L2 0 3.149e-10 6.4e-06 0.267
MIR34A 0 4.096e-10 8.3e-06 0.28
TBC1D21 0 4.556e-10 9.2e-06 0.275

Figure S1.  Get High-res Image As an example, this figure shows the association of HFE to 'Time to Death'. four curves present the cumulative survival rates of 4 quartile subsets of patients. P value = 3.84e-11 with univariate Cox regression analysis using continuous log-2 expression values.

Clinical variable #2: 'AGE'

173 genes related to 'AGE'.

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

AGE Mean (SD) 42.59 (13)
  Significant markers N = 173
  pos. correlated 168
  neg. correlated 5
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
TBX20 0.6324 5.259e-17 1.06e-12
BARHL2 0.6203 2.996e-16 6.06e-12
TBX18 0.6013 3.992e-15 8.07e-11
POM121L2 0.5839 3.655e-14 7.39e-10
NPBWR1 0.5813 5.071e-14 1.03e-09
HOXD8 0.5809 5.307e-14 1.07e-09
FOXB1 0.5663 3.053e-13 6.17e-09
AVPR1A 0.5651 3.504e-13 7.09e-09
PRDM13 0.5634 4.285e-13 8.66e-09
SOX14 0.5626 4.659e-13 9.42e-09

Figure S2.  Get High-res Image As an example, this figure shows the association of TBX20 to 'AGE'. P value = 5.26e-17 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #3: 'GENDER'

2 genes related to 'GENDER'.

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

GENDER Labels N
  FEMALE 62
  MALE 78
     
  Significant markers N = 2
  Higher in MALE 1
  Higher in FEMALE 1
List of 2 genes differentially expressed by 'GENDER'

Table S6.  Get Full Table List of 2 genes differentially expressed by 'GENDER'

T(pos if higher in 'MALE') ttestP Q AUC
KIF4B -6.2 9.528e-09 0.000193 0.7874
MAZ 5.19 8.667e-07 0.0175 0.707

Figure S3.  Get High-res Image As an example, this figure shows the association of KIF4B to 'GENDER'. P value = 9.53e-09 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) 89.21 (10)
  Score N
  50 2
  70 4
  80 11
  90 38
  100 21
     
  Significant markers N = 0
Clinical variable #5: 'HISTOLOGICAL.TYPE'

176 genes related to 'HISTOLOGICAL.TYPE'.

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

HISTOLOGICAL.TYPE Labels N
  ASTROCYTOMA 49
  OLIGOASTROCYTOMA 34
  OLIGODENDROGLIOMA 56
     
  Significant markers N = 176
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
BVES 2.228e-13 4.51e-09
MAPKAP1 1.558e-12 3.15e-08
SLMO1 6.938e-12 1.4e-07
GATA3 5.916e-11 1.2e-06
TNIP1 8.006e-11 1.62e-06
FLJ45983 9.799e-11 1.98e-06
REST 3.564e-10 7.21e-06
GATM 4.035e-10 8.16e-06
ATL3 4.601e-10 9.3e-06
JAG1 5.474e-10 1.11e-05

Figure S4.  Get High-res Image As an example, this figure shows the association of BVES to 'HISTOLOGICAL.TYPE'. P value = 2.23e-13 with ANOVA analysis.

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

No gene related to 'RADIATIONS.RADIATION.REGIMENINDICATION'.

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

RADIATIONS.RADIATION.REGIMENINDICATION Labels N
  NO 83
  YES 57
     
  Significant markers N = 0
Clinical variable #7: 'NEOADJUVANT.THERAPY'

No gene related to 'NEOADJUVANT.THERAPY'.

Table S11.  Basic characteristics of clinical feature: 'NEOADJUVANT.THERAPY'

NEOADJUVANT.THERAPY Labels N
  NO 70
  YES 70
     
  Significant markers N = 0
Methods & Data
Input

  • Expresson data file = LGG.meth.for_correlation.filtered_data.txt

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

  • Number of patients = 140

  • Number of genes = 20231

  • 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

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.

Download Results

This is an experimental feature. The full results of the analysis summarized in this report can be downloaded from the TCGA Data Coordination Center.

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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