Brain Lower Grade Glioma: Correlation between gene methylation status and clinical features
(primary solid tumor cohort)
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 17352 genes and 6 clinical features across 104 samples, statistically thresholded by Q value < 0.05, 4 clinical features related to at least one genes.

  • 163 genes correlated to 'Time to Death'.

    • LOC254559 ,  HS3ST4 ,  SYNPR ,  HPD ,  ZNF492 ,  ...

  • 22 genes correlated to 'AGE'.

    • PAX9 ,  ADAMTSL3 ,  RAB11FIP1 ,  FAM83H ,  RAB6C ,  ...

  • 7 genes correlated to 'GENDER'.

    • UTP14C ,  POLDIP3 ,  FDPS ,  GLUD1 ,  ATAD5 ,  ...

  • 249 genes correlated to 'HISTOLOGICAL.TYPE'.

    • REST ,  BVES ,  SMAD6 ,  KDM4A ,  SNAPC2 ,  ...

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

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=163 shorter survival N=32 longer survival N=131
AGE Spearman correlation test N=22 older N=20 younger N=2
GENDER t test N=7 male N=4 female N=3
KARNOFSKY PERFORMANCE SCORE Spearman correlation test   N=0        
HISTOLOGICAL TYPE ANOVA test N=249        
RADIATIONS RADIATION REGIMENINDICATION t test   N=0        
Clinical variable #1: 'Time to Death'

163 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=18.9)
  censored N = 69
  death N = 35
     
  Significant markers N = 163
  associated with shorter survival 32
  associated with longer survival 131
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
LOC254559 5101 4.725e-10 8.2e-06 0.714
HS3ST4 231 8.371e-10 1.5e-05 0.777
SYNPR 19001 7.387e-09 0.00013 0.732
HPD 0 1.039e-08 0.00018 0.318
ZNF492 101 1.07e-08 0.00019 0.678
PI15 0 1.417e-08 0.00025 0.271
NEIL3 0 1.847e-08 0.00032 0.252
SSTR1 231 2.361e-08 0.00041 0.738
HIST3H2A 431 2.551e-08 0.00044 0.734
ATF3 0 2.737e-08 0.00047 0.325

Figure S1.  Get High-res Image As an example, this figure shows the association of LOC254559 to 'Time to Death'. four curves present the cumulative survival rates of 4 quartile subsets of patients. P value = 4.72e-10 with univariate Cox regression analysis using continuous log-2 expression values.

Clinical variable #2: 'AGE'

22 genes related to 'AGE'.

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

AGE Mean (SD) 42.67 (13)
  Significant markers N = 22
  pos. correlated 20
  neg. correlated 2
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
PAX9 0.6093 6.749e-12 1.17e-07
ADAMTSL3 0.5799 1.116e-10 1.94e-06
RAB11FIP1 0.5486 1.645e-09 2.85e-05
FAM83H 0.5285 8.124e-09 0.000141
RAB6C 0.5271 9.029e-09 0.000157
LOC150786 0.5208 1.447e-08 0.000251
BATF2 -0.4911 1.204e-07 0.00209
SSTR4 0.49 1.295e-07 0.00225
SLC18A2 0.4889 1.397e-07 0.00242
GALNT14 0.488 1.483e-07 0.00257

Figure S2.  Get High-res Image As an example, this figure shows the association of PAX9 to 'AGE'. P value = 6.75e-12 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #3: 'GENDER'

7 genes related to 'GENDER'.

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

GENDER Labels N
  FEMALE 49
  MALE 55
     
  Significant markers N = 7
  Higher in MALE 4
  Higher in FEMALE 3
List of 7 genes differentially expressed by 'GENDER'

Table S6.  Get Full Table List of 7 genes differentially expressed by 'GENDER'

T(pos if higher in 'MALE') ttestP Q AUC
UTP14C 16.39 1.218e-23 2.11e-19 0.9781
POLDIP3 -10.28 1.431e-16 2.48e-12 0.9213
FDPS 8.65 2.04e-13 3.54e-09 0.902
GLUD1 -7.29 8.352e-11 1.45e-06 0.8219
ATAD5 6.82 6.646e-10 1.15e-05 0.8579
TFDP1 -5.44 4.306e-07 0.00747 0.8538
WBP11P1 5.3 7.239e-07 0.0126 0.8048

Figure S3.  Get High-res Image As an example, this figure shows the association of UTP14C to 'GENDER'. P value = 1.22e-23 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) 88.73 (11)
  Score N
  50 2
  70 3
  80 9
  90 25
  100 16
     
  Significant markers N = 0
Clinical variable #5: 'HISTOLOGICAL.TYPE'

249 genes related to 'HISTOLOGICAL.TYPE'.

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

HISTOLOGICAL.TYPE Labels N
  ASTROCYTOMA 33
  OLIGOASTROCYTOMA 26
  OLIGODENDROGLIOMA 44
     
  Significant markers N = 249
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
REST 4.86e-13 8.43e-09
BVES 7.225e-13 1.25e-08
SMAD6 1.117e-11 1.94e-07
KDM4A 2.154e-11 3.74e-07
SNAPC2 1.255e-10 2.18e-06
SLC2A4RG 1.469e-10 2.55e-06
CEPT1 2.598e-10 4.51e-06
DRAM2 2.598e-10 4.51e-06
NRAS 4.566e-10 7.92e-06
DDX20 4.935e-10 8.56e-06

Figure S4.  Get High-res Image As an example, this figure shows the association of REST to 'HISTOLOGICAL.TYPE'. P value = 4.86e-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 62
  YES 42
     
  Significant markers N = 0
Methods & Data
Input

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

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

  • Number of patients = 104

  • Number of genes = 17352

  • 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.

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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