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 17428 genes and 7 clinical features across 42 samples, statistically thresholded by Q value < 0.05, 3 clinical features related to at least one genes.

  • 3 genes correlated to 'AGE'.

    • HOXD8 ,  KCNK17 ,  BARHL2

  • 2 genes correlated to 'GENDER'.

    • UTP14C ,  ACSM1

  • 26 genes correlated to 'HISTOLOGICAL.TYPE'.

    • ZNF362 ,  SLMO1 ,  SLC16A1 ,  C1ORF50 ,  BVES ,  ...

  • No genes correlated to 'Time to Death', '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=0        
AGE Spearman correlation test N=3 older N=3 younger N=0
GENDER t test N=2 male N=1 female N=1
KARNOFSKY PERFORMANCE SCORE Spearman correlation test   N=0        
HISTOLOGICAL TYPE ANOVA test N=26        
RADIATIONS RADIATION REGIMENINDICATION t test   N=0        
NEOADJUVANT THERAPY t 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) 1.2-211.2 (median=36)
  censored N = 23
  death N = 19
     
  Significant markers N = 0
Clinical variable #2: 'AGE'

3 genes related to 'AGE'.

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

AGE Mean (SD) 43 (12)
  Significant markers N = 3
  pos. correlated 3
  neg. correlated 0
List of 3 genes significantly correlated to 'AGE' by Spearman correlation test

Table S3.  Get Full Table List of 3 genes significantly correlated to 'AGE' by Spearman correlation test

SpearmanCorr corrP Q
HOXD8 0.7005 2.413e-07 0.00421
KCNK17 0.6679 1.347e-06 0.0235
BARHL2 0.6546 2.565e-06 0.0447

Figure S1.  Get High-res Image As an example, this figure shows the association of HOXD8 to 'AGE'. P value = 2.41e-07 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #3: 'GENDER'

2 genes related to 'GENDER'.

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

GENDER Labels N
  FEMALE 24
  MALE 18
     
  Significant markers N = 2
  Higher in MALE 1
  Higher in FEMALE 1
List of 2 genes differentially expressed by 'GENDER'

Table S5.  Get Full Table List of 2 genes differentially expressed by 'GENDER'

T(pos if higher in 'MALE') ttestP Q AUC
UTP14C 11.26 1.11e-13 1.93e-09 0.9884
ACSM1 -7.23 1.34e-08 0.000233 0.9236

Figure S2.  Get High-res Image As an example, this figure shows the association of UTP14C to 'GENDER'. P value = 1.11e-13 with T-test analysis.

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

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

Table S6.  Basic characteristics of clinical feature: 'KARNOFSKY.PERFORMANCE.SCORE'

KARNOFSKY.PERFORMANCE.SCORE Mean (SD) 89.6 (11)
  Score N
  50 1
  70 1
  80 3
  90 12
  100 8
     
  Significant markers N = 0
Clinical variable #5: 'HISTOLOGICAL.TYPE'

26 genes related to 'HISTOLOGICAL.TYPE'.

Table S7.  Basic characteristics of clinical feature: 'HISTOLOGICAL.TYPE'

HISTOLOGICAL.TYPE Labels N
  ASTROCYTOMA 12
  OLIGOASTROCYTOMA 14
  OLIGODENDROGLIOMA 16
     
  Significant markers N = 26
List of top 10 genes differentially expressed by 'HISTOLOGICAL.TYPE'

Table S8.  Get Full Table List of top 10 genes differentially expressed by 'HISTOLOGICAL.TYPE'

ANOVA_P Q
ZNF362 1.488e-08 0.000259
SLMO1 2.821e-08 0.000492
SLC16A1 6.191e-08 0.00108
C1ORF50 6.422e-08 0.00112
BVES 7.141e-08 0.00124
NRD1 1.397e-07 0.00243
XRCC1 2.205e-07 0.00384
PAFAH1B3 2.363e-07 0.00412
ZNF227 3.138e-07 0.00547
ZNF790 4.008e-07 0.00698

Figure S3.  Get High-res Image As an example, this figure shows the association of ZNF362 to 'HISTOLOGICAL.TYPE'. P value = 1.49e-08 with ANOVA analysis.

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

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

Table S9.  Basic characteristics of clinical feature: 'RADIATIONS.RADIATION.REGIMENINDICATION'

RADIATIONS.RADIATION.REGIMENINDICATION Labels N
  NO 30
  YES 12
     
  Significant markers N = 0
Clinical variable #7: 'NEOADJUVANT.THERAPY'

No gene related to 'NEOADJUVANT.THERAPY'.

Table S10.  Basic characteristics of clinical feature: 'NEOADJUVANT.THERAPY'

NEOADJUVANT.THERAPY Labels N
  NO 24
  YES 18
     
  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 = 42

  • Number of genes = 17428

  • 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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