Correlation between gene methylation status and clinical features
Acute Myeloid Leukemia (Primary blood derived cancer - Peripheral blood)
16 April 2014 | analyses__2014_04_16
Maintainer Information
Citation Information
doi:10.7908/C1MG7N4S
Maintained by Juok Cho (Broad Institute)
Cite as Broad Institute TCGA Genome Data Analysis Center (2014): Correlation between gene methylation status and clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1MG7N4S
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 19042 genes and 3 clinical features across 194 samples, statistically thresholded by Q value < 0.05, 2 clinical features related to at least one genes.

- 65 genes correlated to 'AGE'.

- TMEM20 , ANGPTL5 , KIAA1377 , JAKMIP1 , AASS , ...

- 12 genes correlated to 'GENDER'.

- FAM35A , GLUD1 , DKFZP434L187 , AP2B1 , KIF4B , ...

- No genes correlated to 'Time to Death'

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=65 | older | N=15 | younger | N=50
GENDER | t test | N=12 | male | N=5 | female | N=7

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) | 0.9-94.1 (median=12)
| censored | N = 63
| death | N = 106
| Significant markers | N = 0

Clinical variable #2: 'AGE'

65 genes related to 'AGE'.

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

AGE | Mean (SD) | 55.11 (16)
| Significant markers | N = 65
| pos. correlated | 15
| neg. correlated | 50

List of top 10 genes significantly correlated to 'AGE' by Spearman correlation test

Table S3. Get Full Table List of top 10 genes significantly correlated to 'AGE' by Spearman correlation test

| SpearmanCorr | corrP | Q
--- | --- | --- | ---
TMEM20 | -0.4691 | 5.241e-12 | 9.98e-08
ANGPTL5 | -0.4301 | 3.881e-10 | 7.39e-06
KIAA1377 | -0.4301 | 3.881e-10 | 7.39e-06
JAKMIP1 | -0.4275 | 5.054e-10 | 9.62e-06
AASS | -0.4139 | 1.989e-09 | 3.79e-05
TBC1D12 | -0.3924 | 1.521e-08 | 0.00029
CD96 | 0.3869 | 2.5e-08 | 0.000476
CAMK2D | -0.3812 | 4.157e-08 | 0.000791
CBLN3 | -0.3809 | 4.265e-08 | 0.000812
KHNYN | -0.3809 | 4.265e-08 | 0.000812

Figure S1. Get High-res Image As an example, this figure shows the association of TMEM20 to 'AGE'. P value = 5.24e-12 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #3: 'GENDER'

12 genes related to 'GENDER'.

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

GENDER | Labels | N
| FEMALE | 89
| MALE | 105
| Significant markers | N = 12
| Higher in MALE | 5
| Higher in FEMALE | 7

List of top 10 genes differentially expressed by 'GENDER'

Table S5. Get Full Table List of top 10 genes differentially expressed by 'GENDER'

| T(pos if higher in 'MALE') | ttestP | Q | AUC
--- | --- | --- | --- | ---
FAM35A | -13.42 | 3.19e-29 | 6.07e-25 | 0.8964
GLUD1 | -13.42 | 3.19e-29 | 6.07e-25 | 0.8964
DKFZP434L187 | 12.68 | 7.369e-27 | 1.4e-22 | 0.9388
AP2B1 | -12.75 | 1.219e-26 | 2.32e-22 | 0.8999
KIF4B | -12.12 | 2.683e-24 | 5.11e-20 | 0.8675
CROCC | -10.69 | 4.77e-21 | 9.08e-17 | 0.8856
LOC389791__1 | 5.55 | 9.298e-08 | 0.00177 | 0.7109
PTGES2__1 | 5.55 | 9.298e-08 | 0.00177 | 0.7109
ATP5J | 5.22 | 5.05e-07 | 0.00961 | 0.7353
GABPA__1 | 5.22 | 5.05e-07 | 0.00961 | 0.7353

Figure S2. Get High-res Image As an example, this figure shows the association of FAM35A to 'GENDER'. P value = 3.19e-29 with T-test analysis.

Methods & Data
Input

- Expresson data file = LAML-TB.meth.by_min_clin_corr.data.txt

- Clinical data file = LAML-TB.merged_data.txt

- Number of patients = 194

- Number of genes = 19042

- Number of clinical features = 3

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

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

In addition to the links below, the full results of the analysis summarized in this report can also be downloaded programmatically using firehose_get, or interactively from either the Broad GDAC website or TCGA Data Coordination Center Portal.

- Analysis Results (MD5 checksum)

- Auxiliary Data (MD5 checksum)

- MAGE-TAB File (MD5 checksum)

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] 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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