Correlation between gene methylation status and clinical features
Prostate Adenocarcinoma (Primary solid tumor)
21 April 2013 | analyses__2013_04_21
Maintainer Information
Citation Information
doi:10.7908/C18W3B9T
Maintained by Juok Cho (Broad Institute)
Cite as Broad Institute TCGA Genome Data Analysis Center (2013): Prostate Adenocarcinoma (Primary solid tumor cohort) - 21 April 2013: Correlation between gene methylation status and clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C18W3B9T
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 17313 genes and 4 clinical features across 150 samples, statistically thresholded by Q value < 0.05, 2 clinical features related to at least one genes.

- 89 genes correlated to 'RADIATIONS.RADIATION.REGIMENINDICATION'.

- NAA20 , ZNF280D , BCAS2 , AASDHPPT , VWDE , ...

- 4 genes correlated to 'COMPLETENESS.OF.RESECTION'.

- WHAMM , CD36 , TGM4 , ZNF219

- No genes correlated to 'AGE', and 'NUMBER.OF.LYMPH.NODES'.

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 |
--- | --- | --- | --- | --- | --- | ---
AGE | Spearman correlation test | N=0 |  |  |  |
RADIATIONS RADIATION REGIMENINDICATION | t test | N=89 | yes | N=56 | no | N=33
COMPLETENESS OF RESECTION | ANOVA test | N=4 |  |  |  |
NUMBER OF LYMPH NODES | Spearman correlation test | N=0 |  |  |  |

Clinical variable #1: 'AGE'

No gene related to 'AGE'.

Table S1. Basic characteristics of clinical feature: 'AGE'

AGE | Mean (SD) | 60.3 (6.8)
| Significant markers | N = 0

Clinical variable #2: 'RADIATIONS.RADIATION.REGIMENINDICATION'

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

Table S2. Basic characteristics of clinical feature: 'RADIATIONS.RADIATION.REGIMENINDICATION'

RADIATIONS.RADIATION.REGIMENINDICATION | Labels | N
| NO | 5
| YES | 145
| Significant markers | N = 89
| Higher in YES | 56
| Higher in NO | 33

List of top 10 genes differentially expressed by 'RADIATIONS.RADIATION.REGIMENINDICATION'

Table S3. Get Full Table List of top 10 genes differentially expressed by 'RADIATIONS.RADIATION.REGIMENINDICATION'

| T(pos if higher in 'YES') | ttestP | Q | AUC
--- | --- | --- | --- | ---
NAA20 | 11.13 | 3.896e-17 | 6.74e-13 | 0.8676
ZNF280D | 10.9 | 1.563e-16 | 2.71e-12 | 0.8207
BCAS2 | 8.86 | 7.037e-15 | 1.22e-10 | 0.7683
AASDHPPT | 9.06 | 7.451e-15 | 1.29e-10 | 0.8761
VWDE | 8.42 | 3.465e-14 | 6e-10 | 0.7793
ZKSCAN4 | 11.29 | 4.078e-13 | 7.06e-09 | 0.8814
CLEC4C | -8.18 | 6.031e-13 | 1.04e-08 | 0.7379
KCNA4 | -8.73 | 6.356e-13 | 1.1e-08 | 0.7545
PIK3CA | 8.44 | 7.373e-13 | 1.28e-08 | 0.8292
VCAN | -9.32 | 8.649e-13 | 1.5e-08 | 0.7655

Figure S1. Get High-res Image As an example, this figure shows the association of NAA20 to 'RADIATIONS.RADIATION.REGIMENINDICATION'. P value = 3.9e-17 with T-test analysis.

Clinical variable #3: 'COMPLETENESS.OF.RESECTION'

4 genes related to 'COMPLETENESS.OF.RESECTION'.

Table S4. Basic characteristics of clinical feature: 'COMPLETENESS.OF.RESECTION'

COMPLETENESS.OF.RESECTION | Labels | N
| R0 | 114
| R1 | 29
| RX | 2
| Significant markers | N = 4

List of 4 genes differentially expressed by 'COMPLETENESS.OF.RESECTION'

Table S5. Get Full Table List of 4 genes differentially expressed by 'COMPLETENESS.OF.RESECTION'

| ANOVA_P | Q
--- | --- | ---
WHAMM | 3.612e-20 | 6.25e-16
CD36 | 3.046e-08 | 0.000527
TGM4 | 1.666e-07 | 0.00288
ZNF219 | 1.139e-06 | 0.0197

Figure S2. Get High-res Image As an example, this figure shows the association of WHAMM to 'COMPLETENESS.OF.RESECTION'. P value = 3.61e-20 with ANOVA analysis.

Clinical variable #4: 'NUMBER.OF.LYMPH.NODES'

No gene related to 'NUMBER.OF.LYMPH.NODES'.

Table S6. Basic characteristics of clinical feature: 'NUMBER.OF.LYMPH.NODES'

NUMBER.OF.LYMPH.NODES | Mean (SD) | 0.21 (0.75)
| Significant markers | N = 0

Methods & Data
Input

- Expresson data file = PRAD-TP.meth.for_correlation.filtered_data.txt

- Clinical data file = PRAD-TP.clin.merged.picked.txt

- Number of patients = 150

- Number of genes = 17313

- Number of clinical features = 4

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.

- Analysis Results (MD5 checksum)

- Auxiliary Data (MD5 checksum)

- MAGE-TAB File (MD5 checksum)

References
[1] Spearman, C, The proof and measurement of association between two things, Amer. J. Psychol 15:72-101 (1904)
[2] Lehmann and Romano, Testing Statistical Hypotheses (3E ed.), New York: Springer. ISBN 0387988645 (2005)
[3] Howell, D, Statistical Methods for Psychology. (5th ed.), Duxbury Press:324-5 (2002)
[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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