Correlation between gene methylation status and clinical features
Prostate Adenocarcinoma (Primary solid tumor)
23 September 2013 | analyses__2013_09_23
Maintainer Information
Citation Information
doi:10.7908/C1KP80H0
Maintained by Juok Cho (Broad Institute)
Cite as Broad Institute TCGA Genome Data Analysis Center (2013): Correlation between gene methylation status and clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1KP80H0
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 20008 genes and 5 clinical features across 162 samples, statistically thresholded by Q value < 0.05, 3 clinical features related to at least one genes.

- 1 gene correlated to 'AGE'.

- LYNX1

- 15 genes correlated to 'PATHOLOGY.N.STAGE'.

- DYRK2 , DLEU2__2 , CCL2 , TDRKH , KCNJ2 , ...

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

- CTNNA3__1 , LRRTM3__1 , CTNNA3 , LRRTM3 , AKR1B10 , ...

- No genes correlated to 'PATHOLOGY.T.STAGE', 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=1 | older | N=1 | younger | N=0
PATHOLOGY T STAGE | Spearman correlation test | N=0 |  |  |  |
PATHOLOGY N STAGE | t test | N=15 | class1 | N=0 | class0 | N=15
COMPLETENESS OF RESECTION | ANOVA test | N=7 |  |  |  |
NUMBER OF LYMPH NODES | Spearman correlation test | N=0 |  |  |  |

Clinical variable #1: 'AGE'

One gene related to 'AGE'.

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

AGE | Mean (SD) | 60.04 (6.8)
| Significant markers | N = 1
| pos. correlated | 1
| neg. correlated | 0

List of one gene significantly correlated to 'AGE' by Spearman correlation test

Table S2. Get Full Table List of one gene significantly correlated to 'AGE' by Spearman correlation test

| SpearmanCorr | corrP | Q
--- | --- | --- | ---
LYNX1 | 0.3624 | 2.313e-06 | 0.0463

Figure S1. Get High-res Image As an example, this figure shows the association of LYNX1 to 'AGE'. P value = 2.31e-06 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #2: 'PATHOLOGY.T.STAGE'

No gene related to 'PATHOLOGY.T.STAGE'.

Table S3. Basic characteristics of clinical feature: 'PATHOLOGY.T.STAGE'

PATHOLOGY.T.STAGE | Mean (SD) | 2.61 (0.55)
|  | N
| 2 | 68
| 3 | 88
| 4 | 5
| Significant markers | N = 0

Clinical variable #3: 'PATHOLOGY.N.STAGE'

15 genes related to 'PATHOLOGY.N.STAGE'.

Table S4. Basic characteristics of clinical feature: 'PATHOLOGY.N.STAGE'

PATHOLOGY.N.STAGE | Labels | N
| class0 | 128
| class1 | 15
| Significant markers | N = 15
| Higher in class1 | 0
| Higher in class0 | 15

List of top 10 genes differentially expressed by 'PATHOLOGY.N.STAGE'

Table S5. Get Full Table List of top 10 genes differentially expressed by 'PATHOLOGY.N.STAGE'

| T(pos if higher in 'class1') | ttestP | Q | AUC
--- | --- | --- | --- | ---
DYRK2 | -6.8 | 3.481e-09 | 6.96e-05 | 0.7432
DLEU2__2 | -6.19 | 6.253e-08 | 0.00125 | 0.8177
CCL2 | -6.08 | 7.085e-08 | 0.00142 | 0.8333
TDRKH | -5.68 | 1.115e-07 | 0.00223 | 0.7054
KCNJ2 | -5.59 | 1.283e-07 | 0.00257 | 0.7307
RRM2 | -5.79 | 3.398e-07 | 0.0068 | 0.7583
NUP54 | -5.28 | 8.503e-07 | 0.017 | 0.6745
DDX21 | -5.26 | 8.622e-07 | 0.0172 | 0.7172
ABCE1 | -5.6 | 1.092e-06 | 0.0218 | 0.7635
ANAPC10 | -5.6 | 1.092e-06 | 0.0218 | 0.7635

Figure S2. Get High-res Image As an example, this figure shows the association of DYRK2 to 'PATHOLOGY.N.STAGE'. P value = 3.48e-09 with T-test analysis.

Clinical variable #4: 'COMPLETENESS.OF.RESECTION'

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

Table S6. Basic characteristics of clinical feature: 'COMPLETENESS.OF.RESECTION'

COMPLETENESS.OF.RESECTION | Labels | N
| R0 | 119
| R1 | 31
| RX | 3
| Significant markers | N = 7

List of 7 genes differentially expressed by 'COMPLETENESS.OF.RESECTION'

Table S7. Get Full Table List of 7 genes differentially expressed by 'COMPLETENESS.OF.RESECTION'

| ANOVA_P | Q
--- | --- | ---
CTNNA3__1 | 3.73e-13 | 7.46e-09
LRRTM3__1 | 3.73e-13 | 7.46e-09
CTNNA3 | 8.428e-13 | 1.69e-08
LRRTM3 | 8.428e-13 | 1.69e-08
AKR1B10 | 2.937e-09 | 5.87e-05
CAPN3 | 6.627e-07 | 0.0133
ZNF814 | 1.515e-06 | 0.0303

Figure S3. Get High-res Image As an example, this figure shows the association of CTNNA3__1 to 'COMPLETENESS.OF.RESECTION'. P value = 3.73e-13 with ANOVA analysis.

Clinical variable #5: 'NUMBER.OF.LYMPH.NODES'

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

Table S8. Basic characteristics of clinical feature: 'NUMBER.OF.LYMPH.NODES'

NUMBER.OF.LYMPH.NODES | Mean (SD) | 0.2 (0.72)
| Significant markers | N = 0

Methods & Data
Input

- Expresson data file = PRAD-TP.meth.by_min_expr_corr.data.txt

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

- Number of patients = 162

- Number of genes = 20008

- Number of clinical features = 5

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

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] 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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