Correlation between gene methylation status and clinical features
Ovarian Serous Cystadenocarcinoma (Primary solid tumor)
16 April 2014 | analyses__2014_04_16
Maintainer Information
Citation Information
doi:10.7908/C1PC3112
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/C1PC3112
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 14047 genes and 6 clinical features across 574 samples, statistically thresholded by Q value < 0.05, 4 clinical features related to at least one genes.

- 181 genes correlated to 'AGE'.

- PCDHGA1__4 , PCDHGA10__3 , PCDHGA2__4 , PCDHGA3__4 , PCDHGA4__4 , ...

- 59 genes correlated to 'PRIMARY.SITE.OF.DISEASE'.

- ZMYM5 , ZCCHC11 , CCNF , FOXN2 , SUV39H2 , ...

- 1324 genes correlated to 'RADIATIONS.RADIATION.REGIMENINDICATION'.

- NID2 , ALDH6A1 , LIN52 , SLC25A17 , HSD17B14 , ...

- 1 gene correlated to 'COMPLETENESS.OF.RESECTION'.

- ADAD2

- No genes correlated to 'Time to Death', and 'KARNOFSKY.PERFORMANCE.SCORE'.

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=181 | older | N=71 | younger | N=110
PRIMARY SITE OF DISEASE | ANOVA test | N=59 |  |  |  |
KARNOFSKY PERFORMANCE SCORE | Spearman correlation test | N=0 |  |  |  |
RADIATIONS RADIATION REGIMENINDICATION | t test | N=1324 | yes | N=1253 | no | N=71
COMPLETENESS OF RESECTION | ANOVA test | N=1 |  |  |  |

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) | 0.3-180.2 (median=28.5)
| censored | N = 263
| death | N = 295
| Significant markers | N = 0

Clinical variable #2: 'AGE'

181 genes related to 'AGE'.

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

AGE | Mean (SD) | 59.79 (12)
| Significant markers | N = 181
| pos. correlated | 71
| neg. correlated | 110

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
--- | --- | --- | ---
PCDHGA1__4 | 0.3019 | 4.094e-13 | 5.75e-09
PCDHGA10__3 | 0.3019 | 4.094e-13 | 5.75e-09
PCDHGA2__4 | 0.3019 | 4.094e-13 | 5.75e-09
PCDHGA3__4 | 0.3019 | 4.094e-13 | 5.75e-09
PCDHGA4__4 | 0.3019 | 4.094e-13 | 5.75e-09
PCDHGA5__4 | 0.3019 | 4.094e-13 | 5.75e-09
PCDHGA6__4 | 0.3019 | 4.094e-13 | 5.75e-09
PCDHGA7__4 | 0.3019 | 4.094e-13 | 5.75e-09
PCDHGA8__3 | 0.3019 | 4.094e-13 | 5.75e-09
PCDHGA9__3 | 0.3019 | 4.094e-13 | 5.75e-09

Figure S1. Get High-res Image As an example, this figure shows the association of PCDHGA1__4 to 'AGE'. P value = 4.09e-13 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #3: 'PRIMARY.SITE.OF.DISEASE'

59 genes related to 'PRIMARY.SITE.OF.DISEASE'.

Table S4. Basic characteristics of clinical feature: 'PRIMARY.SITE.OF.DISEASE'

PRIMARY.SITE.OF.DISEASE | Labels | N
| OMENTUM | 2
| OVARY | 560
| PERITONEUM OVARY | 2
| Significant markers | N = 59

List of top 10 genes differentially expressed by 'PRIMARY.SITE.OF.DISEASE'

Table S5. Get Full Table List of top 10 genes differentially expressed by 'PRIMARY.SITE.OF.DISEASE'

| ANOVA_P | Q
--- | --- | ---
ZMYM5 | 2.129e-68 | 2.99e-64
ZCCHC11 | 1.089e-53 | 1.53e-49
CCNF | 9.443e-48 | 1.33e-43
FOXN2 | 2.05e-44 | 2.88e-40
SUV39H2 | 2.262e-28 | 3.18e-24
RANBP1 | 1.023e-26 | 1.44e-22
TRMT2A | 1.023e-26 | 1.44e-22
DBN1 | 5.833e-26 | 8.19e-22
IRS2 | 1.286e-22 | 1.8e-18
INHA | 3.36e-21 | 4.72e-17

Figure S2. Get High-res Image As an example, this figure shows the association of ZMYM5 to 'PRIMARY.SITE.OF.DISEASE'. P value = 2.13e-68 with ANOVA 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) | 75.64 (13)
| Score | N
| 40 | 2
| 60 | 20
| 80 | 49
| 100 | 7
| Significant markers | N = 0

Clinical variable #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

1324 genes related to 'RADIATIONS.RADIATION.REGIMENINDICATION'.

Table S7. Basic characteristics of clinical feature: 'RADIATIONS.RADIATION.REGIMENINDICATION'

RADIATIONS.RADIATION.REGIMENINDICATION | Labels | N
| NO | 3
| YES | 571
| Significant markers | N = 1324
| Higher in YES | 1253
| Higher in NO | 71

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

Table S8. Get Full Table List of top 10 genes differentially expressed by 'RADIATIONS.RADIATION.REGIMENINDICATION'

| T(pos if higher in 'YES') | ttestP | Q | AUC
--- | --- | --- | --- | ---
NID2 | 27.74 | 7.984e-108 | 1.12e-103 | 0.9685
ALDH6A1 | 20.86 | 2.975e-72 | 4.18e-68 | 0.972
LIN52 | 20.86 | 2.975e-72 | 4.18e-68 | 0.972
SLC25A17 | 20.86 | 7.418e-68 | 1.04e-63 | 0.8578
HSD17B14 | 22.32 | 7.572e-68 | 1.06e-63 | 0.8616
PLEKHA4__1 | 22.32 | 7.572e-68 | 1.06e-63 | 0.8616
LYPD5 | 19.74 | 8.446e-66 | 1.19e-61 | 0.7274
PTPRN | 19.75 | 8.254e-65 | 1.16e-60 | 0.8961
THY1 | 20.36 | 9.25e-65 | 1.3e-60 | 0.8809
ACY1 | -18.88 | 3.911e-62 | 5.49e-58 | 0.7017

Figure S3. Get High-res Image As an example, this figure shows the association of NID2 to 'RADIATIONS.RADIATION.REGIMENINDICATION'. P value = 7.98e-108 with T-test analysis.

Clinical variable #6: 'COMPLETENESS.OF.RESECTION'

One gene related to 'COMPLETENESS.OF.RESECTION'.

Table S9. Basic characteristics of clinical feature: 'COMPLETENESS.OF.RESECTION'

COMPLETENESS.OF.RESECTION | Labels | N
| R0 | 14
| R1 | 26
| R2 | 2
| Significant markers | N = 1

List of one gene differentially expressed by 'COMPLETENESS.OF.RESECTION'

Table S10. Get Full Table List of one gene differentially expressed by 'COMPLETENESS.OF.RESECTION'

| ANOVA_P | Q
--- | --- | ---
ADAD2 | 2.294e-09 | 3.22e-05

Figure S4. Get High-res Image As an example, this figure shows the association of ADAD2 to 'COMPLETENESS.OF.RESECTION'. P value = 2.29e-09 with ANOVA analysis.

Methods & Data
Input

- Expresson data file = OV-TP.meth.by_min_clin_corr.data.txt

- Clinical data file = OV-TP.merged_data.txt

- Number of patients = 574

- Number of genes = 14047

- 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

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

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] Howell, D, Statistical Methods for Psychology. (5th ed.), Duxbury Press:324-5 (2002)
[4] Lehmann and Romano, Testing Statistical Hypotheses (3E ed.), New York: Springer. ISBN 0387988645 (2005)
[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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