Correlation between gene methylation status and clinical features
Liver Hepatocellular Carcinoma (Primary solid tumor)
15 January 2014 | analyses__2014_01_15
Maintainer Information
Citation Information
doi:10.7908/C1WH2NFR
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/C1WH2NFR
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 19646 genes and 8 clinical features across 127 samples, statistically thresholded by Q value < 0.05, 7 clinical features related to at least one genes.

- 3 genes correlated to 'AGE'.

- RASL11B , WTIP , CACNA2D2

- 118 genes correlated to 'NEOPLASM.DISEASESTAGE'.

- SLC12A9 , SEPSECS , SMU1 , C16ORF61 , CENPN , ...

- 1 gene correlated to 'PATHOLOGY.T.STAGE'.

- TPCN2

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

- PTP4A3 , FAM157A , MSC , PCDHGA1__9 , PCDHGA2__9 , ...

- 32 genes correlated to 'PATHOLOGY.M.STAGE'.

- SEPSECS , SLC12A9 , KLHL7 , HSPB11 , LRRC42 , ...

- 12 genes correlated to 'GENDER'.

- ALG11__1 , UTP14C , ALDH3A1 , SLC22A11 , ZNF35 , ...

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

- SEPSECS , BIVM , KDELC1 , CCDC94 , ZNF540 , ...

- 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=3 | older | N=3 | younger | N=0
NEOPLASM DISEASESTAGE | ANOVA test | N=118 |  |  |  |
PATHOLOGY T STAGE | Spearman correlation test | N=1 | higher stage | N=1 | lower stage | N=0
PATHOLOGY N STAGE | t test | N=210 | class1 | N=36 | class0 | N=174
PATHOLOGY M STAGE | ANOVA test | N=32 |  |  |  |
GENDER | t test | N=12 | male | N=3 | female | N=9
COMPLETENESS OF RESECTION | ANOVA test | N=10 |  |  |  |

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) | 0-113 (median=14.6)
| censored | N = 68
| death | N = 54
| Significant markers | N = 0

Clinical variable #2: 'AGE'

3 genes related to 'AGE'.

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

AGE | Mean (SD) | 61.38 (14)
| 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
--- | --- | --- | ---
RASL11B | 0.4408 | 2.675e-07 | 0.00526
WTIP | 0.4188 | 1.172e-06 | 0.023
CACNA2D2 | 0.4075 | 2.4e-06 | 0.0472

Figure S1. Get High-res Image As an example, this figure shows the association of RASL11B to 'AGE'. P value = 2.68e-07 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #3: 'NEOPLASM.DISEASESTAGE'

118 genes related to 'NEOPLASM.DISEASESTAGE'.

Table S4. Basic characteristics of clinical feature: 'NEOPLASM.DISEASESTAGE'

NEOPLASM.DISEASESTAGE | Labels | N
| STAGE I | 48
| STAGE II | 27
| STAGE III | 2
| STAGE IIIA | 30
| STAGE IIIB | 3
| STAGE IIIC | 5
| STAGE IV | 1
| STAGE IVA | 1
| STAGE IVB | 1
| Significant markers | N = 118

List of top 10 genes differentially expressed by 'NEOPLASM.DISEASESTAGE'

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

| ANOVA_P | Q
--- | --- | ---
SLC12A9 | 1.153e-68 | 2.26e-64
SEPSECS | 1.891e-60 | 3.72e-56
SMU1 | 4.847e-43 | 9.52e-39
C16ORF61 | 9.19e-32 | 1.81e-27
CENPN | 9.19e-32 | 1.81e-27
KLHL7 | 5.058e-30 | 9.93e-26
HSPB11 | 1.933e-29 | 3.8e-25
LRRC42 | 1.933e-29 | 3.8e-25
KILLIN | 2.179e-29 | 4.28e-25
PTEN | 2.179e-29 | 4.28e-25

Figure S2. Get High-res Image As an example, this figure shows the association of SLC12A9 to 'NEOPLASM.DISEASESTAGE'. P value = 1.15e-68 with ANOVA analysis.

Clinical variable #4: 'PATHOLOGY.T.STAGE'

One gene related to 'PATHOLOGY.T.STAGE'.

Table S6. Basic characteristics of clinical feature: 'PATHOLOGY.T.STAGE'

PATHOLOGY.T.STAGE | Mean (SD) | 2.02 (0.97)
|  | N
| 1 | 51
| 2 | 30
| 3 | 39
| 4 | 7
| Significant markers | N = 1
| pos. correlated | 1
| neg. correlated | 0

List of one gene significantly correlated to 'PATHOLOGY.T.STAGE' by Spearman correlation test

Table S7. Get Full Table List of one gene significantly correlated to 'PATHOLOGY.T.STAGE' by Spearman correlation test

| SpearmanCorr | corrP | Q
--- | --- | --- | ---
TPCN2 | 0.4313 | 4.157e-07 | 0.00817

Figure S3. Get High-res Image As an example, this figure shows the association of TPCN2 to 'PATHOLOGY.T.STAGE'. P value = 4.16e-07 with Spearman correlation analysis.

Clinical variable #5: 'PATHOLOGY.N.STAGE'

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

Table S8. Basic characteristics of clinical feature: 'PATHOLOGY.N.STAGE'

PATHOLOGY.N.STAGE | Labels | N
| class0 | 83
| class1 | 3
| Significant markers | N = 210
| Higher in class1 | 36
| Higher in class0 | 174

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

Table S9. Get Full Table List of top 10 genes differentially expressed by 'PATHOLOGY.N.STAGE'

| T(pos if higher in 'class1') | ttestP | Q | AUC
--- | --- | --- | --- | ---
PTP4A3 | -15.24 | 5.021e-20 | 9.86e-16 | 0.9598
FAM157A | -14.35 | 2.016e-19 | 3.96e-15 | 0.9438
MSC | -10.72 | 3.534e-17 | 6.94e-13 | 0.9076
PCDHGA1__9 | -9.96 | 7.214e-16 | 1.42e-11 | 0.8916
PCDHGA2__9 | -9.96 | 7.214e-16 | 1.42e-11 | 0.8916
PCDHGA3__8 | -9.96 | 7.214e-16 | 1.42e-11 | 0.8916
PCDHGA4__7 | -9.96 | 7.214e-16 | 1.42e-11 | 0.8916
PCDHGB1__8 | -9.96 | 7.214e-16 | 1.42e-11 | 0.8916
PCDHGB2__7 | -9.96 | 7.214e-16 | 1.42e-11 | 0.8916
THBD | -9.5 | 1.935e-14 | 3.8e-10 | 0.8112

Figure S4. Get High-res Image As an example, this figure shows the association of PTP4A3 to 'PATHOLOGY.N.STAGE'. P value = 5.02e-20 with T-test analysis.

Clinical variable #6: 'PATHOLOGY.M.STAGE'

32 genes related to 'PATHOLOGY.M.STAGE'.

Table S10. Basic characteristics of clinical feature: 'PATHOLOGY.M.STAGE'

PATHOLOGY.M.STAGE | Labels | N
| M0 | 101
| M1 | 2
| MX | 24
| Significant markers | N = 32

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

Table S11. Get Full Table List of top 10 genes differentially expressed by 'PATHOLOGY.M.STAGE'

| ANOVA_P | Q
--- | --- | ---
SEPSECS | 2.855e-20 | 5.61e-16
SLC12A9 | 4.703e-19 | 9.24e-15
KLHL7 | 9.885e-16 | 1.94e-11
HSPB11 | 2.851e-13 | 5.6e-09
LRRC42 | 2.851e-13 | 5.6e-09
C16ORF61 | 5.438e-13 | 1.07e-08
CENPN | 5.438e-13 | 1.07e-08
ERCC2 | 5.137e-12 | 1.01e-07
ALG8 | 2.396e-11 | 4.7e-07
SAMHD1 | 7.836e-11 | 1.54e-06

Figure S5. Get High-res Image As an example, this figure shows the association of SEPSECS to 'PATHOLOGY.M.STAGE'. P value = 2.86e-20 with ANOVA analysis.

Clinical variable #7: 'GENDER'

12 genes related to 'GENDER'.

Table S12. Basic characteristics of clinical feature: 'GENDER'

GENDER | Labels | N
| FEMALE | 49
| MALE | 78
| Significant markers | N = 12
| Higher in MALE | 3
| Higher in FEMALE | 9

List of top 10 genes differentially expressed by 'GENDER'

Table S13. Get Full Table List of top 10 genes differentially expressed by 'GENDER'

| T(pos if higher in 'MALE') | ttestP | Q | AUC
--- | --- | --- | --- | ---
ALG11__1 | 12.44 | 1.128e-17 | 2.22e-13 | 0.95
UTP14C | 12.44 | 1.128e-17 | 2.22e-13 | 0.95
ALDH3A1 | -7.31 | 3.285e-11 | 6.45e-07 | 0.7972
SLC22A11 | -5.79 | 6.67e-08 | 0.00131 | 0.7195
ZNF35 | 5.74 | 7.133e-08 | 0.0014 | 0.7588
MAP3K8 | -5.7 | 1.078e-07 | 0.00212 | 0.7713
NKD1 | -5.4 | 3.387e-07 | 0.00665 | 0.7179
FAM83A | -5.14 | 1.266e-06 | 0.0249 | 0.7462
LOC100131726 | -5.14 | 1.266e-06 | 0.0249 | 0.7462
DHODH | -5.02 | 2.088e-06 | 0.041 | 0.7386

Figure S6. Get High-res Image As an example, this figure shows the association of ALG11__1 to 'GENDER'. P value = 1.13e-17 with T-test analysis.

Clinical variable #8: 'COMPLETENESS.OF.RESECTION'

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

Table S14. Basic characteristics of clinical feature: 'COMPLETENESS.OF.RESECTION'

COMPLETENESS.OF.RESECTION | Labels | N
| R0 | 102
| R1 | 10
| R2 | 1
| RX | 9
| Significant markers | N = 10

List of 10 genes differentially expressed by 'COMPLETENESS.OF.RESECTION'

Table S15. Get Full Table List of 10 genes differentially expressed by 'COMPLETENESS.OF.RESECTION'

| ANOVA_P | Q
--- | --- | ---
SEPSECS | 6.302e-67 | 1.24e-62
BIVM | 4.319e-23 | 8.48e-19
KDELC1 | 4.319e-23 | 8.48e-19
CCDC94 | 7.105e-22 | 1.4e-17
ZNF540 | 5.669e-16 | 1.11e-11
ZNF571 | 5.669e-16 | 1.11e-11
C5ORF42 | 2.076e-13 | 4.08e-09
TBC1D15 | 4.683e-11 | 9.2e-07
C1ORF101 | 4.495e-09 | 8.83e-05
CCDC117 | 1.354e-06 | 0.0266

Figure S7. Get High-res Image As an example, this figure shows the association of SEPSECS to 'COMPLETENESS.OF.RESECTION'. P value = 6.3e-67 with ANOVA analysis.

Methods & Data
Input

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

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

- Number of patients = 127

- Number of genes = 19646

- Number of clinical features = 8

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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