Correlation between gene methylation status and clinical features
Liver Hepatocellular Carcinoma (Primary solid tumor)
23 September 2013 | analyses__2013_09_23
Maintainer Information
Citation Information
doi:10.7908/C1MK6B72
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/C1MK6B72
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 19657 genes and 8 clinical features across 107 samples, statistically thresholded by Q value < 0.05, 8 clinical features related to at least one genes.

- 3 genes correlated to 'Time to Death'.

- E2F8 , C3ORF26 , FILIP1L

- 1 gene correlated to 'AGE'.

- SHOX2

- 112 genes correlated to 'NEOPLASM.DISEASESTAGE'.

- SEPSECS , SLC12A9 , DTD1 , AHR , C16ORF61 , ...

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

- TPCN2

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

- PTP4A3 , THBD , MSC , C19ORF45 , TSFM , ...

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

- SEPSECS , SLC12A9 , DTD1 , KLHL7 , AHR , ...

- 9 genes correlated to 'GENDER'.

- ALG11__2 , UTP14C , ALDH3A1 , ZNF35 , SLC22A11 , ...

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

- SEPSECS , C5ORF42 , BIVM , KDELC1 , CCDC94 , ...

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=3 | shorter survival | N=0 | longer survival | N=3
AGE | Spearman correlation test | N=1 | older | N=1 | younger | N=0
NEOPLASM DISEASESTAGE | ANOVA test | N=112 |  |  |  |
PATHOLOGY T STAGE | Spearman correlation test | N=1 | higher stage | N=1 | lower stage | N=0
PATHOLOGY N STAGE | t test | N=143 | class1 | N=30 | class0 | N=113
PATHOLOGY M STAGE | ANOVA test | N=34 |  |  |  |
GENDER | t test | N=9 | male | N=3 | female | N=6
COMPLETENESS OF RESECTION | ANOVA test | N=14 |  |  |  |

Clinical variable #1: 'Time to Death'

3 genes related to 'Time to Death'.

Table S1. Basic characteristics of clinical feature: 'Time to Death'

Time to Death | Duration (Months) | 0-113 (median=14.4)
| censored | N = 56
| death | N = 47
| Significant markers | N = 3
| associated with shorter survival | 0
| associated with longer survival | 3

List of 3 genes significantly associated with 'Time to Death' by Cox regression test

Table S2. Get Full Table List of 3 genes significantly associated with 'Time to Death' by Cox regression test

| HazardRatio | Wald_P | Q | C_index
--- | --- | --- | --- | ---
E2F8 | 0 | 2.408e-06 | 0.047 | 0.353
C3ORF26 | 0.02 | 2.43e-06 | 0.048 | 0.315
FILIP1L | 0.02 | 2.43e-06 | 0.048 | 0.315

Figure S1. Get High-res Image As an example, this figure shows the association of E2F8 to 'Time to Death'. four curves present the cumulative survival rates of 4 quartile subsets of patients. P value = 2.41e-06 with univariate Cox regression analysis using continuous log-2 expression values.

Clinical variable #2: 'AGE'

One gene related to 'AGE'.

Table S3. Basic characteristics of clinical feature: 'AGE'

AGE | Mean (SD) | 61.23 (14)
| Significant markers | N = 1
| pos. correlated | 1
| neg. correlated | 0

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

Table S4. Get Full Table List of one gene significantly correlated to 'AGE' by Spearman correlation test

| SpearmanCorr | corrP | Q
--- | --- | --- | ---
SHOX2 | 0.4451 | 1.966e-06 | 0.0386

Figure S2. Get High-res Image As an example, this figure shows the association of SHOX2 to 'AGE'. P value = 1.97e-06 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #3: 'NEOPLASM.DISEASESTAGE'

112 genes related to 'NEOPLASM.DISEASESTAGE'.

Table S5. Basic characteristics of clinical feature: 'NEOPLASM.DISEASESTAGE'

NEOPLASM.DISEASESTAGE | Labels | N
| STAGE I | 40
| STAGE II | 23
| STAGE III | 2
| STAGE IIIA | 22
| STAGE IIIB | 3
| STAGE IIIC | 5
| STAGE IV | 1
| STAGE IVA | 1
| STAGE IVB | 1
| Significant markers | N = 112

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

Table S6. Get Full Table List of top 10 genes differentially expressed by 'NEOPLASM.DISEASESTAGE'

| ANOVA_P | Q
--- | --- | ---
SEPSECS | 1.863e-58 | 3.66e-54
SLC12A9 | 7.725e-57 | 1.52e-52
DTD1 | 1.948e-51 | 3.83e-47
AHR | 2.425e-37 | 4.77e-33
C16ORF61 | 8.378e-37 | 1.65e-32
CENPN | 8.378e-37 | 1.65e-32
SLC25A38 | 5.705e-36 | 1.12e-31
KLHL7 | 5.267e-28 | 1.03e-23
HSPB11 | 1.074e-24 | 2.11e-20
LRRC42 | 1.074e-24 | 2.11e-20

Figure S3. Get High-res Image As an example, this figure shows the association of SEPSECS to 'NEOPLASM.DISEASESTAGE'. P value = 1.86e-58 with ANOVA analysis.

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

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

Table S7. Basic characteristics of clinical feature: 'PATHOLOGY.T.STAGE'

PATHOLOGY.T.STAGE | Mean (SD) | 2.02 (0.99)
|  | N
| 1 | 43
| 2 | 25
| 3 | 31
| 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 S8. Get Full Table List of one gene significantly correlated to 'PATHOLOGY.T.STAGE' by Spearman correlation test

| SpearmanCorr | corrP | Q
--- | --- | --- | ---
TPCN2 | 0.4792 | 2.032e-07 | 0.00399

Figure S4. Get High-res Image As an example, this figure shows the association of TPCN2 to 'PATHOLOGY.T.STAGE'. P value = 2.03e-07 with Spearman correlation analysis.

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

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

Table S9. Basic characteristics of clinical feature: 'PATHOLOGY.N.STAGE'

PATHOLOGY.N.STAGE | Labels | N
| class0 | 68
| class1 | 3
| Significant markers | N = 143
| Higher in class1 | 30
| Higher in class0 | 113

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

Table S10. Get Full Table List of top 10 genes differentially expressed by 'PATHOLOGY.N.STAGE'

| T(pos if higher in 'class1') | ttestP | Q | AUC
--- | --- | --- | --- | ---
PTP4A3 | -13.34 | 1.342e-18 | 2.64e-14 | 0.951
THBD | -9.69 | 2.082e-14 | 4.09e-10 | 0.7843
MSC | -9.59 | 2.814e-14 | 5.53e-10 | 0.8873
C19ORF45 | -9.72 | 2.603e-13 | 5.11e-09 | 0.8775
TSFM | -9.18 | 6.296e-13 | 1.24e-08 | 0.9853
ZNF90 | -9.56 | 7.995e-13 | 1.57e-08 | 0.8529
KLHL3 | -8.52 | 2.716e-12 | 5.34e-08 | 0.7696
GRAMD1A | -8.28 | 6.406e-12 | 1.26e-07 | 0.7941
CCNJ | -8.17 | 1.174e-11 | 2.31e-07 | 0.8137
WDR17 | -8.19 | 1.899e-11 | 3.73e-07 | 0.8284

Figure S5. Get High-res Image As an example, this figure shows the association of PTP4A3 to 'PATHOLOGY.N.STAGE'. P value = 1.34e-18 with T-test analysis.

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

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

Table S11. Basic characteristics of clinical feature: 'PATHOLOGY.M.STAGE'

PATHOLOGY.M.STAGE | Labels | N
| M0 | 83
| M1 | 2
| MX | 22
| Significant markers | N = 34

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

Table S12. Get Full Table List of top 10 genes differentially expressed by 'PATHOLOGY.M.STAGE'

| ANOVA_P | Q
--- | --- | ---
SEPSECS | 7.968e-18 | 1.57e-13
SLC12A9 | 3.738e-16 | 7.35e-12
DTD1 | 1.636e-14 | 3.22e-10
KLHL7 | 2.866e-14 | 5.63e-10
AHR | 3.98e-13 | 7.82e-09
C16ORF61 | 1.005e-12 | 1.98e-08
CENPN | 1.005e-12 | 1.98e-08
HSPB11 | 5.833e-12 | 1.15e-07
LRRC42 | 5.833e-12 | 1.15e-07
ALG10B | 5.972e-11 | 1.17e-06

Figure S6. Get High-res Image As an example, this figure shows the association of SEPSECS to 'PATHOLOGY.M.STAGE'. P value = 7.97e-18 with ANOVA analysis.

Clinical variable #7: 'GENDER'

9 genes related to 'GENDER'.

Table S13. Basic characteristics of clinical feature: 'GENDER'

GENDER | Labels | N
| FEMALE | 42
| MALE | 65
| Significant markers | N = 9
| Higher in MALE | 3
| Higher in FEMALE | 6

List of 9 genes differentially expressed by 'GENDER'

Table S14. Get Full Table List of 9 genes differentially expressed by 'GENDER'

| T(pos if higher in 'MALE') | ttestP | Q | AUC
--- | --- | --- | --- | ---
ALG11__2 | 10.85 | 2.313e-14 | 4.55e-10 | 0.9443
UTP14C | 10.85 | 2.313e-14 | 4.55e-10 | 0.9443
ALDH3A1 | -6.46 | 3.8e-09 | 7.47e-05 | 0.7894
ZNF35 | 5.39 | 4.674e-07 | 0.00919 | 0.7557
SLC22A11 | -5.38 | 5.537e-07 | 0.0109 | 0.7271
MAP3K8 | -5.3 | 7.679e-07 | 0.0151 | 0.7711
TINAG | -5.08 | 1.687e-06 | 0.0332 | 0.741
FAM83A | -5.11 | 1.795e-06 | 0.0353 | 0.7641
LOC100131726 | -5.11 | 1.795e-06 | 0.0353 | 0.7641

Figure S7. Get High-res Image As an example, this figure shows the association of ALG11__2 to 'GENDER'. P value = 2.31e-14 with T-test analysis.

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

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

Table S15. Basic characteristics of clinical feature: 'COMPLETENESS.OF.RESECTION'

COMPLETENESS.OF.RESECTION | Labels | N
| R0 | 86
| R1 | 6
| R2 | 1
| RX | 9
| Significant markers | N = 14

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

Table S16. Get Full Table List of top 10 genes differentially expressed by 'COMPLETENESS.OF.RESECTION'

| ANOVA_P | Q
--- | --- | ---
SEPSECS | 2.133e-64 | 4.19e-60
C5ORF42 | 5.531e-24 | 1.09e-19
BIVM | 2.029e-19 | 3.99e-15
KDELC1 | 2.029e-19 | 3.99e-15
CCDC94 | 4.319e-19 | 8.49e-15
ZNF540 | 2.824e-13 | 5.55e-09
ZNF571 | 2.824e-13 | 5.55e-09
C1ORF101 | 1.627e-07 | 0.0032
CAMLG | 4.432e-07 | 0.00871
ZBTB7C | 1.847e-06 | 0.0363

Figure S8. Get High-res Image As an example, this figure shows the association of SEPSECS to 'COMPLETENESS.OF.RESECTION'. P value = 2.13e-64 with ANOVA analysis.

Methods & Data
Input

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

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

- Number of patients = 107

- Number of genes = 19657

- 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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