Correlation between gene methylation status and clinical features
Kidney Renal Papillary Cell Carcinoma (Primary solid tumor)
15 January 2014 | analyses__2014_01_15
Maintainer Information
Citation Information
doi:10.7908/C1765CRM
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/C1765CRM
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 19951 genes and 11 clinical features across 123 samples, statistically thresholded by Q value < 0.05, 6 clinical features related to at least one genes.

- 1 gene correlated to 'AGE'.

- WDR81__1

- 234 genes correlated to 'NEOPLASM.DISEASESTAGE'.

- FBXO39 , PCDHA1__6 , PCDHA10__3 , PCDHA11__1 , PCDHA12__1 , ...

- 85 genes correlated to 'PATHOLOGY.T.STAGE'.

- TRH , DLX6 , DLX6AS , LPPR3 , CDO1 , ...

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

- LRRC26 , PCDHA1__6 , PCDHA10__3 , PCDHA11__1 , PCDHA12__1 , ...

- 15 genes correlated to 'GENDER'.

- FAM35A , GLUD1 , NARFL , PRKRIR , HNRNPD , ...

- 77 genes correlated to 'HISTOLOGICAL.TYPE'.

- ANKS1A , TAF11 , LOC286002__1 , BHLHE41 , SRPK2 , ...

- No genes correlated to 'Time to Death', 'PATHOLOGY.N.STAGE', 'KARNOFSKY.PERFORMANCE.SCORE', 'NUMBERPACKYEARSSMOKED', and 'YEAROFTOBACCOSMOKINGONSET'.

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=1 | older | N=1 | younger | N=0
NEOPLASM DISEASESTAGE | ANOVA test | N=234 |  |  |  |
PATHOLOGY T STAGE | Spearman correlation test | N=85 | higher stage | N=75 | lower stage | N=10
PATHOLOGY N STAGE | Spearman correlation test | N=0 |  |  |  |
PATHOLOGY M STAGE | ANOVA test | N=266 |  |  |  |
GENDER | t test | N=15 | male | N=7 | female | N=8
KARNOFSKY PERFORMANCE SCORE | Spearman correlation test | N=0 |  |  |  |
HISTOLOGICAL TYPE | t test | N=77 | kidney papillary renal cell carcinoma | N=28 | kidney clear cell renal carcinoma | N=49
NUMBERPACKYEARSSMOKED | Spearman correlation test | N=0 |  |  |  |
YEAROFTOBACCOSMOKINGONSET | Spearman correlation test | N=0 |  |  |  |

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) | 0-194.8 (median=13.6)
| censored | N = 98
| death | N = 13
| Significant markers | N = 0

Clinical variable #2: 'AGE'

One gene related to 'AGE'.

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

AGE | Mean (SD) | 60.15 (13)
| Significant markers | N = 1
| pos. correlated | 1
| neg. correlated | 0

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

Table S3. Get Full Table List of one gene significantly correlated to 'AGE' by Spearman correlation test

| SpearmanCorr | corrP | Q
--- | --- | --- | ---
WDR81__1 | 0.4394 | 7.172e-07 | 0.0143

Figure S1. Get High-res Image As an example, this figure shows the association of WDR81__1 to 'AGE'. P value = 7.17e-07 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #3: 'NEOPLASM.DISEASESTAGE'

234 genes related to 'NEOPLASM.DISEASESTAGE'.

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

NEOPLASM.DISEASESTAGE | Labels | N
| STAGE I | 72
| STAGE II | 8
| STAGE III | 30
| STAGE IV | 9
| Significant markers | N = 234

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
--- | --- | ---
FBXO39 | 1.529e-11 | 3.05e-07
PCDHA1__6 | 7.528e-11 | 1.5e-06
PCDHA10__3 | 7.528e-11 | 1.5e-06
PCDHA11__1 | 7.528e-11 | 1.5e-06
PCDHA12__1 | 7.528e-11 | 1.5e-06
PCDHA13 | 7.528e-11 | 1.5e-06
PCDHA2__6 | 7.528e-11 | 1.5e-06
PCDHA3__5 | 7.528e-11 | 1.5e-06
PCDHA4__5 | 7.528e-11 | 1.5e-06
PCDHA5__5 | 7.528e-11 | 1.5e-06

Figure S2. Get High-res Image As an example, this figure shows the association of FBXO39 to 'NEOPLASM.DISEASESTAGE'. P value = 1.53e-11 with ANOVA analysis.

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

85 genes related to 'PATHOLOGY.T.STAGE'.

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

PATHOLOGY.T.STAGE | Mean (SD) | 1.71 (0.93)
|  | N
| 1 | 75
| 2 | 10
| 3 | 37
| 4 | 1
| Significant markers | N = 85
| pos. correlated | 75
| neg. correlated | 10

List of top 10 genes significantly correlated to 'PATHOLOGY.T.STAGE' by Spearman correlation test

Table S7. Get Full Table List of top 10 genes significantly correlated to 'PATHOLOGY.T.STAGE' by Spearman correlation test

| SpearmanCorr | corrP | Q
--- | --- | --- | ---
TRH | 0.5647 | 1.024e-11 | 2.04e-07
DLX6 | 0.5473 | 5.77e-11 | 1.15e-06
DLX6AS | 0.5473 | 5.77e-11 | 1.15e-06
LPPR3 | 0.5343 | 1.952e-10 | 3.89e-06
CDO1 | 0.5248 | 4.656e-10 | 9.29e-06
LEFTY2 | 0.5217 | 6.127e-10 | 1.22e-05
C17ORF93 | 0.5118 | 1.445e-09 | 2.88e-05
PRAC | 0.5118 | 1.445e-09 | 2.88e-05
TULP1 | 0.5117 | 1.466e-09 | 2.92e-05
NSD1 | 0.491 | 8.15e-09 | 0.000163

Figure S3. Get High-res Image As an example, this figure shows the association of TRH to 'PATHOLOGY.T.STAGE'. P value = 1.02e-11 with Spearman correlation analysis.

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

No gene related to 'PATHOLOGY.N.STAGE'.

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

PATHOLOGY.N.STAGE | Mean (SD) | 0.49 (0.68)
|  | N
| 0 | 24
| 1 | 11
| 2 | 4
| Significant markers | N = 0

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

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

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

PATHOLOGY.M.STAGE | Labels | N
| M0 | 48
| M1 | 5
| MX | 62
| Significant markers | N = 266

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

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

| ANOVA_P | Q
--- | --- | ---
LRRC26 | 3.408e-14 | 6.8e-10
PCDHA1__6 | 3.904e-11 | 7.79e-07
PCDHA10__3 | 3.904e-11 | 7.79e-07
PCDHA11__1 | 3.904e-11 | 7.79e-07
PCDHA12__1 | 3.904e-11 | 7.79e-07
PCDHA13 | 3.904e-11 | 7.79e-07
PCDHA2__6 | 3.904e-11 | 7.79e-07
PCDHA3__5 | 3.904e-11 | 7.79e-07
PCDHA4__5 | 3.904e-11 | 7.79e-07
PCDHA5__5 | 3.904e-11 | 7.79e-07

Figure S4. Get High-res Image As an example, this figure shows the association of LRRC26 to 'PATHOLOGY.M.STAGE'. P value = 3.41e-14 with ANOVA analysis.

Clinical variable #7: 'GENDER'

15 genes related to 'GENDER'.

Table S11. Basic characteristics of clinical feature: 'GENDER'

GENDER | Labels | N
| FEMALE | 38
| MALE | 85
| Significant markers | N = 15
| Higher in MALE | 7
| Higher in FEMALE | 8

List of top 10 genes differentially expressed by 'GENDER'

Table S12. Get Full Table List of top 10 genes differentially expressed by 'GENDER'

| T(pos if higher in 'MALE') | ttestP | Q | AUC
--- | --- | --- | --- | ---
FAM35A | -9.94 | 6.262e-13 | 1.25e-08 | 0.9715
GLUD1 | -9.94 | 6.262e-13 | 1.25e-08 | 0.9715
NARFL | -8 | 1.256e-12 | 2.51e-08 | 0.817
PRKRIR | 8.27 | 7.392e-11 | 1.47e-06 | 0.9136
HNRNPD | 6.9 | 9.027e-09 | 0.00018 | 0.8402
ALG11__2 | 6.82 | 4.29e-08 | 0.000856 | 0.9251
UTP14C | 6.82 | 4.29e-08 | 0.000856 | 0.9251
FDPS | 6.38 | 6.377e-08 | 0.00127 | 0.8895
RUSC1__1 | 6.38 | 6.377e-08 | 0.00127 | 0.8895
KIF4B | -5.92 | 1.444e-07 | 0.00288 | 0.7991

Figure S5. Get High-res Image As an example, this figure shows the association of FAM35A to 'GENDER'. P value = 6.26e-13 with T-test analysis.

Clinical variable #8: 'KARNOFSKY.PERFORMANCE.SCORE'

No gene related to 'KARNOFSKY.PERFORMANCE.SCORE'.

Table S13. Basic characteristics of clinical feature: 'KARNOFSKY.PERFORMANCE.SCORE'

KARNOFSKY.PERFORMANCE.SCORE | Mean (SD) | 91.52 (11)
| Score | N
| 40 | 1
| 80 | 3
| 90 | 16
| 100 | 13
| Significant markers | N = 0

Clinical variable #9: 'HISTOLOGICAL.TYPE'

77 genes related to 'HISTOLOGICAL.TYPE'.

Table S14. Basic characteristics of clinical feature: 'HISTOLOGICAL.TYPE'

HISTOLOGICAL.TYPE | Labels | N
| KIDNEY CLEAR CELL RENAL CARCINOMA | 6
| KIDNEY PAPILLARY RENAL CELL CARCINOMA | 117
| Significant markers | N = 77
| Higher in KIDNEY PAPILLARY RENAL CELL CARCINOMA | 28
| Higher in KIDNEY CLEAR CELL RENAL CARCINOMA | 49

List of top 10 genes differentially expressed by 'HISTOLOGICAL.TYPE'

Table S15. Get Full Table List of top 10 genes differentially expressed by 'HISTOLOGICAL.TYPE'

| T(pos if higher in 'KIDNEY PAPILLARY RENAL CELL CARCINOMA') | ttestP | Q | AUC
--- | --- | --- | --- | ---
ANKS1A | 9.53 | 1.24e-13 | 2.47e-09 | 0.8875
TAF11 | 9.53 | 1.24e-13 | 2.47e-09 | 0.8875
LOC286002__1 | -9.81 | 2.164e-13 | 4.32e-09 | 0.8903
BHLHE41 | -8.34 | 2.485e-12 | 4.96e-08 | 0.7678
SRPK2 | 8.62 | 1.381e-11 | 2.75e-07 | 0.9031
KLRC2 | -7.51 | 4.616e-11 | 9.21e-07 | 0.8609
CLEC4G | -7.31 | 5.857e-11 | 1.17e-06 | 0.8205
IBSP | -7.57 | 8.906e-11 | 1.78e-06 | 0.8162
SPOPL | 8.08 | 2.958e-10 | 5.9e-06 | 0.8447
PCBP3 | -6.85 | 4.146e-10 | 8.27e-06 | 0.8276

Figure S6. Get High-res Image As an example, this figure shows the association of ANKS1A to 'HISTOLOGICAL.TYPE'. P value = 1.24e-13 with T-test analysis.

Clinical variable #10: 'NUMBERPACKYEARSSMOKED'

No gene related to 'NUMBERPACKYEARSSMOKED'.

Table S16. Basic characteristics of clinical feature: 'NUMBERPACKYEARSSMOKED'

NUMBERPACKYEARSSMOKED | Mean (SD) | 45.67 (69)
| Significant markers | N = 0

Clinical variable #11: 'YEAROFTOBACCOSMOKINGONSET'

No gene related to 'YEAROFTOBACCOSMOKINGONSET'.

Table S17. Basic characteristics of clinical feature: 'YEAROFTOBACCOSMOKINGONSET'

YEAROFTOBACCOSMOKINGONSET | Mean (SD) | 1973.75 (21)
| Value | N
| 1951 | 1
| 1960 | 1
| 1991 | 1
| 1993 | 1
| Significant markers | N = 0

Methods & Data
Input

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

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

- Number of patients = 123

- Number of genes = 19951

- Number of clinical features = 11

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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