Kidney Renal Papillary Cell Carcinoma: Correlation between gene methylation status and clinical features
(primary solid tumor cohort)
Maintained by Juok Cho (Broad Institute)
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 17247 genes and 8 clinical features across 58 samples, statistically thresholded by Q value < 0.05, 5 clinical features related to at least one genes.

- 1 gene correlated to 'AGE'.

- IGF2BP2

- 3 genes correlated to 'GENDER'.

- NARFL , ATAD5 , CCNYL1

- 51 genes correlated to 'PATHOLOGY.T'.

- DLX6AS , GNASAS , GP2 , GPR150 , PCDHB19P , ...

- 22 genes correlated to 'PATHOLOGICSPREAD(M)'.

- RFX7 , GDPD4 , ERCC2 , DHDH , UNC93A , ...

- 33 genes correlated to 'TUMOR.STAGE'.

- INSM1 , ZNF177 , DLX6AS , CNIH3 , MATK , ...

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

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
GENDER | t test | N=3 | male | N=1 | female | N=2
KARNOFSKY PERFORMANCE SCORE | t test | N=0 |  |  |  |
PATHOLOGY T | Spearman correlation test | N=51 | higher pT | N=40 | lower pT | N=11
PATHOLOGY N | Spearman correlation test | N=0 |  |  |  |
PATHOLOGICSPREAD(M) | ANOVA test | N=22 |  |  |  |
TUMOR STAGE | Spearman correlation test | N=33 | higher stage | N=29 | lower stage | N=4

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) | 1-182.7 (median=21.6)
| censored | N = 44
| death | N = 11
| Significant markers | N = 0

Clinical variable #2: 'AGE'

One gene related to 'AGE'.

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

AGE | Mean (SD) | 60.27 (14)
| 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
--- | --- | --- | ---
IGF2BP2 | 0.619 | 4.721e-07 | 0.00814

Figure S1. Get High-res Image As an example, this figure shows the association of IGF2BP2 to 'AGE'. P value = 4.72e-07 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #3: 'GENDER'

3 genes related to 'GENDER'.

Table S4. Basic characteristics of clinical feature: 'GENDER'

GENDER | Labels | N
| FEMALE | 18
| MALE | 40
| Significant markers | N = 3
| Higher in MALE | 1
| Higher in FEMALE | 2

List of 3 genes differentially expressed by 'GENDER'

Table S5. Get Full Table List of 3 genes differentially expressed by 'GENDER'

| T(pos if higher in 'MALE') | ttestP | Q | AUC
--- | --- | --- | --- | ---
NARFL | -7.15 | 1.997e-09 | 3.44e-05 | 0.8875
ATAD5 | 6.47 | 6.841e-07 | 0.0118 | 0.8889
CCNYL1 | -6.09 | 2.276e-06 | 0.0392 | 0.8847

Figure S2. Get High-res Image As an example, this figure shows the association of NARFL to 'GENDER'. P value = 2e-09 with T-test 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 | Labels | N
| class100 | 5
| class90 | 5
| Significant markers | N = 0

Clinical variable #5: 'PATHOLOGY.T'

51 genes related to 'PATHOLOGY.T'.

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

PATHOLOGY.T | Mean (SD) | 1.91 (1)
|  | N
| T1 | 31
| T2 | 2
| T3 | 24
| T4 | 1
| Significant markers | N = 51
| pos. correlated | 40
| neg. correlated | 11

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

Table S8. Get Full Table List of top 10 genes significantly correlated to 'PATHOLOGY.T' by Spearman correlation test

| SpearmanCorr | corrP | Q
--- | --- | --- | ---
DLX6AS | 0.6973 | 1.19e-09 | 2.05e-05
GNASAS | -0.6915 | 1.858e-09 | 3.2e-05
GP2 | -0.6477 | 3.9e-08 | 0.000673
GPR150 | 0.64 | 6.328e-08 | 0.00109
PCDHB19P | 0.6368 | 7.7e-08 | 0.00133
HBA1 | 0.6368 | 7.716e-08 | 0.00133
SLC2A14 | 0.6329 | 9.774e-08 | 0.00169
DLEU2 | -0.6307 | 1.118e-07 | 0.00193
MATK | 0.6246 | 1.605e-07 | 0.00277
NECAB1 | 0.624 | 1.665e-07 | 0.00287

Figure S3. Get High-res Image As an example, this figure shows the association of DLX6AS to 'PATHOLOGY.T'. P value = 1.19e-09 with Spearman correlation analysis.

Clinical variable #6: 'PATHOLOGY.N'

No gene related to 'PATHOLOGY.N'.

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

PATHOLOGY.N | Mean (SD) | 0.56 (0.7)
|  | N
| N0 | 15
| N1 | 9
| N2 | 3
| Significant markers | N = 0

Clinical variable #7: 'PATHOLOGICSPREAD(M)'

22 genes related to 'PATHOLOGICSPREAD(M)'.

Table S10. Basic characteristics of clinical feature: 'PATHOLOGICSPREAD(M)'

PATHOLOGICSPREAD(M) | Labels | N
| M0 | 37
| M1 | 4
| MX | 16
| Significant markers | N = 22

List of top 10 genes differentially expressed by 'PATHOLOGICSPREAD(M)'

Table S11. Get Full Table List of top 10 genes differentially expressed by 'PATHOLOGICSPREAD(M)'

| ANOVA_P | Q
--- | --- | ---
RFX7 | 2.125e-11 | 3.67e-07
GDPD4 | 7.075e-09 | 0.000122
ERCC2 | 4.114e-08 | 0.000709
DHDH | 1.266e-07 | 0.00218
UNC93A | 1.497e-07 | 0.00258
NFE2L1 | 1.929e-07 | 0.00333
ZPBP | 2.418e-07 | 0.00417
OR10AD1 | 3.672e-07 | 0.00633
PCDHA5 | 3.774e-07 | 0.00651
TRMT6 | 4.743e-07 | 0.00818

Figure S4. Get High-res Image As an example, this figure shows the association of RFX7 to 'PATHOLOGICSPREAD(M)'. P value = 2.12e-11 with ANOVA analysis.

Clinical variable #8: 'TUMOR.STAGE'

33 genes related to 'TUMOR.STAGE'.

Table S12. Basic characteristics of clinical feature: 'TUMOR.STAGE'

TUMOR.STAGE | Mean (SD) | 2.05 (1.2)
|  | N
| Stage 1 | 30
| Stage 2 | 1
| Stage 3 | 19
| Stage 4 | 7
| Significant markers | N = 33
| pos. correlated | 29
| neg. correlated | 4

List of top 10 genes significantly correlated to 'TUMOR.STAGE' by Spearman correlation test

Table S13. Get Full Table List of top 10 genes significantly correlated to 'TUMOR.STAGE' by Spearman correlation test

| SpearmanCorr | corrP | Q
--- | --- | --- | ---
INSM1 | 0.7163 | 3.74e-10 | 6.45e-06
ZNF177 | 0.6711 | 1.111e-08 | 0.000192
DLX6AS | 0.6708 | 1.133e-08 | 0.000195
CNIH3 | 0.6526 | 3.775e-08 | 0.000651
MATK | 0.65 | 4.45e-08 | 0.000767
NSD1 | 0.6407 | 7.973e-08 | 0.00137
TMEM132B | -0.6389 | 8.876e-08 | 0.00153
WDR16 | 0.6318 | 1.363e-07 | 0.00235
HBA1 | 0.6317 | 1.372e-07 | 0.00236
DIO3 | 0.6263 | 1.878e-07 | 0.00324

Figure S5. Get High-res Image As an example, this figure shows the association of INSM1 to 'TUMOR.STAGE'. P value = 3.74e-10 with Spearman correlation analysis.

Methods & Data
Input

- Expresson data file = KIRP-TP.meth.for_correlation.filtered_data.txt

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

- Number of patients = 58

- Number of genes = 17247

- 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

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

This is an experimental feature. The full results of the analysis summarized in this report can be downloaded from the TCGA Data Coordination Center.

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