Kidney Renal Papillary Cell Carcinoma: Correlation between mRNAseq expression and clinical features
(primary solid tumor cohort)
Maintained by Juok Cho (Broad Institute)
Overview
Introduction

This pipeline uses various statistical tests to identify mRNAs whose expression levels correlated to selected clinical features.

Summary

Testing the association between 18174 genes and 8 clinical features across 74 samples, statistically thresholded by Q value < 0.05, 6 clinical features related to at least one genes.

- 1 gene correlated to 'AGE'.

- MCCC2|64087

- 16 genes correlated to 'GENDER'.

- XIST|7503 , RPS4Y1|6192 , KDM5C|8242 , PRKY|5616 , ZFX|7543 , ...

- 43 genes correlated to 'PATHOLOGY.T'.

- UCK2|7371 , PRCC|5546 , MAD2L1|4085 , ACBD6|84320 , EPR1|8475 , ...

- 1 gene correlated to 'PATHOLOGY.N'.

- KIAA0664|23277

- 1 gene correlated to 'PATHOLOGICSPREAD(M)'.

- CA8|767

- 106 genes correlated to 'TUMOR.STAGE'.

- MAD2L1|4085 , UCK2|7371 , PRCC|5546 , EPR1|8475 , GPR19|2842 , ...

- 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=1 | older | N=1 | younger | N=0
GENDER | t test | N=16 | male | N=4 | female | N=12
KARNOFSKY PERFORMANCE SCORE | Spearman correlation test | N=0 |  |  |  |
PATHOLOGY T | Spearman correlation test | N=43 | higher pT | N=41 | lower pT | N=2
PATHOLOGY N | Spearman correlation test | N=1 | higher pN | N=0 | lower pN | N=1
PATHOLOGICSPREAD(M) | ANOVA test | N=1 |  |  |  |
TUMOR STAGE | Spearman correlation test | N=106 | higher stage | N=93 | lower stage | N=13

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) | 0.5-182.7 (median=15.5)
| censored | N = 58
| 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) | 59.75 (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
--- | --- | --- | ---
MCCC2|64087 | 0.5349 | 1.545e-06 | 0.0281

Figure S1. Get High-res Image As an example, this figure shows the association of MCCC2|64087 to 'AGE'. P value = 1.54e-06 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #3: 'GENDER'

16 genes related to 'GENDER'.

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

GENDER | Labels | N
| FEMALE | 22
| MALE | 52
| Significant markers | N = 16
| Higher in MALE | 4
| Higher in FEMALE | 12

List of top 10 genes differentially expressed by 'GENDER'

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

| T(pos if higher in 'MALE') | ttestP | Q | AUC
--- | --- | --- | --- | ---
XIST|7503 | -12.87 | 1.749e-13 | 3.18e-09 | 0.9812
RPS4Y1|6192 | 11.88 | 2.538e-11 | 4.61e-07 | 0.9562
KDM5C|8242 | -8.46 | 2.523e-10 | 4.58e-06 | 0.9423
PRKY|5616 | 7.2 | 3.165e-08 | 0.000575 | 0.9126
ZFX|7543 | -6.71 | 3.323e-08 | 0.000604 | 0.8872
KDM6A|7403 | -6.64 | 3.859e-08 | 0.000701 | 0.9065
TSIX|9383 | -7.35 | 3.863e-08 | 0.000702 | 0.9274
UBA1|7317 | -6.32 | 1.455e-07 | 0.00264 | 0.8741
TXLNG|55787 | -6.05 | 1.724e-07 | 0.00313 | 0.8601
DYNLT1|6993 | 5.79 | 3.751e-07 | 0.00681 | 0.8523

Figure S2. Get High-res Image As an example, this figure shows the association of XIST|7503 to 'GENDER'. P value = 1.75e-13 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 | Mean (SD) | 86.36 (29)
| Score | N
| 0 | 1
| 90 | 5
| 100 | 5
| Significant markers | N = 0

Clinical variable #5: 'PATHOLOGY.T'

43 genes related to 'PATHOLOGY.T'.

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

PATHOLOGY.T | Mean (SD) | 1.86 (0.96)
|  | N
| T1 | 38
| T2 | 9
| T3 | 26
| T4 | 1
| Significant markers | N = 43
| pos. correlated | 41
| neg. correlated | 2

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
--- | --- | --- | ---
UCK2|7371 | 0.66 | 1.592e-10 | 2.89e-06
PRCC|5546 | 0.5992 | 1.703e-08 | 0.00031
MAD2L1|4085 | 0.5921 | 2.761e-08 | 0.000502
ACBD6|84320 | 0.5798 | 6.178e-08 | 0.00112
EPR1|8475 | 0.5777 | 8.741e-08 | 0.00159
CCT3|7203 | 0.5733 | 9.35e-08 | 0.0017
NUF2|83540 | 0.5731 | 9.506e-08 | 0.00173
PTHLH|5744 | 0.5767 | 1.413e-07 | 0.00257
UBE2T|29089 | 0.5666 | 1.421e-07 | 0.00258
GPR19|2842 | 0.5732 | 1.425e-07 | 0.00259

Figure S3. Get High-res Image As an example, this figure shows the association of UCK2|7371 to 'PATHOLOGY.T'. P value = 1.59e-10 with Spearman correlation analysis.

Clinical variable #6: 'PATHOLOGY.N'

One gene related to 'PATHOLOGY.N'.

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

PATHOLOGY.N | Mean (SD) | 0.59 (0.68)
|  | N
| N0 | 15
| N1 | 11
| N2 | 3
| Significant markers | N = 1
| pos. correlated | 0
| neg. correlated | 1

List of one gene significantly correlated to 'PATHOLOGY.N' by Spearman correlation test

Table S10. Get Full Table List of one gene significantly correlated to 'PATHOLOGY.N' by Spearman correlation test

| SpearmanCorr | corrP | Q
--- | --- | --- | ---
KIAA0664|23277 | -0.7639 | 1.422e-06 | 0.0258

Figure S4. Get High-res Image As an example, this figure shows the association of KIAA0664|23277 to 'PATHOLOGY.N'. P value = 1.42e-06 with Spearman correlation analysis.

Clinical variable #7: 'PATHOLOGICSPREAD(M)'

One gene related to 'PATHOLOGICSPREAD(M)'.

Table S11. Basic characteristics of clinical feature: 'PATHOLOGICSPREAD(M)'

PATHOLOGICSPREAD(M) | Labels | N
| M0 | 46
| M1 | 5
| MX | 16
| Significant markers | N = 1

List of one gene differentially expressed by 'PATHOLOGICSPREAD(M)'

Table S12. Get Full Table List of one gene differentially expressed by 'PATHOLOGICSPREAD(M)'

| ANOVA_P | Q
--- | --- | ---
CA8|767 | 7.982e-08 | 0.00145

Figure S5. Get High-res Image As an example, this figure shows the association of CA8|767 to 'PATHOLOGICSPREAD(M)'. P value = 7.98e-08 with ANOVA analysis.

Clinical variable #8: 'TUMOR.STAGE'

106 genes related to 'TUMOR.STAGE'.

Table S13. Basic characteristics of clinical feature: 'TUMOR.STAGE'

TUMOR.STAGE | Mean (SD) | 2.02 (1.2)
|  | N
| Stage 1 | 35
| Stage 2 | 3
| Stage 3 | 20
| Stage 4 | 8
| Significant markers | N = 106
| pos. correlated | 93
| neg. correlated | 13

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

Table S14. Get Full Table List of top 10 genes significantly correlated to 'TUMOR.STAGE' by Spearman correlation test

| SpearmanCorr | corrP | Q
--- | --- | --- | ---
MAD2L1|4085 | 0.7054 | 3.756e-11 | 6.83e-07
UCK2|7371 | 0.6701 | 7.557e-10 | 1.37e-05
PRCC|5546 | 0.644 | 5.435e-09 | 9.88e-05
EPR1|8475 | 0.6391 | 1.01e-08 | 0.000184
GPR19|2842 | 0.6423 | 1.06e-08 | 0.000193
TCTA|6988 | -0.6276 | 1.701e-08 | 0.000309
ORC6L|23594 | 0.6156 | 3.777e-08 | 0.000686
CDCA5|113130 | 0.6149 | 3.956e-08 | 0.000719
PTHLH|5744 | 0.6258 | 4.143e-08 | 0.000753
TROAP|10024 | 0.6128 | 4.53e-08 | 0.000823

Figure S6. Get High-res Image As an example, this figure shows the association of MAD2L1|4085 to 'TUMOR.STAGE'. P value = 3.76e-11 with Spearman correlation analysis.

Methods & Data
Input

- Expresson data file = KIRP-TP.uncv2.mRNAseq_RSEM_normalized_log2.txt

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

- Number of patients = 74

- Number of genes = 18174

- 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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