Kidney Renal Clear Cell Carcinoma: Correlation between mRNA expression and clinical features

Maintained by TCGA GDAC Team (Broad Institute/Dana-Farber Cancer Institute/Harvard Medical School)

Overview

Introduction

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

Summary

Testing the association between 17814 genes and 6 clinical features across 72 samples, statistically thresholded by Q value < 0.05, 4 clinical features related to at least one genes.

14 genes correlated to 'GENDER'.

DDX3Y , RPS4Y1 , CYORF15A , EIF1AY , RPS4Y2 , ...

1 gene correlated to 'PATHOLOGY.T'.

TRIM11

89 genes correlated to 'PATHOLOGY.N'.

M6PRBP1 , FAM64A , GRAP2 , ULK1 , L2HGDH , ...

59 genes correlated to 'PATHOLOGICSPREAD(M)'.

IGFBPL1 , DDX3Y , CYORF15B , EIF1AY , GRIN2A , ...

No genes correlated to 'Time to Death', and 'AGE'.

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=0
GENDER	t test	N=14	male	N=14	female	N=0
PATHOLOGY T	Spearman correlation test	N=1	higher pT	N=1	lower pT	N=0
PATHOLOGY N	t test	N=89	n1	N=42	n0	N=47
PATHOLOGICSPREAD(M)	t test	N=59	m1	N=17	m0	N=42

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)	0.5-101.1 (median=32.6)
	censored	N = 58
	death	N = 13

	Significant markers	N = 0

Clinical variable #2: 'AGE'

No gene related to 'AGE'.

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


AGE	Mean (SD)	60.55 (12)
	Significant markers	N = 0

Clinical variable #3: 'GENDER'

14 genes related to 'GENDER'.

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


GENDER	Labels	N
	FEMALE	29
	MALE	43

	Significant markers	N = 14
	Higher in MALE	14
	Higher in FEMALE	0

List of top 10 genes differentially expressed by 'GENDER'

Table S4. Get Full Table List of top 10 genes differentially expressed by 'GENDER'

	T(pos if higher in 'MALE')	ttestP	Q	AUC
DDX3Y	13.92	3.817e-21	6.8e-17	0.9583
RPS4Y1	14.23	7.605e-21	1.35e-16	0.9615
CYORF15A	13.58	1.934e-20	3.45e-16	0.9687
EIF1AY	13.49	2.22e-20	3.95e-16	0.9647
RPS4Y2	13.38	2.537e-20	4.52e-16	0.9567
JARID1D	12.61	3.928e-19	6.99e-15	0.9607
ZFY	11.99	1.925e-18	3.43e-14	0.9663
CYORF15B	11.16	3.496e-17	6.23e-13	0.9447
UTY	10.82	1.604e-16	2.86e-12	0.9575
USP9Y	9.74	1.181e-14	2.1e-10	0.9318

Figure S1. Get High-res Image As an example, this figure shows the association of DDX3Y to 'GENDER'. P value = 3.82e-21 with T-test analysis.

Clinical variable #4: 'PATHOLOGY.T'

One gene related to 'PATHOLOGY.T'.

Table S5. Basic characteristics of clinical feature: 'PATHOLOGY.T'


PATHOLOGY.T	Mean (SD)	1.67 (0.84)
		N
	T1	41
	T2	14
	T3	17

	Significant markers	N = 1
	pos. correlated	1
	neg. correlated	0

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

Table S6. Get Full Table List of one gene significantly correlated to 'PATHOLOGY.T' by Spearman correlation test

	SpearmanCorr	corrP	Q
TRIM11	0.5385	1.064e-06	0.0189

Figure S2. Get High-res Image As an example, this figure shows the association of TRIM11 to 'PATHOLOGY.T'. P value = 1.06e-06 with Spearman correlation analysis.

Clinical variable #5: 'PATHOLOGY.N'

89 genes related to 'PATHOLOGY.N'.

Table S7. Basic characteristics of clinical feature: 'PATHOLOGY.N'


PATHOLOGY.N	Labels	N
	N0	35
	N1	3

	Significant markers	N = 89
	Higher in N1	42
	Higher in N0	47

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

Table S8. Get Full Table List of top 10 genes differentially expressed by 'PATHOLOGY.N'

	T(pos if higher in 'N1')	ttestP	Q	AUC
M6PRBP1	13.21	4.031e-14	7.14e-10	1
FAM64A	11.83	5.97e-14	1.06e-09	0.9714
GRAP2	-10.15	4.308e-12	7.63e-08	0.9524
ULK1	10.62	9.997e-12	1.77e-07	0.9429
L2HGDH	-9.83	1.033e-11	1.83e-07	0.9714
FCHO1	9.95	1.321e-11	2.34e-07	0.9429
MEG3	9.46	2.7e-11	4.78e-07	0.9524
MRGPRF	9.57	3.256e-11	5.76e-07	0.9429
HSD17B7P2	-9.4	3.921e-11	6.94e-07	0.9714
FLJ40869	16.9	6.929e-11	1.23e-06	1

Figure S3. Get High-res Image As an example, this figure shows the association of M6PRBP1 to 'PATHOLOGY.N'. P value = 4.03e-14 with T-test analysis.

Clinical variable #6: 'PATHOLOGICSPREAD(M)'

59 genes related to 'PATHOLOGICSPREAD(M)'.

Table S9. Basic characteristics of clinical feature: 'PATHOLOGICSPREAD(M)'


PATHOLOGICSPREAD(M)	Labels	N
	M0	67
	M1	5

	Significant markers	N = 59
	Higher in M1	17
	Higher in M0	42

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

Table S10. Get Full Table List of top 10 genes differentially expressed by 'PATHOLOGICSPREAD(M)'

	T(pos if higher in 'M1')	ttestP	Q	AUC
IGFBPL1	-11.03	8.943e-16	1.59e-11	0.9164
DDX3Y	-9.93	5.331e-15	9.5e-11	0.8269
CYORF15B	-10.32	7.768e-14	1.38e-09	0.9254
EIF1AY	-10.79	1.68e-13	2.99e-09	0.9493
GRIN2A	-9.37	1.623e-12	2.89e-08	0.9433
HOXA7	-10.08	9e-12	1.6e-07	0.8955
OAS1	9.36	2.099e-11	3.74e-07	0.9493
UTY	-10.41	6.154e-11	1.1e-06	0.9851
PELI2	-8.93	6.744e-11	1.2e-06	0.8896
FAM9B	-7.63	1.323e-10	2.36e-06	0.8388

Figure S4. Get High-res Image As an example, this figure shows the association of IGFBPL1 to 'PATHOLOGICSPREAD(M)'. P value = 8.94e-16 with T-test analysis.

Methods & Data

Input

Expresson data file = KIRC.medianexp.txt
Clinical data file = KIRC.clin.merged.picked.txt
Number of patients = 72
Number of genes = 17814
Number of clinical features = 6

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

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.

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] 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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