Correlation between mRNAseq expression and clinical features

Rectum Adenocarcinoma (Primary solid tumor)

22 February 2013 | analyses__2013_02_22

Maintainer Information

Citation Information

Maintained by Juok Cho (Broad Institute)

Cite as Broad Institute TCGA Genome Data Analysis Center (2013): Correlation between mRNAseq expression and clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C16W989N

Overview

Introduction

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

Summary

Testing the association between 17926 genes and 8 clinical features across 72 samples, statistically thresholded by Q value < 0.05, 3 clinical features related to at least one genes.

11 genes correlated to 'GENDER'.

XIST|7503 , NLGN4Y|22829 , TSIX|9383 , TMSB4Y|9087 , RPS4Y1|6192 , ...

54 genes correlated to 'HISTOLOGICAL.TYPE'.

EFNA5|1946 , B3GNT6|192134 , GNPNAT1|64841 , AGR3|155465 , AGR2|10551 , ...

7 genes correlated to 'PATHOLOGICSPREAD(M)'.

SCN2A|6326 , CTNNA2|1496 , C1ORF114|57821 , IGDCC3|9543 , MYBPH|4608 , ...

No genes correlated to 'Time to Death', 'AGE', 'PATHOLOGY.T', 'PATHOLOGY.N', and 'TUMOR.STAGE'.

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=11	male	N=8	female	N=3
HISTOLOGICAL TYPE	t test	N=54	rectal mucinous adenocarcinoma	N=41	rectal adenocarcinoma	N=13
PATHOLOGY T	Spearman correlation test	N=0
PATHOLOGY N	Spearman correlation test	N=0
PATHOLOGICSPREAD(M)	t test	N=7	m1	N=1	m0	N=6
TUMOR STAGE	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)	0.9-72.1 (median=10.6)
	censored	N = 39
	death	N = 4

	Significant markers	N = 0

Clinical variable #2: 'AGE'

No gene related to 'AGE'.

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


AGE	Mean (SD)	66.75 (10)
	Significant markers	N = 0

Clinical variable #3: 'GENDER'

11 genes related to 'GENDER'.

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


GENDER	Labels	N
	FEMALE	33
	MALE	39

	Significant markers	N = 11
	Higher in MALE	8
	Higher in FEMALE	3

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
XIST\|7503	-17.89	1.189e-20	2.13e-16	0.9987
NLGN4Y\|22829	12.07	6.01e-15	1.08e-10	0.9731
TSIX\|9383	-11.07	8.536e-14	1.53e-09	0.9636
TMSB4Y\|9087	10.29	2.797e-13	5.01e-09	0.9556
RPS4Y1\|6192	11.83	1.894e-12	3.39e-08	0.996
ZFY\|7544	10.86	4.81e-11	8.62e-07	0.9803
KDM5D\|8284	10.84	4.932e-09	8.84e-05	0.998
PRKY\|5616	6.55	8.702e-09	0.000156	0.8547
UTY\|7404	9.96	8.461e-08	0.00152	0.9886
DDX3Y\|8653	9.82	2.865e-07	0.00513	0.9953

Figure S1. Get High-res Image As an example, this figure shows the association of XIST|7503 to 'GENDER'. P value = 1.19e-20 with T-test analysis.

Clinical variable #4: 'HISTOLOGICAL.TYPE'

54 genes related to 'HISTOLOGICAL.TYPE'.

Table S5. Basic characteristics of clinical feature: 'HISTOLOGICAL.TYPE'


HISTOLOGICAL.TYPE	Labels	N
	RECTAL ADENOCARCINOMA	60
	RECTAL MUCINOUS ADENOCARCINOMA	8

	Significant markers	N = 54
	Higher in RECTAL MUCINOUS ADENOCARCINOMA	41
	Higher in RECTAL ADENOCARCINOMA	13

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

Table S6. Get Full Table List of top 10 genes differentially expressed by 'HISTOLOGICAL.TYPE'

	T(pos if higher in 'RECTAL MUCINOUS ADENOCARCINOMA')	ttestP	Q	AUC
EFNA5\|1946	10.01	3.79e-13	6.72e-09	0.8896
B3GNT6\|192134	9.68	1.746e-10	3.09e-06	0.9576
GNPNAT1\|64841	8.16	3.791e-10	6.72e-06	0.9292
AGR3\|155465	8	4.976e-10	8.82e-06	0.8958
AGR2\|10551	9.07	1.701e-08	0.000301	0.95
SERPINA1\|5265	8.67	3.548e-08	0.000628	0.9333
SPDEF\|25803	8.09	4.428e-08	0.000784	0.9438
PNKD\|25953	-8.62	7.819e-08	0.00138	0.9562
SNX20\|124460	6.51	7.907e-08	0.0014	0.825
ANG\|283	7.8	8.451e-08	0.0015	0.9125

Figure S2. Get High-res Image As an example, this figure shows the association of EFNA5|1946 to 'HISTOLOGICAL.TYPE'. P value = 3.79e-13 with T-test analysis.

Clinical variable #5: 'PATHOLOGY.T'

No gene related to 'PATHOLOGY.T'.

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


PATHOLOGY.T	Mean (SD)	2.71 (0.68)
		N
	T1	5
	T2	15
	T3	48
	T4	4

	Significant markers	N = 0

Clinical variable #6: 'PATHOLOGY.N'

No gene related to 'PATHOLOGY.N'.

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


PATHOLOGY.N	Mean (SD)	0.56 (0.77)
		N
	N0	44
	N1	16
	N2	12

	Significant markers	N = 0

Clinical variable #7: 'PATHOLOGICSPREAD(M)'

7 genes related to 'PATHOLOGICSPREAD(M)'.

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


PATHOLOGICSPREAD(M)	Labels	N
	M0	61
	M1	11

	Significant markers	N = 7
	Higher in M1	1
	Higher in M0	6

List of 7 genes differentially expressed by 'PATHOLOGICSPREAD(M)'

Table S10. Get Full Table List of 7 genes differentially expressed by 'PATHOLOGICSPREAD(M)'

	T(pos if higher in 'M1')	ttestP	Q	AUC
SCN2A\|6326	-7.42	1.171e-09	2.09e-05	0.8819
CTNNA2\|1496	-6.38	8.414e-08	0.0015	0.8827
C1ORF114\|57821	-6.44	2.261e-07	0.00403	0.8429
IGDCC3\|9543	-6.1	3.199e-07	0.0057	0.859
MYBPH\|4608	-6.17	4.345e-07	0.00774	0.9412
RNF214\|257160	5.66	1.032e-06	0.0184	0.8659
TMEM84\|283673	-5.91	1.052e-06	0.0187	0.8485

Figure S3. Get High-res Image As an example, this figure shows the association of SCN2A|6326 to 'PATHOLOGICSPREAD(M)'. P value = 1.17e-09 with T-test analysis.

Clinical variable #8: 'TUMOR.STAGE'

No gene related to 'TUMOR.STAGE'.

Table S11. Basic characteristics of clinical feature: 'TUMOR.STAGE'


TUMOR.STAGE	Mean (SD)	2.28 (1)
		N
	Stage 1	18
	Stage 2	25
	Stage 3	18
	Stage 4	10

	Significant markers	N = 0

Methods & Data

Input

Expresson data file = READ-TP.uncv2.mRNAseq_RSEM_normalized_log2.txt
Clinical data file = READ-TP.clin.merged.picked.txt
Number of patients = 72
Number of genes = 17926
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

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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