Correlation between mRNAseq expression and clinical features

Liver Hepatocellular Carcinoma (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/C1ZC813B

Overview

Introduction

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

Summary

Testing the association between 17911 genes and 7 clinical features across 33 samples, statistically thresholded by Q value < 0.05, 5 clinical features related to at least one genes.

1 gene correlated to 'AGE'.

KRT6B|3854

5 genes correlated to 'GENDER'.

EDA|1896 , PPARGC1A|10891 , NLGN4Y|22829 , PRKY|5616 , TMED4|222068

3 genes correlated to 'DISTANT.METASTASIS'.

ZNF689|115509 , MARCH8|220972 , TNPO3|23534

6 genes correlated to 'COMPLETENESS.OF.RESECTION'.

MARCH8|220972 , ZNF689|115509 , TNPO3|23534 , MAPK8|5599 , MGRN1|23295 , ...

3 genes correlated to 'NEOPLASM.DISEASESTAGE'.

L3MBTL4|91133 , MARCH8|220972 , THNSL2|55258

No genes correlated to 'Time to Death', and 'LYMPH.NODE.METASTASIS'.

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=5	male	N=2	female	N=3
DISTANT METASTASIS	ANOVA test	N=3
LYMPH NODE METASTASIS	t test	N=0
COMPLETENESS OF RESECTION	ANOVA test	N=6
NEOPLASM DISEASESTAGE	ANOVA test	N=3

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)	0.1-83.6 (median=19.8)
	censored	N = 12
	death	N = 17

	Significant markers	N = 0

Clinical variable #2: 'AGE'

One gene related to 'AGE'.

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


AGE	Mean (SD)	59.4 (18)
	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
KRT6B\|3854	0.95	0	0

Figure S1. Get High-res Image As an example, this figure shows the association of KRT6B|3854 to 'AGE'. P value = 0 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #3: 'GENDER'

5 genes related to 'GENDER'.

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


GENDER	Labels	N
	FEMALE	10
	MALE	23

	Significant markers	N = 5
	Higher in MALE	2
	Higher in FEMALE	3

List of 5 genes differentially expressed by 'GENDER'

Table S5. Get Full Table List of 5 genes differentially expressed by 'GENDER'

	T(pos if higher in 'MALE')	ttestP	Q	AUC
EDA\|1896	-6.98	8.388e-08	0.00149	0.9565
PPARGC1A\|10891	-6.39	4.098e-07	0.00726	0.9522
NLGN4Y\|22829	11.05	9.292e-07	0.0165	1
PRKY\|5616	14.16	1.216e-06	0.0215	1
TMED4\|222068	-5.82	2.59e-06	0.0459	0.9478

Figure S2. Get High-res Image As an example, this figure shows the association of EDA|1896 to 'GENDER'. P value = 8.39e-08 with T-test analysis.

Clinical variable #4: 'DISTANT.METASTASIS'

3 genes related to 'DISTANT.METASTASIS'.

Table S6. Basic characteristics of clinical feature: 'DISTANT.METASTASIS'


DISTANT.METASTASIS	Labels	N
	M0	22
	M1	1
	MX	10

	Significant markers	N = 3

List of 3 genes differentially expressed by 'DISTANT.METASTASIS'

Table S7. Get Full Table List of 3 genes differentially expressed by 'DISTANT.METASTASIS'

	ANOVA_P	Q
ZNF689\|115509	6.031e-09	0.000108
MARCH8\|220972	9.157e-08	0.00164
TNPO3\|23534	2.08e-07	0.00373

Figure S3. Get High-res Image As an example, this figure shows the association of ZNF689|115509 to 'DISTANT.METASTASIS'. P value = 6.03e-09 with ANOVA analysis.

Clinical variable #5: 'LYMPH.NODE.METASTASIS'

No gene related to 'LYMPH.NODE.METASTASIS'.

Table S8. Basic characteristics of clinical feature: 'LYMPH.NODE.METASTASIS'


LYMPH.NODE.METASTASIS	Labels	N
	N0	24
	NX	8

	Significant markers	N = 0

Clinical variable #6: 'COMPLETENESS.OF.RESECTION'

6 genes related to 'COMPLETENESS.OF.RESECTION'.

Table S9. Basic characteristics of clinical feature: 'COMPLETENESS.OF.RESECTION'


COMPLETENESS.OF.RESECTION	Labels	N
	R0	18
	R1	6
	R2	1
	RX	6

	Significant markers	N = 6

List of 6 genes differentially expressed by 'COMPLETENESS.OF.RESECTION'

Table S10. Get Full Table List of 6 genes differentially expressed by 'COMPLETENESS.OF.RESECTION'

	ANOVA_P	Q
MARCH8\|220972	3.319e-08	0.000594
ZNF689\|115509	8.656e-08	0.00155
TNPO3\|23534	2.004e-07	0.00359
MAPK8\|5599	7.24e-07	0.013
MGRN1\|23295	1.454e-06	0.026
AP2A1\|160	2.778e-06	0.0497

Figure S4. Get High-res Image As an example, this figure shows the association of MARCH8|220972 to 'COMPLETENESS.OF.RESECTION'. P value = 3.32e-08 with ANOVA analysis.

Clinical variable #7: 'NEOPLASM.DISEASESTAGE'

3 genes related to 'NEOPLASM.DISEASESTAGE'.

Table S11. Basic characteristics of clinical feature: 'NEOPLASM.DISEASESTAGE'


NEOPLASM.DISEASESTAGE	Labels	N
	STAGE I	10
	STAGE II	5
	STAGE III	2
	STAGE IIIA	7
	STAGE IIIB	1
	STAGE IVB	1

	Significant markers	N = 3

List of 3 genes differentially expressed by 'NEOPLASM.DISEASESTAGE'

Table S12. Get Full Table List of 3 genes differentially expressed by 'NEOPLASM.DISEASESTAGE'

	ANOVA_P	Q
L3MBTL4\|91133	6.568e-07	0.0118
MARCH8\|220972	1.672e-06	0.0299
THNSL2\|55258	1.776e-06	0.0318

Figure S5. Get High-res Image As an example, this figure shows the association of L3MBTL4|91133 to 'NEOPLASM.DISEASESTAGE'. P value = 6.57e-07 with ANOVA analysis.

Methods & Data

Input

Expresson data file = LIHC-TP.uncv2.mRNAseq_RSEM_normalized_log2.txt
Clinical data file = LIHC-TP.clin.merged.picked.txt
Number of patients = 33
Number of genes = 17911
Number of clinical features = 7

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.

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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