Correlation between mRNA expression and clinical features

Lung Adenocarcinoma (Primary solid tumor)

17 October 2014 | analyses__2014_10_17

Maintainer Information

Citation Information

Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/Harvard Medical School)

Cite as Broad Institute TCGA Genome Data Analysis Center (2014): Correlation between mRNA expression and clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1J67FTW

Overview

Introduction

This pipeline uses various statistical tests to identify mRNAs whose log2 expression levels correlated to selected clinical features.

Summary

Testing the association between 17814 genes and 10 clinical features across 32 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 2 clinical features related to at least one genes.

1 gene correlated to 'AGE'.

GTF2IRD1

1 gene correlated to 'PATHOLOGY.T.STAGE'.

PAPPA2

No genes correlated to 'Time to Death', 'NEOPLASM.DISEASESTAGE', 'PATHOLOGY.N.STAGE', 'GENDER', 'KARNOFSKY.PERFORMANCE.SCORE', 'NUMBERPACKYEARSSMOKED', 'COMPLETENESS.OF.RESECTION', and 'RACE'.

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 P value < 0.05 and Q value < 0.3.

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=0	younger	N=1
NEOPLASM DISEASESTAGE	Kruskal-Wallis test	N=0
PATHOLOGY T STAGE	Spearman correlation test	N=1	higher stage	N=0	lower stage	N=1
PATHOLOGY N STAGE	Spearman correlation test	N=0
GENDER	Wilcoxon test	N=0
KARNOFSKY PERFORMANCE SCORE	Spearman correlation test	N=0
NUMBERPACKYEARSSMOKED	Spearman correlation test	N=0
COMPLETENESS OF RESECTION	Kruskal-Wallis test	N=0
RACE	Kruskal-Wallis 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 (Years)	14-1728 (median=731.5)
	censored	N = 25
	death	N = 5

	Significant markers	N = 0

Clinical variable #2: 'AGE'

One gene related to 'AGE'.

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


AGE	Mean (SD)	65.7 (11)
	Significant markers	N = 1
	pos. correlated	0
	neg. correlated	1

List of one gene differentially expressed by 'AGE'

Table S3. Get Full Table List of one gene significantly correlated to 'AGE' by Spearman correlation test

	SpearmanCorr	corrP	Q
GTF2IRD1	-0.7129	9.854e-06	0.176

Clinical variable #3: 'NEOPLASM.DISEASESTAGE'

No gene related to 'NEOPLASM.DISEASESTAGE'.

Table S4. Basic characteristics of clinical feature: 'NEOPLASM.DISEASESTAGE'


NEOPLASM.DISEASESTAGE	Labels	N
	STAGE IA	12
	STAGE IB	11
	STAGE IIA	1
	STAGE IIB	3
	STAGE IIIA	3
	STAGE IV	2

	Significant markers	N = 0

Clinical variable #4: 'PATHOLOGY.T.STAGE'

One gene related to 'PATHOLOGY.T.STAGE'.

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


PATHOLOGY.T.STAGE	Mean (SD)	1.66 (0.55)
		N
	1	12
	2	19
	3	1

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

List of one gene differentially expressed by 'PATHOLOGY.T.STAGE'

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

	SpearmanCorr	corrP	Q
PAPPA2	-0.7603	4.455e-07	0.00794

Clinical variable #5: 'PATHOLOGY.N.STAGE'

No gene related to 'PATHOLOGY.N.STAGE'.

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


PATHOLOGY.N.STAGE	Mean (SD)	0.39 (0.72)
		N
	0	23
	1	4
	2	4

	Significant markers	N = 0

Clinical variable #6: 'GENDER'

No gene related to 'GENDER'.

Table S8. Basic characteristics of clinical feature: 'GENDER'


GENDER	Labels	N
	FEMALE	18
	MALE	14

	Significant markers	N = 0

Clinical variable #7: 'KARNOFSKY.PERFORMANCE.SCORE'

No gene related to 'KARNOFSKY.PERFORMANCE.SCORE'.

Table S9. Basic characteristics of clinical feature: 'KARNOFSKY.PERFORMANCE.SCORE'


KARNOFSKY.PERFORMANCE.SCORE	Mean (SD)	56.67 (49)
	Score	N
	0	1
	80	1
	90	1

	Significant markers	N = 0

Clinical variable #8: 'NUMBERPACKYEARSSMOKED'

No gene related to 'NUMBERPACKYEARSSMOKED'.

Table S10. Basic characteristics of clinical feature: 'NUMBERPACKYEARSSMOKED'


NUMBERPACKYEARSSMOKED	Mean (SD)	41.15 (15)
	Significant markers	N = 0

Clinical variable #9: 'COMPLETENESS.OF.RESECTION'

No gene related to 'COMPLETENESS.OF.RESECTION'.

Table S11. Basic characteristics of clinical feature: 'COMPLETENESS.OF.RESECTION'


COMPLETENESS.OF.RESECTION	Labels	N
	R0	26
	R2	1
	RX	2

	Significant markers	N = 0

Clinical variable #10: 'RACE'

No gene related to 'RACE'.

Table S12. Basic characteristics of clinical feature: 'RACE'


RACE	Labels	N
	ASIAN	2
	BLACK OR AFRICAN AMERICAN	1
	WHITE	26

	Significant markers	N = 0

Methods & Data

Input

Expresson data file = LUAD-TP.medianexp.txt
Clinical data file = LUAD-TP.merged_data.txt
Number of patients = 32
Number of genes = 17814
Number of clinical features = 10

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

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

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

In addition to the links below, the full results of the analysis summarized in this report can also be downloaded programmatically using firehose_get, or interactively from either the Broad GDAC website or TCGA Data Coordination Center Portal.

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] Howell, D, Statistical Methods for Psychology. (5th ed.), Duxbury Press:324-5 (2002)

[4] Lehmann and Romano, Testing Statistical Hypotheses (3E ed.), New York: Springer. ISBN 0387988645 (2005)

[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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