Correlation between mRNA expression and clinical features

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 8 clinical features across 527 samples, statistically thresholded by Q value < 0.05, 7 clinical features related to at least one genes.

2 genes correlated to 'Time to Death'.

RPS26 , PPP1R14D

427 genes correlated to 'AGE'.

ESR1 , CNTNAP3 , KRT17 , MAGED4B , KLK6 , ...

6 genes correlated to 'GENDER'.

PI3 , TMEM16C , CACNG1 , RP13-36C9.6 , MAPK4 , ...

1 gene correlated to 'RADIATIONS.RADIATION.REGIMENINDICATION'.

OR13C4

10 genes correlated to 'LYMPH.NODE.METASTASIS'.

PIK3CA , KCNMB2 , GUCA2A , ARID1B , AMTN , ...

1 gene correlated to 'NUMBER.OF.LYMPH.NODES'.

CSDE1

19 genes correlated to 'NEOPLASM.DISEASESTAGE'.

NGFB , OR1M1 , C20ORF71 , PRKACG , OR6K3 , ...

No genes correlated to 'DISTANT.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=2	shorter survival	N=2	longer survival	N=0
AGE	Spearman correlation test	N=427	older	N=205	younger	N=222
GENDER	t test	N=6	male	N=1	female	N=5
RADIATIONS RADIATION REGIMENINDICATION	t test	N=1	yes	N=1	no	N=0
DISTANT METASTASIS	ANOVA test	N=0
LYMPH NODE METASTASIS	ANOVA test	N=10
NUMBER OF LYMPH NODES	Spearman correlation test	N=1	higher number.of.lymph.nodes	N=0	lower number.of.lymph.nodes	N=1
NEOPLASM DISEASESTAGE	ANOVA test	N=19

Clinical variable #1: 'Time to Death'

2 genes related to 'Time to Death'.

Table S1. Basic characteristics of clinical feature: 'Time to Death'


Time to Death	Duration (Months)	0.1-223.4 (median=24.2)
	censored	N = 430
	death	N = 65

	Significant markers	N = 2
	associated with shorter survival	2
	associated with longer survival	0

List of 2 genes significantly associated with 'Time to Death' by Cox regression test

Table S2. Get Full Table List of 2 genes significantly associated with 'Time to Death' by Cox regression test

	HazardRatio	Wald_P	Q	C_index
RPS26	2.7	9.679e-08	0.0017	0.682
PPP1R14D	2.3	2.923e-07	0.0052	0.592

Figure S1. Get High-res Image As an example, this figure shows the association of RPS26 to 'Time to Death'. four curves present the cumulative survival rates of 4 quartile subsets of patients. P value = 9.68e-08 with univariate Cox regression analysis using continuous log-2 expression values.

Clinical variable #2: 'AGE'

427 genes related to 'AGE'.

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


AGE	Mean (SD)	57.88 (13)
	Significant markers	N = 427
	pos. correlated	205
	neg. correlated	222

List of top 10 genes significantly correlated to 'AGE' by Spearman correlation test

Table S4. Get Full Table List of top 10 genes significantly correlated to 'AGE' by Spearman correlation test

	SpearmanCorr	corrP	Q
ESR1	0.4215	4.056e-24	7.22e-20
CNTNAP3	-0.3002	1.939e-12	3.45e-08
KRT17	-0.2978	2.98e-12	5.31e-08
MAGED4B	-0.2961	4.005e-12	7.13e-08
KLK6	-0.293	6.874e-12	1.22e-07
FOXD2	0.2923	7.731e-12	1.38e-07
NUDT16	0.2903	1.079e-11	1.92e-07
PPP1R14C	-0.2899	1.158e-11	2.06e-07
MGC102966	-0.2878	1.652e-11	2.94e-07
SYT8	-0.2875	1.736e-11	3.09e-07

Figure S2. Get High-res Image As an example, this figure shows the association of ESR1 to 'AGE'. P value = 4.06e-24 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #3: 'GENDER'

6 genes related to 'GENDER'.

Table S5. Basic characteristics of clinical feature: 'GENDER'


GENDER	Labels	N
	FEMALE	521
	MALE	6

	Significant markers	N = 6
	Higher in MALE	1
	Higher in FEMALE	5

List of 6 genes differentially expressed by 'GENDER'

Table S6. Get Full Table List of 6 genes differentially expressed by 'GENDER'

	T(pos if higher in 'MALE')	ttestP	Q	AUC
PI3	-9.27	5.072e-11	9.03e-07	0.7086
TMEM16C	-14.25	1.281e-10	2.28e-06	0.9258
CACNG1	18.37	1.822e-08	0.000325	0.961
RP13-36C9.6	-6.21	9.464e-07	0.0169	0.6638
MAPK4	-9.12	1.385e-06	0.0247	0.802
PLA2G3	-10.63	1.726e-06	0.0307	0.8305

Figure S3. Get High-res Image As an example, this figure shows the association of PI3 to 'GENDER'. P value = 5.07e-11 with T-test analysis.

Clinical variable #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

One gene related to 'RADIATIONS.RADIATION.REGIMENINDICATION'.

Table S7. Basic characteristics of clinical feature: 'RADIATIONS.RADIATION.REGIMENINDICATION'


RADIATIONS.RADIATION.REGIMENINDICATION	Labels	N
	NO	148
	YES	379

	Significant markers	N = 1
	Higher in YES	1
	Higher in NO	0

List of one gene differentially expressed by 'RADIATIONS.RADIATION.REGIMENINDICATION'

Table S8. Get Full Table List of one gene differentially expressed by 'RADIATIONS.RADIATION.REGIMENINDICATION'

	T(pos if higher in 'YES')	ttestP	Q	AUC
OR13C4	4.98	1.158e-06	0.0206	0.6251

Figure S4. Get High-res Image As an example, this figure shows the association of OR13C4 to 'RADIATIONS.RADIATION.REGIMENINDICATION'. P value = 1.16e-06 with T-test analysis.

Clinical variable #5: 'DISTANT.METASTASIS'

No gene related to 'DISTANT.METASTASIS'.

Table S9. Basic characteristics of clinical feature: 'DISTANT.METASTASIS'


DISTANT.METASTASIS	Labels	N
	CM0 (I+)	2
	M0	497
	M1	14
	MX	14

	Significant markers	N = 0

Clinical variable #6: 'LYMPH.NODE.METASTASIS'

10 genes related to 'LYMPH.NODE.METASTASIS'.

Table S10. Basic characteristics of clinical feature: 'LYMPH.NODE.METASTASIS'


LYMPH.NODE.METASTASIS	Labels	N
	N0	138
	N0 (I+)	16
	N0 (I-)	101
	N1	59
	N1A	77
	N1B	20
	N1C	2
	N1MI	13
	N2	32
	N2A	29
	N3	10
	N3A	18
	N3C	1
	NX	11

	Significant markers	N = 10

List of 10 genes differentially expressed by 'LYMPH.NODE.METASTASIS'

Table S11. Get Full Table List of 10 genes differentially expressed by 'LYMPH.NODE.METASTASIS'

	ANOVA_P	Q
PIK3CA	8.972e-25	1.6e-20
KCNMB2	3.214e-18	5.73e-14
GUCA2A	2.841e-12	5.06e-08
ARID1B	5.362e-08	0.000955
AMTN	6.805e-08	0.00121
REG1B	7.563e-08	0.00135
SULT1B1	2.291e-07	0.00408
C16ORF44	4.572e-07	0.00814
PDS5A	4.665e-07	0.00831
ZMAT3	1.279e-06	0.0228

Figure S5. Get High-res Image As an example, this figure shows the association of PIK3CA to 'LYMPH.NODE.METASTASIS'. P value = 8.97e-25 with ANOVA analysis.

Clinical variable #7: 'NUMBER.OF.LYMPH.NODES'

One gene related to 'NUMBER.OF.LYMPH.NODES'.

Table S12. Basic characteristics of clinical feature: 'NUMBER.OF.LYMPH.NODES'


NUMBER.OF.LYMPH.NODES	Mean (SD)	1.82 (3.5)
	Significant markers	N = 1
	pos. correlated	0
	neg. correlated	1

List of one gene significantly correlated to 'NUMBER.OF.LYMPH.NODES' by Spearman correlation test

Table S13. Get Full Table List of one gene significantly correlated to 'NUMBER.OF.LYMPH.NODES' by Spearman correlation test

	SpearmanCorr	corrP	Q
CSDE1	-0.2441	6.22e-07	0.0111

Figure S6. Get High-res Image As an example, this figure shows the association of CSDE1 to 'NUMBER.OF.LYMPH.NODES'. P value = 6.22e-07 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #8: 'NEOPLASM.DISEASESTAGE'

19 genes related to 'NEOPLASM.DISEASESTAGE'.

Table S14. Basic characteristics of clinical feature: 'NEOPLASM.DISEASESTAGE'


NEOPLASM.DISEASESTAGE	Labels	N
	STAGE I	42
	STAGE IA	41
	STAGE IB	6
	STAGE IIA	185
	STAGE IIB	111
	STAGE IIIA	77
	STAGE IIIB	15
	STAGE IIIC	19
	STAGE IV	14
	STAGE TIS	1
	STAGE X	16

	Significant markers	N = 19

List of top 10 genes differentially expressed by 'NEOPLASM.DISEASESTAGE'

Table S15. Get Full Table List of top 10 genes differentially expressed by 'NEOPLASM.DISEASESTAGE'

	ANOVA_P	Q
NGFB	5.856e-23	1.04e-18
OR1M1	4.173e-20	7.43e-16
C20ORF71	4.378e-13	7.8e-09
PRKACG	9.103e-13	1.62e-08
OR6K3	9.582e-12	1.71e-07
IFNA7	1.876e-11	3.34e-07
OR1J4	6.796e-10	1.21e-05
GPR52	1.641e-09	2.92e-05
SEBOX	1.812e-09	3.23e-05
TAS2R40	2.263e-09	4.03e-05

Figure S7. Get High-res Image As an example, this figure shows the association of NGFB to 'NEOPLASM.DISEASESTAGE'. P value = 5.86e-23 with ANOVA analysis.

Methods & Data

Input

Expresson data file = BRCA-TP.medianexp.txt
Clinical data file = BRCA-TP.clin.merged.picked.txt
Number of patients = 527
Number of genes = 17814
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.

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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