Colon/Rectal Adenocarcinoma: Correlation between mRNA expression and clinical features

Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/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 9 clinical features across 224 samples, statistically thresholded by Q value < 0.05, 5 clinical features related to at least one genes.

37 genes correlated to 'PRIMARY.SITE.OF.DISEASE'.

PRAC , GPX2 , CEACAM5 , LGALS4 , ZNF529 , ...

31 genes correlated to 'GENDER'.

DDX3Y , EIF1AY , JARID1D , RPS4Y1 , RPS4Y2 , ...

390 genes correlated to 'HISTOLOGICAL.TYPE'.

SLC11A1 , PLAGL2 , PDGFRL , C20ORF177 , AGR2 , ...

1 gene correlated to 'PATHOLOGY.T'.

RBP7

4 genes correlated to 'PATHOLOGICSPREAD(M)'.

KCNC2 , ZNF273 , FLJ44894 , MFNG

No genes correlated to 'Time to Death', 'AGE', '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
PRIMARY SITE OF DISEASE	t test	N=37	rectum	N=26	colon	N=11
GENDER	t test	N=31	male	N=14	female	N=17
HISTOLOGICAL TYPE	ANOVA test	N=390
PATHOLOGY T	Spearman correlation test	N=1	higher pT	N=1	lower pT	N=0
PATHOLOGY N	Spearman correlation test	N=0
PATHOLOGICSPREAD(M)	ANOVA test	N=4
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)	0.9-52 (median=5.5)
	censored	N = 99
	death	N = 15

	Significant markers	N = 0

Clinical variable #2: 'AGE'

No gene related to 'AGE'.

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


AGE	Mean (SD)	69.34 (11)
	Significant markers	N = 0

Clinical variable #3: 'PRIMARY.SITE.OF.DISEASE'

37 genes related to 'PRIMARY.SITE.OF.DISEASE'.

Table S3. Basic characteristics of clinical feature: 'PRIMARY.SITE.OF.DISEASE'


PRIMARY.SITE.OF.DISEASE	Labels	N
	COLON	154
	RECTUM	68

	Significant markers	N = 37
	Higher in RECTUM	26
	Higher in COLON	11

List of top 10 genes differentially expressed by 'PRIMARY.SITE.OF.DISEASE'

Table S4. Get Full Table List of top 10 genes differentially expressed by 'PRIMARY.SITE.OF.DISEASE'

	T(pos if higher in 'RECTUM')	ttestP	Q	AUC
PRAC	7.43	7.085e-12	1.26e-07	0.7771
GPX2	7.24	7.418e-12	1.32e-07	0.7868
CEACAM5	7.27	7.521e-12	1.34e-07	0.7713
LGALS4	7.12	2.569e-11	4.58e-07	0.7524
ZNF529	6.29	1.657e-09	2.95e-05	0.6826
MLH1	5.95	1.052e-08	0.000187	0.6731
HSP90B3P	-5.99	1.217e-08	0.000217	0.732
PPP1R1B	5.68	5.053e-08	9e-04	0.7348
HOXB13	5.85	5.063e-08	0.000902	0.7457
PDZK1IP1	5.73	5.909e-08	0.00105	0.7406

Figure S1. Get High-res Image As an example, this figure shows the association of PRAC to 'PRIMARY.SITE.OF.DISEASE'. P value = 7.09e-12 with T-test analysis.

Clinical variable #4: 'GENDER'

31 genes related to 'GENDER'.

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


GENDER	Labels	N
	FEMALE	107
	MALE	117

	Significant markers	N = 31
	Higher in MALE	14
	Higher in FEMALE	17

List of top 10 genes differentially expressed by 'GENDER'

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

	T(pos if higher in 'MALE')	ttestP	Q	AUC
DDX3Y	25.36	1.631e-67	2.91e-63	0.9711
EIF1AY	22.73	2.061e-59	3.67e-55	0.9638
JARID1D	22.52	3.733e-59	6.65e-55	0.9703
RPS4Y1	22.39	6.001e-58	1.07e-53	0.9426
RPS4Y2	21.49	5.929e-55	1.06e-50	0.9646
CYORF15A	20.74	6.388e-53	1.14e-48	0.9539
UTY	18.72	3.543e-46	6.31e-42	0.9451
CYORF15B	17.06	1.259e-41	2.24e-37	0.9321
ZFY	16.7	6.085e-41	1.08e-36	0.9291
NLGN4Y	12.33	6.163e-26	1.1e-21	0.8686

Figure S2. Get High-res Image As an example, this figure shows the association of DDX3Y to 'GENDER'. P value = 1.63e-67 with T-test analysis.

Clinical variable #5: 'HISTOLOGICAL.TYPE'

390 genes related to 'HISTOLOGICAL.TYPE'.

Table S7. Basic characteristics of clinical feature: 'HISTOLOGICAL.TYPE'


HISTOLOGICAL.TYPE	Labels	N
	COLON ADENOCARCINOMA	128
	COLON MUCINOUS ADENOCARCINOMA	24
	RECTAL ADENOCARCINOMA	58
	RECTAL MUCINOUS ADENOCARCINOMA	7

	Significant markers	N = 390

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

Table S8. Get Full Table List of top 10 genes differentially expressed by 'HISTOLOGICAL.TYPE'

	ANOVA_P	Q
SLC11A1	1.944e-12	3.46e-08
PLAGL2	2.799e-12	4.99e-08
PDGFRL	1.224e-11	2.18e-07
C20ORF177	1.233e-11	2.2e-07
AGR2	7.249e-11	1.29e-06
DUSP4	7.473e-11	1.33e-06
ZNF529	8.005e-11	1.43e-06
SLC19A3	9.183e-11	1.64e-06
HPSE	1.347e-10	2.4e-06
ACSL5	1.445e-10	2.57e-06

Figure S3. Get High-res Image As an example, this figure shows the association of SLC11A1 to 'HISTOLOGICAL.TYPE'. P value = 1.94e-12 with ANOVA analysis.

Clinical variable #6: 'PATHOLOGY.T'

One gene related to 'PATHOLOGY.T'.

Table S9. Basic characteristics of clinical feature: 'PATHOLOGY.T'


PATHOLOGY.T	Mean (SD)	2.79 (0.63)
		N
	T1	9
	T2	46
	T3	150
	T4	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 S10. Get Full Table List of one gene significantly correlated to 'PATHOLOGY.T' by Spearman correlation test

	SpearmanCorr	corrP	Q
RBP7	0.31	2.481e-06	0.0442

Figure S4. Get High-res Image As an example, this figure shows the association of RBP7 to 'PATHOLOGY.T'. P value = 2.48e-06 with Spearman correlation analysis.

Clinical variable #7: 'PATHOLOGY.N'

No gene related to 'PATHOLOGY.N'.

Table S11. Basic characteristics of clinical feature: 'PATHOLOGY.N'


PATHOLOGY.N	Mean (SD)	0.58 (0.8)
		N
	N0	137
	N1	43
	N2	44

	Significant markers	N = 0

Clinical variable #8: 'PATHOLOGICSPREAD(M)'

4 genes related to 'PATHOLOGICSPREAD(M)'.

Table S12. Basic characteristics of clinical feature: 'PATHOLOGICSPREAD(M)'


PATHOLOGICSPREAD(M)	Labels	N
	M0	186
	M1	34
	M1A	1

	Significant markers	N = 4

List of 4 genes differentially expressed by 'PATHOLOGICSPREAD(M)'

Table S13. Get Full Table List of 4 genes differentially expressed by 'PATHOLOGICSPREAD(M)'

	ANOVA_P	Q
KCNC2	9.739e-11	1.73e-06
ZNF273	1.864e-06	0.0332
FLJ44894	1.99e-06	0.0354
MFNG	2.089e-06	0.0372

Figure S5. Get High-res Image As an example, this figure shows the association of KCNC2 to 'PATHOLOGICSPREAD(M)'. P value = 9.74e-11 with ANOVA analysis.

Clinical variable #9: 'TUMOR.STAGE'

No gene related to 'TUMOR.STAGE'.

Table S14. Basic characteristics of clinical feature: 'TUMOR.STAGE'


TUMOR.STAGE	Mean (SD)	2.33 (0.97)
		N
	Stage 1	46
	Stage 2	86
	Stage 3	55
	Stage 4	32

	Significant markers	N = 0

Methods & Data

Input

Expresson data file = COADREAD-TP.medianexp.txt
Clinical data file = COADREAD-TP.clin.merged.picked.txt
Number of patients = 224
Number of genes = 17814
Number of clinical features = 9

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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