Rectum Adenocarcinoma: Correlation between mRNA expression and clinical features

Maintained by TCGA GDAC Team (Broad Institute/Dana-Farber Cancer Institute/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 8 clinical features across 69 samples, statistically thresholded by Q value < 0.05, 2 clinical features related to at least one genes.

11 genes correlated to 'GENDER'.

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

12 genes correlated to 'HISTOLOGICAL.TYPE'.

TLE6 , FBXO2 , USP42 , AGR3 , CARD6 , ...

No genes correlated to 'Time to Death', 'AGE', 'PATHOLOGY.T', 'PATHOLOGY.N', 'PATHOLOGICSPREAD(M)', 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=11	female	N=0
HISTOLOGICAL TYPE	t test	N=12	rectal mucinous adenocarcinoma	N=6	rectal adenocarcinoma	N=6
PATHOLOGY T	Spearman correlation test	N=0
PATHOLOGY N	Spearman correlation test	N=0
PATHOLOGICSPREAD(M)	t test	N=0
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=6)
	censored	N = 35
	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)	66.62 (11)
	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	31
	MALE	38

	Significant markers	N = 11
	Higher in MALE	11
	Higher in FEMALE	0

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
DDX3Y	12.58	3.494e-19	6.22e-15	0.9652
RPS4Y1	11.98	2.93e-17	5.22e-13	0.9244
RPS4Y2	11.51	1.822e-16	3.24e-12	0.9669
EIF1AY	10.97	2.286e-16	4.07e-12	0.9593
JARID1D	10.54	8.569e-16	1.53e-11	0.9491
CYORF15A	10.03	1.685e-14	3e-10	0.9491
UTY	7.97	4.7e-11	8.37e-07	0.9177
CYORF15B	7.8	7.005e-11	1.25e-06	0.9032
ZFY	7.67	1.14e-10	2.03e-06	0.8964
TTTY14	5.84	1.885e-07	0.00336	0.8973

Figure S1. Get High-res Image As an example, this figure shows the association of DDX3Y to 'GENDER'. P value = 3.49e-19 with T-test analysis.

Clinical variable #4: 'HISTOLOGICAL.TYPE'

12 genes related to 'HISTOLOGICAL.TYPE'.

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


HISTOLOGICAL.TYPE	Labels	N
	RECTAL ADENOCARCINOMA	58
	RECTAL MUCINOUS ADENOCARCINOMA	7

	Significant markers	N = 12
	Higher in RECTAL MUCINOUS ADENOCARCINOMA	6
	Higher in RECTAL ADENOCARCINOMA	6

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
TLE6	11.58	2.77e-13	4.93e-09	0.9828
FBXO2	-7.64	2.189e-10	3.9e-06	0.867
USP42	-6.71	2.684e-08	0.000478	0.867
AGR3	6.79	8.791e-08	0.00157	0.8768
CARD6	6.15	1.932e-07	0.00344	0.8448
LOC643641	-6.6	4.407e-07	0.00785	0.8547
MECR	-5.75	4.437e-07	0.0079	0.7734
RAB27B	6.36	4.44e-07	0.00791	0.8645
TTLL7	7.44	7.9e-07	0.0141	0.936
PLCB2	6.6	9.349e-07	0.0166	0.8966

Figure S2. Get High-res Image As an example, this figure shows the association of TLE6 to 'HISTOLOGICAL.TYPE'. P value = 2.77e-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)	2.7 (0.69)
		N
	T1	5
	T2	15
	T3	45
	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)	0.57 (0.78)
		N
	N0	42
	N1	15
	N2	12

	Significant markers	N = 0

Clinical variable #7: 'PATHOLOGICSPREAD(M)'

No gene related to 'PATHOLOGICSPREAD(M)'.

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


PATHOLOGICSPREAD(M)	Labels	N
	M0	57
	M1	12

	Significant markers	N = 0

Clinical variable #8: 'TUMOR.STAGE'

No gene related to 'TUMOR.STAGE'.

Table S10. Basic characteristics of clinical feature: 'TUMOR.STAGE'


TUMOR.STAGE	Mean (SD)	2.27 (1)
		N
	Stage 1	18
	Stage 2	23
	Stage 3	16
	Stage 4	10

	Significant markers	N = 0

Methods & Data

Input

Expresson data file = READ.medianexp.txt
Clinical data file = READ.clin.merged.picked.txt
Number of patients = 69
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

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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