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

Maintained by Juok Cho (Broad Institute)

Overview

Introduction

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

Summary

Testing the association between 404 genes and 10 clinical features across 255 samples, statistically thresholded by Q value < 0.05, 4 clinical features related to at least one genes.

1 gene correlated to 'AGE'.

HSA-MIR-432

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

HSA-MIR-10B , HSA-MIR-190 , HSA-MIR-19A , HSA-MIR-19B-2 , HSA-MIR-30C-2 , ...

17 genes correlated to 'HISTOLOGICAL.TYPE'.

HSA-MIR-10B , HSA-MIR-196B , HSA-MIR-592 , HSA-MIR-1259 , HSA-MIR-224 , ...

1 gene correlated to 'NEOADJUVANT.THERAPY'.

HSA-MIR-141

No genes correlated to 'Time to Death', 'GENDER', '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=1	older	N=0	younger	N=1
PRIMARY SITE OF DISEASE	t test	N=8	rectum	N=7	colon	N=1
GENDER	t test	N=0
HISTOLOGICAL TYPE	ANOVA test	N=17
PATHOLOGY T	Spearman correlation test	N=0
PATHOLOGY N	Spearman correlation test	N=0
PATHOLOGICSPREAD(M)	ANOVA test	N=0
TUMOR STAGE	Spearman correlation test	N=0
NEOADJUVANT THERAPY	t test	N=1	yes	N=1	no	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)	0.9-72.1 (median=9.1)
	censored	N = 121
	death	N = 20

	Significant markers	N = 0

Clinical variable #2: 'AGE'

One gene related to 'AGE'.

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


AGE	Mean (SD)	68.64 (12)
	Significant markers	N = 1
	pos. correlated	0
	neg. correlated	1

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
HSA-MIR-432	-0.2667	2.005e-05	0.0081

Figure S1. Get High-res Image As an example, this figure shows the association of HSA-MIR-432 to 'AGE'. P value = 2.01e-05 with Spearman correlation analysis. The straight line presents the best linear regression.

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

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

Table S4. Basic characteristics of clinical feature: 'PRIMARY.SITE.OF.DISEASE'


PRIMARY.SITE.OF.DISEASE	Labels	N
	COLON	186
	RECTUM	67

	Significant markers	N = 8
	Higher in RECTUM	7
	Higher in COLON	1

List of 8 genes differentially expressed by 'PRIMARY.SITE.OF.DISEASE'

Table S5. Get Full Table List of 8 genes differentially expressed by 'PRIMARY.SITE.OF.DISEASE'

	T(pos if higher in 'RECTUM')	ttestP	Q	AUC
HSA-MIR-10B	-6.63	1.252e-09	5.06e-07	0.7575
HSA-MIR-190	4.95	1.851e-06	0.000746	0.6735
HSA-MIR-19A	4.88	2.71e-06	0.00109	0.6822
HSA-MIR-19B-2	4.61	8.358e-06	0.00335	0.6752
HSA-MIR-30C-2	4.55	1.198e-05	0.00479	0.672
HSA-MIR-106A	4.14	5.95e-05	0.0237	0.6509
HSA-MIR-224	4.05	8.932e-05	0.0355	0.6416
HSA-MIR-425	4	0.0001109	0.044	0.6553

Figure S2. Get High-res Image As an example, this figure shows the association of HSA-MIR-10B to 'PRIMARY.SITE.OF.DISEASE'. P value = 1.25e-09 with T-test analysis.

Clinical variable #4: 'GENDER'

No gene related to 'GENDER'.

Table S6. Basic characteristics of clinical feature: 'GENDER'


GENDER	Labels	N
	FEMALE	127
	MALE	128

	Significant markers	N = 0

Clinical variable #5: 'HISTOLOGICAL.TYPE'

17 genes related to 'HISTOLOGICAL.TYPE'.

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


HISTOLOGICAL.TYPE	Labels	N
	COLON ADENOCARCINOMA	158
	COLON MUCINOUS ADENOCARCINOMA	26
	RECTAL ADENOCARCINOMA	56
	RECTAL MUCINOUS ADENOCARCINOMA	8

	Significant markers	N = 17

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
HSA-MIR-10B	4.124e-10	1.67e-07
HSA-MIR-196B	4.114e-07	0.000166
HSA-MIR-592	1.55e-06	0.000623
HSA-MIR-1259	2.293e-06	0.000919
HSA-MIR-224	2.444e-06	0.000978
HSA-MIR-31	2.952e-06	0.00118
HSA-MIR-362	5.396e-06	0.00215
HSA-MIR-1977	5.749e-06	0.00228
HSA-MIR-582	9.351e-06	0.0037
HSA-MIR-92A-1	1.159e-05	0.00458

Figure S3. Get High-res Image As an example, this figure shows the association of HSA-MIR-10B to 'HISTOLOGICAL.TYPE'. P value = 4.12e-10 with ANOVA analysis.

Clinical variable #6: 'PATHOLOGY.T'

No gene related to 'PATHOLOGY.T'.

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


PATHOLOGY.T	Mean (SD)	2.79 (0.65)
		N
	T1	11
	T2	52
	T3	169
	T4	21

	Significant markers	N = 0

Clinical variable #7: 'PATHOLOGY.N'

No gene related to 'PATHOLOGY.N'.

Table S10. Basic characteristics of clinical feature: 'PATHOLOGY.N'


PATHOLOGY.N	Mean (SD)	0.59 (0.78)
		N
	N0	152
	N1	55
	N2	47

	Significant markers	N = 0

Clinical variable #8: 'PATHOLOGICSPREAD(M)'

No gene related to 'PATHOLOGICSPREAD(M)'.

Table S11. Basic characteristics of clinical feature: 'PATHOLOGICSPREAD(M)'


PATHOLOGICSPREAD(M)	Labels	N
	M0	208
	M1	41
	M1A	2

	Significant markers	N = 0

Clinical variable #9: 'TUMOR.STAGE'

No gene related to 'TUMOR.STAGE'.

Table S12. Basic characteristics of clinical feature: 'TUMOR.STAGE'


TUMOR.STAGE	Mean (SD)	2.36 (0.99)
		N
	Stage 1	52
	Stage 2	90
	Stage 3	64
	Stage 4	38

	Significant markers	N = 0

Clinical variable #10: 'NEOADJUVANT.THERAPY'

One gene related to 'NEOADJUVANT.THERAPY'.

Table S13. Basic characteristics of clinical feature: 'NEOADJUVANT.THERAPY'


NEOADJUVANT.THERAPY	Labels	N
	NO	16
	YES	239

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

List of one gene differentially expressed by 'NEOADJUVANT.THERAPY'

Table S14. Get Full Table List of one gene differentially expressed by 'NEOADJUVANT.THERAPY'

	T(pos if higher in 'YES')	ttestP	Q	AUC
HSA-MIR-141	6.02	5.947e-06	0.0024	0.8146

Figure S4. Get High-res Image As an example, this figure shows the association of HSA-MIR-141 to 'NEOADJUVANT.THERAPY'. P value = 5.95e-06 with T-test analysis.

Methods & Data

Input

Expresson data file = COADREAD.miRseq_RPKM_log2.txt
Clinical data file = COADREAD.clin.merged.picked.txt
Number of patients = 255
Number of genes = 404
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

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)

Made with Nozzle