Head and Neck Squamous Cell Carcinoma: 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 564 genes and 8 clinical features across 308 samples, statistically thresholded by Q value < 0.05, 4 clinical features related to at least one genes.

5 genes correlated to 'Time to Death'.

HSA-MIR-377 , HSA-MIR-154 , HSA-MIR-493 , HSA-MIR-337 , HSA-MIR-654

1 gene correlated to 'PATHOLOGY.N'.

HSA-MIR-195

5 genes correlated to 'RADIATIONS.RADIATION.REGIMENINDICATION'.

HSA-MIR-1274B , HSA-MIR-3676 , HSA-MIR-374A , HSA-MIR-660 , HSA-MIR-532

2 genes correlated to 'NEOADJUVANT.THERAPY'.

HSA-MIR-3676 , HSA-MIR-660

No genes correlated to 'AGE', 'GENDER', 'PATHOLOGY.T', 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=5	shorter survival	N=5	longer survival	N=0
AGE	Spearman correlation test	N=0
GENDER	t test	N=0
PATHOLOGY T	Spearman correlation test	N=0
PATHOLOGY N	Spearman correlation test	N=1	higher pN	N=1	lower pN	N=0
TUMOR STAGE	Spearman correlation test	N=0
RADIATIONS RADIATION REGIMENINDICATION	t test	N=5	yes	N=4	no	N=1
NEOADJUVANT THERAPY	t test	N=2	yes	N=2	no	N=0

Clinical variable #1: 'Time to Death'

5 genes related to 'Time to Death'.

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


Time to Death	Duration (Months)	0.1-210.9 (median=15)
	censored	N = 183
	death	N = 122

	Significant markers	N = 5
	associated with shorter survival	5
	associated with longer survival	0

List of 5 genes significantly associated with 'Time to Death' by Cox regression test

Table S2. Get Full Table List of 5 genes significantly associated with 'Time to Death' by Cox regression test

	HazardRatio	Wald_P	Q	C_index
HSA-MIR-377	1.48	2.217e-06	0.0013	0.634
HSA-MIR-154	1.46	1.031e-05	0.0058	0.635
HSA-MIR-493	1.45	2.314e-05	0.013	0.63
HSA-MIR-337	1.4	2.811e-05	0.016	0.622
HSA-MIR-654	1.38	4.724e-05	0.026	0.622

Figure S1. Get High-res Image As an example, this figure shows the association of HSA-MIR-377 to 'Time to Death'. four curves present the cumulative survival rates of 4 quartile subsets of patients. P value = 2.22e-06 with univariate Cox regression analysis using continuous log-2 expression values.

Clinical variable #2: 'AGE'

No gene related to 'AGE'.

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


AGE	Mean (SD)	61.09 (12)
	Significant markers	N = 0

Clinical variable #3: 'GENDER'

No gene related to 'GENDER'.

Table S4. Basic characteristics of clinical feature: 'GENDER'


GENDER	Labels	N
	FEMALE	86
	MALE	222

	Significant markers	N = 0

Clinical variable #4: 'PATHOLOGY.T'

No gene related to 'PATHOLOGY.T'.

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


PATHOLOGY.T	Mean (SD)	2.91 (1)
		N
	T1	24
	T2	78
	T3	62
	T4	103

	Significant markers	N = 0

Clinical variable #5: 'PATHOLOGY.N'

One gene related to 'PATHOLOGY.N'.

Table S6. Basic characteristics of clinical feature: 'PATHOLOGY.N'


PATHOLOGY.N	Mean (SD)	1.05 (0.96)
		N
	N0	99
	N1	32
	N2	101
	N3	5

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

List of one gene significantly correlated to 'PATHOLOGY.N' by Spearman correlation test

Table S7. Get Full Table List of one gene significantly correlated to 'PATHOLOGY.N' by Spearman correlation test

	SpearmanCorr	corrP	Q
HSA-MIR-195	0.2646	3.684e-05	0.0208

Figure S2. Get High-res Image As an example, this figure shows the association of HSA-MIR-195 to 'PATHOLOGY.N'. P value = 3.68e-05 with Spearman correlation analysis.

Clinical variable #6: 'TUMOR.STAGE'

No gene related to 'TUMOR.STAGE'.

Table S8. Basic characteristics of clinical feature: 'TUMOR.STAGE'


TUMOR.STAGE	Mean (SD)	3.3 (0.98)
		N
	Stage 1	17
	Stage 2	46
	Stage 3	41
	Stage 4	158

	Significant markers	N = 0

Clinical variable #7: 'RADIATIONS.RADIATION.REGIMENINDICATION'

5 genes related to 'RADIATIONS.RADIATION.REGIMENINDICATION'.

Table S9. Basic characteristics of clinical feature: 'RADIATIONS.RADIATION.REGIMENINDICATION'


RADIATIONS.RADIATION.REGIMENINDICATION	Labels	N
	NO	77
	YES	231

	Significant markers	N = 5
	Higher in YES	4
	Higher in NO	1

List of 5 genes differentially expressed by 'RADIATIONS.RADIATION.REGIMENINDICATION'

Table S10. Get Full Table List of 5 genes differentially expressed by 'RADIATIONS.RADIATION.REGIMENINDICATION'

	T(pos if higher in 'YES')	ttestP	Q	AUC
HSA-MIR-1274B	5.58	1.256e-07	7.08e-05	0.696
HSA-MIR-3676	5.38	2.403e-07	0.000135	0.6658
HSA-MIR-374A	-4.63	8.368e-06	0.0047	0.6576
HSA-MIR-660	4.53	1.177e-05	0.0066	0.6511
HSA-MIR-532	4.25	3.735e-05	0.0209	0.6495

Figure S3. Get High-res Image As an example, this figure shows the association of HSA-MIR-1274B to 'RADIATIONS.RADIATION.REGIMENINDICATION'. P value = 1.26e-07 with T-test analysis.

Clinical variable #8: 'NEOADJUVANT.THERAPY'

2 genes related to 'NEOADJUVANT.THERAPY'.

Table S11. Basic characteristics of clinical feature: 'NEOADJUVANT.THERAPY'


NEOADJUVANT.THERAPY	Labels	N
	NO	48
	YES	260

	Significant markers	N = 2
	Higher in YES	2
	Higher in NO	0

List of 2 genes differentially expressed by 'NEOADJUVANT.THERAPY'

Table S12. Get Full Table List of 2 genes differentially expressed by 'NEOADJUVANT.THERAPY'

	T(pos if higher in 'YES')	ttestP	Q	AUC
HSA-MIR-3676	4.47	2.684e-05	0.0151	0.6653
HSA-MIR-660	4.12	8.748e-05	0.0492	0.6508

Figure S4. Get High-res Image As an example, this figure shows the association of HSA-MIR-3676 to 'NEOADJUVANT.THERAPY'. P value = 2.68e-05 with T-test analysis.

Methods & Data

Input

Expresson data file = HNSC.miRseq_RPKM_log2.txt
Clinical data file = HNSC.clin.merged.picked.txt
Number of patients = 308
Number of genes = 564
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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