Head and Neck Squamous Cell Carcinoma: Correlation between mRNAseq expression and clinical features

Maintained by Juok Cho (Broad Institute)

Overview

Introduction

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

Summary

Testing the association between 18327 genes and 8 clinical features across 229 samples, statistically thresholded by Q value < 0.05, 5 clinical features related to at least one genes.

11 genes correlated to 'Time to Death'.

CYB5B|80777 , FGD3|89846 , TXNDC9|10190 , ODF3B|440836 , SLC25A45|283130 , ...

24 genes correlated to 'GENDER'.

XIST|7503 , PRKY|5616 , ZFY|7544 , RPS4Y1|6192 , TSIX|9383 , ...

3 genes correlated to 'PATHOLOGY.T'.

SAMD8|142891 , LRRC8A|56262 , USP32|84669

1 gene correlated to 'TUMOR.STAGE'.

SAMD8|142891

1 gene correlated to 'NEOADJUVANT.THERAPY'.

NBPF14|25832

No genes correlated to 'AGE', 'PATHOLOGY.N', and 'RADIATIONS.RADIATION.REGIMENINDICATION'.

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=11	shorter survival	N=4	longer survival	N=7
AGE	Spearman correlation test	N=0
GENDER	t test	N=24	male	N=9	female	N=15
PATHOLOGY T	Spearman correlation test	N=3	higher pT	N=3	lower pT	N=0
PATHOLOGY N	Spearman correlation test	N=0
TUMOR STAGE	Spearman correlation test	N=1	higher stage	N=1	lower stage	N=0
RADIATIONS RADIATION REGIMENINDICATION	t test	N=0
NEOADJUVANT THERAPY	t test	N=1	yes	N=0	no	N=1

Clinical variable #1: 'Time to Death'

11 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=14.2)
	censored	N = 132
	death	N = 96

	Significant markers	N = 11
	associated with shorter survival	4
	associated with longer survival	7

List of top 10 genes significantly associated with 'Time to Death' by Cox regression test

Table S2. Get Full Table List of top 10 genes significantly associated with 'Time to Death' by Cox regression test

	HazardRatio	Wald_P	Q	C_index
CYB5B\|80777	2.6	3.411e-08	0.00063	0.647
FGD3\|89846	0.63	1.075e-07	0.002	0.34
TXNDC9\|10190	2.2	9.283e-07	0.017	0.612
ODF3B\|440836	0.55	1.653e-06	0.03	0.37
SLC25A45\|283130	0.58	1.661e-06	0.03	0.347
TMED2\|10959	2.4	1.982e-06	0.036	0.663
TOMM34\|10953	2.3	2.219e-06	0.041	0.64
ZNF266\|10781	0.43	2.339e-06	0.043	0.354
ZNF541\|84215	0.79	2.556e-06	0.047	0.319
REC8\|9985	0.73	2.661e-06	0.049	0.371

Figure S1. Get High-res Image As an example, this figure shows the association of CYB5B|80777 to 'Time to Death'. four curves present the cumulative survival rates of 4 quartile subsets of patients. P value = 3.41e-08 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)	61.14 (12)
	Significant markers	N = 0

Clinical variable #3: 'GENDER'

24 genes related to 'GENDER'.

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


GENDER	Labels	N
	FEMALE	59
	MALE	170

	Significant markers	N = 24
	Higher in MALE	9
	Higher in FEMALE	15

List of top 10 genes differentially expressed by 'GENDER'

Table S5. Get Full Table List of top 10 genes differentially expressed by 'GENDER'

	T(pos if higher in 'MALE')	ttestP	Q	AUC
XIST\|7503	-31.56	6.652e-58	1.22e-53	0.9896
PRKY\|5616	19.55	1.722e-38	3.15e-34	0.9741
ZFY\|7544	24.07	2.762e-32	5.06e-28	0.9834
RPS4Y1\|6192	22.54	4.585e-26	8.4e-22	0.9837
TSIX\|9383	-13.99	1.465e-25	2.68e-21	0.9639
NLGN4Y\|22829	17.69	4.072e-23	7.46e-19	0.974
DDX3Y\|8653	19.35	6.966e-20	1.28e-15	0.9733
UTY\|7404	14.45	1.16e-13	2.13e-09	0.9675
HDHD1A\|8226	-8.24	3.306e-13	6.06e-09	0.8239
USP9Y\|8287	13.88	5.006e-13	9.17e-09	0.9658

Figure S2. Get High-res Image As an example, this figure shows the association of XIST|7503 to 'GENDER'. P value = 6.65e-58 with T-test analysis.

Clinical variable #4: 'PATHOLOGY.T'

3 genes related to 'PATHOLOGY.T'.

Table S6. Basic characteristics of clinical feature: 'PATHOLOGY.T'


PATHOLOGY.T	Mean (SD)	3.02 (0.97)
		N
	T1	12
	T2	55
	T3	47
	T4	82

	Significant markers	N = 3
	pos. correlated	3
	neg. correlated	0

List of 3 genes significantly correlated to 'PATHOLOGY.T' by Spearman correlation test

Table S7. Get Full Table List of 3 genes significantly correlated to 'PATHOLOGY.T' by Spearman correlation test

	SpearmanCorr	corrP	Q
SAMD8\|142891	0.3574	2.711e-07	0.00497
LRRC8A\|56262	0.3444	7.729e-07	0.0142
USP32\|84669	0.3335	1.778e-06	0.0326

Figure S3. Get High-res Image As an example, this figure shows the association of SAMD8|142891 to 'PATHOLOGY.T'. P value = 2.71e-07 with Spearman correlation analysis.

Clinical variable #5: 'PATHOLOGY.N'

No gene related to 'PATHOLOGY.N'.

Table S8. Basic characteristics of clinical feature: 'PATHOLOGY.N'


PATHOLOGY.N	Mean (SD)	1.05 (0.96)
		N
	N0	73
	N1	23
	N2	76
	N3	3

	Significant markers	N = 0

Clinical variable #6: 'TUMOR.STAGE'

One gene related to 'TUMOR.STAGE'.

Table S9. Basic characteristics of clinical feature: 'TUMOR.STAGE'


TUMOR.STAGE	Mean (SD)	3.36 (0.93)
		N
	Stage 1	9
	Stage 2	34
	Stage 3	29
	Stage 4	121

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

List of one gene significantly correlated to 'TUMOR.STAGE' by Spearman correlation test

Table S10. Get Full Table List of one gene significantly correlated to 'TUMOR.STAGE' by Spearman correlation test

	SpearmanCorr	corrP	Q
SAMD8\|142891	0.3415	1.172e-06	0.0215

Figure S4. Get High-res Image As an example, this figure shows the association of SAMD8|142891 to 'TUMOR.STAGE'. P value = 1.17e-06 with Spearman correlation analysis.

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

No gene related to 'RADIATIONS.RADIATION.REGIMENINDICATION'.

Table S11. Basic characteristics of clinical feature: 'RADIATIONS.RADIATION.REGIMENINDICATION'


RADIATIONS.RADIATION.REGIMENINDICATION	Labels	N
	NO	62
	YES	167

	Significant markers	N = 0

Clinical variable #8: 'NEOADJUVANT.THERAPY'

One gene related to 'NEOADJUVANT.THERAPY'.

Table S12. Basic characteristics of clinical feature: 'NEOADJUVANT.THERAPY'


NEOADJUVANT.THERAPY	Labels	N
	NO	38
	YES	191

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

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

Table S13. Get Full Table List of one gene differentially expressed by 'NEOADJUVANT.THERAPY'

	T(pos if higher in 'YES')	ttestP	Q	AUC
NBPF14\|25832	-5.21	1.663e-06	0.0305	0.7224

Figure S5. Get High-res Image As an example, this figure shows the association of NBPF14|25832 to 'NEOADJUVANT.THERAPY'. P value = 1.66e-06 with T-test analysis.

Methods & Data

Input

Expresson data file = HNSC.uncv2.mRNAseq_RSEM_normalized_log2.txt
Clinical data file = HNSC.clin.merged.picked.txt
Number of patients = 229
Number of genes = 18327
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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