Lung Squamous Cell Carcinoma: Correlation between gene methylation status and clinical features

Maintained by Juok Cho (Broad Institute)

Overview

Introduction

This pipeline uses various statistical tests to identify genes whose promoter methylation levels correlated to selected clinical features.

Summary

Testing the association between 17552 genes and 11 clinical features across 92 samples, statistically thresholded by Q value < 0.05, 5 clinical features related to at least one genes.

2 genes correlated to 'GENDER'.

KIF4B , DDX43

71 genes correlated to 'HISTOLOGICAL.TYPE'.

BSDC1 , CHMP4A , C16ORF63 , TP53I3 , UQCRB , ...

23 genes correlated to 'PATHOLOGICSPREAD(M)'.

ACTR6 , RASSF8 , RNF8 , CR2 , DEPDC1 , ...

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

CSTF2T , ERBB2 , TMPO , NPBWR1 , HOXC11 , ...

10 genes correlated to 'NEOADJUVANT.THERAPY'.

C16ORF73 , IRF8 , BRE , LOC100302650 , ZBTB38 , ...

No genes correlated to 'Time to Death', 'AGE', 'KARNOFSKY.PERFORMANCE.SCORE', 'PATHOLOGY.T', '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
GENDER	t test	N=2	male	N=0	female	N=2
KARNOFSKY PERFORMANCE SCORE	Spearman correlation test	N=0
HISTOLOGICAL TYPE	ANOVA test	N=71
PATHOLOGY T	Spearman correlation test	N=0
PATHOLOGY N	Spearman correlation test	N=0
PATHOLOGICSPREAD(M)	ANOVA test	N=23
TUMOR STAGE	Spearman correlation test	N=0
RADIATIONS RADIATION REGIMENINDICATION	t test	N=72	yes	N=65	no	N=7
NEOADJUVANT THERAPY	t test	N=10	yes	N=5	no	N=5

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)	0.1-173.8 (median=23.1)
	censored	N = 42
	death	N = 38

	Significant markers	N = 0

Clinical variable #2: 'AGE'

No gene related to 'AGE'.

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


AGE	Mean (SD)	69.5 (8.5)
	Significant markers	N = 0

Clinical variable #3: 'GENDER'

2 genes related to 'GENDER'.

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


GENDER	Labels	N
	FEMALE	26
	MALE	66

	Significant markers	N = 2
	Higher in MALE	0
	Higher in FEMALE	2

List of 2 genes differentially expressed by 'GENDER'

Table S4. Get Full Table List of 2 genes differentially expressed by 'GENDER'

	T(pos if higher in 'MALE')	ttestP	Q	AUC
KIF4B	-7.09	8.534e-09	0.00015	0.9009
DDX43	-5.79	1.617e-07	0.00284	0.8316

Figure S1. Get High-res Image As an example, this figure shows the association of KIF4B to 'GENDER'. P value = 8.53e-09 with T-test analysis.

Clinical variable #4: 'KARNOFSKY.PERFORMANCE.SCORE'

No gene related to 'KARNOFSKY.PERFORMANCE.SCORE'.

Table S5. Basic characteristics of clinical feature: 'KARNOFSKY.PERFORMANCE.SCORE'


KARNOFSKY.PERFORMANCE.SCORE	Mean (SD)	10.91 (27)
	Score	N
	0	18
	20	1
	40	1
	90	2

	Significant markers	N = 0

Clinical variable #5: 'HISTOLOGICAL.TYPE'

71 genes related to 'HISTOLOGICAL.TYPE'.

Table S6. Basic characteristics of clinical feature: 'HISTOLOGICAL.TYPE'


HISTOLOGICAL.TYPE	Labels	N
	LUNG BASALOID SQUAMOUS CELL CARCINOMA	3
	LUNG PAPILLARY SQUAMOUS CELL CARCINOMA	1
	LUNG SQUAMOUS CELL CARCINOMA- NOT OTHERWISE SPECIFIED (NOS)	88

	Significant markers	N = 71

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

Table S7. Get Full Table List of top 10 genes differentially expressed by 'HISTOLOGICAL.TYPE'

	ANOVA_P	Q
BSDC1	1.724e-53	3.03e-49
CHMP4A	4.429e-45	7.77e-41
C16ORF63	2.387e-23	4.19e-19
TP53I3	5.669e-23	9.95e-19
UQCRB	7.76e-23	1.36e-18
C13ORF34	2.149e-21	3.77e-17
C13ORF37	2.149e-21	3.77e-17
NME1	2.476e-18	4.34e-14
PINX1	1.15e-17	2.02e-13
CRLS1	6.637e-15	1.16e-10

Figure S2. Get High-res Image As an example, this figure shows the association of BSDC1 to 'HISTOLOGICAL.TYPE'. P value = 1.72e-53 with ANOVA analysis.

Clinical variable #6: 'PATHOLOGY.T'

No gene related to 'PATHOLOGY.T'.

Table S8. Basic characteristics of clinical feature: 'PATHOLOGY.T'


PATHOLOGY.T	Mean (SD)	1.89 (0.75)
		N
	T1	28
	T2	49
	T3	12
	T4	3

	Significant markers	N = 0

Clinical variable #7: 'PATHOLOGY.N'

No gene related to 'PATHOLOGY.N'.

Table S9. Basic characteristics of clinical feature: 'PATHOLOGY.N'


PATHOLOGY.N	Mean (SD)	0.43 (0.67)
		N
	N0	61
	N1	22
	N2	9

	Significant markers	N = 0

Clinical variable #8: 'PATHOLOGICSPREAD(M)'

23 genes related to 'PATHOLOGICSPREAD(M)'.

Table S10. Basic characteristics of clinical feature: 'PATHOLOGICSPREAD(M)'


PATHOLOGICSPREAD(M)	Labels	N
	M0	82
	M1	1
	MX	9

	Significant markers	N = 23

List of top 10 genes differentially expressed by 'PATHOLOGICSPREAD(M)'

Table S11. Get Full Table List of top 10 genes differentially expressed by 'PATHOLOGICSPREAD(M)'

	ANOVA_P	Q
ACTR6	6.376e-57	1.12e-52
RASSF8	1.742e-38	3.06e-34
RNF8	9.601e-36	1.68e-31
CR2	5.131e-18	9e-14
DEPDC1	3.806e-15	6.68e-11
IVD	5.919e-15	1.04e-10
ZNF30	9.524e-13	1.67e-08
RGS17	3.037e-11	5.33e-07
PGBD1	2.109e-10	3.7e-06
POLR2E	9.241e-10	1.62e-05

Figure S3. Get High-res Image As an example, this figure shows the association of ACTR6 to 'PATHOLOGICSPREAD(M)'. P value = 6.38e-57 with ANOVA analysis.

Clinical variable #9: 'TUMOR.STAGE'

No gene related to 'TUMOR.STAGE'.

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


TUMOR.STAGE	Mean (SD)	1.7 (0.81)
		N
	Stage 1	46
	Stage 2	27
	Stage 3	17
	Stage 4	1

	Significant markers	N = 0

Clinical variable #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

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

Table S13. Basic characteristics of clinical feature: 'RADIATIONS.RADIATION.REGIMENINDICATION'


RADIATIONS.RADIATION.REGIMENINDICATION	Labels	N
	NO	5
	YES	87

	Significant markers	N = 72
	Higher in YES	65
	Higher in NO	7

List of top 10 genes differentially expressed by 'RADIATIONS.RADIATION.REGIMENINDICATION'

Table S14. Get Full Table List of top 10 genes differentially expressed by 'RADIATIONS.RADIATION.REGIMENINDICATION'

	T(pos if higher in 'YES')	ttestP	Q	AUC
CSTF2T	9.2	3.769e-12	6.61e-08	0.9229
ERBB2	8.48	5.029e-12	8.83e-08	0.9425
TMPO	7.44	2.931e-10	5.14e-06	0.9609
NPBWR1	7.09	3.079e-10	5.4e-06	0.8552
HOXC11	8.81	3.251e-10	5.7e-06	0.9034
RYR1	7.21	3.432e-10	6.02e-06	0.8575
RNF135	6.74	1.447e-09	2.54e-05	0.869
SLFN11	6.6	3.191e-09	5.6e-05	0.6759
TRPS1	8.45	3.994e-09	7.01e-05	0.9057
HPGDS	-7.2	4.216e-09	7.4e-05	0.823

Figure S4. Get High-res Image As an example, this figure shows the association of CSTF2T to 'RADIATIONS.RADIATION.REGIMENINDICATION'. P value = 3.77e-12 with T-test analysis.

Clinical variable #11: 'NEOADJUVANT.THERAPY'

10 genes related to 'NEOADJUVANT.THERAPY'.

Table S15. Basic characteristics of clinical feature: 'NEOADJUVANT.THERAPY'


NEOADJUVANT.THERAPY	Labels	N
	NO	11
	YES	81

	Significant markers	N = 10
	Higher in YES	5
	Higher in NO	5

List of 10 genes differentially expressed by 'NEOADJUVANT.THERAPY'

Table S16. Get Full Table List of 10 genes differentially expressed by 'NEOADJUVANT.THERAPY'

	T(pos if higher in 'YES')	ttestP	Q	AUC
C16ORF73	-6.82	6.658e-09	0.000117	0.8284
IRF8	-7.38	2.094e-08	0.000368	0.8305
BRE	6.08	1.376e-07	0.00241	0.8541
LOC100302650	6.08	1.376e-07	0.00241	0.8541
ZBTB38	-5.71	1.526e-07	0.00268	0.7183
ATAD2	5.64	1.937e-07	0.0034	0.7531
HSF5	-5.73	3.584e-07	0.00629	0.8058
FAM122A	5.32	1.416e-06	0.0249	0.7542
CCDC9	5.18	1.694e-06	0.0297	0.7727
HRNR	-5.08	2.384e-06	0.0418	0.6801

Figure S5. Get High-res Image As an example, this figure shows the association of C16ORF73 to 'NEOADJUVANT.THERAPY'. P value = 6.66e-09 with T-test analysis.

Methods & Data

Input

Expresson data file = LUSC.meth.for_correlation.filtered_data.txt
Clinical data file = LUSC.clin.merged.picked.txt
Number of patients = 92
Number of genes = 17552
Number of clinical features = 11

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