Correlation between gene methylation status and clinical features
Lung Squamous Cell Carcinoma (Primary solid tumor)
21 April 2013  |  analyses__2013_04_21
Maintainer Information
Citation Information
doi:10.7908/C1NK3C0H
Maintained by Juok Cho (Broad Institute)
Cite as Broad Institute TCGA Genome Data Analysis Center (2013): Lung Squamous Cell Carcinoma (Primary solid tumor cohort) - 21 April 2013: Correlation between gene methylation status and clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1NK3C0H
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 17536 genes and 14 clinical features across 187 samples, statistically thresholded by Q value < 0.05, 7 clinical features related to at least one genes.

  • 2 genes correlated to 'AGE'.

    • EML4 ,  TSC22D4

  • 4 genes correlated to 'GENDER'.

    • KIF4B ,  UTP14C ,  DDX43 ,  YARS2

  • 149 genes correlated to 'HISTOLOGICAL.TYPE'.

    • SETDB2 ,  SLC27A1 ,  BSDC1 ,  CHMP4A ,  EP300 ,  ...

  • 14 genes correlated to 'PATHOLOGICSPREAD(M)'.

    • PECR ,  TMEM169 ,  ZNF30 ,  IVD ,  L3MBTL2 ,  ...

  • 39 genes correlated to 'RADIATIONS.RADIATION.REGIMENINDICATION'.

    • UNC13A ,  PAPPA ,  APBB1IP ,  TMEM200C ,  TMEM231 ,  ...

  • 43 genes correlated to 'TOBACCOSMOKINGHISTORYINDICATOR'.

    • TP53I3 ,  AP1S3 ,  PI4K2B ,  SYMPK ,  PCNP ,  ...

  • 692 genes correlated to 'COMPLETENESS.OF.RESECTION'.

    • DDX54 ,  EP400 ,  ASB16 ,  ZNF416 ,  NAGLU ,  ...

  • No genes correlated to 'Time to Death', 'KARNOFSKY.PERFORMANCE.SCORE', 'PATHOLOGY.T', 'PATHOLOGY.N', 'TUMOR.STAGE', 'NUMBERPACKYEARSSMOKED', and 'YEAROFTOBACCOSMOKINGONSET'.

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=2 older N=0 younger N=2
GENDER t test N=4 male N=1 female N=3
KARNOFSKY PERFORMANCE SCORE Spearman correlation test   N=0        
HISTOLOGICAL TYPE ANOVA test N=149        
PATHOLOGY T Spearman correlation test   N=0        
PATHOLOGY N Spearman correlation test   N=0        
PATHOLOGICSPREAD(M) ANOVA test N=14        
TUMOR STAGE Spearman correlation test   N=0        
RADIATIONS RADIATION REGIMENINDICATION t test N=39 yes N=37 no N=2
NUMBERPACKYEARSSMOKED Spearman correlation test   N=0        
TOBACCOSMOKINGHISTORYINDICATOR ANOVA test N=43        
YEAROFTOBACCOSMOKINGONSET Spearman correlation test   N=0        
COMPLETENESS OF RESECTION ANOVA test N=692        
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) 0-173.8 (median=11.6)
  censored N = 105
  death N = 69
     
  Significant markers N = 0
Clinical variable #2: 'AGE'

2 genes related to 'AGE'.

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

AGE Mean (SD) 68.18 (8.9)
  Significant markers N = 2
  pos. correlated 0
  neg. correlated 2
List of 2 genes significantly correlated to 'AGE' by Spearman correlation test

Table S3.  Get Full Table List of 2 genes significantly correlated to 'AGE' by Spearman correlation test

SpearmanCorr corrP Q
EML4 -0.3482 1.788e-06 0.0314
TSC22D4 -0.3449 2.268e-06 0.0398

Figure S1.  Get High-res Image As an example, this figure shows the association of EML4 to 'AGE'. P value = 1.79e-06 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #3: 'GENDER'

4 genes related to 'GENDER'.

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

GENDER Labels N
  FEMALE 47
  MALE 140
     
  Significant markers N = 4
  Higher in MALE 1
  Higher in FEMALE 3
List of 4 genes differentially expressed by 'GENDER'

Table S5.  Get Full Table List of 4 genes differentially expressed by 'GENDER'

T(pos if higher in 'MALE') ttestP Q AUC
KIF4B -9.8 2.47e-15 4.33e-11 0.8973
UTP14C 9.08 2.347e-12 4.11e-08 0.9427
DDX43 -5.47 2.442e-07 0.00428 0.7778
YARS2 -5.34 1.285e-06 0.0225 0.7641

Figure S2.  Get High-res Image As an example, this figure shows the association of KIF4B to 'GENDER'. P value = 2.47e-15 with T-test analysis.

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

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

Table S6.  Basic characteristics of clinical feature: 'KARNOFSKY.PERFORMANCE.SCORE'

KARNOFSKY.PERFORMANCE.SCORE Mean (SD) 29.35 (40)
  Significant markers N = 0
Clinical variable #5: 'HISTOLOGICAL.TYPE'

149 genes related to 'HISTOLOGICAL.TYPE'.

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

HISTOLOGICAL.TYPE Labels N
  LUNG BASALOID SQUAMOUS CELL CARCINOMA 4
  LUNG PAPILLARY SQUAMOUS CELL CARCINOMA 1
  LUNG SMALL CELL SQUAMOUS CELL CARCINOMA 1
  LUNG SQUAMOUS CELL CARCINOMA- NOT OTHERWISE SPECIFIED (NOS) 181
     
  Significant markers N = 149
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
SETDB2 2.485e-105 4.36e-101
SLC27A1 1.148e-91 2.01e-87
BSDC1 2.273e-65 3.99e-61
CHMP4A 3.362e-59 5.9e-55
EP300 1.35e-41 2.37e-37
C9ORF114 4.733e-38 8.3e-34
UQCRB 6.745e-32 1.18e-27
NACC2 2.508e-28 4.4e-24
WDR1 3.547e-28 6.22e-24
FNDC3A 6.723e-26 1.18e-21

Figure S3.  Get High-res Image As an example, this figure shows the association of SETDB2 to 'HISTOLOGICAL.TYPE'. P value = 2.48e-105 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) 1.91 (0.74)
  N
  T1 52
  T2 106
  T3 22
  T4 7
     
  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) 0.47 (0.64)
  N
  N0 113
  N1 57
  N2 15
     
  Significant markers N = 0
Clinical variable #8: 'PATHOLOGICSPREAD(M)'

14 genes related to 'PATHOLOGICSPREAD(M)'.

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

PATHOLOGICSPREAD(M) Labels N
  M0 157
  M1 1
  MX 27
     
  Significant markers N = 14
List of top 10 genes differentially expressed by 'PATHOLOGICSPREAD(M)'

Table S12.  Get Full Table List of top 10 genes differentially expressed by 'PATHOLOGICSPREAD(M)'

ANOVA_P Q
PECR 3.068e-17 5.38e-13
TMEM169 3.068e-17 5.38e-13
ZNF30 5.975e-15 1.05e-10
IVD 1.193e-14 2.09e-10
L3MBTL2 4.544e-14 7.97e-10
NEAT1 2.172e-09 3.81e-05
SCAMP5 3.607e-09 6.32e-05
POLR2E 9.76e-09 0.000171
RNF8 1.005e-08 0.000176
CHST10 1.055e-08 0.000185

Figure S4.  Get High-res Image As an example, this figure shows the association of PECR to 'PATHOLOGICSPREAD(M)'. P value = 3.07e-17 with ANOVA analysis.

Clinical variable #9: 'TUMOR.STAGE'

No gene related to 'TUMOR.STAGE'.

Table S13.  Basic characteristics of clinical feature: 'TUMOR.STAGE'

TUMOR.STAGE Mean (SD) 1.68 (0.77)
  N
  Stage 1 92
  Stage 2 60
  Stage 3 31
  Stage 4 1
     
  Significant markers N = 0
Clinical variable #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

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

Table S14.  Basic characteristics of clinical feature: 'RADIATIONS.RADIATION.REGIMENINDICATION'

RADIATIONS.RADIATION.REGIMENINDICATION Labels N
  NO 9
  YES 178
     
  Significant markers N = 39
  Higher in YES 37
  Higher in NO 2
List of top 10 genes differentially expressed by 'RADIATIONS.RADIATION.REGIMENINDICATION'

Table S15.  Get Full Table List of top 10 genes differentially expressed by 'RADIATIONS.RADIATION.REGIMENINDICATION'

T(pos if higher in 'YES') ttestP Q AUC
UNC13A 8.84 7.076e-12 1.24e-07 0.8177
PAPPA 7.36 9.793e-12 1.72e-07 0.7815
APBB1IP 7.36 1.068e-10 1.87e-06 0.6685
TMEM200C 6.21 3.537e-09 6.2e-05 0.6623
TMEM231 6.12 5.368e-09 9.41e-05 0.726
PNMT 6.1 6.148e-09 0.000108 0.8333
ASTN2 6.14 7.189e-09 0.000126 0.6692
POU4F3 7.24 1.078e-08 0.000189 0.8558
HIST3H2BB 5.99 1.136e-08 0.000199 0.6417
FLJ35390 5.96 1.247e-08 0.000219 0.7203

Figure S5.  Get High-res Image As an example, this figure shows the association of UNC13A to 'RADIATIONS.RADIATION.REGIMENINDICATION'. P value = 7.08e-12 with T-test analysis.

Clinical variable #11: 'NUMBERPACKYEARSSMOKED'

No gene related to 'NUMBERPACKYEARSSMOKED'.

Table S16.  Basic characteristics of clinical feature: 'NUMBERPACKYEARSSMOKED'

NUMBERPACKYEARSSMOKED Mean (SD) 51.49 (30)
  Significant markers N = 0
Clinical variable #12: 'TOBACCOSMOKINGHISTORYINDICATOR'

43 genes related to 'TOBACCOSMOKINGHISTORYINDICATOR'.

Table S17.  Basic characteristics of clinical feature: 'TOBACCOSMOKINGHISTORYINDICATOR'

TOBACCOSMOKINGHISTORYINDICATOR Labels N
  CURRENT REFORMED SMOKER FOR < OR = 15 YEARS 82
  CURRENT REFORMED SMOKER FOR > 15 YEARS 39
  CURRENT SMOKER 53
  LIFELONG NON-SMOKER 8
     
  Significant markers N = 43
List of top 10 genes differentially expressed by 'TOBACCOSMOKINGHISTORYINDICATOR'

Table S18.  Get Full Table List of top 10 genes differentially expressed by 'TOBACCOSMOKINGHISTORYINDICATOR'

ANOVA_P Q
TP53I3 2.61e-09 4.58e-05
AP1S3 6.055e-09 0.000106
PI4K2B 1.088e-08 0.000191
SYMPK 2.341e-08 0.000411
PCNP 2.887e-08 0.000506
CA13 3.194e-08 0.00056
BBX 3.353e-08 0.000588
C1ORF174 4.995e-08 0.000876
SLC35A1 7.1e-08 0.00124
LNP1 1.075e-07 0.00188

Figure S6.  Get High-res Image As an example, this figure shows the association of TP53I3 to 'TOBACCOSMOKINGHISTORYINDICATOR'. P value = 2.61e-09 with ANOVA analysis.

Clinical variable #13: 'YEAROFTOBACCOSMOKINGONSET'

No gene related to 'YEAROFTOBACCOSMOKINGONSET'.

Table S19.  Basic characteristics of clinical feature: 'YEAROFTOBACCOSMOKINGONSET'

YEAROFTOBACCOSMOKINGONSET Mean (SD) 1958.7 (12)
  Significant markers N = 0
Clinical variable #14: 'COMPLETENESS.OF.RESECTION'

692 genes related to 'COMPLETENESS.OF.RESECTION'.

Table S20.  Basic characteristics of clinical feature: 'COMPLETENESS.OF.RESECTION'

COMPLETENESS.OF.RESECTION Labels N
  R0 133
  R1 3
  R2 2
  RX 11
     
  Significant markers N = 692
List of top 10 genes differentially expressed by 'COMPLETENESS.OF.RESECTION'

Table S21.  Get Full Table List of top 10 genes differentially expressed by 'COMPLETENESS.OF.RESECTION'

ANOVA_P Q
DDX54 3.201e-22 5.61e-18
EP400 2.038e-21 3.57e-17
ASB16 2.069e-21 3.63e-17
ZNF416 2.714e-21 4.76e-17
NAGLU 3.197e-21 5.61e-17
ANKRD13D 3.26e-21 5.72e-17
SS18L1 3.275e-21 5.74e-17
NCOR2 3.347e-21 5.87e-17
TRIM56 5.288e-21 9.27e-17
ARID3A 6.281e-21 1.1e-16

Figure S7.  Get High-res Image As an example, this figure shows the association of DDX54 to 'COMPLETENESS.OF.RESECTION'. P value = 3.2e-22 with ANOVA analysis.

Methods & Data
Input

  • Expresson data file = LUSC-TP.meth.for_correlation.filtered_data.txt

  • Clinical data file = LUSC-TP.clin.merged.picked.txt

  • Number of patients = 187

  • Number of genes = 17536

  • Number of clinical features = 14

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)
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