Correlation between gene methylation status and clinical features
Lung Squamous Cell Carcinoma (Primary solid tumor)
23 September 2013  |  analyses__2013_09_23
Maintainer Information
Citation Information
doi:10.7908/C1GH9G8C
Maintained by Juok Cho (Broad Institute)
Cite as Broad Institute TCGA Genome Data Analysis Center (2013): Correlation between gene methylation status and clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1GH9G8C
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 20242 genes and 13 clinical features across 260 samples, statistically thresholded by Q value < 0.05, 9 clinical features related to at least one genes.

  • 8 genes correlated to 'AGE'.

    • KIAA1143 ,  KIF15 ,  APOD ,  TSC22D4 ,  EML4 ,  ...

  • 17 genes correlated to 'NEOPLASM.DISEASESTAGE'.

    • ORAI3 ,  FIGNL1 ,  RWDD2B ,  C20ORF12 ,  POLR3F ,  ...

  • 15 genes correlated to 'PATHOLOGY.M.STAGE'.

    • ORAI3 ,  FIGNL1 ,  TWISTNB ,  MTMR2 ,  AGPS ,  ...

  • 7 genes correlated to 'GENDER'.

    • ALG11__2 ,  UTP14C ,  ATP5J ,  GABPA__1 ,  KIF4B ,  ...

  • 1 gene correlated to 'KARNOFSKY.PERFORMANCE.SCORE'.

    • C7ORF63

  • 133 genes correlated to 'HISTOLOGICAL.TYPE'.

    • CAB39L ,  SETDB2 ,  DDX42 ,  CAB39L__1 ,  SETDB2__1 ,  ...

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

    • ERBB4 ,  TMEM231 ,  BASP1 ,  LOC285696 ,  NR6A1 ,  ...

  • 11 genes correlated to 'YEAROFTOBACCOSMOKINGONSET'.

    • SMARCAL1 ,  C18ORF22 ,  FAF1 ,  RPL3 ,  SNORD43 ,  ...

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

    • ZNF416 ,  ASB16__1 ,  C17ORF65__2 ,  EFHC1 ,  ZNF136 ,  ...

  • No genes correlated to 'Time to Death', 'PATHOLOGY.T.STAGE', 'PATHOLOGY.N.STAGE', and 'NUMBERPACKYEARSSMOKED'.

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=8 older N=2 younger N=6
NEOPLASM DISEASESTAGE ANOVA test N=17        
PATHOLOGY T STAGE Spearman correlation test   N=0        
PATHOLOGY N STAGE Spearman correlation test   N=0        
PATHOLOGY M STAGE ANOVA test N=15        
GENDER t test N=7 male N=4 female N=3
KARNOFSKY PERFORMANCE SCORE Spearman correlation test N=1 higher score N=1 lower score N=0
HISTOLOGICAL TYPE ANOVA test N=133        
RADIATIONS RADIATION REGIMENINDICATION t test N=61 yes N=59 no N=2
NUMBERPACKYEARSSMOKED Spearman correlation test   N=0        
YEAROFTOBACCOSMOKINGONSET Spearman correlation test N=11 higher yearoftobaccosmokingonset N=11 lower yearoftobaccosmokingonset N=0
COMPLETENESS OF RESECTION ANOVA test N=904        
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=9)
  censored N = 160
  death N = 70
     
  Significant markers N = 0
Clinical variable #2: 'AGE'

8 genes related to 'AGE'.

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

AGE Mean (SD) 67.58 (9)
  Significant markers N = 8
  pos. correlated 2
  neg. correlated 6
List of 8 genes significantly correlated to 'AGE' by Spearman correlation test

Table S3.  Get Full Table List of 8 genes significantly correlated to 'AGE' by Spearman correlation test

SpearmanCorr corrP Q
KIAA1143 0.3284 9.495e-08 0.00192
KIF15 0.3284 9.495e-08 0.00192
APOD -0.3139 3.632e-07 0.00735
TSC22D4 -0.3118 4.393e-07 0.00889
EML4 -0.3096 5.323e-07 0.0108
NECAB2 -0.3069 6.779e-07 0.0137
RBP1 -0.296 1.726e-06 0.0349
VPS13D__1 -0.2951 1.868e-06 0.0378

Figure S1.  Get High-res Image As an example, this figure shows the association of KIAA1143 to 'AGE'. P value = 9.5e-08 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #3: 'NEOPLASM.DISEASESTAGE'

17 genes related to 'NEOPLASM.DISEASESTAGE'.

Table S4.  Basic characteristics of clinical feature: 'NEOPLASM.DISEASESTAGE'

NEOPLASM.DISEASESTAGE Labels N
  STAGE I 2
  STAGE IA 52
  STAGE IB 76
  STAGE II 1
  STAGE IIA 41
  STAGE IIB 42
  STAGE IIIA 37
  STAGE IIIB 5
  STAGE IV 2
     
  Significant markers N = 17
List of top 10 genes differentially expressed by 'NEOPLASM.DISEASESTAGE'

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

ANOVA_P Q
ORAI3 4.863e-29 9.84e-25
FIGNL1 7.868e-23 1.59e-18
RWDD2B 1.755e-22 3.55e-18
C20ORF12 1.408e-19 2.85e-15
POLR3F 1.408e-19 2.85e-15
TWISTNB 6.609e-13 1.34e-08
MTMR2 3.245e-12 6.57e-08
AGPS 7.81e-12 1.58e-07
LOC100130691 7.81e-12 1.58e-07
LETMD1 5.734e-11 1.16e-06

Figure S2.  Get High-res Image As an example, this figure shows the association of ORAI3 to 'NEOPLASM.DISEASESTAGE'. P value = 4.86e-29 with ANOVA analysis.

Clinical variable #4: 'PATHOLOGY.T.STAGE'

No gene related to 'PATHOLOGY.T.STAGE'.

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

PATHOLOGY.T.STAGE Mean (SD) 1.9 (0.7)
  N
  1 70
  2 152
  3 31
  4 7
     
  Significant markers N = 0
Clinical variable #5: 'PATHOLOGY.N.STAGE'

No gene related to 'PATHOLOGY.N.STAGE'.

Table S7.  Basic characteristics of clinical feature: 'PATHOLOGY.N.STAGE'

PATHOLOGY.N.STAGE Mean (SD) 0.44 (0.65)
  N
  0 164
  1 69
  2 22
     
  Significant markers N = 0
Clinical variable #6: 'PATHOLOGY.M.STAGE'

15 genes related to 'PATHOLOGY.M.STAGE'.

Table S8.  Basic characteristics of clinical feature: 'PATHOLOGY.M.STAGE'

PATHOLOGY.M.STAGE Labels N
  M0 209
  M1 2
  MX 47
     
  Significant markers N = 15
List of top 10 genes differentially expressed by 'PATHOLOGY.M.STAGE'

Table S9.  Get Full Table List of top 10 genes differentially expressed by 'PATHOLOGY.M.STAGE'

ANOVA_P Q
ORAI3 4.336e-34 8.78e-30
FIGNL1 7.567e-28 1.53e-23
TWISTNB 1.011e-16 2.05e-12
MTMR2 9.493e-16 1.92e-11
AGPS 7.686e-14 1.56e-09
LOC100130691 7.686e-14 1.56e-09
PECR 1.229e-13 2.49e-09
TMEM169 1.229e-13 2.49e-09
SLC12A7 1.149e-07 0.00233
L3MBTL2 1.484e-07 0.003

Figure S3.  Get High-res Image As an example, this figure shows the association of ORAI3 to 'PATHOLOGY.M.STAGE'. P value = 4.34e-34 with ANOVA analysis.

Clinical variable #7: 'GENDER'

7 genes related to 'GENDER'.

Table S10.  Basic characteristics of clinical feature: 'GENDER'

GENDER Labels N
  FEMALE 64
  MALE 196
     
  Significant markers N = 7
  Higher in MALE 4
  Higher in FEMALE 3
List of 7 genes differentially expressed by 'GENDER'

Table S11.  Get Full Table List of 7 genes differentially expressed by 'GENDER'

T(pos if higher in 'MALE') ttestP Q AUC
ALG11__2 11.64 3.697e-18 7.48e-14 0.9569
UTP14C 11.64 3.697e-18 7.48e-14 0.9569
ATP5J 9.01 6.874e-17 1.39e-12 0.8018
GABPA__1 9.01 6.874e-17 1.39e-12 0.8018
KIF4B -9.77 7.985e-16 1.62e-11 0.8573
YARS2 -6.4 8.241e-09 0.000167 0.7551
DDX43 -5.75 5.793e-08 0.00117 0.7427

Figure S4.  Get High-res Image As an example, this figure shows the association of ALG11__2 to 'GENDER'. P value = 3.7e-18 with T-test analysis.

Clinical variable #8: 'KARNOFSKY.PERFORMANCE.SCORE'

One gene related to 'KARNOFSKY.PERFORMANCE.SCORE'.

Table S12.  Basic characteristics of clinical feature: 'KARNOFSKY.PERFORMANCE.SCORE'

KARNOFSKY.PERFORMANCE.SCORE Mean (SD) 29.35 (40)
  Significant markers N = 1
  pos. correlated 1
  neg. correlated 0
List of one gene significantly correlated to 'KARNOFSKY.PERFORMANCE.SCORE' by Spearman correlation test

Table S13.  Get Full Table List of one gene significantly correlated to 'KARNOFSKY.PERFORMANCE.SCORE' by Spearman correlation test

SpearmanCorr corrP Q
C7ORF63 0.7449 1.54e-06 0.0312

Figure S5.  Get High-res Image As an example, this figure shows the association of C7ORF63 to 'KARNOFSKY.PERFORMANCE.SCORE'. P value = 1.54e-06 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #9: 'HISTOLOGICAL.TYPE'

133 genes related to 'HISTOLOGICAL.TYPE'.

Table S14.  Basic characteristics of clinical feature: 'HISTOLOGICAL.TYPE'

HISTOLOGICAL.TYPE Labels N
  LUNG BASALOID SQUAMOUS CELL CARCINOMA 9
  LUNG PAPILLARY SQUAMOUS CELL CARICNOMA 4
  LUNG SMALL CELL SQUAMOUS CELL CARCINOMA 1
  LUNG SQUAMOUS CELL CARCINOMA- NOT OTHERWISE SPECIFIED (NOS) 246
     
  Significant markers N = 133
List of top 10 genes differentially expressed by 'HISTOLOGICAL.TYPE'

Table S15.  Get Full Table List of top 10 genes differentially expressed by 'HISTOLOGICAL.TYPE'

ANOVA_P Q
CAB39L 1.733e-75 3.51e-71
SETDB2 1.733e-75 3.51e-71
DDX42 2.229e-75 4.51e-71
CAB39L__1 1.262e-55 2.56e-51
SETDB2__1 1.262e-55 2.56e-51
EP300 5.298e-51 1.07e-46
OSCAR 3.088e-28 6.25e-24
QKI 7.525e-28 1.52e-23
WDR1 6.924e-26 1.4e-21
DNAJB4 1.078e-24 2.18e-20

Figure S6.  Get High-res Image As an example, this figure shows the association of CAB39L to 'HISTOLOGICAL.TYPE'. P value = 1.73e-75 with ANOVA analysis.

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

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

Table S16.  Basic characteristics of clinical feature: 'RADIATIONS.RADIATION.REGIMENINDICATION'

RADIATIONS.RADIATION.REGIMENINDICATION Labels N
  NO 10
  YES 250
     
  Significant markers N = 61
  Higher in YES 59
  Higher in NO 2
List of top 10 genes differentially expressed by 'RADIATIONS.RADIATION.REGIMENINDICATION'

Table S17.  Get Full Table List of top 10 genes differentially expressed by 'RADIATIONS.RADIATION.REGIMENINDICATION'

T(pos if higher in 'YES') ttestP Q AUC
ERBB4 7.5 4.643e-12 9.4e-08 0.688
TMEM231 6.99 2.613e-11 5.29e-07 0.7248
BASP1 7.02 2.074e-10 4.2e-06 0.7728
LOC285696 7.02 2.074e-10 4.2e-06 0.7728
NR6A1 6.77 4.542e-10 9.19e-06 0.7424
PNMT 6.94 2.591e-09 5.24e-05 0.772
STK38 8.11 2.989e-09 6.05e-05 0.8069
BRSK2 7.59 4.054e-09 8.2e-05 0.7888
LDHB 5.99 9.875e-09 2e-04 0.7908
KIAA2018 7.58 1.213e-08 0.000246 0.7924

Figure S7.  Get High-res Image As an example, this figure shows the association of ERBB4 to 'RADIATIONS.RADIATION.REGIMENINDICATION'. P value = 4.64e-12 with T-test analysis.

Clinical variable #11: 'NUMBERPACKYEARSSMOKED'

No gene related to 'NUMBERPACKYEARSSMOKED'.

Table S18.  Basic characteristics of clinical feature: 'NUMBERPACKYEARSSMOKED'

NUMBERPACKYEARSSMOKED Mean (SD) 51.26 (29)
  Significant markers N = 0
Clinical variable #12: 'YEAROFTOBACCOSMOKINGONSET'

11 genes related to 'YEAROFTOBACCOSMOKINGONSET'.

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

YEAROFTOBACCOSMOKINGONSET Mean (SD) 1960.22 (12)
  Significant markers N = 11
  pos. correlated 11
  neg. correlated 0
List of top 10 genes significantly correlated to 'YEAROFTOBACCOSMOKINGONSET' by Spearman correlation test

Table S20.  Get Full Table List of top 10 genes significantly correlated to 'YEAROFTOBACCOSMOKINGONSET' by Spearman correlation test

SpearmanCorr corrP Q
SMARCAL1 0.3757 2.571e-07 0.00521
C18ORF22 0.369 4.322e-07 0.00875
FAF1 0.354 1.345e-06 0.0272
RPL3 0.3537 1.366e-06 0.0277
SNORD43 0.3537 1.366e-06 0.0277
SEL1L3 0.3482 2.038e-06 0.0412
RBBP9 0.3478 2.103e-06 0.0426
COX6B1 0.3472 2.194e-06 0.0444
RNU5D 0.3457 2.452e-06 0.0496
RNU5E 0.3457 2.452e-06 0.0496

Figure S8.  Get High-res Image As an example, this figure shows the association of SMARCAL1 to 'YEAROFTOBACCOSMOKINGONSET'. P value = 2.57e-07 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #13: 'COMPLETENESS.OF.RESECTION'

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

Table S21.  Basic characteristics of clinical feature: 'COMPLETENESS.OF.RESECTION'

COMPLETENESS.OF.RESECTION Labels N
  R0 198
  R1 4
  R2 2
  RX 13
     
  Significant markers N = 904
List of top 10 genes differentially expressed by 'COMPLETENESS.OF.RESECTION'

Table S22.  Get Full Table List of top 10 genes differentially expressed by 'COMPLETENESS.OF.RESECTION'

ANOVA_P Q
ZNF416 2.724e-31 5.51e-27
ASB16__1 3.18e-31 6.44e-27
C17ORF65__2 3.18e-31 6.44e-27
EFHC1 3.561e-30 7.21e-26
ZNF136 1.586e-29 3.21e-25
EME2 1.792e-28 3.63e-24
TSEN2 2.696e-28 5.46e-24
C21ORF58 2.884e-28 5.84e-24
TAGLN 3.995e-27 8.08e-23
RRP1B__1 9.993e-27 2.02e-22

Figure S9.  Get High-res Image As an example, this figure shows the association of ZNF416 to 'COMPLETENESS.OF.RESECTION'. P value = 2.72e-31 with ANOVA analysis.

Methods & Data
Input

  • Expresson data file = LUSC-TP.meth.by_min_expr_corr.data.txt

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

  • Number of patients = 260

  • Number of genes = 20242

  • Number of clinical features = 13

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

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

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

In addition to the links below, the full results of the analysis summarized in this report can also be downloaded programmatically using firehose_get, or interactively from either the Broad GDAC website or TCGA Data Coordination Center Portal.

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] Howell, D, Statistical Methods for Psychology. (5th ed.), Duxbury Press:324-5 (2002)
[4] Lehmann and Romano, Testing Statistical Hypotheses (3E ed.), New York: Springer. ISBN 0387988645 (2005)
[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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