Correlation between gene methylation status and clinical features
Lung Squamous Cell Carcinoma (Primary solid tumor)
23 May 2013  |  analyses__2013_05_23
Maintainer Information
Citation Information
doi:10.7908/C1RJ4GHF
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/C1RJ4GHF
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 20217 genes and 12 clinical features across 203 samples, statistically thresholded by Q value < 0.05, 9 clinical features related to at least one genes.

  • 3 genes correlated to 'AGE'.

    • TSC22D4 ,  KIAA1143 ,  KIF15

  • 5 genes correlated to 'GENDER'.

    • KIF4B ,  ALG11__2 ,  UTP14C ,  YARS2 ,  KIAA1529

  • 162 genes correlated to 'HISTOLOGICAL.TYPE'.

    • SLC27A1 ,  BSDC1 ,  CAB39L ,  SETDB2 ,  CHMP4A__1 ,  ...

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

    • PNMT ,  TMEM231 ,  TDRKH ,  STK38 ,  MSI1 ,  ...

  • 1 gene correlated to 'YEAROFTOBACCOSMOKINGONSET'.

    • GRWD1

  • 17 genes correlated to 'DISTANT.METASTASIS'.

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

  • 7 genes correlated to 'LYMPH.NODE.METASTASIS'.

    • CCDC53 ,  MED12L__2 ,  P2RY13 ,  RASGEF1C ,  FLJ36777 ,  ...

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

    • CCDC42B__1 ,  DDX54 ,  EP400 ,  ASB16 ,  C17ORF65__1 ,  ...

  • 21 genes correlated to 'NEOPLASM.DISEASESTAGE'.

    • KIAA0101 ,  C20ORF12 ,  POLR3F ,  RWDD2B ,  ZNF30 ,  ...

  • No genes correlated to 'Time to Death', 'KARNOFSKY.PERFORMANCE.SCORE', 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=3 older N=2 younger N=1
GENDER t test N=5 male N=2 female N=3
KARNOFSKY PERFORMANCE SCORE Spearman correlation test   N=0        
HISTOLOGICAL TYPE ANOVA test N=162        
RADIATIONS RADIATION REGIMENINDICATION t test N=31 yes N=31 no N=0
NUMBERPACKYEARSSMOKED Spearman correlation test   N=0        
YEAROFTOBACCOSMOKINGONSET Spearman correlation test N=1 higher yearoftobaccosmokingonset N=1 lower yearoftobaccosmokingonset N=0
DISTANT METASTASIS ANOVA test N=17        
LYMPH NODE METASTASIS ANOVA test N=7        
COMPLETENESS OF RESECTION ANOVA test N=818        
NEOPLASM DISEASESTAGE ANOVA test N=21        
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=10.8)
  censored N = 119
  death N = 71
     
  Significant markers N = 0
Clinical variable #2: 'AGE'

3 genes related to 'AGE'.

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

AGE Mean (SD) 68.26 (8.8)
  Significant markers N = 3
  pos. correlated 2
  neg. correlated 1
List of 3 genes significantly correlated to 'AGE' by Spearman correlation test

Table S3.  Get Full Table List of 3 genes significantly correlated to 'AGE' by Spearman correlation test

SpearmanCorr corrP Q
TSC22D4 -0.3529 4.206e-07 0.0085
KIAA1143 0.3384 1.311e-06 0.0265
KIF15 0.3384 1.311e-06 0.0265

Figure S1.  Get High-res Image As an example, this figure shows the association of TSC22D4 to 'AGE'. P value = 4.21e-07 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #3: 'GENDER'

5 genes related to 'GENDER'.

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

GENDER Labels N
  FEMALE 48
  MALE 155
     
  Significant markers N = 5
  Higher in MALE 2
  Higher in FEMALE 3
List of 5 genes differentially expressed by 'GENDER'

Table S5.  Get Full Table List of 5 genes differentially expressed by 'GENDER'

T(pos if higher in 'MALE') ttestP Q AUC
KIF4B -9.96 1.009e-15 2.04e-11 0.8934
ALG11__2 9.72 2.266e-13 4.58e-09 0.9628
UTP14C 9.72 2.266e-13 4.58e-09 0.9628
YARS2 -5.4 9.806e-07 0.0198 0.7594
KIAA1529 -4.96 1.893e-06 0.0383 0.6805

Figure S2.  Get High-res Image As an example, this figure shows the association of KIF4B to 'GENDER'. P value = 1.01e-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'

162 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 CARICNOMA 1
  LUNG SMALL CELL SQUAMOUS CELL CARCINOMA 1
  LUNG SQUAMOUS CELL CARCINOMA- NOT OTHERWISE SPECIFIED (NOS) 197
     
  Significant markers N = 162
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
SLC27A1 3.835e-99 7.75e-95
BSDC1 1.6e-69 3.23e-65
CAB39L 4.878e-60 9.86e-56
SETDB2 4.878e-60 9.86e-56
CHMP4A__1 1.743e-59 3.52e-55
EP300 1.749e-44 3.54e-40
C9ORF114 4.808e-41 9.72e-37
ENDOG__1 4.808e-41 9.72e-37
UQCRB 3.98e-33 8.04e-29
WDR1 2.715e-30 5.49e-26

Figure S3.  Get High-res Image As an example, this figure shows the association of SLC27A1 to 'HISTOLOGICAL.TYPE'. P value = 3.84e-99 with ANOVA analysis.

Clinical variable #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

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

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

RADIATIONS.RADIATION.REGIMENINDICATION Labels N
  NO 10
  YES 193
     
  Significant markers N = 31
  Higher in YES 31
  Higher in NO 0
List of top 10 genes differentially expressed by 'RADIATIONS.RADIATION.REGIMENINDICATION'

Table S10.  Get Full Table List of top 10 genes differentially expressed by 'RADIATIONS.RADIATION.REGIMENINDICATION'

T(pos if higher in 'YES') ttestP Q AUC
PNMT 6.42 9.409e-10 1.9e-05 0.8301
TMEM231 6.2 3.189e-09 6.45e-05 0.6969
TDRKH 6.05 1.065e-08 0.000215 0.6772
STK38 6.95 2.398e-08 0.000485 0.778
MSI1 6.08 1.011e-07 0.00204 0.7347
UNC13A 6.83 2.453e-07 0.00496 0.7674
ENPP4 5.27 3.548e-07 0.00717 0.5927
KIAA2018 6.59 3.771e-07 0.00762 0.7746
MRPL36__1 5.21 5.082e-07 0.0103 0.5767
NDUFS6 5.21 5.082e-07 0.0103 0.5767

Figure S4.  Get High-res Image As an example, this figure shows the association of PNMT to 'RADIATIONS.RADIATION.REGIMENINDICATION'. P value = 9.41e-10 with T-test analysis.

Clinical variable #7: 'NUMBERPACKYEARSSMOKED'

No gene related to 'NUMBERPACKYEARSSMOKED'.

Table S11.  Basic characteristics of clinical feature: 'NUMBERPACKYEARSSMOKED'

NUMBERPACKYEARSSMOKED Mean (SD) 50.61 (30)
  Significant markers N = 0
Clinical variable #8: 'YEAROFTOBACCOSMOKINGONSET'

One gene related to 'YEAROFTOBACCOSMOKINGONSET'.

Table S12.  Basic characteristics of clinical feature: 'YEAROFTOBACCOSMOKINGONSET'

YEAROFTOBACCOSMOKINGONSET Mean (SD) 1959.25 (12)
  Significant markers N = 1
  pos. correlated 1
  neg. correlated 0
List of one gene significantly correlated to 'YEAROFTOBACCOSMOKINGONSET' by Spearman correlation test

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

SpearmanCorr corrP Q
GRWD1 0.3879 1.841e-06 0.0372

Figure S5.  Get High-res Image As an example, this figure shows the association of GRWD1 to 'YEAROFTOBACCOSMOKINGONSET'. P value = 1.84e-06 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #9: 'DISTANT.METASTASIS'

17 genes related to 'DISTANT.METASTASIS'.

Table S14.  Basic characteristics of clinical feature: 'DISTANT.METASTASIS'

DISTANT.METASTASIS Labels N
  M0 163
  M1 1
  MX 37
     
  Significant markers N = 17
List of top 10 genes differentially expressed by 'DISTANT.METASTASIS'

Table S15.  Get Full Table List of top 10 genes differentially expressed by 'DISTANT.METASTASIS'

ANOVA_P Q
ZNF30 6.699e-16 1.35e-11
L3MBTL2 6.151e-15 1.24e-10
IVD 1.195e-14 2.42e-10
PECR 2.305e-11 4.66e-07
TMEM169 2.305e-11 4.66e-07
NEAT1 8.219e-10 1.66e-05
SCAMP5 1.632e-09 3.3e-05
CHST10 2.018e-09 4.08e-05
POLR2E 5.373e-09 0.000109
CERKL 1.027e-08 0.000208

Figure S6.  Get High-res Image As an example, this figure shows the association of ZNF30 to 'DISTANT.METASTASIS'. P value = 6.7e-16 with ANOVA analysis.

Clinical variable #10: 'LYMPH.NODE.METASTASIS'

7 genes related to 'LYMPH.NODE.METASTASIS'.

Table S16.  Basic characteristics of clinical feature: 'LYMPH.NODE.METASTASIS'

LYMPH.NODE.METASTASIS Labels N
  N0 127
  N1 58
  N2 16
  NX 2
     
  Significant markers N = 7
List of 7 genes differentially expressed by 'LYMPH.NODE.METASTASIS'

Table S17.  Get Full Table List of 7 genes differentially expressed by 'LYMPH.NODE.METASTASIS'

ANOVA_P Q
CCDC53 8.448e-27 1.71e-22
MED12L__2 2.617e-12 5.29e-08
P2RY13 2.617e-12 5.29e-08
RASGEF1C 1.489e-10 3.01e-06
FLJ36777 2.51e-07 0.00507
C1ORF49 6.933e-07 0.014
PCTP 2.414e-06 0.0488

Figure S7.  Get High-res Image As an example, this figure shows the association of CCDC53 to 'LYMPH.NODE.METASTASIS'. P value = 8.45e-27 with ANOVA analysis.

Clinical variable #11: 'COMPLETENESS.OF.RESECTION'

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

Table S18.  Basic characteristics of clinical feature: 'COMPLETENESS.OF.RESECTION'

COMPLETENESS.OF.RESECTION Labels N
  R0 142
  R1 3
  R2 2
  RX 11
     
  Significant markers N = 818
List of top 10 genes differentially expressed by 'COMPLETENESS.OF.RESECTION'

Table S19.  Get Full Table List of top 10 genes differentially expressed by 'COMPLETENESS.OF.RESECTION'

ANOVA_P Q
CCDC42B__1 2.302e-23 4.65e-19
DDX54 2.302e-23 4.65e-19
EP400 9.54e-23 1.93e-18
ASB16 1.008e-22 2.04e-18
C17ORF65__1 1.008e-22 2.04e-18
ZNF416 1.282e-22 2.59e-18
NAGLU 1.525e-22 3.08e-18
ANKRD13D 1.53e-22 3.09e-18
SS18L1__1 1.605e-22 3.24e-18
NCOR2 1.628e-22 3.29e-18

Figure S8.  Get High-res Image As an example, this figure shows the association of CCDC42B__1 to 'COMPLETENESS.OF.RESECTION'. P value = 2.3e-23 with ANOVA analysis.

Clinical variable #12: 'NEOPLASM.DISEASESTAGE'

21 genes related to 'NEOPLASM.DISEASESTAGE'.

Table S20.  Basic characteristics of clinical feature: 'NEOPLASM.DISEASESTAGE'

NEOPLASM.DISEASESTAGE Labels N
  STAGE I 1
  STAGE IA 41
  STAGE IB 60
  STAGE II 1
  STAGE IIA 27
  STAGE IIB 36
  STAGE IIIA 28
  STAGE IIIB 5
  STAGE IV 1
     
  Significant markers N = 21
List of top 10 genes differentially expressed by 'NEOPLASM.DISEASESTAGE'

Table S21.  Get Full Table List of top 10 genes differentially expressed by 'NEOPLASM.DISEASESTAGE'

ANOVA_P Q
KIAA0101 1.773e-20 3.58e-16
C20ORF12 7.588e-20 1.53e-15
POLR3F 7.588e-20 1.53e-15
RWDD2B 2.221e-17 4.49e-13
ZNF30 7.288e-13 1.47e-08
IVD 7.725e-13 1.56e-08
L3MBTL2 1.188e-11 2.4e-07
TLR2 1.359e-10 2.75e-06
HIST1H2AL 7.266e-09 0.000147
PECR 2.317e-08 0.000468

Figure S9.  Get High-res Image As an example, this figure shows the association of KIAA0101 to 'NEOPLASM.DISEASESTAGE'. P value = 1.77e-20 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 = 203

  • Number of genes = 20217

  • Number of clinical features = 12

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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