Correlation between gene methylation status and clinical features
Head and Neck Squamous Cell Carcinoma (Primary solid tumor)
23 May 2013  |  analyses__2013_05_23
Maintainer Information
Citation Information
doi:10.7908/C1NZ85PP
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/C1NZ85PP
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 20139 genes and 9 clinical features across 306 samples, statistically thresholded by Q value < 0.05, 8 clinical features related to at least one genes.

  • 1 gene correlated to 'Time to Death'.

    • ZNF266

  • 4 genes correlated to 'AGE'.

    • SLC35D3 ,  XKR6 ,  C12ORF23 ,  HAND1

  • 8 genes correlated to 'GENDER'.

    • KIF4B ,  FRG1B ,  C17ORF70 ,  NLRP2 ,  RPA1 ,  ...

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

    • FLII ,  CKS2 ,  QRSL1 ,  RTN4IP1 ,  SNRNP40 ,  ...

  • 5 genes correlated to 'NUMBERPACKYEARSSMOKED'.

    • MMADHC ,  C2ORF77__1 ,  KLHL23__2 ,  PHOSPHO2__1 ,  RAB21

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

    • C19ORF60 ,  NR4A2 ,  UFM1 ,  C2CD3__1 ,  PPME1__1 ,  ...

  • 2 genes correlated to 'NUMBER.OF.LYMPH.NODES'.

    • SLC47A2 ,  THSD4

  • 55 genes correlated to 'NEOPLASM.DISEASESTAGE'.

    • FLJ40852__1 ,  WEE2 ,  UFM1 ,  C2CD3__1 ,  PPME1__1 ,  ...

  • No genes correlated to '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=1 shorter survival N=1 longer survival N=0
AGE Spearman correlation test N=4 older N=4 younger N=0
GENDER t test N=8 male N=2 female N=6
RADIATIONS RADIATION REGIMENINDICATION t test N=13 yes N=2 no N=11
NUMBERPACKYEARSSMOKED Spearman correlation test N=5 higher numberpackyearssmoked N=0 lower numberpackyearssmoked N=5
YEAROFTOBACCOSMOKINGONSET Spearman correlation test   N=0        
LYMPH NODE METASTASIS ANOVA test N=96        
NUMBER OF LYMPH NODES Spearman correlation test N=2 higher number.of.lymph.nodes N=2 lower number.of.lymph.nodes N=0
NEOPLASM DISEASESTAGE ANOVA test N=55        
Clinical variable #1: 'Time to Death'

One gene 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=14.8)
  censored N = 185
  death N = 118
     
  Significant markers N = 1
  associated with shorter survival 1
  associated with longer survival 0
List of one gene significantly associated with 'Time to Death' by Cox regression test

Table S2.  Get Full Table List of one gene significantly associated with 'Time to Death' by Cox regression test

HazardRatio Wald_P Q C_index
ZNF266 9001 1.313e-07 0.0026 0.611

Figure S1.  Get High-res Image As an example, this figure shows the association of ZNF266 to 'Time to Death'. four curves present the cumulative survival rates of 4 quartile subsets of patients. P value = 1.31e-07 with univariate Cox regression analysis using continuous log-2 expression values.

Clinical variable #2: 'AGE'

4 genes related to 'AGE'.

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

AGE Mean (SD) 61.16 (12)
  Significant markers N = 4
  pos. correlated 4
  neg. correlated 0
List of 4 genes significantly correlated to 'AGE' by Spearman correlation test

Table S4.  Get Full Table List of 4 genes significantly correlated to 'AGE' by Spearman correlation test

SpearmanCorr corrP Q
SLC35D3 0.2949 1.489e-07 0.003
XKR6 0.2841 4.331e-07 0.00872
C12ORF23 0.271 1.501e-06 0.0302
HAND1 0.2686 1.863e-06 0.0375

Figure S2.  Get High-res Image As an example, this figure shows the association of SLC35D3 to 'AGE'. P value = 1.49e-07 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #3: 'GENDER'

8 genes related to 'GENDER'.

Table S5.  Basic characteristics of clinical feature: 'GENDER'

GENDER Labels N
  FEMALE 85
  MALE 221
     
  Significant markers N = 8
  Higher in MALE 2
  Higher in FEMALE 6
List of 8 genes differentially expressed by 'GENDER'

Table S6.  Get Full Table List of 8 genes differentially expressed by 'GENDER'

T(pos if higher in 'MALE') ttestP Q AUC
KIF4B -12.39 1.189e-24 2.39e-20 0.8686
FRG1B -6.53 1.531e-09 3.08e-05 0.7352
C17ORF70 -5.5 9.359e-08 0.00188 0.6581
NLRP2 5.31 3.84e-07 0.00773 0.6949
RPA1 -5.04 8.201e-07 0.0165 0.5742
BRCA1__1 -5.03 1.453e-06 0.0293 0.6859
NBR2__1 -5.03 1.453e-06 0.0293 0.6859
SLC22A3 4.91 1.494e-06 0.0301 0.5732

Figure S3.  Get High-res Image As an example, this figure shows the association of KIF4B to 'GENDER'. P value = 1.19e-24 with T-test analysis.

Clinical variable #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'

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

Table S7.  Basic characteristics of clinical feature: 'RADIATIONS.RADIATION.REGIMENINDICATION'

RADIATIONS.RADIATION.REGIMENINDICATION Labels N
  NO 79
  YES 227
     
  Significant markers N = 13
  Higher in YES 2
  Higher in NO 11
List of top 10 genes differentially expressed by 'RADIATIONS.RADIATION.REGIMENINDICATION'

Table S8.  Get Full Table List of top 10 genes differentially expressed by 'RADIATIONS.RADIATION.REGIMENINDICATION'

T(pos if higher in 'YES') ttestP Q AUC
FLII 6.05 6.185e-09 0.000125 0.6935
CKS2 -5.6 1.16e-07 0.00234 0.7077
QRSL1 -5.47 1.936e-07 0.0039 0.6873
RTN4IP1 -5.47 1.936e-07 0.0039 0.6873
SNRNP40 -5.42 2.88e-07 0.0058 0.6969
ZCCHC17 -5.42 2.88e-07 0.0058 0.6969
LSM12 -5.39 3.869e-07 0.00779 0.6954
RPL7L1 -5.23 5.575e-07 0.0112 0.6828
MIR632 -5.2 7.475e-07 0.015 0.6906
ZNF207 -5.2 7.475e-07 0.015 0.6906

Figure S4.  Get High-res Image As an example, this figure shows the association of FLII to 'RADIATIONS.RADIATION.REGIMENINDICATION'. P value = 6.18e-09 with T-test analysis.

Clinical variable #5: 'NUMBERPACKYEARSSMOKED'

5 genes related to 'NUMBERPACKYEARSSMOKED'.

Table S9.  Basic characteristics of clinical feature: 'NUMBERPACKYEARSSMOKED'

NUMBERPACKYEARSSMOKED Mean (SD) 49.6 (37)
  Significant markers N = 5
  pos. correlated 0
  neg. correlated 5
List of 5 genes significantly correlated to 'NUMBERPACKYEARSSMOKED' by Spearman correlation test

Table S10.  Get Full Table List of 5 genes significantly correlated to 'NUMBERPACKYEARSSMOKED' by Spearman correlation test

SpearmanCorr corrP Q
MMADHC -0.3684 9.646e-07 0.0194
C2ORF77__1 -0.3653 1.212e-06 0.0244
KLHL23__2 -0.3653 1.212e-06 0.0244
PHOSPHO2__1 -0.3653 1.212e-06 0.0244
RAB21 -0.3641 1.318e-06 0.0265

Figure S5.  Get High-res Image As an example, this figure shows the association of MMADHC to 'NUMBERPACKYEARSSMOKED'. P value = 9.65e-07 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #6: 'YEAROFTOBACCOSMOKINGONSET'

No gene related to 'YEAROFTOBACCOSMOKINGONSET'.

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

YEAROFTOBACCOSMOKINGONSET Mean (SD) 1964.57 (12)
  Significant markers N = 0
Clinical variable #7: 'LYMPH.NODE.METASTASIS'

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

Table S12.  Basic characteristics of clinical feature: 'LYMPH.NODE.METASTASIS'

LYMPH.NODE.METASTASIS Labels N
  N0 99
  N1 33
  N2 6
  N2A 4
  N2B 57
  N2C 32
  N3 5
  NX 60
     
  Significant markers N = 96
List of top 10 genes differentially expressed by 'LYMPH.NODE.METASTASIS'

Table S13.  Get Full Table List of top 10 genes differentially expressed by 'LYMPH.NODE.METASTASIS'

ANOVA_P Q
C19ORF60 4.446e-12 8.95e-08
NR4A2 4.787e-12 9.64e-08
UFM1 1.412e-11 2.84e-07
C2CD3__1 5.243e-11 1.06e-06
PPME1__1 5.243e-11 1.06e-06
DNAJC10 6.57e-11 1.32e-06
POLI 8.015e-11 1.61e-06
C11ORF45 9.231e-11 1.86e-06
KCNJ5 9.231e-11 1.86e-06
CAB39L__1 9.597e-11 1.93e-06

Figure S6.  Get High-res Image As an example, this figure shows the association of C19ORF60 to 'LYMPH.NODE.METASTASIS'. P value = 4.45e-12 with ANOVA analysis.

Clinical variable #8: 'NUMBER.OF.LYMPH.NODES'

2 genes related to 'NUMBER.OF.LYMPH.NODES'.

Table S14.  Basic characteristics of clinical feature: 'NUMBER.OF.LYMPH.NODES'

NUMBER.OF.LYMPH.NODES Mean (SD) 2.72 (5.3)
  Significant markers N = 2
  pos. correlated 2
  neg. correlated 0
List of 2 genes significantly correlated to 'NUMBER.OF.LYMPH.NODES' by Spearman correlation test

Table S15.  Get Full Table List of 2 genes significantly correlated to 'NUMBER.OF.LYMPH.NODES' by Spearman correlation test

SpearmanCorr corrP Q
SLC47A2 0.32 7.977e-07 0.0161
THSD4 0.3123 1.51e-06 0.0304

Figure S7.  Get High-res Image As an example, this figure shows the association of SLC47A2 to 'NUMBER.OF.LYMPH.NODES'. P value = 7.98e-07 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #9: 'NEOPLASM.DISEASESTAGE'

55 genes related to 'NEOPLASM.DISEASESTAGE'.

Table S16.  Basic characteristics of clinical feature: 'NEOPLASM.DISEASESTAGE'

NEOPLASM.DISEASESTAGE Labels N
  STAGE I 17
  STAGE II 47
  STAGE III 41
  STAGE IVA 149
  STAGE IVB 6
     
  Significant markers N = 55
List of top 10 genes differentially expressed by 'NEOPLASM.DISEASESTAGE'

Table S17.  Get Full Table List of top 10 genes differentially expressed by 'NEOPLASM.DISEASESTAGE'

ANOVA_P Q
FLJ40852__1 2.802e-09 5.64e-05
WEE2 2.802e-09 5.64e-05
UFM1 3.089e-09 6.22e-05
C2CD3__1 4.26e-09 8.58e-05
PPME1__1 4.26e-09 8.58e-05
EIF4A1__1 5.502e-09 0.000111
C1ORF151 6.099e-09 0.000123
C11ORF45 7.224e-09 0.000145
HDLBP 7.211e-09 0.000145
KCNJ5 7.224e-09 0.000145

Figure S8.  Get High-res Image As an example, this figure shows the association of FLJ40852__1 to 'NEOPLASM.DISEASESTAGE'. P value = 2.8e-09 with ANOVA analysis.

Methods & Data
Input

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

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

  • Number of patients = 306

  • Number of genes = 20139

  • Number of clinical features = 9

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)
Copyright © 2013 Broad Institute TCGA GDAC as part of the TCGA Research Network. All rights reserved.
Made with Nozzle