Head and Neck 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 20233 genes and 8 clinical features across 292 samples, statistically thresholded by Q value < 0.05, 7 clinical features related to at least one genes.

  • 1 gene correlated to 'Time to Death'.

    • CYP51A1

  • 2 genes correlated to 'AGE'.

    • RIPPLY2 ,  PBX4

  • 4 genes correlated to 'GENDER'.

    • KIF4B ,  UTP14C ,  MGC21881 ,  FRG1B

  • 1 gene correlated to 'PATHOLOGY.T'.

    • PRO1768

  • 2 genes correlated to 'PATHOLOGY.N'.

    • CD74 ,  VAMP8

  • 1 gene correlated to 'TUMOR.STAGE'.

    • IPO7

  • 1 gene correlated to 'RADIATIONS.RADIATION.REGIMENINDICATION'.

    • PTPN1

  • No genes correlated to 'NEOADJUVANT.THERAPY'

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=2 older N=2 younger N=0
GENDER t test N=4 male N=2 female N=2
PATHOLOGY T Spearman correlation test N=1 higher pT N=0 lower pT N=1
PATHOLOGY N Spearman correlation test N=2 higher pN N=2 lower pN N=0
TUMOR STAGE Spearman correlation test N=1 higher stage N=1 lower stage N=0
RADIATIONS RADIATION REGIMENINDICATION t test N=1 yes N=0 no N=1
NEOADJUVANT THERAPY t test   N=0        
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=14.2)
  censored N = 169
  death N = 121
     
  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
CYP51A1 7501 9.455e-07 0.019 0.525

Figure S1.  Get High-res Image As an example, this figure shows the association of CYP51A1 to 'Time to Death'. four curves present the cumulative survival rates of 4 quartile subsets of patients. P value = 9.45e-07 with univariate Cox regression analysis using continuous log-2 expression values.

Clinical variable #2: 'AGE'

2 genes related to 'AGE'.

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

AGE Mean (SD) 61.29 (12)
  Significant markers N = 2
  pos. correlated 2
  neg. correlated 0
List of 2 genes significantly correlated to 'AGE' by Spearman correlation test

Table S4.  Get Full Table List of 2 genes significantly correlated to 'AGE' by Spearman correlation test

SpearmanCorr corrP Q
RIPPLY2 0.3198 2.281e-08 0.000462
PBX4 0.2885 5.288e-07 0.0107

Figure S2.  Get High-res Image As an example, this figure shows the association of RIPPLY2 to 'AGE'. P value = 2.28e-08 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #3: 'GENDER'

4 genes related to 'GENDER'.

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

GENDER Labels N
  FEMALE 82
  MALE 210
     
  Significant markers N = 4
  Higher in MALE 2
  Higher in FEMALE 2
List of 4 genes differentially expressed by 'GENDER'

Table S6.  Get Full Table List of 4 genes differentially expressed by 'GENDER'

T(pos if higher in 'MALE') ttestP Q AUC
KIF4B -8.24 8.995e-14 1.82e-09 0.7829
UTP14C 7.74 5.51e-12 1.11e-07 0.7997
MGC21881 5.37 2.911e-07 0.00589 0.6824
FRG1B -5.3 6.541e-07 0.0132 0.6764

Figure S3.  Get High-res Image As an example, this figure shows the association of KIF4B to 'GENDER'. P value = 8.99e-14 with T-test analysis.

Clinical variable #4: 'PATHOLOGY.T'

One gene related to 'PATHOLOGY.T'.

Table S7.  Basic characteristics of clinical feature: 'PATHOLOGY.T'

PATHOLOGY.T Mean (SD) 2.93 (1)
  N
  T1 21
  T2 76
  T3 61
  T4 99
     
  Significant markers N = 1
  pos. correlated 0
  neg. correlated 1
List of one gene significantly correlated to 'PATHOLOGY.T' by Spearman correlation test

Table S8.  Get Full Table List of one gene significantly correlated to 'PATHOLOGY.T' by Spearman correlation test

SpearmanCorr corrP Q
PRO1768 -0.2907 2.128e-06 0.0431

Figure S4.  Get High-res Image As an example, this figure shows the association of PRO1768 to 'PATHOLOGY.T'. P value = 2.13e-06 with Spearman correlation analysis.

Clinical variable #5: 'PATHOLOGY.N'

2 genes related to 'PATHOLOGY.N'.

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

PATHOLOGY.N Mean (SD) 1.04 (0.96)
  N
  N0 96
  N1 31
  N2 97
  N3 4
     
  Significant markers N = 2
  pos. correlated 2
  neg. correlated 0
List of 2 genes significantly correlated to 'PATHOLOGY.N' by Spearman correlation test

Table S10.  Get Full Table List of 2 genes significantly correlated to 'PATHOLOGY.N' by Spearman correlation test

SpearmanCorr corrP Q
CD74 0.3274 4.272e-07 0.00864
VAMP8 0.3205 7.682e-07 0.0155

Figure S5.  Get High-res Image As an example, this figure shows the association of CD74 to 'PATHOLOGY.N'. P value = 4.27e-07 with Spearman correlation analysis.

Clinical variable #6: 'TUMOR.STAGE'

One gene related to 'TUMOR.STAGE'.

Table S11.  Basic characteristics of clinical feature: 'TUMOR.STAGE'

TUMOR.STAGE Mean (SD) 3.3 (0.97)
  N
  Stage 1 15
  Stage 2 46
  Stage 3 40
  Stage 4 151
     
  Significant markers N = 1
  pos. correlated 1
  neg. correlated 0
List of one gene significantly correlated to 'TUMOR.STAGE' by Spearman correlation test

Table S12.  Get Full Table List of one gene significantly correlated to 'TUMOR.STAGE' by Spearman correlation test

SpearmanCorr corrP Q
IPO7 0.2969 1.594e-06 0.0322

Figure S6.  Get High-res Image As an example, this figure shows the association of IPO7 to 'TUMOR.STAGE'. P value = 1.59e-06 with Spearman correlation analysis.

Clinical variable #7: 'RADIATIONS.RADIATION.REGIMENINDICATION'

One gene related to 'RADIATIONS.RADIATION.REGIMENINDICATION'.

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

RADIATIONS.RADIATION.REGIMENINDICATION Labels N
  NO 78
  YES 214
     
  Significant markers N = 1
  Higher in YES 0
  Higher in NO 1
List of one gene differentially expressed by 'RADIATIONS.RADIATION.REGIMENINDICATION'

Table S14.  Get Full Table List of one gene differentially expressed by 'RADIATIONS.RADIATION.REGIMENINDICATION'

T(pos if higher in 'YES') ttestP Q AUC
PTPN1 -5.02 1.596e-06 0.0323 0.6936

Figure S7.  Get High-res Image As an example, this figure shows the association of PTPN1 to 'RADIATIONS.RADIATION.REGIMENINDICATION'. P value = 1.6e-06 with T-test analysis.

Clinical variable #8: 'NEOADJUVANT.THERAPY'

No gene related to 'NEOADJUVANT.THERAPY'.

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

NEOADJUVANT.THERAPY Labels N
  NO 48
  YES 244
     
  Significant markers N = 0
Methods & Data
Input

  • Expresson data file = HNSC.meth.for_correlation.filtered_data.txt

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

  • Number of patients = 292

  • Number of genes = 20233

  • Number of clinical features = 8

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

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] 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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