This pipeline uses various statistical tests to identify genes whose promoter methylation levels correlated to selected clinical features.
Testing the association between 17460 genes and 8 clinical features across 283 samples, statistically thresholded by Q value < 0.05, 7 clinical features related to at least one genes.

2 genes correlated to 'Time to Death'.

ZNF266 , HES7

4 genes correlated to 'AGE'.

SLC35D3 , XKR6 , DES , HAND1

8 genes correlated to 'GENDER'.

KIF4B , LOC96610 , FH , FRG1B , SLC22A3 , ...

5 genes correlated to 'PATHOLOGY.N'.

SLC47A2 , ESRRA , AVPI1 , FGD2 , TMCO4

1 gene correlated to 'TUMOR.STAGE'.

LOC400657

2 genes correlated to 'RADIATIONS.RADIATION.REGIMENINDICATION'.

ZCCHC17 , NEAT1

4 genes correlated to 'NEOADJUVANT.THERAPY'.

ASRGL1 , ZCCHC17 , NEAT1 , BMP6

No genes correlated to 'PATHOLOGY.T'
Complete statistical result table is provided in Supplement Table 1
Clinical feature  Statistical test  Significant genes  Associated with  Associated with  

Time to Death  Cox regression test  N=2  shorter survival  N=2  longer survival  N=0 
AGE  Spearman correlation test  N=4  older  N=4  younger  N=0 
GENDER  t test  N=8  male  N=5  female  N=3 
PATHOLOGY T  Spearman correlation test  N=0  
PATHOLOGY N  Spearman correlation test  N=5  higher pN  N=5  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=2  yes  N=1  no  N=1 
NEOADJUVANT THERAPY  t test  N=4  yes  N=3  no  N=1 
Time to Death  Duration (Months)  0.1210.9 (median=14.8) 
censored  N = 164  
death  N = 116  
Significant markers  N = 2  
associated with shorter survival  2  
associated with longer survival  0 
AGE  Mean (SD)  61.38 (12) 
Significant markers  N = 4  
pos. correlated  4  
neg. correlated  0 
SpearmanCorr  corrP  Q  

SLC35D3  0.3014  2.369e07  0.00414 
XKR6  0.2843  1.161e06  0.0203 
DES  0.2803  1.667e06  0.0291 
HAND1  0.2781  2.025e06  0.0353 
GENDER  Labels  N 
FEMALE  78  
MALE  205  
Significant markers  N = 8  
Higher in MALE  5  
Higher in FEMALE  3 
T(pos if higher in 'MALE')  ttestP  Q  AUC  

KIF4B  10.87  4.768e20  8.32e16  0.8499 
LOC96610  7.33  1.097e11  1.92e07  0.7691 
FH  6.68  7e10  1.22e05  0.7549 
FRG1B  6.64  1.55e09  2.71e05  0.7637 
SLC22A3  5.21  3.752e07  0.00655  0.5934 
TMEM232  5.28  5.434e07  0.00948  0.7019 
NLRP2  5.04  1.474e06  0.0257  0.6957 
TTC21A  4.88  1.933e06  0.0337  0.6335 
PATHOLOGY.T  Mean (SD)  2.93 (1) 
N  
T1  20  
T2  75  
T3  57  
T4  98  
Significant markers  N = 0 
PATHOLOGY.N  Mean (SD)  1.03 (0.96) 
N  
N0  94  
N1  31  
N2  93  
N3  4  
Significant markers  N = 5  
pos. correlated  5  
neg. correlated  0 
SpearmanCorr  corrP  Q  

SLC47A2  0.3503  8.254e08  0.00144 
ESRRA  0.3181  1.305e06  0.0228 
AVPI1  0.3141  1.795e06  0.0313 
FGD2  0.3138  1.845e06  0.0322 
TMCO4  0.3095  2.592e06  0.0453 
TUMOR.STAGE  Mean (SD)  3.3 (0.97) 
N  
Stage 1  14  
Stage 2  46  
Stage 3  38  
Stage 4  147  
Significant markers  N = 1  
pos. correlated  1  
neg. correlated  0 
SpearmanCorr  corrP  Q  

LOC400657  0.2967  2.28e06  0.0398 
2 genes related to 'RADIATIONS.RADIATION.REGIMENINDICATION'.
RADIATIONS.RADIATION.REGIMENINDICATION  Labels  N 
NO  75  
YES  208  
Significant markers  N = 2  
Higher in YES  1  
Higher in NO  1 
T(pos if higher in 'YES')  ttestP  Q  AUC  

ZCCHC17  5.43  2.816e07  0.00492  0.7006 
NEAT1  5.23  4.465e07  0.00779  0.6804 
NEOADJUVANT.THERAPY  Labels  N 
NO  45  
YES  238  
Significant markers  N = 4  
Higher in YES  3  
Higher in NO  1 

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

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

Number of patients = 283

Number of genes = 17460

Number of clinical features = 8
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. KaplanMeier survival curves were plot using the four quartile subgroups of patients based on expression levels
For continuous numerical clinical features, Spearman's rank correlation coefficients (Spearman 1904) and twotailed P values were estimated using 'cor.test' function in R
For twoclass clinical features, twotailed Student's t test with unequal variance (Lehmann and Romano 2005) was applied to compare the log2expression levels between the two clinical classes using 't.test' function in R
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.
This is an experimental feature. The full results of the analysis summarized in this report can be downloaded from the TCGA Data Coordination Center.