This pipeline uses various statistical tests to identify miRs whose expression levels correlated to selected clinical features.
Testing the association between 564 genes and 8 clinical features across 308 samples, statistically thresholded by Q value < 0.05, 4 clinical features related to at least one genes.

5 genes correlated to 'Time to Death'.

HSAMIR377 , HSAMIR154 , HSAMIR493 , HSAMIR337 , HSAMIR654

1 gene correlated to 'PATHOLOGY.N'.

HSAMIR195

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

HSAMIR1274B , HSAMIR3676 , HSAMIR374A , HSAMIR660 , HSAMIR532

2 genes correlated to 'NEOADJUVANT.THERAPY'.

HSAMIR3676 , HSAMIR660

No genes correlated to 'AGE', 'GENDER', 'PATHOLOGY.T', and 'TUMOR.STAGE'.
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=5  shorter survival  N=5  longer survival  N=0 
AGE  Spearman correlation test  N=0  
GENDER  t test  N=0  
PATHOLOGY T  Spearman correlation test  N=0  
PATHOLOGY N  Spearman correlation test  N=1  higher pN  N=1  lower pN  N=0 
TUMOR STAGE  Spearman correlation test  N=0  
RADIATIONS RADIATION REGIMENINDICATION  t test  N=5  yes  N=4  no  N=1 
NEOADJUVANT THERAPY  t test  N=2  yes  N=2  no  N=0 
Time to Death  Duration (Months)  0.1210.9 (median=15) 
censored  N = 183  
death  N = 122  
Significant markers  N = 5  
associated with shorter survival  5  
associated with longer survival  0 
HazardRatio  Wald_P  Q  C_index  

HSAMIR377  1.48  2.217e06  0.0013  0.634 
HSAMIR154  1.46  1.031e05  0.0058  0.635 
HSAMIR493  1.45  2.314e05  0.013  0.63 
HSAMIR337  1.4  2.811e05  0.016  0.622 
HSAMIR654  1.38  4.724e05  0.026  0.622 
AGE  Mean (SD)  61.09 (12) 
Significant markers  N = 0 
GENDER  Labels  N 
FEMALE  86  
MALE  222  
Significant markers  N = 0 
PATHOLOGY.T  Mean (SD)  2.91 (1) 
N  
T1  24  
T2  78  
T3  62  
T4  103  
Significant markers  N = 0 
PATHOLOGY.N  Mean (SD)  1.05 (0.96) 
N  
N0  99  
N1  32  
N2  101  
N3  5  
Significant markers  N = 1  
pos. correlated  1  
neg. correlated  0 
SpearmanCorr  corrP  Q  

HSAMIR195  0.2646  3.684e05  0.0208 
TUMOR.STAGE  Mean (SD)  3.3 (0.98) 
N  
Stage 1  17  
Stage 2  46  
Stage 3  41  
Stage 4  158  
Significant markers  N = 0 
5 genes related to 'RADIATIONS.RADIATION.REGIMENINDICATION'.
RADIATIONS.RADIATION.REGIMENINDICATION  Labels  N 
NO  77  
YES  231  
Significant markers  N = 5  
Higher in YES  4  
Higher in NO  1 
T(pos if higher in 'YES')  ttestP  Q  AUC  

HSAMIR1274B  5.58  1.256e07  7.08e05  0.696 
HSAMIR3676  5.38  2.403e07  0.000135  0.6658 
HSAMIR374A  4.63  8.368e06  0.0047  0.6576 
HSAMIR660  4.53  1.177e05  0.0066  0.6511 
HSAMIR532  4.25  3.735e05  0.0209  0.6495 
NEOADJUVANT.THERAPY  Labels  N 
NO  48  
YES  260  
Significant markers  N = 2  
Higher in YES  2  
Higher in NO  0 
T(pos if higher in 'YES')  ttestP  Q  AUC  

HSAMIR3676  4.47  2.684e05  0.0151  0.6653 
HSAMIR660  4.12  8.748e05  0.0492  0.6508 

Expresson data file = HNSC.miRseq_RPKM_log2.txt

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

Number of patients = 308

Number of genes = 564

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.