(primary solid tumor cohort)
This pipeline uses various statistical tests to identify miRs whose expression levels correlated to selected clinical features.
Testing the association between 487 genes and 4 clinical features across 154 samples, statistically thresholded by Q value < 0.05, 1 clinical feature related to at least one genes.

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

HSAMIR449A , HSAMIR548O

No genes correlated to 'AGE', 'COMPLETENESS.OF.RESECTION', and 'NUMBER.OF.LYMPH.NODES'.
Complete statistical result table is provided in Supplement Table 1
Clinical feature  Statistical test  Significant genes  Associated with  Associated with  

AGE  Spearman correlation test  N=0  
RADIATIONS RADIATION REGIMENINDICATION  t test  N=2  yes  N=2  no  N=0 
COMPLETENESS OF RESECTION  ANOVA test  N=0  
NUMBER OF LYMPH NODES  Spearman correlation test  N=0 
AGE  Mean (SD)  60.4 (6.9) 
Significant markers  N = 0 
2 genes related to 'RADIATIONS.RADIATION.REGIMENINDICATION'.
RADIATIONS.RADIATION.REGIMENINDICATION  Labels  N 
NO  5  
YES  149  
Significant markers  N = 2  
Higher in YES  2  
Higher in NO  0 
T(pos if higher in 'YES')  ttestP  Q  AUC  

HSAMIR449A  4.48  3.792e05  0.0167  0.6234 
HSAMIR548O  6.96  0.0001066  0.0468  0.7953 
COMPLETENESS.OF.RESECTION  Labels  N 
R0  118  
R1  29  
RX  2  
Significant markers  N = 0 

Expresson data file = PRADTP.miRseq_RPKM_log2.txt

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

Number of patients = 154

Number of genes = 487

Number of clinical features = 4
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 multiclass clinical features (ordinal or nominal), oneway analysis of variance (Howell 2002) was applied to compare the log2expression levels between different clinical classes using 'anova' 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.