This pipeline uses various statistical tests to identify miRs whose expression levels correlated to selected clinical features.
Testing the association between 469 miRs and 10 clinical features across 66 samples, statistically thresholded by Q value < 0.05, 3 clinical features related to at least one miRs.

1 miR correlated to 'Time to Death'.

HSAMIR130B

3 miRs correlated to 'NEOPLASM.DISEASESTAGE'.

HSAMIR376A1 , HSAMIR130B , HSAMIR181B1

1 miR correlated to 'PATHOLOGY.T.STAGE'.

HSAMIR181B1

No miRs correlated to 'AGE', 'PATHOLOGY.N.STAGE', 'PATHOLOGY.M.STAGE', 'GENDER', 'KARNOFSKY.PERFORMANCE.SCORE', 'NUMBERPACKYEARSSMOKED', and 'YEAROFTOBACCOSMOKINGONSET'.
Complete statistical result table is provided in Supplement Table 1
Clinical feature  Statistical test  Significant miRs  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=0  
NEOPLASM DISEASESTAGE  ANOVA test  N=3  
PATHOLOGY T STAGE  Spearman correlation test  N=1  higher stage  N=1  lower stage  N=0 
PATHOLOGY N STAGE  Spearman correlation test  N=0  
PATHOLOGY M STAGE  ANOVA test  N=0  
GENDER  t test  N=0  
KARNOFSKY PERFORMANCE SCORE  Spearman correlation test  N=0  
NUMBERPACKYEARSSMOKED  Spearman correlation test  N=0  
YEAROFTOBACCOSMOKINGONSET  Spearman correlation test  N=0 
Time to Death  Duration (Months)  0.6151.9 (median=63.9) 
censored  N = 57  
death  N = 8  
Significant markers  N = 1  
associated with shorter survival  1  
associated with longer survival  0 
HazardRatio  Wald_P  Q  C_index  

HSAMIR130B  2.5  5.591e05  0.026  0.753 
AGE  Mean (SD)  51.52 (14) 
Significant markers  N = 0 
NEOPLASM.DISEASESTAGE  Labels  N 
STAGE I  21  
STAGE II  25  
STAGE III  14  
STAGE IV  6  
Significant markers  N = 3 
ANOVA_P  Q  

HSAMIR376A1  4.077e08  1.91e05 
HSAMIR130B  2.075e05  0.00971 
HSAMIR181B1  9.782e05  0.0457 
PATHOLOGY.T.STAGE  Mean (SD)  2.02 (0.85) 
N  
1  21  
2  25  
3  18  
4  2  
Significant markers  N = 1  
pos. correlated  1  
neg. correlated  0 
SpearmanCorr  corrP  Q  

HSAMIR181B1  0.505  1.53e05  0.00717 
PATHOLOGY.N.STAGE  Mean (SD)  0.16 (0.47) 
N  
0  40  
1  3  
2  2  
Significant markers  N = 0 
PATHOLOGY.M.STAGE  Labels  N 
M0  34  
M1  2  
MX  9  
Significant markers  N = 0 
GENDER  Labels  N 
FEMALE  27  
MALE  39  
Significant markers  N = 0 
No miR related to 'KARNOFSKY.PERFORMANCE.SCORE'.
KARNOFSKY.PERFORMANCE.SCORE  Mean (SD)  89.09 (9.4) 
Score  N  
70  1  
80  2  
90  5  
100  3  
Significant markers  N = 0 
NUMBERPACKYEARSSMOKED  Mean (SD)  25.09 (22) 
Significant markers  N = 0 

Expresson data file = KICHTP.miRseq_RPKM_log2.txt

Clinical data file = KICHTP.merged_data.txt

Number of patients = 66

Number of miRs = 469

Number of clinical features = 10
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 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 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.
In addition to the links below, the full results of the analysis summarized in this report can also be downloaded programmatically using firehose_get, or interactively from either the Broad GDAC website or TCGA Data Coordination Center Portal.