This pipeline uses various statistical tests to identify mRNAs whose expression levels correlated to selected clinical features.
Testing the association between 18632 genes and 6 clinical features across 562 samples, statistically thresholded by Q value < 0.05, 5 clinical features related to at least one genes.

192 genes correlated to 'AGE'.

STS , GREB1 , DEPDC6 , GNPNAT1 , SLCO1A2 , ...

2 genes correlated to 'PRIMARY.SITE.OF.DISEASE'.

SPINK8 , PTBP1

1 gene correlated to 'KARNOFSKY.PERFORMANCE.SCORE'.

WDR60

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

FLJ22662 , WDR62 , GLIPR1L2 , ATHL1 , ST6GALNAC6 , ...

1 gene correlated to 'COMPLETENESS.OF.RESECTION'.

IL1RAPL2

No genes correlated to 'Time to Death'
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=0  
AGE  Spearman correlation test  N=192  older  N=74  younger  N=118 
PRIMARY SITE OF DISEASE  ANOVA test  N=2  
KARNOFSKY PERFORMANCE SCORE  Spearman correlation test  N=1  higher score  N=1  lower score  N=0 
RADIATIONS RADIATION REGIMENINDICATION  t test  N=30  yes  N=15  no  N=15 
COMPLETENESS OF RESECTION  ANOVA test  N=1 
Time to Death  Duration (Months)  0.3180.2 (median=28.3) 
censored  N = 265  
death  N = 292  
Significant markers  N = 0 
AGE  Mean (SD)  59.71 (12) 
Significant markers  N = 192  
pos. correlated  74  
neg. correlated  118 
SpearmanCorr  corrP  Q  

STS  0.3073  1.644e13  3.06e09 
GREB1  0.3024  4.055e13  7.56e09 
DEPDC6  0.3011  5.244e13  9.77e09 
GNPNAT1  0.2964  1.238e12  2.31e08 
SLCO1A2  0.2854  8.665e12  1.61e07 
EIF4E3  0.2846  9.966e12  1.86e07 
NPAL2  0.2761  4.274e11  7.96e07 
NLK  0.275  5.1e11  9.5e07 
BRCC3  0.2749  5.214e11  9.71e07 
APPL2  0.2744  5.623e11  1.05e06 
PRIMARY.SITE.OF.DISEASE  Labels  N 
OMENTUM  2  
OVARY  558  
PERITONEUM OVARY  2  
Significant markers  N = 2 
One gene related to 'KARNOFSKY.PERFORMANCE.SCORE'.
KARNOFSKY.PERFORMANCE.SCORE  Mean (SD)  75.64 (13) 
Score  N  
40  2  
60  20  
80  49  
100  7  
Significant markers  N = 1  
pos. correlated  1  
neg. correlated  0 
SpearmanCorr  corrP  Q  

WDR60  0.504  2.551e06  0.0475 
30 genes related to 'RADIATIONS.RADIATION.REGIMENINDICATION'.
RADIATIONS.RADIATION.REGIMENINDICATION  Labels  N 
NO  3  
YES  559  
Significant markers  N = 30  
Higher in YES  15  
Higher in NO  15 
T(pos if higher in 'YES')  ttestP  Q  AUC  

FLJ22662  24.23  4.068e41  7.58e37  0.867 
WDR62  13.77  2.368e35  4.41e31  0.7513 
GLIPR1L2  16.48  6.348e35  1.18e30  0.7794 
ATHL1  12.49  3.399e31  6.33e27  0.6535 
ST6GALNAC6  17.15  2.815e20  5.24e16  0.799 
SDK1  15  2.746e15  5.12e11  0.768 
CYP4A11  8.73  5.398e15  1.01e10  0.6184 
LOC388161  22.94  2.065e13  3.85e09  0.9207 
TMC5  8.42  5.86e12  1.09e07  0.5897 
C9ORF114  11.98  2.006e11  3.74e07  0.7358 
COMPLETENESS.OF.RESECTION  Labels  N 
R0  14  
R1  27  
R2  1  
Significant markers  N = 1 
ANOVA_P  Q  

IL1RAPL2  6.299e07  0.0117 

Expresson data file = OVTP.medianexp.txt

Clinical data file = OVTP.merged_data.txt

Number of patients = 562

Number of genes = 18632

Number of clinical features = 6
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.