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 564 samples, statistically thresholded by Q value < 0.05, 5 clinical features related to at least one genes.
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1 gene correlated to 'Time to Death'.
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ZFHX4
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178 genes correlated to 'AGE'.
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STS , GREB1 , GNPNAT1 , DEPDC6 , EIF4E3 , ...
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3 genes correlated to 'PRIMARY.SITE.OF.DISEASE'.
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SPINK8 , PTBP1 , EBI3
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1 gene correlated to 'KARNOFSKY.PERFORMANCE.SCORE'.
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WDR60
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25 genes correlated to 'RADIATIONS.RADIATION.REGIMENINDICATION'.
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RAPGEF1 , SNX9 , EIF4G2 , PRRX1 , ZNF507 , ...
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No genes correlated to 'NEOADJUVANT.THERAPY'
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=1 | shorter survival | N=1 | longer survival | N=0 |
AGE | Spearman correlation test | N=178 | older | N=72 | younger | N=106 |
PRIMARY SITE OF DISEASE | ANOVA test | N=3 | ||||
KARNOFSKY PERFORMANCE SCORE | Spearman correlation test | N=1 | higher score | N=1 | lower score | N=0 |
RADIATIONS RADIATION REGIMENINDICATION | t test | N=25 | yes | N=18 | no | N=7 |
NEOADJUVANT THERAPY | t test | N=0 |
Time to Death | Duration (Months) | 0.3-180.2 (median=28.3) |
censored | N = 267 | |
death | N = 292 | |
Significant markers | N = 1 | |
associated with shorter survival | 1 | |
associated with longer survival | 0 |
HazardRatio | Wald_P | Q | C_index | |
---|---|---|---|---|
ZFHX4 | 1.64 | 1.27e-06 | 0.024 | 0.574 |
AGE | Mean (SD) | 59.69 (12) |
Significant markers | N = 178 | |
pos. correlated | 72 | |
neg. correlated | 106 |
SpearmanCorr | corrP | Q | |
---|---|---|---|
STS | -0.3021 | 3.889e-13 | 7.25e-09 |
GREB1 | -0.2914 | 2.762e-12 | 5.15e-08 |
GNPNAT1 | -0.2909 | 3.005e-12 | 5.6e-08 |
DEPDC6 | -0.2908 | 3.077e-12 | 5.73e-08 |
EIF4E3 | -0.284 | 1.02e-11 | 1.9e-07 |
SLCO1A2 | 0.2838 | 1.06e-11 | 1.97e-07 |
GEMIN8 | -0.2729 | 6.715e-11 | 1.25e-06 |
NPAL2 | -0.2697 | 1.136e-10 | 2.12e-06 |
CCDC91 | 0.269 | 1.268e-10 | 2.36e-06 |
PRPS2 | -0.2678 | 1.554e-10 | 2.89e-06 |
PRIMARY.SITE.OF.DISEASE | Labels | N |
OMENTUM | 2 | |
OVARY | 560 | |
PERITONEUM (OVARY) | 2 | |
Significant markers | N = 3 |
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 | 0.5131 | 1.561e-06 | 0.0291 |
25 genes related to 'RADIATIONS.RADIATION.REGIMENINDICATION'.
RADIATIONS.RADIATION.REGIMENINDICATION | Labels | N |
NO | 3 | |
YES | 561 | |
Significant markers | N = 25 | |
Higher in YES | 18 | |
Higher in NO | 7 |
T(pos if higher in 'YES') | ttestP | Q | AUC | |
---|---|---|---|---|
RAPGEF1 | 21.48 | 9.569e-49 | 1.78e-44 | 0.8378 |
SNX9 | -15.62 | 4.157e-33 | 7.74e-29 | 0.7623 |
EIF4G2 | 14.29 | 1.684e-23 | 3.14e-19 | 0.7629 |
PRRX1 | 13.58 | 9.302e-20 | 1.73e-15 | 0.6542 |
ZNF507 | -23.25 | 5.634e-15 | 1.05e-10 | 0.9002 |
ACTL7B | -11.43 | 8.136e-15 | 1.52e-10 | 0.7094 |
CNKSR2 | -8.76 | 1.192e-14 | 2.22e-10 | 0.7249 |
AKAP9 | 16.8 | 2.418e-14 | 4.5e-10 | 0.7903 |
CRIP2 | 9.78 | 3.517e-14 | 6.55e-10 | 0.694 |
NF2 | 17.14 | 3.701e-13 | 6.89e-09 | 0.8491 |
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Expresson data file = OV.medianexp.txt
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Clinical data file = OV.clin.merged.picked.txt
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Number of patients = 564
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Number of genes = 18632
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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. Kaplan-Meier 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 two-tailed P values were estimated using 'cor.test' function in R
For multi-class clinical features (ordinal or nominal), one-way analysis of variance (Howell 2002) was applied to compare the log2-expression levels between different clinical classes using 'anova' function in R
For two-class clinical features, two-tailed Student's t test with unequal variance (Lehmann and Romano 2005) was applied to compare the log2-expression 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.