This pipeline uses various statistical tests to identify mRNAs whose expression levels correlated to selected clinical features.
Testing the association between 12042 genes and 6 clinical features across 523 samples, statistically thresholded by Q value < 0.05, 5 clinical features related to at least one genes.
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15 genes correlated to 'Time to Death'.
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CLEC5A , RANBP17 , EFEMP2 , DIRAS3 , ATP5C1 , ...
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73 genes correlated to 'AGE'.
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FBXO17 , KIAA0495 , RANBP17 , NOL3 , C14ORF45 , ...
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21 genes correlated to 'GENDER'.
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DDX3Y , RPS4Y1 , JARID1D , EIF1AY , NLGN4Y , ...
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1 gene correlated to 'KARNOFSKY.PERFORMANCE.SCORE'.
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TM4SF20
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497 genes correlated to 'HISTOLOGICAL.TYPE'.
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CDH8 , KIAA0774 , RYR2 , KCNV1 , CLDN3 , ...
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No genes correlated to 'RADIATIONS.RADIATION.REGIMENINDICATION'
Complete statistical result table is provided in Supplement Table 1
Clinical feature | Statistical test | Significant genes | Associated with | Associated with | ||
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Time to Death | Cox regression test | N=15 | shorter survival | N=10 | longer survival | N=5 |
AGE | Spearman correlation test | N=73 | older | N=61 | younger | N=12 |
GENDER | t test | N=21 | male | N=13 | female | N=8 |
KARNOFSKY PERFORMANCE SCORE | Spearman correlation test | N=1 | higher score | N=1 | lower score | N=0 |
HISTOLOGICAL TYPE | ANOVA test | N=497 | ||||
RADIATIONS RADIATION REGIMENINDICATION | t test | N=0 |
Time to Death | Duration (Months) | 0.1-127.6 (median=9.9) |
censored | N = 116 | |
death | N = 407 | |
Significant markers | N = 15 | |
associated with shorter survival | 10 | |
associated with longer survival | 5 |
HazardRatio | Wald_P | Q | C_index | |
---|---|---|---|---|
CLEC5A | 1.23 | 7.442e-08 | 9e-04 | 0.582 |
RANBP17 | 0.39 | 1.046e-07 | 0.0013 | 0.418 |
EFEMP2 | 1.29 | 1.15e-07 | 0.0014 | 0.54 |
DIRAS3 | 1.2 | 2.886e-07 | 0.0035 | 0.554 |
ATP5C1 | 0.62 | 3.108e-07 | 0.0037 | 0.447 |
SIGLEC9 | 2.2 | 4.912e-07 | 0.0059 | 0.56 |
HIST3H2A | 0.83 | 4.99e-07 | 0.006 | 0.428 |
ZIC3 | 0.44 | 1.578e-06 | 0.019 | 0.438 |
TIMP1 | 1.21 | 1.876e-06 | 0.023 | 0.55 |
KIAA0495 | 1.63 | 2.181e-06 | 0.026 | 0.55 |
AGE | Mean (SD) | 57.75 (15) |
Significant markers | N = 73 | |
pos. correlated | 61 | |
neg. correlated | 12 |
SpearmanCorr | corrP | Q | |
---|---|---|---|
FBXO17 | 0.3036 | 1.29e-12 | 1.55e-08 |
KIAA0495 | 0.2924 | 9.085e-12 | 1.09e-07 |
RANBP17 | -0.2884 | 1.781e-11 | 2.14e-07 |
NOL3 | 0.2798 | 7.302e-11 | 8.79e-07 |
C14ORF45 | 0.2744 | 1.744e-10 | 2.1e-06 |
TUSC3 | -0.2738 | 1.904e-10 | 2.29e-06 |
DRG2 | 0.2629 | 1.03e-09 | 1.24e-05 |
C5ORF21 | 0.2502 | 6.598e-09 | 7.94e-05 |
C9ORF6 | 0.2476 | 9.508e-09 | 0.000114 |
PCNP | 0.2448 | 1.409e-08 | 0.00017 |
GENDER | Labels | N |
FEMALE | 205 | |
MALE | 318 | |
Significant markers | N = 21 | |
Higher in MALE | 13 | |
Higher in FEMALE | 8 |
T(pos if higher in 'MALE') | ttestP | Q | AUC | |
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DDX3Y | 37.87 | 8.65e-149 | 1.04e-144 | 0.9583 |
RPS4Y1 | 40.71 | 3.227e-144 | 3.89e-140 | 0.9504 |
JARID1D | 34.74 | 9.382e-138 | 1.13e-133 | 0.957 |
EIF1AY | 34.83 | 4.969e-137 | 5.98e-133 | 0.9499 |
NLGN4Y | 30.22 | 3.925e-111 | 4.73e-107 | 0.9385 |
UTY | 26.81 | 3.743e-99 | 4.51e-95 | 0.9435 |
USP9Y | 22.04 | 2.372e-75 | 2.86e-71 | 0.9337 |
CYORF15B | 22.1 | 7.411e-75 | 8.92e-71 | 0.9434 |
ZFY | 14.69 | 5.257e-41 | 6.33e-37 | 0.8441 |
HDHD1A | -10.41 | 2.185e-22 | 2.63e-18 | 0.7637 |
One gene related to 'KARNOFSKY.PERFORMANCE.SCORE'.
KARNOFSKY.PERFORMANCE.SCORE | Mean (SD) | 77.12 (14) |
Significant markers | N = 1 | |
pos. correlated | 1 | |
neg. correlated | 0 |
SpearmanCorr | corrP | Q | |
---|---|---|---|
TM4SF20 | 0.2356 | 2.62e-06 | 0.0316 |
HISTOLOGICAL.TYPE | Labels | N |
GLIOBLASTOMA MULTIFORME (GBM) | 4 | |
TREATED PRIMARY GBM | 20 | |
UNTREATED PRIMARY (DE NOVO) GBM | 499 | |
Significant markers | N = 497 |
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Expresson data file = GBM-TP.medianexp.txt
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Clinical data file = GBM-TP.clin.merged.picked.txt
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Number of patients = 523
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Number of genes = 12042
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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 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 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 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.