(primary solid tumor cohort)
This pipeline uses various statistical tests to identify mRNAs whose expression levels correlated to selected clinical features.
Testing the association between 18254 genes and 4 clinical features across 834 samples, statistically thresholded by Q value < 0.05, 3 clinical features related to at least one genes.
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4 genes correlated to 'Time to Death'.
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DIP2B|57609 , PGK1|5230 , CAND1|55832 , IRF2|3660
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689 genes correlated to 'AGE'.
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ESR1|2099 , TFPI2|7980 , LRFN5|145581 , TMEFF1|8577 , SOBP|55084 , ...
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18 genes correlated to 'GENDER'.
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NLGN4Y|22829 , ZFY|7544 , PRKY|5616 , C7ORF10|79783 , SYT9|143425 , ...
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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 | ||
---|---|---|---|---|---|---|
Time to Death | Cox regression test | N=4 | shorter survival | N=3 | longer survival | N=1 |
AGE | Spearman correlation test | N=689 | older | N=163 | younger | N=526 |
GENDER | t test | N=18 | male | N=8 | female | N=10 |
RADIATIONS RADIATION REGIMENINDICATION | t test | N=0 |
Time to Death | Duration (Months) | 0-223.4 (median=18.9) |
censored | N = 683 | |
death | N = 94 | |
Significant markers | N = 4 | |
associated with shorter survival | 3 | |
associated with longer survival | 1 |
HazardRatio | Wald_P | Q | C_index | |
---|---|---|---|---|
DIP2B|57609 | 2.9 | 1.611e-07 | 0.0029 | 0.635 |
PGK1|5230 | 2 | 1.673e-06 | 0.031 | 0.685 |
CAND1|55832 | 2.1 | 1.686e-06 | 0.031 | 0.629 |
IRF2|3660 | 0.28 | 1.983e-06 | 0.036 | 0.329 |
AGE | Mean (SD) | 58.18 (13) |
Significant markers | N = 689 | |
pos. correlated | 163 | |
neg. correlated | 526 |
SpearmanCorr | corrP | Q | |
---|---|---|---|
ESR1|2099 | 0.3524 | 9.285e-26 | 1.69e-21 |
TFPI2|7980 | -0.268 | 4.569e-15 | 8.34e-11 |
LRFN5|145581 | -0.2692 | 5.372e-15 | 9.81e-11 |
TMEFF1|8577 | -0.2528 | 1.31e-13 | 2.39e-09 |
SOBP|55084 | -0.2517 | 1.69e-13 | 3.08e-09 |
DBX2|440097 | -0.2673 | 4.243e-13 | 7.74e-09 |
DZIP1|22873 | -0.2461 | 5.847e-13 | 1.07e-08 |
PCDH18|54510 | -0.2459 | 6.176e-13 | 1.13e-08 |
RELN|5649 | -0.2468 | 9.321e-13 | 1.7e-08 |
FXYD6|53826 | -0.2409 | 1.831e-12 | 3.34e-08 |
GENDER | Labels | N |
FEMALE | 825 | |
MALE | 9 | |
Significant markers | N = 18 | |
Higher in MALE | 8 | |
Higher in FEMALE | 10 |
T(pos if higher in 'MALE') | ttestP | Q | AUC | |
---|---|---|---|---|
NLGN4Y|22829 | 41.17 | 2.294e-12 | 4.12e-08 | 1 |
ZFY|7544 | 41.94 | 1.786e-11 | 3.21e-07 | 1 |
PRKY|5616 | 27.63 | 6.061e-10 | 1.09e-05 | 1 |
C7ORF10|79783 | 8.61 | 3.928e-09 | 7.06e-05 | 0.6626 |
SYT9|143425 | 12.51 | 5.949e-09 | 0.000107 | 0.7954 |
GSTA1|2938 | -16.04 | 8.563e-09 | 0.000154 | 0.8829 |
MMP11|4320 | 11.05 | 1.626e-08 | 0.000292 | 0.7461 |
RND2|8153 | 12.35 | 2.543e-08 | 0.000457 | 0.8361 |
SNORA74B|677841 | -12 | 8.287e-08 | 0.00149 | 0.8348 |
HTR4|3360 | -12.07 | 1.118e-07 | 0.00201 | 0.7909 |
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Expresson data file = BRCA-TP.uncv2.mRNAseq_RSEM_normalized_log2.txt
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Clinical data file = BRCA-TP.clin.merged.picked.txt
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Number of patients = 834
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Number of genes = 18254
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Number of clinical features = 4
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 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.