This pipeline uses various statistical tests to identify miRs whose expression levels correlated to selected clinical features.
Testing the association between 506 genes and 5 clinical features across 817 samples, statistically thresholded by Q value < 0.05, 4 clinical features related to at least one genes.
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2 genes correlated to 'Time to Death'.
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HSA-MIR-874 , HSA-MIR-148B
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25 genes correlated to 'AGE'.
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HSA-MIR-424 , HSA-MIR-381 , HSA-MIR-31 , HSA-MIR-99A , HSA-MIR-542 , ...
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3 genes correlated to 'RADIATIONS.RADIATION.REGIMENINDICATION'.
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HSA-MIR-374C , HSA-MIR-3607 , HSA-MIR-489
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7 genes correlated to 'NEOADJUVANT.THERAPY'.
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HSA-MIR-374C , HSA-MIR-3607 , HSA-MIR-1180 , HSA-MIR-26A-1 , HSA-MIR-3676 , ...
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No genes correlated to 'GENDER'
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=2 | shorter survival | N=2 | longer survival | N=0 |
AGE | Spearman correlation test | N=25 | older | N=2 | younger | N=23 |
GENDER | t test | N=0 | ||||
RADIATIONS RADIATION REGIMENINDICATION | t test | N=3 | yes | N=0 | no | N=3 |
NEOADJUVANT THERAPY | t test | N=7 | yes | N=3 | no | N=4 |
Time to Death | Duration (Months) | 0-223.4 (median=19) |
censored | N = 664 | |
death | N = 94 | |
Significant markers | N = 2 | |
associated with shorter survival | 2 | |
associated with longer survival | 0 |
HazardRatio | Wald_P | Q | C_index | |
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HSA-MIR-874 | 1.57 | 1.037e-05 | 0.0052 | 0.606 |
HSA-MIR-148B | 1.78 | 6.237e-05 | 0.031 | 0.625 |
AGE | Mean (SD) | 58.26 (13) |
Significant markers | N = 25 | |
pos. correlated | 2 | |
neg. correlated | 23 |
SpearmanCorr | corrP | Q | |
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HSA-MIR-424 | -0.2216 | 1.56e-10 | 7.89e-08 |
HSA-MIR-381 | -0.2139 | 6.764e-10 | 3.42e-07 |
HSA-MIR-31 | -0.213 | 1.308e-09 | 6.59e-07 |
HSA-MIR-99A | -0.2057 | 3.019e-09 | 1.52e-06 |
HSA-MIR-542 | -0.2044 | 3.778e-09 | 1.9e-06 |
HSA-MIR-598 | -0.1996 | 8.862e-09 | 4.44e-06 |
HSA-MIR-652 | -0.1965 | 1.505e-08 | 7.52e-06 |
HSA-LET-7C | -0.1774 | 3.37e-07 | 0.000168 |
HSA-MIR-450B | -0.1705 | 9.763e-07 | 0.000486 |
HSA-MIR-125B-1 | -0.1652 | 2.08e-06 | 0.00103 |
GENDER | Labels | N |
FEMALE | 808 | |
MALE | 9 | |
Significant markers | N = 0 |
3 genes related to 'RADIATIONS.RADIATION.REGIMENINDICATION'.
RADIATIONS.RADIATION.REGIMENINDICATION | Labels | N |
NO | 189 | |
YES | 628 | |
Significant markers | N = 3 | |
Higher in YES | 0 | |
Higher in NO | 3 |
T(pos if higher in 'YES') | ttestP | Q | AUC | |
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HSA-MIR-374C | -4.73 | 3.893e-06 | 0.00197 | 0.6313 |
HSA-MIR-3607 | -4.3 | 2.14e-05 | 0.0108 | 0.5726 |
HSA-MIR-489 | -4.3 | 2.917e-05 | 0.0147 | 0.6304 |
NEOADJUVANT.THERAPY | Labels | N |
NO | 299 | |
YES | 518 | |
Significant markers | N = 7 | |
Higher in YES | 3 | |
Higher in NO | 4 |
T(pos if higher in 'YES') | ttestP | Q | AUC | |
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HSA-MIR-374C | -5.75 | 1.614e-08 | 8.17e-06 | 0.6404 |
HSA-MIR-3607 | -5.21 | 2.362e-07 | 0.000119 | 0.5764 |
HSA-MIR-1180 | 4.7 | 3.18e-06 | 0.0016 | 0.5965 |
HSA-MIR-26A-1 | -4.5 | 7.912e-06 | 0.00398 | 0.575 |
HSA-MIR-3676 | 4.41 | 1.232e-05 | 0.00618 | 0.5909 |
HSA-MIR-361 | 4.29 | 2.067e-05 | 0.0104 | 0.5903 |
HSA-MIR-320E | -3.93 | 9.988e-05 | 0.0499 | 0.5878 |
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Expresson data file = BRCA.miRseq_RPKM_log2.txt
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Clinical data file = BRCA.clin.merged.picked.txt
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Number of patients = 817
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Number of genes = 506
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Number of clinical features = 5
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.