This pipeline uses various statistical tests to identify miRs whose expression levels correlated to selected clinical features.
Testing the association between 508 genes and 5 clinical features across 838 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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26 genes correlated to 'AGE'.
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HSA-MIR-424 , HSA-MIR-381 , HSA-MIR-31 , HSA-MIR-598 , HSA-MIR-99A , ...
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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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5 genes correlated to 'NEOADJUVANT.THERAPY'.
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HSA-MIR-374C , HSA-MIR-3607 , HSA-MIR-26A-1 , HSA-MIR-1180 , HSA-MIR-361
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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=26 | older | N=2 | younger | N=24 |
GENDER | t test | N=0 | ||||
RADIATIONS RADIATION REGIMENINDICATION | t test | N=3 | yes | N=0 | no | N=3 |
NEOADJUVANT THERAPY | t test | N=5 | yes | N=2 | no | N=3 |
Time to Death | Duration (Months) | 0-223.4 (median=18.9) |
censored | N = 684 | |
death | N = 95 | |
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 | 1.58 | 7.068e-06 | 0.0036 | 0.606 |
HSA-MIR-148B | 1.79 | 5.138e-05 | 0.026 | 0.627 |
AGE | Mean (SD) | 58.41 (13) |
Significant markers | N = 26 | |
pos. correlated | 2 | |
neg. correlated | 24 |
SpearmanCorr | corrP | Q | |
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HSA-MIR-424 | -0.2234 | 6.369e-11 | 3.24e-08 |
HSA-MIR-381 | -0.2111 | 6.876e-10 | 3.49e-07 |
HSA-MIR-31 | -0.2037 | 4.305e-09 | 2.18e-06 |
HSA-MIR-598 | -0.1956 | 1.151e-08 | 5.81e-06 |
HSA-MIR-99A | -0.1945 | 1.403e-08 | 7.07e-06 |
HSA-MIR-542 | -0.1935 | 1.676e-08 | 8.43e-06 |
HSA-MIR-652 | -0.1854 | 6.519e-08 | 3.27e-05 |
HSA-LET-7C | -0.1739 | 4.17e-07 | 0.000209 |
HSA-MIR-125B-1 | -0.1589 | 3.818e-06 | 0.00191 |
HSA-MIR-450B | -0.159 | 3.849e-06 | 0.00192 |
GENDER | Labels | N |
FEMALE | 829 | |
MALE | 9 | |
Significant markers | N = 0 |
3 genes related to 'RADIATIONS.RADIATION.REGIMENINDICATION'.
RADIATIONS.RADIATION.REGIMENINDICATION | Labels | N |
NO | 193 | |
YES | 645 | |
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 | -4.78 | 3.1e-06 | 0.00157 | 0.6323 |
HSA-MIR-3607 | -4.3 | 2.122e-05 | 0.0108 | 0.5721 |
HSA-MIR-489 | -4.25 | 3.472e-05 | 0.0176 | 0.6254 |
NEOADJUVANT.THERAPY | Labels | N |
NO | 303 | |
YES | 535 | |
Significant markers | N = 5 | |
Higher in YES | 2 | |
Higher in NO | 3 |
T(pos if higher in 'YES') | ttestP | Q | AUC | |
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HSA-MIR-374C | -5.78 | 1.409e-08 | 7.16e-06 | 0.6401 |
HSA-MIR-3607 | -5.43 | 7.725e-08 | 3.92e-05 | 0.5805 |
HSA-MIR-26A-1 | -4.74 | 2.543e-06 | 0.00129 | 0.5792 |
HSA-MIR-1180 | 4.7 | 3.195e-06 | 0.00161 | 0.5955 |
HSA-MIR-361 | 4.02 | 6.66e-05 | 0.0336 | 0.5823 |
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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 = 838
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Number of genes = 508
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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.