This pipeline uses various statistical tests to identify miRs whose expression levels correlated to selected clinical features.
Testing the association between 817 miRs and 6 clinical features across 562 samples, statistically thresholded by Q value < 0.05, 3 clinical features related to at least one miRs.
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5 miRs correlated to 'AGE'.
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HSA-MIR-30B* , HSA-MIR-30D* , HSA-MIR-30B , HUR_4 , HSA-MIR-30D
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14 miRs correlated to 'PRIMARY.SITE.OF.DISEASE'.
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EBV-MIR-BART6-5P , HSA-MIR-96* , HSA-MIR-614 , KSHV-MIR-K12-9* , HSA-MIR-26A-2* , ...
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6 miRs correlated to 'RADIATIONS.RADIATION.REGIMENINDICATION'.
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HSA-MIR-125B-1* , HSA-MIR-338-5P , EBV-MIR-BART17-5P , HSA-MIR-589* , HSA-MIR-518D-5P , ...
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No miRs correlated to 'Time to Death', 'KARNOFSKY.PERFORMANCE.SCORE', and 'NEOADJUVANT.THERAPY'.
Complete statistical result table is provided in Supplement Table 1
Clinical feature | Statistical test | Significant miRs | Associated with | Associated with | ||
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Time to Death | Cox regression test | N=0 | ||||
AGE | Spearman correlation test | N=5 | older | N=1 | younger | N=4 |
PRIMARY SITE OF DISEASE | ANOVA test | N=14 | ||||
KARNOFSKY PERFORMANCE SCORE | Spearman correlation test | N=0 | ||||
RADIATIONS RADIATION REGIMENINDICATION | t test | N=6 | yes | N=6 | no | N=0 |
NEOADJUVANT THERAPY | t test | N=0 |
Time to Death | Duration (Months) | 0.3-180.2 (median=28.2) |
censored | N = 265 | |
death | N = 292 | |
Significant markers | N = 0 |
AGE | Mean (SD) | 59.68 (12) |
Significant markers | N = 5 | |
pos. correlated | 1 | |
neg. correlated | 4 |
SpearmanCorr | corrP | Q | |
---|---|---|---|
HSA-MIR-30B* | -0.2227 | 1.28e-07 | 0.000105 |
HSA-MIR-30D* | -0.2187 | 2.161e-07 | 0.000176 |
HSA-MIR-30B | -0.2048 | 1.254e-06 | 0.00102 |
HUR_4 | 0.1972 | 3.101e-06 | 0.00252 |
HSA-MIR-30D | -0.1902 | 6.917e-06 | 0.00562 |
PRIMARY.SITE.OF.DISEASE | Labels | N |
OMENTUM | 2 | |
OVARY | 558 | |
PERITONEUM (OVARY) | 2 | |
Significant markers | N = 14 |
ANOVA_P | Q | |
---|---|---|
EBV-MIR-BART6-5P | 5.151e-15 | 4.21e-12 |
HSA-MIR-96* | 6.953e-13 | 5.67e-10 |
HSA-MIR-614 | 2.311e-12 | 1.88e-09 |
KSHV-MIR-K12-9* | 2.283e-11 | 1.86e-08 |
HSA-MIR-26A-2* | 1.475e-10 | 1.2e-07 |
EBV-MIR-BART9 | 2.28e-10 | 1.85e-07 |
HSA-MIR-600 | 6.428e-10 | 5.21e-07 |
HSA-MIR-548C-3P | 6.817e-10 | 5.52e-07 |
HSA-MIR-374A* | 4.249e-09 | 3.44e-06 |
HCMV-MIR-UL36* | 1.328e-08 | 1.07e-05 |
No miR 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 = 0 |
6 miRs related to 'RADIATIONS.RADIATION.REGIMENINDICATION'.
RADIATIONS.RADIATION.REGIMENINDICATION | Labels | N |
NO | 3 | |
YES | 559 | |
Significant markers | N = 6 | |
Higher in YES | 6 | |
Higher in NO | 0 |
T(pos if higher in 'YES') | ttestP | Q | AUC | |
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HSA-MIR-125B-1* | 12.11 | 2.011e-17 | 1.64e-14 | 0.7895 |
HSA-MIR-338-5P | 12.61 | 1.412e-15 | 1.15e-12 | 0.802 |
EBV-MIR-BART17-5P | 7.93 | 6.434e-14 | 5.24e-11 | 0.6887 |
HSA-MIR-589* | 13.1 | 1.673e-12 | 1.36e-09 | 0.7651 |
HSA-MIR-518D-5P | 6.99 | 4.74e-10 | 3.85e-07 | 0.6786 |
HSA-MIR-486-5P | 15.93 | 2.182e-08 | 1.77e-05 | 0.8253 |
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Expresson data file = PANCANCER.mirna__h_mirna_8x15kv2__unc_edu__Level_3__unc_DWD_Batch_adjusted__data.data.txt
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Clinical data file = PANCANCER.clin.merged.picked.txt
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Number of patients = 562
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Number of miRs = 817
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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.