This pipeline uses various statistical tests to identify miRs whose expression levels correlated to selected clinical features.
Testing the association between 534 miRs and 6 clinical features across 560 samples, statistically thresholded by Q value < 0.05, 3 clinical features related to at least one miRs.
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4 miRs correlated to 'Time to Death'.
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HSA-MIR-222 , HSA-MIR-221 , HSA-MIR-34A , HSA-MIR-148A
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4 miRs correlated to 'AGE'.
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HSA-MIR-210 , HSA-MIR-148A , HSA-MIR-552 , HSA-MIR-29B
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6 miRs correlated to 'HISTOLOGICAL.TYPE'.
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HSA-MIR-139 , HSA-MIR-29C , HSA-MIR-433 , HSA-MIR-137 , HSA-MIR-330 , ...
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No miRs correlated to 'GENDER', 'KARNOFSKY.PERFORMANCE.SCORE', and 'RADIATIONS.RADIATION.REGIMENINDICATION'.
Complete statistical result table is provided in Supplement Table 1
Clinical feature | Statistical test | Significant miRs | Associated with | Associated with | ||
---|---|---|---|---|---|---|
Time to Death | Cox regression test | N=4 | shorter survival | N=4 | longer survival | N=0 |
AGE | Spearman correlation test | N=4 | older | N=3 | younger | N=1 |
GENDER | t test | N=0 | ||||
KARNOFSKY PERFORMANCE SCORE | Spearman correlation test | N=0 | ||||
HISTOLOGICAL TYPE | ANOVA test | N=6 | ||||
RADIATIONS RADIATION REGIMENINDICATION | t test | N=0 |
Time to Death | Duration (Months) | 0.1-127.6 (median=9.9) |
censored | N = 112 | |
death | N = 448 | |
Significant markers | N = 4 | |
associated with shorter survival | 4 | |
associated with longer survival | 0 |
HazardRatio | Wald_P | Q | C_index | |
---|---|---|---|---|
HSA-MIR-222 | 1.27 | 3.524e-10 | 1.9e-07 | 0.564 |
HSA-MIR-221 | 1.31 | 1.807e-07 | 9.6e-05 | 0.555 |
HSA-MIR-34A | 1.19 | 4e-05 | 0.021 | 0.547 |
HSA-MIR-148A | 1.18 | 8.284e-05 | 0.044 | 0.554 |
AGE | Mean (SD) | 57.91 (14) |
Significant markers | N = 4 | |
pos. correlated | 3 | |
neg. correlated | 1 |
SpearmanCorr | corrP | Q | |
---|---|---|---|
HSA-MIR-210 | 0.2017 | 1.491e-06 | 0.000796 |
HSA-MIR-148A | 0.1947 | 3.462e-06 | 0.00184 |
HSA-MIR-552 | -0.1699 | 5.304e-05 | 0.0282 |
HSA-MIR-29B | 0.1682 | 6.366e-05 | 0.0338 |
GENDER | Labels | N |
FEMALE | 218 | |
MALE | 342 | |
Significant markers | N = 0 |
No miR related to 'KARNOFSKY.PERFORMANCE.SCORE'.
KARNOFSKY.PERFORMANCE.SCORE | Mean (SD) | 77.37 (15) |
Significant markers | N = 0 |
HISTOLOGICAL.TYPE | Labels | N |
GLIOBLASTOMA MULTIFORME (GBM) | 8 | |
TREATED PRIMARY GBM | 20 | |
UNTREATED PRIMARY (DE NOVO) GBM | 532 | |
Significant markers | N = 6 |
ANOVA_P | Q | |
---|---|---|
HSA-MIR-139 | 9.232e-07 | 0.000493 |
HSA-MIR-29C | 2.043e-06 | 0.00109 |
HSA-MIR-433 | 5.361e-06 | 0.00285 |
HSA-MIR-137 | 2.015e-05 | 0.0107 |
HSA-MIR-330 | 3.539e-05 | 0.0188 |
HSA-MIR-29B | 5.565e-05 | 0.0294 |
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Expresson data file = GBM-TP.mirna__h_mirna_8x15k__unc_edu__Level_3__unc_DWD_Batch_adjusted__data.data.txt
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Clinical data file = GBM-TP.merged_data.txt
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Number of patients = 560
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Number of miRs = 534
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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.
In addition to the links below, the full results of the analysis summarized in this report can also be downloaded programmatically using firehose_get, or interactively from either the Broad GDAC website or TCGA Data Coordination Center Portal.