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 488 samples, statistically thresholded by Q value < 0.05, 3 clinical features related to at least one miRs.
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3 miRs correlated to 'Time to Death'.
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HSA-MIR-222 , HSA-MIR-221 , HSA-MIR-148A
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2 miRs correlated to 'AGE'.
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HSA-MIR-148A , HSA-MIR-210
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3 miRs correlated to 'HISTOLOGICAL.TYPE'.
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HSA-MIR-29C , HSA-MIR-137 , HSA-MIR-29B
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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 | ||
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Time to Death | Cox regression test | N=3 | shorter survival | N=3 | longer survival | N=0 |
AGE | Spearman correlation test | N=2 | older | N=2 | younger | N=0 |
GENDER | t test | N=0 | ||||
KARNOFSKY PERFORMANCE SCORE | Spearman correlation test | N=0 | ||||
HISTOLOGICAL TYPE | ANOVA test | N=3 | ||||
RADIATIONS RADIATION REGIMENINDICATION | t test | N=0 |
Time to Death | Duration (Months) | 0.1-127.6 (median=10.3) |
censored | N = 103 | |
death | N = 385 | |
Significant markers | N = 3 | |
associated with shorter survival | 3 | |
associated with longer survival | 0 |
HazardRatio | Wald_P | Q | C_index | |
---|---|---|---|---|
HSA-MIR-222 | 1.26 | 2.342e-08 | 1.3e-05 | 0.558 |
HSA-MIR-221 | 1.3 | 3.051e-06 | 0.0016 | 0.55 |
HSA-MIR-148A | 1.2 | 5.951e-05 | 0.032 | 0.562 |
AGE | Mean (SD) | 57.59 (15) |
Significant markers | N = 2 | |
pos. correlated | 2 | |
neg. correlated | 0 |
SpearmanCorr | corrP | Q | |
---|---|---|---|
HSA-MIR-148A | 0.2091 | 3.164e-06 | 0.00169 |
HSA-MIR-210 | 0.1946 | 1.498e-05 | 0.00798 |
GENDER | Labels | N |
FEMALE | 188 | |
MALE | 300 | |
Significant markers | N = 0 |
No miR related to 'KARNOFSKY.PERFORMANCE.SCORE'.
KARNOFSKY.PERFORMANCE.SCORE | Mean (SD) | 77.61 (14) |
Significant markers | N = 0 |
HISTOLOGICAL.TYPE | Labels | N |
GLIOBLASTOMA MULTIFORME (GBM) | 6 | |
TREATED PRIMARY GBM | 20 | |
UNTREATED PRIMARY (DE NOVO) GBM | 462 | |
Significant markers | N = 3 |
ANOVA_P | Q | |
---|---|---|
HSA-MIR-29C | 1.082e-05 | 0.00578 |
HSA-MIR-137 | 3.494e-05 | 0.0186 |
HSA-MIR-29B | 5.487e-05 | 0.0292 |
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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.clin.merged.picked.txt
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Number of patients = 488
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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.
This is an experimental feature. The full results of the analysis summarized in this report can be downloaded from the TCGA Data Coordination Center.