(primary solid tumor cohort)
This pipeline uses various statistical tests to identify miRs whose expression levels correlated to selected clinical features.
Testing the association between 534 miRs and 5 clinical features across 482 samples, statistically thresholded by Q value < 0.05, 2 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-148A , HSA-MIR-34A
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3 miRs correlated to 'AGE'.
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HSA-MIR-148A , HSA-MIR-210 , HSA-MIR-339
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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
Table 1. Get Full Table This table shows the clinical features, statistical methods used, and the number of miRs that are significantly associated with each clinical feature at Q value < 0.05.
Clinical feature | Statistical test | Significant miRs | Associated with | Associated with | ||
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Time to Death | Cox regression test | N=4 | shorter survival | N=4 | longer survival | N=0 |
AGE | Spearman correlation test | N=3 | older | N=3 | younger | N=0 |
GENDER | t test | N=0 | ||||
KARNOFSKY PERFORMANCE SCORE | Spearman correlation test | N=0 | ||||
RADIATIONS RADIATION REGIMENINDICATION | t test | N=0 |
Table S1. Basic characteristics of clinical feature: 'Time to Death'
Time to Death | Duration (Months) | 0.1-127.6 (median=10.3) |
censored | N = 103 | |
death | N = 379 | |
Significant markers | N = 4 | |
associated with shorter survival | 4 | |
associated with longer survival | 0 |
Table S2. Get Full Table List of 4 miRs significantly associated with 'Time to Death' by Cox regression test
HazardRatio | Wald_P | Q | C_index | |
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HSA-MIR-222 | 1.27 | 6.105e-09 | 3.3e-06 | 0.563 |
HSA-MIR-221 | 1.32 | 1.292e-06 | 0.00069 | 0.554 |
HSA-MIR-148A | 1.21 | 2.856e-05 | 0.015 | 0.564 |
HSA-MIR-34A | 1.2 | 7.211e-05 | 0.038 | 0.54 |
Figure S1. Get High-res Image As an example, this figure shows the association of HSA-MIR-222 to 'Time to Death'. four curves present the cumulative survival rates of 4 quartile subsets of patients. P value = 6.11e-09 with univariate Cox regression analysis using continuous log-2 expression values.
![](V1ex.png)
Table S3. Basic characteristics of clinical feature: 'AGE'
AGE | Mean (SD) | 57.53 (15) |
Significant markers | N = 3 | |
pos. correlated | 3 | |
neg. correlated | 0 |
Table S4. Get Full Table List of 3 miRs significantly correlated to 'AGE' by Spearman correlation test
SpearmanCorr | corrP | Q | |
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HSA-MIR-148A | 0.2148 | 1.933e-06 | 0.00103 |
HSA-MIR-210 | 0.1952 | 1.587e-05 | 0.00846 |
HSA-MIR-339 | 0.1809 | 6.511e-05 | 0.0346 |
Figure S2. Get High-res Image As an example, this figure shows the association of HSA-MIR-148A to 'AGE'. P value = 1.93e-06 with Spearman correlation analysis. The straight line presents the best linear regression.
![](V2ex.png)
Table S5. Basic characteristics of clinical feature: 'GENDER'
GENDER | Labels | N |
FEMALE | 186 | |
MALE | 296 | |
Significant markers | N = 0 |
No miR related to 'KARNOFSKY.PERFORMANCE.SCORE'.
Table S6. Basic characteristics of clinical feature: 'KARNOFSKY.PERFORMANCE.SCORE'
KARNOFSKY.PERFORMANCE.SCORE | Mean (SD) | 77.61 (14) |
Significant markers | N = 0 |
No miR related to 'RADIATIONS.RADIATION.REGIMENINDICATION'.
Table S7. Basic characteristics of clinical feature: 'RADIATIONS.RADIATION.REGIMENINDICATION'
RADIATIONS.RADIATION.REGIMENINDICATION | Labels | N |
NO | 327 | |
YES | 155 | |
Significant markers | N = 0 |
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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 = 482
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Number of miRs = 534
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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.