This pipeline uses various statistical tests to identify miRs whose expression levels correlated to selected clinical features.
Testing the association between 415 miRs and 5 clinical features across 453 samples, statistically thresholded by Q value < 0.05, 2 clinical features related to at least one miRs.
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3 miRs correlated to 'AGE'.
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HSA-MIR-1269 , HSA-MIR-30D , HSA-MIR-767
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3 miRs correlated to 'RADIATIONS.RADIATION.REGIMENINDICATION'.
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HSA-MIR-34A , HSA-MIR-501 , HSA-MIR-500
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No miRs correlated to 'Time to Death', 'PRIMARY.SITE.OF.DISEASE', and 'KARNOFSKY.PERFORMANCE.SCORE'.
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=0 | ||||
AGE | Spearman correlation test | N=3 | older | N=2 | younger | N=1 |
PRIMARY SITE OF DISEASE | ANOVA test | N=0 | ||||
KARNOFSKY PERFORMANCE SCORE | Spearman correlation test | N=0 | ||||
RADIATIONS RADIATION REGIMENINDICATION | t test | N=3 | yes | N=1 | no | N=2 |
Table S1. Basic characteristics of clinical feature: 'Time to Death'
Time to Death | Duration (Months) | 0.3-180.2 (median=30.1) |
censored | N = 198 | |
death | N = 252 | |
Significant markers | N = 0 |
Table S2. Basic characteristics of clinical feature: 'AGE'
AGE | Mean (SD) | 59.76 (12) |
Significant markers | N = 3 | |
pos. correlated | 2 | |
neg. correlated | 1 |
Table S3. Get Full Table List of 3 miRs significantly correlated to 'AGE' by Spearman correlation test
SpearmanCorr | corrP | Q | |
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HSA-MIR-1269 | 0.3265 | 1.639e-12 | 6.8e-10 |
HSA-MIR-30D | -0.2601 | 2.587e-08 | 1.07e-05 |
HSA-MIR-767 | 0.1997 | 5.641e-05 | 0.0233 |
Figure S1. Get High-res Image As an example, this figure shows the association of HSA-MIR-1269 to 'AGE'. P value = 1.64e-12 with Spearman correlation analysis. The straight line presents the best linear regression.
![](V2ex.png)
Table S4. Basic characteristics of clinical feature: 'PRIMARY.SITE.OF.DISEASE'
PRIMARY.SITE.OF.DISEASE | Labels | N |
OMENTUM | 2 | |
OVARY | 450 | |
PERITONEUM OVARY | 1 | |
Significant markers | N = 0 |
No miR related to 'KARNOFSKY.PERFORMANCE.SCORE'.
Table S5. Basic characteristics of clinical feature: 'KARNOFSKY.PERFORMANCE.SCORE'
KARNOFSKY.PERFORMANCE.SCORE | Mean (SD) | 75.31 (13) |
Score | N | |
40 | 2 | |
60 | 17 | |
80 | 39 | |
100 | 6 | |
Significant markers | N = 0 |
3 miRs related to 'RADIATIONS.RADIATION.REGIMENINDICATION'.
Table S6. Basic characteristics of clinical feature: 'RADIATIONS.RADIATION.REGIMENINDICATION'
RADIATIONS.RADIATION.REGIMENINDICATION | Labels | N |
NO | 3 | |
YES | 450 | |
Significant markers | N = 3 | |
Higher in YES | 1 | |
Higher in NO | 2 |
Table S7. Get Full Table List of 3 miRs differentially expressed by 'RADIATIONS.RADIATION.REGIMENINDICATION'
T(pos if higher in 'YES') | ttestP | Q | AUC | |
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HSA-MIR-34A | 9.66 | 1.891e-06 | 0.00063 | 0.723 |
HSA-MIR-501 | -6.95 | 1.914e-06 | 0.000636 | 0.6844 |
HSA-MIR-500 | -6.29 | 3.313e-06 | 0.0011 | 0.6511 |
Figure S2. Get High-res Image As an example, this figure shows the association of HSA-MIR-34A to 'RADIATIONS.RADIATION.REGIMENINDICATION'. P value = 1.89e-06 with T-test analysis.
![](V5ex.png)
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Expresson data file = OV-TP.miRseq_RPKM_log2.txt
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Clinical data file = OV-TP.merged_data.txt
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Number of patients = 453
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Number of miRs = 415
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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 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.
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.