This pipeline uses various statistical tests to identify miRs whose expression levels correlated to selected clinical features.
Testing the association between 556 genes and 5 clinical features across 469 samples, statistically thresholded by Q value < 0.05, 4 clinical features related to at least one genes.
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3 genes correlated to 'Time to Death'.
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HSA-MIR-497 , HSA-LET-7G , HSA-LET-7B
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45 genes correlated to 'AGE'.
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HSA-MIR-424 , HSA-MIR-1247 , HSA-MIR-337 , HSA-MIR-935 , HSA-MIR-199A-1 , ...
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102 genes correlated to 'HISTOLOGICAL.TYPE'.
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HSA-MIR-9-3 , HSA-MIR-9-2 , HSA-MIR-9-1 , HSA-MIR-934 , HSA-MIR-34A , ...
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9 genes correlated to 'RADIATIONS.RADIATION.REGIMENINDICATION'.
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HSA-MIR-3613 , HSA-MIR-128-1 , HSA-MIR-128-2 , HSA-MIR-107 , HSA-MIR-628 , ...
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No genes correlated to 'COMPLETENESS.OF.RESECTION'
Complete statistical result table is provided in Supplement Table 1
Clinical feature | Statistical test | Significant genes | Associated with | Associated with | ||
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Time to Death | Cox regression test | N=3 | shorter survival | N=0 | longer survival | N=3 |
AGE | Spearman correlation test | N=45 | older | N=4 | younger | N=41 |
HISTOLOGICAL TYPE | ANOVA test | N=102 | ||||
RADIATIONS RADIATION REGIMENINDICATION | t test | N=9 | yes | N=6 | no | N=3 |
COMPLETENESS OF RESECTION | ANOVA test | N=0 |
Time to Death | Duration (Months) | 0-191.8 (median=20.9) |
censored | N = 413 | |
death | N = 53 | |
Significant markers | N = 3 | |
associated with shorter survival | 0 | |
associated with longer survival | 3 |
HazardRatio | Wald_P | Q | C_index | |
---|---|---|---|---|
HSA-MIR-497 | 0.66 | 1.653e-05 | 0.0092 | 0.336 |
HSA-LET-7G | 0.51 | 2.347e-05 | 0.013 | 0.337 |
HSA-LET-7B | 0.6 | 8.118e-05 | 0.045 | 0.293 |
AGE | Mean (SD) | 63.66 (11) |
Significant markers | N = 45 | |
pos. correlated | 4 | |
neg. correlated | 41 |
SpearmanCorr | corrP | Q | |
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HSA-MIR-424 | -0.3223 | 9.039e-13 | 5.03e-10 |
HSA-MIR-1247 | -0.2656 | 6.231e-09 | 3.46e-06 |
HSA-MIR-337 | -0.2469 | 6.245e-08 | 3.46e-05 |
HSA-MIR-935 | 0.2677 | 7.937e-08 | 4.39e-05 |
HSA-MIR-199A-1 | -0.2374 | 2.03e-07 | 0.000112 |
HSA-MIR-516A-1 | 0.3031 | 2.114e-07 | 0.000116 |
HSA-MIR-214 | -0.2361 | 2.441e-07 | 0.000134 |
HSA-MIR-409 | -0.2358 | 2.464e-07 | 0.000135 |
HSA-MIR-34A | -0.2356 | 2.513e-07 | 0.000138 |
HSA-MIR-199A-2 | -0.2331 | 3.396e-07 | 0.000186 |
HISTOLOGICAL.TYPE | Labels | N |
ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA | 362 | |
MIXED SEROUS AND ENDOMETRIOID | 18 | |
SEROUS ENDOMETRIAL ADENOCARCINOMA | 89 | |
Significant markers | N = 102 |
ANOVA_P | Q | |
---|---|---|
HSA-MIR-9-3 | 2.203e-33 | 1.22e-30 |
HSA-MIR-9-2 | 5.277e-30 | 2.93e-27 |
HSA-MIR-9-1 | 6.31e-30 | 3.5e-27 |
HSA-MIR-934 | 3.937e-25 | 2.18e-22 |
HSA-MIR-34A | 1.663e-21 | 9.18e-19 |
HSA-MIR-375 | 1.059e-19 | 5.83e-17 |
HSA-MIR-195 | 3.877e-19 | 2.13e-16 |
HSA-MIR-221 | 4.03e-19 | 2.21e-16 |
HSA-MIR-190B | 1.024e-18 | 5.61e-16 |
HSA-MIR-452 | 1.145e-18 | 6.26e-16 |
9 genes related to 'RADIATIONS.RADIATION.REGIMENINDICATION'.
RADIATIONS.RADIATION.REGIMENINDICATION | Labels | N |
NO | 131 | |
YES | 338 | |
Significant markers | N = 9 | |
Higher in YES | 6 | |
Higher in NO | 3 |
T(pos if higher in 'YES') | ttestP | Q | AUC | |
---|---|---|---|---|
HSA-MIR-3613 | -5.21 | 3.665e-07 | 0.000204 | 0.6549 |
HSA-MIR-128-1 | 4.94 | 1.322e-06 | 0.000734 | 0.6401 |
HSA-MIR-128-2 | 4.72 | 3.686e-06 | 0.00204 | 0.6316 |
HSA-MIR-107 | 4.56 | 7.554e-06 | 0.00418 | 0.6194 |
HSA-MIR-628 | -4.54 | 8.46e-06 | 0.00467 | 0.6273 |
HSA-MIR-181D | 4.36 | 1.855e-05 | 0.0102 | 0.6296 |
HSA-MIR-361 | 4.34 | 2.05e-05 | 0.0113 | 0.6183 |
HSA-MIR-146A | -4.21 | 3.541e-05 | 0.0194 | 0.6175 |
HSA-MIR-103-1 | 3.99 | 8.724e-05 | 0.0478 | 0.6166 |
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Expresson data file = UCEC-TP.miRseq_RPKM_log2.txt
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Clinical data file = UCEC-TP.clin.merged.picked.txt
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Number of patients = 469
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Number of genes = 556
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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.