This pipeline uses various statistical tests to identify miRs whose log2 expression levels correlated to selected clinical features. The input file " UVM-TP.miRseq_RPKM_log2.txt " is generated in the pipeline miRseq_Preprocess in the stddata run.
Testing the association between 536 miRs and 7 clinical features across 80 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 2 clinical features related to at least one miRs.
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30 miRs correlated to 'DAYS_TO_DEATH_OR_LAST_FUP'.
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HSA-MIR-508 , HSA-MIR-506 , HSA-MIR-514-3 , HSA-MIR-514-1 , HSA-MIR-181A-1 , ...
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30 miRs correlated to 'YEARS_TO_BIRTH'.
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HSA-MIR-15A , HSA-MIR-181A-2 , HSA-LET-7D , HSA-MIR-197 , HSA-MIR-20A , ...
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No miRs correlated to 'PATHOLOGIC_STAGE', 'PATHOLOGY_T_STAGE', 'PATHOLOGY_M_STAGE', 'GENDER', and 'RADIATION_THERAPY'.
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 P value < 0.05 and Q value < 0.3.
| Clinical feature | Statistical test | Significant miRs | Associated with | Associated with | ||
|---|---|---|---|---|---|---|
| DAYS_TO_DEATH_OR_LAST_FUP | Cox regression test | N=30 | N=NA | N=NA | ||
| YEARS_TO_BIRTH | Spearman correlation test | N=30 | older | N=10 | younger | N=20 |
| PATHOLOGIC_STAGE | Kruskal-Wallis test | N=0 | ||||
| PATHOLOGY_T_STAGE | Spearman correlation test | N=0 | ||||
| PATHOLOGY_M_STAGE | Wilcoxon test | N=0 | ||||
| GENDER | Wilcoxon test | N=0 | ||||
| RADIATION_THERAPY | Wilcoxon test | N=0 |
Table S1. Basic characteristics of clinical feature: 'DAYS_TO_DEATH_OR_LAST_FUP'
| DAYS_TO_DEATH_OR_LAST_FUP | Duration (Months) | 0.1-85.5 (median=25.4) |
| censored | N = 56 | |
| death | N = 23 | |
| Significant markers | N = 30 | |
| associated with shorter survival | NA | |
| associated with longer survival | NA |
Table S2. Get Full Table List of top 10 miRs significantly associated with 'Time to Death' by Cox regression test. For the survival curves, it compared quantile intervals at c(0, 0.25, 0.50, 0.75, 1) and did not try survival analysis if there is only one interval.
| logrank_P | Q | C_index | |
|---|---|---|---|
| HSA-MIR-508 | 6.92e-08 | 1.6e-05 | 0.133 |
| HSA-MIR-506 | 1.02e-07 | 1.6e-05 | 0.135 |
| HSA-MIR-514-3 | 1.17e-07 | 1.6e-05 | 0.137 |
| HSA-MIR-514-1 | 1.24e-07 | 1.6e-05 | 0.131 |
| HSA-MIR-181A-1 | 1.52e-07 | 1.6e-05 | 0.173 |
| HSA-MIR-212 | 2.67e-07 | 2.4e-05 | 0.796 |
| HSA-MIR-509-1 | 5.15e-07 | 3.4e-05 | 0.133 |
| HSA-MIR-509-3 | 5.15e-07 | 3.4e-05 | 0.131 |
| HSA-MIR-513C | 2.46e-06 | 0.00015 | 0.141 |
| HSA-MIR-148B | 3.77e-06 | 2e-04 | 0.731 |
Table S3. Basic characteristics of clinical feature: 'YEARS_TO_BIRTH'
| YEARS_TO_BIRTH | Mean (SD) | 61.65 (14) |
| Significant markers | N = 30 | |
| pos. correlated | 10 | |
| neg. correlated | 20 |
Table S4. Get Full Table List of top 10 miRs significantly correlated to 'YEARS_TO_BIRTH' by Spearman correlation test
| SpearmanCorr | corrP | Q | |
|---|---|---|---|
| HSA-MIR-15A | 0.4398 | 4.475e-05 | 0.024 |
| HSA-MIR-181A-2 | -0.408 | 0.0001724 | 0.0328 |
| HSA-LET-7D | -0.3973 | 0.0002637 | 0.0328 |
| HSA-MIR-197 | -0.3958 | 0.0002791 | 0.0328 |
| HSA-MIR-20A | 0.3908 | 0.0003387 | 0.0328 |
| HSA-MIR-191 | -0.384 | 0.000438 | 0.0328 |
| HSA-MIR-18A | 0.3807 | 0.000494 | 0.0328 |
| HSA-MIR-744 | -0.3791 | 0.0005242 | 0.0328 |
| HSA-MIR-30C-1 | -0.3777 | 0.0005516 | 0.0328 |
| HSA-MIR-7-1 | -0.3567 | 0.001163 | 0.0623 |
Table S5. Basic characteristics of clinical feature: 'PATHOLOGIC_STAGE'
| PATHOLOGIC_STAGE | Labels | N |
| STAGE IIA | 12 | |
| STAGE IIB | 27 | |
| STAGE IIIA | 25 | |
| STAGE IIIB | 10 | |
| STAGE IIIC | 1 | |
| STAGE IV | 4 | |
| Significant markers | N = 0 |
Table S6. Basic characteristics of clinical feature: 'PATHOLOGY_T_STAGE'
| PATHOLOGY_T_STAGE | Mean (SD) | 3.25 (0.74) |
| N | ||
| T2 | 14 | |
| T3 | 32 | |
| T4 | 34 | |
| Significant markers | N = 0 |
Table S7. Basic characteristics of clinical feature: 'PATHOLOGY_M_STAGE'
| PATHOLOGY_M_STAGE | Labels | N |
| class0 | 51 | |
| class1 | 4 | |
| Significant markers | N = 0 |
Table S8. Basic characteristics of clinical feature: 'GENDER'
| GENDER | Labels | N |
| FEMALE | 35 | |
| MALE | 45 | |
| Significant markers | N = 0 |
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Expresson data file = UVM-TP.miRseq_RPKM_log2.txt
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Clinical data file = UVM-TP.merged_data.txt
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Number of patients = 80
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Number of miRs = 536
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Number of clinical features = 7
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Further details on clinical features selected for this analysis, please find a documentation on selected CDEs (Clinical Data Elements). The first column of the file is a formula to convert values and the second column is a clinical parameter name.
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There are also useful links about clinical features.
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Survival time data
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Survival time data is a combined value of days_to_death and days_to_last_followup. For each patient, it creates a combined value 'days_to_death_or_last_fup' using conversion process below.
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if 'vital_status'==1(dead), 'days_to_last_followup' is always NA. Thus, uses 'days_to_death' value for 'days_to_death_or_fup'
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if 'vital_status'==0(alive),
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if 'days_to_death'==NA & 'days_to_last_followup'!=NA, uses 'days_to_last_followup' value for 'days_to_death_or_fup'
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if 'days_to_death'!=NA, excludes this case in survival analysis and report the case.
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if 'vital_status'==NA,excludes this case in survival analysis and report the case.
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cf. In certain diesase types such as SKCM, days_to_death parameter is replaced with time_from_specimen_dx or time_from_specimen_procurement_to_death .
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This analysis excluded clinical variables that has only NA values.
For survival clinical features, logrank test in univariate Cox regression analysis with proportional hazards model (Andersen and Gill 1982) was used to estimate the P values comparing quantile intervals using the 'coxph' function in R. Kaplan-Meier survival curves were plotted using quantile intervals at c(0, 0.25, 0.50, 0.75, 1). If there is only one interval group, it will not try survival analysis.
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 groups (mutant or wild-type) of continuous type of clinical data, wilcoxon rank sum test (Mann and Whitney, 1947) was applied to compare their mean difference using 'wilcox.test(continuous.clinical ~ as.factor(group), exact=FALSE)' function in R. This test is equivalent to the Mann-Whitney test.
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.