This pipeline uses various statistical tests to identify miRs whose expression levels correlated to selected clinical features.
Testing the association between 517 miRs and 8 clinical features across 52 samples, statistically thresholded by Q value < 0.05, 2 clinical features related to at least one miRs.
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2 miRs correlated to 'AGE'.
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HSA-MIR-708 , HSA-MIR-149
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3 miRs correlated to 'GENDER'.
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HSA-MIR-584 , HSA-MIR-1266 , HSA-MIR-3662
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No miRs correlated to 'Time to Death', 'NEOPLASM.DISEASESTAGE', 'PATHOLOGY.T.STAGE', 'PATHOLOGY.N.STAGE', 'PATHOLOGY.M.STAGE', and 'NUMBERPACKYEARSSMOKED'.
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=0 | ||||
AGE | Spearman correlation test | N=2 | older | N=0 | younger | N=2 |
NEOPLASM DISEASESTAGE | ANOVA test | N=0 | ||||
PATHOLOGY T STAGE | Spearman correlation test | N=0 | ||||
PATHOLOGY N STAGE | Spearman correlation test | N=0 | ||||
PATHOLOGY M STAGE | ANOVA test | N=0 | ||||
GENDER | t test | N=3 | male | N=0 | female | N=3 |
NUMBERPACKYEARSSMOKED | Spearman correlation test | N=0 |
Time to Death | Duration (Months) | 0-30.7 (median=1) |
censored | N = 36 | |
death | N = 13 | |
Significant markers | N = 0 |
AGE | Mean (SD) | 62.94 (12) |
Significant markers | N = 2 | |
pos. correlated | 0 | |
neg. correlated | 2 |
SpearmanCorr | corrP | Q | |
---|---|---|---|
HSA-MIR-708 | -0.5804 | 6.473e-06 | 0.00335 |
HSA-MIR-149 | -0.5521 | 2.205e-05 | 0.0114 |
NEOPLASM.DISEASESTAGE | Labels | N |
STAGE I | 1 | |
STAGE IA | 3 | |
STAGE IB | 2 | |
STAGE II | 1 | |
STAGE IIA | 15 | |
STAGE IIB | 10 | |
STAGE III | 5 | |
STAGE IIIA | 7 | |
STAGE IIIB | 5 | |
STAGE IIIC | 1 | |
STAGE IV | 1 | |
Significant markers | N = 0 |
PATHOLOGY.T.STAGE | Mean (SD) | 2.53 (0.76) |
N | ||
1 | 6 | |
2 | 14 | |
3 | 29 | |
4 | 2 | |
Significant markers | N = 0 |
PATHOLOGY.N.STAGE | Mean (SD) | 0.65 (0.77) |
N | ||
0 | 26 | |
1 | 18 | |
2 | 6 | |
3 | 1 | |
Significant markers | N = 0 |
PATHOLOGY.M.STAGE | Labels | N |
M0 | 40 | |
M1A | 1 | |
MX | 6 | |
Significant markers | N = 0 |
GENDER | Labels | N |
FEMALE | 7 | |
MALE | 45 | |
Significant markers | N = 3 | |
Higher in MALE | 0 | |
Higher in FEMALE | 3 |
T(pos if higher in 'MALE') | ttestP | Q | AUC | |
---|---|---|---|---|
HSA-MIR-584 | -6.65 | 3.405e-08 | 1.68e-05 | 0.8635 |
HSA-MIR-1266 | -5.21 | 3.733e-05 | 0.0184 | 0.8265 |
HSA-MIR-3662 | -5.7 | 3.957e-05 | 0.0195 | 0.8915 |
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Expresson data file = ESCA-TP.miRseq_RPKM_log2.txt
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Clinical data file = ESCA-TP.merged_data.txt
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Number of patients = 52
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Number of miRs = 517
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Number of clinical features = 8
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.