This pipeline uses various statistical tests to identify miRs whose log2 expression levels correlated to selected clinical features.
Testing the association between 523 miRs and 9 clinical features across 150 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 5 clinical features related to at least one miRs.
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5 miRs correlated to 'Time to Death'.
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HSA-MIR-3648 , HSA-MIR-103-2 , HSA-MIR-1291 , HSA-MIR-505 , HSA-MIR-3653
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10 miRs correlated to 'AGE'.
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HSA-MIR-375 , HSA-MIR-944 , HSA-MIR-29B-2 , HSA-MIR-29B-1 , HSA-MIR-29A , ...
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6 miRs correlated to 'NEOPLASM.DISEASESTAGE'.
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HSA-MIR-708 , HSA-MIR-192 , HSA-MIR-194-1 , HSA-MIR-194-2 , HSA-MIR-205 , ...
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8 miRs correlated to 'PATHOLOGY.T.STAGE'.
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HSA-MIR-190 , HSA-MIR-206 , HSA-MIR-125B-1 , HSA-MIR-378 , HSA-MIR-556 , ...
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50 miRs correlated to 'RACE'.
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HSA-MIR-205 , HSA-MIR-944 , HSA-MIR-708 , HSA-MIR-149 , HSA-MIR-224 , ...
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No miRs correlated to 'PATHOLOGY.N.STAGE', 'PATHOLOGY.M.STAGE', 'GENDER', and 'NUMBERPACKYEARSSMOKED'.
Complete statistical result table is provided in Supplement Table 1
Clinical feature | Statistical test | Significant miRs | Associated with | Associated with | ||
---|---|---|---|---|---|---|
Time to Death | Cox regression test | N=5 | shorter survival | N=5 | longer survival | N=0 |
AGE | Spearman correlation test | N=10 | older | N=6 | younger | N=4 |
NEOPLASM DISEASESTAGE | Kruskal-Wallis test | N=6 | ||||
PATHOLOGY T STAGE | Spearman correlation test | N=8 | higher stage | N=3 | lower stage | N=5 |
PATHOLOGY N STAGE | Spearman correlation test | N=0 | ||||
PATHOLOGY M STAGE | Kruskal-Wallis test | N=0 | ||||
GENDER | Wilcoxon test | N=0 | ||||
NUMBERPACKYEARSSMOKED | Spearman correlation test | N=0 | ||||
RACE | Kruskal-Wallis test | N=50 |
Time to Death | Duration (Months) | 0-122.1 (median=7.6) |
censored | N = 87 | |
death | N = 55 | |
Significant markers | N = 5 | |
associated with shorter survival | 5 | |
associated with longer survival | 0 |
HazardRatio | Wald_P | Q | C_index | |
---|---|---|---|---|
HSA-MIR-3648 | 1.26 | 2.88e-05 | 0.015 | 0.679 |
HSA-MIR-103-2 | 2.2 | 2.917e-05 | 0.015 | 0.696 |
HSA-MIR-1291 | 1.5 | 0.0001852 | 0.097 | 0.644 |
HSA-MIR-505 | 1.99 | 0.0002297 | 0.12 | 0.643 |
HSA-MIR-3653 | 1.57 | 0.0002493 | 0.13 | 0.648 |
AGE | Mean (SD) | 63.35 (12) |
Significant markers | N = 10 | |
pos. correlated | 6 | |
neg. correlated | 4 |
SpearmanCorr | corrP | Q | |
---|---|---|---|
HSA-MIR-375 | 0.3427 | 1.77e-05 | 0.00925 |
HSA-MIR-944 | -0.3454 | 2.093e-05 | 0.0109 |
HSA-MIR-29B-2 | 0.3344 | 2.889e-05 | 0.015 |
HSA-MIR-29B-1 | 0.3326 | 3.195e-05 | 0.0166 |
HSA-MIR-29A | 0.3195 | 6.758e-05 | 0.0351 |
HSA-MIR-708 | -0.3127 | 9.788e-05 | 0.0507 |
HSA-MIR-149 | -0.2976 | 0.0002173 | 0.112 |
HSA-MIR-147B | 0.3094 | 0.0002217 | 0.114 |
HSA-MIR-34A | 0.2865 | 0.0003781 | 0.195 |
HSA-MIR-193B | -0.2825 | 0.0004607 | 0.237 |
NEOPLASM.DISEASESTAGE | Labels | N |
STAGE I | 8 | |
STAGE IA | 4 | |
STAGE IB | 6 | |
STAGE II | 1 | |
STAGE IIA | 34 | |
STAGE IIB | 26 | |
STAGE III | 24 | |
STAGE IIIA | 11 | |
STAGE IIIB | 8 | |
STAGE IIIC | 6 | |
STAGE IV | 3 | |
STAGE IVA | 2 | |
Significant markers | N = 6 |
ANOVA_P | Q | |
---|---|---|
HSA-MIR-708 | 3.852e-05 | 0.0201 |
HSA-MIR-192 | 6.688e-05 | 0.0349 |
HSA-MIR-194-1 | 9.142e-05 | 0.0476 |
HSA-MIR-194-2 | 0.0001034 | 0.0537 |
HSA-MIR-205 | 0.0001782 | 0.0925 |
HSA-MIR-944 | 0.0004103 | 0.213 |
PATHOLOGY.T.STAGE | Mean (SD) | 2.4 (0.84) |
N | ||
0 | 1 | |
1 | 25 | |
2 | 33 | |
3 | 74 | |
4 | 4 | |
Significant markers | N = 8 | |
pos. correlated | 3 | |
neg. correlated | 5 |
SpearmanCorr | corrP | Q | |
---|---|---|---|
HSA-MIR-190 | -0.3591 | 1.638e-05 | 0.00856 |
HSA-MIR-206 | -0.406 | 4.052e-05 | 0.0212 |
HSA-MIR-125B-1 | 0.3421 | 4.272e-05 | 0.0223 |
HSA-MIR-378 | -0.3322 | 7.321e-05 | 0.0381 |
HSA-MIR-556 | -0.3741 | 0.0001477 | 0.0766 |
HSA-MIR-199A-1 | 0.3118 | 0.000208 | 0.108 |
HSA-MIR-215 | -0.307 | 0.0002637 | 0.136 |
HSA-MIR-199A-2 | 0.2927 | 0.0005176 | 0.267 |
PATHOLOGY.N.STAGE | Mean (SD) | 0.7 (0.8) |
N | ||
0 | 63 | |
1 | 55 | |
2 | 11 | |
3 | 6 | |
Significant markers | N = 0 |
PATHOLOGY.M.STAGE | Labels | N |
M0 | 111 | |
M1 | 2 | |
M1A | 3 | |
MX | 17 | |
Significant markers | N = 0 |
GENDER | Labels | N |
FEMALE | 21 | |
MALE | 129 | |
Significant markers | N = 0 |
NUMBERPACKYEARSSMOKED | Mean (SD) | 35.5 (21) |
Significant markers | N = 0 |
RACE | Labels | N |
ASIAN | 37 | |
BLACK OR AFRICAN AMERICAN | 2 | |
WHITE | 93 | |
Significant markers | N = 50 |
ANOVA_P | Q | |
---|---|---|
HSA-MIR-205 | 3.094e-09 | 1.62e-06 |
HSA-MIR-944 | 4.023e-09 | 2.1e-06 |
HSA-MIR-708 | 4.789e-09 | 2.49e-06 |
HSA-MIR-149 | 5.34e-09 | 2.78e-06 |
HSA-MIR-224 | 4.62e-08 | 2.4e-05 |
HSA-MIR-34B | 4.62e-08 | 2.4e-05 |
HSA-MIR-193B | 1.138e-07 | 5.88e-05 |
HSA-MIR-194-1 | 1.497e-07 | 7.72e-05 |
HSA-MIR-365-2 | 2.822e-07 | 0.000145 |
HSA-MIR-365-1 | 3.543e-07 | 0.000182 |
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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 = 150
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Number of miRs = 523
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Number of clinical features = 9
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.