This pipeline uses various statistical tests to identify miRs whose expression levels correlated to selected clinical features.
Testing the association between 550 miRs and 8 clinical features across 150 samples, statistically thresholded by Q value < 0.05, 2 clinical features related to at least one miRs.
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1 miR correlated to 'Time to Death'.
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HSA-MIR-570
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3 miRs correlated to 'PATHOLOGY.N.STAGE'.
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HSA-MIR-29B-1 , HSA-MIR-200C , HSA-MIR-29B-2
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No miRs correlated to 'AGE', 'NEOPLASM.DISEASESTAGE', 'PATHOLOGY.T.STAGE', 'PATHOLOGY.M.STAGE', 'GENDER', and 'COMPLETENESS.OF.RESECTION'.
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=1 | shorter survival | N=1 | longer survival | N=0 |
AGE | Spearman correlation test | N=0 | ||||
NEOPLASM DISEASESTAGE | ANOVA test | N=0 | ||||
PATHOLOGY T STAGE | Spearman correlation test | N=0 | ||||
PATHOLOGY N STAGE | t test | N=3 | class1 | N=2 | class0 | N=1 |
PATHOLOGY M STAGE | ANOVA test | N=0 | ||||
GENDER | t test | N=0 | ||||
COMPLETENESS OF RESECTION | ANOVA test | N=0 |
Time to Death | Duration (Months) | 0-113 (median=13.9) |
censored | N = 86 | |
death | N = 61 | |
Significant markers | N = 1 | |
associated with shorter survival | 1 | |
associated with longer survival | 0 |
HazardRatio | Wald_P | Q | C_index | |
---|---|---|---|---|
HSA-MIR-570 | 1.63 | 8.376e-05 | 0.046 | 0.609 |
AGE | Mean (SD) | 61.26 (14) |
Significant markers | N = 0 |
NEOPLASM.DISEASESTAGE | Labels | N |
STAGE I | 56 | |
STAGE II | 35 | |
STAGE III | 2 | |
STAGE IIIA | 33 | |
STAGE IIIB | 4 | |
STAGE IIIC | 6 | |
STAGE IV | 1 | |
STAGE IVA | 1 | |
STAGE IVB | 2 | |
Significant markers | N = 0 |
PATHOLOGY.T.STAGE | Mean (SD) | 2.01 (0.96) |
N | ||
1 | 59 | |
2 | 39 | |
3 | 43 | |
4 | 9 | |
Significant markers | N = 0 |
PATHOLOGY.N.STAGE | Labels | N |
class0 | 98 | |
class1 | 3 | |
Significant markers | N = 3 | |
Higher in class1 | 2 | |
Higher in class0 | 1 |
T(pos if higher in 'class1') | ttestP | Q | AUC | |
---|---|---|---|---|
HSA-MIR-29B-1 | 5.63 | 2.543e-07 | 9.23e-05 | 0.7143 |
HSA-MIR-200C | -4.84 | 1.374e-05 | 0.00497 | 0.7075 |
HSA-MIR-29B-2 | 4.66 | 5.921e-05 | 0.0214 | 0.6905 |
PATHOLOGY.M.STAGE | Labels | N |
M0 | 118 | |
M1 | 3 | |
MX | 29 | |
Significant markers | N = 0 |
GENDER | Labels | N |
FEMALE | 58 | |
MALE | 92 | |
Significant markers | N = 0 |
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Expresson data file = LIHC-TP.miRseq_RPKM_log2.txt
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Clinical data file = LIHC-TP.merged_data.txt
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Number of patients = 150
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Number of miRs = 550
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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.