This pipeline uses various statistical tests to identify miRs whose expression levels correlated to selected clinical features.
Testing the association between 545 miRs and 8 clinical features across 123 samples, statistically thresholded by Q value < 0.05, 1 clinical feature related to at least one miRs.
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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 'Time to Death', '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=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=14.6) |
censored | N = 66 | |
death | N = 52 | |
Significant markers | N = 0 |
AGE | Mean (SD) | 61.45 (14) |
Significant markers | N = 0 |
NEOPLASM.DISEASESTAGE | Labels | N |
STAGE I | 46 | |
STAGE II | 26 | |
STAGE III | 2 | |
STAGE IIIA | 29 | |
STAGE IIIB | 3 | |
STAGE IIIC | 5 | |
STAGE IV | 1 | |
STAGE IVA | 1 | |
STAGE IVB | 1 | |
Significant markers | N = 0 |
PATHOLOGY.T.STAGE | Mean (SD) | 2.02 (0.97) |
N | ||
1 | 49 | |
2 | 29 | |
3 | 38 | |
4 | 7 | |
Significant markers | N = 0 |
PATHOLOGY.N.STAGE | Labels | N |
class0 | 80 | |
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 | 5.64 | 2.895e-07 | 0.000105 | 0.7458 |
HSA-MIR-200C | -5.11 | 3.95e-06 | 0.00143 | 0.75 |
HSA-MIR-29B-2 | 4.78 | 2.653e-05 | 0.00958 | 0.7208 |
PATHOLOGY.M.STAGE | Labels | N |
M0 | 98 | |
M1 | 2 | |
MX | 23 | |
Significant markers | N = 0 |
GENDER | Labels | N |
FEMALE | 46 | |
MALE | 77 | |
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 = 123
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Number of miRs = 545
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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.