This pipeline uses various statistical tests to identify miRs whose expression levels correlated to selected clinical features.
Testing the association between 517 miRs and 7 clinical features across 22 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 'NEOPLASM.DISEASESTAGE'.
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HSA-MIR-148A
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1 miR correlated to 'GENDER'.
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HSA-MIR-628
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No miRs correlated to 'Time to Death', 'AGE', 'PATHOLOGY.T.STAGE', 'PATHOLOGY.N.STAGE', and 'NUMBERPACKYEARSSMOKED'.
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 Q value < 0.05.
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=0 | ||||
NEOPLASM DISEASESTAGE | ANOVA test | N=1 | ||||
PATHOLOGY T STAGE | Spearman correlation test | N=0 | ||||
PATHOLOGY N STAGE | Spearman correlation test | N=0 | ||||
GENDER | t test | N=1 | male | N=0 | female | N=1 |
NUMBERPACKYEARSSMOKED | Spearman correlation test | N=0 |
Table S1. Basic characteristics of clinical feature: 'Time to Death'
Time to Death | Duration (Months) | 0-30.7 (median=3.2) |
censored | N = 12 | |
death | N = 6 | |
Significant markers | N = 0 |
Table S2. Basic characteristics of clinical feature: 'AGE'
AGE | Mean (SD) | 66 (11) |
Significant markers | N = 0 |
Table S3. Basic characteristics of clinical feature: 'NEOPLASM.DISEASESTAGE'
NEOPLASM.DISEASESTAGE | Labels | N |
STAGE IA | 2 | |
STAGE IB | 1 | |
STAGE IIA | 3 | |
STAGE IIB | 7 | |
STAGE IIIA | 5 | |
STAGE IIIB | 3 | |
STAGE IIIC | 1 | |
Significant markers | N = 1 |
Table S4. Get Full Table List of one miR differentially expressed by 'NEOPLASM.DISEASESTAGE'
ANOVA_P | Q | |
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HSA-MIR-148A | 4.164e-05 | 0.0214 |
Figure S1. Get High-res Image As an example, this figure shows the association of HSA-MIR-148A to 'NEOPLASM.DISEASESTAGE'. P value = 4.16e-05 with ANOVA analysis.
![](V3ex.png)
Table S5. Basic characteristics of clinical feature: 'PATHOLOGY.T.STAGE'
PATHOLOGY.T.STAGE | Mean (SD) | 2.41 (0.91) |
N | ||
1 | 4 | |
2 | 7 | |
3 | 9 | |
4 | 2 | |
Significant markers | N = 0 |
Table S6. Basic characteristics of clinical feature: 'PATHOLOGY.N.STAGE'
PATHOLOGY.N.STAGE | Mean (SD) | 0.86 (0.83) |
N | ||
0 | 8 | |
1 | 10 | |
2 | 3 | |
3 | 1 | |
Significant markers | N = 0 |
Table S7. Basic characteristics of clinical feature: 'GENDER'
GENDER | Labels | N |
FEMALE | 3 | |
MALE | 19 | |
Significant markers | N = 1 | |
Higher in MALE | 0 | |
Higher in FEMALE | 1 |
Table S8. Get Full Table List of one miR differentially expressed by 'GENDER'
T(pos if higher in 'MALE') | ttestP | Q | AUC | |
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HSA-MIR-628 | -5.6 | 1.949e-05 | 0.00793 | 0.9474 |
Figure S2. Get High-res Image As an example, this figure shows the association of HSA-MIR-628 to 'GENDER'. P value = 1.95e-05 with T-test analysis.
![](V6ex.png)
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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 = 22
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Number of miRs = 517
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Number of clinical features = 7
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.