This pipeline uses various statistical tests to identify miRs whose expression levels correlated to selected clinical features.
Testing the association between 469 miRs and 10 clinical features across 66 samples, statistically thresholded by Q value < 0.05, 3 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-130B
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3 miRs correlated to 'NEOPLASM.DISEASESTAGE'.
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HSA-MIR-376A-1 , HSA-MIR-130B , HSA-MIR-181B-1
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1 miR correlated to 'PATHOLOGY.T.STAGE'.
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HSA-MIR-181B-1
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No miRs correlated to 'AGE', 'PATHOLOGY.N.STAGE', 'PATHOLOGY.M.STAGE', 'GENDER', 'KARNOFSKY.PERFORMANCE.SCORE', 'NUMBERPACKYEARSSMOKED', and 'YEAROFTOBACCOSMOKINGONSET'.
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=1 | shorter survival | N=1 | longer survival | N=0 |
AGE | Spearman correlation test | N=0 | ||||
NEOPLASM DISEASESTAGE | ANOVA test | N=3 | ||||
PATHOLOGY T STAGE | Spearman correlation test | N=1 | higher stage | N=1 | lower stage | N=0 |
PATHOLOGY N STAGE | Spearman correlation test | N=0 | ||||
PATHOLOGY M STAGE | ANOVA test | N=0 | ||||
GENDER | t test | N=0 | ||||
KARNOFSKY PERFORMANCE SCORE | Spearman correlation test | N=0 | ||||
NUMBERPACKYEARSSMOKED | Spearman correlation test | N=0 | ||||
YEAROFTOBACCOSMOKINGONSET | Spearman correlation test | N=0 |
Table S1. Basic characteristics of clinical feature: 'Time to Death'
Time to Death | Duration (Months) | 0.6-151.9 (median=63.9) |
censored | N = 57 | |
death | N = 8 | |
Significant markers | N = 1 | |
associated with shorter survival | 1 | |
associated with longer survival | 0 |
Table S2. Get Full Table List of one miR significantly associated with 'Time to Death' by Cox regression test
HazardRatio | Wald_P | Q | C_index | |
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HSA-MIR-130B | 2.5 | 5.591e-05 | 0.026 | 0.753 |
Figure S1. Get High-res Image As an example, this figure shows the association of HSA-MIR-130B to 'Time to Death'. four curves present the cumulative survival rates of 4 quartile subsets of patients. P value = 5.59e-05 with univariate Cox regression analysis using continuous log-2 expression values.
![](V1ex.png)
Table S3. Basic characteristics of clinical feature: 'AGE'
AGE | Mean (SD) | 51.52 (14) |
Significant markers | N = 0 |
Table S4. Basic characteristics of clinical feature: 'NEOPLASM.DISEASESTAGE'
NEOPLASM.DISEASESTAGE | Labels | N |
STAGE I | 21 | |
STAGE II | 25 | |
STAGE III | 14 | |
STAGE IV | 6 | |
Significant markers | N = 3 |
Table S5. Get Full Table List of 3 miRs differentially expressed by 'NEOPLASM.DISEASESTAGE'
ANOVA_P | Q | |
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HSA-MIR-376A-1 | 4.077e-08 | 1.91e-05 |
HSA-MIR-130B | 2.075e-05 | 0.00971 |
HSA-MIR-181B-1 | 9.782e-05 | 0.0457 |
Figure S2. Get High-res Image As an example, this figure shows the association of HSA-MIR-376A-1 to 'NEOPLASM.DISEASESTAGE'. P value = 4.08e-08 with ANOVA analysis.
![](V3ex.png)
Table S6. Basic characteristics of clinical feature: 'PATHOLOGY.T.STAGE'
PATHOLOGY.T.STAGE | Mean (SD) | 2.02 (0.85) |
N | ||
1 | 21 | |
2 | 25 | |
3 | 18 | |
4 | 2 | |
Significant markers | N = 1 | |
pos. correlated | 1 | |
neg. correlated | 0 |
Table S7. Get Full Table List of one miR significantly correlated to 'PATHOLOGY.T.STAGE' by Spearman correlation test
SpearmanCorr | corrP | Q | |
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HSA-MIR-181B-1 | 0.505 | 1.53e-05 | 0.00717 |
Figure S3. Get High-res Image As an example, this figure shows the association of HSA-MIR-181B-1 to 'PATHOLOGY.T.STAGE'. P value = 1.53e-05 with Spearman correlation analysis.
![](V4ex.png)
Table S8. Basic characteristics of clinical feature: 'PATHOLOGY.N.STAGE'
PATHOLOGY.N.STAGE | Mean (SD) | 0.16 (0.47) |
N | ||
0 | 40 | |
1 | 3 | |
2 | 2 | |
Significant markers | N = 0 |
Table S9. Basic characteristics of clinical feature: 'PATHOLOGY.M.STAGE'
PATHOLOGY.M.STAGE | Labels | N |
M0 | 34 | |
M1 | 2 | |
MX | 9 | |
Significant markers | N = 0 |
Table S10. Basic characteristics of clinical feature: 'GENDER'
GENDER | Labels | N |
FEMALE | 27 | |
MALE | 39 | |
Significant markers | N = 0 |
No miR related to 'KARNOFSKY.PERFORMANCE.SCORE'.
Table S11. Basic characteristics of clinical feature: 'KARNOFSKY.PERFORMANCE.SCORE'
KARNOFSKY.PERFORMANCE.SCORE | Mean (SD) | 89.09 (9.4) |
Score | N | |
70 | 1 | |
80 | 2 | |
90 | 5 | |
100 | 3 | |
Significant markers | N = 0 |
Table S12. Basic characteristics of clinical feature: 'NUMBERPACKYEARSSMOKED'
NUMBERPACKYEARSSMOKED | Mean (SD) | 25.09 (22) |
Significant markers | N = 0 |
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Expresson data file = KICH-TP.miRseq_RPKM_log2.txt
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Clinical data file = KICH-TP.merged_data.txt
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Number of patients = 66
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Number of miRs = 469
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Number of clinical features = 10
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.