This pipeline uses various statistical tests to identify miRs whose expression levels correlated to selected clinical features.
Testing the association between 504 genes and 8 clinical features across 103 samples, statistically thresholded by Q value < 0.05, 4 clinical features related to at least one genes.
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3 genes correlated to 'Time to Death'.
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HSA-MIR-141 , HSA-MIR-200C , HSA-MIR-937
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7 genes correlated to 'PATHOLOGY.T'.
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HSA-MIR-452 , HSA-MIR-1293 , HSA-MIR-224 , HSA-MIR-217 , HSA-MIR-200A , ...
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8 genes correlated to 'PATHOLOGICSPREAD(M)'.
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HSA-MIR-3607 , HSA-MIR-3647 , HSA-MIR-1245 , HSA-MIR-1248 , HSA-MIR-424 , ...
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8 genes correlated to 'TUMOR.STAGE'.
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HSA-MIR-224 , HSA-MIR-452 , HSA-MIR-200A , HSA-MIR-200B , HSA-MIR-216A , ...
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No genes correlated to 'AGE', 'GENDER', 'KARNOFSKY.PERFORMANCE.SCORE', and 'PATHOLOGY.N'.
Complete statistical result table is provided in Supplement Table 1
Clinical feature | Statistical test | Significant genes | Associated with | Associated with | ||
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Time to Death | Cox regression test | N=3 | shorter survival | N=3 | longer survival | N=0 |
AGE | Spearman correlation test | N=0 | ||||
GENDER | t test | N=0 | ||||
KARNOFSKY PERFORMANCE SCORE | Spearman correlation test | N=0 | ||||
PATHOLOGY T | Spearman correlation test | N=7 | higher pT | N=5 | lower pT | N=2 |
PATHOLOGY N | Spearman correlation test | N=0 | ||||
PATHOLOGICSPREAD(M) | ANOVA test | N=8 | ||||
TUMOR STAGE | Spearman correlation test | N=8 | higher stage | N=5 | lower stage | N=3 |
Time to Death | Duration (Months) | 0-182.7 (median=13.7) |
censored | N = 82 | |
death | N = 14 | |
Significant markers | N = 3 | |
associated with shorter survival | 3 | |
associated with longer survival | 0 |
HazardRatio | Wald_P | Q | C_index | |
---|---|---|---|---|
HSA-MIR-141 | 1.66 | 1.807e-05 | 0.0091 | 0.762 |
HSA-MIR-200C | 1.56 | 3.342e-05 | 0.017 | 0.75 |
HSA-MIR-937 | 4.1 | 8.872e-05 | 0.045 | 0.849 |
AGE | Mean (SD) | 59.69 (12) |
Significant markers | N = 0 |
GENDER | Labels | N |
FEMALE | 33 | |
MALE | 70 | |
Significant markers | N = 0 |
No gene related to 'KARNOFSKY.PERFORMANCE.SCORE'.
KARNOFSKY.PERFORMANCE.SCORE | Mean (SD) | 87.62 (24) |
Score | N | |
0 | 1 | |
40 | 1 | |
90 | 10 | |
100 | 9 | |
Significant markers | N = 0 |
PATHOLOGY.T | Mean (SD) | 1.76 (0.92) |
N | ||
T1 | 58 | |
T2 | 13 | |
T3 | 31 | |
T4 | 1 | |
Significant markers | N = 7 | |
pos. correlated | 5 | |
neg. correlated | 2 |
SpearmanCorr | corrP | Q | |
---|---|---|---|
HSA-MIR-452 | 0.4499 | 1.866e-06 | 0.000941 |
HSA-MIR-1293 | 0.4951 | 1.957e-06 | 0.000984 |
HSA-MIR-224 | 0.4451 | 2.77e-06 | 0.00139 |
HSA-MIR-217 | 0.4467 | 5.061e-06 | 0.00254 |
HSA-MIR-200A | -0.4185 | 1.086e-05 | 0.00543 |
HSA-MIR-216A | 0.5284 | 1.43e-05 | 0.00714 |
HSA-MIR-200B | -0.3911 | 4.423e-05 | 0.022 |
PATHOLOGY.N | Mean (SD) | 0.54 (0.7) |
N | ||
N0 | 20 | |
N1 | 11 | |
N2 | 4 | |
Significant markers | N = 0 |
PATHOLOGICSPREAD(M) | Labels | N |
M0 | 54 | |
M1 | 5 | |
MX | 35 | |
Significant markers | N = 8 |
ANOVA_P | Q | |
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HSA-MIR-3607 | 1.108e-07 | 5.59e-05 |
HSA-MIR-3647 | 2.632e-07 | 0.000132 |
HSA-MIR-1245 | 6.938e-06 | 0.00348 |
HSA-MIR-1248 | 3.803e-05 | 0.0191 |
HSA-MIR-424 | 3.944e-05 | 0.0197 |
HSA-MIR-126 | 4.953e-05 | 0.0247 |
HSA-MIR-16-1 | 6.744e-05 | 0.0336 |
HSA-MIR-26A-1 | 8.222e-05 | 0.0409 |
TUMOR.STAGE | Mean (SD) | 1.87 (1.1) |
N | ||
Stage 1 | 53 | |
Stage 2 | 7 | |
Stage 3 | 23 | |
Stage 4 | 9 | |
Significant markers | N = 8 | |
pos. correlated | 5 | |
neg. correlated | 3 |
SpearmanCorr | corrP | Q | |
---|---|---|---|
HSA-MIR-224 | 0.5389 | 3.585e-08 | 1.81e-05 |
HSA-MIR-452 | 0.5284 | 6.191e-08 | 3.11e-05 |
HSA-MIR-200A | -0.4953 | 5.171e-07 | 0.00026 |
HSA-MIR-200B | -0.4615 | 3.655e-06 | 0.00183 |
HSA-MIR-216A | 0.5595 | 4.975e-06 | 0.00249 |
HSA-MIR-217 | 0.4581 | 6.368e-06 | 0.00318 |
HSA-MIR-1293 | 0.471 | 1.756e-05 | 0.00875 |
HSA-MIR-429 | -0.4228 | 2.681e-05 | 0.0133 |
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Expresson data file = KIRP-TP.miRseq_RPKM_log2.txt
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Clinical data file = KIRP-TP.clin.merged.picked.txt
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Number of patients = 103
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Number of genes = 504
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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 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 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 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.
This is an experimental feature. The full results of the analysis summarized in this report can be downloaded from the TCGA Data Coordination Center.