This pipeline uses various statistical tests to identify miRs whose expression levels correlated to selected clinical features.
Testing the association between 560 genes and 7 clinical features across 160 samples, statistically thresholded by Q value < 0.05, 4 clinical features related to at least one genes.
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4 genes correlated to 'Time to Death'.
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HSA-MIR-10A , HSA-MIR-346 , HSA-MIR-155 , HSA-MIR-15B
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1 gene correlated to 'AGE'.
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HSA-MIR-195
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28 genes correlated to 'HISTOLOGICAL.TYPE'.
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HSA-MIR-1262 , HSA-MIR-186 , HSA-MIR-3074 , HSA-MIR-23A , HSA-MIR-455 , ...
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1 gene correlated to 'RADIATIONS.RADIATION.REGIMENINDICATION'.
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HSA-MIR-1262
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No genes correlated to 'GENDER', 'KARNOFSKY.PERFORMANCE.SCORE', and 'NEOADJUVANT.THERAPY'.
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=4 | shorter survival | N=3 | longer survival | N=1 |
AGE | Spearman correlation test | N=1 | older | N=1 | younger | N=0 |
GENDER | t test | N=0 | ||||
KARNOFSKY PERFORMANCE SCORE | Spearman correlation test | N=0 | ||||
HISTOLOGICAL TYPE | ANOVA test | N=28 | ||||
RADIATIONS RADIATION REGIMENINDICATION | t test | N=1 | yes | N=0 | no | N=1 |
NEOADJUVANT THERAPY | t test | N=0 |
Time to Death | Duration (Months) | 0-211.2 (median=17.4) |
censored | N = 111 | |
death | N = 48 | |
Significant markers | N = 4 | |
associated with shorter survival | 3 | |
associated with longer survival | 1 |
HazardRatio | Wald_P | Q | C_index | |
---|---|---|---|---|
HSA-MIR-10A | 1.45 | 4.4e-08 | 2.5e-05 | 0.72 |
HSA-MIR-346 | 0.52 | 4.794e-08 | 2.7e-05 | 0.293 |
HSA-MIR-155 | 1.84 | 2.771e-07 | 0.00015 | 0.742 |
HSA-MIR-15B | 1.7 | 1.372e-05 | 0.0076 | 0.775 |
AGE | Mean (SD) | 43.48 (13) |
Significant markers | N = 1 | |
pos. correlated | 1 | |
neg. correlated | 0 |
SpearmanCorr | corrP | Q | |
---|---|---|---|
HSA-MIR-195 | 0.3172 | 4.381e-05 | 0.0245 |
GENDER | Labels | N |
FEMALE | 68 | |
MALE | 92 | |
Significant markers | N = 0 |
No gene related to 'KARNOFSKY.PERFORMANCE.SCORE'.
KARNOFSKY.PERFORMANCE.SCORE | Mean (SD) | 89 (11) |
Significant markers | N = 0 |
HISTOLOGICAL.TYPE | Labels | N |
ASTROCYTOMA | 53 | |
OLIGOASTROCYTOMA | 39 | |
OLIGODENDROGLIOMA | 67 | |
Significant markers | N = 28 |
ANOVA_P | Q | |
---|---|---|
HSA-MIR-1262 | 3.679e-11 | 2.06e-08 |
HSA-MIR-186 | 4.893e-08 | 2.73e-05 |
HSA-MIR-3074 | 5.264e-08 | 2.94e-05 |
HSA-MIR-23A | 5.95e-08 | 3.31e-05 |
HSA-MIR-455 | 1.003e-07 | 5.58e-05 |
HSA-MIR-3127 | 2.805e-07 | 0.000156 |
HSA-MIR-1226 | 9.253e-07 | 0.000513 |
HSA-MIR-576 | 3.299e-06 | 0.00182 |
HSA-MIR-3923 | 3.772e-06 | 0.00208 |
HSA-MIR-2114 | 6.665e-06 | 0.00367 |
One gene related to 'RADIATIONS.RADIATION.REGIMENINDICATION'.
RADIATIONS.RADIATION.REGIMENINDICATION | Labels | N |
NO | 94 | |
YES | 66 | |
Significant markers | N = 1 | |
Higher in YES | 0 | |
Higher in NO | 1 |
T(pos if higher in 'YES') | ttestP | Q | AUC | |
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HSA-MIR-1262 | -4.34 | 2.759e-05 | 0.0155 | 0.7031 |
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Expresson data file = LGG.miRseq_RPKM_log2.txt
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Clinical data file = LGG.clin.merged.picked.txt
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Number of patients = 160
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Number of genes = 560
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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 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.