This pipeline uses various statistical tests to identify miRs whose expression levels correlated to selected clinical features.
Testing the association between 556 genes and 6 clinical features across 207 samples, statistically thresholded by Q value < 0.05, 3 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-346 , HSA-MIR-10A , HSA-MIR-155
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2 genes correlated to 'AGE'.
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HSA-MIR-34A , HSA-MIR-320D-2
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34 genes correlated to 'HISTOLOGICAL.TYPE'.
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HSA-MIR-1262 , HSA-MIR-3074 , HSA-MIR-186 , HSA-MIR-23A , HSA-MIR-455 , ...
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No genes correlated to 'GENDER', 'KARNOFSKY.PERFORMANCE.SCORE', and 'RADIATIONS.RADIATION.REGIMENINDICATION'.
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=2 | longer survival | N=1 |
AGE | Spearman correlation test | N=2 | older | N=2 | younger | N=0 |
GENDER | t test | N=0 | ||||
KARNOFSKY PERFORMANCE SCORE | Spearman correlation test | N=0 | ||||
HISTOLOGICAL TYPE | ANOVA test | N=34 | ||||
RADIATIONS RADIATION REGIMENINDICATION | t test | N=0 |
Time to Death | Duration (Months) | 0-211.2 (median=13.4) |
censored | N = 156 | |
death | N = 50 | |
Significant markers | N = 3 | |
associated with shorter survival | 2 | |
associated with longer survival | 1 |
HazardRatio | Wald_P | Q | C_index | |
---|---|---|---|---|
HSA-MIR-346 | 0.52 | 9.232e-09 | 5.1e-06 | 0.278 |
HSA-MIR-10A | 1.46 | 2.038e-08 | 1.1e-05 | 0.729 |
HSA-MIR-155 | 1.85 | 1.008e-07 | 5.6e-05 | 0.749 |
AGE | Mean (SD) | 43.22 (13) |
Significant markers | N = 2 | |
pos. correlated | 2 | |
neg. correlated | 0 |
SpearmanCorr | corrP | Q | |
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HSA-MIR-34A | 0.2962 | 1.467e-05 | 0.00815 |
HSA-MIR-320D-2 | 0.316 | 5.554e-05 | 0.0308 |
GENDER | Labels | N |
FEMALE | 89 | |
MALE | 118 | |
Significant markers | N = 0 |
No gene related to 'KARNOFSKY.PERFORMANCE.SCORE'.
KARNOFSKY.PERFORMANCE.SCORE | Mean (SD) | 88.48 (10) |
Significant markers | N = 0 |
HISTOLOGICAL.TYPE | Labels | N |
ASTROCYTOMA | 63 | |
OLIGOASTROCYTOMA | 54 | |
OLIGODENDROGLIOMA | 89 | |
Significant markers | N = 34 |
ANOVA_P | Q | |
---|---|---|
HSA-MIR-1262 | 3.98e-12 | 2.21e-09 |
HSA-MIR-3074 | 5.65e-10 | 3.14e-07 |
HSA-MIR-186 | 2.817e-08 | 1.56e-05 |
HSA-MIR-23A | 5.322e-08 | 2.94e-05 |
HSA-MIR-455 | 5.876e-08 | 3.24e-05 |
HSA-MIR-21 | 6.729e-08 | 3.71e-05 |
HSA-MIR-576 | 2.999e-07 | 0.000165 |
HSA-MIR-219-1 | 3.152e-07 | 0.000173 |
HSA-MIR-3065 | 8.647e-07 | 0.000474 |
HSA-MIR-2114 | 1.314e-06 | 0.000719 |
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Expresson data file = LGG-TP.miRseq_RPKM_log2.txt
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Clinical data file = LGG-TP.clin.merged.picked.txt
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Number of patients = 207
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Number of genes = 556
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Number of clinical features = 6
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.