This pipeline uses various statistical tests to identify miRs whose expression levels correlated to selected clinical features.
Testing the association between 493 genes and 7 clinical features across 104 samples, statistically thresholded by Q value < 0.05, 5 clinical features related to at least one genes.
-
2 genes correlated to 'Time to Death'.
-
HSA-MIR-141 , HSA-MIR-200C
-
1 gene correlated to 'GENDER'.
-
HSA-MIR-3173
-
7 genes correlated to 'DISTANT.METASTASIS'.
-
HSA-MIR-3607 , HSA-MIR-3647 , HSA-MIR-1245 , HSA-MIR-3653 , HSA-MIR-26A-1 , ...
-
6 genes correlated to 'LYMPH.NODE.METASTASIS'.
-
HSA-MIR-200B , HSA-MIR-224 , HSA-MIR-452 , HSA-MIR-200A , HSA-MIR-217 , ...
-
5 genes correlated to 'NEOPLASM.DISEASESTAGE'.
-
HSA-MIR-224 , HSA-MIR-452 , HSA-MIR-200B , HSA-MIR-92A-1 , HSA-MIR-200A
-
No genes correlated to 'AGE', and 'KARNOFSKY.PERFORMANCE.SCORE'.
Complete statistical result table is provided in Supplement Table 1
Clinical feature | Statistical test | Significant genes | Associated with | Associated with | ||
---|---|---|---|---|---|---|
Time to Death | Cox regression test | N=2 | shorter survival | N=2 | longer survival | N=0 |
AGE | Spearman correlation test | N=0 | ||||
GENDER | t test | N=1 | male | N=1 | female | N=0 |
KARNOFSKY PERFORMANCE SCORE | Spearman correlation test | N=0 | ||||
DISTANT METASTASIS | ANOVA test | N=7 | ||||
LYMPH NODE METASTASIS | ANOVA test | N=6 | ||||
NEOPLASM DISEASESTAGE | ANOVA test | N=5 |
Time to Death | Duration (Months) | 0-182.7 (median=13.7) |
censored | N = 83 | |
death | N = 14 | |
Significant markers | N = 2 | |
associated with shorter survival | 2 | |
associated with longer survival | 0 |
HazardRatio | Wald_P | Q | C_index | |
---|---|---|---|---|
HSA-MIR-141 | 1.66 | 1.398e-05 | 0.0069 | 0.776 |
HSA-MIR-200C | 1.56 | 3.357e-05 | 0.017 | 0.745 |
AGE | Mean (SD) | 59.64 (12) |
Significant markers | N = 0 |
GENDER | Labels | N |
FEMALE | 34 | |
MALE | 70 | |
Significant markers | N = 1 | |
Higher in MALE | 1 | |
Higher in FEMALE | 0 |
T(pos if higher in 'MALE') | ttestP | Q | AUC | |
---|---|---|---|---|
HSA-MIR-3173 | 4.24 | 8.781e-05 | 0.0433 | 0.7756 |
No gene related to 'KARNOFSKY.PERFORMANCE.SCORE'.
KARNOFSKY.PERFORMANCE.SCORE | Mean (SD) | 87.73 (23) |
Score | N | |
0 | 1 | |
40 | 1 | |
90 | 11 | |
100 | 9 | |
Significant markers | N = 0 |
DISTANT.METASTASIS | Labels | N |
M0 | 55 | |
M1 | 5 | |
MX | 36 | |
Significant markers | N = 7 |
ANOVA_P | Q | |
---|---|---|
HSA-MIR-3607 | 7.251e-08 | 3.57e-05 |
HSA-MIR-3647 | 9.616e-08 | 4.73e-05 |
HSA-MIR-1245 | 2.738e-05 | 0.0134 |
HSA-MIR-3653 | 3.219e-05 | 0.0158 |
HSA-MIR-26A-1 | 3.479e-05 | 0.017 |
HSA-MIR-126 | 7.638e-05 | 0.0373 |
HSA-MIR-1248 | 9.26e-05 | 0.0451 |
LYMPH.NODE.METASTASIS | Labels | N |
N0 | 20 | |
N1 | 12 | |
N2 | 4 | |
NX | 68 | |
Significant markers | N = 6 |
ANOVA_P | Q | |
---|---|---|
HSA-MIR-200B | 6.946e-07 | 0.000342 |
HSA-MIR-224 | 1.473e-06 | 0.000725 |
HSA-MIR-452 | 1.781e-06 | 0.000874 |
HSA-MIR-200A | 6.894e-06 | 0.00338 |
HSA-MIR-217 | 3.341e-05 | 0.0163 |
HSA-MIR-421 | 9.449e-05 | 0.0461 |
NEOPLASM.DISEASESTAGE | Labels | N |
STAGE I | 53 | |
STAGE II | 7 | |
STAGE III | 24 | |
STAGE IV | 9 | |
Significant markers | N = 5 |
ANOVA_P | Q | |
---|---|---|
HSA-MIR-224 | 2.664e-06 | 0.00131 |
HSA-MIR-452 | 1.903e-05 | 0.00936 |
HSA-MIR-200B | 2.832e-05 | 0.0139 |
HSA-MIR-92A-1 | 4.656e-05 | 0.0228 |
HSA-MIR-200A | 9.274e-05 | 0.0453 |
-
Expresson data file = KIRP-TP.miRseq_RPKM_log2.txt
-
Clinical data file = KIRP-TP.clin.merged.picked.txt
-
Number of patients = 104
-
Number of genes = 493
-
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.