This pipeline uses various statistical tests to identify miRs whose expression levels correlated to selected clinical features.
Testing the association between 496 genes and 9 clinical features across 868 samples, statistically thresholded by Q value < 0.05, 6 clinical features related to at least one genes.
-
1 gene correlated to 'Time to Death'.
-
HSA-MIR-874
-
26 genes correlated to 'AGE'.
-
HSA-MIR-424 , HSA-MIR-31 , HSA-MIR-598 , HSA-MIR-542 , HSA-MIR-99A , ...
-
301 genes correlated to 'HISTOLOGICAL.TYPE'.
-
HSA-MIR-210 , HSA-MIR-301A , HSA-MIR-616 , HSA-MIR-501 , HSA-MIR-3653 , ...
-
3 genes correlated to 'DISTANT.METASTASIS'.
-
HSA-MIR-2276 , HSA-MIR-374C , HSA-MIR-3174
-
6 genes correlated to 'LYMPH.NODE.METASTASIS'.
-
HSA-MIR-374C , HSA-MIR-874 , HSA-MIR-197 , HSA-MIR-92A-1 , HSA-MIR-574 , ...
-
9 genes correlated to 'NEOPLASM.DISEASESTAGE'.
-
HSA-MIR-143 , HSA-MIR-210 , HSA-LET-7F-2 , HSA-MIR-200A , HSA-MIR-374C , ...
-
No genes correlated to 'GENDER', 'RADIATIONS.RADIATION.REGIMENINDICATION', and 'NUMBER.OF.LYMPH.NODES'.
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=1 | shorter survival | N=1 | longer survival | N=0 |
AGE | Spearman correlation test | N=26 | older | N=2 | younger | N=24 |
GENDER | t test | N=0 | ||||
HISTOLOGICAL TYPE | ANOVA test | N=301 | ||||
RADIATIONS RADIATION REGIMENINDICATION | t test | N=0 | ||||
DISTANT METASTASIS | ANOVA test | N=3 | ||||
LYMPH NODE METASTASIS | ANOVA test | N=6 | ||||
NUMBER OF LYMPH NODES | Spearman correlation test | N=0 | ||||
NEOPLASM DISEASESTAGE | ANOVA test | N=9 |
Time to Death | Duration (Months) | 0-223.4 (median=18.2) |
censored | N = 714 | |
death | N = 95 | |
Significant markers | N = 1 | |
associated with shorter survival | 1 | |
associated with longer survival | 0 |
HazardRatio | Wald_P | Q | C_index | |
---|---|---|---|---|
HSA-MIR-874 | 1.56 | 1.448e-05 | 0.0072 | 0.601 |
AGE | Mean (SD) | 58.47 (13) |
Significant markers | N = 26 | |
pos. correlated | 2 | |
neg. correlated | 24 |
SpearmanCorr | corrP | Q | |
---|---|---|---|
HSA-MIR-424 | -0.2292 | 8.548e-12 | 4.24e-09 |
HSA-MIR-31 | -0.2155 | 3.211e-10 | 1.59e-07 |
HSA-MIR-598 | -0.1939 | 8.58e-09 | 4.24e-06 |
HSA-MIR-542 | -0.1847 | 4.309e-08 | 2.12e-05 |
HSA-MIR-99A | -0.1839 | 4.908e-08 | 2.41e-05 |
HSA-MIR-381 | -0.1813 | 7.735e-08 | 3.8e-05 |
HSA-MIR-652 | -0.1663 | 8.55e-07 | 0.000419 |
HSA-LET-7C | -0.1637 | 1.269e-06 | 0.000621 |
HSA-MIR-202 | -0.1998 | 2.237e-06 | 0.00109 |
HSA-MIR-450B | -0.1518 | 7.253e-06 | 0.00353 |
GENDER | Labels | N |
FEMALE | 859 | |
MALE | 9 | |
Significant markers | N = 0 |
HISTOLOGICAL.TYPE | Labels | N |
INFILTRATING DUCTAL CARCINOMA | 677 | |
INFILTRATING LOBULAR CARCINOMA | 111 | |
MEDULLARY CARCINOMA | 4 | |
MIXED HISTOLOGY (PLEASE SPECIFY) | 26 | |
MUCINOUS CARCINOMA | 10 | |
OTHER SPECIFY | 40 | |
Significant markers | N = 301 |
ANOVA_P | Q | |
---|---|---|
HSA-MIR-210 | 3.587e-38 | 1.78e-35 |
HSA-MIR-301A | 5.299e-28 | 2.62e-25 |
HSA-MIR-616 | 1.558e-24 | 7.7e-22 |
HSA-MIR-501 | 1.458e-23 | 7.19e-21 |
HSA-MIR-3653 | 3.212e-23 | 1.58e-20 |
HSA-MIR-197 | 5.929e-23 | 2.91e-20 |
HSA-MIR-301B | 1.929e-22 | 9.45e-20 |
HSA-MIR-1307 | 4.027e-22 | 1.97e-19 |
HSA-MIR-324 | 6.042e-22 | 2.95e-19 |
HSA-MIR-1306 | 9.848e-22 | 4.8e-19 |
No gene related to 'RADIATIONS.RADIATION.REGIMENINDICATION'.
RADIATIONS.RADIATION.REGIMENINDICATION | Labels | N |
NO | 210 | |
YES | 658 | |
Significant markers | N = 0 |
DISTANT.METASTASIS | Labels | N |
CM0 (I+) | 2 | |
M0 | 771 | |
M1 | 14 | |
MX | 81 | |
Significant markers | N = 3 |
ANOVA_P | Q | |
---|---|---|
HSA-MIR-2276 | 1.961e-07 | 9.72e-05 |
HSA-MIR-374C | 4.281e-06 | 0.00212 |
HSA-MIR-3174 | 3.358e-05 | 0.0166 |
LYMPH.NODE.METASTASIS | Labels | N |
N0 | 255 | |
N0 (I+) | 22 | |
N0 (I-) | 131 | |
N0 (MOL+) | 1 | |
N1 | 103 | |
N1A | 129 | |
N1B | 32 | |
N1C | 2 | |
N1MI | 24 | |
N2 | 50 | |
N2A | 53 | |
N3 | 18 | |
N3A | 30 | |
N3B | 2 | |
N3C | 1 | |
NX | 15 | |
Significant markers | N = 6 |
ANOVA_P | Q | |
---|---|---|
HSA-MIR-374C | 3.314e-08 | 1.64e-05 |
HSA-MIR-874 | 4.3e-08 | 2.13e-05 |
HSA-MIR-197 | 2.88e-05 | 0.0142 |
HSA-MIR-92A-1 | 5.184e-05 | 0.0256 |
HSA-MIR-574 | 6.08e-05 | 0.0299 |
HSA-MIR-92A-2 | 6.799e-05 | 0.0334 |
NUMBER.OF.LYMPH.NODES | Mean (SD) | 2.21 (4.4) |
Significant markers | N = 0 |
NEOPLASM.DISEASESTAGE | Labels | N |
STAGE I | 72 | |
STAGE IA | 67 | |
STAGE IB | 7 | |
STAGE II | 8 | |
STAGE IIA | 294 | |
STAGE IIB | 195 | |
STAGE III | 2 | |
STAGE IIIA | 124 | |
STAGE IIIB | 25 | |
STAGE IIIC | 41 | |
STAGE IV | 14 | |
STAGE TIS | 1 | |
STAGE X | 17 | |
Significant markers | N = 9 |
ANOVA_P | Q | |
---|---|---|
HSA-MIR-143 | 5.031e-09 | 2.5e-06 |
HSA-MIR-210 | 1.406e-08 | 6.96e-06 |
HSA-LET-7F-2 | 1.133e-07 | 5.6e-05 |
HSA-MIR-200A | 3.455e-06 | 0.0017 |
HSA-MIR-374C | 1.038e-05 | 0.0051 |
HSA-MIR-125B-2 | 4.096e-05 | 0.0201 |
HSA-MIR-338 | 6.775e-05 | 0.0332 |
HSA-LET-7A-2 | 8.604e-05 | 0.0421 |
HSA-LET-7A-1 | 9.022e-05 | 0.044 |
-
Expresson data file = BRCA-TP.miRseq_RPKM_log2.txt
-
Clinical data file = BRCA-TP.clin.merged.picked.txt
-
Number of patients = 868
-
Number of genes = 496
-
Number of clinical features = 9
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.