(All_Samples cohort)
This pipeline uses various statistical tests to identify miRs whose expression levels correlated to selected clinical features.
Testing the association between 598 genes and 8 clinical features across 197 samples, statistically thresholded by Q value < 0.05, 3 clinical features related to at least one genes.
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12 genes correlated to 'PRIMARY.SITE.OF.DISEASE'.
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HSA-MIR-205 , HSA-MIR-203 , HSA-MIR-141 , HSA-MIR-200C , HSA-MIR-200B , ...
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3 genes correlated to 'RADIATIONS.RADIATION.REGIMENINDICATION'.
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HSA-MIR-381 , HSA-MIR-379 , HSA-MIR-487B
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1 gene correlated to 'DISTANT.METASTASIS'.
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HSA-MIR-3654
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No genes correlated to 'Time to Death', 'AGE', 'GENDER', 'LYMPH.NODE.METASTASIS', and 'NEOPLASM.DISEASESTAGE'.
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=0 | ||||
AGE | Spearman correlation test | N=0 | ||||
PRIMARY SITE OF DISEASE | ANOVA test | N=12 | ||||
GENDER | t test | N=0 | ||||
RADIATIONS RADIATION REGIMENINDICATION | t test | N=3 | yes | N=3 | no | N=0 |
DISTANT METASTASIS | ANOVA test | N=1 | ||||
LYMPH NODE METASTASIS | ANOVA test | N=0 | ||||
NEOPLASM DISEASESTAGE | ANOVA test | N=0 |
Time to Death | Duration (Months) | 0-357.4 (median=40.3) |
censored | N = 97 | |
death | N = 90 | |
Significant markers | N = 0 |
AGE | Mean (SD) | 56.68 (16) |
Significant markers | N = 0 |
PRIMARY.SITE.OF.DISEASE | Labels | N |
DISTANT METASTASIS | 26 | |
PRIMARY TUMOR | 25 | |
REGIONAL CUTANEOUS OR SUBCUTANEOUS TISSUE (INCLUDES SATELLITE AND IN-TRANSIT METASTASIS) | 36 | |
REGIONAL LYMPH NODE | 110 | |
Significant markers | N = 12 |
ANOVA_P | Q | |
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HSA-MIR-205 | 1.856e-20 | 1.11e-17 |
HSA-MIR-203 | 8.781e-14 | 5.24e-11 |
HSA-MIR-141 | 2.495e-13 | 1.49e-10 |
HSA-MIR-200C | 2.793e-13 | 1.66e-10 |
HSA-MIR-200B | 5.309e-12 | 3.15e-09 |
HSA-MIR-200A | 2.948e-11 | 1.75e-08 |
HSA-MIR-3652 | 5.013e-06 | 0.00297 |
HSA-MIR-342 | 5.886e-06 | 0.00348 |
HSA-MIR-3184 | 6.081e-06 | 0.00359 |
HSA-MIR-429 | 7.066e-06 | 0.00416 |
GENDER | Labels | N |
FEMALE | 73 | |
MALE | 124 | |
Significant markers | N = 0 |
3 genes related to 'RADIATIONS.RADIATION.REGIMENINDICATION'.
RADIATIONS.RADIATION.REGIMENINDICATION | Labels | N |
NO | 3 | |
YES | 194 | |
Significant markers | N = 3 | |
Higher in YES | 3 | |
Higher in NO | 0 |
T(pos if higher in 'YES') | ttestP | Q | AUC | |
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HSA-MIR-381 | 5.85 | 1.147e-07 | 4.88e-05 | 0.6894 |
HSA-MIR-379 | 5.7 | 3.759e-06 | 0.00159 | 0.6924 |
HSA-MIR-487B | 6.62 | 5.352e-06 | 0.00226 | 0.7213 |
DISTANT.METASTASIS | Labels | N |
M0 | 171 | |
M1 | 2 | |
M1A | 2 | |
M1B | 2 | |
M1C | 3 | |
Significant markers | N = 1 |
ANOVA_P | Q | |
---|---|---|
HSA-MIR-3654 | 4.753e-05 | 0.0283 |
LYMPH.NODE.METASTASIS | Labels | N |
N0 | 107 | |
N1 | 2 | |
N1A | 8 | |
N1B | 17 | |
N2 | 1 | |
N2A | 5 | |
N2B | 14 | |
N2C | 6 | |
N3 | 17 | |
NX | 5 | |
Significant markers | N = 0 |
NEOPLASM.DISEASESTAGE | Labels | N |
I OR II NOS | 4 | |
STAGE I | 17 | |
STAGE IA | 10 | |
STAGE IB | 15 | |
STAGE II | 19 | |
STAGE IIA | 8 | |
STAGE IIB | 10 | |
STAGE IIC | 22 | |
STAGE III | 9 | |
STAGE IIIA | 7 | |
STAGE IIIB | 21 | |
STAGE IIIC | 25 | |
STAGE IV | 7 | |
Significant markers | N = 0 |
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Expresson data file = SKCM-All_Samples.miRseq_RPKM_log2.txt
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Clinical data file = SKCM-All_Samples.clin.merged.picked.txt
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Number of patients = 197
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Number of genes = 598
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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 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 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 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.