(BRAF_Hotspot_Mutants cohort)
This pipeline uses various statistical tests to identify genes whose promoter methylation levels correlated to selected clinical features.
Testing the association between 17172 genes and 7 clinical features across 64 samples, statistically thresholded by Q value < 0.05, 4 clinical features related to at least one genes.
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105 genes correlated to 'PRIMARY.SITE.OF.DISEASE'.
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PSPN , AMY2B , RTN4RL2 , IRAK4 , PUS7L , ...
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531 genes correlated to 'DISTANT.METASTASIS'.
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MUL1 , SFRP4 , CCNG1 , POLR3K , MAN2A1 , ...
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42 genes correlated to 'LYMPH.NODE.METASTASIS'.
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TGM4 , CRIPAK , ITGB6 , MEPE , NHEDC1 , ...
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17 genes correlated to 'NEOPLASM.DISEASESTAGE'.
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C4ORF3 , SRPK2 , FAM107B , HEMK1 , C2CD4C , ...
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No genes correlated to 'Time to Death', 'AGE', and 'GENDER'.
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=0 | ||||
AGE | Spearman correlation test | N=0 | ||||
PRIMARY SITE OF DISEASE | ANOVA test | N=105 | ||||
GENDER | t test | N=0 | ||||
DISTANT METASTASIS | ANOVA test | N=531 | ||||
LYMPH NODE METASTASIS | ANOVA test | N=42 | ||||
NEOPLASM DISEASESTAGE | ANOVA test | N=17 |
Time to Death | Duration (Months) | 4.2-84.7 (median=13.7) |
censored | N = 16 | |
death | N = 17 | |
Significant markers | N = 0 |
AGE | Mean (SD) | 50.17 (16) |
Significant markers | N = 0 |
PRIMARY.SITE.OF.DISEASE | Labels | N |
DISTANT METASTASIS | 6 | |
PRIMARY TUMOR | 1 | |
REGIONAL CUTANEOUS OR SUBCUTANEOUS TISSUE (INCLUDES SATELLITE AND IN-TRANSIT METASTASIS) | 11 | |
REGIONAL LYMPH NODE | 46 | |
Significant markers | N = 105 |
ANOVA_P | Q | |
---|---|---|
PSPN | 5.699e-71 | 9.79e-67 |
AMY2B | 1.357e-39 | 2.33e-35 |
RTN4RL2 | 6.09e-34 | 1.05e-29 |
IRAK4 | 8.495e-30 | 1.46e-25 |
PUS7L | 8.495e-30 | 1.46e-25 |
SCAMP4 | 5.363e-28 | 9.21e-24 |
MIER2 | 2.768e-22 | 4.75e-18 |
KLHDC4 | 6.838e-21 | 1.17e-16 |
RPTOR | 1.044e-19 | 1.79e-15 |
ACRV1 | 1.798e-19 | 3.09e-15 |
GENDER | Labels | N |
FEMALE | 21 | |
MALE | 43 | |
Significant markers | N = 0 |
DISTANT.METASTASIS | Labels | N |
M0 | 53 | |
M1 | 1 | |
M1A | 1 | |
M1B | 1 | |
M1C | 2 | |
Significant markers | N = 531 |
ANOVA_P | Q | |
---|---|---|
MUL1 | 3.595e-72 | 6.17e-68 |
SFRP4 | 2.051e-62 | 3.52e-58 |
CCNG1 | 8.506e-43 | 1.46e-38 |
POLR3K | 4.528e-38 | 7.77e-34 |
MAN2A1 | 1.457e-37 | 2.5e-33 |
TPD52L2 | 2.203e-37 | 3.78e-33 |
C11ORF80 | 9.745e-37 | 1.67e-32 |
COPS3 | 7.502e-36 | 1.29e-31 |
LOC728758 | 3.011e-33 | 5.17e-29 |
F2RL1 | 3.967e-32 | 6.81e-28 |
LYMPH.NODE.METASTASIS | Labels | N |
N0 | 31 | |
N1 | 1 | |
N1A | 2 | |
N1B | 6 | |
N2A | 1 | |
N2B | 5 | |
N2C | 3 | |
N3 | 7 | |
NX | 2 | |
Significant markers | N = 42 |
ANOVA_P | Q | |
---|---|---|
TGM4 | 2.142e-30 | 3.68e-26 |
CRIPAK | 1.178e-25 | 2.02e-21 |
ITGB6 | 4.141e-23 | 7.11e-19 |
MEPE | 1.019e-20 | 1.75e-16 |
NHEDC1 | 2.102e-19 | 3.61e-15 |
SUCNR1 | 3.43e-19 | 5.89e-15 |
FREM2 | 1.625e-18 | 2.79e-14 |
RNASEL | 3.014e-17 | 5.17e-13 |
DGCR6 | 7.16e-17 | 1.23e-12 |
TMEM182 | 2.198e-16 | 3.77e-12 |
NEOPLASM.DISEASESTAGE | Labels | N |
I OR II NOS | 1 | |
STAGE I | 10 | |
STAGE IA | 2 | |
STAGE IB | 4 | |
STAGE II | 8 | |
STAGE IIA | 3 | |
STAGE IIC | 2 | |
STAGE III | 4 | |
STAGE IIIA | 2 | |
STAGE IIIB | 7 | |
STAGE IIIC | 9 | |
STAGE IV | 3 | |
Significant markers | N = 17 |
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Expresson data file = SKCM-BRAF_Hotspot_Mutants.meth.for_correlation.filtered_data.txt
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Clinical data file = SKCM-BRAF_Hotspot_Mutants.clin.merged.picked.txt
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Number of patients = 64
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Number of genes = 17172
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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 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.