(primary solid tumor cohort)
This pipeline computes the correlation between significantly recurrent gene mutations and molecular subtypes.
Testing the association between mutation status of 3 genes and 8 molecular subtypes across 28 patients, no significant finding detected with P value < 0.05 and Q value < 0.25.
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No gene mutations related to molecuar subtypes.
Clinical Features |
CN CNMF |
METHLYATION CNMF |
RPPA CNMF |
RPPA CHIERARCHICAL |
MRNASEQ CNMF |
MRNASEQ CHIERARCHICAL |
MIRSEQ CNMF |
MIRSEQ CHIERARCHICAL |
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nMutated (%) | nWild-Type | Chi-square test | Fisher's exact test | Fisher's exact test | Fisher's exact test | Fisher's exact test | Fisher's exact test | Fisher's exact test | Fisher's exact test | |
TP53 | 11 (39%) | 17 |
0.0685 (1.00) |
0.179 (1.00) |
0.553 (1.00) |
0.143 (1.00) |
0.0438 (0.92) |
0.743 (1.00) |
0.873 (1.00) |
1 (1.00) |
FBXW7 | 5 (18%) | 23 |
0.025 (0.579) |
0.0241 (0.579) |
0.651 (1.00) |
0.834 (1.00) |
0.123 (1.00) |
0.0372 (0.817) |
1 (1.00) |
1 (1.00) |
NFE2L2 | 4 (14%) | 24 |
0.267 (1.00) |
0.435 (1.00) |
0.212 (1.00) |
1 (1.00) |
0.791 (1.00) |
0.36 (1.00) |
0.659 (1.00) |
1 (1.00) |
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Mutation data file = BLCA-TP.mutsig.cluster.txt
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Molecular subtypes file = BLCA-TP.transferedmergedcluster.txt
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Number of patients = 28
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Number of significantly mutated genes = 3
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Number of Molecular subtypes = 8
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Exclude genes that fewer than K tumors have mutations, K = 3
For multi-class clinical features (nominal or ordinal), Chi-square tests (Greenwood and Nikulin 1996) were used to estimate the P values using the 'chisq.test' function in R
For binary or multi-class clinical features (nominal or ordinal), two-tailed Fisher's exact tests (Fisher 1922) were used to estimate the P values using the 'fisher.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.