This pipeline maps genes, with mutation or copy number alteration AND this alteration is highly correlated with mRNA expression, to pathways curated in the KEGG and BIOCARTA databases. It identifies pathways significantly enriched with these genes. The pipeline also identifies pathways significantly enriched with marker genes of each expression subtype of cancer.
genes with mutation: identified by the Mutation_Significance pipeline
genes with copy number alteration: identified by the CopyNumber_Gistic2 pipeline
correlation between copy number and mRNA expression: identified by the Correlate_CopyNumber_vs_mRNA pipeline
marker genes and expression subtypes: identified by the mRNAConsensusClustering pipeline
There are 33 genes with significant mutation (Q value <= 0.1) and 342 genes with significant copy number alteration (Q value <= 0.25). The identified marker genes (Q value <= 0.01 or within top 2000) are 238 for subtype 1, 238 for subtype 2, 238 for subtype 3. Pathways significantly enriched with these genes (Q value <= 0.01) are identified :
33 pathways significantly enriched with genes with copy number alteration or mutation.
0 pathways significantly enriched with marker genes of gene expression subtype 1
7 pathways significantly enriched with marker genes of gene expression subtype 2
1 pathways significantly enriched with marker genes of gene expression subtype 3
Table 1. Get Full Table Top Pathways enriched with genes with copy number alteration or mutation. Nof Genes : No. of genes in this pathway. Nof CNV_Mut : No. of genes with copy number alteration or mutation in this pathway. Enrichment , P value and Q value : See Methods & Data below. CNV_Mut Genes in Pathway: genes with copy number alteration or mutation in this pathway.
| Pathway | Nof Genes | Nof CNV_Mut | Enrichment | P value | Q value |
|---|---|---|---|---|---|
| KEGG_ENDOMETRIAL_CANCER | 52 | 12 | 3.7 | 0 | 0 |
| KEGG_PROSTATE_CANCER | 89 | 12 | 2.9 | 0 | 0 |
| KEGG_THYROID_CANCER | 29 | 8 | 4 | 0 | 0 |
| KEGG_PATHWAYS_IN_CANCER | 328 | 20 | 1.8 | 0 | 0.0001 |
| KEGG_BLADDER_CANCER | 42 | 8 | 3.4 | 0 | 0.0001 |
HRAS,PTEN,CTNNB1,PIK3CA,MYC,TP53,CCND1,ERBB2,CDH1,KRAS,PIK3R1,NRAS
HRAS,PTEN,CTNNB1,CCNE1,PIK3CA,TP53,CCND1,FGFR2,ERBB2,KRAS,PIK3R1,NRAS
HRAS,CDH1,CTNNB1,KRAS,MYC,TP53,NRAS,CCND1
HRAS,CTNNB1,PIK3CA,ERBB2,KRAS,FH,PTEN,CCNE1,MYC,CTBP1,TP53,CCND1,NRAS,FGFR2,TRAF2,ARNT,TRAF4,CASP8,CDH1,PIK3R1
HRAS,ERBB2,CDH1,KRAS,MYC,TP53,NRAS,CCND1
Table 2. Get Full Table Top Pathways enriched with marker genes of gene expression subtype 1 . Nof Genes : No. of genes in this pathway. Nof Marker : No. of marker genes of gene expression subtype 1 in this pathway. Enrichment , P value and Q value : See Methods & Data below. Marker Gene in Pathway: markers of gene expression subtype 1 in this pathway
| Pathway | Nof Genes | Nof Marker | Enrichment | P value | Q value |
|---|---|---|---|---|---|
| KEGG_AMINO_SUGAR_AND_NUCLEOTIDE_SUGAR_METABOLISM | 44 | 4 | 3.1 | 0.0011 | 0.38 |
| BIOCARTA_RELA_PATHWAY | 16 | 0 | 0 | 1 | 1 |
| BIOCARTA_NO1_PATHWAY | 31 | 0 | 0 | 1 | 1 |
| BIOCARTA_CSK_PATHWAY | 24 | 0 | 0 | 1 | 1 |
| BIOCARTA_AMI_PATHWAY | 20 | 0 | 0 | 1 | 1 |
GNE,GNPNAT1,GALK2,FUK
Table 3. Get Full Table Top Pathways enriched with marker genes of gene expression subtype 2 . Nof Genes : No. of genes in this pathway. Nof Marker : No. of marker genes of gene expression subtype 2 in this pathway. Enrichment , P value and Q value : See Methods & Data below. Marker Gene in Pathway: markers of gene expression subtype 2 in this pathway
| Pathway | Nof Genes | Nof Marker | Enrichment | P value | Q value |
|---|---|---|---|---|---|
| KEGG_CELL_CYCLE | 128 | 25 | 2.7 | 0 | 0 |
| KEGG_RIBOSOME | 87 | 13 | 2.2 | 0 | 0.0002 |
| KEGG_SPLICEOSOME | 126 | 15 | 2.1 | 0 | 0.0002 |
| KEGG_OOCYTE_MEIOSIS | 114 | 15 | 2.1 | 0 | 0.0002 |
| KEGG_P53_SIGNALING_PATHWAY | 69 | 11 | 2.3 | 0 | 0.0005 |
DBF4,TTK,PTTG1,CCNA2,RBL1,ANAPC4,SKP2,ESPL1,ORC1L,CHEK1,CDC20,PLK1,E2F1,CCNE1,CDC45L,MCM2,MCM4,CDK2,BUB1B,GADD45B,GADD45A,BUB1,CDC25C,CCNB1,CCNB2
RPL19,RPL15,RPLP0,RPS4X,RPS15A,RPL9,RPL3,RPL7A,RPL26,RPS9,RPS6,RPL13A,RPL22
CHERP,CTNNBL1,RBM8A,LSM3,HNRNPC,SNRPB2,HNRNPA1,PPIH,PRPF3,SNRNP200,THOC4,PRPF38A,SR140,SF3B4,USP39
PTTG1,PGR,SGOL1,ANAPC4,ESPL1,CDC20,PLK1,CCNE1,CDK2,CALML3,BUB1,CDC25C,CCNB1,RPS6KA6,CCNB2
PMAIP1,GTSE1,CCNE1,TP53I3,CDK2,RRM2,GADD45B,GADD45A,CHEK1,CCNB1,CCNB2
Table 4. Get Full Table Top Pathways enriched with marker genes of gene expression subtype 3 . Nof Genes : No. of genes in this pathway. Nof Marker : No. of marker genes of gene expression subtype 3 in this pathway. Enrichment , P value and Q value : See Methods & Data below. Marker Gene in Pathway: markers of gene expression subtype 3 in this pathway
| Pathway | Nof Genes | Nof Marker | Enrichment | P value | Q value |
|---|---|---|---|---|---|
| KEGG_CELL_CYCLE | 128 | 19 | 2 | 0 | 0.0001 |
| BIOCARTA_G2_PATHWAY | 24 | 6 | 2.7 | 0.0002 | 0.041 |
| KEGG_OOCYTE_MEIOSIS | 114 | 13 | 1.6 | 0.0004 | 0.045 |
| KEGG_GLUTATHIONE_METABOLISM | 50 | 7 | 1.9 | 0.0027 | 0.22 |
| BIOCARTA_MCM_PATHWAY | 18 | 4 | 2.5 | 0.0045 | 0.23 |
TTK,PTTG1,CDKN2A,ANAPC4,CHEK1,CHEK2,CDC20,E2F1,CCNE1,MCM2,MCM4,MDM2,BUB1B,GADD45B,ORC2L,BUB1,CDC25C,CDC25B,CCNB2
CHEK1,CHEK2,CDC25C,CDC25B,RPS6KA1,MDM2
PTTG1,MOS,ANAPC4,CDC20,CCNE1,CALML3,BUB1,CALML6,CDC25C,RPS6KA6,CCNB2,ADCY9,RPS6KA1
GGT1,GSTM4,GSTZ1,GSTK1,ODC1,GGT7,MGST2
MCM2,MCM4,CCNE1,ORC2L
Let genes with copy number alteration or mutation be query genes. Let marker genes of specific identified subtypes be query genes. The Enrichment is calculated as:
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Enrichment = log2 (# of query genes in the pathway/# No of query genes) - log2 (# of genes in the pathway/# of human genes)
The statistical signficance of the pathways that are enriched with genes with copy number alteration or mutation, and the pathways that are enriched with markers genes of specific identified subtypes is measured by P value.
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P value = Fisher exact P value
The Q value is for adjusting P value for multiple testing. A public available R package is used to calculate the Q value.
In addition to the links below, the full results of the analysis summarized in this report can also be downloaded programmatically using firehose_get, or interactively from either the Broad GDAC website or TCGA Data Coordination Center Portal.