Cancer Cell Metabolism Gene Database

  Cancer Cell Metabolism Gene DB

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Bioinformatics and Systems Medicine Laboratory Bioinformatics and Systems Medicine Laboratory

Gene Summary

Phenotypic Information (metabolism pathway, cancer, disease, phenome)

Mutations: SVs, CNVs, SNVs

Gene expression: GE, Protein, DEGE, CNV vs GE

Gene-Gene Network Information: Co-Expression Network, Interacting Genes & KEGG

Pharmacological Information: Drug-Gene Network

Cross referenced IDs

Gene Summary for AGPS
Basic gene info.Gene symbolAGPS
Gene namealkylglycerone phosphate synthase
SynonymsADAP-S|ADAS|ADHAPS|ADPS|ALDHPSY
CytomapUCSC genome browser: 2q31.2
Genomic locationchr2 :178257470-178408564
Type of geneprotein-coding
RefGenesNM_003659.3,
Ensembl idENSG00000018510
Descriptionaging-associated gene 5 proteinaging-associated protein 5alkyl-DHAP synthasealkyldihydroxyacetonephosphate synthase, peroxisomalalkylglycerone-phosphate synthase
Modification date20141207
dbXrefs MIM : 603051
HGNC : HGNC
Ensembl : ENSG00000018510
HPRD : 04337
Vega : OTTHUMG00000132530
ProteinUniProt:
go to UniProt's Cross Reference DB Table
ExpressionCleanEX: HS_AGPS
BioGPS: 8540
Gene Expression Atlas: ENSG00000018510
The Human Protein Atlas: ENSG00000018510
PathwayNCI Pathway Interaction Database: AGPS
KEGG: AGPS
REACTOME: AGPS
ConsensusPathDB
Pathway Commons: AGPS
MetabolismMetaCyc: AGPS
HUMANCyc: AGPS
RegulationEnsembl's Regulation: ENSG00000018510
miRBase: chr2 :178,257,470-178,408,564
TargetScan: NM_003659
cisRED: ENSG00000018510
ContextiHOP: AGPS
cancer metabolism search in PubMed: AGPS
UCL Cancer Institute: AGPS
Assigned class in ccmGDBC

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Phenotypic Information for AGPS(metabolism pathway, cancer, disease, phenome)
check002.gifCancer Description
Cancer CGAP: AGPS
Familial Cancer Database: AGPS
* This gene is included in those cancer gene databases.

Oncogene 1

Tumor Suppressor gene 2

Cancer Gene Census 3

CancerGenes 4

Network of Cancer Gene 5

Significant driver gene in

Therapeutic Vulnerabilities in Cancer1

cf) number; DB name
1 Oncogene; http://nar.oxfordjournals.org/content/35/suppl_1/D721.long,
2 Tumor Suppressor gene; https://bioinfo.uth.edu/TSGene/,
3 Cancer Gene Census; http://www.nature.com/nrc/journal/v4/n3/abs/nrc1299.html,
4 CancerGenes; http://nar.oxfordjournals.org/content/35/suppl_1/D721.long,
5 Network of Cancer Gene; http://ncg.kcl.ac.uk/index.php,
1Therapeutic Vulnerabilities in Cancer; http://cbio.mskcc.org/cancergenomics/statius/

check002.gifMetabolic Pathway Description
REACTOME_PEROXISOMAL_LIPID_METABOLISM
REACTOME_METABOLISM_OF_LIPIDS_AND_LIPOPROTEINS

check002.gifOthers
OMIM
Orphanet
DiseaseKEGG Disease: AGPS
MedGen: AGPS (Human Medical Genetics with Condition)
ClinVar: AGPS
PhenotypeMGI: AGPS (International Mouse Phenotyping Consortium)
PhenomicDB: AGPS

Mutations for AGPS
* Under tables are showing count per each tissue to give us broad intuition about tissue specific mutation patterns.You can go to the detailed page for each mutation database's web site.

check002.gifStructural Variants in COSMIC: go to COSMIC mutation histogram

- Statistics for Tissue and Mutation typeTop
- For Inter-chromosomal Variations
* Inter-chromosomal variantions includes 'interchromosomal amplicon to amplicon', 'interchromosomal amplicon to non-amplified dna', 'interchromosomal insertion', 'Interchromosomal unknown type'.
- For Intra-chromosomal Variations
There's no intra-chromosomal structural variation.
SampleSymbol_aChr_aStart_aEnd_aSymbol_bChr_bStart_bEnd_b
cf) Tissue number; Tissue name (1;Breast, 2;Central_nervous_system, 3;Haematopoietic_and_lymphoid_tissue, 4;Large_intestine, 5;Liver, 6;Lung, 7;Ovary, 8;Pancreas, 9;Prostate, 10;Skin, 11;Soft_tissue, 12;Upper_aerodigestive_tract)

check002.gifRelated fusion transcripts : go to Chitars2.0
* From mRNA Sanger sequences, Chitars2.0 arranged chimeric transcripts. This table shows AGPS related fusion information.
IDHead GeneTail Gene
AccessionGene_aqStart_aqEnd_aChromosome_atStart_atEnd_aGene_aqStart_aqEnd_aChromosome_atStart_atEnd_a
DB074890RMI21353161137474411375187AGPS3545542178257504178257704
BC008192HEATR2179197810105825405AGPS91914432178408042178408566
AW889598HNRNPAB81365177637986177638114AGPS1182352178328933178329049

check002.gifOther DBs for Structural Variants
Structural Variants in Ensembl: go to Ensembl Structural variation
Structural Variants in dbVar: go to dbVar

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check002.gifCopy Number Variations in COSMIC: go to COSMIC mutation CNV/Expr
 
Mutation type/ Tissue IDbrcacnscervendomehaematopokidnLintestliverlungnsovarypancreprostskinstomathyrourina
Total # sample1    1  8        
GAIN (# sample)1    1  8        
LOSS (# sample)        1        
cf) Tissue ID; Tissue type (1; Breast, 2; Central_nervous_system, 3; Cervix, 4; Endometrium, 5; Haematopoietic_and_lymphoid_tissue, 6; Kidney, 7; Large_intestine, 8; Liver, 9; Lung, 10; NS, 11; Ovary, 12; Pancreas, 13; Prostate, 14; Skin, 15; Stomach, 16; Thyroid, 17; Urinary_tract)

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check002.gifSNV Counts per Each Loci in COSMIC data: go to COSMIC point mutation

 : Non-synonymous mutation, : Synonymous mutation, Circle size denotes number of samples.
Maximum mutation count=4

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check002.gifSomatic Mutation Counts per Tissue in COSMIC data
Stat. for Non-Synonymous SNVs
(# total SNVs=42)
Stat. for Synonymous SNVs
(# total SNVs=17)
Stat. for Deletions
(# total SNVs=1)
Stat. for Insertions
(# total SNVs=1)

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check002.gifTop 10 SNVs Having the Most Samples in COSMIC data
* When you move the cursor on each content, you can see more deailed mutation information on the Tooltip. Those are primary_site,primary_histology,mutation(aa),pubmedID.
GRCh37 positionMutation(aa)Unique sampleID count
chr2:178299132-178299132p.K143T2
chr2:178301756-178301756p.R204Q2
chr2:178378584-178378584p.T549P2
chr2:178378585-178378585p.T549I2
chr2:178402821-178402821p.Q625Q2
chr2:178364459-178364459p.?2
chr2:178310273-178310273p.?2
chr2:178285070-178285070p.E112K2
chr2:178362475-178362475p.K448K1
chr2:178301755-178301755p.R204R1

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check002.gifSNV Counts per Each Loci in TCGA data

 : non-synonymous mutation, : synonymous mutation, Circle size denotes number of samples.
maximum mutation count=3

Point Mutation/ Tissue ID1234567891011121314151617181920
# sample41 9  3 12 721  3317
# mutation41 9  3 12 921  3318
nonsynonymous SNV31 4  3 12 41   32 6
synonymous SNV1  5       511   112
cf) Tissue ID; Tissue type (1; BLCA[Bladder Urothelial Carcinoma], 2; BRCA[Breast invasive carcinoma], 3; CESC[Cervical squamous cell carcinoma and endocervical adenocarcinoma], 4; COAD[Colon adenocarcinoma], 5; GBM[Glioblastoma multiforme], 6; Glioma Low Grade, 7; HNSC[Head and Neck squamous cell carcinoma], 8; KICH[Kidney Chromophobe], 9; KIRC[Kidney renal clear cell carcinoma], 10; KIRP[Kidney renal papillary cell carcinoma], 11; LAML[Acute Myeloid Leukemia], 12; LUAD[Lung adenocarcinoma], 13; LUSC[Lung squamous cell carcinoma], 14; OV[Ovarian serous cystadenocarcinoma ], 15; PAAD[Pancreatic adenocarcinoma], 16; PRAD[Prostate adenocarcinoma], 17; SKCM[Skin Cutaneous Melanoma], 18:STAD[Stomach adenocarcinoma], 19:THCA[Thyroid carcinoma], 20:UCEC[Uterine Corpus Endometrial Carcinoma])

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check002.gifTop 10 SNVs Having the Most Samples in TCGA data
* We represented just top 10 SNVs. When you move the cursor on each content, you can see more deailed mutation information on the Tooltip. Those are primary_site, primary_histology, mutation(aa), pubmedID.
Genomic PositionMutation(aa)Unique sampleID count
chr2:178402821p.Q625Q2
chr2:178301756p.R204Q2
chr2:178386078p.T593T2
chr2:178326734p.K389T1
chr2:178364455p.A602S1
chr2:178326740p.M201I1
chr2:178370235p.R419C1
chr2:178402841p.I606V1
chr2:178301781p.R204R1
chr2:178346790p.L420R1

check002.gifOther DBs for Point Mutations
Point Mutation Table of Ensembl: go to Ensembl variation table
Mutation of cBioPortal: go to cBioPortal's Cross-cancer alteration summary

check002.gifCopy Number for AGPS in TCGA
* Copy number data were extracted from TCGA using R package TCGA-Assembler. The URLs of all public data files on TCGA DCC data server were gathered on Jan-05-2015. Function ProcessCNAData in TCGA-Assembler package was used to obtain gene-level copy number value which is calculated as the average copy number of the genomic region of a gene.
cf) Tissue ID[Tissue type]: BLCA[Bladder Urothelial Carcinoma], BRCA[Breast invasive carcinoma], CESC[Cervical squamous cell carcinoma and endocervical adenocarcinoma], COAD[Colon adenocarcinoma], GBM[Glioblastoma multiforme], Glioma Low Grade, HNSC[Head and Neck squamous cell carcinoma], KICH[Kidney Chromophobe], KIRC[Kidney renal clear cell carcinoma], KIRP[Kidney renal papillary cell carcinoma], LAML[Acute Myeloid Leukemia], LUAD[Lung adenocarcinoma], LUSC[Lung squamous cell carcinoma], OV[Ovarian serous cystadenocarcinoma ], PAAD[Pancreatic adenocarcinoma], PRAD[Prostate adenocarcinoma], SKCM[Skin Cutaneous Melanoma], STAD[Stomach adenocarcinoma], THCA[Thyroid carcinoma], UCEC[Uterine Corpus Endometrial Carcinoma]

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Gene Expression for AGPS

check002.gifGene Expression in Cancer Cell-lines (CCLE)
* CCLE gene expression data were extracted from CCLE_Expression_Entrez_2012-10-18.res: Gene-centric RMA-normalized mRNA expression data.

check002.gifDifferential Gene Expression in Primary Tumors (TCGA)
* Normalized gene expression data of RNASeqV2 was extracted from TCGA using R package TCGA-Assembler. The URLs of all public data files on TCGA DCC data server were gathered at Jan-05-2015. Only eight cancer types have enough normal control samples for differential expression analysis.
(t test, adjusted p<0.05 (using Benjamini-Hochberg FDR))
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check002.gifCNV vs Gene Expression Plot
* This plots show the correlation between CNV and gene expression.

: Open all plots for all cancer types


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Gene-Gene Network Information
check002.gifCo-Expressed gene's network Plot
* Co-Expression network figures were drawn using R package igraph. Only the top 20 genes with the highest correlations were shown.
Red circle: input gene, orange circle: cell metabolism gene, sky circle: other gene

: Open all plots for all cancer types

AGPS,DNAH7,DNAL1,EDEM3,FAM73A,GCC2,IBTK,
KIF27,KLF3,LOC100130691,NEK4,NFE2L2,PJA2,POLK,
TMF1,TRIP11,TTC30A,TTC30B,UBR3,USP33,ZNF484
AGPS,ASAP2,CDC27,EPS15,EXOC5,FAM160B1,GNG12,
KATNAL1,KDELC2,LUZP6,PHACTR2,DESI2,RECQL,SEC23A,
SEPT7,SERINC1,SNX6,TCF4,TMEM167A,TMX3,VAMP7

AGL,AGPS,ARHGAP11A,C2orf69,CDC27,FAM98B,FAR1,
GNPNAT1,HNRNPR,LRRC8B,MAPK1,MRPL19,MYO1B,NCKAP1,
R3HDM1,SRBD1,STXBP4,TMTC3,TNPO1,UBR3,UGGT1
AGPS,CAPRIN1,CDC25A,DARS2,DLAT,ECT2,FBXO45,
G3BP1,HDAC2,HSPA9,HSPD1,LARP4,LARS2,MRPL19,
NAA50,NARS,PRPF40A,SERBP1,SRSF1,SRPK1,STT3B
check002.gifCo-Expressed gene's Protein-protein interaction Network Plot
* Co-Expression network figures were drawn using R package igraph. Only the top 20 genes with the highest correlations were shown.
Red circle: input gene, orange circle: cell metabolism gene, sky circle: other gene

: Open all plots for all cancer types

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check002.gifInteracting Genes (from Pathway Commons)

: Open all interacting genes' information including KEGG pathway for all interacting genes from DAVID

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Pharmacological Information for AGPS


There's no related Drug.
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Cross referenced IDs for AGPS
* We obtained these cross-references from Uniprot database. It covers 150 different DBs, 18 categories. http://www.uniprot.org/help/cross_references_section

: Open all cross reference information



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