Cancer Cell Metabolism Gene Database

  Cancer Cell Metabolism Gene DB

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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 ITPKA
Basic gene info.Gene symbolITPKA
Gene nameinositol-trisphosphate 3-kinase A
SynonymsIP3-3KA|IP3KA
CytomapUCSC genome browser: 15q15.1
Genomic locationchr15 :41786055-41795757
Type of geneprotein-coding
RefGenesNM_002220.2,
Ensembl idENSG00000137825
DescriptionIP3 3-kinase AIP3K Ainositol 1,4,5-trisphosphate 3-kinase AinsP 3-kinase A
Modification date20141222
dbXrefs MIM : 147521
HGNC : HGNC
Ensembl : ENSG00000137825
HPRD : 00941
Vega : OTTHUMG00000130343
ProteinUniProt: P23677
go to UniProt's Cross Reference DB Table
ExpressionCleanEX: HS_ITPKA
BioGPS: 3706
Gene Expression Atlas: ENSG00000137825
The Human Protein Atlas: ENSG00000137825
PathwayNCI Pathway Interaction Database: ITPKA
KEGG: ITPKA
REACTOME: ITPKA
ConsensusPathDB
Pathway Commons: ITPKA
MetabolismMetaCyc: ITPKA
HUMANCyc: ITPKA
RegulationEnsembl's Regulation: ENSG00000137825
miRBase: chr15 :41,786,055-41,795,757
TargetScan: NM_002220
cisRED: ENSG00000137825
ContextiHOP: ITPKA
cancer metabolism search in PubMed: ITPKA
UCL Cancer Institute: ITPKA
Assigned class in ccmGDBA - This gene has a literature evidence and it belongs to cancer gene.
References showing role of ITPKA in cancer cell metabolism1. Hoofd C, Devreker F, Deneubourg L, Deleu S, Nguyen TM, et al. (2012) A specific increase in inositol 1,4,5-trisphosphate 3-kinase B expression upon differentiation of human embryonic stem cells. Cell Signal 24: 1461-1470. doi: 10.1016/j.cellsig.2012.03.006. go to article

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Phenotypic Information for ITPKA(metabolism pathway, cancer, disease, phenome)
check002.gifCancer Description
Cancer CGAP: ITPKA
Familial Cancer Database: ITPKA
* 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
KEGG_INOSITOL_PHOSPHATE_METABOLISM

check002.gifOthers
OMIM 147521; gene.
Orphanet
DiseaseKEGG Disease: ITPKA
MedGen: ITPKA (Human Medical Genetics with Condition)
ClinVar: ITPKA
PhenotypeMGI: ITPKA (International Mouse Phenotyping Consortium)
PhenomicDB: ITPKA

Mutations for ITPKA
* 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
There's no structural variation information in COSMIC data for this gene.

check002.gifRelated fusion transcripts : go to Chitars2.0
* From mRNA Sanger sequences, Chitars2.0 arranged chimeric transcripts. This table shows ITPKA related fusion information.
IDHead GeneTail Gene
AccessionGene_aqStart_aqEnd_aChromosome_atStart_atEnd_aGene_aqStart_aqEnd_aChromosome_atStart_atEnd_a
BE175234ITPKA187154178881341788899ITPKA84175154178890741788998

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 # sample   1             
GAIN (# sample)                 
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=2

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check002.gifSomatic Mutation Counts per Tissue in COSMIC data
Stat. for Non-Synonymous SNVs
(# total SNVs=16)
Stat. for Synonymous SNVs
(# total SNVs=2)
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
chr15:41793924-41793924p.A226A2
chr15:41793928-41793928p.R228C1
chr15:41794645-41794645p.R352*1
chr15:41786312-41786312p.G63R1
chr15:41793985-41793985p.Q247K1
chr15:41794660-41794660p.R357C1
chr15:41786384-41786384p.E87*1
chr15:41794232-41794232p.R281W1
chr15:41794697-41794697p.V369A1
chr15:41786466-41786466p.L114P1

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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=2

Point Mutation/ Tissue ID1234567891011121314151617181920
# sample21 1  2 2  31   12  
# mutation21 1  2 2  31   12  
nonsynonymous SNV21 1  1 2  21   11  
synonymous SNV      1    1     1  
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
chr15:41793928p.R228C2
chr15:41794697p.L114P1
chr15:41786466p.P177S1
chr15:41794987p.Y186F1
chr15:41793700p.L222R1
chr15:41795237p.T301T1
chr15:41793728p.V310I1
chr15:41795316p.S341S1
chr15:41793911p.T349M1
chr15:41794294p.R357C1

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 ITPKA 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 ITPKA

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

BAG1,BLVRB,C15orf59,LINC00313,CRYM,EGFL7,ENTPD8,
MZT2B,FAM195A,GPX4,HEXIM2,ITPKA,LOC113230,MAB21L2,
P2RX4,PISD,SIGIRR,SIL1,SLC6A19,TMEM205,TSPAN1
ALPP,ARL14,B3GNT6,CTSE,HNF4A,ITPKA,MAGEA4,
MSLN,MUC21,NAPSA,NKX2-1,PAEP,PLA2G10,SERPINB3,
SFTA3,SFTPA1,SFTPB,SFTPC,TRIM15,UPK3B,XKRX

ACOT7,AKR7A3,BRI3BP,GUCD1,CIB1,E2F2,EFHD2,
GAL3ST1,GCHFR,ITPK1,ITPKA,LTK,NAGS,PLCB3,
PLLP,PTPRH,RTN4R,SLC9A3R1,TRIM15,WRAP53,XPNPEP1
ACVR1B,C1orf210,CDKN2B,CTSD,EPN1,GPN2,IL17RE,
ITPKA,KIAA1522,MGAT4B,NAAA,OAF,PIGS,PRKCZ,
SLC26A6,SPINT1,SPIRE2,TMC4,VILL,VIPR1,VSIG10
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 ITPKA
check002.gifCross-referenced pharmacological DB IDs from Uniprot
DB CategoryDB NameDB's ID and Url link
ChemistryBindingDB P23677; -.
Organism-specific databasesPharmGKB PA29975; -.
Organism-specific databasesCTD 3706; -.

check002.gifDrug-Gene Interaction Network
* Gene Centered Interaction Network.
* Drug Centered Interaction Network.
DrugBank IDTarget NameDrug GroupsGeneric NameDrug Centered NetworkDrug Structure
DB01863inositol-trisphosphate 3-kinase AexperimentalInositol 1,3,4,5-Tetrakisphosphate
DB03401inositol-trisphosphate 3-kinase AexperimentalD-Myo-Inositol-1,4,5-Triphosphate
DB03431inositol-trisphosphate 3-kinase AexperimentalAdenosine-5'-Diphosphate
DB04395inositol-trisphosphate 3-kinase AexperimentalPhosphoaminophosphonic Acid-Adenylate Ester


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Cross referenced IDs for ITPKA
* 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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