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 PRKAA2
Basic gene info.Gene symbolPRKAA2
Gene nameprotein kinase, AMP-activated, alpha 2 catalytic subunit
SynonymsAMPK|AMPK2|AMPKa2|PRKAA
CytomapUCSC genome browser: 1p31
Genomic locationchr1 :57110989-57181008
Type of geneprotein-coding
RefGenesNM_006252.3,
Ensembl idENSG00000162409
Description5'-AMP-activated protein kinase catalytic subunit alpha-25'-AMP-activated protein kinase, catalytic alpha-2 chainACACA kinaseAMP-activated protein kinase alpha-2 subunit variant 2AMP-activated protein kinase alpha-2 subunit variant 3AMPK alpha 2AMPK
Modification date20141207
dbXrefs MIM : 600497
HGNC : HGNC
Ensembl : ENSG00000162409
HPRD : 02735
Vega : OTTHUMG00000008282
ProteinUniProt: P54646
go to UniProt's Cross Reference DB Table
ExpressionCleanEX: HS_PRKAA2
BioGPS: 5563
Gene Expression Atlas: ENSG00000162409
The Human Protein Atlas: ENSG00000162409
PathwayNCI Pathway Interaction Database: PRKAA2
KEGG: PRKAA2
REACTOME: PRKAA2
ConsensusPathDB
Pathway Commons: PRKAA2
MetabolismMetaCyc: PRKAA2
HUMANCyc: PRKAA2
RegulationEnsembl's Regulation: ENSG00000162409
miRBase: chr1 :57,110,989-57,181,008
TargetScan: NM_006252
cisRED: ENSG00000162409
ContextiHOP: PRKAA2
cancer metabolism search in PubMed: PRKAA2
UCL Cancer Institute: PRKAA2
Assigned class in ccmGDBB - This gene belongs to cancer gene.

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Phenotypic Information for PRKAA2(metabolism pathway, cancer, disease, phenome)
check002.gifCancer Description
Cancer CGAP: PRKAA2
Familial Cancer Database: PRKAA2
* 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_INTEGRATION_OF_ENERGY_METABOLISM
REACTOME_METABOLISM_OF_LIPIDS_AND_LIPOPROTEINS

check002.gifOthers
OMIM 600497; gene.
Orphanet
DiseaseKEGG Disease: PRKAA2
MedGen: PRKAA2 (Human Medical Genetics with Condition)
ClinVar: PRKAA2
PhenotypeMGI: PRKAA2 (International Mouse Phenotyping Consortium)
PhenomicDB: PRKAA2

Mutations for PRKAA2
* 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
ovaryPRKAA2chr15715887757158897PPP2R1Achr195269582852695848
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 PRKAA2 related fusion information.
IDHead GeneTail Gene
AccessionGene_aqStart_aqEnd_aChromosome_atStart_atEnd_aGene_aqStart_aqEnd_aChromosome_atStart_atEnd_a
AI732856PRKAA22741515716990757171911PRKAA241247115717193457171993

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       2
GAIN (# sample)        1       2
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=5

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check002.gifSomatic Mutation Counts per Tissue in COSMIC data
Stat. for Non-Synonymous SNVs
(# total SNVs=64)
Stat. for Synonymous SNVs
(# total SNVs=17)
Stat. for Deletions
(# total SNVs=6)
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
chr1:57157094-57157094p.F90fs*33
chr1:57173294-57173294p.S523G3
chr1:57170075-57170075p.R407Q3
chr1:57173243-57173243p.T506A3
chr1:57161759-57161759p.R239C3
chr1:57161760-57161760p.R239H2
chr1:57169985-57169985p.S377I2
chr1:57169911-57169911p.M352I2
chr1:57157092-57157092p.D88Y2
chr1:57169752-57169752p.K299K2

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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=5

Point Mutation/ Tissue ID1234567891011121314151617181920
# sample22 16  3    843 1248 9
# mutation32 17  3    843 1259 13
nonsynonymous SNV32 12  2    542 1196 10
synonymous SNV   5  1    3 1  63 3
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
chr1:57173243p.T506A3
chr1:57161759p.F158F2
chr1:57169756p.R442K2
chr1:57173245p.R239C2
chr1:57169985p.M352I2
chr1:57170075p.E168K2
chr1:57158174p.S377N2
chr1:57169911p.R407Q2
chr1:57159464p.T506T2
chr1:57171786p.E301K2

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 PRKAA2 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 PRKAA2

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

ACLY,AR,C1orf168,C9orf152,DPY19L3,EDEM3,GALNT7,
GCC2,GDPD1,LCOR,LRRC8B,MUC5B,MYSM1,NSF,
PRKAA2,SECISBP2L,SLC44A4,SPATA6,TRIP11,UCP3,YPEL2
ASB14,ASB4,BEST3,CAPN3,CMYA5,IP6K3,JPH1,
KCNQ5,MYBPC1,MYLK3,MYLK4,MYOM2,MYOZ2,OBSCN,
PHKA1,PPP2R3A,PRKAA2,SYNPO2L,TXLNB,UGT3A1,WDR62

ASAP1,DYNLRB2,ETNK2,GFRA3,HFM1,HOOK3,HSF1,
LAPTM4B,LRRC14,PRKAA2,PTP4A3,RB1CC1,SCRIB,TSNARE1,
UBR5,ZFP41,ZNF34,ZNF517,ZNF696,ZNF707,ZSCAN10
AMOTL1,ARHGAP10,CLMP,CPED1,CAP2,DCBLD2,EML1,
MAPK4,MSRB3,NPHP1,PALLD,PARD6G,PKD2,PRKAA2,
PTMS,SOBP,SORBS2,SUSD4,TMEM47,ZAK,ZMYND11
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 PRKAA2
check002.gifCross-referenced pharmacological DB IDs from Uniprot
DB CategoryDB NameDB's ID and Url link
ChemistryBindingDB P54646; -.
ChemistryChEMBL CHEMBL2116; -.
ChemistryGuidetoPHARMACOLOGY 1542; -.
Organism-specific databasesPharmGKB PA33745; -.
Organism-specific databasesCTD 5563; -.

check002.gifDrug-Gene Interaction Network
* Gene Centered Interaction Network.
* Drug Centered Interaction Network.
DrugBank IDTarget NameDrug GroupsGeneric NameDrug Centered NetworkDrug Structure
DB00131protein kinase, AMP-activated, alpha 2 catalytic subunitapproved; nutraceuticalAdenosine monophosphate
DB00171protein kinase, AMP-activated, alpha 2 catalytic subunitapproved; nutraceuticalAdenosine triphosphate
DB00412protein kinase, AMP-activated, alpha 2 catalytic subunitapproved; investigationalRosiglitazone
DB01132protein kinase, AMP-activated, alpha 2 catalytic subunitapproved; investigationalPioglitazone
DB00331protein kinase, AMP-activated, alpha 2 catalytic subunitapprovedMetformin


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Cross referenced IDs for PRKAA2
* 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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