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 APOB
Basic gene info.Gene symbolAPOB
Gene nameapolipoprotein B
SynonymsFLDB|LDLCQ4
CytomapUCSC genome browser: 2p24-p23
Genomic locationchr2 :21224300-21266945
Type of geneprotein-coding
RefGenesNM_000384.2,
Ensembl idENSG00000084674
Descriptionapo B-100apoB-100apoB-48apolipoprotein B (including Ag(x) antigen)apolipoprotein B-100apolipoprotein B48mutant Apo B 100
Modification date20141222
dbXrefs MIM : 107730
HGNC : HGNC
Ensembl : ENSG00000084674
HPRD : 00133
Vega : OTTHUMG00000090785
ProteinUniProt:
go to UniProt's Cross Reference DB Table
ExpressionCleanEX: HS_APOB
BioGPS: 338
Gene Expression Atlas: ENSG00000084674
The Human Protein Atlas: ENSG00000084674
PathwayNCI Pathway Interaction Database: APOB
KEGG: APOB
REACTOME: APOB
ConsensusPathDB
Pathway Commons: APOB
MetabolismMetaCyc: APOB
HUMANCyc: APOB
RegulationEnsembl's Regulation: ENSG00000084674
miRBase: chr2 :21,224,300-21,266,945
TargetScan: NM_000384
cisRED: ENSG00000084674
ContextiHOP: APOB
cancer metabolism search in PubMed: APOB
UCL Cancer Institute: APOB
Assigned class in ccmGDBC

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

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

Mutations for APOB
* 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 APOB related fusion information.
IDHead GeneTail Gene
AccessionGene_aqStart_aqEnd_aChromosome_atStart_atEnd_aGene_aqStart_aqEnd_aChromosome_atStart_atEnd_a
BG562874APOB136722122726821228146ALB35966047427917474280877
AI052800SUPT6H1197172702905227029248APOB19647022123478521235060
AF119905MALAT111173116526943965270612APOB1160171922122871821229276
BX091114APOB159222126150321262094SLBP586718416991791699311
BP238983DDX19B6287167040587570406156APOB28746122122605321227161

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)                1
LOSS (# sample)                 
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=6

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check002.gifSomatic Mutation Counts per Tissue in COSMIC data
Stat. for Non-Synonymous SNVs
(# total SNVs=636)
Stat. for Synonymous SNVs
(# total SNVs=204)
Stat. for Deletions
(# total SNVs=17)
Stat. for Insertions
(# total SNVs=5)

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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
chr2:21230434-21230434p.F3102L5
chr2:21235160-21235160p.L1527P5
chr2:21235202-21235202p.R1513Q5
chr2:21230014-21230014p.L3242L4
chr2:21236086-21236086p.R1388C4
chr2:21230633-21230633p.S3036Y4
chr2:21230565-21230565p.R3059C4
chr2:21235442-21235442p.S1433L4
chr2:21230333-21230333p.R3136H3
chr2:21263900-21263900p.T98I3

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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=6

Point Mutation/ Tissue ID1234567891011121314151617181920
# sample122458215 29 16228737221410535438
# mutation1525711317 32 162212744231421558489
nonsynonymous SNV101877213 25 10129326161414941470
synonymous SNV57 424 7 61 34187  6617 19
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
chr2:21230434p.F3102L6
chr2:21235160p.L1527P5
chr2:21235442p.S1433L4
chr2:21225676p.R3059C3
chr2:21229930p.I4126I3
chr2:21236264p.G4206G3
chr2:21265342p.F3270F3
chr2:21230565p.F1328F3
chr2:21225916p.E2079K3
chr2:21229324p.T3472T3

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 APOB 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 APOB

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

APCS,APOA2,APOA4,APOB,APOC3,ARG1,C8A,
C9,CREB3L3,CRP,F2,FABP1,FGF23,HP,
ITIH1,MT1B,PLG,SERPINA7,SERPINC1,SLC17A2,TM4SF5
ADH5,ANO6,APOB,PQLC2L,EIF4EBP2,ESYT1,FAM89A,
FBXL5,GBE1,GPR180,LPL,MAPK10,MARC1,NAT8L,
PCYOX1,PDE3B,PEX19,PPARG,PRKAR2B,RGS22,STBD1

APOA1,APOA4,APOB,APOC3,BEND2,CPO,EPHA7,
FMO9P,G6PC,GABRB1,GSTA2,KCNJ13,LOC285733,LYZL2,
MGAM,MS4A10,PRSS38,SERPINA10,SLC2A2,SNORA13,SPATA21
AADAC,APOA1,APOA4,APOB,APOC3,C17orf78,CRISP1,
FAM99A,FAM99B,GSTA5,KCNJ13,LCE3E,LOC388428,MOS,
ONECUT3,OR10H1,OR10H5,OR4N5,PWAR4___F2RL3___PAWR,SLC2A2,SPANXN3
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 APOB
check002.gifCross-referenced pharmacological DB IDs from Uniprot
DB CategoryDB NameDB's ID and Url link

check002.gifDrug-Gene Interaction Network
* Gene Centered Interaction Network.
* Drug Centered Interaction Network.
DrugBank IDTarget NameDrug GroupsGeneric NameDrug Centered NetworkDrug Structure
DB01095apolipoprotein BapprovedFluvastatin
DB00335apolipoprotein BapprovedAtenolol
DB01029apolipoprotein Bapproved; investigationalIrbesartan
DB00930apolipoprotein BapprovedColesevelam
DB00264apolipoprotein Bapproved; investigationalMetoprolol


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Cross referenced IDs for APOB
* 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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