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 MTOR
Basic gene info.Gene symbolMTOR
Gene namemechanistic target of rapamycin (serine/threonine kinase)
SynonymsFRAP|FRAP1|FRAP2|RAFT1|RAPT1
CytomapUCSC genome browser: 1p36.2
Genomic locationchr1 :11166587-11322608
Type of geneprotein-coding
RefGenesNM_004958.3,
Ensembl idENSG00000198793
DescriptionFK506 binding protein 12-rapamycin associated protein 2FK506-binding protein 12-rapamycin complex-associated protein 1FKBP-rapamycin associated proteinFKBP12-rapamycin complex-associated protein 1mammalian target of rapamycinrapamycin and FKBP12 targ
Modification date20141222
dbXrefs MIM : 601231
HGNC : HGNC
Ensembl : ENSG00000198793
HPRD : 03134
Vega : OTTHUMG00000002001
ProteinUniProt: P42345
go to UniProt's Cross Reference DB Table
ExpressionCleanEX: HS_MTOR
BioGPS: 2475
Gene Expression Atlas: ENSG00000198793
The Human Protein Atlas: ENSG00000198793
PathwayNCI Pathway Interaction Database: MTOR
KEGG: MTOR
REACTOME: MTOR
ConsensusPathDB
Pathway Commons: MTOR
MetabolismMetaCyc: MTOR
HUMANCyc: MTOR
RegulationEnsembl's Regulation: ENSG00000198793
miRBase: chr1 :11,166,587-11,322,608
TargetScan: NM_004958
cisRED: ENSG00000198793
ContextiHOP: MTOR
cancer metabolism search in PubMed: MTOR
UCL Cancer Institute: MTOR
Assigned class in ccmGDBA - This gene has a literature evidence and it belongs to cancer gene.
References showing role of MTOR in cancer cell metabolism1. Yuan L, Sheng X, Willson AK, Roque DR, Stine JE, et al. (2015) Glutamine promotes ovarian cancer cell proliferation through the mTOR/S6 pathway. Endocrine-related cancer 22: 577-591. go to article
2. Hsu C-C, Wu L-C, Hsia C-Y, Yin P-H, Chi CW, et al. (2015) Energy metabolism determines the sensitivity of human hepatocellular carcinoma cells to mitochondrial inhibitors and biguanide drugs. Oncology reports. go to article
3. Demel H-R, Feuerecker B, Piontek G, Seidl C, Blechert B, et al. (2015) Effects of topoisomerase inhibitors that induce DNA damage response on glucose metabolism and PI3K/Akt/mTOR signaling in multiple myeloma cells. American Journal of Cancer Research 5: 1649. go to article

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Phenotypic Information for MTOR(metabolism pathway, cancer, disease, phenome)
check002.gifCancer Description
Cancer CGAP: MTOR
Familial Cancer Database: MTOR
* 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 KIRC 6,

Therapeutic Vulnerabilities in Cancer7

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,
6 http://www.nature.com/nature/journal/v499/n7456/full/nature12222.html,
7Therapeutic Vulnerabilities in Cancer; http://cbio.mskcc.org/cancergenomics/statius/

check002.gifMetabolic Pathway Description
Nat Rev Drug Discovery, 2013, 12: 829, doi: 10.1038/nrd4145

check002.gifOthers
OMIM 601231; gene.
Orphanet
DiseaseKEGG Disease: MTOR
MedGen: MTOR (Human Medical Genetics with Condition)
ClinVar: MTOR
PhenotypeMGI: MTOR (International Mouse Phenotyping Consortium)
PhenomicDB: MTOR

Mutations for MTOR
* 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
* Intra-chromosomal variantions includes 'intrachromosomal amplicon to amplicon', 'intrachromosomal amplicon to non-amplified dna', 'intrachromosomal deletion', 'intrachromosomal fold-back inversion', 'intrachromosomal inversion', 'intrachromosomal tandem duplication', 'Intrachromosomal unknown type', 'intrachromosomal with inverted orientation', 'intrachromosomal with non-inverted orientation'.
SampleSymbol_aChr_aStart_aEnd_aSymbol_bChr_bStart_bEnd_b
ovaryMTORchr11117103211171052MTORchr11117121011171230
ovaryMTORchr11117388111173901EXOSC10chr11115336211153382
ovaryMTORchr11117448011174500SMARCA2chr920872542087274
ovaryMTORchr11124483711244857ERBB3chr125649489556494915
ovaryMTORchr11124994811249968PAQR6chr1156217772156217792
ovaryMTORchr11125188111251901MTORchr11124724411247264
ovaryMTORchr11128367211283692chr11069549910695519
ovaryMTORchr11131385211313872MTORchr11129937411299394
pancreasMTORchr11131158111311781SLC4A4chr47210516072105360
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 MTOR related fusion information.
IDHead GeneTail Gene
AccessionGene_aqStart_aqEnd_aChromosome_atStart_atEnd_aGene_aqStart_aqEnd_aChromosome_atStart_atEnd_a
BF845885RASGEF1B3740848236598182366349MTOR40353111131779111317919
BG194684PNPLA1120463627406936275482MTOR19523811120907211209117
BE149450HK129161107113964571139779MTOR14940411116707411167330
BF845891RASGEF1B2839548236598182366351MTOR39051811131779111317919
BE830802MTOR512711118675611187093MTOR11929611118212911184677
BE380007MTOR113211118440311184534SOCS4130633145551489655515402
BF845998RASGEF1B1037548236598182366349MTOR37046311131782611317919
DB084769OCIAD1134844883308048850463MTOR34956711119315211194523

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=15

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check002.gifSomatic Mutation Counts per Tissue in COSMIC data
Stat. for Non-Synonymous SNVs
(# total SNVs=314)
Stat. for Synonymous SNVs
(# total SNVs=44)
Stat. for Deletions
(# total SNVs=6)
Stat. for Insertions
(# total SNVs=0)
There's no inserted snv.

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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
chr1:11184573-11184573p.S2215Y12
chr1:11190804-11190804p.E1799K8
chr1:11188164-11188164p.T1977K5
chr1:11169361-11169361p.R2505P5
chr1:11188078-11188078p.V2006F5
chr1:11272938-11272938p.A1105T4
chr1:11174395-11174395p.L2427Q4
chr1:11169375-11169375p.I2500M4
chr1:11174426-11174426p.V2417M3
chr1:11187102-11187102p.H2106Y3

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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=11

Point Mutation/ Tissue ID1234567891011121314151617181920
# sample2152419 72343 27119 21923 35
# mutation21524010 72303 31129 22328 48
nonsynonymous SNV2112327 62262 2799 21221 43
synonymous SNV 4 83 1 41 43   117 5
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
chr1:11184573p.S2215Y9
chr1:11190804p.E1799K6
chr1:11188164p.T1977K5
chr1:11272938p.A1105T4
chr1:11169375p.I2500M3
chr1:11189847p.F1888I3
chr1:11217230p.C1483Y3
chr1:11300494p.L1460P2
chr1:11189841p.A2056V2
chr1:11300544p.F1103Y2

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 MTOR 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 MTOR

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

ARID1A,ASXL2,BIRC6,DDI2,EYA3,EMC1,CEP104,
KIF1B,KPNA6,LZIC,MFN2,MTF1,MTOR,NOL9,
RAD54L2,REST,RSC1A1,SPEN,UBE4B,UBR4,VPS13D
ANKRD52,ATE1,C10orf12,NOP9,C9orf129,CKAP5,DDI2,
GBF1,GCN1L1,GIGYF2,HERC2,HUWE1,IDE,LMTK2,
KMT2B___KMT2D,MTOR,NCOR1,PRPF8,RGP1,UBE3B,UBR4

ARID1A,DDI2,DNAJC16,EIF4G3,HERC1,EMC1,CEP104,
KIF1B,LUZP1,MFN2,MTOR,NOL9,NSD1,RAD54L2,
RSC1A1,SPEN,UBE4B,UBR4,VPS13D,WASF2,ZBTB40
ALMS1,ARID1A,BAZ1B,CHD8,DYNC1H1,EP400,GCN1L1,
GIGYF2,HCFC1,HERC2,HUWE1,MTOR,KAT6A,NSD1,
PRKDC,PRPF8,SNRNP200,TPR,TRRAP,VPS13D,YLPM1
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 MTOR
check002.gifCross-referenced pharmacological DB IDs from Uniprot
DB CategoryDB NameDB's ID and Url link
ChemistryBindingDB P42345; -.
ChemistryChEMBL CHEMBL2221341; -.
ChemistryGuidetoPHARMACOLOGY 2109; -.
Organism-specific databasesPharmGKB PA28360; -.
Organism-specific databasesCTD 2475; -.

check002.gifDrug-Gene Interaction Network
* Gene Centered Interaction Network.
* Drug Centered Interaction Network.
DrugBank IDTarget NameDrug GroupsGeneric NameDrug Centered NetworkDrug Structure
DB00337mechanistic target of rapamycin (serine/threonine kinase)approved; investigationalPimecrolimus
DB00877mechanistic target of rapamycin (serine/threonine kinase)approved; investigationalSirolimus
DB01590mechanistic target of rapamycin (serine/threonine kinase)approved; investigationalEverolimus
DB06287mechanistic target of rapamycin (serine/threonine kinase)approved; investigationalTemsirolimus


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Cross referenced IDs for MTOR
* 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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