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 SGMS1
Basic gene info.Gene symbolSGMS1
Gene namesphingomyelin synthase 1
SynonymsMOB|MOB1|SMS1|TMEM23|hmob33
CytomapUCSC genome browser: 10q11.2
Genomic locationchr10 :52065344-52383737
Type of geneprotein-coding
RefGenesNM_147156.3,
Ensembl idENSG00000198964
Descriptionmedulla oblongata-derived proteinphosphatidylcholine:ceramide cholinephosphotransferase 1protein Mobtransmembrane protein 23
Modification date20141207
dbXrefs MIM : 611573
HGNC : HGNC
Ensembl : ENSG00000198964
HPRD : 18203
Vega : OTTHUMG00000018231
ProteinUniProt:
go to UniProt's Cross Reference DB Table
ExpressionCleanEX: HS_SGMS1
BioGPS: 259230
Gene Expression Atlas: ENSG00000198964
The Human Protein Atlas: ENSG00000198964
PathwayNCI Pathway Interaction Database: SGMS1
KEGG: SGMS1
REACTOME: SGMS1
ConsensusPathDB
Pathway Commons: SGMS1
MetabolismMetaCyc: SGMS1
HUMANCyc: SGMS1
RegulationEnsembl's Regulation: ENSG00000198964
miRBase: chr10 :52,065,344-52,383,737
TargetScan: NM_147156
cisRED: ENSG00000198964
ContextiHOP: SGMS1
cancer metabolism search in PubMed: SGMS1
UCL Cancer Institute: SGMS1
Assigned class in ccmGDBC

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Phenotypic Information for SGMS1(metabolism pathway, cancer, disease, phenome)
check002.gifCancer Description
Cancer CGAP: SGMS1
Familial Cancer Database: SGMS1
* 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_SPHINGOLIPID_METABOLISM
REACTOME_PHOSPHOLIPID_METABOLISM
REACTOME_SPHINGOLIPID_METABOLISM
REACTOME_METABOLISM_OF_LIPIDS_AND_LIPOPROTEINS

check002.gifOthers
OMIM
Orphanet
DiseaseKEGG Disease: SGMS1
MedGen: SGMS1 (Human Medical Genetics with Condition)
ClinVar: SGMS1
PhenotypeMGI: SGMS1 (International Mouse Phenotyping Consortium)
PhenomicDB: SGMS1

Mutations for SGMS1
* 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
There's no inter-chromosomal structural variation.
- 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
breastSGMS1chr105214941352149813SGMS1chr105215109352151493
breastSGMS1chr105223380152233801SGMS1chr105224009752240097
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 SGMS1 related fusion information.
IDHead GeneTail Gene
AccessionGene_aqStart_aqEnd_aChromosome_atStart_atEnd_aGene_aqStart_aqEnd_aChromosome_atStart_atEnd_a
BF836346EPB41L5424272120857824120885494SGMS1419532105219566652195779
BF917180SGMS118123105206671852066823TUBGCP211866310135098969135103347
BF845226MLLT1016447102202314122023574SGMS1436696105206662452066888

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 # sample121  1  2 1 1 2  
GAIN (# sample)1       1        
LOSS (# sample) 21  1  1 1 1 2  
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=3

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check002.gifSomatic Mutation Counts per Tissue in COSMIC data
Stat. for Non-Synonymous SNVs
(# total SNVs=28)
Stat. for Synonymous SNVs
(# total SNVs=10)
Stat. for Deletions
(# total SNVs=1)
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
chr10:52103748-52103748p.D43N2
chr10:52103484-52103484p.E131*2
chr10:52103662-52103662p.H71H2
chr10:52103403-52103403p.E158K2
chr10:52103686-52103686p.M63I2
chr10:52103696-52103696p.L60R2
chr10:52066960-52066960p.W395L2
chr10:52103418-52103418p.I153F2
chr10:52103437-52103437p.F146F2
chr10:52087029-52087029p.R226Q2

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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
# sample 2 5  2  1 231  144 6
# mutation 2 6  2  1 231  194 6
nonsynonymous SNV 1 4  1  1 23   123 6
synonymous SNV 1 2  1      1  71  
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
chr10:52103403p.M63I2
chr10:52103686p.E158K2
chr10:52071042p.G278G1
chr10:52103623p.I193I1
chr10:52087030p.S53Y1
chr10:52103387p.T271I1
chr10:52071078p.P168L1
chr10:52103662p.P47T1
chr10:52087052p.L262L1
chr10:52071083p.P167S1

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 SGMS1 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 SGMS1

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

ANO6,CCDC186,CAB39,ERCC6,FAM160B1,HERC4,IPMK,
MOB1A,NUMB,PARG,PPP2R5E,PRKAA1,REEP3,SEC24A,
SGMS1,SLC10A7,SLC30A7,SLC35F5,TC2N,TRIP11,ZBTB21
ACTR2,AGPS,CCNYL1,CLIC4,CPNE8,CSGALNACT2,EIF5A2,
EVI5,EXOC5,GNG12,RAB8B,RBMS1,RECQL,SDCBP,
SEPT7,SGMS1,SGPP1,SGTB,TMEM167A,TMX3,VAMP7

GSKIP,CDC42EP1,DAPP1,FUT8,LGR4,LIMA1,LMO4,
MBP,PDLIM5,RAB27B,REEP3,RNF19B,SGMS1,SGMS2,
SLC41A2,SOCS6,SRD5A3,STS,TC2N,TNFAIP8,TNFRSF11A
ANKRD29,ANO6,B3GALT2,C4orf3,LINC00242,CYP2U1,KPNA5,
KRT222,UBA6-AS1,MAGEH1,MCFD2,OSBPL8,SEC22C,SGMS1,
SHF,SRR,ST7,STK3,TMX4,ZNF16,ZNF639
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 SGMS1


There's no related Drug.
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Cross referenced IDs for SGMS1
* 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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