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 SPTLC2
Basic gene info.Gene symbolSPTLC2
Gene nameserine palmitoyltransferase, long chain base subunit 2
SynonymsHSN1C|LCB2|LCB2A|NSAN1C|SPT2|hLCB2a
CytomapUCSC genome browser: 14q24.3
Genomic locationchr14 :77972339-78083110
Type of geneprotein-coding
RefGenesNM_004863.3,
Ensembl idENSG00000100596
DescriptionLCB 2SPT 2long chain base biosynthesis protein 2aserine palmitoyltransferase 2serine palmitoyltransferase, subunit IIserine-palmitoyl-CoA transferase 2
Modification date20141207
dbXrefs MIM : 605713
HGNC : HGNC
Ensembl : ENSG00000100596
HPRD : 05755
Vega : OTTHUMG00000171540
ProteinUniProt:
go to UniProt's Cross Reference DB Table
ExpressionCleanEX: HS_SPTLC2
BioGPS: 9517
Gene Expression Atlas: ENSG00000100596
The Human Protein Atlas: ENSG00000100596
PathwayNCI Pathway Interaction Database: SPTLC2
KEGG: SPTLC2
REACTOME: SPTLC2
ConsensusPathDB
Pathway Commons: SPTLC2
MetabolismMetaCyc: SPTLC2
HUMANCyc: SPTLC2
RegulationEnsembl's Regulation: ENSG00000100596
miRBase: chr14 :77,972,339-78,083,110
TargetScan: NM_004863
cisRED: ENSG00000100596
ContextiHOP: SPTLC2
cancer metabolism search in PubMed: SPTLC2
UCL Cancer Institute: SPTLC2
Assigned class in ccmGDBC

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Phenotypic Information for SPTLC2(metabolism pathway, cancer, disease, phenome)
check002.gifCancer Description
Cancer CGAP: SPTLC2
Familial Cancer Database: SPTLC2
* 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: SPTLC2
MedGen: SPTLC2 (Human Medical Genetics with Condition)
ClinVar: SPTLC2
PhenotypeMGI: SPTLC2 (International Mouse Phenotyping Consortium)
PhenomicDB: SPTLC2

Mutations for SPTLC2
* 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
ovarySPTLC2chr147802137078021390SPTLC2chr147800811478008134
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 SPTLC2 related fusion information.
IDHead GeneTail Gene
AccessionGene_aqStart_aqEnd_aChromosome_atStart_atEnd_aGene_aqStart_aqEnd_aChromosome_atStart_atEnd_a
N98751SURF462299136228340136228561SPTLC2230402147797255977972728
AW376098SPTLC21257147797869277987860SPTLC2254320147797864877978714
AW376106SPTLC25274147797869277987873SPTLC2271337147797864877978714
AW803684SPTLC214107147797242877972521ACTB92182755672585567348

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    11
GAIN (# sample)1         2     1
LOSS (# sample)        1      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=4

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check002.gifSomatic Mutation Counts per Tissue in COSMIC data
Stat. for Non-Synonymous SNVs
(# total SNVs=44)
Stat. for Synonymous SNVs
(# total SNVs=7)
Stat. for Deletions
(# total SNVs=0)
Stat. for Insertions
(# total SNVs=0)
There's no deleted snv.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
chr14:77987880-77987880p.R450R3
chr14:77978689-77978689p.R543W2
chr14:77987898-77987898p.E444K2
chr14:77978747-77978747p.?2
chr14:78021720-78021720p.P367S2
chr14:77984470-77984470p.V494I2
chr14:77978644-77978644p.E558K2
chr14:78021755-78021755p.T355I2
chr14:78045415-78045415p.Y122C2
chr14:78018519-78018519p.A408D2

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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=4

Point Mutation/ Tissue ID1234567891011121314151617181920
# sample32110  2 111531  54 4
# mutation4219  2 111531  54 5
nonsynonymous SNV3216  2  1143   43 4
synonymous SNV1  3    1  1 1  11 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
chr14:77987880p.R450G3
chr14:78063612p.R543Q2
chr14:77978688p.T82S2
chr14:77984460p.E485D1
chr14:78021718p.L268V1
chr14:78043216p.E61K1
chr14:77984470p.F482I1
chr14:78063656p.L257M1
chr14:78021720p.I45I1
chr14:78043230p.E455Q1

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 SPTLC2 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 SPTLC2

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

ALDH6A1,ALKBH1,AR,BTBD7,GPATCH2L,DCAF5,GTF2A1,
IDE,KIAA1244,CIPC,MPP5,NUMB,PPP2R5E,PSEN1,
RPS6KA5,SEL1L,SLC39A9,SPTLC2,TC2N,TRIP11,YLPM1
ANAPC1,AQR,CEP128,C9orf41,CDK12,CLCN3,CPD,
DPY19L3,FAM73A,GPR107,HDX,HFE,IPMK,SCYL2,
SPG11,SPTLC2,TMEM181,TMTC2,XYLB,ZDHHC20,ZFYVE16

ALDH6A1,BTBD7,CDC42BPB,DOCK5,DYNC1H1,HEATR5A,AREL1,
KIAA0586,MPP5,MUC4,NUMB,PPP2R5E,RCOR1,SEL1L,
SHROOM3,SMEK1,SPTLC2,TC2N,TMED10,TRIP11,YLPM1
AIM1,ARFGEF2,ARHGAP32,PRR14L,CLTC,CNOT1,DCAF7,
DNAJC16,FAM120A,FNBP1L,KDM4A,LARP4B,MAML2,MLEC,
OSBP,RALGAPA2,SPG11,SPTLC2,SYNE2,ZBED4,ZBTB10
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 SPTLC2
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
DB00114serine palmitoyltransferase, long chain base subunit 2nutraceuticalPyridoxal Phosphate
DB00133serine palmitoyltransferase, long chain base subunit 2approved; nutraceuticalL-Serine


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Cross referenced IDs for SPTLC2
* 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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