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 SEC13
Basic gene info.Gene symbolSEC13
Gene nameSEC13 homolog (S. cerevisiae)
SynonymsD3S1231E|SEC13L1|SEC13R|npp-20
CytomapUCSC genome browser: 3p25-p24
Genomic locationchr3 :10342614-10362725
Type of geneprotein-coding
RefGenesNM_001136026.2,
NM_001136232.2,NM_001278946.1,NM_030673.3,NM_183352.2,
Ensembl idENSG00000157020
DescriptionSEC13-like 1 isoformSEC13-like protein 1SEC13-related proteinprotein SEC13 homolog
Modification date20141207
dbXrefs MIM : 600152
HGNC : HGNC
Ensembl : ENSG00000157020
HPRD : 02538
Vega : OTTHUMG00000128671
ProteinUniProt:
go to UniProt's Cross Reference DB Table
ExpressionCleanEX: HS_SEC13
BioGPS: 6396
Gene Expression Atlas: ENSG00000157020
The Human Protein Atlas: ENSG00000157020
PathwayNCI Pathway Interaction Database: SEC13
KEGG: SEC13
REACTOME: SEC13
ConsensusPathDB
Pathway Commons: SEC13
MetabolismMetaCyc: SEC13
HUMANCyc: SEC13
RegulationEnsembl's Regulation: ENSG00000157020
miRBase: chr3 :10,342,614-10,362,725
TargetScan: NM_001136026
cisRED: ENSG00000157020
ContextiHOP: SEC13
cancer metabolism search in PubMed: SEC13
UCL Cancer Institute: SEC13
Assigned class in ccmGDBC

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Phenotypic Information for SEC13(metabolism pathway, cancer, disease, phenome)
check002.gifCancer Description
Cancer CGAP: SEC13
Familial Cancer Database: SEC13
* 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_PROTEINS

check002.gifOthers
OMIM
Orphanet
DiseaseKEGG Disease: SEC13
MedGen: SEC13 (Human Medical Genetics with Condition)
ClinVar: SEC13
PhenotypeMGI: SEC13 (International Mouse Phenotyping Consortium)
PhenomicDB: SEC13

Mutations for SEC13
* 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 SEC13 related fusion information.
IDHead GeneTail Gene
AccessionGene_aqStart_aqEnd_aChromosome_atStart_atEnd_aGene_aqStart_aqEnd_aChromosome_atStart_atEnd_a
BG256737MRPS10133964217525542175596SEC1333572531034327010343668
DW441224TOMM71826272285762722862498SEC1325737931034276510342887
BE161424KHNYN2477142490876724908820SEC137254931034300910353671

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
There's no copy number variation information in COSMIC data for this gene.

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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=25)
Stat. for Synonymous SNVs
(# total SNVs=8)
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
chr3:10354352-10354352p.S76L2
chr3:10346764-10346764p.A221T2
chr3:10357052-10357052p.I39I2
chr3:10354321-10354321p.W86*2
chr3:10357109-10357109p.Q20H2
chr3:10346828-10346828p.D199D2
chr3:10354338-10354338p.R81W1
chr3:10342967-10342967p.E316G1
chr3:10345799-10345799p.K256*1
chr3:10347261-10347261p.N189S1

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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
# sample21 3    31 412  53 7
# mutation21 3    31 412  53 7
nonsynonymous SNV11 2    11 312  32 4
synonymous SNV1  1    2  1    21 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
chr3:10346828p.I25I,SEC132
chr3:10357052p.D185D,SEC132
chr3:10342967p.G124G,SEC131
chr3:10346776p.E302A,SEC131
chr3:10354321p.P99P,SEC131
chr3:10342968p.E302Q,SEC131
chr3:10346789p.S295S,SEC131
chr3:10354336p.C96C,SEC131
chr3:10342987p.G281R,SEC131
chr3:10346791p.D89N,SEC131

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 SEC13 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 SEC13

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

ARPC4,KXD1,PRADC1,BRK1,CIDECP,COPE,CTU2,
DDX49,FAM58A,IL17RC,JAGN1,MED8,MOGS,MTMR14,
OGG1,POLR2F,PRDX4,RPUSD3,SEC13,TADA3,TATDN2
ADRM1,AP2S1,ARHGDIA,ASNA1,KXD1,CHMP2A,COPE,
CUEDC2,DRAP1,GNB2,MRPL28,NAA10,PSMC3,PSMC4,
LAMTOR2,SDF4,SEC13,SLC39A3,USP5,YIF1A,YKT6

AGR2,ARF4,ARPC4,GSKIP,C19orf10,CYSTM1,CIB1,
COPE,CYB561D2,JAGN1,MPDU1,MRPL54,NDUFC1,POLR2L,
PPIB,RPN1,S100P,SEC13,SEC61B,SERF2,SRA1
AKR1A1,ATOX1,COA4,CNPY2,COQ6,GLRX,GLYATL1,
GOSR2,GSTO1,NTMT1,MRPL55,P4HB,PPAPDC1B,SEC13,
SEC24D,SLC35B1,SNRPA1,TFEC,TSPO2,YIF1A,YIPF5
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 SEC13


There's no related Drug.
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Cross referenced IDs for SEC13
* 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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