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Phenotypic Information (metabolism pathway, cancer, disease, phenome) | |
Gene-Gene Network Information: Co-Expression Network, Interacting Genes & KEGG | |
Gene Summary for MED22 |
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Phenotypic Information for MED22(metabolism pathway, cancer, disease, phenome) |
Cancer Description | |
Cancer | CGAP: MED22 |
Familial Cancer Database: MED22 |
* This gene is included in those cancer gene databases. |
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Oncogene 1 | Significant driver gene in |
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/ |
Metabolic Pathway Description | |
REACTOME_METABOLISM_OF_LIPIDS_AND_LIPOPROTEINS |
Others | |
OMIM | |
Orphanet | |
Disease | KEGG Disease: MED22 |
MedGen: MED22 (Human Medical Genetics with Condition) | |
ClinVar: MED22 | |
Phenotype | MGI: MED22 (International Mouse Phenotyping Consortium) |
PhenomicDB: MED22 |
Mutations for MED22 |
* 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. |
Structural Variants in COSMIC: go to COSMIC mutation histogram |
There's no structural variation information in COSMIC data for this gene. |
Related fusion transcripts : go to Chitars2.0 |
* From mRNA Sanger sequences, Chitars2.0 arranged chimeric transcripts. This table shows MED22 related fusion information. |
ID | Head Gene | Tail Gene | Accession | Gene_a | qStart_a | qEnd_a | Chromosome_a | tStart_a | tEnd_a | Gene_a | qStart_a | qEnd_a | Chromosome_a | tStart_a | tEnd_a |
Other DBs for Structural Variants |
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Copy 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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SNV Counts per Each Loci in COSMIC data: go to COSMIC point mutation |
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Somatic Mutation Counts per Tissue in COSMIC data |
Stat. for Non-Synonymous SNVs (# total SNVs=8) | (# total SNVs=6) |
(# total SNVs=0) | (# total SNVs=0) |
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Top 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 position | Mutation(aa) | Unique sampleID count |
chr9:136212036-136212036 | p.A65A | 1 |
chr9:136210997-136210997 | p.E132E | 1 |
chr9:136212099-136212099 | p.D44D | 1 |
chr9:136211030-136211030 | p.D121D | 1 |
chr9:136212104-136212104 | p.E43Q | 1 |
chr9:136211045-136211045 | p.L116L | 1 |
chr9:136213393-136213393 | p.? | 1 |
chr9:136211062-136211062 | p.E111K | 1 |
chr9:136213404-136213404 | p.K38N | 1 |
chr9:136211065-136211065 | p.E110K | 1 |
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SNV Counts per Each Loci in TCGA data |
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Point Mutation/ Tissue ID | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 |
# sample | 2 | 2 |   | 1 | 1 |   | 1 |   | 1 |   |   | 1 | 1 | 1 |   |   | 1 | 3 |   | 3 |
# mutation | 2 | 2 |   | 1 | 1 |   | 1 |   | 1 |   |   | 1 | 1 | 1 |   |   | 1 | 3 |   | 4 |
nonsynonymous SNV | 1 | 2 |   | 1 | 1 |   | 1 |   |   |   |   | 1 | 1 | 1 |   |   |   | 3 |   |   |
synonymous SNV | 1 |   |   |   |   |   |   |   | 1 |   |   |   |   |   |   |   | 1 |   |   | 4 |
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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Top 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 Position | Mutation(aa) | Unique sampleID count |
chr9:136208458 | p.S167L | 2 |
chr9:136211045 | p.L116L,MED22 | 1 |
chr9:136213407 | p.E110K,MED22 | 1 |
chr9:136211065 | p.L108L,MED22 | 1 |
chr9:136213479 | p.R101H,MED22 | 1 |
chr9:136211069 | p.I98T,MED22 | 1 |
chr9:136213509 | p.A97V,MED22 | 1 |
chr9:136211091 | p.E73D,MED22 | 1 |
chr9:136211100 | p.A65A,MED22 | 1 |
chr9:136211103 | p.D44D,MED22 | 1 |
Other DBs for Point Mutations |
Copy Number for MED22 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 MED22 |
Gene 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. |
Differential 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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CNV vs Gene Expression Plot |
* This plots show the correlation between CNV and gene expression. |
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Gene-Gene Network Information |
Co-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 |
ANAPC2,ASB6,C9orf114,C9orf69,RABL6,CIZ1,NELFB, FAM73B,FBXW5,PPP1R26,MED22,PMPCA,POMT1,REXO4, SDCCAG3,SNAPC4,SURF6,TRAF2,UBAC1,USP20,WDR5 | ALKBH4,ARFGAP1,ARMC7,C19orf25,SMG9,CCDC102A,DDX41, EHMT2,GHDC,LOC148413,MAN1B1,MED22,MOGS,NAT9, NDOR1,RANBP3,SLC12A9,TRIM11,USP20,WDR46,XAB2 |
ABL1,PRRC2B,BRD3,C9orf69,CDK5RAP2,CEP250,CIZ1, NELFB,EHMT1,GIGYF1,MED22,NOTCH1,NUP214,RAPGEF1, REXO4,SNAPC4,TBC1D13,TRAF2,USP20,VAV2,WDR5 | ABCA3,CCDC102A,COL8A2,CRTC3,FAM117A,FNBP1,FOXJ2, FRMD4A,HEATR6,LOXL1,MAGEE1,MECP2,MED22,NFIX, PACS1,PKN1,RANBP3,SOX12,ST5,STAT5B,TLN1 |
Co-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 |
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Interacting Genes (from Pathway Commons) |
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Pharmacological Information for MED22 |
There's no related Drug. |
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Cross referenced IDs for MED22 |
* We obtained these cross-references from Uniprot database. It covers 150 different DBs, 18 categories. http://www.uniprot.org/help/cross_references_section |
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