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Tbio
FAM83D
Protein FAM83D
Protein Summary
Description
Probable proto-oncogene that regulates cell proliferation, growth, migration and epithelial to mesenchymal transition. Through the degradation of FBXW7, may act indirectly on the expression and downstream signaling of MTOR, JUN and MYC (PubMed:24344117). May play also a role in cell proliferation through activation of the ERK1/ERK2 signaling cascade (PubMed:25646692). May also be important for proper chromosome congression and alignment during mitosis through its interaction with KIF22.
These are targets about which virtually nothing is known. They do not have known drug or small molecule activities - AND - satisfy two or more of the following criteria:
GENEVA (GENe Expression Variance Analysis) allows you to identify RNA-sequencing datasets from the Gene Expression Omnibus (GEO) that contain conditions modulating a gene or a gene signature.
Nearest Tclin calculations are only available for non-Tclin targets that have KEGG Pathway annotations.
Pathways (0)
No pathways found
Interacting Pathways
Reactome is a manually curated, peer reviewed knowledgebase of human biological pathways and processes, available online as an open access resource that can be freely used and distributed by all members of the biological research community. This view of the Reactome database displays the pathways functionally interacting with FAM83D.
Overrepresented Terms from Abstracts 100 terms from up to 100 abstracts, scaled based on the degree of overrepresentation (Fisher's Exact Test p-Value).
Yu et al., Toxicology in vitro : an international journal published in association with BIBRA, 2024-03
Abstract: (show)
N6-methyladenosine (m6A) modification, the most abundant methylation modification on eukaryotic mRNAs, was implicated in the tumourigenesis. This study aimed to explore the role of methyltransferase like 3 (METTL3) in triple-negative breast cancer progression and its underlying mechanisms. FAM83D was markedly elevated in triple-negative breast cancer tissues and cells, and high expression of FAM83D was related to the poor prognosis of triple-negative breast cancer patients. FAM83D knockdown significantly retarded cell proliferation, invasion, stemness, and accelerated cell apoptosis in triple-negative breast cancer cells. On the contrary, overexpression of FAM83D promoted the malignant behaviors. METTL3 could interact with FAM83D and mediate m6A modification of FAM838D. Moreover, METTL3 positively regulated FAM83D expression, and FAM83D overexpression could block the inhibition effects of MRTTL3 knockdown on the malignant behaviors. METTL3 knockdown decreased FAM83D expression to inhibit the Wnt/β-catenin pathway. In addition, knockdown of FAM83D also showed the repressive effects on tumor growth in triple-negative breast cancer in vivo. These findings suggested that METTL3 could modulate FAM83D protein expression through m6A modification to aggravate triple-negative breast cancer progression via the Wnt/β-catenin pathway.
Geng et al., Cell cycle (Georgetown, Tex.), 2023-04
Abstract: (show)
Family with sequence similarity of 83D (FAM83D) is overexpressed in various cancers. However, no pan-cancer analysis is presently available. In the present study, we used a bioinformatics analysis to explore the diagnostic and prognostic value of FAM83D expression levels in human cancers. The GEPIA 2, TIMER 2.0, ENCORI, and DriverDBV3 databases were used to evaluate FAM83D expression levels. The potential prognostic value of FAM83D expression was analyzed using the GEPIA 2, UALCAN, and TISIB databases. The driver gene and promoter methylation levels regarding FAM83D were evaluated using the TIMER 2.0 and UALCAN databases. To further analyze interactive networks for FAM83D, FAM83D-binding proteins and related genes were determined using STRING and Gene MANIA analytic tools. Highly expressed FAM83D could be associated with mutated TP53 and promoter DNA methylation. Relative network analysis suggested that FAM83D was mainly involved in the progesterone-mediated oocyte maturation pathway, cell cycle regulation, and several other signaling pathways. Therefore, the differential expression of FAM83D could serve as a diagnostic and prognostic biomarker for various cancers. Our study revealed useful information about the differential expression of FAM83D, prognostic values, and potential functional networks in a variety of cancers, providing valuable substantive and methodological information to explore the underlying mechanisms.Abbreviations: BP: Biological processes; CC: Cellular components; DAVID: Database for Annotation, Visualization, and Integrated Discovery; DFS: Disease-free survival; ENCORI: Encyclopedia of RNA Interactomes; FAM83: Family with sequence similarity 83; FAM83D: Family with sequence similarity of 83D; GEO: Gene Expression Omnibus; GEPIAx2: Gene Expression Profiling Interactive Analysis 2; GO: Gene Ontology; GTEx: Genotype-Tissue Expression; KEGG: Kyoto Encyclopedia of Genes and Genomes; KIRC: Kidney renal clear cell carcinoma; LIHC: Liver hepatocellular carcinoma; LUAD: Lung adenocarcinoma; MF: Molecular functions miRNA: microRNA; OS: Overall survival; PAAD: Pancreatic adenocarcinoma; PPI: Protein - protein interaction; RNA-seq: RNA-sequencing; TCGA: The Cancer Genome Atlas; TIMER 2.0: Tumor Immune Estimation Resource 2.0; UALCAN: University of Alabama at Birmingham Cancer; UCEC: Uterine corpus endometrial carcinoma.
FAM83D (family with sequence similarity 83, member D) is of particular interest in tumorigenesis and tumor progression. Ovarian cancer is the leading cause of cancer-related death in women all over the world. This study aims to research the association between FAM83D and ovarian cancer (OC).
FAM83D has been demonstrated to contribute to tumorigenesis. However, its immune effects in hepatocellular carcinoma (HCC) have not been reported. This study aimed to identify the immune role of FAM83D in HCC. FAM83D was over-expressed in HCC and contributed to poor prognosis according to the results of data analysis based on The Cancer Genome Atlas (TCGA). Afterward, the levels of immune cells infiltration were found to be correlated with the expression level of FAM83D in HCC. Through TISIDB and cBioPortal network tools, a total of 82 FAM83D-associated genes were screened out, including 12 immunoinhibitors, 20 immunostimulators and 50 tightly co-expressed genes. TCGA cohort was divided into train set and test set on the basis of the proportion of 7:3. According to FAM83D-associated immunomodulators, a four gene predicted model was established using train set via the Cox regression analysis. Survival analysis demonstrated that the overall survival (OS) of high-risk HCC patients was poor compared with the patients in low-risk group. The reliability and predicted power of the risk-score model were identified by a receiver operating characteristic (ROC) curve. A risk-score based nomogram as well as a calibration curve, which were created could be used to anticipate patient's 1-year, 3-year and 5-year survival probabilities. The test set was used to validate these results. Our findings showed that the FAM83D gene was related with HCC immunity. The immune marker chosen could be a promising biomarker for HCC prognosis.
Zouboulis et al., Journal of the European Academy of Dermatology and Venereology : JEADV, 2020-07
Abstract: (show)
Apocrine glands have been long considered as the initial targeted skin compartment in hidradenitis suppurativa/acne inversa (HS).
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Description of the protein which includes the UniProt Function and the NCBI Gene Summary.
Uniprot linked accession values, symbols or commonly used abbreviations associated with this particular target.
Approved gene symbol with link to HUGO Gene Nomenclature Committee.
Ensembl identifier links.
List of abbreviations or acronyms of the full target name.
Radar plot depicting the variety of knowledge obtained by Pharos for a particular target. The more spikes in the plot, the more variety. The longer the length, the higher the quantity of that particular knowledge. Clicking the illumination graph opens an expanded view to explore the plot fuller by seeing plot with annotations of the different radii.
Table representing the top 5 knowledge attributes in the illumination graph. The knowledge value property is on a scale of 0 to 1.
Gene symbols, accession ids and various other target identifiers. Also contains the illumination graph which highlights the amount of knowledge available. Click the "?" for more details.
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Hierarchical classifications for this protein from different ontologies. Click the "?" for more details.
Descriptions of the IDG illumination levels, highlighting the milestones attained in research for this target. Click the "?" for more details.
Jensen Lab generated fractional counting score for the prevalence of this gene in Pubmed articles.
Total count of NCBI Gene Reference Into Function hits for target listed in parenthesis, and summary table with links to publications per PMID with the specific text in article that includes the reported target.
Number of antibodies for this target listed in antibodypedia.com
Number of Gene Ontology (GO) annotations for this target, consisting of the sum of GO Functions and GO Processes.
Ligands associated with a target, listed in ChEMBL, with activity over a cutoff relative to the targetclass.
Approved drugs associated with a target.
Expression data from several sources shown as a heatmap of tissues and data sources, and as a shaded anatomogram. Click the "?" for more details.
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Bar chart summarizing the number of times each residue appears in the sequence. The bars represent the actual counts, while the gold lines represent the expected counts given the frequency of the amino acids in all human genes.
Amino acid sequence of the target protein.
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The protein sequence aligned with structural and functional annotations, as well as disease variants.
Amino acid sequence, and a detailed sequence structure viewer via the Uniprot Protvista viewer. Click the "?" for more details.
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List of protein-protein interactions associated with this gene. Interactions are reported from Reactome, BioPlex, and StringDB. StringDB score must be above 0.400 to be shown here. Explore on the String-DB website to see lower likelihood targets. Click the "?" for more details.
Transcription regulator protein BACH1
A broad classification of protein families
TIN-X metric for the relative scarcity of specific publications for this target.
BioPlex score representing the probability of the protein protein interaction.
BioPlex score representing the probability that the interaction detected was non-specific.
String-DB score representing the confidence in the protein protein interaction.
Data Sources reporting this protein-protein interaction.
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BioPlex score representing the probability that the interacting protein was wrongly identified.
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A listing of the nearest upstream and downstream targets from KEGG pathways that have reached the Tclin designation. Click the "?" for more details.
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Details about predicted viral interactions with this protein, from P-HIPSTer. Click the "?" for more details.
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Function terms describe molecular activities performed by gene products. Terms may be broad, such as "catalytic activity" or narrow, such as "adenylate cyclase activity".
Component terms describe locations relative to cellular structures in which a gene product performs a function.
Process terms describe larger "biological programs" accomplished by multiple molecular activities. Like Functions, Process terms can be broad, such as "DNA repair", or narrow, such as "pyrimidine nucleobase biosynthetic process".
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This is a list of diseases associated with this target, compiled by several resources. Each
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Identifier the data source is using for this disease association.
Based on data from Expression Atlas, this measure quantifies the change in expression between the disease and non-disease states which yielded this association between disease and target.
The significance of the association between disease and target based on the Expression Atlas log2foldchange.
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Occurrence of this target in up to 10 categories of UniProt keywords.
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Orthologous proteins from other species, from OMA, EggNOG, and Inparanoid. Click the "?" for more details.
Statistics about the occurence of this target in literature, extracted via text mining. GeneRIFs,and text-mined publications are also displayed. Click the "?" for more details.
The Pubmed Score (also sometimes referred to as the Jensen Score) is
derived from text mining a set of Pubmed abstracts.
Timeline of pubtator scores for each available year.
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This component shows publications, as determined by manual curation and entity matching to text in the title or abstract. Displayed data is from NCBI. Additional references based on entity matching to the abstracts and full text (when available) from JensenLab is available in the download. Click the "?" for more details.
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What's new in Pharos 3.18?
There are several new features in Pharos version 3.18, including updated data for Publications and Expression, and added features for displaying data from the Pharos community.