Ask about this productRelated genes to: FAM53A Blocking Peptide
- Gene:
- FAM53A NIH gene
- Name:
- family with sequence similarity 53 member A
- Previous symbol:
- -
- Synonyms:
- DNTNP
- Chromosome:
- 4p16.3
- Locus Type:
- gene with protein product
- Date approved:
- 2005-06-17
- Date modifiied:
- 2018-04-09
Related products to: FAM53A Blocking Peptide
Related articles to: FAM53A Blocking Peptide
- Genome-wide association studies (GWAS) have detected several susceptibility variants for urinary bladder cancer, but how gene regulation affects disease development remains unclear. To extend GWAS findings, we conducted a transcriptome-wide association study (TWAS) using PrediXcan to predict gene expression levels in whole blood using genome-wide genotype data for 6180 bladder cancer cases and 5699 controls included in the database of Genotypes and Phenotypes (dbGaP). Logistic regression was used to estimate adjusted gene-level odds ratios (OR) per 1-standard deviation higher expression with 95% confidence intervals (CI) for bladder cancer risk. We further assessed associations for individual single-nucleotide polymorphisms (SNPs) used to predict expression levels and proximal loci for genes identified in gene-level analyses with false-discovery rate (FDR) correction. TWAS identified four genes for which expression levels were associated with bladder cancer risk: SLC39A3 (OR = 0.91, CI = 0.87-0.95, FDR = 0.015), ZNF737 (OR = 0.91, CI = 0.88-0.95, FDR = 0.016), FAM53A (OR = 1.09, CI = 1.05-1.14, FDR = 0.022), and PPP1R2 (OR = 1.09, CI = 1.05-1.13, FDR = 0.049). Findings from this TWAS enhance our understanding of how genetically-regulated gene expression affects bladder cancer development and point to potential prevention and treatment targets. - Source: PubMed
Publication date: 2025/01/09
Li SitingGui JiangKaragas Margaret RPassarelli Michael N - There has been a growing interest in the role of the subchondral bone and its resident osteoclasts in the progression of osteoarthritis (OA). A recent genome-wide association study (GWAS) identified 100 independent association signals for OA traits. Most of these signals are led by noncoding variants, suggesting that genetic regulatory effects may drive many of the associations. We have generated a unique human osteoclast-like cell-specific expression quantitative trait locus (eQTL) resource for studying the genetics of bone disease. Considering the potential role of osteoclasts in the pathogenesis of OA, we performed an integrative analysis of this dataset with the recently published OA GWAS results. Summary data-based Mendelian randomization (SMR) and colocalization analyses identified 38 genes with a potential role in OA, including some that have been implicated in Mendelian diseases with joint/skeletal abnormalities, such as BICRA, EIF6, CHST3, and FBN2. Several OA GWAS signals demonstrated colocalization with more than one eQTL peak, including at 19q13.32 (hip OA with BCAM, PRKD2, and BICRA eQTL). We also identified a number of eQTL signals colocalizing with more than one OA trait, including FAM53A, GCAT, HMGN1, MGAT4A, RRP7BP, and TRIOBP. An SMR analysis identified 3 loci with evidence of pleiotropic effects on OA-risk and gene expression: LINC01481, CPNE1, and EIF6. Both CPNE1 and EIF6 are located at 20q11.22, a locus harboring 2 other strong OA candidate genes, GDF5 and UQCC1, suggesting the presence of an OA-risk gene cluster. In summary, we have used our osteoclast-specific eQTL dataset to identify genes potentially involved with the pathogenesis of OA. - Source: PubMed
Mullin Benjamin HZhu KunBrown Suzanne JMullin ShelbyDudbridge FrankPavlos Nathan JRichards J BrentGrundberg ElinBell Jordana TZeggini EleftheriaWalsh John PXu JiakeWilson Scott G - Diabetic kidney disease is the leading cause of end-stage kidney disease worldwide; however, the integration of high-dimensional trans-omics data to predict this diabetic complication is rare. We develop artificial intelligence (AI)-assisted models using machine learning algorithms to identify a biomarker signature that predisposes high risk patients with diabetes mellitus (DM) to diabetic kidney disease based on clinical information, untargeted metabolomics, targeted lipidomics and genome-wide single nucleotide polymorphism (SNP) datasets. This involves 618 individuals who are split into training and testing cohorts of 557 and 61 subjects, respectively. Three models are developed. In model 1, the top 20 features selected by AI give an accuracy rate of 0.83 and an area under curve (AUC) of 0.89 when differentiating DM and non-DM individuals. In model 2, among DM patients, a biomarker signature of 10 AI-selected features gives an accuracy rate of 0.70 and an AUC of 0.76 when identifying subjects at high risk of renal impairment. In model 3, among non-DM patients, a biomarker signature of 25 AI-selected features gives an accuracy rate of 0.82 and an AUC of 0.76 when pinpointing subjects at high risk of chronic kidney disease. In addition, the performance of the three models is rigorously verified using an independent validation cohort. Intriguingly, analysis of the protein-protein interaction network of the genes containing the identified SNPs (RPTOR, CLPTM1L, ALDH1L1, LY6D, PCDH9, B3GNTL1, CDS1, ADCYAP and FAM53A) reveals that, at the molecular level, there seems to be interconnected factors that have an effect on the progression of renal impairment among DM patients. In conclusion, our findings reveal the potential of employing machine learning algorithms to augment traditional methods and our findings suggest what molecular mechanisms may underlie the complex interaction between DM and chronic kidney disease. Moreover, the development of our AI-assisted models will improve precision when diagnosing renal impairment in predisposed patients, both DM and non-DM. Finally, a large prospective cohort study is needed to validate the clinical utility and mechanistic implications of these biomarker signatures. - Source: PubMed
Publication date: 2022/11/02
Wu I-WenTsai Tsung-HsienLo Chi-JenChou Yi-JuYeh Chi-HsiaoChan Yun-HsuanChen Jun-HongHsu Paul Wei-ChePan Heng-ChihHsu Heng-JungChen Chun-YuLee Chin-ChanShyu Yu-ChiauLin Chih-LangCheng Mei-LingLai Chi-ChunSytwu Huey-KangTsai Ting-Fen - To analyze ultrasonographic finding in fetuses with Wolf-Hirschhorn syndrome (WHS) and refine the critical region on chromosome 4p16.3 for WHS-associated fetal growth retardation (FGR). - Source: PubMed
Zheng WentingChen BaojiangYin ZhijunHuang XuezhenLiang Yingliang - Family with sequence similarity 53-member A (FAM53A) is an uncharacterized protein with a suspected but unclear role in tumorigenesis. In this study, we examined its role in breast cancer. Immunohistochemical staining of specimens from 199 cases of breast cancer demonstrated that FAM53A levels were negatively correlated with p53 status. In the p53 wild-type breast cancer cell line MCF-7, FAM53A overexpression inhibited cell migration, invasion, and proliferation, downregulated the expression of Snail, cyclin D1, RhoA, RhoC, and MMP9, and decreased mitogen-activated protein kinase kinase (MEK) and extracellular-signal regulated kinase (ERK) phosphorylation. Concurrently, it upregulated E-cadherin and p21 expression levels. Interestingly, opposite trends were observed in the p53-null breast cancer cell line MDA-MB-231. The MEK inhibitor PD98059 reduced the biological effects of FAM53A knockdown in MCF-7 cells and FAM53A overexpression in MDA-MB-231 cells, suggesting that FAM53A affects breast cancer through the MEK-ERK pathway. Silencing in MCF-7 cells and stably expressing wild-type p53 in MDA-MB-231 cells confirmed that the effects of FAM53A signaling through the MEK/ERK pathway depended on the p53 status of the cells. These results suggest that FAM53A acts as a tumor suppressor in p53-positive breast cancer by modulating the MEK-ERK pathway, but may be a potential candidate for targeted anticancer therapies in p53-negative breast cancer. - Source: PubMed
Publication date: 2019/11/14
Zhang JieSun MingfangHao MiaomiaoDiao KexinWang JianLi ShipingCao QixueMi Xiaoyi