Ask about this productRelated genes to: RBMS3 antibody
- Gene:
- RBMS3 NIH gene
- Name:
- RNA binding motif single stranded interacting protein 3
- Previous symbol:
- -
- Synonyms:
- -
- Chromosome:
- 3p24.1
- Locus Type:
- gene with protein product
- Date approved:
- 2001-06-25
- Date modifiied:
- 2016-10-05
Related products to: RBMS3 antibody
Related articles to: RBMS3 antibody
- Tumor metastasis is a major cause of poor prognosis in ovarian cancer, and angiogenesis plays an important role in metastatic progression. RBMS3 has been shown to possess tumor-suppressive functions in several cancers, but its specific molecular mechanisms in ovarian cancer remain unclear. In this study, we investigated the molecular mechanisms and functions of RBMS3 in ovarian cancer using in vitro assays (including angiogenesis and migration assays), in vivo nude mouse tumor models, immunoprecipitation, western blotting, immunofluorescence, mRNA stability assays, and methylation assays. We found that RBMS3 forms a protein complex with ELAVL1 and UPF1. Mechanistically, RBMS3 reduces the stability of ELAVL1 mRNA at the post-transcriptional level, leading to downregulation of downstream oncogenic targets, including VEGF-A and IL-6. In addition, RBMS3 was found to inhibit UPF1 methylation, although the functional consequences of this modification remain to be further characterized. In vivo experiments showed that RBMS3 overexpression significantly inhibited ovarian tumor growth and reduces tumor vascular density. Functionally, overexpression of RBMS3 suppressed angiogenesis and invasion of ovarian cancer cells and reduced STAT3 phosphorylation levels. Restoring ELAVL1 expression reversed the inhibitory effects of RBMS3 on these malignant phenotypes and STAT3 phosphorylation. Our study reveals for the first time that RBMS3 coordinates a dual regulatory mechanism by bringing ELAVL1 and UPF1 together into a functional complex. This complex synergistically suppresses ovarian cancer at the post-transcriptional level by reducing ELAVL1 mRNA stability and inhibiting UPF1 methylation. These findings suggest that RBMS3 serves as a key regulator of dual mechanisms and provide a basis for targeting the RBMS3-ELAVL1-UPF1 axis in ovarian cancer therapy. - Source: PubMed
Publication date: 2026/04/17
Wang YixiaoSun HuiHou XiaoyaXing WenluXu ZhengguoZhang QingsongYang Bo - In this study, we investigated the genomic basis of key body measurement and weight traits in Iraqi Awassi sheep using a multi-locus genome-wide association approach. A total of 315 yearling animals were phenotyped for body length, chest depth, heart girth, withers height, and body weight, and genotyped using the Ovine 50K SNP BeadChip. Genome-wide association analyses were performed within the BLUPmrMLM framework to improve the detection of loci with moderate-to-small effects. Significant associations were identified using an LOD-based threshold (LOD ≥ 5), followed by positional annotation of nearby genes and functional enrichment analyses to infer their potential biological relevance. Multiple genomic regions were associated with the evaluated traits. Among the most biologically plausible candidate genes were and for body length, for chest depth, and for heart girth, for body weight, and , , and for withers height. Functional enrichment analyses indicated the involvement of pathways related to integrin-mediated signaling, focal adhesion and integrin complexes, extracellular matrix organization, and post-transcriptional regulation, suggesting coordinated effects of cell-matrix interactions and gene-expression regulation on body size and conformation. Overall, these findings refine the genomic landscape underlying body weight and morphometric variation in Awassi sheep and provide a focused set of loci for future validation and possible application in marker-assisted and genomic selection programs. - Source: PubMed
Publication date: 2026/03/10
Bayraktar MervanHasan Hussein FShoshin Omer - Gastrointestinal cancer is a common malignant tumor with high incidence and poor prognosis. Accurate prediction of prognosis can improve the treatment of cancer patients, but the clinical features currently used provide insufficient information. This study aimed to establish an efficient survival prediction model for gastrointestinal cancer based on gene expression and clinical data. - Source: PubMed
Publication date: 2026/02/04
Liu ShicaiZhang Han - Heterogeneity in cancer gene expression is typically linked to genetic and epigenetic alterations, yet the extent of contribution from posttranscriptional regulation remains unclear. Here, we systematically measured messenger RNA (mRNA) dynamics across diverse breast cancer models, revealing that mRNA stability substantially shapes gene expression variability. To decipher these dynamics, we developed GreyHound, an interpretable multimodal deep-learning framework integrating RNA sequence features and RNA binding protein (RBP) expression. GreyHound identified an extensive network of RBPs and their regulons underlying variations in mRNA stability, including a regulatory axis centered on RBP RBMS3 and redox regulator TXNIP. depletion resulted in targeted transcript destabilization-associated with poor clinical outcomes and enhanced metastatic potential in xenograft models. In vivo epistasis studies confirmed that RBMS3-mediated regulation of mRNA stability drives this metastasis-suppressive program. These findings identify a key posttranscriptional mechanism in breast cancer and illustrate how interpretable models of RNA dynamics can uncover regulatory programs in disease. - Source: PubMed
Publication date: 2026/03/11
Karner HeatherMittmann Tabea CChen Vicky WBorah Ashir ALangen AndreasYousefi HassanFish LisaZaro Balyn WNavickas AlbertasGoodarzi Hani - Alternative splicing contributes to the carcinogenic process of non-small cell lung cancer. Although extensive efforts have characterized cancer-associated alternative splicing events, the upstream regulators governing these aberrant splicing events remain poorly understood. - Source: PubMed
Publication date: 2026/03/02
Yan YuMeng HuaGuo HuanWei ShengCheng Shanshan