Ask about this productRelated genes to: SH3BP5 Blocking Peptide
- Gene:
- SH3BP5 NIH gene
- Name:
- SH3 domain binding protein 5
- Previous symbol:
- -
- Synonyms:
- Sab
- Chromosome:
- 3p25.1
- Locus Type:
- gene with protein product
- Date approved:
- 1999-09-07
- Date modifiied:
- 2016-10-05
Related products to: SH3BP5 Blocking Peptide
Related articles to: SH3BP5 Blocking Peptide
- Recent reports have shown the development of autoantibodies in patients with atherosclerosis. The purpose of this study is to examine whether these autoantibodies are associated with the risk of incident stroke in community-based population. - Source: PubMed
Publication date: 2026/06/01
Yamagishi KazumasaHiwasa TakakiSun WanluSata MizukiYamaji TaikiIwasaki MotokiInoue ManamiSawada NorieYoshida YoichiKubota MasaakiMatsutani TomooHiguchi YoshinoriLi Shu-YangZhang Bo-ShiMachida ToshioGoto Ken-IchiroOhno MikikoNishi EiichiroWang HaoKudo TakashiDoi HirofumiTomiyoshi GoNakamura RikaMuraki IsaoKokubo YoshihiroSaito IsaoYatsuya HiroshiIso HiroyasuTsugane Shoichiro - Gestational diabetes mellitus (GDM) is a common pregnancy complication linked to adverse outcomes, highlighting the need for new diagnostic markers. This study aimed to identify oxidative stress-related genes as potential biomarkers for GDM using integrated bioinformatics and experimental validation. - Source: PubMed
Publication date: 2026/03/06
He HaiyuWu Wen - Cervical cancer (CC) and ovarian cancer (OC) are among the most prevalent and lethal gynecological malignancies in women, necessitating the identification of reliable biomarkers for early diagnosis and prognosis. This study integrates bioinformatics and Healthcare 4.0 to identify key biomarkers associated with these cancers. Differentially expressed genes (DEGs) were identified from two microarray datasets. mRMR followed by SVM-RFE was applied to the identified DEGs to extract the most significant ML-based DEGs (MDEGs). The predictive ability of the selected gene subsets was further evaluated via multiple classifiers, where attention-based long short-term memory (AttLSTM) consistently achieved the best performance across both datasets. In parallel, WGCNA was conducted to identify coexpression-associated genes (CAGs) from significant modules in each dataset. A PPI network (PPIN) was constructed using the genes common to MDEGs and CAGs and was analyzed via Cytoscape. Four hub genes, MCM3, FOXM1, SH3BP5, and PAPSS2, were identified via the degree method. mRNA expression analysis revealed that FOXM1 and MCM3 were upregulated, whereas SH3BP5 and PAPSS2 were downregulated in cancer tissues compared with normal tissues. ROC curve analysis demonstrated the high prognostic significance of these hub genes, with substantial AUC scores indicating strong discriminatory power. Furthermore, molecular docking analysis with an FDA-approved drug compound confirmed the significant binding affinity between these genes and the drug molecules. These findings suggest that FOXM1, MCM3, SH3BP5, and PAPSS2 could serve as biomarkers for early prognosis, diagnosis, and targeted therapy in patients with cervical and ovarian cancer. - Source: PubMed
Publication date: 2026/02/12
Sarker SakibAhammed EmonHosen Md FarukMiah Mohammad Badrul AlamIslam Mohammad AmanulGhimire DeepakHwang YoungbaeHosen A S M Sanwar - Diffuse large B-cell lymphoma (DLBCL) is a highly heterogeneous and aggressive hematologic malignancy, with the activated B-cell-like (ABC) subtype displaying particularly poor prognosis due to inherent treatment resistance and elevated recurrence rates. Despite advances in targeted therapies and immunotherapies, a significant proportion of patients experience relapse or refractory disease, highlighting the urgent need for novel biomarkers and innovative therapeutic strategies to improve clinical outcomes. - Source: PubMed
Publication date: 2025/09/24
Wu TongYang YiZong YuanZhao JiawenZhao XiaoyuLi LeiGao YimingLi NingJiang LitingXie Yinyin - Preeclampsia is a complex pregnancy disorder that poses significant health risks to both mother and fetus. Despite its clinical importance, the underlying molecular mechanisms remain poorly understood. In this study, we developed an integrative deep learning and bioinformatics approach to identify potential biomarkers for preeclampsia. Three microarray datasets related to preeclampsia were initially analyzed to select a preliminary gene subset based on $P$-values. Feature selection was then performed in two consecutive rounds: first, the Fisher score method was applied to extract significant genes, followed by the minimum Redundancy Maximum Relevance method to refine the subset further. These selected gene subsets were trained using our proposed Attention-based Convolutional Neural Network (AttCNN), which achieved the highest classification accuracy compared with other models. From the experiments, a set of 58 common genes was identified between differentially expressed genes and the final optimized subset. Here, Gene Ontology and KEGG pathway enrichment analyses highlighted key biological processes and pathways associated with preeclampsia. Subsequently, a protein-protein interaction network was constructed, identifying 10 hub genes: TSC22D1, IRF3, MME, SRSF10, SOD1, HK2, ERO1L, SH3BP5, UBC, and ZFAND5. Further analysis of gene regulatory networks, including transcription factor-gene, gene-microRNA, and drug-gene interactions, revealed that seven hub genes (HK2, SRSF10, SOD1, ERO1L, IRF3, MME, and SH3BP5) were strongly associated with preeclampsia. Molecular docking analysis showed that HK2, SH3BP5, and SOD1 exhibited significant binding affinities with two preeclampsia drugs. These findings suggest that the identified hub genes hold promise as biomarkers for early prognosis, diagnosis, and potential therapeutic targets for preeclampsia. - Source: PubMed
Sarker SakibMahmud S M HasanHosen Md FarukMichael Goh Kah OngShoombuatong Watshara