Ask about this productRelated genes to: HS2ST1 Blocking Peptide
- Gene:
- HS2ST1 NIH gene
- Name:
- heparan sulfate 2-O-sulfotransferase 1
- Previous symbol:
- -
- Synonyms:
- KIAA0448
- Chromosome:
- 1p22.3
- Locus Type:
- gene with protein product
- Date approved:
- 1999-10-14
- Date modifiied:
- 2014-11-19
Related products to: HS2ST1 Blocking Peptide
Related articles to: HS2ST1 Blocking Peptide
- Despite therapeutic advances, relapse remains the leading cause of death in patients with acute myeloid leukemia (AML). Growth factor signaling controls AML survival, proliferation, relapse, and chemotherapy resistance. Here, we studied heparan sulfate proteoglycans, a class of molecules that bind growth factors via their heparan sulfate chains to change their signaling ability. Heparan sulfate-growth factor interactions are controlled by the addition of sulfate groups catalyzed by heparan sulfotransferases, such as those encoded by and . Using AML patient cohort analyses, we demonstrate that increased expression is associated with worse survival and increased relapse risk for AML patients harboring -rearrangements. Using cell line derived xenografts, we show that AML cells depleted of , but not , have increased bone marrow leukemic burden. Further, AML cells depleted of are more sensitive to cytarabine than Control cells, suggesting that regulates AML chemotherapy resistance. Heparan sulfate antagonism with surfen synergized with cytarabine to further support AML cell death compared to cytarabine alone. Mechanistically, we demonstrate that depletion in AML cells reduces TGF-β1-mediated signaling, which diminishes cell survival upon cytarabine treatment. Together, our data show that promotes AML cell chemotherapy resistance by supporting TGF-β1 signaling. - Source: PubMed
Publication date: 2026/02/13
Termini ChristinaWoodruff KelseyPatel DiyaPeplinski JackSetiawan NicolletteHagen MatthewMeshinchi Soheil - Glioblastoma (GBM) is an aggressive brain tumor with highly variable patient outcomes due to pronounced molecular heterogeneity. Prognosis remains dismal (median survival ∼15 months) and current prognostic models often function as "black boxes," lacking interpretability and limiting clinical utility. There is an urgent need for interpretable prognostic tools to better stratify GBM patients. This study performed cross-cohort, cross-platform transcriptome data integration (TCGA RNA-seq and GEO microarrays) and incorporated inferred immunogenomic features to capture GBM's complexity. An automated machine learning (AutoML) pipeline tested over 100 algorithmic combinations to build an optimal survival prediction model. The final model is a 22-gene signature, and SHAP (SHapley Additive exPlanations) analysis was applied to explain each gene's contribution to risk. Key genes identified by the model (e.g. UBE2W, EID1, HS2ST1) were validated by qRT-PCR and Western blot, confirming their dysregulated expression in GBM cell lines. The 22-gene model achieved a concordance index of ∼0.72 and was validated on independent cohorts (TCGA training and GEO validation), demonstrating robust performance. It effectively stratified patients into high- and low-risk groups with significant survival differences. High-risk tumors were associated with an immune-cell-enriched yet immune-evasive microenvironment, showing greater infiltration of immunosuppressive cells and higher TIDE scores (indicating immune escape). In contrast, low-risk patients had a more favorable immune profile, and their tumors were predicted to be more sensitive to multiple chemotherapeutic agents. This interpretable transcriptome-based integrative prognostic model can serve as a valuable tool for GBM risk stratification and may guide therapeutic decision-making by highlighting potential targets. Not only does it improve outcome prediction, but it also identifies novel prognostic biomarkers, holding promise for personalized treatment and clinical translation in glioblastoma. - Source: PubMed
Publication date: 2026/02/13
Shan MinZhao Zhi-LongLiang Shi-MinDu Nan-DiSheng Wei-WeiShen JieChen Xiao-Hua - The impact of metabolism-related genes on inflammatory bowel disease remains unclear. This study aimed to identify the causal relationships between metabolism-related genes and inflammatory bowel disease. - Source: PubMed
Publication date: 2026/01/22
Zhou HanXie KexinAn HongjinFeng YueYuan YifanGan Huatian - Growing insights into gut microbiota reveal their surprising role in shaping external traits in fish, including the regulation of skin pigmentation. This study explores whether black-spot pigmentation influences the abundance of gut microbiota. We investigate how black-spot pigmentation in Oujiang color common carp shapes gut microbiome composition, gene expression, and metabolite, revealing a coordinated gut-skin color axis. To validate these findings, we used a TYR knockout group, which included both mutant black-spotted (TYR ) and non black-spotted (TYR ) fish, enabling functional comparison across pigmentation phenotypes. - Source: PubMed
Publication date: 2025/10/02
Kanika N HKe JMandal R NGuo Z YCai S LHou XChen X WWang JWang C H - Wilms tumor (WT) lacks precise molecular subtyping tools, which limits the development of personalized therapies. To address this issue, we investigated whether NK cell-related genes (NKGs) could refine the molecular subtyping of WT, aiming to identify novel therapeutic strategies. - Source: PubMed
Publication date: 2025/08/28
Hong PengHu ZaihongLin JieCui KongkongGao ZhiqiangTian XiaomaoLin TaoShi QinlinWei Guanghui