ARHGAP26 _ OPHN1L
- Known as:
- ARHGAP26 _ OPHN1L
- Catalog number:
- GTX15916
- Product Quantity:
- 100 µg
- Category:
- -
- Supplier:
- ACR
- Gene target:
- ARHGAP26 _ OPHN1L
Related genes to: ARHGAP26 _ OPHN1L
- Gene:
- ARHGAP26 NIH gene
- Name:
- Rho GTPase activating protein 26
- Previous symbol:
- -
- Synonyms:
- GRAF, KIAA0621, OPHN1L, OPHN1L1
- Chromosome:
- 5q31.3
- Locus Type:
- gene with protein product
- Date approved:
- 2004-05-19
- Date modifiied:
- 2019-04-23
Related products to: ARHGAP26 _ OPHN1L
Related articles to: ARHGAP26 _ OPHN1L
- - Source: PubMed
Publication date: 2025/12/30
Long AichunLi TianyunHuang ChengShi YuweiZhang Cuiwei - BRAF, when mutated at V600E, is a well-known potent early oncogenic driver in papillary thyroid carcinoma (PTC), with potential prognostic and therapeutic implications. Non-V600E mutations are less common and without clear functional or therapeutic significance. One class of non-V600E mutations is BRAF gene fusions, which typically involve the C-terminal kinase domain of BRAF joined to a wide repertoire of potential N-terminal fusion partners. The aim of this study was to employ a sequential algorithmic approach to identify patients with BRAF fusions based on an integrated analysis of histologic, immunohistochemistry (IHC), and molecular (NGS) features of BRAF-rearranged PTCs. Nine patients with PTC previously scrutinized as BRAF V600E negative by IHC were analyzed by NGS. The studied 9 cases showed conventional PTC growth; 2 cases displayed a minor high-grade component (tall cell and hobnailing, < 20%), 1 case qualified as high-grade differentiated thyroid carcinoma (presence of necrosis and mitotic activity > 5 MF/ 2 mm; adjacent conventional PTC was present), and 1 case represented neck (lymph node) recurrence after 10 years. BRAF fusions were detected in all cases (10 different fusion partners: NRF1, MKRN1, MACF1, MTDH1, ARHGAP26, STRBF, FCHSDH2, POM121C, UBAP2L, SND1). To our knowledge, 7 of these fusions have not been reported so far in PTC (NRF1::BRAF, MTDH1::BRAF, ARHGAP26::BRAF, BRAF::STRBF, FCHSDH2::BRAF, BRAF::POM121C, UBAP2L::BRAF). In 3 PTCs, BRAF fusions were sole genomic events. Concurrent TERT (c.-124C > T) mutations were detected in 3 PTCs; pathogenic IGF2 amplification was present in another PTC, in addition to BRAF fusion. Two simultaneous fusions BRAF::STRBF and FCHSDH2::BRAF were found in one case of PTC; two BRAF fusions (BRAF::POM121C; UBAP2L::BRAF) co-existed with 2 FOXO1 fusions (FOXO1::TES, YWHAG::FOXO1) in one PTC. In summary, we report 7 new BRAF fusions in PTC BRAF V600E-WT. Additional clinical research is needed to elucidate the behavior of BRAF fusion-driven thyroid carcinomas and the therapeutic utility of MAPK pathway inhibitors. - Source: PubMed
Publication date: 2026/02/16
McGrath NathanLiang LiBakkar RaniaGernon Thomas JMaghami EllieAfkhami MichelleBell Diana - During evolution, organisms evolve mainly through natural and artificial selection, leaving distinctive signatures on genomic coordinates. Such genomic regions offer valuable insights into the molecular mechanisms that influence quantitative traits. India harbours a diverse buffalo population with Murrah breed exhibiting exceptional milk production and quality, notably a high fat and solids-not-fat content. Therefore, the present investigation focused on exploring selection signatures within the genome of the Murrah buffalo through whole-genome resequencing. A total of 17 472 799 SNPs were identified, which were further utilized for identification of selection signatures using site frequency spectrum-based Tajima's D and Nucleotide Diversity; and linkage disequilibrium-based iHS approaches. A total of 248 regions under selection overlapped with 64 QTLs across various traits (milk, production, reproduction, meat and carcass, health, and exterior) on chromosomes 5, 9, and 17. A majority of the identified QTLs (39) were associated with milk-related traits, with 27 QTLs specifically linked to milk fat content. Notably, genes such as , and mapped within the QTLs under selection are implicated in milk traits, while is associated with growth. Hub genes included 3 (milk); (reproduction); (body confirmation), and (heat tolerance). This study lays the groundwork for targeted breeding efforts aimed at enhancing milk production in buffalo. - Source: PubMed
Surati UtsavNiranjan Saket KPundir Rakesh KumarKoul YmberzalVohra VikasGandham Ravi KumarKumar Amod - Renal clear cell carcinoma (KIRC), the most common subtype of renal cell carcinoma, is characterized by high metastatic potential and heterogeneity.The expression pattern of the ARHGAP26 gene in KIRC, its link to patient prognosis, and its role in the TME immunoregulatory network are not well understood, with significant research gaps. We will analyze ARHGAP26 expression using TCGA and GSCALite databases and assess its association with the tumor microenvironment using the ESTIMATE algorithm. Additionally, we will use the GEO database to examine ARHGAP26 expression across different cell subsets in KIRC and evaluate its correlation with immune cell infiltration using TIMER 2.0. Immunohistochemistry (IHC) will be used to confirm differences in ARHGAP26 expression between renal clear cell carcinoma (KIRC) and adjacent normal tissues. ARHGAP26 expression is higher in KIRC, with a significant difference from normal tissues (p < 0.001). In KIRC patients, high ARHGAP26 expression is linked to longer overall, progression-free, and disease-specific survival, suggesting a tumor suppressor role, though it does not affect the disease-free interval. High ARHGAP26 expression may remodel the tumor stroma and alter the tumor microenvironment by changing the tumor-to-non-tumor cell ratio. - Source: PubMed
Publication date: 2025/11/10
Long AichunHuang ChengLi TianyunShi YuweiZhang Cuiwei - Retinal degeneration comprises a diverse group of progressive disorders leading to visual impairment and ultimately blindness. These include inherited retinal dystrophies (IRDs), diabetic retinopathy (DR), age-related macular degeneration (AMD), and glaucoma, affecting millions worldwide. The underlying pathology involves dysfunction and death of photoreceptor cells and the retinal pigment epithelium (RPE), driven by various stress-induced cell death mechanisms. Although multiple pathways have been reported, the relative contribution of each remains incompletely understood, highlighting the need for further investigation. Therefore, we studied how different stress types that induce retinal degeneration alter the global gene expression profile in vivo (C57BL/6 mice), aiming to identify predominant cell death mechanisms as well as key genes and networks. Retinal toxicity was induced using established models of oxidative stress, hypoxia, endoplasmic reticulum (ER) stress, and chronic inflammation. Transcriptomic profiling revealed both unique and convergent gene expression changes associated with each stressor. In total, 170, 328, 146, and 151 genes were significantly altered under oxidative stress, inflammation, ER stress, and hypoxia, respectively (Log2 fold change >2 or <-2; p < 0.05). Genes such as Arhgap26, Ccdc9, Ube2e2, and Fndc3b were commonly dysregulated across ER stress, inflammation, and oxidative stress, whereas Nfix, Elp6, Naca, and Plcd3 were selectively altered in oxidative stress, inflammation, ER stress, and hypoxia, respectively. Analysis of cell death-related gene subsets revealed that pyroptosis was commonly activated across different stress types. Additionally, autophagy-mediated cell death, ferroptosis, and extrinsic apoptosis were preferentially associated with oxidative stress, chronic inflammation, and hypoxia, respectively. Both ER and oxidative stress models showed strong activation of autophagy-associated cell death. Together, these findings delineate distinct molecular signatures and predominant cell death mechanisms triggered by specific stressors, providing important insights that could aid in developing targeted therapies to prevent or slow retinal degeneration. - Source: PubMed
Publication date: 2025/12/01
Sarkar SubhradeepKannan RamarajPanigrahi TrailokyanathVeeramani KarthickrajaMb ThirumaleshShanker Bhattacharya ShomGhosh Arkasubhra