FXYD6

Last update 08 May 2025

Basic Info

Synonyms

FXYD domain containing ion transport regulator 6, FXYD domain-containing ion transport regulator 6, FXYD6

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Introduction

Associates with and regulates the activity of the sodium/potassium-transporting ATPase (NKA) which catalyzes the hydrolysis of ATP coupled with the exchange of Na(+) and K(+) ions across the plasma membrane. Reduces the apparent affinity for intracellular Na(+) with no change in the apparent affinity for extracellular K(+) (PubMed:33231612). In addition to modulating NKA kinetics, may also function as a regulator of NKA localization to the plasma membrane (By similarity).

100 Clinical Results associated with FXYD6

100 Translational Medicine associated with FXYD6

0 Patents (Medical) associated with FXYD6

Literatures (Medical) associated with FXYD6

01 Apr 2025·Cytotechnology

FXYD6 is transcriptionally activated by KLF10 to suppress the aggressiveness of gastric cancer cells

Article

Author: Zhou, Xin ; Liu, Chao ; Xu, Rui ; Wang, Guangsheng ; Zhu, Chenyu

Despite improvements in therapeutic approaches, the mortality rate of gastric cancer (GC) remains unacceptably high. Evidence suggests that FXYD domain containing ion transport regulator 6 (FXYD6) is downregulated in GC. However, its exact function and the molecular mechanism in GC are still unclear. FXYD6 expression in different cell lines was estimated using RT-qPCR. Western blotting was employed for protein expression detection. Cell counting kit-8 assay, colony formation assay, and flow cytometry were implemented to assess GC cell viability, proliferation, and apoptosis, respectively. Bioinformatics analysis as well as chromatin immunoprecipitation and luciferase reporter assays were utilized for verifying FXYD6 interaction with the transcription factor Krüppel-like factor 10 (KLF10). The results showed that FXYD6 displayed a decreased level in GC cell lines. Impaired proliferative ability and enhanced apoptotic capacity were observed in GC cells overexpressing FXYD6. KLF10 expression is positively correlated with FXYD6 expression in GC samples. KLF10 binds to the FXYD6 promoter to enhance its transcription. FXYD6 depletion counteracted KLF10 upregulation-triggered reduction in GC cell proliferation and elevation in apoptosis. In conclusion, KLF10 activates FXYD6 transcription, thereby impeding GC cell proliferation and promoting cell apoptosis.

01 Mar 2025·Interdisciplinary Sciences: Computational Life Sciences

Cell Fate Dynamics Reconstruction Identifies TPT1 and PTPRZ1 Feedback Loops as Master Regulators of Differentiation in Pediatric Glioblastoma-Immune Cell Networks

Article

Author: Uthamacumaran, Abicumaran

Pediatric glioblastoma is a complex dynamical disease that is difficult to treat due to its multiple adaptive behaviors driven largely by phenotypic plasticity. Integrated data science and network theory pipelines offer novel approaches to studying glioblastoma cell fate dynamics, particularly phenotypic transitions over time. Here we used various single-cell trajectory inference algorithms to infer signaling dynamics regulating pediatric glioblastoma-immune cell networks. We identified GATA2, PTPRZ1, TPT1, MTRNR2L1/2, OLIG1/2, SOX11, FXYD6, SEZ6L, PDGFRA, EGFR, S100B, WNT, TNF __-mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"-__α , and NF-kB as critical transition genes or signals regulating glioblastoma-immune network dynamics, revealing potential clinically relevant targets. Further, we reconstructed glioblastoma cell fate attractors and found complex bifurcation dynamics within glioblastoma phenotypic transitions, suggesting that a causal pattern may be driving glioblastoma evolution and cell fate decision-making. Together, our findings have implications for developing targeted therapies against glioblastoma, and the continued integration of quantitative approaches and artificial intelligence (AI) to understand pediatric glioblastoma tumor-immune interactions.

27 Jan 2025·Journal of Cancer

Integrative multi-omics and machine learning identify a robust signature for discriminating prognosis and therapeutic targets in bladder cancer

Article

Author: Yang, Chadanfeng ; Li, Haihao ; Lv, Dihao ; Huang, Yinglong ; Wang, Haifeng ; Ding, Mingxia ; Gong, Chen ; Fu, Shi ; Wang, Jiansong ; Chen, Xiaorong ; Tan, Zhiyong

Background: Bladder cancer (BLCA) is a common malignant tumor whose pathogenesis has not yet been fully elucidated. This study analyzed prognostic genes in BLCA by integrating transcriptomics and proteomics data, and established prognostic models, aiming to offer novel insights for BLCA therapy. Methods: Transcriptomic, proteomic, and protein acetylation sequencing were conducted on six BLCA tumor tissues and six paraneoplastic tissue samples. Furthermore, data from TCGA-BLCA, GSE13507, and single-cell RNA sequencing (scRNA-seq) datasets were integrated. Initially, differential expression analysis identified candidate genes regulated by acetylation. These genes were further refined by intersecting with scRNA-DEG obtained from the scRNA-seq dataset, resulting in the identification of key genes. Subsequently, consistency clustering analysis was performed based on these key genes. Prognostic models were then developed utilizing Cox regression analysis and least absolute shrinkage and selection operator (LASSO) Cox regression. Independent prognostic factors were determined through independent prognostic analysis, followed by the establishment of a nomogram model. Additionally, gene set enrichment analysis (GSEA), immune cell infiltration analysis, mutation analysis, and drug sensitivity analysis were conducted between the two risk groups to elucidate underlying mechanisms. Results: A total of 15 key genes were obtained by crossing 284 candidate genes with 510 scRNA-DEGs. Patients in the TCGA-BLCA dataset were categorized into two subtypes based on the 15 key genes. Next, a risk model was developed using five prognostic genes (CTSE, XAGE2, MAP1A, CASQ2, and FXYD6), and a nomogram model was developed using age, pathologic T, pathologic N, and risk score. A total of 1089 GO entries and 49 KEGG pathways, including cytokine-cytokine receptor interactions, ECM receptor interactions, etc., were involved in all genes in both risk groups. The immunization score, matrix score, and ESTIMATE score were significantly higher in the low-risk group than in the high-risk group. Conclusion: CTSE, XAGE2, MAP1A, CASQ2 and FXYD6 were selected as prognostic genes in BLCA, risk model and nomogram model predicting the prognosis of BLCA patients were constructed. These were helpful for prognostic assessment of BLCA.

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FXYD6

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