Ask about this productRelated genes to: UBXD3 antibody
- Gene:
- UBXN10 NIH gene
- Name:
- UBX domain protein 10
- Previous symbol:
- UBXD3
- Synonyms:
- FLJ25429
- Chromosome:
- 1p36.12
- Locus Type:
- gene with protein product
- Date approved:
- 2004-01-09
- Date modifiied:
- 2019-03-20
Related products to: UBXD3 antibody
Related articles to: UBXD3 antibody
- Lysine acetylation plays a critical role in prostate cancer (PCa) by modulating androgen receptor (AR) signaling. However, the exact mechanisms by which lysine acetylation impacts PCa prognosis remain unclear. The aim of this study was to investigate the mechanism by which lysine acetylation affects PCa prognosis by modulating the AR signaling pathway. - Source: PubMed
Publication date: 2025/08/26
Cao BinChen HuijunZhang LutingXiao FangLiu QiaotingTang LizhenYou TaoOuyang Qiufang - Breast cancer is the second leading cause of female mortality globally. Effective diagnostic tools, such as biosensors that utilize reliable biomarkers, are essential for early detection, particularly in low-income countries. This study introduces a novel bioinformatics pipeline that uses machine learning algorithms (MLAs) to identify genetic biomarkers for classifying breast cancer into non-malignant, non-triple-negative, and triple-negative categories. Five Gene Selection Approaches (GSAs) were employed: LASSO (Least Absolute Shrinkage and Selection Operator), Membrane LASSO, Surfaceome LASSO, Network Analysis, and Feature Importance Score (FIS). We implemented three factorial designs to assess the impact of MLAs and GSAs on classification performance (F1 Macro and Accuracy) in both cell lines and patient samples. Using Recursive Feature Elimination (RFE) and Genetic Algorithms (GAs) in the first four GSAs, we reduced the gene count to eight per GSA while maintaining an F1 Macro ≥80 %. Consequently, 95.5 % of our treatments with these gene sets achieved an F1 Macro or Accuracy ranging from 70.3 % to 97.2 %. We analyzed 37 genes for their predictive power in terms of five-year survival and relapse-free survival and compared them with genes from four commercial panels. Notably, thirteen genes (MFSD2A, TMEM74, SFRP1, UBXN10, CACNA1H, ERBB2, SIDT1, TMEM129, MME, FLRT2, CA12, ESR1, and TBC1D9) showed significant predictive capabilities for up to five years of survival. TBC1D9, UBXN10, SFRP1, and MME were significant for relapse-free survival after five years. The FOXC1, MLPH, FOXA1, ESR1, ERBB2, and SFRP1 genes also matched those described in commercial panels. The influence of MLA on F1 Macro and Accuracy was not statistically significant. Altogether, the genetic biomarkers identified in this study hold potential for use in biosensors aimed at breast cancer diagnosis and treatment. - Source: PubMed
Publication date: 2025/07/11
Mayoral-Peña KalaumariGonzález Peña Omar IsraelArtzi Nataliede Donato Marcos - Hypothalamic hamartoma (HH) is a rare suprasellar developmental lesion that resembles ectopically located grey matter within the hypothalamus. Genetic mutations in genes involved in the sonic hedgehog intracellular pathway have been reported in humans with HH. Hypothalamic hamartoma has been reported in dogs; however, no genetic mutation has been associated with it. The aim of this study was to phenotypically and genetically characterize presumptive sporadic HH in a dog. - Source: PubMed
Publication date: 2025/05/27
Liatis TheofanisAttree ElizabethRuiz De Alejos Blanco LauraSantens PatrickDe Stefani AlbertaPsifidi Androniki - Prostate adenocarcinoma (PRAD) is a common cancer diagnosis among men globally, yet large gaps in our knowledge persist with respect to the molecular bases of its progression and aggression. It is mostly indolent and slow-growing, but aggressive prostate cancers need to be recognized early for optimising treatment, with a view to reducing mortality. - Source: PubMed
Balraj Alex StanleyMuthamilselvan SangeethaRaja RachanaaPalaniappan Ashok - Prostate cancer is among the most central sources of cancer-related mortalities. In order to find novel candidates for therapeutic strategies in this kind of cancer, we developed an in-silico method for identification of competing endogenous RNA network. - Source: PubMed
Publication date: 2024/02/28
Taheri MohammadSafarzadeh ArashGhafouri-Fard SoudehBaniahmad Aria