Ask about this productRelated genes to: RPLP0 Blocking Peptide
- Gene:
- RPLP0 NIH gene
- Name:
- ribosomal protein lateral stalk subunit P0
- Previous symbol:
- -
- Synonyms:
- PRLP0, P0, L10E, RPP0, LP0
- Chromosome:
- 12q24.23
- Locus Type:
- gene with protein product
- Date approved:
- 1993-12-16
- Date modifiied:
- 2016-10-05
Related products to: RPLP0 Blocking Peptide
Related articles to: RPLP0 Blocking Peptide
- To investigate selected transcripts from urinary cells regarding their potential to predict risk reclassification in patients with prostate cancer (PCa) on active surveillance (AS). - Source: PubMed
Publication date: 2026/08/20
Borkowetz AngelikaGräfe SebastianKwe JeremyFuessel SusanneThomas ChristianErdmann Kati - Glioma ranks among the most intractable malignancies. Given the intricate anatomy of the brain, complete surgical resection is seldom achievable, making radiotherapy a necessary adjunct. Nevertheless, radioresistance, which is closely linked to recurrence, still represents a critical clinical barrier. Effective tumor biomarkers and novel detection methods are urgently required to predict treatment resistance and monitor therapeutic response. Here, we employed surface-enhanced Raman spectroscopy (SERS) coupled with proteomics to profile, for the first time, the characteristic spectral patterns of exosomes secreted by our established radioresistant glioma cells. We further uncovered specific shifts in protein expression during the development of radioresistance, including glycolysis-related proteins (ALDOA and GAPDH) and ribosomal proteins (RPS5 and RPLP0). These proteins are correlated with glioma prognosis. Moreover, bioinformatic analysis revealed that expression levels of all four genes positively correlate with tumor malignancy grade. Furthermore, we established a machine learning-based diagnostic model, Principal component analysis and convolutional neural network (PCA-CNN), for the accurate identification of exosomes derived from radioresistant glioma cells. These findings validate exosomes as a strong candidate biomarker for predicting radioresistance. This approach enables rapid and reliable assessment of radiotherapy resistance in glioma, paving the way for personalized and precise clinical management. - Source: PubMed
Publication date: 2026/07/18
Wu QiongQiu SufangLin DuoLin WanzunWeng Youliang - Clear cell renal cell carcinoma (ccRCC) is characterized by intratumoral heterogeneity and a complex immune microenvironment, which contribute to disease progression and therapeutic resistance. Although ribosomal proteins have been implicated in tumor biology, the clinical relevance, microenvironmental impact, and biological role of ribosomal protein lateral stalk subunit P0 (RPLP0) in ccRCC remain unclear. - Source: PubMed
Publication date: 2026/07/17
Wang BinLiu HongquanGuo YichengMa JianZhang YanweiLi QianZou QingsongWu Jitao - Synovial tissue plays a key role in osteoarthritis (OA) pathogenesis. Gene expression analysis is widely used to investigate underlying pathomechanisms; however, accurate normalization of target mRNA expression depends on the use of stable reference genes. This study evaluated the suitability of common reference genes for determining synovial tissue mRNA expression, considering various factors. In the synovial tissue of 20 patients with end-stage OA, the stability of the expression of 10 reference genes (GAPDH, RPLP0, YWHAZ, TBP, PPIA, EEF1A1, ACTB, HRPT1, SDHA, and RPL13A) was evaluated using four different analysis methods (NormFinder, geNorm, the ΔCt method, and Pearson correlation analysis). The most stable genes were identified by means of a subsequent ranking analysis. Both the entire sample pool and various subgroups (sex, age, BMI, and synovitis score) were considered. RPL13A, PPIA, and EEF1A1 were identified as the most stable reference genes overall, with minor variability across subgroups. In contrast, GAPDH proved to be the least suitable reference gene, showing the most variable expression. The stability of the reference gene might be affected by sex, age, and BMI and this should be taken into account. - Source: PubMed
Publication date: 2026/07/17
Freitag DianaWildemann BrittEitner Annett - The present study evaluated the stability of candidate reference genes during adipogenic differentiation of 3T3-L1 cells cultured on different extracellular matrices. The aim was to investigate the effects of matrix composition and differentiation stage on the expression of candidate housekeeping genes and to compare validation strategies in dynamic in vitro models. Eleven candidate reference genes (, , , , , , , , , , and ) were analyzed by RT-qPCR in 3T3-L1 cells cultured on TC, collagen, gelatin, and Matrigel at Days 7 and 14 of differentiation. Gene stability was assessed using geNorm, NormFinder, RefFinder, comparative ΔCt, BestKeeper, generalized linear model (GLM), linear mixed model (LMM), and correlation analyses with the adipogenic markers and . The results demonstrated that the expression of most housekeeping genes was influenced by matrix composition, differentiation stage, or their interaction. and exhibited the strongest condition-dependent variability and pronounced matrix sensitivity. and showed significant correlations with both and , while correlated with , suggesting that these reference genes may not be fully independent of adipogenic status. demonstrated markedly contrasting rankings across analytical approaches, highlighting limitations of single-method stability assessment. The findings confirm that universal housekeeping genes are unlikely to exist across different matrix conditions and differentiation stages. The results highlight the need for multi-level validation strategies and experimentally validated normalization panels to minimize normalization bias and avoid misleading RT-qPCR expression profiles. Functional validation identified and as the most suitable two-gene normalization panel for the experimental model evaluated, whereas remained a strong complementary reference gene candidate. - Source: PubMed
Publication date: 2026/06/10
Todorova BetinaIvanova ZhenyaGrigorova Natalia