RGS20 antibody Host Rabbit
- Known as:
- RGS20 (anti-) Host Rabbit
- Catalog number:
- 'H00008601-D01P
- Product Quantity:
- 100
- Category:
- -
- Supplier:
- ACR
- Gene target:
- RGS20 antibody Host Rabbit
Related genes to: RGS20 antibody Host Rabbit
- Gene:
- RGS20 NIH gene
- Name:
- regulator of G protein signaling 20
- Previous symbol:
- -
- Synonyms:
- RGSZ1, ZGAP1
- Chromosome:
- 8q11.23
- Locus Type:
- gene with protein product
- Date approved:
- 2001-02-09
- Date modifiied:
- 2017-04-13
Related products to: RGS20 antibody Host Rabbit
Related articles to: RGS20 antibody Host Rabbit
- Clear cell renal cell carcinoma (ccRCC) is an angiogenic tumor originating from proximal tubule epithelial cells. Ammonia induced cell death is closely associated with carcinogenesis, but its potential mechanism in ccRCC remains unclear and requires further investigation. - Source: PubMed
Publication date: 2026/04/23
Jiang YaoZhang TianFan JieSu YanshengQiao ShaoyiJi JintaoHu XiangnanZhou ShuchangWei YingjuanDu LinaYang BoZhang Wuhe - The poor prognosis of lung adenocarcinoma (LUAD) remains unimproved. This study aimed to identify lymph node metastasis (LNM)-related and cellular immunity-related prognostic genes in LUAD and propose novel strategies to improve its prognosis. LUAD-related datasets were obtained from public databases. Prognostic genes and a prognostic model were obtained through various bioinformatics analyzes, and the immunotherapy response in risk groups was assessed. Subsequently, the expression levels of prognostic genes and the intercellular communication relationships were explored at the single-cell level. Moreover, malignant cells were identified, and their differentiation mechanisms were explored via inferCNV analysis. Additionally, FURIN was silenced and overexpressed to investigate its effects on the invasion, metastasis, and lymphangiogenesis of LUAD cells in vitro. RGS20, KYNU, RAET1E, FGF12, GJB2, CACNA2D2, FURIN, and GDF10 were identified as prognostic genes with LNM. In 4 datasets, LUAD patients with the high LNM and immune cell-related risk scores exhibited higher mortality rates compared to those in the low-risk group. Furthermore, individuals in the low-risk group demonstrated a greater propensity to derive advantages from immunotherapeutic interventions. Epithelial cells were identified as key cells, with CACNA2D2 being significantly up-regulated during their late-stage differentiation. Basal cells, the malignant subset within epithelial cells, showed elevated FURIN expression in the pre-differentiation phase, which declined in the middle and late phases. Functionally, FURIN was found to enhance the migratory and proliferative capacities of LUAD cells. Moreover, we demonstrated that FURIN accelerated lymphatic metastasis and lymphangiogenesis in vitro. In this paper, we identified LUAD prognostic genes with LNM and immune cell signatures, emphasized treating LUAD patients according to LNM- and immune cell-related risk scores, and provided novel ideas on how to improve poor prognosis and develop targeted therapy for LUAD. - Source: PubMed
Lin ChuanChen XuanSun YongTang XiaomeiJiang Yi - BACKGROUND: Lung adenocarcinoma (LUAD) represents an aggressive malignancy characterized by high metastatic potential. Emerging evidence suggests mitochondrial DNA methylation (MTDM) plays a pivotal role in regulating gene expression through protein synthesis modulation, yet its mechanistic involvement in LUAD pathogenesis remains poorly understood. METHODS: We systematically identified differentially expressed MTDM-related genes (DEMTDMRGs) through intersection analysis of differentially expressed genes and weighted gene co-expression network modules. Functional enrichment analysis was performed for the identified 339 DEMTDMRGs. Prognostic gene signatures were established using machine learning algorithms, followed by comprehensive validation of the risk model through Kaplan-Meier and ROC analyses. The clinical utility was further evaluated via nomogram construction. Immune cell infiltration patterns and drug sensitivity were analyzed across risk strata. Pathway enrichment was investigated through GSEA. Single-cell RNA sequencing elucidated cell-type specific expression patterns of prognostic genes, with subsequent experimental validation by qRT-PCR. RESULTS: Functional analysis revealed DEMTDMRGs were significantly enriched in cell cycle regulation, ferroptosis, and ABC transporter pathways. Our machine learning-derived prognostic model incorporating six genes (GJB3, RGS20, PTPRH, GPR37, STK32A, and CNTNAP2) demonstrated robust predictive capacity (1-year AUC = 0.82). The riskScore emerged as an independent prognostic factor (HR = 1.87, 95%CI = 1.32–2.65). Distinct immune infiltration patterns were observed between risk groups, with 15 immune cell subsets showing differential abundance. Pathway analysis identified 27 KEGG and 30 HALLMARK pathways, with particular enrichment in cell proliferation and metabolic processes. High-risk patients exhibited enhanced sensitivity to AZD7762 (P = 0.003). Single-cell resolution analysis localized predominant expression of five prognostic genes (GJB3, RGS20, PTPRH, GPR37, and STK32A) in epithelial cells, with elevated expression in tumor samples. Experimental validation confirmed significant overexpression of all six genes in LUAD cells. CONCLUSION: Our study elucidates the multifaceted roles of MTDM in LUAD pathogenesis and establishes a novel six-gene signature with prognostic and therapeutic implications. The identified biomarkers not only predict immunotherapy response but also provide accurate risk stratification, offering new perspectives for precision oncology in LUAD management. - Source: PubMed
Publication date: 2026/02/10
Ding JianCheng GangXue QianGuo WeizhenCheng YikunYang ChengTong JiabingLi ZegengGao Yating - BACKGROUND: Lung adenocarcinoma (LUAD) is a leading cause of cancer-related mortality worldwide. Although established biomarkers guide targeted therapy, their utility in prognostic stratification remains limited. In this study, we. METHODS: ASPH mRNA expression across pan-cancers and in LUAD was analyzed using the TIMER and The Cancer Genome Atlas (TCGA) databases. Immunohistochemical (IHC) staining was performed on 120 clinical LUAD specimens to validate ASPH protein expression. Kaplan-Meier survival analysis was used to assess the association between ASPH expression and overall survival (OS) as well as progression-free survival (PFS). Univariate and multivariate Cox proportional hazards regression models were applied to evaluate the prognostic value of ASPH. Gene Set Enrichment Analysis (GSEA) and Pearson co-expression analysis were conducted to explore potential molecular mechanisms. RESULTS: ASPH was significantly upregulated in LUAD tissues at both mRNA and protein levels compared with corresponding normal lung tissues (all p < 0.001). High ASPH expression was closely correlated with aggressive clinicopathological features, including larger tumor size, T-stage parameters, lymph node/distant metastasis, and advanced clinical stage (all p < 0.05). Kaplan-Meier analysis showed that high ASPH expression was associated with poorer OS and PFS (both p < 0.001). Multivariate Cox regression confirmed ASPH as an independent prognostic factor for OS (TCGA cohort: HR = 1.01, 95% CI = 1.00–1.01, p = 0.003; clinical cohort: HR = 1.85, 95% CI = 1.03–3.30, p = 0.04) and PFS (clinical cohort: HR = 1.84, 95% CI = 1.08–3.14, p = 0.03). Co-expression analysis revealed ASPH was positively correlated with oncogenes RGS20 (r = 0.547) and MET (r = 0.516). GSEA indicated ASPH-coexpressed genes were enriched in pathways related to cell proliferation (ribosome, DNA replication, cell cycle), immune regulation (granulocyte activation, neutrophil-mediated immunity), and cell adhesion. CONCLUSION: Overexpression of ASPH is significantly associated with poor prognosis and disease progression in LUAD patients, supporting its utility as a complementary prognostic tool and candidate therapeutic target for LUAD. - Source: PubMed
Publication date: 2026/01/31
Zheng QingzhuWeng JiamiaoZhu BinLi MingjieZhu XianjinCao Yingping - - Source: PubMed
Publication date: 2025/12/16
Yang LinLi ShuoZhou QiaoLiu Wei