更新于：2024-11-01

ATPase x IFNAR

更新于：2024-11-01

基本信息

关联

项与 ATPase x IFNAR 相关的药物

ATP-IFNα

靶点

ATPase x IFNAR

作用机制

ATPase抑制剂 [+1]

在研机构

Bonum Therapeutics, Inc.初创企业

原研机构

Bonum Therapeutics, Inc.初创企业

在研适应症

肿瘤

非在研适应症-

最高研发阶段临床前

首次获批国家/地区-

首次获批日期1800-01-20

100 项与 ATPase x IFNAR 相关的临床结果

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100 项与 ATPase x IFNAR 相关的转化医学

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0 项与 ATPase x IFNAR 相关的专利（医药）

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项与 ATPase x IFNAR 相关的文献（医药）

2024-08-05·Frontiers in Bioscience-Scholar

GWAS-Identified Loci are Associated with Obesity and Type 2 Diabetes Mellitus in Patients with Severe COVID-19

Article

作者: Sergeeva, Vera ; Dorofeeva, Anna ; Loktionov, Alexey ; Kobzeva, Ksenia ; Bushueva, Olga

BACKGROUNDComorbidities such as obesity and type 2 diabetes mellitus (T2DM) have emerged as critical risk factors exacerbating the severity and mortality of COVID-19. Meanwhile, numerous genome-wide association studies (GWAS) have identified single nucleotide polymorphisms (SNPs) associated with increased susceptibility to severe COVID-19.AIMThis study investigated whether SNPs previously identified by GWAS as risk factors for severe COVID-19 also correlate with common comorbidities-obesity and T2DM-in hospitalized patients with severe COVID-19.METHODSDNA samples from 199 hospitalized COVID-19 patients were genotyped using probe-based PCR for 10 GWAS SNPs previously implicated in severe COVID-19 outcomes (rs143334143 CCHCR1, rs111837807 CCHCR1, rs17078346 SLC6A20-LZTFL1, rs17713054 SLC6A20-LZTFL1, rs7949972 ELF5, rs61882275 ELF5, rs12585036 ATP11A, rs67579710 THBS3, THBS3-AS1, rs12610495 DPP9, rs9636867 IFNAR2).RESULTSThe analysis revealed significant associations between certain SNPs and the increased risk of obesity and T2DM in severe COVID-19 patients. Specifically, rs17713054 SLC6A20-LZTFL1 (risk allele A; odds ratio (OR) = 2.34, 95% confidence interval (CI) = 1.24-4.4, p = 0.007) and rs7949972 ELF5 SNP (risk allele T; OR = 1.79, 95% CI = 1.11-2.91, p = 0.015) were associated with increased risk of obesity. SNP rs9636867 IFNAR2 was associated with a higher risk of T2DM (risk allele G, OR = 8.28, 95% CI = 1.69-40.64, p = 0.027). Using the model-based multifactor dimensionality reduction (MB-MDR) approach, the six most significant gene-gene interaction patterns associated with obesity in severe COVID-19 patients were identified and included five polymorphic loci: rs7949972, rs17713054, rs61882275, rs12585036, and rs143334143, participating in two or more of the most significant G-G interactions (pperm < 0.05). In total, the best models of G-G interactions associated with T2DM in patients with severe COVID-19 included eight polymorphic loci, six of which, rs7949972, rs61882275, rs12585036, rs143334143, rs67579710, and rs12610495, were involved in two or more of the most significant G-G interactions.CONCLUSIONSOur study provides novel insights into the genetic associations between GWAS-identified SNPs and the risk of obesity and T2DM in patients with severe COVID-19.

2024-04-01·Heliyon

Genetic variants in ATP2B2 as risk factors for mortality in patients unrelated but not associated with families with severe COVID-19

Article

作者: Fierro-Piña, Aurelio ; Falfán-Valencia, Ramcés ; Córdoba-Lanus, Elizabeth ; López-Bielma, María Fernanda ; Abarca-Rojano, Edgar ; Chávez-Galán, Leslie ; Jaime-Capetillo, María Esther ; Romero-Villaseñor, Priscila ; Fricke-Galindo, Ingrid ; Buendía-Roldán, Ivette ; Pérez-Rubio, Gloria

IntroductionThe severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the causative agent of Coronavirus Disease 2019 (COVID-19). The disease has a wide range of clinical manifestations, from asymptomatic to severe. Ancestral contribution, sex, immune response, and genetic factors influence the presentation of the disease. The objective of the present study was to validate these genetic variants in patients with severe COVID-19 who died and in survivor patients. Methods: Single nucleotide variants (SNVs) in six genes: ATPase plasma membrane Ca²⁺ transporting 2 (ATP2B2), transmembrane serine protease 2 (TMPRSS2), dedicator of cytokinesis 2 (DOCK2), (interferon alpha and beta receptor subunit 2) IFNAR2, tumor necrosis factor receptor superfamily, member 1A (TNFRSF1A), and tumor necrosis factor receptor superfamily, member 1B (TNFRSF1B), were explored in two groups: the first consisted of severe COVID-19-related patients (familial cases from 58 families, n = 130), and the second group of unrelated severe COVID-19 patients (n = 1045). In each study group, death was evaluated as the outcome.ResultsIn non-related patients with severe COVID-19, carriers of GG genotype (rs2289274) in the ATP2B2 gene showed a high-risk probability of non-surviving (OR = 1.43). Survival analysis to 75 days indicates that carriers of GG have a higher risk than GA or AA genotypes (p = 0.0059). The haplotype GG (rs2289273-rs2289274) in ATP2B2 was found to be associated with a high risk of death in severe non-related COVID-19 patients. No significant associations were found between severe COVID-19-related patients and SNVs in ATP2B2, TMPRSS2, DOCK2, IFNAR2, TNFRSF1A, or TNFRSF1B.ConclusionsUnrelated patients with severe COVID-19 that carry the GG genotype (rs2289274) in ATP2B2 showed a high death risk. Survival analysis to 75 days indicates that carriers of GG have a higher risk of non-survival compared to GA or AA genotypes.

2022-11-01·Zhonghua nan ke xue = National journal of andrology

[Establishment of a prostate cancer prognostic risk model based on the TCGA database and inflammation-related genes].

Article

作者: Zhong, Rong-Fang ; Fan, Song ; Ling, Jia-Cheng ; Meng, Jia-Lin ; Wu, Jun-Chao

OBJECTIVETo investigate the effect of inflammation-related genes on the prognosis of prostate cancer (PCa).METHODSWe downloaded PCa-related clinical data and mRNA sequencing data from the database Cancer Genome Atlas (TCGA) and inflammation-related pathway gene sets from MsigDB. Using univariate regression and LASSO regression analyses, we screened inflammation-related genes for the construction of a prognostic risk model and evaluated the performance of the model in predicting the prognosis of PCa by Kaplan-Meier and ROC analyses. Based on the nomogram, we calculated the risk scores of the patients, divided them into a high-risk and a low-risk group based on the median values of their risk scores, identified differentially expressed genes for enrichment analysis and verified the expression level of SPHK1 in the PCa tissue microarrays by immunohistochemical staining.RESULTSTotally 19 inflammation-related genes were identified from 172 candidate genes for the construction of the prognostic risk model, including the risk genes CD14, PIK3R5, GABBR1, RELA, IRF7, SCARF1, MSR1, SPHK1, OSM and STAB1, and the protective genes AQP9, LPAR1, ATP2C1, NDP, CXCL6, P2RY2, DCBLD2, PCDH7, and IFNAR1. Kaplan-Meier analysis showed that the patients with high risk scores had a significantly lower recurrence-free survival and a worse prognosis than those with low risk scores. Differentially expressed genes were involved mainly in the activation of inflammatory response pathways. Immunohistochemical results indicated that the expression of SPHK1 was significantly higher in the tumorous than in the normal tissue and increased with the Gleason score. There was a correlation between the SPHK1 expression and envelope invasion.CONCLUSIONThe prognostic risk model of inflammation-related genes constructed based on the TCGA database can effectively predict the prognosis of PCa.