Last update 01 Nov 2024

RASGRP1

Last update 01 Nov 2024

Basic Info

Synonyms

Calcium and DAG-regulated guanine nucleotide exchange factor II, calcium- and diacylglycerol-regulated guanine nucleotide exchange factor II, CalDAG-GEFII

+ [6]

Introduction

Functions as a calcium- and diacylglycerol (DAG)-regulated nucleotide exchange factor specifically activating Ras through the exchange of bound GDP for GTP (PubMed:15899849, PubMed:23908768, PubMed:27776107, PubMed:29155103). Activates the Erk/MAP kinase cascade (PubMed:15899849). Regulates T-cell/B-cell development, homeostasis and differentiation by coupling T-lymphocyte/B-lymphocyte antigen receptors to Ras (PubMed:10807788, PubMed:12839994, PubMed:27776107, PubMed:29155103). Regulates NK cell cytotoxicity and ITAM-dependent cytokine production by activation of Ras-mediated ERK and JNK pathways (PubMed:19933860). Functions in mast cell degranulation and cytokine secretion, regulating FcERI-evoked allergic responses. May also function in differentiation of other cell types (PubMed:12845332).

Drugs associated with RASGRP1

Synthetic bryostatin analogues compounds 1(University of Alberta)

Target

RASGRP1

Mechanism

RASGRP1 agonists

Active Org.

University of Alberta

Originator Org.

University of Alberta

Active Indication

Immune System Diseases [+1]

Inactive Indication-

Drug Highest PhasePreclinical

First Approval Ctry. / Loc.-

First Approval Date20 Jan 1800

Synthetic bryostatin analogues compounds 2(University of Alberta)

Target

RASGRP1

Mechanism

RASGRP1 agonists

Active Org.

University of Alberta

Originator Org.

University of Alberta

Active Indication

Immune System Diseases [+1]

Inactive Indication-

Drug Highest PhasePreclinical

First Approval Ctry. / Loc.-

First Approval Date20 Jan 1800

100 Clinical Results associated with RASGRP1

100 Translational Medicine associated with RASGRP1

0 Patents (Medical) associated with RASGRP1

257

Literatures (Medical) associated with RASGRP1

31 Dec 2024·Hematology

Construction of a five-gene-based prognostic model for relapsed/refractory acute lymphoblastic leukemia

Article

Author: Li, Ying ; Zhou, Bi ; Fang, Jing ; Wu, De ; Min, BoJie ; Chen, Qin ; Huang, Jin ; Zhu, Feng ; Liu, WenYuan

BACKGROUNDRelapsed/refractory acute lymphoblastic leukemia (R/R ALL) continues to be a major cause of mortality in children worldwide, with around 15% of ALL patients experiencing relapse and approximately 10% eventually dying from the disease. Early identification of R/R ALL in children has posed a longstanding clinical challenge.METHODGenetic analysis of survival outcomes in pediatric patients with ALL from the TARGET-ALL dataset revealed five risk score factors identified through the intersection of differential genes (relapse/non-relapse) from the GSE17703 and GSE6092 databases. A risk score equation was formulated using these factors and validated against prognostic data from 46 ALL cases at our institution. Patients from multiple datasets were stratified into high and low-score groups based on this equation. Protein-protein interaction networks (PPI) were then constructed using the intersecting differential genes from all three datasets to identify hub nodes and predict interacting transcription factors. Additionally, genes related to cell pyroptosis with varying expression across these datasets were screened, and a multifactorial ROC curve (incorporating risk score and differential expression of pyroptosis-related genes) was generated. Furthermore, relationships among variables in the predictive model were depicted using a nomogram, and model efficacy was assessed through decision curve analysis (DCA).RESULTSBy analyzing the TARGET-ALL, GSE17703, and GSE6092 databases, we developed a prognostic risk assessment model for pediatric ALL incorporating BAG2, EPHA4, FBXO9, SNX10, and WNK1. Validation of this model was conducted using data from 46 pediatric ALL cases obtained from our institution. Following the identification of 27 differentially expressed genes, we constructed a PPI and identified the top 10 hub genes (PTPRC, BTK, LCK, PRKCQ, CD3D, CD27, CD3G, BLNK, RASGRP1, VPREB1). Using this network, we predicted the top 5 transcription factors (HOXB4, MYC, SOX2, E2F1, NANOG). ROC and DCA were conducted on pyroptosis-related genes exhibiting differential expression and risk scores. Subsequently, a nomogram was generated, demonstrating the effectiveness of the risk score in predicting prognosis for pediatric ALL patients.CONCLUSIONSWe have developed a risk prediction model for pediatric R/R ALL utilizing the genes BAG2, EPHA4, FBXO9, SNX10, and WNK1. This model provides a scientific foundation for early identification of R/R ALL in children.

01 Oct 2024·Veterinary Parasitology

Analysis of potential genes, immunological and antioxidant profiles associated with trypanosomiasis susceptibility in dromedary camels

Article

Author: Barghash, Safaa M ; Ateya, Ahmed I ; Wassif, Islam M ; Osman, Wafaa A ; Elsayed, Ahmed A ; El-Kattan, Adel M

Trypanosomiasis is associated with tissue damage and may trigger an immunological response. These tissue lesions are linked to metabolic issues and oxidative stress. The current study aimed to investigate the immunological, antioxidant, and metabolic changes that may be connected to camel trypanosomiasis. Blood samples were collected from 54 camels and allocated into two groups: The control group (35 camels) and the infected group (19 camels). The genes TLR2, TLR5, IL-17, MARCHF3, RASGRP1, EPS15L1, PPIE, ASB16, CMPK2, LPCAT1, FPGT, GPHN, TNNI3K, DIO3, keap1, and OXSR1 were significantly up-regulated in trypanosomiasis camels. However, down-regulation was observed for the genes Nrf2, PRDX6, and NDUFS5. PCR-DNA sequencing was used to identify nucleotide sequence polymorphisms in the immune (TLR2, TLR5, IL-17, MARCHF3, RASGRP1, and EPS15L1), metabolic (PPIE, ASB16, CMPK2, LPCAT1, FPGT, GPHN, TNNI3K, and DIO3), and antioxidant (Nrf2, Keap1, PRDX6, NDUFS5, and OXSR1) genes between healthy and trypanosomiasis-affected camels. Exploring the serum profile also showed a significant (P ˂ 0.05) increase in Hp, SAA, Cp, IL-1β, IL-6, IL 10, TNF-α, and MDA, with significant (P ˂ 0.05) reduction in the serum levels of CAT, SOD, GSH, T3, and T4 in diseased camels compared with healthy ones. Our findings confirm the significance of nucleotide variations, gene expression patterns, and the biochemical profile of the investigated markers as indicators for the susceptibility of trypanosomiasis in dromedary camels and may be utilized to create management strategies.

01 Oct 2024·Cytokine

Natural killer cell-associated prognosis model characterizes immune landscape and treatment efficacy of diffuse large B cell lymphoma

Article

Author: Zeng, Yunxin ; Yu, Kuai ; Xiao, Wei ; Deng, Xuefei

PURPOSENK cells are essential for the detection, identification and prediction of cancer. However, so far, there is no prognostic risk model based on NK cell-related genes to predict the prognosis and treatment outcome of DLBCL patients. This study aimed to explore a risk assessment model that could accurately predict the prognosis and treatment efficacy of DLBCL.METHODSBioinformatics analysis of the expression profiles of DLBCL samples in the GEO database was performed. Cox regression and LASSO regression analysis were used to determine NK cell-related genes associated with patient's prognosis. Based on these genes, a risk assessment model was constructed to predict the prognosis of patients and the effectiveness of treatment. Finally, qRT-PCR was used to verify the expression of gene tags in clinical samples.RESULTSWe identified seven prognosis-related NK cell-related genes (MAP2K1, PRKCB, TNFRSF10B, IL18, LAMP1, RASGRP1, and SP110), and DLBCL patients were divided into low- and high-risk groups based on these genes. Survival analysis showed that the prognosis of patients with low-risk group was better. Pathway enrichment analysis showed that the differentially expressed genes between the two risk groups were related to immune response pathways. Compared with the high-risk group, the low-risk group had higher infiltration of immune cells in tumor tissues. Besides, compared with high-risk group, low-risk patients by immunotherapy or other commonly used anti-tumor drugs might have better efficacy after treatment. In addition, qRT-PCR showed that the expression of risk genes including TNFRSF10B, IL18 and LAMP1 were significantly increased in most DLBCL samples compared to control samples, while the expression of protective genes including MAP2K1, PRKCB, RASGRP1 and SP110 were significantly decreased.CONCLUSIONThe NK cell-related gene signatures were proved to be a reliable indicator of the success of immunotherapy in patients with DLBCL, thus providing a unique evaluation method.

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RASGRP1

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