Last update 01 Nov 2024

RHOB

Last update 01 Nov 2024

Basic Info

Synonyms

ARH6, ARHB, h6

+ [9]

Introduction

Mediates apoptosis in neoplastically transformed cells after DNA damage. Not essential for development but affects cell adhesion and growth factor signaling in transformed cells. Plays a negative role in tumorigenesis as deletion causes tumor formation. Involved in intracellular protein trafficking of a number of proteins. Targets PKN1 to endosomes and is involved in trafficking of the EGF receptor from late endosomes to lysosomes. Also required for stability and nuclear trafficking of AKT1/AKT which promotes endothelial cell survival during vascular development. Serves as a microtubule-dependent signal that is required for the myosin contractile ring formation during cell cycle cytokinesis. Required for genotoxic stress-induced cell death in breast cancer cells.

Drugs associated with RHOB

BA-240

Target

RHOB

Mechanism

RHOB inhibitors

Active Org.-

Originator Org.

BioAxone Therapeutic, Inc.

Active Indication-

Inactive Indication

Glaucoma [+1]

Drug Highest PhasePending

First Approval Ctry. / Loc.-

First Approval Date20 Jan 1800

100 Clinical Results associated with RHOB

100 Translational Medicine associated with RHOB

0 Patents (Medical) associated with RHOB

909

Literatures (Medical) associated with RHOB

01 Dec 2024·Molecular Biology Reports

MiR-21-5p knockdown inhibits epithelial to mesenchymal transition in A549 lung adenocarcinoma cells by upregulating RhoB

Article

Author: Khair, Hiba Hussein A ; Karagöz, Işık Didem

BACKGROUNDMiR-21-5p is a highly expressed microRNA that plays an important role in various cancer-promoting processes, including anchorage-independent growth, invasion, migration metastasis, and drug resistance in lung cancer. Studies indicate that miR-21-5p may contribute to these processes by promoting epithelial-mesenchymal transition (EMT). Ras homolog gene family member B (RhoB), a gene downregulated by miR-21-5p, has also been linked to EMT in lung cancer. However, the role of the miR-21-5p/RhoB axis in EMT regulation in lung adenocarcinoma remains unclear. In this study, we aimed to investigate the regulatory role of the miR-21-5p/RhoB axis in EMT and related in vitro functional characteristics such as migration, invasion, cisplatin resistance, and the formation of tumor spheroids.METHODS AND RESULTSA549 cells were transfected with the miR-21-5p inhibitor, RhoB siRNA, and their corresponding negative controls. Wound healing, transwell invasion, Methyl thiazole tetrazolium (MTT), and sphere formation assays were also performed to evaluate the migration, invasion, cisplatin resistance, and anchorage-independent growth of A549 cells. RT-qPCR was used to determine the mRNA expression levels of EMT markers. MiR-21-5p knockdown inhibited migration, invasion, cisplatin resistance, and sphere formation while upregulating E-cadherin and downregulating Slug. Furthermore, RhoB silencing restored EMT and related in vitro functional characteristics in A549 cells.CONCLUSIONSKnockdown of miR-21-5p inhibits EMT and related in vitro functional characteristics by upregulating RhoB, suggesting that miR-21-5p may promote EMT through downregulation of RhoB.

16 Oct 2024·Clinical and Experimental Immunology

Identification of angiogenesis-related genes and molecular subtypes for psoriasis based on random forest algorithm

Article

Author: Ru, Yi ; Wang, Rui-Ping ; Jiang, Jing-Si ; Zhang, Ying ; Zhang, Meng-Jie ; Luo, Ying ; Xue, Ting-Ting ; Song, Jian-Kun ; Luo, Yue ; Kuai, Le ; Li, Bin ; Fei, Xiao-Ya

AbstractPsoriasis is a chronic immune-mediated recurrent skin disease causing systemic damage. Increased angiogenesis has been reported to participate in the progression of psoriasis. However, angiogenesis-related genes (ARGs) in psoriasis have not been systematically elucidated. Therefore, we aim to identify potential biomarkers and subtypes using two algorithmsr. Transcriptome sequencing data of patients with psoriasis were obtained, in which differentially expressed genes were assessed by principal component analysis. A diagnostic model was developed using random forest algorithm and validated by receiver operating characteristic (ROC) curves. Subsequently, we performed consensus clustering to calculate angiogenesis-associated molecular subtypes of psoriasis. Additionally, a correlation analysis was conducted between ARGs and immune cell infiltration. Finally, validation of potential ARG genes was performed by quantitative real-time PCR (qRT-PCR). We identified 29 differentially expressed ARGs, including 13 increased and 16 decreased. Ten ARGs, CXCL8, ANG, EGF, HTATIP2, ANGPTL4, TNFSF12, RHOB, PML, FOXO4, and EMCN were subsequently sifted by the diagnostic model based on a random forest algorithm. Analysis of the ROC curve (area under the curve [AUC] = 1.0) indicated high diagnostic performance in internal validation. The correlation analysis suggested that CXCL8 has a high positive correlation with neutrophil (R =0.8, P < 0.0001) and interleukins pathway (R = 0.79, P < 0.0001). Furthermore, two ARG-mediated subtypes were obtained, indicating potential heterogeneity. Finally, the qRT-PCR demonstrated that the mRNA expression levels of CXCL8 and ANGPTL4 were elevated in psoriasis patients, with a reduced expression of EMCN observed. The current paper indicated potential ARG-related biomarkers of psoriasis, including CXCL8, ANGPTL4, and EMCN, with two molecular subtypes.

01 Oct 2024·ACS Omega

New and Highly Accurate Static Young’s Modulus Model Using Machine Learning Techniques

Article

Author: Darkwah-Owusu, Victor ; Mahmood, Syed Mohammad ; Alakbari, Fahd Saeed ; Tsegab, Haylay ; Babikir, Ismailalwali ; Bamumen, Salem Saleh ; Hagar, Haithm Salah

Static Young's modulus (E _s) is a critical property required in numerous petroleum calculations. Various models to forecast E _s have been proposed in the literature. However, existing models, by and large, lack precision and are confined to specific data set ranges. This study proposes an alternative approach for E _s determination, utilizing different machine learning methods, such as an adaptive neuro-fuzzy inference system (ANFIS). In these proposed methods, the predictor variables include bulk formation density (RHOB), shear wave velocity (DTs), and compressional wave velocity (DTc). The models were trained on a data set comprising 1853 hydrocarbon reservoir rock samples from globally diverse locations. They were evaluated using trend, group error, and statistical error analyses. To test the efficacy of the proposed models, the optimally performing model was identified and used to detect the rock types along with the previously published models. Results indicated that ANFIS is the optimum model and can predict E _s with an average absolute percentage relative error (AAPRE) of 5.1% and a correlation coefficient (R) of 0.9602. The ANFIS method has some benefits over other machine learning approaches insofar as its superiority in reaching a quicker decision about the mapped relationship between the inputs and outputs because it combines artificial neural networks and fuzzy logic in one tool. The ANFIS can perform a highly nonlinear mapping and displays a better learning ability. The proposed ANFIS model demonstrates its ability to capture accurate physical relationships between input rock properties and E _s through trend analysis, which shows that increasing the RHOB increases the E _s. Contrarily, increasing the DTc and DTs reduces the E _s. Furthermore, the ANFIS model can accurately detect the rock types based on its E _s determinations. This research demonstrates the importance of accurately predicting E _s for the proper identification of rock types. Thus, this study offers potential advancements in geological assessments of hydrocarbon reservoirs and improvements in many petroleum engineering applications.

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