ARHGAP9

Last update 08 May 2025

Basic Info

Synonyms

10C, ARHGAP9, MGC1295

+ [3]

Introduction

GTPase activator for the Rho-type GTPases by converting them to an inactive GDP-bound state. Has a substantial GAP activity toward CDC42 and RAC1 and less toward RHOA. Has a role in regulating adhesion of hematopoietic cells to the extracellular matrix. Binds phosphoinositides, and has the highest affinity for phosphatidylinositol 3,4,5-trisphosphate, followed by phosphatidylinositol 3,4-bisphosphate and phosphatidylinositol 4,5-bisphosphate.

100 Clinical Results associated with ARHGAP9

100 Translational Medicine associated with ARHGAP9

0 Patents (Medical) associated with ARHGAP9

Literatures (Medical) associated with ARHGAP9

01 Mar 2025·JCO Precision Oncology

Prediction of Patients With High-Risk Osteosarcoma on the Basis of XGBoost Algorithm Using Transcriptome and Methylation Data From SGH-OS Cohort

Article

Author: Song, Yunjie ; Cai, Zhengdong ; Xu, Wenyuan ; Wang, Zhuoying ; Tian, Kai ; Jiang, Yafei ; Cheng, Zhiwei ; Wang, Gangyang ; Hua, Yingqi ; Sun, Wei ; Zhang, Lulu ; Meng, Huanliang ; Zhao, Weisong ; Dai, Zhenwu

PURPOSEOsteosarcoma (OS) is the most prevalent primary malignant bone sarcoma, characterized by its high rates of metastasis and mortality. In our previous multiomics analysis of the Shanghai General Hospital OS (SGH-OS) cohort, we identified four distinct OS subtypes, each with unique molecular characteristics and clinical outcomes. Of particular importance was the identification of the MYC-driven subtype, which exhibited the poorest prognosis and was referred to as high-risk OS. A diagnostic tool is needed for clinicians to identify high-risk OS in advance. The purpose of this study is to develop a classifier capable of accurately predicting the high-risk OS subtype using transcriptome and methylation data.METHODSIn this study, using eXtreme Gradient Boosting (XGBoost) with Bayesian optimization, we developed a classification model by integrating transcriptome and methylation data from our internal SGH-OS cohort. We further validated the model's predictive performance with the external TARGET-OS cohort.RESULTSUsing the XGBoost algorithm, we developed a classifier incorporating nine genes (ARHGAP9, CADM1, CPE, DUSP3, FGFR1, GALNT3, IGF2BP3, KIF26A, ZFP3). In our internal cohort, the classifier exhibited excellent predictive performance, with an area under the receiver operating characteristics curve (AUC) of 0.999 and an overall accuracy of 0.989. Furthermore, the classifier successfully stratified two groups with distinct survival outcomes in the external TARGET-OS cohort. Notably, our analysis revealed a positive correlation between IGF2BP3 and MYC signaling pathways, highlighting IGF2BP3 as a potential therapeutic target in high-risk OS.CONCLUSIONOur classifier demonstrated excellent predictive performance in identifying patients with high-risk OS, offering the potential to enhance treatment decision making and optimize patient management strategies.

01 Jan 2025·Translational Oncology

Machine learning-derived natural killer cell signature predicts prognosis and therapeutic response in clear cell renal cell carcinoma

Article

Author: Liu, Kezhi ; Chen, Jinwei ; Chen, Minyu ; Wang, Jieyan ; Chen, Wei ; Jin, Meiyu ; Liang, Yanping ; Luo, Junhang ; Chen, Jiajie ; Liang, Hui ; Deng, Qiong ; Luo, Jinchen ; Cen, Junjie ; Zhou, Xinwei ; Lin, Mingjie ; Wang, Zhu

BACKGROUNDNatural killer cells, interconnected with patient prognosis and treatment response, play a pivotal role in the tumor immune microenvironment and may serve as potential novel predictive biomarkers for renal cell carcinoma.METHODSClear cell renal cell carcinoma transcriptome data and the corresponding clinical data were obtained from the Cancer Genome Atlas (TCGA) database. Single-cell sequencing data were sourced from the Gene Expression Omnibus (GEO) database. A risk model was established by integrating ten different machine learning algorithms, which resulted in 101 combined models. The model with the highest average C-index was selected for further analysis, and was assessed using nomogram, time-dependent receiver operating characteristics (ROC) and Kaplan-Meier survival analysis. The differences in immune infiltration fractions, clinicopathological features, and response to various targeted therapies and immunotherapy between high- and low-risk groups were investigated. Furthermore, qRT-PCR, IHC, colony formation test, CCK8 assay and flow cytometry were conducted to explore the expression pattern and function of ARHGAP9 in our own patient samples and renal cancer cell lines.RESULTSTotally, 156 NK cell-related genes and 5189 prognosis-related genes were identified, and 36 genes of their intersection demonstrated prognostic value. A risk model with 18 genes was established by Coxboost plus plsRcox, which can accurately predict the prognosis of ccRCC patients. Significant correlations were determined between risk score and tumor malignancy and immune cell infiltration. Meanwhile, a combination of tumor mutation burden plus risk score could have higher accuracy of predicting clinical outcomes. Moreover, high-risk group patients were more likely to be responsive to targeted therapy but show no response to immunotherapy.CONCLUSIONSIntricate signaling interactions between NK cells and various cellular subgroups were depicted and the developmental trajectory of NK cells was elucidated. A NK cells-related risk model was established, which can provide reliable prognostic information and identified patients with more probability of benefiting from therapy.

01 Dec 2024·Asian Journal of Surgery

ARHGAP9 may be used as a novel prognostic biomarker associated with tumour immunity and cellular composition in ccRCC

Letter

Author: Wang, Wenyu ; Ma, Xiaohong ; Wang, Yini ; Wu, Jitao

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ARHGAP9

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