TBC1D2

Last update 08 May 2025

Basic Info

Synonyms

Armus, PARIS-1, PARIS1

+ [6]

Introduction

Acts as a GTPase-activating protein for RAB7A. Signal effector acting as a linker between RAC1 and RAB7A, leading to RAB7A inactivation and subsequent inhibition of cadherin degradation and reduced cell-cell adhesion.

100 Clinical Results associated with TBC1D2

100 Translational Medicine associated with TBC1D2

0 Patents (Medical) associated with TBC1D2

Literatures (Medical) associated with TBC1D2

15 Jan 2024·Oral health & preventive dentistry

A Prognostic Model Based on Cisplatin-Resistance Related Genes in Oral Squamous Cell Carcinoma.

Article

Author: Sun, Lu ; Liu, Siyu ; Lu, Rong ; Yang, Qian

PURPOSETo screen for the cisplatin resistance-related prognostic signature in oral squamous cell carcinoma (OSCC) and assess its correlation with the immune microenvironment.MATERIALS AND METHODSThe gene expression data associated with OSCC and cisplatin-resistance were downloaded from TCGA and GEO databases. Cisplatin-resistant genes were selected through taking the intersection of differentially expressed genes (DEGs) between tumor and control groups as well as between cisplatin-resistant samples and parental samples. Then, prognosis-related cisplatin-resistant genes were further selected by univariate Cox regression and LASSO regression analyses to construct a survival prognosis model. A GSEA (gene set enrichment analysis) between two risk groups was conducted with the MSigDB v7.1 database. Finally, the immune landscape of the sample was studied using CIBERSORT. The IC50 values of 57 drugs were predicted using pRRophetic 0.5.RESULTSA total 230 candidate genes were obtained. Then 7 drug-resistant genes were selected for prognostic risk-score (RS) signature construction using LASSO regression analysis, including STC2, TBC1D2, ADM, NDRG1, OLR1, PDGFA and ANO1. RS was an independent prognostic factor. Additionally, a nomogram model was established to predict the 1-, 2-, and 3-year survival rates of samples. The GSEA identified several differential pathways between two risk groups, such as EMT, hypoxia, and oxidative phosphorylation. Fifteen immune cells were statistically significantly different in infiltration level between the two groups, such as macrophages M2, and resting NK cells. A total of 57 drugs had statistically significantly different IC50 values between two risk groups.CONCLUSIONThese model genes and immune cells may play a role in predicting the prognosis and chemoresistance in OSCC.

01 May 2023·Neurobiology of Aging

Development and validation of a 13-gene signature associated with immune function for the detection of Alzheimer's disease

Article

Author: Hou, Tingting ; Zhu, Min ; Tan, Qihua ; Du, Yifeng ; Qiu, Chengxuan ; Jia, Longfei

Current knowledge of Alzheimer's disease (AD) etiology and effective therapy remains limited. Thus, the identification of biomarkers is crucial to improve the detection and treatment of patients with AD. Using robust rank aggregation method to analyze the microarray data from Gene Expression Omnibus database, we identified 1138 differentially expressed genes in AD. We then explored 13 hub genes by weighted gene co-expression network analysis, least absolute shrinkage, and selection operator, and logistic regression in the training dataset. The detection model, which composed of CD163, CDC42SE1, CECR6, CSF1R, CYP27A1, EIF4E3, H2AFJ, IFIT2, IL10RA, KIAA1324, PSTPIP1, SLA, and TBC1D2 genes, along with APOE gene, showed that the area under the curve for detecting AD was 0.821 (95% confidence interval [CI] = 0.782-0.861) and the model was validated in ADNI dataset (area under the curve = 0.776; 95%CI = 0.686-0.865). Notably, the 13 genes in the model were highly enriched in immune function. These findings have implications for the detection and therapeutic target of AD.

13 Jan 2023·BMC cancer

A meta-validated immune infiltration-related gene model predicts prognosis and immunotherapy sensitivity in HNSCC.

Article

Author: Chu, Ling ; Zhuang, Quan ; Cao, Qingtai ; Li, Xinyu ; Lei, Hanyu ; Ding, Yinghe

AbstractBackgroundTumor microenvironment (TME) is of great importance to regulate the initiation and advance of cancer. The immune infiltration patterns of TME have been considered to impact the prognosis and immunotherapy sensitivity in Head and Neck squamous cell carcinoma (HNSCC). Whereas, specific molecular targets and cell components involved in the HNSCC tumor microenvironment remain a twilight zone.MethodsImmune scores of TCGA-HNSCC patients were calculated via ESTIMATE algorithm, followed by weighted gene co-expression network analysis (WGCNA) to filter immune infiltration-related gene modules. Univariate, the least absolute shrinkage and selection operator (LASSO), and multivariate cox regression were applied to construct the prognostic model. The predictive capacity was validated by meta-analysis including external dataset GSE65858, GSE41613 and GSE686. Model candidate genes were verified at mRNA and protein levels using public database and independent specimens of immunohistochemistry. Immunotherapy-treated cohort GSE159067, TIDE and CIBERSORT were used to evaluate the features of immunotherapy responsiveness and immune infiltration in HNSCC.ResultsImmune microenvironment was significantly associated with the prognosis of HNSCC patients. Total 277 immune infiltration-related genes were filtered by WGCNA and involved in various immune processes. Cox regression identified nine prognostic immune infiltration-related genes (MORF4L2, CTSL1, TBC1D2, C5orf15, LIPA, WIPF1, CXCL13, TMEM173, ISG20) to build a risk score. Most candidate genes were highly expressed in HNSCC tissues at mRNA and protein levels. Survival meta-analysis illustrated high prognostic accuracy of the model in the discovery cohort and validation cohort. Higher proportion of progression-free outcomes, lower TIDE scores and higher expression levels of immune checkpoint genes indicated enhanced immunotherapy responsiveness in low-risk patients. Decreased memory B cells, CD8+ T cells, follicular helper T cells, regulatory T cells, and increased activated dendritic cells and activated mast cells were identified as crucial immune cells in the TME of high-risk patients.ConclusionsThe immune infiltration-related gene model was well-qualified and provided novel biomarkers for the prognosis of HNSCC.

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