GMNN

Last update 08 May 2025

Basic Info

Synonyms

Gem, Geminin, geminin DNA replication inhibitor

+ [1]

Introduction

Inhibits DNA replication by preventing the incorporation of MCM complex into pre-replication complex (pre-RC) (PubMed:14993212, PubMed:20129055, PubMed:24064211, PubMed:9635433). It is degraded during the mitotic phase of the cell cycle (PubMed:14993212, PubMed:24064211, PubMed:9635433). Its destruction at the metaphase-anaphase transition permits replication in the succeeding cell cycle (PubMed:14993212, PubMed:24064211, PubMed:9635433). Inhibits histone acetyltransferase activity of KAT7/HBO1 in a CDT1-dependent manner, inhibiting histone H4 acetylation and DNA replication licensing (PubMed:20129055). Inhibits the transcriptional activity of a subset of Hox proteins, enrolling them in cell proliferative control (PubMed:22615398).

100 Clinical Results associated with GMNN

100 Translational Medicine associated with GMNN

0 Patents (Medical) associated with GMNN

468

Literatures (Medical) associated with GMNN

01 Jun 2025·Acta Histochemica

Immunohistochemistry and machine learning study of DNA replication-associated proteins in uterine epithelial tumors and precursor lesions

Article

Author: Ishii, Keiko ; Kimura, Fumikazu ; Ikehata, Koyo ; Ohshima, Kengo ; Urata, Takumi ; Yamaguchi, Masahiro

Endometrioid adenocarcinoma (EA) has been on the increase in recent years in developed countries. Early detection of endometrioid adenocarcinoma in the endometrial corpus is crucial for patient prognosis and early treatment, although their distinction can sometimes be challenging. In this study, we focused on DNA replication-related proteins through immunohistochemical analysis and investigated whether the discrimination between EA and their precursor lesions is achievable using machine learning techniques. The research utilized tissue specimens from 100 cases, including EA of different grades (Grade 1; G1, Grade 2; G2, Grade 3; G3) and their precursor lesions (endometrial hyperplasia without atypia; EH, endometrial atypical hyperplasia: AH). Immunohistochemical analysis of DNA replication-related proteins, such as ORC1, Cdt1, Cdc6, MCM7, Cdc7, and Geminin, was conducted for each case, measuring the Labeling Index (LI) and optical density (OD) of protein expression. Furthermore, we performed statistical significance tests and machine learning -discriminant analysis using LI and OD as inputs, employing non-linear Support Vector Machines (NSVM). The NSVM discriminant analysis demonstrated the accuracy of over 85 % between EH and each differentiation grade of EA, the accuracy is also similar for AH and each differentiation grade of EA. In addition, changing the combination of DNA replication-related proteins used for discrimination resulted in a high accuracy (95-100 %). A discriminant analysis with NSVM using the LI and OD of DNA replication-related proteins may enable the differentiation of EA from its precursor lesions.

01 Mar 2025·The Clinical Respiratory Journal

Bioinformatic Insights and XGBoost Identify Shared Genetics in Chronic Obstructive Pulmonary Disease and Type 2 Diabetes

Article

Author: Yi, Chao ; Zhan, Yiqiang ; Ji, Qianqian ; Chen, Xiaoliang ; Han, Xiaojie ; Meng, Yaxian

ABSTRACTBackgroundThe correlation between chronic obstructive pulmonary disease (COPD) and Type 2 diabetes mellitus (T2DM) has long been recognized, but their shared molecular underpinnings remain elusive. This study aims to uncover common genetic markers and pathways in COPD and T2DM, providing insights into their molecular crosstalk.MethodsUtilizing the Gene Expression Omnibus (GEO) database, we analyzed gene expression datasets from six COPD and five T2DM studies. A multifaceted bioinformatics approach, encompassing the limma R package, unified matrix analysis, and weighted gene co‐expression network analysis (WGCNA), was deployed to identify differentially expressed genes (DEGs) and hub genes. Functional enrichment and protein–protein interaction (PPI) analyses were conducted, followed by cross‐species validation in Mus musculus models. Machine learning techniques, including random forest and LASSO regression, were applied for further validation, culminating in the development of a prognostic model using XGBoost.ResultsOur analysis revealed shared DEGs such as KIF1C, CSTA, GMNN, and PHGDH in both COPD and T2DM. Cross‐species comparison identified common genes including PON1 and CD14, exhibiting varying expression patterns. The random forest and LASSO regression identified six critical genes, with our XGBoost model demonstrating significant predictive accuracy (AUC = 0.996 for COPD).ConclusionsThis study identifies key genetic markers shared between COPD and T2DM, providing new insights into their molecular pathways. Our XGBoost model exhibited high predictive accuracy for COPD, highlighting the potential utility of these markers. These findings offer promising biomarkers for early detection and enhance our understanding of the diseases' interplay. Further validation in larger cohorts is recommended.

01 Oct 2024·Cellular Signalling

Methylation and transcriptome analyses construct a prognostic model and reveal the suppressor role of VMO1 in lung adenocarcinoma

Article

Author: Liu, Yingjie ; Wang, Lishui ; Geng, Xueyan ; Liu, Shichao ; Zhang, Zeyu ; Yin, Maopeng ; Zheng, Guixi

BACKGROUNDDNA methylation is an important epigenetic mechanism of gene regulation. The aberrant DNA methylation has been found to play an important role in the initiation and progression of tumors.RESULTSTranscriptome and DNA methylation data of lung adenocarcinoma (LUAD) patients were co-analyzed and 95 methylation-driven genes (MDGs) was found in relation to LUAD. A prognostic model based on 3 MDGs (GMNN, SPINK2 and VMO1) was constructed by Univariate and Multivariate cox regression analyses. The risk score generated from the prognostic model could be used to classify LUAD patients into high and low risk groups. Furthermore, it was found that the risk score was associated with tumor microenvironment (TME) and clinical characteristics (survival status and T stage) of patients. Interestingly, we identified and validated that the patients in the low-risk group responded better to immunotherapy treatment. Then, a nomogram model based on the risk score and clinical characteristics was established which showed significant prediction value. The down-regulation and hypermethylation levels of vitelline membrane outer layer protein 1 homolog (VMO1) were verified in paired LUAD tumor and non-tumor tissues by pyrosequencing assay and RT-qPCR. Furthermore, MTT, migration and wound healing assays were performed with lentivector-mediated ectopic over-expression and 5-Aza-dC demethylation followed by siRNA rescue experiments to investigate the role of VMO1 in LUAD cells. Our results indicated that VMO1 could inhibit proliferation and migration of A549 and NCI-H1299 cells.CONCLUSIONSIn summary, our experiments constructed a prognostic model with high capacity for risk prediction in LUAD patients. VMO1 had a malignant suppressor role in LUAD cells. The correlation between risk score and TME might elucidate a potential mechanism of oncogenesis and provide an avenue for further therapeutic targets.

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