KIF24

Last update 08 May 2025

Basic Info

Synonyms

bA571F15.4, C9orf48, FLJ10933

+ [4]

Introduction

Microtubule-dependent motor protein that acts as a negative regulator of ciliogenesis by mediating recruitment of CCP110 to mother centriole in cycling cells, leading to restrict nucleation of cilia at centrioles. Mediates depolymerization of microtubules of centriolar origin, possibly to suppress aberrant cilia formation (PubMed:21620453). Following activation by NEK2 involved in disassembly of primary cilium during G2/M phase but does not disassemble fully formed ciliary axonemes. As cilium assembly and disassembly is proposed to coexist in a dynamic equilibrium may suppress nascent cilium assembly and, potentially, ciliar re-assembly in cells that have already disassembled their cilia ensuring the completion of cilium removal in the later stages of the cell cycle (PubMed:26290419). Plays an important role in recruiting MPHOSPH9, a negative regulator of cilia formation to the distal end of mother centriole (PubMed:30375385).

100 Clinical Results associated with KIF24

100 Translational Medicine associated with KIF24

0 Patents (Medical) associated with KIF24

Literatures (Medical) associated with KIF24

01 Sep 2023·Heliyon

Integrative analysis of cancer-associated fibroblast signature in gastric cancer

Article

Author: Shi, Yuntao ; Mak, Tsz Kin ; Li, Kuan ; Huo, Mingyu ; Zhang, Changhua ; Zhao, Zidan

BackgroundCAFs regulate the signaling of GC cells by promoting their migration, invasion, and proliferation and the function of immune cells as well as their location and migration in the TME by remodeling the extracellular matrix (ECM). This study explored the understanding of the heterogeneity of CAFs in TME and laid the groundwork for GC biomarker and precision treatment development.MethodsThe scRNA-seq and bulk RNA-seq datasets were obtained from GEO and TCGA. The prognostic significance of various CAFs subtypes was investigated using ssGSEA combined with Kaplan-Meier analysis. POSTN expression in GC tissues and CAFs was detected using immunohistochemistry, immunofluorescence, and Western blotting. Differential expression analysis identified the differentially expressed genes (DEGs) between normal and tumor samples in TCGA-STAD. Pearson correlation analysis identified DEGs associated with adverse prognosis CAF subtype, and univariate Cox regression analysis determined prognostic genes associated with CAFs. LASSO regression analysis and Multivariate Cox regression were used to build a prognosis model for CAFs.ResultsWe identified five CAFs subtypes in GC, with the CAF_0 subtype associated with poor prognosis. The abundance of CAF_0 correlated with T stage, clinical stage, histological type, and immune cell infiltration levels. Periostin (POSTN) exhibited increased expression in both GC tissues and CAFs and was linked to poor prognosis in GC patients. Through LASSO and multivariate Cox regression analysis, three genes (CXCR4, MATN3, and KIF24) were selected to create the CAFs-score. We developed a nomogram to facilitate the clinical application of the CAFs-score. Notably, the CAFs signature showed significant correlations with immune cells, stromal components, and immunological scores, suggesting its pivotal role in the tumor microenvironment (TME). Furthermore, CAFs-score demonstrated prognostic value in assessing immunotherapy outcomes, highlighting its potential as a valuable biomarker to guide therapeutic decisions.ConclusionCAF_0 subtype in TME is the cause of poor prognosis in GC patients. Furthermore, CAFs-score constructed from the CAF_0 subtype can be used to determine the clinical prognosis, immune infiltration, clinicopathological characteristics, and assessment of personalized treatment of GC patients.

01 Aug 2023·Journal of Neurology

Recent advances in novel mutation genes of Parkinson's disease

Review

Author: Wu, Xinyu ; Yang, Jie ; Song, Yuning

With increasing life expectancy, a growing number of individuals are being affected by Parkinson's Disease (PD), a Neurodegenerative Disease (ND). Approximately, 5-10% of PD is explained by genetic causes linked to known PD genes. With improvements in genetic testing and high-throughput technologies, more PD-associated susceptibility genes have been reported in recent years. However, a comprehensive review of the pathogenic mechanisms and physiological roles of these genes is still lacking. This article reviews novel genes with putative or confirmed pathogenic mutations in PD reported since 2019, summarizes the physiological functions and potential associations with PD. Newly reported PD-related genes include ANK2, DNAH1, STAB1, NOTCH2NLC, UQCRC1, ATP10B, TFG, CHMP1A, GIPC1, KIF21B, KIF24, SLC25A39, SPTBN1 and TOMM22. However, the evidence for pathogenic effects of many of these genes is inconclusive. A variety of novel PD-associated genes have been identified through clinical cases of PD patients and analysis of Genome-Wide Association Studies (GWAS). However, more evidence is needed in confirm the strong association of novel genes with disease.

25 Apr 2023·Biomolecules

Epigenetic and Tumor Microenvironment for Prognosis of Patients with Gastric Cancer.

Article

Author: Zhang, Kun ; Lin, Rong ; Wang, Weijun ; Wu, Zenghong ; Fan, Mengke

BACKGROUNDEpigenetics studies heritable or inheritable mechanisms that regulate gene expression rather than altering the DNA sequence. However, no research has investigated the link between TME-related genes (TRGs) and epigenetic-related genes (ERGs) in GC.METHODSA complete review of genomic data was performed to investigate the relationship between the epigenesis tumor microenvironment (TME) and machine learning algorithms in GC.RESULTSFirstly, TME-related differential expression of genes (DEGs) performed non-negative matrix factorization (NMF) clustering analysis and determined two clusters (C1 and C2). Then, Kaplan-Meier curves for overall survival (OS) and progression-free survival (PFS) rates suggested that cluster C1 predicted a poorer prognosis. The Cox-LASSO regression analysis identified eight hub genes (SRMS, MET, OLFML2B, KIF24, CLDN9, RNF43, NETO2, and PRSS21) to build the TRG prognostic model and nine hub genes (TMPO, SLC25A15, SCRG1, ISL1, SOD3, GAD1, LOXL4, AKR1C2, and MAGEA3) to build the ERG prognostic model. Additionally, the signature's area under curve (AUC) values, survival rates, C-index scores, and mean squared error (RMS) curves were evaluated against those of previously published signatures, which revealed that the signature identified in this study performed comparably. Meanwhile, based on the IMvigor210 cohort, a statistically significant difference in OS between immunotherapy and risk scores was observed. It was followed by LASSO regression analysis which identified 17 key DEGs and a support vector machine (SVM) model identified 40 significant DEGs, and based on the Venn diagram, eight co-expression genes (ENPP6, VMP1, LY6E, SHISA6, TMEM158, SYT4, IL11, and KLK8) were discovered.CONCLUSIONThe study identified some hub genes that could be useful in predicting prognosis and management in GC.

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KIF24

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