KLF8

Last update 08 May 2025

Basic Info

Synonyms

Basic krueppel-like factor 3, BKLF3, DXS741

+ [5]

Introduction

Transcriptional repressor and activator. Binds to CACCC-boxes promoter elements. Also binds the GT-box of cyclin D1 promoter and mediates cell cycle progression at G(1) phase as a downstream target of focal adhesion kinase (FAK).

100 Clinical Results associated with KLF8

100 Translational Medicine associated with KLF8

0 Patents (Medical) associated with KLF8

145

Literatures (Medical) associated with KLF8

01 Dec 2024·Thoracic Cancer

CircSEC24A induces KLF8 expression to promote the malignant progression of non‐small cell lung cancer by regulating miR‐1253

Article

Author: Yang, Jinhua ; Xiong, Wei

AbstractObjectivesThis study aimed to analyze the role of circSEC24A in non‐small cell lung cancer (NSCLC) and its underlying mechanism.MethodsRNA levels of circSEC24A, microRNA‐1253 (miR‐1253), and KLF transcription factor 8 (KLF8) were detected by quantitative real‐time polymerase chain reaction. Protein expression was analyzed by western blot or immunohistochemistry assay. Cell proliferation and apoptosis were investigated by colony formation assay, 5‐ethynyl‐2′‐deoxyuridine assay, and flow cytometry analysis. Glycolysis was evaluated by commercial kits. Dual‐luciferase reporter assay and RNA immunoprecipitation assay were conducted to identify the associations among circSEC24A, miR‐1253, and KLF8. Xenograft mouse model assay was used to evaluate the effect of circSEC24A on tumor tumorigenesis.ResultsCircSEC24A and KLF8 were upregulated, while miR‐1253 was downregulated in NSCLC. CircSEC24A knockdown inhibited proliferation and glycolysis but induced the apoptosis of NSCLC cells. CircSEC24A acted as a miR‐1253 sponge and regulated NSCLC cell malignancy by targeting miR‐1253. KLF8 was identified as a target of miR‐1253, and its overexpression attenuated miR‐1253‐induced effects in NSCLC cells. Besides, circSEC24A upregulated KLF8 by sponging miR‐1253. Further, circSEC24A knockdown suppressed NSCLC cell tumorigenesis in vivo.ConclusionsCircSEC24A silencing inhibited NSCLC cell malignancy through the miR‐1253/KLF8 pathway, providing a potential therapeutic target for NSCLC.

01 Sep 2024·Heliyon

Bioinformatics analysis of the role of RNA modification regulators in polycystic ovary syndrome

Article

Author: You, Zilin ; Ning, Shuting ; Quan, Kewei ; Deng, Gaopi

PurposePolycystic ovary syndrome (PCOS) is the most common metabolic and endocrine disorder affecting women of reproductive age. The pathogenesis of PCOS is influenced by factors such as race, genetics, environment, hyperandrogenemia, hyperinsulinemia, and obesity. However, the molecular mechanisms linking RNA modification and PCOS remain underexplored. This study aims to investigate the potential genetic and molecular pathways connecting RNA modification with PCOS through bioinformatics analyses.MethodsThe GSE34526, GSE5850, and GSE98421 datasets were obtained from the National Center for Biotechnology Information Gene Expression Omnibus database. We identified intersecting differentially expressed genes (DEGs) and RNA modification-related genes within the GSE34526 dataset and visualized the overlaps using a Venn diagram. Subsequent analyses included Gene Ontology (GO), pathway enrichment (Kyoto Encyclopedia of Genes and Genomes), gene set enrichment analysis (GSEA), gene set variation analysis (GSVA), and immune infiltration analysis. Additionally, we constructed a protein-protein interaction network as well as mRNA-miRNA, mRNA-RNA binding protein, and mRNA-transcription factor (TF) regulatory networks. The expression and receiver operating characteristic curves of hub genes were also identified.ResultsThe expression of several RNA modification-related DEGs (RMRDEGs) (ALYREF, NUDT1, AGO2, TET2, YTHDF2, and TRMT61B) showed significant differences in PCOS patients. GSEA and GSVA indicated that RMRDEGs were enriched in the hedgehog, MAPK, JAK STAT, and Notch pathways. Key transcription factors, including SP7, KLF8, HCFC1, IRF1, and MLLT1, were identified in the TF regulatory networks.ConclusionsThese findings suggest that there are gene and miRNA profile alterations exist in PCOS patients and highlight immune-related differences. This knowledge could pave the way for new research directions in the diagnosis and treatment of PCOS.

01 Jun 2024·Computers in Biology and Medicine

Using histopathology latent diffusion models as privacy-preserving dataset augmenters improves downstream classification performance

Article

Author: Jendrusch, Michael ; Pearson, Alexander T ; Kather, Jakob Nikolas ; Schirris, Yoni ; Niehues, Jan M ; Kloor, Matthias ; Hewitt, Katherine J ; Zigutyte, Laura ; Wagner, Sophia Janine ; Muti, Hannah Sophie ; Truhn, Daniel ; Müller-Franzes, Gustav ; Veldhuizen, Gregory P

Latent diffusion models (LDMs) have emerged as a state-of-the-art image generation method, outperforming previous Generative Adversarial Networks (GANs) in terms of training stability and image quality. In computational pathology, generative models are valuable for data sharing and data augmentation. However, the impact of LDM-generated images on histopathology tasks compared to traditional GANs has not been systematically studied. We trained three LDMs and a styleGAN2 model on histology tiles from nine colorectal cancer (CRC) tissue classes. The LDMs include 1) a fine-tuned version of stable diffusion v1.4, 2) a Kullback-Leibler (KL)-autoencoder (KLF8-DM), and 3) a vector quantized (VQ)-autoencoder deploying LDM (VQF8-DM). We assessed image quality through expert ratings, dimensional reduction methods, distribution similarity measures, and their impact on training a multiclass tissue classifier. Additionally, we investigated image memorization in the KLF8-DM and styleGAN2 models. All models provided a high image quality, with the KLF8-DM achieving the best Frechet Inception Distance (FID) and expert rating scores for complex tissue classes. For simpler classes, the VQF8-DM and styleGAN2 models performed better. Image memorization was negligible for both styleGAN2 and KLF8-DM models. Classifiers trained on a mix of KLF8-DM generated and real images achieved a 4% improvement in overall classification accuracy, highlighting the usefulness of these images for dataset augmentation. Our systematic study of generative methods showed that KLF8-DM produces the highest quality images with negligible image memorization. The higher classifier performance in the generatively augmented dataset suggests that this augmentation technique can be employed to enhance histopathology classifiers for various tasks.

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KLF8

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