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Last update 08 May 2025

FTH1

Last update 08 May 2025

Basic Info

Synonyms

Cell proliferation-inducing gene 15 protein, Ferritin H subunit, Ferritin heavy chain

+ [8]

Introduction

Stores iron in a soluble, non-toxic, readily available form. Important for iron homeostasis. Has ferroxidase activity (PubMed:9003196). Iron is taken up in the ferrous form and deposited as ferric hydroxides after oxidation (PubMed:9003196). Also plays a role in delivery of iron to cells (By similarity). Mediates iron uptake in capsule cells of the developing kidney (By similarity). Delivery to lysosomes is mediated by the cargo receptor NCOA4 for autophagic degradation and release of iron (PubMed:24695223, PubMed:26436293).

Drugs associated with FTH1

EdX-17

Target

FTH1

Mechanism

FTH1 agonists

Active Org.-

Originator Org.

EpimedX, LLC

Active Indication-

Inactive Indication

Anemia, Sickle Cell

Drug Highest PhasePending

First Approval Ctry. / Loc.-

First Approval Date20 Jan 1800

100 Clinical Results associated with FTH1

100 Translational Medicine associated with FTH1

0 Patents (Medical) associated with FTH1

1,418

Literatures (Medical) associated with FTH1

31 Dec 2025·Hematology

ALKBH3-mediated m1A demethylation promotes the malignant progression of acute myeloid leukemia by regulating ferroptosis through the upregulation of ATF4 expression

Article

Author: Liu, Xin ; Pan, Xinghua

To investigate the role of ALKBH3 in acute myeloid leukemia (AML), we constructed an animal model of xenotransplantation of AML. Our study demonstrated that ALKBH3-mediated m1A demethylation inhibits ferroptosis in KG-1 cells by increasing ATF4 expression, thus promoting the development of AML. These findings suggest that reducing ALKBH3 expression may be a potential strategy to mitigate AML progression.Background: Acute myeloid leukemia (AML) is characterized by the unrestrained proliferation of myeloid cells. Studies have shown that ALKBH3 is upregulated in most tumors, but the role of ALKBH3 in AML remains unclear.Methods: In this study, we investigated the function of ALKBH3 in AML cells (KG-1) by immunofluorescence, ELISA, flow cytometry, HE staining, and Western blotting.Results: Our results revealed that ALKBH3 is upregulated in AML and that the downregulation of ALKBH3 inhibited KG-1 cell proliferation and promoted cell apoptosis; at the same time, ALKBH3 upregulated ATF4 expression through m1A demethylation, and the knockdown of ATF4 resulted in increased ferrous iron content; TFR1, ACSL4, and PTGS2 expression; and ROS and MDA levels, whereas SOD and GSH levels and the expression levels of ATF4, SLC7A11, GPX4, and FTH1 decreased in KG-1 cells, thereby promoting ferroptosis. Mechanistically, ALKBH3-mediated m1A demethylation suppressed ferroptosis in KG-1 cells by increasing ATF4 expression, thereby promoting the development of AML.Conclusions: Our study indicated that reducing the expression of ALKBH3 might be a potential target for improving AML symptoms.

31 Dec 2025·Renal Failure

Efficacy and potential pharmacological mechanism of Astragalus-Salvia miltiorrhiza combination in diabetic nephropathy: integrating meta-analysis, network pharmacology, molecular docking, and experimental validation

Article

Author: Chen, Zedong ; Xiao, Ya ; Jiang, Pingping ; Zhu, Mingmin ; Zhao, Xiaoshan ; Zhong, Jingying ; Yuan, Jing ; Chen, Jianfeng ; Liang, Huiyu ; Lin, Shufan

BACKGROUNDDiabetic nephropathy (DN) is a diabetes mellitus (DM)-induced complication that poses high morbidity and mortality risks. The Astragalus and Salvia miltiorrhiza couplet medicines (AS) are commonly employed in DN clinical treatment in China, but their clinical efficacy and potential pharmacological mechanisms are yet to be evaluated.MATERIAL AND METHODSA meta-analysis of 15 studies involving 1,443 patients was conducted. Furthermore, network pharmacology predicted components and targets, which were verified by molecular docking and in vivo validation.RESULTSIn our meta-analysis, AS notably elevated clinical outcomes and renal function among patients with DN. Meanwhile, when the treatment duration exceeds 12 weeks, AS demonstrated a significant reduction in fasting blood glucose levels, indicating a time-dependent effect. Moreover, based on network pharmacology results, AS likely enhanced clinical outcomes by interacting with vital signaling pathways, including PI3K/Akt, MAPK, and NF-kappa B. Molecular docking studies have confirmed that PTGS2, the key therapeutic target of AS, can be closely combined with bioactive components GLY, quercetin, apigenin, and daidzein. Additionally, in vivo experiments have corroborated that AS can ameliorate renal function, UACR, and biomarkers associated with iron metabolism, such as GPX4, PTGS2, FTH1, and FTL1.CONCLUSIONThrough rigorous experimental validation, our study demonstrates AS's significant clinical efficacy in managing DN. Specifically, AS has been shown to enhance renal function, ameliorate renal fibrosis, and positively influence iron metabolism. Despite these promising outcomes, future research with a larger sample size must be conducted to further substantiate these findings.

01 Dec 2025·Inflammation Research

Single-cell hdWGCNA reveals a novel diagnostic model and signature genes of macrophages associated with chronic obstructive pulmonary disease

Article

Author: Zhou, Xianqiang ; Feng, Cuiling ; Zhang, Yixin ; Jin, Zhengjie ; Wang, Hongtao ; Meng, Yufeng ; Yang, Jie

BACKGROUNDChronic obstructive pulmonary disease (COPD) is the leading cause of respiratory system-related mortality worldwide. Although COPD is associated with immune regulation, its underlying mechanisms remain unclear.METHODSCells from the single-cell RNA sequencing (scRNA-seq) datasets were subjected to clustering analysis and cell type identification to isolate immune cell subgroups specifically expressed in COPD. High-dimensional weighted gene co-expression network analysis (hdWGCNA) was used to identify hub genes related to the immune cell subpopulations. Machine learning algorithms were applied to identify diagnostic genes in the immune cell subpopulations and construct clinical diagnostic models for COPD. In bulk RNA sequencing data, AUC curves were used to assess the stability of the diagnostic models in predicting COPD.RESULTSThrough 2 rounds of clustering analysis, the macrophage subgroups 1, 2, 7, 11, and 13 which specifically expressed in COPD (COPD_Mφ) were identified. HdWGCNA analysis revealed a hub set of genes closely related to COPD_Mφ from black, blue, yellow, and brown modules. Nonnegative Matrix Factorization (NMF) analysis separated the COPD samples into 2 clusters, with significant increases in the infiltration of Monocytic_lineage, Myeloid_dendritic_cells, and Neutrophils in cluster 1 (P < 0.001). Univariate logistic regression and LASSO regression analyses identified 11 feature genes associated with COPD_Mφ, including CST3, LGALS3, CSTB, S100A10, CYBA, S100A11, ARPC3, FTH1, PFN1, MAN2B1, and RPL39. The RF and convolutional neural network (CNN) models constructed using these feature genes effectively distinguished between normal and COPD patients. Among them, S100A10, RPL39, and FTH1 exhibited differential expression between COPD patients and normal individuals and could serve as potential clinical diagnostic markers for COPD.CONCLUSIONSThe study provides new insights into the immune mechanisms of COPD and lays the theoretical foundation for its future clinical diagnosis and personalized treatment.

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FTH1

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