FGF5

Last update 08 May 2025

Basic Info

Synonyms

FGF-5, FGF5, fibroblast growth factor 5

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Introduction

Plays an important role in the regulation of cell proliferation and cell differentiation. Required for normal regulation of the hair growth cycle. Functions as an inhibitor of hair elongation by promoting progression from anagen, the growth phase of the hair follicle, into catagen the apoptosis-induced regression phase (By similarity).

100 Clinical Results associated with FGF5

100 Translational Medicine associated with FGF5

0 Patents (Medical) associated with FGF5

491

Literatures (Medical) associated with FGF5

01 Jun 2025·SLAS Technology

Identifcation of the FGF family as therapeutic targets and prognostic biomarkers in the microenvironment of head and neck squamous cell carcinoma

Article

Author: Wei, Jian ; Xu, Yingjiao ; Gao, Yingchun ; Zhang, Xuan ; Tian, Yumei ; Liu, Xuqian ; Nie, Minhai ; Zhang, Li

BACKGROUNDAlmost 90 % of head and neck malignancies are malignant squamous cell cancers, making it the sixth most common malignancy in the developing countries, with an overall five-year overall survival rate about 40 %-50 %. Early diagnosis and treatment can bring a better prognosis. Fibroblast growth factor (FGF) is an important polypeptide in vivo. Studies have found that FGF signal has carcinogenic potential and participates in a variety of carcinogenic behaviors. Some experiments have proved that FGF signal has the function of tumor inhibition in some cases, and the role of FGF signalling in tissue repair and homeostasis suggest a role for FGF in targeted therapy and prognosis. However, its manifestation and predictive role in HNSC have not been clearly defined.METHODSGenome-wide expression analysis of Oncomine evaluated the evaluation of FGF family expression in HNSC. Expression analysis and HNSC data set were used to obtain FGF family expression data and T statistic was applied for analysis. The differential mRNA expression levels in tumor versus normal tissues, as well as the correlation with pathological staging and prognosis, were examined using the GEPIA single-gene analysis tool for the FGF family.FGF family altered CO expression and network modules were obtained from cBioportal and analyzed in 520 HNSC samples.Pro-protein interaction (PPI) flow network is performed on the differentially ordered FGF clusters using STRING, Gene Operating System (GO) domain domain enrichment as well as Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis are performed on the FGF cluster and its neighbouring genes using DAVID6.8, key transcriptional factors (TF) of FGF family was analyzed by TRRUST, correlation between FGF family level and autoimmune cell migration was evaluated by TIMER, and biological analysis of FGF family kinase target enrichment was performed using LinkInterpreter.RESULTSOnly the expression of FGF6 in HNSC was down-regulated in all FGF family(FC=2),Transcriptional level of FGF1, FGF2, FGF5, FGF7-14, FGF17-19, FGF21 and FGF22 was upregulated in HNSC .In terms of the relative level of FGF family in HNSC, the greatest amount of FGF11. In different pathological stages of HNSC, the expression of FGF was meaningless (P>0.05), and FGF3-6, FGF8-10, FGF14, FGF16, FGF17, FGF1921, FGF23 showed no significant difference in different HNSC stages. Low expression of FGF5 and high expression of FGF22 had low overall survival(OS) rate of HNSC(P =0.012, P =0.0015). In addition, enrichment analysis of FGF family in HNSC showed that it was highly abundant in PI3K-Akt signaling pathway, MAPK and rasper pathway. Our data showed that ATF4, STAT, RELA, NFKB1 are key transcription target of the FGF family, NLK, LOCK1, LYN, ZAP70, MAP2K3, RPS6KA4, AURKB, ATR, ROCK1, MYLK2, CAMK2A, EGFR, MAPK3, MAP3K8, SYK, LCK, HCK, PKN2, RPS6KA1, BUB1, CDK5, ITK, FYN, TBK1, ATM, CDK2, PTK2 are kinase targets of the FGF family. We identified a relationship between the modulation of FGF expression and cellular infiltration, such as B lymphocytes, CD4+ T cells and macrophages dendritic cells.CONCLUSIONSOur data may shed new light on the choice of immunotherapeutic targets and predictive biomarkers in HNSC.

01 Jun 2025·Archives of Gerontology and Geriatrics

The connection between 91 inflammatory cytokines and frailty mediated by 1400 metabolites: An exploratory two-step Mendelian randomization analysis

Article

Author: Wen, Bo ; Liu, Sisi ; Wang, Hua ; Zhang, Chao ; Huang, Daolai ; Wu, Xianghua ; Wei, Shizhuang

BACKGROUNDFrailty, a common, multifaceted, and significant geriatric condition, involves crucial roles of inflammation and metabolic factors in its onset and progression. Nevertheless, the ambiguities and complexities in earlier observational studies make current research into their interactions somewhat insufficient. Our goals were to clarify the causal link between inflammatory cytokines and frailty and to explore the potential mediating effect of metabolites using Mendelian randomization (MR) analysis.METHODSUtilizing detailed summary-level data from genome-wide association studies, we conducted two-sample Mendelian randomization analyses to evaluate the potential causal connection between 91 inflammatory cytokines and the frailty index, along with the possible mediating pathways that involve 1400 metabolites. For our main analysis, we applied the inverse variance weighted method. To evaluate the potential mediating pathways of metabolites, a two-step MR analysis was utilized.RESULTSWe identified 8 inflammatory cytokines that were genetically associated with the frailty index, we subsequently identified 2 mediated relationships, with 2 metabolites acting as potential mediators between 2 inflammatory cytokines and frailty index. The 8 inflammatory cytokines were fractalkine (CX3CL1), interleukin-33 (IL-33), leukemia inhibitory factor receptor (LIF-R), monocyte chemoattractant protein-1 (CCL8), CC motif chemokine 4 (CCL4), C-X-C motif chemokine 10 (CXCL10), fibroblast growth factor 5 (FGF-5), and TNF-beta (TNFB) levels.CONCLUSIONSThe findings of this study demonstrate a direct connection between inflammatory cytokines and the frailty index, as well as two pathways mediated by metabolites. These biomarkers contribute valuable insights into the foundational mechanisms of frailty, presenting a novel research avenue for upcoming clinical studies.

01 Apr 2025·Liver International

Low‐Level Viremia Impairs Efficacy of Immune Checkpoint Inhibitors in Unresectable Hepatocellular Carcinoma

Article

Author: Hu, Xiaoyun ; Li, Wenli ; Yang, Keli ; Su, Kaiyan ; Li, Qi ; Yang, Qing ; Chen, Jinzhang ; Yan, Jingquan ; Li, Rong ; Guan, Yujuan ; Zhu, Wei ; Zhao, Miaoxian ; Zang, Mengya ; Zhu, Peilin ; Zhong, Manhua ; Chen, Yongru ; Yuan, Guosheng ; Lin, Zhanzhou

ABSTRACTBackground and AimsThe impact of low‐level viremia(LLV) on the efficacy of immune checkpoint inhibitors (ICIs) in unresectable hepatocellular carcinoma(uHCC) patients remains unclear. This study aims to investigate the effect of LLV on the outcomes of ICIs‐based therapy in patients with uHCC.MethodsIn this multicenter retrospective study, we included patients with uHCC who received ICIs‐based therapy at four centres between January 2019 and December 2022. All patients were positive for HBsAg and were on nucleos(t)ide analogues (NAs) antiviral therapy. Propensity score matching (PSM) and inverse probability of treatment weighting (IPTW) were used to balance baseline characteristics between the LLV and maintained virological response (MVR) groups. Proteomic analysis was performed on a subset of patients to identify differential protein expression.ResultsA total of 329 patients (mean age 56 years; 92.4% male; 70.8% BCLC stage C) were included, with 170 patients in the LLV group and 159 in the MVR group. The objective response rate (ORR) was significantly lower in the LLV group compared to the MVR group (21.2% vs. 36.5%, p = 0.002), as was the disease control rate (DCR) (78.8% vs. 92.5%, p < 0.001). Median progression‐free survival (mPFS) was shorter in the LLV group (7.6 vs. 12.6 months, p < 0.001), as was median overall survival (mOS) (22.8 vs. 40.0 months, p < 0.001). These differences remained consistent after PSM and IPTW adjustments. Multivariate analysis identified LLV as the only independent risk factor for overall survival (hazard ratio [HR] 0.522, 95% CI 0.348–0.781; p = 0.002). Proteomic analysis revealed significant differences in the expression of Flt3L, SLAMF1 and FGF‐5 proteins between the LLV and MVR groups.ConclusionLLV is associated with poorer responses to ICIs‐based therapy and reduced survival in patients with HBV‐related uHCC.

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FGF5

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