Last update 01 Nov 2024

PLXNA4

Last update 01 Nov 2024

Basic Info

Synonyms

DKFZp434G0625PRO34003, FAYV2820, KIAA1550

+ [5]

Introduction

Coreceptor for SEMA3A. Necessary for signaling by class 3 semaphorins and subsequent remodeling of the cytoskeleton. Plays a role in axon guidance in the developing nervous system. Class 3 semaphorins bind to a complex composed of a neuropilin and a plexin. The plexin modulates the affinity of the complex for specific semaphorins, and its cytoplasmic domain is required for the activation of down-stream signaling events in the cytoplasm (By similarity).

Drugs associated with PLXNA4

SGL-10060

Target

PLXNA4 x SEMA3A

Mechanism

PLXNA4 modulators [+1]

Active Org.

Seagull AS

Originator Org.

Seagull AS

Active Indication

Solid tumor

Inactive Indication-

Drug Highest PhasePreclinical

First Approval Ctry. / Loc.-

First Approval Date20 Jan 1800

100 Clinical Results associated with PLXNA4

100 Translational Medicine associated with PLXNA4

0 Patents (Medical) associated with PLXNA4

Literatures (Medical) associated with PLXNA4

01 Jun 2024·Virus Research

Can inflammatory plasma proteins predict Long COVID or Fatigue severity after SARS-CoV-2 infection?

Article

Author: Freuer, Dennis ; Warm, Tobias Dominik ; Kirchberger, Inge ; Hyhlik-Dürr, Alexander ; Goßlau, Yvonne ; Schmitz, Timo ; Linseisen, Jakob ; Meisinger, Christa

OBJECTIVETo investigate whether specific immune response plasma proteins can predict an elevated risk of developing Long COVID symptoms or fatigue severity after SARS-CoV-2 infection.METHODSThis study was based on 257 outpatients with test-confirmed SARS-CoV-2 infection between February 2020 and January 2021. At least 12 weeks after the acute infection, 92 plasma proteins were measured using the Olink Target 96 immune response panel (median time between acute infection and venous blood sampling was 38.8 [IQR: 24.0-48.0] weeks). The presence of Long COVID symptoms and fatigue severity was assessed 115.8 [92.5-118.6] weeks after the acute infection by a follow-up postal survey. Long COVID (yes/no) was defined as having one or more of the following symptoms: fatigue, shortness of breath, concentration or memory problems. The severity of fatigue was assessed using the Fatigue Assessment Scale (FAS). In multivariable-adjusted logistic and linear regression models the associations between each plasma protein (exposure) and Long COVID (yes/no) or severity of fatigue were investigated.RESULTSNine plasma proteins were significantly associated with Long COVID before, but not after adjusting for multiple testing (FDR-adjustment): DFFA, TRIM5, TRIM21, HEXIM1, SRPK2, PRDX5, PIK3AP1, IFNLR1 and HCLS1. Moreover, a total of 10 proteins were significantly associated with severity of fatigue before FDR-adjustment: SRPK2, ITGA6, CLEC4G, HEXIM1, PPP1R9B, PLXNA4, PRDX5, DAPP1, STC1 and HCLS1. Only SRPK2 and ITGA6 remained significantly associated after FDR-adjustment.CONCLUSIONSThis study demonstrates that certain immune response plasma proteins might play an important role in the pathophysiology of Long COVID and severity of fatigue after SARS-CoV-2 infection.

08 Feb 2024·JCI Insight

Sema6D forward signaling impairs T cell activation and proliferation in head and neck cancer

Article

Author: Izumi, Mayuko ; Morita, Takayoshi ; Naito, Maiko ; Kumanogoh, Atsushi ; Kinugasa-Katayama, Yumi ; Koyama, Shohei ; Yamashita, Kazuo ; Takemoto, Norihiko ; Tahara, Makoto ; Aoshi, Taiki ; Nakanishi, Yoshimitsu ; Villa, Jordan Kelly ; Naito, Yujiro ; Mizuno, Yumiko ; Kang, Sujin ; Yamaguchi, Yuta ; Futami, Yu ; Yaga, Moto ; Masuhiro, Kentaro ; Nishide, Masayuki ; Inohara, Hidenori ; Hirai, Takashi ; Kuge, Tomoki ; Nojima, Satoshi ; Kato, Yasuhiro ; Matsuura, Kazuto ; Hoshi, Yuta ; Tsuda, Takeshi ; Takamatsu, Hyota ; Enokida, Tomohiro ; Takeda, Yoshito

Immune checkpoint inhibitors (ICIs) are indicated for a diverse range of cancer types, and characterizing the tumor immune microenvironment is critical for optimizing therapeutic strategies, including ICIs. T cell infiltration and activation status in the tumor microenvironment greatly affects the efficacy of ICIs. Here, we show that semaphorin 6D (Sema6D) forward signaling, which is reportedly involved in coordinating the orientation of cell development and migration as a guidance factor, impaired the infiltration and activation of tumor-specific CD8+ T cells in murine oral tumors. Sema6D expressed by nonhematopoietic cells was responsible for this phenotype. Plexin-A4, a receptor for Sema6D, inhibited T cell infiltration and partially suppressed CD8+ T cell activation and proliferation induced by Sema6D stimulation. Moreover, mouse oral tumors, which are resistant to PD-1-blocking treatment in wild-type mice, showed a response to the treatment in Sema6d-KO mice. Finally, analyses of public data sets of human head and neck squamous cell carcinoma, pan-cancer cohorts, and a retrospective cohort study showed that SEMA6D was mainly expressed by nonhematopoietic cells such as cancer cells, and SEMA6D expression was significantly negatively correlated with CD8A, PDCD1, IFNG, and GZMB expression. Thus, targeting Sema6D forward signaling is a promising option for increasing ICI efficacy.

01 Feb 2024·Alternative therapies in health and medicine

Construction of Prognostic Model and miRNA-mRNA Regulatory Network for Lung Squamous Cell Carcinoma.

Article

Author: Zhao, Lun ; Fu, Qi ; Zhang, Haoran ; Shi, Mohan

The purpose of this paper was to construct a prognostic model, miRNA-mRNA regulatory network and protein-protein interaction (PPI) network for lung squamous cell carcinoma (LUSC) used data from the cancer genome atlas (TCGA) database. In this study, we first downloaded and sorted out the expression matrix containing 19962 mRNA transcripts (including 502 LUSC and 51 normal control (NC) samples) and the expression matrix containing 2205 miRNA transcripts (including 478 LUSC and 45 NC samples) from the TCGA database. We obtained 389 differentially expressed miRNAs (DE-miRNAs), of which 305 were upregulated and 84 down-regulated DE-miRNAs. Next, a total of 7 prognosis-related DE-miRNAs (PDE-miRNAs) were identified by Cox regression analysis, and the prognosis model consisting of three PDE-miRNAs (hsa-miR-4746-5p, hsa-miR-556-3p and hsa-miR-489-3p) was optimized. Then, we drew the survival curves and found that the survival rates of the three PDE-miRNA high and low expression groups and the survival rates of the high-risk and low-risk patients in the prognosis model had significant statistical differences. In addition, the receiver operating characteristics (ROC) curve analysis and independent prognostic analysis confirmed that the prognostic model we built has a relatively accurate ability to predict the grouping and prognosis of LUSC patients. Finally, Cox regression analysis were used to construct the miRNA-mRNA regulatory network, which showed the regulatory relationship between PDE-miRNAs and targeted mRNAs. Moreover, we constructed the PPI network composed of 145 targeted mRNAs and the subnetwork composed of 10 hub-targeted mRNAs (FCGR3A, IL13, CCR2, PPARGC1A, FCGR3B, ACSL1, PLXNA4, LPL, KAT2B and AOC3), which showed the interaction between targeted mRNAs. The above results indicated that the prognosis model we built can predict LUSC patients relatively accurately. The miRNA-mRNA regulatory network and the PPI network of targeted mRNAs illustrated the regulatory mechanisms and interactions between RNAs, which were of certain reference significance for us to further understand the molecular pathogenesis of LUSC and for clinical early diagnosis and treatment.

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PLXNA4

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