Ask about this productRelated genes to: PLA2G4B antibody
- Gene:
- PLA2G4B NIH gene
- Name:
- phospholipase A2 group IVB
- Previous symbol:
- -
- Synonyms:
- cPLA2-beta, HsT16992
- Chromosome:
- 15q15.1
- Locus Type:
- gene with protein product
- Date approved:
- 1998-11-20
- Date modifiied:
- 2016-10-05
Related products to: PLA2G4B antibody
Related articles to: PLA2G4B antibody
- The gut-skin axis represents a critical but poorly understood pathway in atopic dermatitis (AD) pathogenesis. This study aimed to establish causal relationships between gut microbiota and AD risk while identifying key molecular bridges and therapeutic targets. We integrated multiple analytical approaches, including single-cell RNA sequencing analysis of skin biopsies from five AD patients and four healthy controls, intercellular communication network analysis, pseudotime trajectory inference, reverse drug prediction, molecular docking, and molecular dynamics simulations. Analysis revealed increased keratinocyte heterogeneity and enhanced immune cell communication in atopic dermatitis (AD) samples. Intersection analysis between gut microbial metabolite-associated genes and skin pathology-related genes identified seven key bridging genes (, , , , , ). Functional annotation indicated that these genes are primarily involved in vitamin precursor metabolism, suggesting that the group influences AD pathogenesis mainly through vitamin precursor-mediated pathways that regulate systemic immune responses. Pseudotime trajectory analysis demonstrated dynamic temporal gene expression patterns during disease progression. Molecular docking revealed an unexpectedly high-affinity binding between methotrexate and (binding energy = -10.4 kcal/mol), which exceeded its binding affinity for the classical target (-7.5 kcal/mol). Molecular dynamics simulations further confirmed the stable binding conformation of the protein-ligand complexes. This study provides mechanistic insights into how the group influences atopic dermatitis through vitamin precursor-mediated systemic immune modulation and identifies as a novel therapeutic target. The findings provide mechanistic insights into the gut-skin axis and support developing precision medicine approaches integrating microbiome interventions with targeted pharmacotherapy for AD management. - Source: PubMed
Publication date: 2025/12/05
Cao FangLiu AoNanTong JiaoyangGuo CuiZhang HuiPang YaobinTang KexinYu QianyingGuo Jing - Ferroptosis and mitochondrial metabolism are closely associated with the pathological processes of various diseases. However, the role of ferroptosis-related genes (FRGs) and mitochondrial metabolism-related genes (MMRGs) in poor ovarian response (POR) remains unexplored. - Source: PubMed
Publication date: 2025/11/14
Cai YunyingLin NaYin YijieTian MeiWu ZeSu Heng - Type 2 diabetes mellitus (T2DM) is a metabolic disorder caused by insufficient insulin secretion from pancreatic beta cells or reduced insulin sensitivity in peripheral tissues. It has a high clinical incidence rate and can lead to various complications, significantly impacting patients' quality of life and lifespan. Syringin is a natural active compound, and studies have shown that it has certain therapeutic effects on diabetes, but its mechanism of action remains unclear. - Source: PubMed
Publication date: 2025/10/21
Wan ChunleiSun SiyuDu YuqingHan YuxingLi XueyingYang YueCao JianingMiu JinChen PengZhang YuexinLiu ShaoboZhang Lei - Ageing, marked by cumulative molecular damage, now leaves most adults spending nearly a decade in poor health. To date, no therapies directly target the ageing process. We performed a large-scale genome-wide association study to identify potential drug targets for extending health span. - Source: PubMed
Publication date: 2025/09/26
Cai ZhikangYang YueQu PengFu SensongLi Xu - Colorectal cancer (CRC) is a prevalent tumor in the gastrointestinal system. Non-apoptotic regulatory cell death-related genes (NARCDs) play a critical role in tumor development and progression. This research aims to explore the predictive value of NARCDs in CRC and to elucidate their possible biological roles. Transcriptome data for CRC were obtained from the Cancer Genome Atlas (TCGA) database and Gene Expression Omnibus (GEO). Both univariate and multivariate regression analyses, as well as Least Absolute Shrinkage and Selection Operator (LASSO) regression, were utilized to pinpoint the prognostic genes. The expression of the characterised genes in CRC cells was also examined using quantitative reverse transcription polymerase chain reaction (qRT-PCR). The prognostic ability of NARCDs features was assessed using Kaplan-Meier survival curves and receiver operating characteristic (ROC) curves. The predictive performance of the comprehensive nomogram was evaluated using calibration curves and decision curve analysis. Additionally, single-sample Gene Set Enrichment Analysis (ssGSEA) was utilized to analyze immune cell density and functional immune scores. Furthermore, the CellMiner database was applied to identify antitumor drugs that were highly correlated with the feature genes. This project developed an innovative risk model utilizing seven NARCDs characteristic genes (JMJD7-PLA2G4B, CDKN2A, PANX2, FABP4, GSDMC, NOD2, and DYNC1I1) to estimate the survival rate of CRC patients. The prognostic features were recognized as independent indicators for CRC, demonstrating satisfactory predictive efficacy in both the training and validation cohorts. The model achieved AUC values of 0.748, 0718 and 0.668 for 1-, 3- and 5-years in the TCGA training set, respectively. In the low-risk group, patients exhibited a more pronounced potential benefit from immunotherapy and showed higher levels of immune cell infiltration. Furthermore, drug sensitivity analyses indicated that individuals with reduced risk scores demonstrated greater responsiveness to pharmacological therapies. Finally, qRT-PCR results further confirmed our findings. We successfully developed a predictive feature model consisting of seven NARCDs, offering fresh insight into the prognostic evaluation of CRC patients and establishing a theoretical basis for crafting personalized treatment approaches. - Source: PubMed
Publication date: 2025/08/04
Liu HuiLi Dezhi