Human PIK3R5 cDNA Clone
- Known as:
- Human PIK3R5 complementary Desoxyribonucleic acid Clone
- Catalog number:
- DC05799
- Category:
- -
- Supplier:
- Abgen
- Gene target:
- Human PIK3R5 cDNA Clone
Related genes to: Human PIK3R5 cDNA Clone
- Gene:
- PIK3R5 NIH gene
- Name:
- phosphoinositide-3-kinase regulatory subunit 5
- Previous symbol:
- -
- Synonyms:
- P101-PI3K, p101
- Chromosome:
- 17p13.1
- Locus Type:
- gene with protein product
- Date approved:
- 2004-10-13
- Date modifiied:
- 2015-11-17
Related products to: Human PIK3R5 cDNA Clone
Related articles to: Human PIK3R5 cDNA Clone
- Most genetic variants in the human genome reside in non-coding regions, where they can perturb regulatory element activity to influence gene expression, thereby contributing to various phenotypes and diseases. However, identifying functionally relevant non-coding genetic variation remains challenging. Here we integrate personal genomics, allele-specific gene regulation, and deep learning predictions to map the impact of non-coding variation in its native allelic and regulatory context. Leveraging whole-chromosome haplotypes and allele-specific analyses, we establish regulatory links within individual human genomes, enabling us to evaluate functional consequences of both common and rare variants. We identify and validate hundreds of cell-type-specific transcription factor binding events disrupted by genetic variants, revealing known and novel mechanisms that underlie allele-specific chromatin accessibility and gene expression. Using this framework, we discovered a rare variant that disrupted an OCT2 binding site within a distal enhancer, thereby modulating the expression of PIK3R5 gene. Our study establishes a generalisable strategy for interpreting non-coding regulatory variation, enabling systematic dissection of variant effects across diverse biological systems and offering a framework to investigate disease mechanisms. - Source: PubMed
Publication date: 2026/04/29
Magnitov Mikhail Dvan der Weide Robin HWitvliet Aster FHernández-Quiles MiguelMartinović MorenoTeunissen HansBraccioli LucaVermeulen Michielde Wit Elzo - Acute myeloid leukemia (AML) is an aggressive hematologic malignancy characterized by profound molecular heterogeneity and high relapse rates, posing significant clinical challenges. Programmed cell death (PCD), encompassing diverse regulated modalities such as apoptosis, necroptosis, and ferroptosis, plays a key role in leukemogenesis and therapeutic response; however, a comprehensive prognostic framework integrating multi-modal PCD pathways in AML remains elusive. In this study, we performed a systematic transcriptomic analysis of 1624 genes associated with 13 distinct PCD forms. A novel computational pipeline combining a variational autoencoder (VAE) for dimensionality reduction and a multilayer perceptron (MLP) for classification was employed to identify robust PCD-related biomarkers, interpreted via SHapley Additive exPlanations (SHAP) analysis. This approach identified 48 candidate genes with discriminative potential between AML and normal bone marrow. Unsupervised consensus clustering based on these genes delineated two molecular subtypes exhibiting divergent clinical outcomes and immune microenvironment profiles. The subtype demonstrated an immunosuppressive phenotype, characterized by enriched regulatory T cells, M2 macrophages, and elevated expression of inhibitory immune checkpoints, correlating with inferior survival. We developed an 8-gene prognostic signature (, , , , , , and ) that effectively categorized patients into high- and low-risk groups with notable survival differences, validated across independent cohorts. A prognostic nomogram combining the risk score, age, and cytogenetic risk enhanced the prediction accuracy for overall survival. Our study presents an integrative model that connects multi-modal PCD pathways to AML prognosis, offering a new molecular subtyping system and a clinically applicable risk assessment tool for improved prognostication and personalized treatment strategies. - Source: PubMed
Publication date: 2026/03/27
Zhang ChunlongNi HaisenZhao ZiyiZhao Ning - Spinal cord injury (SCI) refers to trauma to the spinal cord resulting in functional deficits. Dysregulation of the phosphoinositide 3-kinase/serine-threonine kinase (PI3K/AKT) pathway significantly contributes to the pathogenesis of SCI. This study evaluated the role of PI3K/AKT-associated biomarkers in SCI. Transcriptomic data from SCI samples deposited in the Gene Expression Omnibus (GEO) database were analyzed. An integrative approach combining differential expression profiling, machine learning, and experimental validation was employed to identify PI3K/AKT-related biomarkers. Combinatorial strategies-including functional enrichment analysis, immune microenvironment characterization, in silico drug prediction, and ligand-receptor docking-were used to elucidate potential biomarker-driven pathological mechanisms. Quantitative reverse transcription PCR (RT-qPCR) was performed to validate biomarker expression. Four biomarkers-FGF2, IL-6, PIK3R5, and TLR2-were successfully identified. Additionally, ELOVL6, IDI1, and SQLE were co-enriched in multiple pathways, including those associated with graft-versus-host disease (GVHD) in mice. TLR2 expression exhibited the strongest positive correlation with M2 macrophages (ρ = 0.74, P < 0.001) and the strongest negative correlation with neurons (ρ = -0.73, P < 0.001). Protein-ligand interaction analysis showed the highest binding scores of TLR2 with CHEMBL1836411, resveratrol hexanoic acid, and diprovocim-1. Molecular docking further confirmed a strong binding affinity between the TLR2 receptor and these compounds. RT-qPCR demonstrated significantly elevated transcript levels of Fgf2, Il6, Pik3r5, and Tlr2 in SCI samples (P < 0.01), corroborating the bioinformatic predictions. This study identifies FGF2, IL-6, PIK3R5, and TLR2 as key biomarkers in SCI, providing potential therapeutic targets for SCI treatment. - Source: PubMed
Publication date: 2026/04/20
Chen TaibangYu LichaoCai ZhijunChen Lingqiang - Understanding the molecular mechanisms underlying disease resistance in is of fundamental importance for enhancing the health and sustainability of salmonid aquaculture. In this study, we performed an integrative multi-omics analysis combining transcriptome sequencing and whole-genome resequencing to systematically identify immune-related genes and single-nucleotide polymorphisms (SNPs) associated with resistance to infection. Transcriptomic profiling identified multiple differentially expressed genes involved in immune and signaling pathways, including interferon-induced protein 44-like (IFI44L), PI3K family members (PIK3R2, PIK3R5), and NF-κB inhibitors (NFKBIA, NFKBIAA). These genes were significantly enriched in key immune regulatory pathways such as chemokine, PI3K-AKT, and NF-κB signaling, indicating their potential roles in modulating host defense responses. The integration of transcriptome and whole-genome resequencing data further revealed 104 SNPs distributed across immune-related genes that effectively differentiated resistant (R) and susceptible (S) populations. Among these, six candidate SNP loci were validated by PCR and demonstrated strong discriminatory power for resistant individuals, with multiplex PCR (MPCR) achieving an overall identification accuracy of 88.33%. The proportion of resistant genotypes within the population reached 14.82%, suggesting the progressive formation of a disease-resistant strain. Overall, our findings provide novel and comprehensive insights into the genetic and transcriptional regulation of disease resistance in . The identified immune-related genes and SNP markers represent valuable molecular resources for selective breeding and the development of health-oriented aquaculture strategies in salmonids. - Source: PubMed
Publication date: 2026/03/25
Zhou JiansheSun ShuaijieWang WanliangZhang YaoqiongWang ZhuangZhuang - Salivary duct carcinoma (SDC) is a rare and aggressive malignant tumor, with scarce reports on its pathogenesis and treatment methods. This study aims to explore the potential biomarkers of SDC. SDC cases were analyzed using Illumina microarrays, and Limma identified differentially expressed genes (DEGs) between cancer and normal tissues. An intersection of these DEGs with the differential genes from the GSE138581 dataset was obtained through Venn analysis to identify the core DEGs. The core DEGs were analyzed by search Tool for the retrieval of interacting genes/proteins (STRING) and cytoscape to identify the genes most related to the disease in SDC patients. Core genes were enriched with gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG); their pan-cancer implications and immune aspects were studied. Finally, the core DEGs were initially validated by immunohistochemistry in SDC samples. Fifty DEGs were found by exome sequencing, 3158 by the GSE138581 microarray. Thirteen common genes were identified by Venn analysis, and ten core genes, including Forkhead box M1 (FOXM1), collagen, type XI, alpha 1 (COL11A1), and neuron navigator 2 (NAV2), were selected. GO enrichment of core genes included positive regulation of calcium ion transport and ATPase activity, and KEGG analysis focused on related pathways. Expression was evaluated across 34 cancers from the TCGA database, and immune DEG Phosphoinositide-3-kinase regulatory subunit 5 (PIK3R5) was identified. The obtained DEGs were further validated by immunohistochemistry (IHC). Identifying the DEGs not only helps improve our understanding of SDC pathogenesis but also promises to identify potential biomarkers and new therapeutic targets. - Source: PubMed
Publication date: 2026/01/17
Zhang RuiqiZhu XingyaoMa HongmeiSun ErcanXiang XuepingHuang FengboHong BoLiu YuhuaZou Hong