CD8a, Porcine

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1 699.43 €

Known as:: CD8a, Porcine
Catalog number:: Y103341
Product Quantity:: 500 ug
Category:: -
Supplier:: ABM
Gene target:: CD8a Porcine

Related genes to: CD8a, Porcine

Gene:: CD8A ^{NIH gene}
Name:: CD8a molecule
Previous symbol:: CD8
Synonyms:: -
Chromosome:: 2p11.2
Locus Type:: gene with protein product
Date approved:: 1986-01-01
Date modifiied:: 2019-04-23

Related products to: CD8a, Porcine

Related articles to: CD8a, Porcine

Perioperative inflammation, anesthesia modulation and cellular signaling in lung cancer: an integrated transcriptomic and single-cell analysis.
Patients undergoing lung cancer surgery experience significant systemic inflammatory responses and anesthetic interventions during the perioperative period, which can affect immune status and the tumor microenvironment. Different anesthetic techniques variably regulate inflammation, cytokine signaling, and tissue protection. It remains unclear, however, whether perioperative inflammatory signals leave identifiable molecular patterns at the tissue level. Furthermore, analyses integrating multi-layered data are currently lacking. We performed an integrated transcriptomic and single-cell analysis to identify shared molecular patterns linking perioperative inflammation, anesthetic modulation, and the lung cancer tumor microenvironment, and to localize these pathways to specific cellular communication axes. - Source: PubMed
Publication date: 2026/03/26
Lu WeixiangAi QingWang ZeZhang TaoDeng ShiyuHe JianxingShao Wenlong
Association of Arylacetamide Deacetylase-Mediated Extracellular Matrix Remodeling With Immune Exclusion in Pancreatic Cancer.
Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive cancer with poor prognosis, characterized by a desmoplastic tumor microenvironment (TME) that limits immune cell infiltration and diminishes response to immunotherapy. Arylacetamide deacetylase (AADAC) is a lipid-processing enzyme, but its role in tumor progression, stromal organization, and immune modulation remains unclear. - Source: PubMed
Publication date: 2026/05/08
Wu ChaoYang Jun
Integrative single-cell transcriptomics and co-expression network analysis identify SIMALR as a prognostic immune-related lncRNA in breast cancer: in silico analysis and validation.
This study aimed to identify and characterize irlncRNAs associated with prognosis and immune modulation in breast cancer. We integrated single-cell RNA sequencing, hdWGCNA, and bulk RNA-seq differential expression analysis results to identify candidate irlncRNAs. The top candidate, SIMALR, was further investigated using immune, survival, mutation analysis, and GSEA. RT-qPCR preliminary validation was performed on patient tissues. SIMALR was linked to favorable survival and enriched in immune pathways, including T-cell receptor signaling, Natural Killer (NK) cell cytotoxicity, and antigen processing. Pearson analysis showed co-expression of SIMALR-related genes (CD8A, CD4, TNF, LCP2, ITGB2) in key immune populations. High SIMALR As per standard instruction, "Statement of Significance" section should not be captured. Hence, the "Clinical significance" section was deleted. Please check and confirm if presented correctly; otherwise, please amend. expression in tumor cells is associated with enhanced secretion of Th1-attracting chemokines (CXCL9/10/11, CCL5), recruitment of CD8 + T cells, activated dendritic cells, and both M1/M2 macrophages. RT-qPCR confirmed higher SIMALR expression in tumors. Due to limited availability of clinical specimens, the RT-qPCR analysis was performed on paired tissue samples from six patients, and therefore the results should be considered a preliminary validation. SIMALR may contribute to anti-tumor immunity, highlighting its potential as a promising biomarker and therapeutic target in breast cancer. - Source: PubMed
Publication date: 2026/05/09
Balangi FatemehSamadi PouriaMaghool FatemehDaneshvar HamidTabatabaeian MaryamAmjadi ElhamSedghy Farnaz
Interpreting and Validating a Deep Learning Model Predictive of Spatial Morphologic-Molecular Patterns in Lung Adenocarcinoma, Using Ground Truth Immunohistochemistry Images.
Lung adenocarcinoma (LUAD), the most common subtype of non-small cell lung cancer, exhibits profound histological and molecular heterogeneity. While genomic profiling has identified key oncogenic drivers and immune signatures, its use is limited by cost, technical demands and tissue availability. In addition, spatial transcriptomics provides spatially resolved molecular insights but remains challenging and time-consuming. To address this gap, we developed XpressO-Lung, an explanatory deep learning model that predicts gene expression heterogeneity spatially in tumor and its microenvironment on hematoxylin and eosin based diagnostic (Dx) whole-slide images (WSIs) by learning associations between tissue morphology and the corresponding bulk-transcriptomic data. Utilizing 200 LUAD cases from The Cancer Genome Atlas, XpressO-Lung predicted spatial expression patterns of NAPSA, TP53I3, CD8A, TTF1, KRT7, CDKN2A, FOXO1, KEAP1, RB1 and TP53 on Dx-WSIs with AUCs ranging from 0.64 to 0.92. The predicted spatial gene expression patterns aligned with the known morphologic interactions of the tumor and its microenvironment, capturing biological events directly on Dx-WSIs. These spatio-morpho-molecular associations were further validated using immunohistochemistry on an external set of clinical samples at Dartmouth Health, demonstrating concordance between model-predicted spatial patterns and observed histomorphologic features. By coupling predictive performance with spatial interpretability of gene expression on Dx-WSIs, the XpressO-Lung model bridges histopathology and bulk-transcriptomics, enabling explainable spatio-morpho-genomic analyses to advance biomarker discovery, therapeutic stratification and precision oncology in LUAD. - Source: PubMed
Publication date: 2026/04/23
Rao Vibha RWorkman Adrienne APalisoul Scott MLimoge Cassandra JVaickus Louis JZanazzi George JLu LiangLiu XiaoyingSukhadia Shrey S
Identification of Cell Subpopulation-Specific Driver Genes Reveals Ideal Candidates for Renal Cell Carcinoma Immunotherapy.
With the rapid development of cancer treatment, immunotherapy has revolutionized renal cell carcinoma (RCC) treatment, yet patient responses remain heterogeneous. Here, a computational pipeline was constructed by integrating single-cell and bulk RNA sequencing data to identify immune-related candidate driver genes and characterize their impact on RCC immunotherapy. Based on gene regulatory networks (GRN), 25 immune-related candidate driver genes were identified, leading to the stratification of patients into three clusters (C1-C3). Compared to the C2/C3 cluster, the C1 cluster exhibited elevated immune infiltration, tumor mutation burden and checkpoint expression, which may represent immunotherapy responders. Dynamic analysis of GRNs revealed the critical role of candidate driver genes in predicting the efficacy of immunotherapy. , and in lymphoid cells of C1 participated in anti-tumor immune response by impacting target genes , , and . , , , and were up-regulated in clusters C2 and C3, leading to tumor progression and immune evasion by influencing target genes , and . In conclusion, integration of the transcriptome with molecular networks provided a network-based framework to uncover immune-related candidate driver genes for stratifying RCC patients, thereby serving as potential therapeutic targets to improve the outcome of RCC immunotherapy. - Source: PubMed
Publication date: 2026/04/13
Yin XiangzheWang LuSun YanwuLi ShiyiYu WentongWang SiyaoGeng ZhichaoZhao HongyingWang Li

CD8a, Porcine

Related genes to: CD8a, Porcine

Related products to: CD8a, Porcine

Related articles to: CD8a, Porcine

Perioperative inflammation, anesthesia modulation and cellular signaling in lung cancer: an integrated transcriptomic and single-cell analysis.

Association of Arylacetamide Deacetylase-Mediated Extracellular Matrix Remodeling With Immune Exclusion in Pancreatic Cancer.

Integrative single-cell transcriptomics and co-expression network analysis identify SIMALR as a prognostic immune-related lncRNA in breast cancer: in silico analysis and validation.

Interpreting and Validating a Deep Learning Model Predictive of Spatial Morphologic-Molecular Patterns in Lung Adenocarcinoma, Using Ground Truth Immunohistochemistry Images.

Identification of Cell Subpopulation-Specific Driver Genes Reveals Ideal Candidates for Renal Cell Carcinoma Immunotherapy.