Ask about this productRelated genes to: CD8A Blocking Peptide
- 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 Blocking Peptide
Related articles to: CD8A Blocking Peptide
- 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 - 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 - 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 - In an earlier murine model of myocardial infarction (MI), we showed that CD8 cells and myeloid dendritic cells (mDCs) infiltrate the infarcted myocardium within the first week. However, in humans, the spatial interplay between CD8 T cells and dendritic cells in the spatial context of human myocardial infarction remains underexplored. In the present study, we applied spatial transcriptomics and functional assays to characterize immune-stromal dynamics in infarcted myocardium and peripheral blood. Spatial transcriptomics analysis of infarcted human myocardium at days 2 and 6 post-MI, combined with peripheral blood flow cytometry and EPC colony-forming assays, was performed. Cell composition, pathway enrichment, and cell-to-cell communication analyses were conducted to map immune-stromal cells' dynamics across time points. Spatial mapping identified dynamic shifts in immune, fibroblast, and endothelial populations, with fibroblasts and endothelial cells remaining abundant throughout. CD8 T cells accumulated in ischemic regions while their circulating levels declined. Gene Ontology and pathway analyses of CD8A transcripts revealed enrichment of proinflammatory and NF-κB survival programs. ITGAX/CD33/THBD APCs progressively increased within infarct zones, activating antigen-presentation and leukocyte chemotaxis pathways. Early (day 2) APC-endothelial crosstalk showed the strongest predicted recruitment signals for CD8 T cells, which diminished by day 6. Finally, EPC colony-forming capacity showed a tendency for reduction in MI patients and inversely correlated with coronary lesion burden, indicating impaired vascular repair potential. This integrative spatial and functional study demonstrates that APC-driven CD8 recruitment and EPC dysfunction are key features of human MI. Immune-endothelial niches facilitate early cytotoxic T-cell infiltration, while progenitor depletion limits vascular regeneration. These findings provide mechanistic insight into immune-vascular imbalance during infarct healing and highlight potential therapeutic targets to modulate inflammation and restore vascular repair. - Source: PubMed
Publication date: 2026/03/26
Salybekov Amankeldi AShaikalamova SaidaKinzhebay AimanWolfien MarkusAsahara Takayuki - Glioblastoma (GBM) is one of the most aggressive brain tumors with a poor prognosis despite current treatment modalities. This study aimed to identify genes whose high expression is paradoxically associated with both poor survival and enhanced immune activity, as potential targets for combination chemotherapeutic and immunotherapeutic strategies. - Source: PubMed
Publication date: 2026/04/27
Han Myung-HoonNoh Yung-KyunKim HyunkeeKim Kyu ShikKim Dong-HoonJung Un SukLee Kyung SukKwon Mi JungChae Seoung WanMin Kyueng-Whan