SLP76 _ LCP2
- Known as:
- SLP76 _ LCP2
- Catalog number:
- Y213372
- Product Quantity:
- 200ul
- Category:
- -
- Supplier:
- ABM
- Gene target:
- SLP76 _ LCP2
Related genes to: SLP76 _ LCP2
- Gene:
- LCP2 NIH gene
- Name:
- lymphocyte cytosolic protein 2
- Previous symbol:
- SLP76
- Synonyms:
- SLP-76
- Chromosome:
- 5q35.1
- Locus Type:
- gene with protein product
- Date approved:
- 1996-03-12
- Date modifiied:
- 2015-11-10
Related products to: SLP76 _ LCP2
76 Kda Tyrosine Phosphoprotein Slp-76 (LCP2) Mouse anti-Human Monoclonal (aa216-434) (SLP-76 03) Antibody76 Kda Tyrosine Phosphoprotein Slp-76 (LCP2) Mouse anti-Human Monoclonal (aa216-434) (SLP-76/03) Antibodyanti-LCP2anti-LCP2anti-LCP2anti-LCP2 SLP-76 (SLP-76 03)anti-LCP2 SLP-76 (SLP-76 03) type: Primary antibodies host: Mouseanti-LCP2 , Mouse monoclonal to LCP2, Isotype IgG2b, Host Mouseanti-LCP2 / SLP-76anti-LCP2 / SLP-76 (aa216-434) (SLP-76/03)anti-LCP2 type: Primary antibodies host: Mouseanti-LCP2 type: Primary antibodies host: RabbitAnti-LCP2, Rabbit Polyclonal to LCP2, Isotype , Host RabbitAntibodies: Mouse Monoclonal to SLP76, Species Reactivity: Mouse, Human, Porcine, Clone: SLP-76_03, Isotype: IgG2bAntibodies: Mouse Monoclonal to SLP76, Species Reactivity: Mouse, Human, Porcine, Clone: SLP-76_03, Isotype: IgG2b Related articles to: SLP76 _ LCP2
- 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 - Our research objective is to construct a prognostic model of tumor-infiltrating lymphocytes related genes (TILRGs) for predicting the survival of cases with diffuse large B-cell lymphoma (DLBCL). - Source: PubMed
Publication date: 2026/05/01
Zhao JingSu KunquanZhong Shoubin - Although genome-wide association studies (GWAS) have identified loci associated with type 1 diabetes, the specific pathways and regulatory networks linking these loci to disease pathology remain largely unknown. We hypothesised that type 1 diabetes genetic risk factors disrupt tissue-specific biological pathways and gene networks that ultimately lead to beta cell loss. - Source: PubMed
Publication date: 2026/04/15
Blencowe MontgomerySaleem ZaraLiu RuoshuiWang MargaretTseng I-HsinWier JulianMutter StefanVaida FlorinGuo YiSandholm NiinaAckeifi CourtneyKaufman Daniel LYang Xia - Ovarian cancer is a rare cancer, it has the worst prognosis and the highest mortality rate, especially in high-grade serous ovarian cancer (HGSOC). High-throughput data generation is developed and provides an opportunity to investigate molecular pathways involved in cancer progression. The purpose of this study is to explore the role of main genes linked to the immune system and immune microenvironment in HGSOC using bioinformatics approaches to introduce promising biomarkers. - Source: PubMed
Publication date: 2025/11/28
Fatehi RaziehTabatabaiefar MohammadAminBehnamfar FaribaKhanahmad Hossein - Lung cancer is one of the most common malignancies, characterized by a wide prognosis spectrum, different histological subtypes, and a high mortality rate. Hemostatic system imbalance in patients with lung cancer often leads to increased mortality. Intracellular RNAs that share common miRNA binding sites create a competing endogenous RNA (ceRNA) network that plays an important role in gene expression regulation. The emerging role of ceRNAs in tumor development is increasingly being recognized; however, their connection to hemostatic system imbalance in lung squamous cell carcinoma (LUSC) remains unclear. In this study, RNA-seq data of LUSC and normal tissues were downloaded from the TCGA data portal. Differentially expressed mRNAs (DEmRNAs), miRNAs (DEmiRNAs), and lncRNAs (DElncRNAs) between LUSC and corresponding paracancerous tissues were analyzed using the DESeq2 package in R statistical software. Hemostasis-related genes linked to coagulation and complement cascades (hsa04610) and platelet activation (hsa04611) pathways were identified using the KEGGREST package. The ceRNA network associated with system hemostasis was constructed using differentially expressed RNAs (DERNAs), including mRNAs, lncRNAs, and miRNAs. The GO and KEGG enrichment analysis of DEmRNAs was conducted using the enrichR package. Hazard ratio (HR) and Kaplan-Meier curve were employed to assess the prognostic value of DERNAs using the survival and survminer packages. A ceRNA network comprising 100 hemostasis-related genes, 5 miRNAs, and 57 lncRNAs was constructed. Of these, 19 hemostasis genes, one miRNA (miR-23-3p), and 6 lncRNAs (LINC01615, LINC00707, LINC00702, FEZF1-AS1, DLX6-AS1, CLRN1-AS1) were significantly associated with prognosis in LUSC. Based on correlation analysis, MEF2C-AS1/miR-429/F8, RAP1A, GNAI2, C3AR1, F13A1, P2RY12, LCP2, C1QC axis and CASC11, CASC9, PVT1, BBOX1-AS1/ miR-23b-3p/ PLAU axis may represent key pathways involved in hemostatic system imbalance and the pathogenesis of LUSC. Our analysis revealed a complex ceRNA network associated with system hemostasis and the prognosis of LUSC. These findings may contribute to the development of personalized therapies and valuable prognostic biomarkers for LUSC patients. - Source: PubMed
Publication date: 2025/12/17
Mirazimi YasinGharechahi Javad