EFNA1 Human

What is the molecular structure and cellular localization of EFNA1 in human cells?

EFNA1 is a member of the EFNA family with approximately 30-40% similarity to other family members. It features an extracellular receptor-binding domain of approximately 20 kDa and is anchored to the cell membrane by glycosyl phosphatidylinositol (GPI) linkage . For studying EFNA1 localization, researchers should employ immunofluorescence microscopy with specific antibodies validated for human EFNA1, combined with membrane markers to confirm its cell surface expression. Western blotting with membrane fraction isolation can provide quantitative assessment of its membrane association.

What are the primary binding partners of EFNA1 and how should their interactions be studied?

EFNA1 binds to multiple EphA family receptors (EphA1–5) . To study these interactions, researchers can employ several complementary approaches: (1) Co-immunoprecipitation followed by mass spectrometry to identify novel binding partners; (2) Surface plasmon resonance to quantify binding affinities; (3) Proximity ligation assays to visualize interactions in situ; and (4) FRET-based approaches to monitor dynamic interactions in living cells. When designing such experiments, it's crucial to include proper controls for specificity, such as using other EFNA family members to determine binding selectivity.

How is EFNA1 expression regulated at the transcriptional and post-translational levels?

EFNA1 expression is regulated by multiple mechanisms. At the transcriptional level, hypoxia has been identified as a key inducer of EFNA1 expression . To investigate transcriptional regulation, researchers should perform chromatin immunoprecipitation (ChIP) to identify transcription factors binding to the EFNA1 promoter, coupled with luciferase reporter assays to validate functional significance. For post-translational regulation, phosphorylation analysis and protein stability assays should be conducted. Additionally, researchers should consider analyzing microRNA regulation using prediction algorithms followed by functional validation with luciferase reporters containing EFNA1 3'UTR regions.

How does EFNA1 expression vary across different human cancer types compared to normal tissues?

EFNA1 expression is significantly upregulated in multiple cancer types compared to corresponding normal tissues. Analysis of TCGA data reveals higher EFNA1 expression in bladder urothelial carcinoma, breast invasive carcinoma, cervical squamous cell carcinoma, cholangiocarcinoma, colon adenocarcinoma, esophageal carcinoma, head and neck squamous cell carcinoma, kidney renal clear cell carcinoma, liver hepatocellular carcinoma, lung adenocarcinoma, prostate adenocarcinoma, rectum adenocarcinoma, stomach adenocarcinoma, thymoma, and uterine corpus endometrial carcinoma . Interestingly, lower expression is observed in kidney chromophobe . When investigating expression patterns, researchers should employ both RNA-seq and protein-level validation through immunohistochemistry, ensuring appropriate normalization across tissue types.

What is the prognostic significance of EFNA1 expression in different cancers and how should it be analyzed?

EFNA1 expression has significant prognostic value in several cancer types. Higher EFNA1 expression correlates with poorer outcomes in low-grade glioma (LGG), esophageal carcinoma (ESCA), and cervical squamous cell carcinoma (CESC) . To properly assess prognostic value, researchers should:

• Stratify patients by EFNA1 expression levels (typically using median as cutoff)

• Perform Kaplan-Meier survival analysis with log-rank tests

• Calculate hazard ratios using Cox regression models

• Adjust for confounding variables through multivariate Cox regression

• Validate findings in independent cohorts

What is the relationship between EFNA1 expression and clinicopathological features in low-grade glioma?

In LGG, EFNA1 expression correlates with several clinicopathological features. Higher EFNA1 expression is significantly associated with WHO grade III status compared to grade II, and with 1p19q non-codeletion status . To investigate such relationships, researchers should employ the Wilcoxon signed-rank test when comparing EFNA1 expression between different clinicopathological groups. Multiple testing correction should be applied when assessing correlations across numerous features. Additionally, researchers should consider molecular subtypes and integrate genomic alterations data to develop a comprehensive understanding of EFNA1's role in specific cancer contexts.

What are the optimal approaches for manipulating EFNA1 expression in experimental models?

To effectively manipulate EFNA1 expression, researchers can employ several complementary approaches:

• For knockdown studies: shRNA-mediated knockdown has been successfully used in esophageal carcinoma cells . Design multiple shRNAs targeting different regions of EFNA1 mRNA to control for off-target effects.

• For knockout models: CRISPR-Cas9 system targeting early exons of EFNA1, followed by single-cell cloning and validation by sequencing, Western blot, and qPCR.

• For overexpression studies: Construct expression vectors with different promoters (constitutive or inducible) to achieve varying expression levels.

• For functional studies: Include rescue experiments by reintroducing wild-type or mutant EFNA1 to confirm phenotype specificity.

Each approach should include appropriate controls and validation at both mRNA and protein levels.

How should researchers design experiments to investigate EFNA1's role in immune cell infiltration?

To investigate EFNA1's role in immune cell infiltration, researchers should adopt a multi-faceted approach:

• Computational analysis: Use tools like TIMER2.0 and CIBERSORT to estimate immune cell infiltration levels from bulk RNA-seq data and correlate with EFNA1 expression .

• In vitro validation: Co-culture EFNA1-manipulated cancer cells with immune cells (e.g., CD4+ T cells, myeloid dendritic cells, neutrophils) to assess migration and interaction.

• In vivo studies: Generate EFNA1 knockout or overexpressing tumor models and analyze tumor-infiltrating lymphocytes by flow cytometry and immunohistochemistry.

• Mechanistic studies: Investigate cytokine/chemokine production by EFNA1-expressing cells using cytokine arrays or targeted ELISAs.

This comprehensive approach can help elucidate how EFNA1 influences the immune microenvironment in tumors.

What statistical methods and bioinformatic tools are most appropriate for analyzing EFNA1 in large-scale genomic datasets?

For robust analysis of EFNA1 in large-scale genomic datasets, researchers should employ:

• Data acquisition and preprocessing: Access data through repositories like TCGA via the UCSC Xena browser, and normalize using packages like "limma" in R to remove batch effects .

• Differential expression analysis: Apply appropriate statistical tests based on data distribution (e.g., Wilcoxon signed-rank test for non-parametric data).

• Survival analysis: Implement Kaplan-Meier and Cox regression using "Survminer" and "Survival" packages in R .

• Immune infiltration analysis: Utilize TIMER2.0 and CIBERSORT algorithms with partial Spearman's correlation to assess relationships between EFNA1 expression and immune cell populations .

• Pathway analysis: Perform Gene Set Enrichment Analysis (GSEA) using appropriate gene set databases (e.g., KEGG, GO) with at least 1000 permutations and a significance threshold of p<0.05 .

How does EFNA1 contribute to tumor angiogenesis and what experimental approaches best capture this function?

EFNA1 has been implicated in angiogenesis processes . To investigate this function:

• In vitro angiogenesis assays: Use tube formation assays with endothelial cells exposed to conditioned media from EFNA1-manipulated cancer cells.

• Spheroid-based models: Generate 3D tumor spheroids with varying EFNA1 expression to observe vascular sprouting.

• In vivo models: Employ matrigel plug assays with EFNA1-expressing cells or recombinant protein to quantify vessel formation.

• Mechanistic studies: Analyze expression of angiogenic factors (VEGF, bFGF) in response to EFNA1 modulation.

• Imaging approaches: Use intravital microscopy in animal models to observe real-time vessel formation in EFNA1-manipulated tumors.

When designing these experiments, researchers should consider the bidirectional signaling nature of Eph/ephrin interactions.

How does hypoxia regulate EFNA1 expression and what are the downstream effects in cancer progression?

Hypoxia has been identified as an inducer of EFNA1 expression . To investigate this regulatory mechanism:

• Hypoxia models: Culture cells in controlled oxygen chambers (1-2% O₂) or treat with hypoxia-mimetic agents like CoCl₂ or deferoxamine.

• Molecular analysis: Perform ChIP assays to detect HIF-1α binding to the EFNA1 promoter and validate with reporter assays.

• Time-course experiments: Monitor EFNA1 expression changes during acute and chronic hypoxia at both mRNA and protein levels.

• Functional consequences: Assess how hypoxia-induced EFNA1 affects cell migration, invasion, and resistance to apoptosis.

• In vivo validation: Use hypoxia markers (pimonidazole) in tumor sections to correlate hypoxic regions with EFNA1 expression.

Understanding this axis could provide insights into EFNA1's role in aggressive tumor behavior under hypoxic conditions.

What is the relationship between EFNA1 and genetic alterations such as 1p19q codeletion in gliomas?

EFNA1 expression is significantly increased in the 1p19q non-codeletion group of LGG patients . To further investigate this relationship:

• Stratification approach: Categorize glioma samples based on 1p19q status through FISH or molecular testing.

• Expression comparison: Analyze EFNA1 expression between codeletion and non-codeletion groups using appropriate statistical tests.

• Mechanistic investigation: Determine if EFNA1 is directly regulated by genes in the 1p19q regions through knockdown/overexpression studies.

• Prognostic interaction: Assess whether EFNA1 expression modifies the prognostic value of 1p19q codeletion status.

• Therapeutic implications: Evaluate whether EFNA1 expression influences response to treatments typically effective in 1p19q codeleted tumors.

This relationship could provide insights into molecular subtyping and treatment stratification for glioma patients.

How can EFNA1 be effectively targeted for cancer therapy and what are the current methodological challenges?

Developing EFNA1-targeted therapies presents several challenges that researchers should address:

• Antibody-based approaches: Generate and characterize antibodies specific to EFNA1 (avoiding cross-reactivity with other EFNA family members that share 30-40% similarity) .

• Small molecule inhibitors: Design molecules that disrupt EFNA1-EphA receptor interactions, requiring structural biology approaches.

• Gene therapy strategies: Develop siRNA or CRISPR-based approaches for targeted EFNA1 silencing in tumor cells.

• Delivery challenges: Address the membrane-anchored nature of EFNA1 through appropriate targeting strategies.

• Patient selection: Establish clear expression thresholds that predict therapeutic response based on prognostic data.

Rigorous preclinical testing in appropriate models that recapitulate the tumor microenvironment is essential before clinical translation.

What is the value of EFNA1 as a biomarker in different cancer types and how should it be validated?

To establish EFNA1 as a clinically useful biomarker:

• Expression analysis: Quantify EFNA1 levels in large, well-annotated patient cohorts using standardized methods.

• Cutoff determination: Establish optimal expression thresholds for prognostic stratification using ROC curve analysis.

• Multivariate validation: Confirm independent prognostic value through multivariate analyses adjusted for established clinical factors.

• Multi-cohort validation: Verify findings across independent patient cohorts from different geographical regions.

• Integration with other markers: Determine whether EFNA1 provides added value to existing biomarker panels.

For LGG specifically, this approach has established EFNA1 as an independent prognostic factor for patient outcomes .

How should researchers design functional studies to translate EFNA1 findings from bioinformatic analyses to preclinical models?

To effectively translate bioinformatic findings to functional studies:

• Hypothesis generation: Use GSEA results to identify enriched pathways in EFNA1-high vs. EFNA1-low expression groups .

• Cell line selection: Choose models that reflect the cancer type and molecular features identified in bioinformatic analyses.

• Functional assays: Design experiments targeting the specific biological processes identified (e.g., cell proliferation, migration, immune response).

• Validation in multiple models: Confirm findings across different cell lines and patient-derived models.

• In vivo studies: Develop appropriate animal models that recapitulate the clinical features associated with EFNA1 expression.

This translational approach bridges computational findings with experimental validation, strengthening the evidence for EFNA1's biological significance.