LDLRAD2 Antibody

What is LDLRAD2 and why is it significant in cancer research?

LDLRAD2 (Low Density Lipoprotein Receptor Class A Domain Containing 2) is a transmembrane protein located on chromosome 1p36.12 that has emerged as a significant biomarker in cancer research, particularly gastric cancer. Research indicates that LDLRAD2 expression is significantly upregulated in gastric cancer tissues and correlates with poor prognosis in patients . Mechanistically, LDLRAD2 promotes epithelial-mesenchymal transition (EMT), migration, invasion, and metastasis of gastric cancer cells by interacting with the Wnt/β-catenin signaling pathway . When selecting an LDLRAD2 antibody for cancer research, prioritize antibodies validated for immunohistochemistry with human tissue samples and those demonstrating specific staining patterns correlating with clinical outcomes.

How does LDLRAD2 expression correlate with clinical features in gastric cancer?

LDLRAD2 expression shows significant correlation with aggressive clinical features in gastric cancer. According to research data, high LDLRAD2 expression is associated with:

What are the optimal protocols for using LDLRAD2 antibodies in Western blot applications?

For optimal Western blot results with LDLRAD2 antibodies:

• Sample preparation: Harvest cells and lyse in RIPA buffer supplemented with protease inhibitors. Pass the lysate 10 times through a number 7 needle to ensure complete lysis .

• Protein quantification: Determine protein concentration using the Lowry method (Bio-Rad) .

• Gel electrophoresis: Load 20-30 μg of protein per lane on 8-10% SDS-PAGE gels.

• Transfer conditions: Use nitrocellulose membranes at 100V for 90 minutes in cold transfer buffer.

• Blocking: Block membranes with 5% non-fat dry milk in TBST for 1 hour at room temperature.

• Primary antibody: Dilute LDLRAD2 antibody 1:1000 in blocking solution and incubate overnight at 4°C.

• Washing: Wash 3 times for 10 minutes each with TBST.

• Detection: Use HRP-conjugated secondary antibodies and enhanced chemiluminescence detection.

Note that LDLRAD2 typically appears at approximately 95-100 kDa on Western blots, and validation should include positive controls from gastric cancer cell lines with known LDLRAD2 expression levels, such as BGC-823 (high expression) and MGC-803 (low expression) .

How can I optimize immunohistochemical staining with LDLRAD2 antibodies for prognostic studies?

For robust prognostic studies using LDLRAD2 immunohistochemistry:

• Tissue processing: Use formalin-fixed paraffin-embedded (FFPE) tissue sections at 4-5 μm thickness.

• Antigen retrieval: Perform heat-induced epitope retrieval in citrate buffer (pH 6.0) for 20 minutes.

• Blocking: Block endogenous peroxidase activity with 3% H₂O₂ for 10 minutes, followed by protein blocking.

• Primary antibody: Incubate with LDLRAD2 antibody at optimized concentration (typically 1:100-1:200) overnight at 4°C.

• Detection system: Use a high-sensitivity polymer detection system for visualization.

• Scoring system: Implement a semi-quantitative scoring system combining staining intensity (0-3) and percentage of positive cells (0-100%), with final scores ranging from 0-300.

• Cutoff determination: Define high versus low expression based on median scores from your cohort, or correlate with survival data to identify the most clinically relevant threshold.

• Controls: Include both positive controls (gastric cancer tissue with known high LDLRAD2 expression) and negative controls (antibody diluent only) in each batch to ensure staining reliability and reproducibility .

How does LDLRAD2 mechanistically interact with the Wnt/β-catenin pathway in cancer progression?

LDLRAD2 functions as a positive regulator of the Wnt/β-catenin pathway through a specific protein-protein interaction mechanism. Research has demonstrated that:

• LDLRAD2 directly interacts with Axin1 as confirmed by co-immunoprecipitation assays .

• This interaction prevents Axin1 from binding to cytoplasmic β-catenin, thereby inhibiting the β-catenin degradation complex .

• Consequently, β-catenin accumulates in the cytoplasm and translocates to the nucleus, as evidenced by increased nuclear β-catenin in LDLRAD2-overexpressing cells .

• Nuclear β-catenin activates transcription of downstream Wnt target genes including c-myc, VEGF, Twist, and MMP7, which promote cancer cell proliferation, invasion, and metastasis .

This mechanism can be verified using antibody-based techniques including:

• Co-immunoprecipitation assays with LDLRAD2 antibodies to pull down protein complexes

• Chromatin immunoprecipitation (ChIP) assays to assess β-catenin binding to target gene promoters

• Immunofluorescence microscopy to visualize β-catenin nuclear translocation

For investigating protein interactions, use antibodies specifically validated for immunoprecipitation applications, with suitable controls to rule out non-specific binding.

What are the differences in epitope recognition between monoclonal and polyclonal LDLRAD2 antibodies for research applications?

Monoclonal and polyclonal LDLRAD2 antibodies differ significantly in their epitope recognition and research applications:

Monoclonal LDLRAD2 antibodies:

• Recognize a single epitope with high specificity

• Provide consistent results across different batches

• Superior for detecting specific domains (e.g., the LDL receptor class A domain)

• Less sensitive to conformational changes in the protein

• Optimal for quantitative applications and specific domain targeting

• May fail to detect LDLRAD2 if the single epitope is masked or altered

Polyclonal LDLRAD2 antibodies:

• Recognize multiple epitopes across the protein

• Higher sensitivity for detecting low-abundance LDLRAD2

• Better for detecting denatured protein in Western blots

• More robust against minor protein modifications

• Useful for initial characterization studies

• May exhibit higher background and cross-reactivity

For domain-specific studies investigating LDLRAD2 interactions with Axin1 or β-catenin, monoclonal antibodies targeting relevant domains provide more precise results. For general detection of LDLRAD2 expression in clinical samples, polyclonal antibodies may offer higher sensitivity, particularly in tissues with lower expression levels.

How can I design knockdown/knockout experiments to validate LDLRAD2 antibody specificity?

A robust validation strategy for LDLRAD2 antibodies should include:

• siRNA knockdown approach:

  • Transfect cells with LDLRAD2-specific siRNA (typically 3 different sequences)

  • Include scrambled siRNA as negative control

  • Confirm knockdown efficiency at mRNA level using qRT-PCR

  • Perform Western blot using LDLRAD2 antibody

  • A specific antibody will show significantly reduced signal in knockdown samples

• CRISPR/Cas9 knockout approach:

  • Design sgRNAs targeting early exons of LDLRAD2

  • Generate knockout cell lines using CRISPR/Cas9

  • Confirm knockout by genomic sequencing

  • Analyze by Western blot and immunofluorescence

  • Complete signal loss should occur with specific antibodies

• Overexpression validation:

  • Transfect cells with LDLRAD2 expression vector

  • Use empty vector as control

  • Confirm increased LDLRAD2 expression by Western blot

  • This validates antibody's ability to detect increased target levels

• Multi-technique confirmation:

  • Compare results across Western blot, immunofluorescence, and flow cytometry

  • Consistent reduction/loss of signal across methods confirms specificity

Based on the literature, BGC-823 (high LDLRAD2 expression) and MGC-803 (low expression) gastric cancer cell lines provide excellent model systems for such validation studies .

What are the best strategies to resolve discrepancies between LDLRAD2 protein levels detected by antibodies versus mRNA expression data?

When facing discrepancies between LDLRAD2 protein and mRNA levels:

• Methodological verification:

  • Ensure antibody specificity using the validation approaches described in FAQ 4.1

  • Verify primer specificity for qRT-PCR through melt curve analysis and sequencing

  • Check for potential LDLRAD2 splice variants that might be detected differentially

• Post-transcriptional regulation analysis:

  • Investigate microRNA-mediated regulation by performing microRNA profiling

  • Assess protein stability using cycloheximide chase assays

  • Measure protein half-life under different conditions

• Translational efficiency assessment:

  • Perform polysome profiling to analyze LDLRAD2 mRNA translation efficiency

  • Investigate ribosome occupancy using Ribo-seq approaches

• Protein degradation pathways:

  • Treat cells with proteasome inhibitors (MG132) or lysosomal inhibitors (chloroquine)

  • Monitor LDLRAD2 protein accumulation by Western blot

  • Determine if protein degradation mechanisms affect LDLRAD2 levels

• Technical considerations:

  • Use multiple antibodies targeting different epitopes

  • Employ absolute quantification methods for both protein (SILAC) and mRNA (digital PCR)

  • Consider single-cell analysis to assess cell-to-cell variability

When analyzing gastric cancer samples, research has shown that while both LDLRAD2 mRNA and protein levels are elevated compared to normal tissues, the correlation between them may vary across patients, potentially due to tumor heterogeneity and post-transcriptional regulation mechanisms .

How can LDLRAD2 antibodies be utilized in multiplexed imaging platforms for tumor microenvironment analysis?

LDLRAD2 antibodies can be integrated into multiplexed imaging platforms through:

• Multiplex immunofluorescence (mIF):

  • Conjugate LDLRAD2 antibodies with specific fluorophores

  • Combine with antibodies against other markers (E-cadherin, N-cadherin, β-catenin)

  • Implement tyramide signal amplification for enhanced sensitivity

  • Use multispectral imaging to resolve overlapping signals

  • Analyze co-localization patterns of LDLRAD2 with EMT markers

• Imaging mass cytometry (IMC):

  • Label LDLRAD2 antibodies with rare earth metals

  • Simultaneously detect >40 proteins on a single tissue section

  • Analyze spatial distribution in relation to immune cells and stromal components

  • Perform neighborhood analysis to identify cellular interactions

• Cyclic immunofluorescence (CycIF):

  • Use repeated rounds of staining, imaging, and signal elimination

  • Include LDLRAD2 antibodies in appropriate cycles

  • Build comprehensive spatial maps of protein expression

• Analysis considerations:

  • Implement machine learning algorithms for pattern recognition

  • Quantify LDLRAD2 expression in specific cell populations

  • Correlate spatial patterns with clinical outcomes

This approach can reveal how LDLRAD2-expressing cancer cells interact with the tumor microenvironment and whether spatial distribution patterns correlate with metastatic potential, particularly in gastric cancer where LDLRAD2 has been shown to promote invasion and metastasis through Wnt/β-catenin signaling .

What are the considerations for developing therapeutic antibodies targeting LDLRAD2 in cancer?

Development of therapeutic antibodies targeting LDLRAD2 requires addressing several key considerations:

• Target validation:

  • Confirm overexpression in tumor versus normal tissues across multiple cohorts

  • Validate functional role through in vivo models (xenografts with LDLRAD2 knockdown/overexpression)

  • Determine whether LDLRAD2 is accessible (surface expression) for antibody binding

• Epitope selection:

  • Target domains critical for LDLRAD2-Axin1 interaction

  • Focus on regions that mediate Wnt/β-catenin pathway activation

  • Consider structural biology approaches to identify optimal binding sites

• Antibody engineering:

  • Develop humanized or fully human antibodies to minimize immunogenicity

  • Consider format options (IgG, Fab, scFv) based on tissue penetration requirements

  • Evaluate potential for antibody-drug conjugates if internalization occurs

• Functional screening:

  • Test candidates for ability to block LDLRAD2-Axin1 interaction

  • Assess inhibition of β-catenin nuclear translocation

  • Measure effects on EMT markers and invasion in vitro

  • Evaluate anti-metastatic potential in in vivo models

• Combination strategies:

  • Test synergy with established Wnt pathway inhibitors

  • Evaluate combination with immune checkpoint inhibitors

  • Investigate potential for overcoming chemotherapy resistance

Given that LDLRAD2 promotes gastric cancer progression through a well-defined mechanism involving Wnt/β-catenin pathway activation , therapeutic antibodies blocking this interaction represent a rational approach for targeted therapy, particularly for patients with high LDLRAD2 expression who currently have poor prognosis.

How should researchers interpret varying staining patterns observed with LDLRAD2 antibodies in different tissue types?

When interpreting variable LDLRAD2 staining patterns:

• Subcellular localization differences:

  • Predominantly membrane staining: Indicates normal functional localization

  • Increased cytoplasmic staining: May suggest internalization or trafficking alterations

  • Nuclear staining: Requires validation as potential non-specific binding

  • Compare patterns to known LDLRAD2 biology (expected membrane localization)

• Tissue-specific considerations:

  • Normal tissues: Typically low expression with membranous pattern

  • Gastric cancer: Increased expression with both membranous and cytoplasmic patterns

  • Other cancers: Document patterns for comparison with functional studies

• Heterogeneity analysis:

  • Quantify percentage of positive cells within samples

  • Analyze intra-tumoral heterogeneity patterns

  • Correlate with histological subtypes (intestinal vs. diffuse in gastric cancer)

  • Map expression to invasive fronts versus tumor centers

• Technical validation:

  • Confirm specificity with multiple antibodies targeting different epitopes

  • Validate unusual patterns with orthogonal techniques (in situ hybridization)

  • Include appropriate positive controls (e.g., BGC-823 cells with known high expression)

• Interpretation framework:

  • Develop a standardized scoring system incorporating both intensity and pattern

  • Consider digital pathology approaches for quantitative assessment

  • Correlate patterns with molecular features (Wnt pathway activation markers)

Studies have shown that in gastric cancer, LDLRAD2 expression is not uniform across all cases, with variations correlating with Lauren classification (intestinal vs. diffuse) and clinical stage , suggesting biological significance to these pattern differences.

What statistical approaches are most appropriate for analyzing LDLRAD2 expression data in patient cohorts?

For robust statistical analysis of LDLRAD2 expression in patient cohorts: