CDCA4 Antibody

What is CDCA4 and why is it important in cancer research?

CDCA4 is a member of the TRIP-Br family of proteins with potential functions in both transcriptional control and cell cycle regulation. It contains homologous sequence motifs including SERTA and a PHD-bromo-binding site that interacts with bromodomain-containing transcriptional cofactors . CDCA4 has gained significance in cancer research due to its consistent overexpression across multiple cancer types and its strong association with poor prognosis. Studies have demonstrated that CDCA4 is significantly upregulated in liver hepatocellular carcinoma (LIHC) , lung adenocarcinoma (LUAD) , and osteosarcoma , making it a valuable biomarker for cancer diagnosis, prognosis, and potentially a therapeutic target.

What are the recommended applications for CDCA4 antibody in research settings?

CDCA4 antibody (such as 11625-1-AP) can be utilized in multiple research applications:

• Western Blot (WB): Recommended dilution of 1:500-1:3000

• Immunohistochemistry (IHC)

• Immunofluorescence (IF)

• Immunoprecipitation (IP)

• Co-Immunoprecipitation (CoIP)

• ELISA

The antibody has been successfully tested in various cell lines including HEK-293, HeLa, MCF-7, and PC-3 cells . For optimal results, it is recommended to titrate the antibody in each testing system as sensitivity may be sample-dependent.

What is the molecular weight of CDCA4 and how does this affect antibody detection?

While the calculated molecular weight of CDCA4 is 26 kDa (241 amino acids), the observed molecular weight in experimental settings is typically 33 kDa . This discrepancy is important for researchers to note when interpreting Western blot results. The difference between calculated and observed molecular weights may be due to post-translational modifications such as phosphorylation or glycosylation. When validating CDCA4 antibodies, researchers should look for bands at approximately 33 kDa rather than the theoretical 26 kDa to confirm specific detection.

How can CDCA4 antibody be utilized in studying cancer immune microenvironment?

CDCA4 expression has been significantly associated with immune cell infiltration in tumor microenvironments. For investigating this relationship:

• Begin with immunohistochemistry using CDCA4 antibody on tumor tissue sections to establish expression levels

• Combine with immune cell markers to perform multiplex immunofluorescence imaging

• Correlate CDCA4 expression with specific immune cell populations

Research has shown that CDCA4 expression negatively correlates with infiltration of Mast cells, Eosinophils, Th17, B cells, T cells, CD8 T cells, and positively correlates with T gamma delta, Th2, and activated DCs . This approach provides insights into how CDCA4 might regulate immune responses in the tumor microenvironment.

What experimental controls are critical when using CDCA4 antibody for co-immunoprecipitation studies?

When performing co-immunoprecipitation (CoIP) experiments with CDCA4 antibody:

• Essential controls:

  • IgG control: Use matched isotype (rabbit IgG) as negative control

  • Input sample: Reserve 5-10% of pre-IP lysate to confirm target protein presence

  • Reverse IP: Perform reciprocal IP using antibody against suspected interaction partner

• Experimental protocol:

  • Prepare cell lysate in IP lysis buffer containing protease inhibitors

  • Centrifuge at 12,000g for 15 minutes at 4°C

  • Incubate supernatant with anti-CDCA4 antibody (5 μg) and magnetic beads overnight at 4°C

  • After washing, elute with 2× SDS-PAGE Sample Loading Buffer at 100°C for 10 minutes

  • Analyze immunocomplexes by SDS/PAGE and immunoblotting

This approach has successfully identified interaction between CDCA4 and CARM1 in NSCLC cells, demonstrating how CDCA4 may regulate autophagy and EMT .

How can CDCA4 knockdown/overexpression be validated using CDCA4 antibody?

To validate genetic manipulation of CDCA4 expression:

For accurate validation, it's essential to perform both mRNA and protein detection, as post-transcriptional regulation may affect correlation between mRNA and protein levels. Studies have employed this comprehensive validation approach to confirm CDCA4 knockdown and overexpression in functional studies examining its role in cell proliferation, migration, and EMT .

How does CDCA4 expression correlate with patient outcomes in different cancer types?

Extensive research has established CDCA4 as a prognostic biomarker across multiple cancer types:

These findings collectively suggest that CDCA4 antibody-based detection methods may serve as valuable prognostic tools in clinical cancer research across different malignancies.

What signaling pathways are associated with CDCA4 in cancer progression?

CDCA4 has been implicated in multiple cancer-related signaling pathways:

• Cell Cycle Regulation:

  • GSEA analysis reveals CDCA4 association with cell cycle progression

  • Knockdown studies show cell cycle arrest in S phase

• PI3K/AKT Pathway:

  • CDCA4 regulates PI3K/AKT signaling by:

    • Decreasing phosphate and tensin homolog (PTEN) expression

    • Increasing p-PI3K and p-AKT levels

  • This pathway is critical for cancer cell proliferation

• Autophagy and EMT:

  • CDCA4 interacts with CARM1 to modulate autophagy

  • Inhibition of CDCA4 induces EMT while inhibiting autophagy

  • CDCA4 suppresses migration and invasion of NSCLC cells via autophagy regulation

When designing experiments to study these pathways, researchers should use CDCA4 antibody in combination with antibodies targeting key pathway proteins to establish mechanistic relationships.

How can CDCA4 antibody be used to analyze immune infiltration patterns in tumors?

To analyze immune infiltration patterns in relation to CDCA4:

• Methodological approach:

  • Perform IHC with CDCA4 antibody on tumor sections

  • Categorize samples into high and low CDCA4 expression groups

  • Use ssGSEA (single-sample Gene Set Enrichment Analysis) to assess immune cell population differences

  • Perform Spearman correlation analysis between CDCA4 expression and immune cell infiltration

• Research findings:

  • CDCA4 expression negatively correlates with:

    • Mast cells, Eosinophils, Th17 cells, B cells, CD8 T cells

    • T central memory, follicular helper T cells

    • NK cells and NK CD56 bright cells

  • CDCA4 expression positively correlates with:

    • T gamma delta, Th2 cells

    • Activated dendritic cells

    • NK CD56 dim cells

• Clinical implications:

  • Reduced CD4+ T cell infiltration with increased CD8+ T cells and B cells is associated with worse prognosis in patients with high CDCA4/5 expression

  • CDCA4 expression correlates with tumor mutational burden (TMB), potentially affecting immunotherapy response

This approach provides insights into how CDCA4 might be involved in modulating the tumor immune microenvironment.

How can researchers troubleshoot discrepancies in CDCA4 antibody specificity across different experimental systems?

When encountering specificity issues with CDCA4 antibody:

• Verification strategies:

  • Validate with positive and negative controls (cell lines known to express or lack CDCA4)

  • Perform blocking peptide competition assay

  • Confirm with orthogonal detection methods (multiple antibodies targeting different epitopes)

  • Use CDCA4 knockdown/knockout samples as negative controls

• Technical considerations:

  • Optimize antibody concentration (1:500-1:3000 for WB)

  • Adjust incubation conditions (time, temperature)

  • Modify blocking reagents (5% BSA recommended)

  • Consider tissue/sample processing methods (fixation can affect epitope recognition)

• Species cross-reactivity:

  • The 11625-1-AP antibody has confirmed reactivity with human and mouse samples

  • When using in other species, validation studies are essential

These approaches help ensure that observed signals are specific to CDCA4 rather than non-specific binding.

What are the optimal conditions for using CDCA4 antibody in different experimental applications?

Optimal conditions vary by application:

• Western Blotting:

  • Dilution: 1:500-1:3000

  • Blocking: 5% BSA in TBS-T

  • Primary antibody incubation: Overnight at 4°C

  • Observed molecular weight: 33 kDa

  • Positive controls: HEK-293, HeLa, MCF-7, PC-3 cells

• Immunohistochemistry:

  • Antigen retrieval: Citrate buffer pH 6.0, heat-induced

  • Blocking: 5% BSA

  • Primary antibody: Follow manufacturer's recommended dilution

  • Detection: HRP-conjugated secondary antibody system

• Immunoprecipitation:

  • Lysis buffer: IP Lysis buffer with protease inhibitors

  • Antibody amount: 5 μg per reaction

  • Incubation: Overnight at 4°C with magnetic beads

  • Washing: Multiple washes to reduce background

  • Elution: 2× SDS-PAGE Sample Loading Buffer at 100°C

• Storage and handling:

  • Store at -20°C

  • Stable for one year after shipment

  • Contains 0.02% sodium azide and 50% glycerol pH 7.3

Each application may require optimization based on specific sample types and experimental conditions.

How should researchers interpret discrepancies between mRNA and protein levels of CDCA4 in experimental settings?

Discrepancies between CDCA4 mRNA and protein levels are common and should be systematically analyzed:

• Potential causes:

  • Post-transcriptional regulation (miRNAs, RNA-binding proteins)

  • Post-translational modifications affecting protein stability

  • Protein degradation pathways (ubiquitin-proteasome system)

  • Technical variability in detection methods

• Recommended validation approach:

  • Perform both RT-PCR and Western blot analysis

  • Include time-course experiments to capture temporal dynamics

  • Assess correlation between mRNA and protein across multiple samples

  • Consider measuring protein half-life using cycloheximide chase assay

• Interpretation framework:

  • Strong correlation: Suggests transcriptional regulation predominates

  • Weak correlation: Indicates significant post-transcriptional regulation

  • Inverse correlation: May suggest negative feedback mechanisms

Research on CDCA4 in lung adenocarcinoma has employed comprehensive validation through multiple methods including RT-PCR, Western blotting, and IHC to establish expression patterns , demonstrating the importance of using complementary approaches for accurate interpretation.

How can bioinformatic analyses complement experimental CDCA4 antibody studies?

Integrating bioinformatic analyses with experimental CDCA4 antibody studies provides comprehensive insights:

• Public database utilization:

  • TCGA database: Access expression data from 513 LUAD patients to validate antibody findings

  • GTEX database: Compare with normal tissue expression patterns

  • GEPIA database: Investigate pathological stages and survival correlation

• Analytical approaches:

  • GSEA (Gene Set Enrichment Analysis): Identify pathways associated with CDCA4

  • CIBERSORT algorithm: Determine immune cell scores in relation to CDCA4 expression

  • ESTIMATE program: Calculate immune scores, stromal scores, and estimate scores

• Integration methodology:

  • Validate antibody-based protein detection with mRNA expression data

  • Correlate protein levels with clinical outcomes and molecular features

  • Use protein-protein interaction networks to predict functional relationships

Studies have successfully employed this integrated approach to identify CDCA4's role in cancer progression and immune infiltration , demonstrating how computational methods enhance and extend antibody-based findings.

What statistical approaches are most appropriate for analyzing CDCA4 expression data in relation to clinical outcomes?

Robust statistical analysis of CDCA4 expression data requires:

Research has demonstrated that patients with high CDCA4 expression have significantly worse prognosis, with multivariate analysis confirming CDCA4 as an independent risk factor for poor outcomes in lung adenocarcinoma (DSS HR=1.674; 95% CI=1.112-2.521, P=0.014; OS HR=1.427, 95% CI=1.017-2.003, P=0.04) .

How can researchers address potential confounding factors when interpreting CDCA4 antibody results in tissue samples?

To address confounding factors in CDCA4 tissue analysis:

• Sample-related confounders:

  • Tissue heterogeneity: Use microdissection to isolate specific regions

  • Sample processing variables: Standardize fixation time and conditions

  • Batch effects: Include appropriate controls in each experimental batch

  • Patient demographics: Stratify analysis by age, gender, and other relevant factors

• Experimental design strategies:

  • Use paired tumor-normal samples when possible

  • Include multiple control tissues from different sources

  • Perform technical replicates to assess reproducibility

  • Document and report all pre-analytical variables

• Analytical approaches:

  • Multivariate analysis to adjust for known confounders

  • Propensity score matching to balance groups

  • Sensitivity analysis to test robustness of findings

Research has shown that CDCA4 expression correlates with gender and age in osteosarcoma patients , highlighting the importance of considering these variables in analysis. Similarly, studies in lung adenocarcinoma have employed multivariate analysis to establish CDCA4 as an independent prognostic factor while controlling for other clinicopathological variables .