CDCA3 Antibody

What is CDCA3 and what are its primary biological functions?

CDCA3 (Cell division cycle-associated protein 3), also known as TOME1 (Trigger of mitotic entry protein 1) or GRCC8 (Gene-rich cluster protein C8), is a 29 kDa F-box-like protein that plays a crucial role in cell cycle regulation. It functions primarily by:

• Participating in E3 ligase complexes that mediate the ubiquitination and degradation of WEE1 kinase at the G2/M phase transition

• Facilitating entry into mitosis through cell cycle checkpoint regulation

• Contributing to various biological activities related to cell cycle progression including DNA replication and nuclear division

The protein contains 286 encoded amino acids and contributes to both physiological and pathological processes by regulating various downstream cytokines .

Which applications are CDCA3 antibodies suitable for?

Based on validated research applications, CDCA3 antibodies are suitable for multiple experimental techniques including:

• Western blotting (WB) for protein expression analysis

• Immunohistochemistry on paraffin-embedded tissues (IHC-P) for tissue localization studies

• Immunofluorescence (IF) and immunocytochemistry (ICC) for cellular localization

Recommended dilutions vary by application:

For optimal IHC results, antigen retrieval with TE buffer pH 9.0 is suggested, although citrate buffer pH 6.0 may serve as an alternative .

What are the recommended positive controls for CDCA3 antibody validation?

When validating CDCA3 antibodies, the following positive controls have been documented as effective:

• Cell lines: HepG2 cells and A431 cells show detectable expression of CDCA3

• Tissue samples: Human placenta, human breast cancer tissue, and human liver cancer tissue have demonstrated positive CDCA3 expression in IHC applications

For negative control validation, using the same samples with either the immunizing peptide or with primary antibody omitted is recommended to confirm antibody specificity .

How is CDCA3 expression associated with cancer progression and prognosis?

CDCA3 has emerged as a significant prognostic biomarker across multiple cancer types:

• Renal Cell Carcinoma (RCC): CDCA3 exhibits lower expression at RNA level in benign tissues but elevated expression in RCC. Higher CDCA3 expression correlates with poor immunotherapy outcomes

• Glioma: Higher CDCA3 expression indicates increased malignancy and poorer prognosis. Expression levels correlate positively with glioma grade, with significantly higher expression in glioblastoma (GBM) compared to low-grade gliomas

• Non-Small Cell Lung Cancer (NSCLC): CDCA3 serves as a prognostic biomarker and potential therapeutic target, with elevated levels enhancing sensitivity to platinum-based chemotherapy

• Other Cancers: Increased CDCA3 expression has been observed in bladder tumors, oral cancer, and hepatocellular carcinoma (HCC)

The protein's involvement in crucial cell cycle checkpoints appears to underlie its role in cancer progression across these different malignancies.

How does CDCA3 expression influence response to immunotherapy in cancer patients?

CDCA3 expression has significant implications for immunotherapy response, particularly in renal cell carcinoma:

These findings suggest CDCA3 expression could serve as a predictive biomarker for selecting patients who might benefit from immunotherapy. The mechanism appears to involve CDCA3's suppression of several immune pathways, including the MHC class II protein complex, potentially facilitating immune evasion in high-expression tumors .

What are the optimal tissue preparation and antigen retrieval methods for CDCA3 immunohistochemistry?

For optimal CDCA3 detection in immunohistochemistry, the following protocol has been validated:

• Tissue Fixation: Standard formalin fixation and paraffin embedding (FFPE) of tissues is appropriate for CDCA3 detection

• Sectioning: 4-5 μm thick sections are typically used for IHC analysis

• Antigen Retrieval:

  • Primary recommendation: TE buffer at pH 9.0

  • Alternative method: Citrate buffer at pH 6.0

  • Heat-induced epitope retrieval (HIER) methods are generally more effective than enzymatic retrieval for nuclear antigens like CDCA3

• Antibody Incubation:

  • Primary antibody dilution: 1:50-1:500 (optimize for specific antibody and tissue)

  • Incubation time: Typically overnight at 4°C or 1-2 hours at room temperature

• Detection System: Standard HRP/DAB detection systems have proven effective for visualizing CDCA3 in tissue sections

The optimization of antigen retrieval conditions is particularly important as CDCA3's nuclear localization may require more rigorous epitope unmasking techniques.

What are the key considerations for Western blot analysis of CDCA3?

For successful Western blot detection of CDCA3, researchers should consider these critical parameters:

• Sample Preparation:

  • Cell lysates from HepG2 cells have been validated as positive controls

  • Appropriate lysis buffers containing protease inhibitors are essential for preserving CDCA3 integrity

• Gel Electrophoresis:

  • Expected molecular weight: 29 kDa for the native protein

  • 10-12% SDS-PAGE gels are typically sufficient for good resolution

• Transfer Conditions:

  • Standard PVDF or nitrocellulose membranes are suitable

  • Transfer efficiency should be verified using reversible staining methods

• Antibody Conditions:

  • Primary antibody dilution: Typically 1:500 for CDCA3 detection

  • Blocking: 5% non-fat milk or BSA in TBST is recommended

  • Secondary antibody selection should match the host species of the primary antibody

• Specificity Controls:

  • Use of immunizing peptide as a competitive inhibitor to confirm band specificity

  • β-Actin is commonly used as a loading control

• Visualization:

  • Both chemiluminescence and fluorescence-based detection methods are suitable

  • Quantification using software like ImageJ allows for comparative analysis

How can CDCA3 expression be effectively quantified in clinical samples?

Quantification of CDCA3 expression in clinical samples can be achieved through several complementary approaches:

• Immunohistochemistry Scoring:

  • Semi-quantitative scoring systems evaluating both staining intensity (0-3) and percentage of positive cells

  • H-score calculation: (1 × % of weakly stained cells) + (2 × % of moderately stained cells) + (3 × % of strongly stained cells)

  • Digital pathology analysis using software like QuPath or ImageJ can provide more objective quantification

• RNA Expression Analysis:

  • RT-qPCR using validated primers specific for CDCA3

  • RNA-seq analysis from tumor samples, with normalization to appropriate housekeeping genes

  • Public datasets (TCGA, CGGA, Rembrandt) provide benchmark expression values across tumor grades

• Protein Quantification:

  • Western blot densitometry analysis normalized to loading controls

  • Tissue microarray (TMA) analysis for high-throughput screening

• Prognostic Assessment:

  • CDCA3 can be included in column charts as a parameter for predicting 3- and 5-year survival risk (C index = 0.86 for glioma)

  • Integration with other molecular markers (e.g., MGMT methylation status and IDH mutation status in gliomas) enhances prognostic accuracy

How does CDCA3 influence the tumor immune microenvironment?

CDCA3 has significant effects on the tumor immune microenvironment, particularly through these mechanisms:

• Immune Pathway Suppression:

  • CDCA3 suppresses several immune pathways, including MHC class II protein complex and MHC protein complex

  • MHC class II proteins are primarily expressed by antigen-presenting cells (dendritic cells, macrophages, B cells) and present exogenous antigens to CD4+ T cells

  • High CDCA3 expression may indicate immune evasion mechanisms in tumors

• Immune Cell Infiltration:

  • Positive correlation between CDCA3 expression and CD8+ T cell infiltration has been observed

  • Significant positive correlation between CDCA3 expression and Th2 cells in kidney cancers (KIRC, KIRP, and KICH)

  • Th2 cells may hinder antitumor immunity by:

    • Promoting tumor growth through cytokine secretion

    • Interfering with cytotoxic CD8+ T cell function

    • Altering the balance of Th1/Th2 responses

• Immunotherapy Response:

  • Low CDCA3 expression correlates with better response to immune checkpoint blockade therapy

  • The balance and cytokine secretion of Th1 and Th2 cells may shift after immune checkpoint blockade therapy in a cancer-dependent manner

These findings suggest that CDCA3 expression analysis could help identify patients likely to benefit from immunotherapy and inform the development of combination strategies targeting both CDCA3 and immune pathways.

What are the key considerations when investigating CDCA3's role in cell cycle regulation?

When investigating CDCA3's function in cell cycle regulation, researchers should consider:

• Interaction with Cell Cycle Checkpoints:

  • CDCA3 has strong associations with key cell cycle checkpoint genes

  • Bioinformatic analyses show correlations between CDCA3 and CDK1, CDK2, CDK4, CDK6, CDKN3, and CDKN2C

  • These interactions suggest CDCA3 could serve as an indicator for cell cycle therapy efficacy

• Experimental Approaches:

  • Flow cytometry for cell cycle phase distribution analysis after CDCA3 knockdown/overexpression

  • Immunoprecipitation studies to identify CDCA3's binding partners in the cell cycle machinery

  • ChIP-seq to determine if CDCA3 regulates cell cycle gene expression

  • Live-cell imaging with fluorescent cell cycle reporters to visualize CDCA3's effects on mitotic entry timing

• Therapeutic Implications:

  • Inhibitors targeting CDK4 and CDK6 have shown effectiveness in tumor therapy

  • CDCA3's close association with these CDKs suggests potential synergistic therapeutic approaches

  • CDCA3 could serve as a novel target for cell cycle-based cancer treatments

• Methodological Considerations:

  • Using synchronized cell populations to precisely determine CDCA3's role at specific cell cycle phases

  • Employing CRISPR/Cas9 genome editing for precise manipulation of CDCA3 expression

  • Utilizing phospho-specific antibodies to detect cell cycle-dependent post-translational modifications of CDCA3

How can CDCA3 expression patterns be integrated with other molecular markers for improved cancer prognostication?

Integration of CDCA3 with other molecular markers can enhance prognostic accuracy through:

• Multi-marker Panels:

  • For gliomas: Combining CDCA3 with established markers like MGMT methylation status and IDH mutation status significantly improves prognostic accuracy

  • For RCC: Integration with immune infiltration markers and PD-L1 expression provides better prediction of immunotherapy response

• Computational Approaches:

  • Machine learning algorithms can identify optimal combinations of markers

  • Nomograms incorporating CDCA3 and other markers can predict 3- and 5-year survival probabilities (C index = 0.86)

• Pathway Analysis Integration:

  • Gene Set Enrichment Analysis (GSEA) and Gene Ontology (GO) analysis reveal that CDCA3's biological processes are primarily concentrated in cell cycle-related activities

  • Integration of this pathway information with expression data provides mechanistic insights into prognostic correlations

• Multi-omics Integration:

  • Combining CDCA3 protein expression (IHC data) with:

    • Transcriptomic data (RNA-seq)

    • Methylation profiles

    • Mutation data

    • Immune profiling (e.g., Th1/Th2 ratios, CD8+ T cell status)

  • This comprehensive approach provides a more nuanced understanding of tumor biology and patient stratification

What are common challenges in CDCA3 antibody applications and how can they be addressed?

Researchers frequently encounter these challenges when working with CDCA3 antibodies:

• Weak or Absent Signal in Western Blots:

  • Possible Causes: Insufficient protein, degradation, inefficient transfer

  • Solutions:

    • Increase protein loading (50-100 μg recommended for detecting endogenous CDCA3)

    • Use fresh lysates with complete protease inhibitor cocktail

    • Optimize transfer conditions for proteins in the 29 kDa range

    • Try longer primary antibody incubation (overnight at 4°C)

• High Background in Immunohistochemistry:

  • Possible Causes: Insufficient blocking, non-specific antibody binding

  • Solutions:

    • Extend blocking time (1-2 hours with 5-10% normal serum)

    • Optimize antibody dilution (start with 1:100 and adjust as needed)

    • Try alternative antigen retrieval methods (compare TE buffer pH 9.0 vs. citrate buffer pH 6.0)

    • Include 0.1-0.3% Triton X-100 in antibody diluent to reduce non-specific binding

• Variability Between Tissue Samples:

  • Possible Causes: Fixation differences, tissue heterogeneity

  • Solutions:

    • Standardize fixation protocols (24 hours in 10% neutral buffered formalin recommended)

    • Use tissue microarrays for comparative studies to ensure identical processing

    • Include internal positive controls (e.g., placenta tissue)

• Inconsistent Results in Different Applications:

  • Possible Causes: Epitope accessibility varies between native and denatured forms

  • Solutions:

    • Select antibodies validated for specific applications

    • For multi-application studies, consider using antibodies recognizing different epitopes

    • Verify results with alternative detection methods (e.g., mRNA expression)

How should researchers interpret contradictory CDCA3 expression data across different experimental platforms?

When faced with contradictory CDCA3 expression data, consider these analytical approaches:

What emerging technologies could advance CDCA3 research in cancer biology?

Several cutting-edge technologies hold promise for deepening our understanding of CDCA3's role in cancer:

• Single-Cell Analysis:

  • Single-cell RNA-seq can reveal heterogeneity in CDCA3 expression within tumors

  • Single-cell proteomics can identify co-expression patterns with other cancer biomarkers

  • Spatial transcriptomics can map CDCA3 expression in the context of tumor microenvironment

• CRISPR-Based Functional Genomics:

  • CRISPR activation/inhibition screens can identify genes that synthetically interact with CDCA3

  • Base editing approaches can introduce specific mutations to assess their impact on CDCA3 function

  • CRISPR-based lineage tracing can track the fate of CDCA3-expressing cells during tumor progression

• Advanced Imaging Techniques:

  • Super-resolution microscopy can visualize CDCA3's subcellular localization with unprecedented detail

  • Live-cell imaging with tagged CDCA3 can reveal dynamic changes during cell cycle progression

  • Multiplex imaging platforms can simultaneously detect CDCA3 and multiple immune markers in tissue sections

• Liquid Biopsy Applications:

  • Circulating tumor DNA analysis for CDCA3 alterations as a non-invasive biomarker

  • Exosomal protein profiling for CDCA3 as a potential blood-based biomarker

  • Integration with other circulating biomarkers for comprehensive prognostic assessment

These technologies could address current limitations in CDCA3 research, particularly regarding tumor heterogeneity, dynamic regulation, and non-invasive monitoring.

What are the most promising therapeutic strategies targeting CDCA3 in cancer?

Based on current understanding of CDCA3's functions, several therapeutic approaches show potential:

• Direct CDCA3 Inhibition:

  • Small molecule inhibitors disrupting CDCA3's E3 ligase complex formation

  • Targeted protein degradation approaches (PROTACs) to selectively degrade CDCA3

  • Peptide-based inhibitors targeting key protein-protein interactions

• Combination Therapies:

  • CDK4/6 inhibitors combined with CDCA3-targeting approaches, leveraging their close association in cell cycle regulation

  • Immune checkpoint inhibitors with CDCA3 modulation to enhance immunotherapy response in high CDCA3-expressing tumors

  • Cell cycle checkpoint inhibitors combined with CDCA3 targeting for synergistic anti-tumor effects

• Biomarker-Guided Treatment Selection:

  • Using CDCA3 expression levels to guide selection of platinum-based chemotherapy in NSCLC

  • Stratifying patients for immunotherapy based on CDCA3 expression and associated immune markers

  • Developing companion diagnostics for CDCA3 to support precision medicine approaches

• RNA-Based Therapeutics:

  • siRNA or antisense oligonucleotides targeting CDCA3 mRNA

  • mRNA vaccines incorporating CDCA3 as a tumor-associated antigen

  • CRISPR-based gene editing to modify CDCA3 expression in adoptive cell therapies