ZNF582 Antibody

What is ZNF582 and why is it significant in cancer research?

ZNF582 (Zinc Finger Protein 582) is a transcription factor that has emerged as a significant biomarker in cervical cancer research. Studies have demonstrated that ZNF582 gene methylation has high diagnostic value for detecting cervical intraepithelial neoplasia type III and worse (CIN3+), with a pooled sensitivity of 0.71 (95% CI: 0.67–0.75) and specificity of 0.81 (95% CI: 0.79–0.83) . Beyond its diagnostic potential, ZNF582 plays a crucial role in treatment response, as overexpression of ZNF582 protein has been associated with increased resistance to radiation and chemotherapy in cervical cancer cell lines . This dual significance in both diagnosis and treatment response prediction makes ZNF582 a valuable target for cancer research.

What are the validated applications for ZNF582 antibody in cancer research?

ZNF582 antibody has been validated for multiple applications in cancer research:

• Western Blotting: Successfully used at 1:1,000 dilution with overnight incubation at 4°C for protein quantification in cell lysates .

• Immunohistochemistry (IHC): Effectively applied at 1:200 dilution with 1-hour room temperature incubation to visualize protein expression in tissue sections .

• Immunofluorescence (IF): Used at 1:200 dilution to examine subcellular localization of ZNF582 protein .

• Mechanistic Studies: Applied in functional investigations to study how ZNF582 protein expression affects treatment response in cancer cells .

What is the relationship between ZNF582 gene methylation and protein expression?

Research has established a clear inverse relationship between ZNF582 gene methylation and protein expression:

• Negative ZNF582 gene methylation status correlates with high ZNF582 protein expression

• Positive ZNF582 gene methylation status correlates with low ZNF582 protein expression

This relationship is particularly significant in the context of cervical adenocarcinoma (CAC), where patients with negative ZNF582 methylation (thus higher protein expression) demonstrated worse prognoses and increased resistance to chemoradiotherapy . This inverse correlation provides valuable insights into the epigenetic regulation of ZNF582 and its functional consequences in cancer biology.

What evidence supports ZNF582 as a biomarker for cervical cancer?

Several lines of evidence support ZNF582 as a biomarker for cervical cancer:

The data show that sequential combined testing of HPV DNA and ZNF582 methylation achieved significantly higher diagnostic accuracy than HPV DNA testing alone . Additionally, ZNF582 methylation levels were found to be significantly higher in cervical cancer tissues compared to non-cancer tissues, particularly in adenosquamous carcinoma compared to adenocarcinoma .

What is the recommended protocol for detecting ZNF582 protein using Western blot?

The following optimized protocol is recommended for detecting ZNF582 protein in cell lines using Western blot:

• Sample Preparation:

  • Lyse cells in RIPA buffer supplemented with protease inhibitors

  • Incubate on ice for 30 minutes with occasional vortexing

  • Centrifuge at 14,000 × g for 15 minutes at 4°C

  • Determine protein concentration using BCA assay

• SDS-PAGE and Transfer:

  • Load 20-50 μg of protein per lane on an SDS-PAGE gel

  • Transfer to nitrocellulose membranes at appropriate voltage

• Antibody Incubation:

  • Block membrane in 5% nonfat milk in TBST for 1 hour at room temperature

  • Incubate with ZNF582 primary antibody (1:1,000 dilution) overnight at 4°C

  • Wash 3 times with TBST

  • Incubate with HRP-conjugated secondary antibody for 1.5 hours

• Detection:

  • Apply ECL substrate and detect signal using an imaging system

  • Use β-actin or GAPDH as loading control

  • Quantify band intensity using appropriate software

This protocol has been successfully used to detect ZNF582 protein in Hela cells and to measure expression changes following gene transfection .

How should I optimize ZNF582 antibody for immunohistochemistry in tissue samples?

Optimizing ZNF582 antibody for immunohistochemistry requires attention to several methodological factors:

• Tissue Processing:

  • Use appropriate fixation (e.g., 10% neutral-buffered formalin)

  • Section tissues at optimal thickness (4-5 μm)

• Antigen Retrieval:

  • Perform heat-induced epitope retrieval using appropriate buffer

  • Optimize retrieval time and temperature

• Blocking and Antibody Incubation:

  • Block endogenous peroxidase activity

  • Block non-specific binding with appropriate serum

  • Incubate with ZNF582 primary antibody at 1:200 dilution for 1 hour at room temperature

• Detection and Counterstaining:

  • Apply appropriate detection system (HRP/DAB)

  • Counterstain, dehydrate, and mount

• Controls:

  • Include positive control (tissue known to express ZNF582)

  • Include negative control (primary antibody omitted)

  • Consider including tissues with known methylation status as reference

This protocol has been successfully applied in cervical adenocarcinoma research and may require adjustment based on specific laboratory conditions .

How can I use ZNF582 antibody to study treatment resistance mechanisms?

Investigating ZNF582's role in treatment resistance involves several experimental approaches:

• Gene Modification Studies:

  • Transfect cells with ZNF582 expression vectors to establish overexpression models

  • Use ZNF582 antibody to confirm successful transfection

  • Compare treatment response between transfected and control cells

• Functional Assays:

  • Perform MTT assays to evaluate cell viability after treatment in cells with different ZNF582 expression levels

  • Use colony formation assays to assess radiation sensitivity

  • Apply flow cytometry to analyze cell cycle distribution and apoptosis

• Expression Analysis in Clinical Samples:

  • Compare ZNF582 protein expression in treatment-resistant versus treatment-sensitive tumors

  • Analyze expression patterns before and after neoadjuvant chemoradiotherapy (NCRT)

  • Correlate expression with clinical outcomes

Research has demonstrated that overexpression of ZNF582 protein increases resistance to radiation and cisplatin treatment in Hela cells . Additionally, patients negative for ZNF582 methylation (with higher protein expression) showed worse prognoses, indicating a potential mechanism for treatment resistance .

What controls should be included when using ZNF582 antibody?

Proper controls are essential when using ZNF582 antibody:

• Positive Controls:

  • ZNF582-transfected cells with confirmed overexpression

  • Cervical adenocarcinoma tissues with known negative methylation status (high expression)

  • Cell lines with established ZNF582 expression

• Negative Controls:

  • Primary antibody omission to assess secondary antibody specificity

  • Isotype control antibody

  • Tissues with confirmed ZNF582 methylation (low expression)

• Technical Controls:

  • Western Blot: Loading control (β-actin, GAPDH); molecular weight marker

  • IHC/IF: Adjacent normal tissue; antigen competition controls

• Validation Controls:

  • Correlation between protein detection and methylation status

  • Comparison of results from multiple detection methods

These controls ensure reliable and interpretable results when using ZNF582 antibody across different experimental applications.

How can I design experiments to investigate the relationship between ZNF582 methylation and protein expression?

Designing experiments to investigate this relationship requires an integrated approach:

• Paired Methylation and Protein Analysis:

  • Divide tissue samples for parallel methylation and protein analysis

  • Assess ZNF582 methylation using quantitative methylation-specific PCR (qMSP)

  • Determine protein expression using Western blot or IHC with ZNF582 antibody

  • Calculate correlation coefficients between methylation index and protein levels

• In Vitro Demethylation Studies:

  • Treat cells with DNA methyltransferase inhibitors

  • Confirm demethylation effect using qMSP

  • Measure changes in ZNF582 protein expression using Western blot

• Clinical Sample Analysis:

  • Stratify patient samples based on methylation status

  • Compare protein expression between methylation-positive and methylation-negative groups

  • Correlate with clinical parameters and outcomes

Published research has established that negative ZNF582 gene methylation status correlates with high protein expression, while positive methylation status correlates with low protein expression in cervical adenocarcinoma tissues .

What role does ZNF582 play in chemoradiotherapy resistance?

Evidence indicates that ZNF582 plays a significant role in chemoradiotherapy resistance:

• Clinical Evidence:

  • Patients negative for ZNF582 methylation (with higher protein expression) had worse prognoses

  • ZNF582 methylation levels were reduced in patients who received neoadjuvant chemoradiotherapy (NCRT) compared to those who did not

• Experimental Evidence:

  • Overexpression of ZNF582 protein in Hela cells increased resistance to radiation

  • ZNF582-overexpressing cells showed increased resistance to cisplatin treatment

• Potential Mechanisms:

  • ZNF582 may regulate genes involved in DNA damage repair

  • It might influence apoptotic pathways in response to treatment

  • ZNF582 could affect cell cycle progression under treatment stress

This evidence suggests that ZNF582 could serve as both a predictive biomarker for treatment response and a potential therapeutic target for overcoming resistance in cervical cancer.

How can ZNF582 antibody be integrated into multiplex biomarker studies?

ZNF582 antibody can be effectively integrated into multiplex biomarker studies through:

• Multiplex Immunohistochemistry/Immunofluorescence:

  • Combine ZNF582 antibody with antibodies against other cancer biomarkers

  • Employ sequential staining protocols with appropriate blocking steps

  • Use different fluorophores for distinct visualization

  • Apply spectral unmixing for signal separation

• Multi-parameter Flow Cytometry:

  • Include ZNF582 antibody in panels with proliferation and apoptosis markers

  • Correlate ZNF582 expression with other cellular parameters

  • Analyze subpopulations based on multiple marker expression

• Tissue Microarray Analysis:

  • Apply ZNF582 antibody alongside other biomarkers on tissue microarrays

  • Develop quantitative scoring systems for protein expression

  • Perform cluster analysis to identify biomarker patterns

• Integrated Molecular Testing:

  • Combine ZNF582 protein detection with methylation analysis

  • Correlate with HPV testing and other molecular markers

  • Develop comprehensive diagnostic algorithms

Research has shown that sequential combined testing of HPV DNA and ZNF582 methylation achieved higher diagnostic accuracy than single tests , suggesting the value of integrated biomarker approaches.

What factors might explain discrepancies between ZNF582 methylation status and protein expression?

Several factors might explain discrepancies between ZNF582 methylation and protein expression:

• Partial Methylation Effects:

  • Incomplete methylation across CpG islands may permit some transcription

  • Specific CpG sites may have different functional impacts

• Post-transcriptional Regulation:

  • microRNAs or RNA-binding proteins may regulate ZNF582 mRNA stability

  • Alternative splicing could affect protein production

• Post-translational Modifications:

  • Protein degradation pathways may affect ZNF582 protein stability

  • Modifications may influence antibody epitope recognition

• Intratumoral Heterogeneity:

  • Different cell populations within samples may show varied expression patterns

  • Sampling location might affect observed methylation-expression relationships

• Treatment Effects:

  • Chemoradiotherapy can alter methylation patterns, as shown by reduced ZNF582 methylation levels in NCRT patients

  • Dynamic changes may occur during disease progression or treatment

Understanding these factors is crucial for correctly interpreting ZNF582 data in research and clinical applications.

How does ZNF582 testing compare with traditional cervical cancer screening methods?

ZNF582 testing offers several advantages compared to traditional screening methods:

The data demonstrate that sequential combined testing of HPV DNA and ZNF582 methylation achieved significantly improved diagnostic accuracy (AUC: 0.876) compared to HPV DNA testing alone (AUC: 0.669) . The pooled diagnostic odds ratio (DOR) for ZNF582 methylation was 12.72, indicating good discriminatory test performance for CIN3+ detection .

How might ZNF582 detection be integrated into personalized medicine approaches?

ZNF582 detection has significant potential for integration into personalized medicine:

• Treatment Response Prediction:

  • ZNF582 expression status could guide treatment selection

  • Patients with high expression (methylation-negative) might benefit from intensified treatment regimens due to potential resistance

• Risk Stratification:

  • ZNF582 methylation status can help stratify patients by risk level

  • Combined with other biomarkers, it could identify high-risk subgroups

• Therapy Monitoring:

  • Changes in ZNF582 methylation levels during treatment could serve as response markers

  • Research has shown methylation levels are reduced in patients receiving chemoradiotherapy

• Target Development:

  • Understanding ZNF582's role in treatment resistance could lead to targeted therapies

  • Inhibiting ZNF582 might restore sensitivity to standard treatments

• Integrated Testing Algorithms:

  • Development of multi-marker panels incorporating ZNF582

  • Sequential testing strategies with HPV and other biomarkers

The combination of diagnostic and prognostic value makes ZNF582 particularly valuable for personalized approaches to cervical cancer management.

What are the potential applications of ZNF582 antibody in studying other cancer types?

While ZNF582 research has primarily focused on cervical cancer, its antibody has potential applications in studying other cancer types:

• HPV-Related Cancers:

  • Head and neck squamous cell carcinomas

  • Anal and penile cancers

  • Vulvar and vaginal cancers

• Other Gynecological Cancers:

  • Ovarian cancer

  • Endometrial cancer

  • Investigation of common resistance mechanisms

• Epithelial Cancers with Methylation Aberrations:

  • Lung cancer

  • Colorectal cancer

  • Exploration of epigenetic regulation similarities

• Comparative Oncology Studies:

  • Investigation of ZNF582 function across cancer types

  • Identification of common pathways affected by ZNF582

The mechanisms by which ZNF582 contributes to treatment resistance in cervical cancer may be relevant to other malignancies, making cross-cancer investigations valuable for understanding broader patterns of treatment response.

How can researchers validate ZNF582 as a predictive biomarker for treatment response?

Validating ZNF582 as a predictive biomarker requires a systematic approach:

• Retrospective Cohort Studies:

  • Analyze archived samples from patients with known treatment outcomes

  • Compare ZNF582 expression between responders and non-responders

  • Calculate sensitivity, specificity, and predictive values

• Prospective Clinical Trials:

  • Measure baseline ZNF582 expression before treatment

  • Monitor response according to standardized criteria

  • Perform multivariate analysis to control for confounding factors

• Mechanistic Validation:

  • Conduct in vitro studies with cell lines of varying ZNF582 expression levels

  • Perform in vivo studies using patient-derived xenografts

  • Identify downstream targets and pathways

• Technical Validation:

  • Standardize detection methods across laboratories

  • Establish reference materials and quality controls

  • Determine clinically relevant cutoff values

• Independent Validation:

  • Test in diverse patient populations

  • Validate in multiple healthcare settings

  • Confirm reproducibility across different testing platforms

Initial research demonstrating that ZNF582 overexpression increases resistance to radiation and cisplatin treatment provides a strong foundation for further validation studies.

What are common challenges when using ZNF582 antibody in research applications?

Researchers may encounter several challenges when using ZNF582 antibody:

• Variable Expression Levels:

  • Expression depends on methylation status, which varies between samples

  • May require optimized detection methods for low-expressing samples

• Specificity Concerns:

  • Potential cross-reactivity with other zinc finger proteins

  • Requires careful validation with appropriate controls

• Technical Variability:

  • Lot-to-lot variations in antibody performance

  • Fixation and processing effects on epitope accessibility

• Heterogeneous Expression:

  • Intratumoral variation in expression patterns

  • Requires careful sampling and analysis strategies

• Treatment-Induced Changes:

  • ZNF582 methylation levels can be altered by chemoradiotherapy

  • Timing of sample collection becomes critical

Understanding these challenges allows researchers to implement appropriate controls and optimization strategies to ensure reliable results.

How can I optimize ZNF582 antibody detection in samples with low protein expression?

For samples with low ZNF582 protein expression, consider these optimization strategies:

• Sample Preparation Enhancement:

  • Use stronger extraction buffers

  • Increase protein concentration through precipitation methods

  • Consider subcellular fractionation to enrich for nuclear proteins

• Signal Amplification:

  • Employ tyramide signal amplification for immunohistochemistry

  • Use high-sensitivity detection systems for Western blot

  • Consider biotin-streptavidin systems for signal enhancement

• Antibody Optimization:

  • Reduce antibody dilution (e.g., from 1:1,000 to 1:500 for Western blot)

  • Extend incubation time (overnight at 4°C)

  • Test different antibody clones if available

• Protocol Modifications:

  • Optimize antigen retrieval conditions for IHC

  • Extend exposure time for Western blot imaging

  • Use PVDF membranes instead of nitrocellulose for greater protein binding

• Consider Methylation Status:

  • Samples with high ZNF582 methylation naturally have lower protein expression

  • Use methylation status to anticipate expression levels and adjust protocols accordingly

These approaches can help detect ZNF582 protein even in samples with low expression due to methylation or other regulatory mechanisms.

What metrics should be used to evaluate ZNF582 as a diagnostic or prognostic biomarker?

Comprehensive evaluation of ZNF582 as a biomarker should include these metrics:

• Diagnostic Performance:

  • Sensitivity and specificity for detecting disease

  • Positive and negative predictive values

  • Area under the ROC curve (AUC)

  • Diagnostic odds ratio (DOR)

• Prognostic Performance:

  • Hazard ratios from Cox regression analysis

  • Kaplan-Meier survival curves stratified by ZNF582 status

  • Concordance index (C-index)

  • Net reclassification improvement

• Technical Performance:

  • Reproducibility (intra- and inter-laboratory)

  • Precision (repeatability)

  • Analytical sensitivity and specificity

  • Stability across sample processing methods

• Clinical Utility:

  • Impact on clinical decision-making

  • Cost-effectiveness

  • Comparison with standard biomarkers

  • Integration potential with existing testing algorithms

Research has established ZNF582 methylation as having good diagnostic performance with an AUC of 0.85 and a DOR of 12.72 for detecting CIN3+ . Sequential combined testing with HPV DNA further improved performance (AUC: 0.876) .

How should researchers report ZNF582 protein expression results in publications?

For comprehensive and reproducible reporting of ZNF582 protein expression, researchers should include:

• Antibody Details:

  • Manufacturer and catalog number

  • Clone designation if monoclonal

  • Host species and isotype

  • Working dilution and incubation conditions

• Experimental Methods:

  • Detailed protocols for sample preparation

  • Antigen retrieval method for IHC

  • Detection system specifications

  • Image acquisition parameters

• Quantification Methods:

  • Scoring system for IHC (e.g., H-score, Allred score)

  • Software and algorithms for digital image analysis

  • Densitometry methods for Western blot

  • Normalization procedures

• Controls and Validation:

  • Positive and negative controls used

  • Validation experiments performed

  • Correlation with other methods (e.g., methylation status)

• Data Representation:

  • Representative images at standardized magnifications

  • Quantitative data with appropriate statistical analysis

  • Correlation with clinical parameters

  • Raw data availability statement

Such comprehensive reporting ensures reproducibility and facilitates meta-analyses like those that have established ZNF582 methylation as a valuable biomarker for cervical cancer screening .