DDX53 Antibody

What is DDX53 and why is it significant in cancer research?

DDX53 is a probable ATP-dependent RNA helicase belonging to the DEAD-box protein family. It contains several domains characteristic of these helicases, which participate in ATP-dependent RNA unwinding . DDX53 has gained significant research interest as a cancer testis antigen (CTA), making it particularly relevant in oncology. Recent studies demonstrate its role in various cancers, including esophageal carcinoma (ESCA) where it shows correlation with disease-free survival metrics . The significance extends to endometrial cancer, where DDX53 positively correlates with cancer progression, metastasis, and chemoresistance .

For researchers, DDX53's value lies in its potential as both a biomarker and therapeutic target. The protein's restricted expression pattern (primarily in cancer and testicular tissues) makes antibodies against it valuable tools for investigating cancer-specific mechanisms while minimizing off-target effects in normal tissues.

DDX53 antibodies have been validated for multiple research applications:

• Western Blotting (WB): Used to detect and quantify DDX53 protein expression levels in cell or tissue lysates. Multiple commercially available antibodies show robust performance in this application .

• Immunohistochemistry (IHC): Used for spatial localization of DDX53 in tissue sections, particularly in formalin-fixed, paraffin-embedded (FFPE) samples. IHC applications are crucial for correlating DDX53 expression with histopathological features .

• ELISA: Allows quantitative measurement of DDX53 in human serum, plasma, cell culture supernatants, and tissue homogenates. The sandwich enzyme immunoassay technique is commonly employed, using antibodies specifically pre-coated onto microplates .

• Immunoprecipitation (IP): Enables isolation of DDX53 protein complexes to study protein-protein interactions, post-translational modifications, or to concentrate the protein prior to other analyses .

When designing experiments, researchers should always validate each antibody for their specific application and experimental conditions.

What is the optimal protocol for using DDX53 antibodies in Western Blotting?

For optimal Western Blotting with DDX53 antibodies:

• Sample preparation:

  • Extract proteins using RIPA or NP-40 buffer supplemented with protease inhibitors

  • Determine protein concentration (Bradford or BCA assay)

  • Prepare 20-50 μg of total protein per lane in reducing sample buffer

• Gel electrophoresis and transfer:

  • Separate proteins on 10% SDS-PAGE (DDX53 is approximately 68-70 kDa)

  • Transfer to PVDF membrane (optimized for higher molecular weight proteins)

• Antibody incubation:

  • Block with 5% non-fat milk or BSA for 1 hour at room temperature

  • Incubate with primary DDX53 antibody (1:500-1:1000 dilution, optimized based on specific product)

  • Incubate overnight at 4°C with gentle agitation

  • Wash 3× with TBST

  • Incubate with HRP-conjugated secondary antibody (1:5000-1:10000) for 1 hour

  • Wash 4× with TBST

• Detection and validation:

  • Develop using ECL substrate and document with imaging system

  • Include positive control (testicular tissue or DDX53-expressing cancer cells)

  • Include negative control (normal tissue known to lack DDX53 expression)

  • Verify specificity by molecular weight comparison

This protocol should be optimized based on the specific antibody being used and sample characteristics.

How can I optimize immunohistochemistry experiments with DDX53 antibodies?

For robust and reproducible IHC results with DDX53 antibodies:

• Tissue processing and preparation:

  • Use freshly prepared 10% neutral-buffered formalin for fixation (12-24 hours)

  • Process and embed in paraffin following standard protocols

  • Section at 4-5 μm thickness onto positively charged slides

• Antigen retrieval optimization:

  • Test both heat-induced epitope retrieval methods:

    • Citrate buffer (pH 6.0) for 20 minutes

    • EDTA buffer (pH 9.0) for 20 minutes

  • Determine optimal retrieval method empirically for your specific antibody

• Antibody incubation:

  • Block endogenous peroxidase with 3% H₂O₂ (10 minutes)

  • Block non-specific binding with serum-free protein block (30 minutes)

  • Incubate with primary DDX53 antibody (typically 1:100-1:200 dilution)

  • Optimize incubation time (1 hour at room temperature or overnight at 4°C)

• Detection and counterstaining:

  • Use polymer-based detection systems for enhanced sensitivity

  • Develop with DAB chromogen (monitor microscopically to optimize timing)

  • Counterstain with hematoxylin

  • Evaluate both nuclear and cytoplasmic staining patterns for DDX53

• Controls:

  • Include positive control tissues (testicular tissue or known DDX53-positive tumors)

  • Include negative controls (antibody diluent without primary antibody)

  • Consider using a peptide competition assay to verify specificity

These recommendations should be adjusted based on the specific antibody datasheet and empirical optimization.

How can I validate the specificity of DDX53 antibodies for research?

Validating DDX53 antibody specificity requires multiple complementary approaches:

• Positive and negative cell/tissue controls:

  • Confirm staining in tissues known to express DDX53 (testicular tissue, certain cancer types)

  • Verify absence of staining in tissues known to lack DDX53 expression (most normal tissues)

• Molecular validation techniques:

  • RNA interference: Compare antibody signals in DDX53-knockdown and control cells

  • Recombinant expression: Overexpress DDX53 in a negative cell line and confirm increased signal

  • Peptide competition: Pre-incubate antibody with immunizing peptide to block specific binding

• Multiple antibody validation:

  • Use antibodies from different sources targeting different epitopes

  • Compare staining patterns across different applications (WB, IHC, IF)

  • Correlate protein detection with mRNA expression data

• Knockout/knockin models:

  • Use CRISPR/Cas9-mediated DDX53 knockout cells as negative controls

  • Use DDX53-GFP fusion protein expression to confirm co-localization with antibody signal

• Mass spectrometry validation:

  • Perform immunoprecipitation with DDX53 antibody

  • Analyze precipitated proteins by mass spectrometry to confirm DDX53 identity

This multi-layered validation approach ensures confidence in experimental results and minimizes the risk of non-specific binding artifacts.

How do DDX53 expression patterns correlate with cancer progression?

DDX53 expression demonstrates significant correlations with cancer progression across multiple tumor types:

• Esophageal Carcinoma (ESCA):

  • Chemical complementarity between TCR CDR3s and DDX53 shows correlation with disease-free survival

  • Patients with higher TCR CDR3-DDX53 complementarity demonstrate worse disease-free survival outcomes

  • Interestingly, high complementarity samples often show lower DDX53 expression, suggesting immune-mediated selection pressure against DDX53-expressing cells

• Endometrial Cancer (EC):

  • DDX53 positively correlates with EC progression and metastasis

  • DDX53 upregulates chemoresistance markers in EC cells

  • miR-429 has been identified as a novel miRNA targeting DDX53, suppressing EC proliferation and invasion

• Methodological approach to studying these correlations:

  • Perform retrospective tissue microarray analysis of DDX53 expression across cancer stages

  • Correlate expression with clinicopathological parameters (tumor grade, stage, invasion depth)

  • Conduct Kaplan-Meier survival analysis stratifying patients by DDX53 expression levels

  • Integrate with molecular profiling data to identify co-expression patterns

These findings suggest DDX53's role as both a biomarker for cancer progression and a potential therapeutic target, with expression patterns providing prognostic information across multiple cancer types.

What methodologies can identify DDX53's role in chemoresistance mechanisms?

To investigate DDX53's involvement in chemoresistance, researchers can employ these methodological approaches:

• Gene expression modulation studies:

  • Generate DDX53-overexpressing and knockdown cell models

  • Assess sensitivity to chemotherapeutic agents (e.g., paclitaxel) using MTT/MTS assays

  • Determine IC50 values across a concentration range (10⁻¹ to 10⁵ nM)

  • Measure apoptosis markers (Annexin V/PI staining) following drug treatment

• Pathway analysis:

  • Perform RNA-seq or proteomics on DDX53-modulated cells

  • Identify altered pathways related to drug metabolism and resistance

  • Validate key pathway components through Western blotting

  • Use pathway inhibitors to determine rescue effects

• Clinical correlation studies:

  • Analyze DDX53 expression in patient samples pre- and post-chemotherapy

  • Compare DDX53 levels between responders and non-responders

  • Correlate with established chemoresistance markers

• Mechanism investigation:

  • Assess DDX53's effect on drug efflux pumps (P-glycoprotein, MRP1)

  • Examine impact on DNA damage repair pathways

  • Investigate changes in EMT markers and stemness properties

  • Analyze alterations in apoptotic threshold

• Therapeutic targeting strategies:

  • Test combination therapies (chemotherapeutics plus DDX53 inhibition)

  • Evaluate chemosensitization effects of targeting DDX53

  • Explore synthetic lethality approaches with DDX53 modulation

Recent data shows DDX53 upregulates chemoresistance and mesenchymal markers in endometrial cancer cells, suggesting its direct involvement in drug resistance mechanisms .

How can DDX53 antibodies be used in immuno-oncology research?

DDX53 antibodies offer valuable tools for immuno-oncology research through various applications:

• Tumor-immune interaction studies:

  • Multiplex immunofluorescence with DDX53 and immune cell markers

  • Spatial analysis of DDX53-expressing cells relative to tumor-infiltrating lymphocytes

  • Correlation of DDX53 expression with immune checkpoint molecules

• Immuno-editing investigation:

  • Recent research suggests DDX53 may be subject to immuno-editing processes

  • Studies in ESCA revealed that high TCR CDR3-DDX53 chemical complementarity correlated with tumor samples lacking DDX53 expression

  • This supports the hypothesis that immune pressure selects for DDX53-negative tumor cell populations

• Research methodology:

  • Single-cell analysis of DDX53 expression in tumor microenvironment

  • TCR repertoire analysis in relation to DDX53 expression patterns

  • Functional T-cell assays against DDX53-expressing targets

  • Evaluation of DDX53 as a potential target for immunotherapy

• Clinical translation potential:

  • Assessment of DDX53 as a cancer vaccine antigen

  • Development of DDX53-targeted chimeric antigen receptor (CAR) T-cells

  • Monitoring DDX53-specific T-cell responses during immunotherapy

These approaches can provide insights into DDX53's potential as both a biomarker and target in immuno-oncology, leveraging its restricted expression pattern in cancer tissues.

Why might I observe inconsistent staining patterns with DDX53 antibodies?

Inconsistent DDX53 antibody staining patterns may result from several methodological and biological factors:

• Antibody-related factors:

  • Epitope accessibility: Different fixation methods may affect epitope exposure

  • Antibody specificity: Some antibodies may recognize multiple isoforms or related proteins

  • Lot-to-lot variability: Manufacturing differences between antibody batches

  • Recommended dilution range: Using appropriate dilutions (typically 1:100-1:200 for IHC)

• Sample-related factors:

  • Fixation time: Over or under-fixation affects protein crosslinking and epitope preservation

  • Tissue processing: Inconsistent processing between samples

  • Antigen retrieval: Suboptimal conditions for epitope unmasking

  • Sample age: Epitope degradation in older FFPE blocks

• Biological heterogeneity:

  • Intratumoral heterogeneity: DDX53 expression may vary within the same tumor

  • Disease stage variation: Expression changes during cancer progression

  • Immune selection pressure: Evidence suggests DDX53-expressing cells may be eliminated by immune responses

  • Post-translational modifications: Affecting epitope recognition

• Technical solutions:

  • Standardize fixation and processing protocols

  • Optimize antibody concentration through titration experiments

  • Test multiple antigen retrieval methods (pH 6.0 citrate vs. pH 9.0 EDTA)

  • Include multiple controls in each experiment

  • Consider using automated staining platforms for consistency

  • Evaluate multiple tissue regions to account for heterogeneity

Understanding these factors will help researchers troubleshoot inconsistent results and design more robust experiments.

How should I interpret contradictory results regarding DDX53 expression in different studies?

When faced with contradictory DDX53 expression data across studies, consider these analysis approaches:

• Methodological differences assessment:

  • Antibody comparison: Different antibodies target distinct epitopes, potentially affecting detection

  • Detection technique sensitivity: RNA-seq vs. qPCR vs. Western blot vs. IHC thresholds

  • Scoring systems: Varied quantification methods across studies (H-score, percentage positive, intensity)

  • Sample preparation: Fresh frozen vs. FFPE tissue processing effects

• Biological context evaluation:

  • Cancer type specificity: Expression patterns vary between cancer types

  • Tumor microenvironment influence: Immune infiltration may affect DDX53 expression

  • Treatment status: Pre- vs. post-treatment samples show different patterns

  • Patient population characteristics: Age, gender, ethnicity affecting expression

• Data integration approaches:

  • Meta-analysis: Systematically combine data across multiple studies

  • Multiplatform validation: Confirm findings using complementary techniques

  • Public database mining: Compare with TCGA, GTEx, or Human Protein Atlas data

  • Single-cell analysis: Evaluate cellular heterogeneity not captured in bulk studies

• Specific example from literature:

  • Recent esophageal cancer research showed apparent contradictions where high TCR-DDX53 complementarity correlated with worse prognosis but lower DDX53 expression

  • This was resolved by understanding immune selection pressure, suggesting an immunoediting mechanism where DDX53-expressing cells are eliminated

By systematically evaluating these factors, researchers can reconcile seemingly contradictory results and develop a more nuanced understanding of DDX53 biology.

What controls are essential when using DDX53 antibodies in cancer research?

A robust control strategy is critical when working with DDX53 antibodies:

• Positive controls:

  • Tissue controls: Include testicular tissue (known to express DDX53)

  • Cell line controls: Use cancer cell lines with validated DDX53 expression

  • Recombinant protein: Include purified DDX53 protein in Western blots

  • Transfected cells: Cells overexpressing DDX53 as positive control

• Negative controls:

  • Technical negative controls: Primary antibody omission

  • Isotype controls: Non-specific antibody of same isotype and concentration

  • Normal tissue: Most normal tissues should lack DDX53 expression

  • Knockdown/knockout cells: DDX53 siRNA or CRISPR-modified cells

• Specificity controls:

  • Peptide competition: Pre-absorption with immunizing peptide

  • Multiple antibodies: Use antibodies targeting different epitopes

  • Molecular weight verification: Confirm expected ~68-70 kDa band in Western blots

• Experimental validation controls:

  • Dilution series: Titration to determine optimal antibody concentration

  • Reproducibility controls: Technical and biological replicates

  • Cross-reactivity assessment: Test in multiple species if cross-reactivity is claimed

• Documentation practices:

  • Maintain detailed records of antibody source, lot number, and dilution

  • Document all experimental conditions for reproducibility

  • Include control images/data in publications and reports

These comprehensive controls help ensure experimental validity and facilitate troubleshooting when unexpected results occur.

How is miR-429 regulation of DDX53 changing our understanding of cancer progression?

Recent research has revealed important regulatory mechanisms involving miR-429 and DDX53:

• Discovery and mechanism:

  • miR-429 has been identified as a novel miRNA targeting DDX53 in endometrial cancer

  • This miRNA suppresses endometrial cancer proliferation and invasion through DDX53 inhibition

  • The regulatory relationship establishes a new pathway controlling cancer progression

• Functional implications:

  • DDX53 upregulates chemoresistance and mesenchymal markers in endometrial cancer cells

  • miR-429 suppression of DDX53 reverses these effects

  • This provides a potential therapeutic approach through miRNA-based strategies

• Research methodology for studying this relationship:

  • miRNA target prediction algorithms to identify potential DDX53 regulators

  • Luciferase reporter assays to validate direct binding

  • miRNA mimic and inhibitor experiments to assess functional effects

  • Correlation analysis of miR-429 and DDX53 expression in patient samples

  • Phenotypic assays (proliferation, invasion, drug sensitivity) following modulation

• Therapeutic implications:

  • miR-429 mimics could potentially serve as DDX53-targeting therapeutics

  • Combination approaches with conventional chemotherapy may overcome resistance

  • Biomarker potential for patient stratification based on miR-429/DDX53 axis

This research adds a new dimension to DDX53 biology, highlighting its regulation by the miRNA network and opening avenues for therapeutic intervention targeting this regulatory axis.

How can researchers interpret DDX53's role in immuno-editing mechanisms?

Recent findings suggest DDX53 may be involved in cancer immuno-editing processes:

• Observational evidence:

  • In esophageal carcinoma (ESCA), tumor samples with high TCR CDR3-DDX53 chemical complementarity often show lower DDX53 expression

  • This pattern suggests immune selection against DDX53-expressing tumor cells

  • The phenomenon represents a potential example of the "escape" phase of cancer immuno-editing

• Experimental approach to studying this phenomenon:

  • Chemical complementarity assessment: Evaluate TCR CDR3-DDX53 interactions

  • Correlation analysis: Compare complementarity scores with DDX53 expression levels

  • Survival correlation: Analyze disease-free survival based on complementarity metrics

  • Immunohistochemical validation: Spatial analysis of DDX53 and immune cell markers

• Methodological considerations for researchers:

  • Use multiparametric flow cytometry to analyze DDX53-specific T-cell populations

  • Employ single-cell sequencing to identify heterogeneity in DDX53 expression

  • Conduct longitudinal sampling to track DDX53 expression changes during treatment

  • Develop functional assays to assess T-cell reactivity against DDX53-expressing targets

• Implications for immunotherapy research:

  • DDX53 may represent a naturally immunogenic tumor antigen

  • Understanding immune evasion through DDX53 loss could inform resistance mechanisms

  • Potential for combinatorial approaches targeting multiple cancer-testis antigens

  • Development of strategies to overcome antigen-loss variants