KRT81 Antibody

What is KRT81 and what is its biological function?

KRT81, also known as KRTHB1, MLN137, or HB1, is a type II hair keratin composed of 505 amino acids that is predominantly expressed in the cortex of growing hair. It plays a crucial role in maintaining the structural integrity and resilience of hair fibers by forming heterotetramer structures with type I keratins, which are essential for the assembly of hair and nail fibers .

The presence of KRT81 in non-dividing differentiating cells in the hair follicle indicates its important role during hair cycle stages, providing the mechanical strength needed for protective and sensory functions . Beyond its structural role in hair biology, recent research has revealed KRT81's significance in cancer biology, particularly in triple-negative breast cancer (TNBC) and pancreatic ductal adenocarcinoma (PDAC) .

What antibodies against KRT81 are commonly available for research applications?

Several validated KRT81 antibodies are available for research applications, including:

• Mouse monoclonal antibodies (e.g., clone 3B10-5B10) - Suitable for Western blotting, immunoprecipitation, immunofluorescence, immunohistochemistry with paraffin-embedded sections, and ELISA; specifically recognizes human KRT81 .

• Rabbit polyclonal antibodies - Appropriate for Western blotting applications with human samples, offering an alternative epitope recognition profile that may complement monoclonal antibody applications .

Both antibody types have been validated for research applications, with monoclonal antibodies typically offering higher specificity but potentially more limited epitope recognition compared to polyclonal alternatives.

How should researchers select the appropriate KRT81 antibody for their experimental design?

Selection of the appropriate KRT81 antibody should be based on:

• Application compatibility: Confirm the antibody has been validated for your intended application (WB, IHC-P, ICC/IF, etc.).

• Species reactivity: Verify the antibody recognizes KRT81 in your species of interest. Currently available commercial antibodies primarily target human KRT81 .

• Clonality considerations: Monoclonal antibodies like clone 3B10-5B10 offer high specificity but recognize a single epitope, while polyclonal antibodies may provide broader epitope recognition but potentially higher background .

• Validation evidence: Review published literature citing the antibody and manufacturer validation data, including Western blot images showing the expected band size (approximately 55 kDa) .

• Experimental controls: Consider whether positive and negative controls are available for your research context, particularly when studying KRT81 in cancer subtypes where expression varies significantly .

What is the expression pattern of KRT81 in normal versus cancerous tissues?

KRT81 shows distinct expression patterns between normal and cancerous tissues:

Normal tissues:

• Predominantly expressed in the cortex of growing hair follicles

• Present in non-dividing differentiating cells during hair cycle stages

• Low expression in most epithelial tissues

Cancer tissues:

• Significantly increased expression in Triple-negative breast cancer (TNBC) compared to non-TNBC and normal tissues (logFC = 2.354, p = 5.9e-05)

• Used as a marker for subtyping pancreatic ductal adenocarcinoma (PDAC), with KRT81-positive PDAC subtypes associated with worse survival outcomes

• Expression correlates with tumor progression, metastasis, and immunotherapy response in multiple cancer types

This differential expression pattern makes KRT81 a valuable marker for cancer research, particularly in identifying aggressive cancer subtypes and predicting treatment responses.

How is KRT81 used as a prognostic marker in cancer research?

KRT81 has emerged as a significant prognostic marker in multiple cancer types:

In Triple-negative breast cancer (TNBC):

• Lower KRT81 expression correlates with better patient outcomes

• High KRT81 expression associated with reduced survival time (HR = 2.79, 95% CI: 1.2–6.47, p = .0012)

• AUC values for predicting 1-, 3-, and 5-year survival were 0.689, 0.846, and 0.862, respectively

• Functions as an independent predictive factor in multivariate Cox regression analysis

In Pancreatic ductal adenocarcinoma (PDAC):

• KRT81-positive subtype associated with the worst survival outcomes

• IHC-based subtyping using KRT81 and HNF1A has significant prognostic value

• Classification system consistently validated across multiple patient cohorts

• May help predict treatment response to specific therapeutic agents like erlotinib

In both cancer types, IHC-based detection of KRT81 provides a practical method for patient stratification that can help guide treatment decisions and predict clinical outcomes.

What are the optimal protocols for KRT81 antibody use in immunohistochemistry?

For optimal KRT81 detection in immunohistochemistry (IHC), researchers should follow these methodological guidelines:

• Sample preparation:

  • Formalin-fixed, paraffin-embedded (FFPE) tissue sections (4-5 μm thickness)

  • Heat-induced epitope retrieval using citrate buffer (pH 6.0)

• Antibody selection and dilution:

  • For monoclonal antibodies: 3-5 μg/ml working concentration (e.g., clone 3B10-5B10)

  • For polyclonal antibodies: Optimize dilution through titration experiments

• Staining interpretation:

  • For cancer subtyping: Use semi-quantitative scoring systems

  • In PDAC research: Evaluate alongside HNF1A for comprehensive subtyping

  • In TNBC research: Assess correlation with clinical parameters and immune cell markers

• Validation controls:

  • Positive controls: Hair follicle sections (strong expression in cortex)

  • Negative controls: Primary antibody omission

  • Technical validation: Comparison with RNA expression data when available

The consistent application of standardized IHC protocols is essential for reliable KRT81 detection across different research settings and clinical applications.

What is the relationship between KRT81 expression and immune cell infiltration in cancer?

KRT81 expression has been shown to significantly correlate with immune cell infiltration patterns in cancer, particularly in TNBC:

• Correlation with specific immune cell populations:

  • Positive correlation with follicular helper T (Tfh) cells (cor = 0.33, p = .0001)

  • Negative correlation with naïve CD8+ T cell infiltration (cor = 0.27, p = .01)

  • Associated with altered proportions of 22 different immune cell types

• Impact on tumor immune microenvironment:

  • High-KRT81 tumors show elevated stromal and immune scores

  • Strong correlation with immune checkpoint expression, particularly inhibitory CD276 (cor = 0.387, p = .003)

  • Potential role in immune evasion mechanisms

• Methodological approaches to study this relationship:

  • CIBERSORT method for calculating immune cell proportions

  • ESTIMATE package for computing stromal and immune scores

  • Spearman correlation analysis for quantifying relationships

  • Co-culture experiments to confirm functional interactions

These findings suggest KRT81 may influence cancer progression partly through modulation of the tumor immune microenvironment, offering potential therapeutic targets for immunotherapy approaches.

How does KRT81 expression predict immunotherapy and chemotherapy responses?

KRT81 expression levels have demonstrated predictive value for treatment responses in cancer:

Immunotherapy response prediction:

• Higher TIDE scores (Tumor Immune Dysfunction and Exclusion) in high-KRT81 groups (p = .029), indicating greater potential for immune evasion and reduced benefit from immune checkpoint inhibitor treatment

• Decreased immunophenotype scores (IPS) in high-KRT81 TNBC patients, suggesting lower immunogenicity

• Across multiple datasets (GSE67501, GSE78220, and IMvigor 210), non-responders to immunotherapy showed significantly higher KRT81 expression than responders (p = .035, p = .016, p = .045, respectively)

Chemotherapy sensitivity prediction:

• Higher IC50 values for cisplatin (p = .022) and doxorubicin (p = .013) in high-KRT81 groups, indicating resistance

• Lower IC50 values for docetaxel (p = .028) and paclitaxel (p = .024), suggesting increased sensitivity

These findings position KRT81 as a valuable biomarker for guiding precision oncology approaches, potentially helping clinicians select patients most likely to benefit from specific treatment modalities.

What molecular mechanisms underlie KRT81's role in cancer progression?

Research has revealed several molecular mechanisms through which KRT81 contributes to cancer progression:

• Effects on tumor cell properties:

  • Promotion of cancer cell proliferation, migration, invasion, and adhesion

  • Involvement in regulating cell adhesion molecules and focal adhesion mechanisms

  • Association with ECM receptor interactions and actin cytoskeleton regulation

• Immune modulation pathways:

  • Potential role in inhibiting CD8+ T cells, confirmed through co-culture experiments

  • High correlation with immune checkpoint CD276, suggesting involvement in immune evasion

  • Enrichment in immune-related pathways including B cell receptor signaling and Fc gamma receptor-mediated phagocytosis

• Metabolic influences:

  • Low-KRT81 groups show enrichment in metabolic pathways including arginine and proline metabolism

  • Association with drug metabolism cytochrome P450, fatty acid metabolism, and PPAR signaling

  • Potential impact on steroid hormone biosynthesis

Understanding these diverse mechanisms provides potential targets for therapeutic intervention and explains KRT81's multifaceted role in cancer biology beyond its structural functions in normal tissues.

What are common technical challenges when working with KRT81 antibodies?

Researchers working with KRT81 antibodies may encounter several technical challenges:

• Specificity concerns:

  • Cross-reactivity with other keratin family members due to structural similarities

  • Need for careful validation with appropriate positive and negative controls

  • Confirmation of expected molecular weight (approximately 55 kDa for main band)

• Detection sensitivity:

  • Variable expression levels across different tissue types and disease states

  • Need for optimized antibody concentrations (typically 1-5 μg/ml depending on application)

  • Selection of appropriate detection systems for weakly expressed samples

• Reproducibility issues:

  • Batch-to-batch variation between antibody preparations

  • Inconsistencies in epitope accessibility due to fixation and processing differences

  • Variations in scoring and interpretation systems across research groups

To address these challenges, researchers should thoroughly validate antibodies before experimental use, include appropriate controls, and standardize protocols across experimental batches.

How can researchers validate the specificity of KRT81 antibodies?

Proper validation of KRT81 antibody specificity is crucial for reliable research outcomes and can be accomplished through:

• Western blot validation:

  • Confirmation of expected band size (approximately 55 kDa)

  • Comparison between KRT81-transfected and non-transfected cell lysates

  • Testing in multiple cell lines with known KRT81 expression profiles

• Genetic approaches:

  • Use of KRT81 knockout or knockdown models as negative controls

  • Overexpression systems as positive controls

  • siRNA-mediated silencing to confirm specificity

• Comparative antibody testing:

  • Parallel testing with multiple antibodies targeting different KRT81 epitopes

  • Correlation with mRNA expression data from the same samples

  • Comparison between monoclonal and polyclonal antibody results

• Cross-platform validation:

  • Correlation between IHC, Western blot, and immunofluorescence results

  • Confirmation with mass spectrometry-based protein identification when possible

  • Independent verification using antibodies from different suppliers

Thorough validation ensures that experimental findings truly reflect KRT81 biology rather than technical artifacts or cross-reactivity.

What are recommended controls for KRT81 antibody experiments?

Proper experimental controls are essential for reliable KRT81 antibody applications:

Positive controls:

• Hair follicle tissue sections (cortex shows strong KRT81 expression)

• KRT81-transfected cell lines (293T cells with confirmed overexpression)

• TNBC tissue samples (known to express higher KRT81 levels)

Negative controls:

• Primary antibody omission controls

• Non-transfected cell lines for comparison with transfected lines

• Normal breast tissue (shows lower expression compared to TNBC)

Procedural controls:

• Isotype-matched control antibodies to assess non-specific binding

• Gradient dilution series to optimize signal-to-noise ratio

• Multiple detection systems to confirm specificity of staining pattern

Validation controls:

• Correlation with KRT81 mRNA expression

• Comparison between monoclonal and polyclonal antibody results

• Parallel testing of multiple samples with known expression levels

Implementing these controls enables confident interpretation of experimental results and facilitates troubleshooting when unexpected outcomes occur.

How can KRT81 be utilized in cancer subtyping and patient stratification?

KRT81 has proven valuable for cancer subtyping and patient stratification across multiple cancer types:

Pancreatic Ductal Adenocarcinoma (PDAC):

• Using IHC-based detection of KRT81 and HNF1A, PDAC can be classified into distinct subtypes with significant prognostic differences

• The KRT81-positive subtype shows the worst survival outcomes

• The HNF1A-positive subtype demonstrates the best prognosis

• The double-negative subtype exhibits intermediate survival

• This classification system has been validated in multiple patient cohorts (269 and 286 patients) with consistent prognostic significance (p < 0.001)

Triple-Negative Breast Cancer (TNBC):

• KRT81 expression serves as an independent predictive factor for TNBC patient outcomes

• Lower KRT81 expression correlates significantly with better prognosis

• Assessment alongside clinical parameters (TNM staging) improves predictive power

• Nomogram models incorporating KRT81 expression show high accuracy in survival probability assessment

These applications demonstrate KRT81's potential as a practical biomarker for routine clinical use, offering valuable prognostic information that could guide treatment decisions.

What methodologies are recommended for studying KRT81's role in immune modulation?

To investigate KRT81's role in immune modulation, researchers should consider these methodological approaches:

• Immune cell correlation analyses:

  • CIBERSORT method to calculate proportions of 22 immune cell types

  • ESTIMATE package to compute stromal and immune scores

  • Spearman correlation analysis to quantify relationships between KRT81 expression and immune cell populations

• Functional validation studies:

  • Co-culture experiments between KRT81-expressing cancer cells and immune cells

  • KRT81 knockout/knockdown followed by immune function assessment

  • Overexpression systems to confirm observed phenotypes

• Checkpoint interaction analysis:

  • Correlation studies between KRT81 and immune checkpoint molecules (e.g., CD276)

  • Co-immunoprecipitation to detect physical interactions

  • Dual immunostaining to assess co-localization patterns

• Treatment response prediction:

  • TIDE score calculation to assess immune evasion potential

  • Immunophenotype score (IPS) evaluation for immunogenicity assessment

  • Retrospective analysis of treatment response data from clinical cohorts

These complementary approaches provide a comprehensive understanding of KRT81's impact on the tumor immune microenvironment and its potential as an immunotherapeutic target.

How should researchers interpret contradictory findings on KRT81 expression across different cancer types?

When faced with contradictory findings regarding KRT81 expression and function across cancer types, researchers should consider:

• Tissue context specificity:

  • KRT81's function may be highly context-dependent, varying between tissue types

  • Expression patterns should be interpreted within the specific cancer type being studied

  • Comparison between cancer types should acknowledge fundamental biological differences

• Methodological variations:

  • Different antibodies may recognize distinct epitopes, leading to discrepant results

  • Varied scoring systems and cutoff values can affect classification outcomes

  • Detection techniques (IHC vs. Western blot vs. RNA-seq) may not always correlate perfectly

• Statistical considerations:

  • Sample size variations between studies affect statistical power

  • Patient cohort characteristics (demographics, treatment history) impact outcomes

  • Different statistical methods for determining significance may yield varied results

• Biological complexity:

  • KRT81 likely participates in multiple cellular pathways with context-specific effects

  • Its role may change during disease progression or treatment

  • Interaction with other biomarkers may modify its prognostic significance

When interpreting contradictory findings, researchers should systematically evaluate these factors and consider replication studies with standardized methodologies.

What are emerging applications of KRT81 in precision medicine approaches?

KRT81 shows significant potential in several emerging precision medicine applications:

• Predictive biomarker for immunotherapy selection:

  • Lower KRT81 expression correlates with better response to immune checkpoint inhibitors

  • TIDE and IPS scores associated with KRT81 levels help predict immunotherapy benefit

  • Potential for patient selection to avoid unnecessary toxicity in unlikely responders

• Chemotherapy response prediction:

  • Differential sensitivity patterns to specific chemotherapeutic agents based on KRT81 expression

  • High-KRT81 tumors show resistance to cisplatin and doxorubicin

  • High-KRT81 tumors demonstrate sensitivity to docetaxel and paclitaxel

  • Potential for guiding chemotherapy selection in personalized treatment plans

• Combined prognostic models:

  • Integration with clinical parameters in nomogram models

  • Improved survival probability assessment accuracy

  • Potential for more precise risk stratification

• Targeted therapy development:

  • KRT81's role in promoting cancer cell proliferation, migration, and invasion makes it a potential therapeutic target

  • Its involvement in immune evasion mechanisms suggests possibilities for combination with immunotherapies

  • Understanding its interaction with CD276 may lead to novel immunomodulatory approaches

These applications highlight KRT81's potential contribution to more personalized cancer treatment strategies that maximize efficacy while minimizing unnecessary side effects.

What are promising future research directions for KRT81 in cancer biology?

Several promising research directions could advance our understanding of KRT81 in cancer biology:

• Mechanistic studies:

  • Detailed investigation of how KRT81 suppresses CD8+ T cell function

  • Elucidation of the molecular interaction between KRT81 and CD276

  • Exploration of signaling pathways mediating KRT81's effects on cell proliferation and invasion

• Therapeutic targeting strategies:

  • Development of small molecule inhibitors targeting KRT81 function

  • Exploration of antibody-drug conjugates targeting KRT81-expressing cells

  • Investigation of combination approaches with immune checkpoint inhibitors

• Expanded cancer type investigations:

  • Extension of KRT81 prognostic studies to additional cancer types

  • Comparative analysis across cancer types to identify common mechanisms

  • Exploration in rare cancer subtypes with limited treatment options

• Clinical implementation studies:

  • Prospective validation of KRT81-based subtyping in clinical trials

  • Development of standardized diagnostic assays for clinical implementation

  • Economic and outcomes analyses of KRT81-guided treatment selection

• Single-cell analysis approaches:

  • Further single-cell RNA sequencing to identify KRT81-expressing cell populations

  • Spatial transcriptomics to understand KRT81's role in the tumor microenvironment

  • Multi-omics integration to develop comprehensive models of KRT81 function

These research directions could significantly advance our understanding of KRT81's role in cancer biology and potentially lead to new therapeutic strategies.

How might advances in antibody technology impact KRT81 research?

Emerging antibody technologies are likely to enhance KRT81 research in several ways:

• Next-generation recombinant antibodies:

  • Improved specificity through protein engineering approaches

  • Enhanced reproducibility through recombinant production

  • Better characterized epitope targeting for consistent results

  • Reduced batch-to-batch variation compared to traditional hybridoma methods

• Multiplexed detection systems:

  • Simultaneous assessment of KRT81 alongside other markers

  • Co-localization studies with immune markers in the same tissue section

  • Quantitative spatial analysis of KRT81 expression patterns

  • Integration with digital pathology platforms for enhanced analysis

• Novel imaging approaches:

  • Super-resolution microscopy for subcellular localization studies

  • Intravital imaging for real-time assessment of KRT81 dynamics

  • Mass cytometry for high-dimensional protein profiling

  • Tissue clearing techniques for 3D visualization of KRT81 expression

• In vivo targeting and imaging:

  • Development of KRT81-targeted probes for non-invasive imaging

  • Potential theranostic applications combining imaging and therapy

  • Antibody-based delivery systems for targeted therapeutic approaches

These technological advances promise to deepen our understanding of KRT81 biology and accelerate translation to clinical applications.

What are the implications of recent KRT81 findings for clinical trial design?

Recent KRT81 findings have significant implications for future clinical trial design:

• Biomarker-driven stratification:

  • Incorporation of KRT81 expression as a stratification factor in immunotherapy trials

  • Selection of patients for specific chemotherapy regimens based on KRT81 status

  • Combined biomarker approaches integrating KRT81 with other established markers

• Combination therapy approaches:

  • Trials evaluating KRT81-targeting strategies with immune checkpoint inhibitors

  • Investigation of chemotherapy combinations based on KRT81-associated sensitivity patterns

  • Targeted approaches addressing KRT81's interaction with CD276

• Trial endpoint considerations:

  • Inclusion of immune monitoring endpoints in KRT81-stratified trials

  • Assessment of treatment-induced changes in KRT81 expression

  • Correlation of dynamic KRT81 changes with clinical outcomes

• Diagnostic standardization needs:

  • Development of companion diagnostic assays with standardized scoring

  • Validation of KRT81 IHC protocols across multiple laboratories

  • Establishment of clinically relevant cutoff values for high vs. low expression

These considerations could enhance the efficiency of clinical trials by identifying patient populations most likely to benefit from specific therapeutic approaches, potentially accelerating the development of personalized cancer treatments.