PFKFB4 Antibody, HRP conjugated

What is PFKFB4 and why is it significant in cancer research?

PFKFB4 (6-Phosphofructo-2-Kinase/Fructose-2,6-Biphosphatase 4) is a bidirectional glycolytic enzyme possessing both kinase and phosphatase functions that plays a crucial role in metabolic reprogramming, recognized as a cancer hallmark. Studies have demonstrated that PFKFB4 is significantly upregulated in multiple cancer types, including gastric cancer, hepatocellular carcinoma, and glioblastoma. The enzyme serves as a biomarker for poor prognosis in gastric cancer patients, with immunohistochemistry studies showing that PFKFB4 expression is significantly higher in tumor tissues compared to adjacent normal tissues (median score 4 vs. 3; P=0.0002) . PFKFB4 is primarily localized in both cytoplasm and nucleus, with stronger expression typically observed in the cytoplasm, though nuclear expression increases in high-expressing tumors . This protein is intimately linked to tumor hypoxia and supports rapid tumor growth while mitigating consequential oxidative stress .

What are the optimal conditions for Western blot experiments using PFKFB4 antibodies?

For successful Western blot detection of PFKFB4, researchers should follow these methodological guidelines based on published protocols:

• Sample preparation: Load 50 μg of whole cell lysate per lane with NFDM/TBST buffer

• Antibody dilution: Use anti-PFKFB4 antibody at 1/1000 dilution

• Controls: Include both positive controls (known PFKFB4-expressing cells) and negative controls (PFKFB4 siRNA-treated cells)

• Loading control: GAPDH antibody at 1/200000 dilution is recommended

• Detection system: For HRP-conjugated antibodies, high-sensitivity ECL substrate allows detection in the mid-femtogram range

Research has validated this approach using HeLa cells transfected with either scrambled siRNA (control) or PFKFB4-specific siRNA, demonstrating clear specificity of the antibody signal . When troubleshooting inconsistent results, verify protein loading, transfer efficiency, and consider the impact of post-translational modifications on antibody recognition.

How should immunohistochemistry protocols be optimized for PFKFB4 detection?

Published studies using PFKFB4 antibodies for immunohistochemistry provide the following validated protocol:

• Tissue preparation:

  • Formalin-fixed and paraffin-embedded tissue sections

  • Heat at 65°C for 1 hour

  • Deparaffinize in xylene and rehydrate through graded ethanol

• Antigen retrieval:

  • Use EDTA buffer (pH 8.0)

  • Block endogenous peroxidase with 3% hydrogen peroxide at room temperature for 10 min

• Antibody incubation:

  • Block with 10% goat serum for 10 min at 37°C

  • Anti-PFKFB4 antibody at 1:100 dilution

  • Incubate at 37°C for 1 hour followed by overnight at 4°C

• Detection and scoring:

  • For semi-quantitative analysis, score staining intensity (0-3+) and percentage of positive cells (0-4+)

  • Calculate final score as the product of intensity and percentage scores

  • Use median score as cut-off for high versus low expression classification

This methodology has successfully distinguished PFKFB4 expression patterns between tumor and normal tissues, with significant clinical correlations observed in multiple cancer types.

How can I validate the specificity of PFKFB4 antibody in my experimental system?

Comprehensive validation of PFKFB4 antibody specificity requires multiple approaches:

• Genetic manipulation controls:

  • siRNA knockdown: Compare signal between PFKFB4-specific siRNA and scrambled control

  • CRISPR/Cas9 knockout: Generate PFKFB4-null cells for absolute negative control

  • Overexpression: Test signal in cells with forced PFKFB4 expression

• Sample type validation:

  • Compare antibody performance across multiple cell lines with known PFKFB4 expression

  • Include hypoxia-treated samples (known to induce PFKFB4) as positive controls

  • Test in multiple tissue types to confirm consistent detection

• Technical validation:

  • Compare results between different antibody lots

  • Verify correlation between protein and mRNA expression when possible

  • For HRP-conjugated antibodies, include enzyme activity controls

Research has shown that PFKFB4 can be detected in hypoxia-induced samples, with HIF-1α knockdown significantly reducing PFKFB4 expression at both mRNA and protein levels . This provides a useful biological system for antibody validation.

How does PFKFB4 expression relate to immune infiltration in tumors?

Recent studies have revealed significant correlations between PFKFB4 expression and immune cell infiltration in colorectal cancer:

Analysis using the TIMER2.0 and CAMOIP databases demonstrated robust correlations between PFKFB4 expression and various immune cell populations, suggesting PFKFB4 may impact the tumor immune microenvironment . This relationship indicates potential implications for immunotherapy response, with PFKFB4 potentially serving as a marker for immune status. When designing studies to investigate this relationship, researchers should include multiple immune cell markers and consider spatial relationships between PFKFB4-expressing cells and immune infiltrates.

What is the relationship between PFKFB4 expression and TP53 mutations?

Multiple studies have identified a significant association between PFKFB4 expression and TP53 mutational status:

• Statistical association:

  • TCGA data analysis shows TP53 mutations (P=1.736E-4) as one of the top genetic alterations co-occurring with PFKFB4 overexpression

  • This association is specific, as PFKFB4 does not correlate with other common mutations like CTNNB1

  • HBV-associated HCCs with TP53 mutations express significantly higher levels of PFKFB4

• Functional relationship:

  • p53 appears to function as an upstream repressor of PFKFB4

  • TP53 mutations may release this repression, leading to PFKFB4 upregulation

  • This regulatory mechanism suggests targeting PFKFB4 could be particularly effective in TP53-mutated cancers

When investigating PFKFB4 in cancer contexts, researchers should consider stratifying samples by TP53 mutational status to identify potential subgroup-specific effects and therapeutic vulnerabilities.

How can PFKFB4 antibodies be used to study the relationship between PFKFB4 and hypoxia signaling?

PFKFB4 has complex interactions with hypoxia signaling, particularly the HIF-1α pathway:

• PFKFB4 regulation by hypoxia:

  • Hypoxia induces PFKFB4 expression via HIF-1α

  • ChIP assays have identified HIF-1α binding to specific HRE sites at -166 and -402 upstream regions from the PFKFB4 promoter transcription start site

  • HIF-1α knockdown, but not HIF-2α, significantly reduces PFKFB4 expression

• PFKFB4 regulation of HIF-1α:

  • PFKFB4 interacts with FBXO28 to regulate HIF-1α ubiquitylation and subsequent proteasomal degradation

  • This creates a potential feedback mechanism affecting hypoxic adaptation

Methodological approaches to study this relationship include:

• Co-immunoprecipitation using PFKFB4 antibodies to detect interactions with HIF-1α and FBXO28

• ChIP-seq to map HIF-1α binding across the genome under PFKFB4 manipulation

• Immunofluorescence to co-localize PFKFB4 with HIF-1α in hypoxic regions of tumors

Researchers should carefully select antibodies that recognize the relevant protein domains involved in these interactions.

How should I account for heterogeneous PFKFB4 staining patterns in tumor samples?

Heterogeneous PFKFB4 staining is commonly observed in tumor samples and requires careful interpretation:

• Biological factors contributing to heterogeneity:

  • Intratumoral hypoxic gradients (PFKFB4 is hypoxia-induced)

  • Varying TP53 mutational status within the tumor

  • Cellular differentiation state differences

  • Regional metabolic adaptation

• Methodological approaches to address heterogeneity:

  • Use a standardized scoring system that accounts for both intensity and percentage of positive cells

  • Example from literature: "Staining intensity was scored as follows: 0, negative; 1+, light yellow; 2+, yellowish brown; and 3+, brown. The number of stained cells was also scored and divided into four groups: 0, no positively stained cells; 1+, ≤10%; 2+, 11–50%; 3+, 51–75%; and 4+, >75%"

  • Employ multiple field analysis (minimum 5 fields per sample)

  • Consider digital pathology quantification methods for objective assessment

• Data interpretation strategies:

  • Correlate staining patterns with hypoxia markers (e.g., CA9, GLUT1)

  • Analyze staining in context of regional lymphocyte infiltration

  • Compare subcellular localization patterns (nuclear vs. cytoplasmic)

Research has shown that PFKFB4 expression is "mainly expressed in the cytoplasm and nucleus of the cells, and distributed diffusely throughout the tumor tissues," with cytoplasmic expression typically stronger than nuclear .

What reference genes should be selected when studying PFKFB4 expression under hypoxic conditions?

Selection of appropriate reference genes is critical when studying PFKFB4 under hypoxic conditions:

• Hypoxia impact on reference genes:

  • Many common housekeeping genes change expression under hypoxia

  • Research demonstrates that "hypoxia in the tumor microenvironment could profoundly affect gene expression"

  • A panel of 8 commonly used reference genes should be evaluated for stability

• Validation approach:

  • Test multiple candidate reference genes under your specific hypoxic conditions

  • Analyze expression stability using algorithms like GeNorm or NormFinder

  • Select a combination of the most stable references (minimum 3 recommended)

• Methodological recommendations:

  • Include time-matched controls for each hypoxic timepoint

  • Consider using exogenous spike-in controls for absolute quantification

  • Report all reference genes used and their validation data

Published studies have employed careful examination of reference gene expression "in RNA-seq data generated from a panel of hypoxic-treated HCC cell lines and a cohort of paired HCC patient samples" to identify stable references, an approach that should be adapted for each experimental system.

How can PFKFB4 antibodies be used to evaluate PFKFB4 as a prognostic biomarker?

Multiple studies have established PFKFB4 as a potential prognostic biomarker in various cancers:

• Standardized assessment protocol:

  • Use validated IHC scoring systems with defined cutoffs

  • Example from gastric cancer research: "specimens with an immunoreactivity score >4 were defined as having high PFKFB4 expression, while those with an immunoreactivity score <4 were defined as having low PFKFB4 expression"

  • Ensure blinded assessment by at least two pathologists

• Clinicopathological correlation methodology:

  • Analyze associations using appropriate statistical tests (χ² test or Fisher's exact test)

  • Example correlations from research: "The percentage of specimens with high PFKFB4 expression was markedly increased in the <65 age group compared with that in the ≥65 age group (49.5 vs. 26.3%; P=0.005)"

  • Use multivariate analysis to assess independent prognostic value

• Survival analysis approach:

  • Kaplan-Meier survival analysis with logrank test

  • Stratification by PFKFB4 expression levels

  • In colon adenocarcinoma, differential survival times were observed: "the group with low PFKFB4 expression exhibited median survival times of 32 months for RFS, 72 months for OS, and 53 months for PPS" versus "the group with high PFKFB4 expression demonstrated median survival times of 42 months for RFS, 130 months for OS, and 25 months for PPS"

• Implementation in precision oncology:

  • Consider combining PFKFB4 assessment with other biomarkers (e.g., TP53 mutation status)

  • Evaluate response prediction to metabolism-targeting therapies

  • Integrate with molecular subtyping approaches

These methodological considerations ensure robust evaluation of PFKFB4's potential as a clinically relevant prognostic biomarker.

How might PFKFB4 antibodies be employed in metabolomic studies of cancer?

Integration of PFKFB4 protein analysis with metabolomics offers powerful insights into cancer metabolism:

• Experimental design approaches:

  • Paired protein-metabolite analysis in the same samples

  • PFKFB4 manipulation (knockout/knockdown) followed by targeted metabolomics

  • Spatial correlation of PFKFB4 expression with metabolic gradients in tumor sections

• Metabolic pathways of interest:

  • Glycolysis intermediates, particularly fructose-2,6-bisphosphate levels

  • Pentose phosphate pathway metabolites

  • Redox balance indicators (NADPH/NADP+ ratio)

  • Nucleotide synthesis precursors

• Methodological considerations:

  • Time-resolved analysis to capture dynamic metabolic changes

  • Compartment-specific metabolite analysis where possible

  • Correlation of metabolite levels with PFKFB4 subcellular localization

Research using "CRISPR/CRISPR-associated protein 9 (Cas9)-mediated PFKFB4 knockout cells" has enabled "functional characterization in vivo, targeted metabolomic profiling, as well as RNA sequencing analysis to comprehensively examine the impact of PFKFB4 loss in HCC" . This integrated approach reveals how PFKFB4-dependent metabolic reprogramming supports cancer progression and suggests potential metabolic vulnerabilities for therapeutic targeting.