RRM2 Antibody

What is RRM2 and what are its primary functions in cellular biology?

RRM2 is a crucial small subunit of ribonucleotide reductase (RR), an enzyme that catalyzes the conversion of ribonucleoside diphosphate (NDP) to 2-deoxyribonucleoside diphosphate (dNDP), an essential step for DNA replication and cell proliferation. The complete RR enzyme consists of one large subunit (RRM1) and two small subunits (RRM2 and RRM2B) . RRM2 plays a significant role in DNA synthesis and has been implicated in various cancer pathways. Recent research has also shown that RRM2 inhibits Wnt signaling, suggesting its involvement in multiple cellular processes beyond nucleotide metabolism .

The protein structure of RRM2 shares approximately 80% similarity in amino acid sequence with RRM2B, though their functions differ significantly. While RRM2B has demonstrated tumor suppressor properties, RRM2 is frequently overexpressed in malignant tissues compared to surrounding normal tissues, particularly in colorectal cancer, suggesting its crucial role in tumorigenesis and metastasis .

What are the standard applications of RRM2 antibodies in research?

RRM2 antibodies are versatile research tools with applications across multiple experimental techniques:

• Immunohistochemistry (IHC): For detecting RRM2 expression in paraffin-embedded tissue sections (such as cerebrum or colon carcinoma)

• Western blotting (WB): For protein quantification and size verification

• Immunoprecipitation (IP): For isolating RRM2 and its binding partners

• Immunocytochemistry/Immunofluorescence (ICC/IF): For cellular localization studies, as demonstrated in HeLa cells

When selecting an RRM2 antibody, researchers should consider the validated applications and species reactivity. For example, commercially available rabbit recombinant monoclonal antibodies have been cited in numerous publications and demonstrated effectiveness in human samples .

How does RRM2 expression correlate with cancer progression?

Through comprehensive bioinformatics analyses across multiple cancer types, RRM2 expression has been shown to be significantly upregulated in tumor tissues compared to normal tissues . Higher RRM2 expression correlates with:

These findings consistently indicate that RRM2 expression could serve as a prognostic biomarker for various cancers, particularly HCC, where expression level strongly correlates with both morbidity and prognosis .

How can researchers analyze the relationship between RRM2 and immune infiltration in tumors?

The relationship between RRM2 expression and immune infiltration can be analyzed using several bioinformatics approaches:

• Correlation analysis with immune cell populations: Using platforms like TIMER2.0 to estimate immune cell infiltration and analyze correlations with RRM2 expression

• Analysis of specific immune cell subsets: Researchers have found that RRM2 expression correlates with various immune cells:

  • Positive correlation with Th2 cells, T helper cells, and T follicular helper (TFH) cells

  • Negative correlation with neutrophils, dendritic cells (DCs), CD8 T cells, and cytotoxic cells

• Immune checkpoint analysis: Examining correlations between RRM2 expression and immune checkpoint molecules to predict immunotherapy response

• Tumor Immune Dysfunction and Exclusion (TIDE) analysis: This approach can be used to predict immunotherapeutic response outcomes based on RRM2 expression in melanoma immunotherapy cohorts

Researchers focusing on particular cancer types should examine the specific immune cell subpopulations that show significant differences between high and low RRM2 expression groups, as these patterns may vary across cancer types .

What methodological approaches are recommended for studying RRM2's role in cancer therapeutic resistance?

RRM2 has been implicated in both chemoresistance and radioresistance mechanisms. To study these relationships, researchers can employ several approaches:

• Gene silencing techniques: siRNA-mediated gene silencing to downregulate RRM2 expression and observe effects on therapeutic sensitivity

• Combination therapy models: Testing RRM2 inhibition in combination with chemotherapy or radiotherapy in:

  • Cell models (fibrosarcoma cells, pancreatic ductal adenocarcinoma cells)

  • Animal models (lung squamous cell carcinoma, pancreatic cancer, breast cancer, renal cell carcinoma)

• Expression analysis following treatment: Examining RRM2 upregulation after exposure to ionizing radiation or UV, which has been documented in several studies

• Resistance mechanism studies: Investigating how RRM2 contributes to resistance against specific agents like GTI-2040, tamoxifen, adriamycin, and cisplatin in breast cancer cells

The experimental design should include appropriate controls and time-course analyses to capture the dynamic changes in RRM2 expression during treatment and resistance development.

How can RRM2 antibodies be validated for research applications?

Rigorous validation of RRM2 antibodies is critical for ensuring reliable results. A comprehensive validation approach includes:

• Specificity testing:

  • Western blot analysis to confirm the correct molecular weight (predicted band size: 45 kDa)

  • Testing in multiple cell lines and tissue types

  • Using positive and negative control tissues (e.g., human cerebrum serves as a negative/low expression control)

• Knock-down/knock-out validation:

  • siRNA-mediated knockdown of RRM2 followed by antibody testing to confirm signal reduction

  • CRISPR/Cas9-mediated knockout as a definitive negative control

• Cross-reactivity assessment:

  • Parallel testing with RRM2B antibodies to ensure specificity, given the 80% sequence similarity

  • Secondary antibody-only controls to rule out non-specific binding

• Application-specific validations:

  • For IHC: Optimizing antigen retrieval methods (e.g., heat-mediated antigen retrieval with Tris-EDTA buffer at pH 9.0)

  • For IF: Testing different fixation methods and antibody concentrations

Which signaling pathways are associated with RRM2 expression in cancer?

Gene Set Enrichment Analysis (GSEA) has revealed that elevated RRM2 expression is significantly enriched in several critical cancer-related pathways:

• PD1 signaling pathway: Suggesting a role in immune regulation and potential immunotherapy response

• P27 pathway: Indicating involvement in cell cycle regulation

• T cell receptor signaling pathway: Pointing to interactions with immune response mechanisms

• DNA synthesis and repair pathways: Consistent with RRM2's role in providing precursors for DNA synthesis

Researchers investigating RRM2's role in these pathways should design experiments that can distinguish between correlation and causation, potentially using genetic manipulation approaches combined with pathway inhibitors to elucidate the precise mechanisms involved.

How does RRM2 influence macrophage polarization and tumor microenvironment?

RRM2 has been shown to play a significant role in regulating macrophage polarization and the tumor microenvironment:

• Macrophage infiltration: RRM2 expression positively correlates with macrophage infiltration, particularly M2 macrophages (tumor-associated macrophages or TAMs) in multiple cancer types

• Polarization regulation: RRM2 influences macrophage polarization both in vitro and in vivo:

  • Inhibition of RRM2 promotes M1 polarization (anti-tumor phenotype)

  • Inhibition of RRM2 suppresses M2 polarization (pro-tumor phenotype)

• TCGA data correlation: Analysis of TCGA-KIRC dataset has demonstrated a positive correlation between RRM2 levels and the number of M2 macrophages

These findings suggest that RRM2 may contribute to tumor progression not only through its direct effects on cancer cell proliferation but also by modulating the immune microenvironment toward a more tumor-permissive state.

What are the optimal protocols for RRM2 antibody use in immunohistochemistry?

For optimal RRM2 immunohistochemistry, researchers should follow these methodological recommendations:

• Sample preparation:

  • Paraffin embedding of tissue samples

  • Sectioning at appropriate thickness (typically 4-5 μm)

• Antigen retrieval:

  • Heat-mediated antigen retrieval with Tris-EDTA buffer (pH 9.0, Epitope Retrieval Solution 2)

  • Recommended duration: 20 minutes

• Antibody incubation:

  • Primary antibody dilution: 1/1000 (may vary by antibody source)

  • Incubation time: 30 minutes at room temperature

  • Secondary detection system: Polymer-based detection systems (e.g., LeicaDS9800 Bond® Polymer Refine Detection)

• Controls:

  • Negative/low expression control tissues (e.g., human cerebrum)

  • Secondary antibody-only control (PBS instead of primary antibody)

  • Positive control tissues (e.g., colon carcinoma)

• Counterstaining and visualization:

  • Hematoxylin counterstaining for nuclear visualization

  • Automated platforms (e.g., Leica Biosystems BOND® RX instrument) may provide consistent results

How can researchers optimize RRM2 antibody-based immunofluorescence studies?

For successful immunofluorescence studies using RRM2 antibodies:

• Cell preparation:

  • Select appropriate cell lines known to express RRM2 (e.g., HeLa cells)

  • Optimize cell density for clear visualization

• Antibody parameters:

  • Recommended dilution: 1/100 for immunofluorescence applications

  • Combine with appropriate fluorophore-conjugated secondary antibodies

  • Include nuclear counterstaining (e.g., DAPI)

• Imaging considerations:

  • Use confocal microscopy for precise subcellular localization

  • Capture multiple z-stack images to ensure complete visualization

  • Apply appropriate exposure settings to prevent photobleaching

• Multiplex staining:

  • When combining with other markers, ensure antibodies are raised in different host species

  • Test for potential cross-reactivity before full experiments

  • Consider sequential staining protocols for challenging combinations

How can RRM2 antibodies contribute to immunotherapy research?

RRM2 antibodies can facilitate immunotherapy research through several approaches:

• Biomarker studies: Analyzing RRM2 expression as a potential predictor of immunotherapy response

  • RRM2 expression correlates with multiple immunotherapy response indicators, including:

    • Immune checkpoint expression

    • Tumor mutation burden (TMB)

    • Microsatellite instability (MSI)

    • Neoantigen presence

    • Cytotoxic T lymphocyte infiltration

• Target validation: Investigating RRM2 as a direct immunotherapeutic target

  • Development of anti-RRM2 antibody drugs that can inhibit tumor proliferation and metastasis

  • Surface-modified gold nanoparticle probes with anti-RRM2 antibodies for detection purposes

• Combination therapy studies: Exploring how RRM2 inhibition might enhance immunotherapy effectiveness

  • Analyzing the relationship between RRM2 expression and natural killer T (NKT) cell infiltration using algorithms like XCELL

  • Studying correlations between RRM2 and immunomodulators, including immune stimulators, MHC molecules, chemokines, and receptors

What are the methodological approaches for developing anti-RRM2 antibody-based therapeutics?

The development of anti-RRM2 antibody-based therapeutics involves several methodological steps:

• Target validation:

  • Confirming RRM2's elevated expression in malignant tissues compared to surrounding normal tissues

  • Verifying RRM2's role in tumorigenesis through functional studies

• Antibody generation:

  • Production of mono-specific anti-RRM2 antibodies with inhibitory effects on tumor proliferation and metastasis

  • Purification of recombinant RRM2 protein for antibody development

• Nanotechnology applications:

  • Preparation of gold nanoparticle probes with surface-modified anti-RRM2 antibodies

  • Utilizing these probes to detect RRM2 in patient samples

• Functional testing:

  • Evaluating antibody effects on RRM2 enzymatic activity

  • Assessing impact on cancer cell proliferation, migration, and invasion

  • Testing in appropriate animal models before clinical translation

This methodological framework represents a promising approach for developing novel immunotherapeutic agents targeting RRM2 in cancer treatment.

How should researchers interpret contradictory findings regarding RRM2's role in different cancer types?

When faced with contradictory findings regarding RRM2's role across different cancers, researchers should consider:

• Cancer-specific context:

  • RRM2's function may be influenced by the unique molecular and cellular context of each cancer type

  • The same molecular player can have different impacts depending on the specific genetic background of the tumor

• Methodological differences:

  • Variations in detection methods, antibodies, scoring systems, and cutoff values

  • Differences in sample preparation, storage conditions, and experimental protocols

• Analysis approach:

  • Use multiple platforms and databases to confirm findings (e.g., TCGA, TIMER2.0, TIDE)

  • Conduct meta-analyses across studies to identify consistent patterns

  • Perform subgroup analyses based on cancer stage, grade, or molecular subtype

• Functional validation:

  • Complement correlation studies with functional validation in cell lines and animal models

  • Use genetic manipulation approaches (siRNA, CRISPR) to confirm causality

  • Test hypotheses in multiple model systems to ensure robustness

What statistical methods are recommended for analyzing RRM2 expression data in relation to patient outcomes?

For rigorous statistical analysis of RRM2 expression data and patient outcomes, researchers should consider:

• Survival analysis methods:

  • Kaplan-Meier survival curves with log-rank tests to compare high vs. low RRM2 expression groups

  • Cox proportional hazards models to assess RRM2 as an independent prognostic factor

  • Time-dependent ROC curves to evaluate the predictive accuracy of RRM2 expression

• Correlation analyses:

  • Spearman correlation for analyzing relationships between RRM2 and immune cell markers

  • Pearson correlation for normally distributed continuous variables

• Multiple testing correction:

  • Apply Benjamini-Hochberg procedure to control false discovery rate

  • Consider family-wise error rate control for confirmatory analyses

• Multivariate analyses:

  • Include relevant clinicopathological variables (stage, grade, age, etc.)

  • Test for interaction effects between RRM2 and other molecular markers

  • Consider machine learning approaches for complex pattern recognition

• Validation strategies:

  • Split-sample validation (training and testing sets)

  • External validation using independent cohorts

  • Cross-validation techniques to assess model stability

By following these methodological recommendations, researchers can generate more reliable and reproducible findings regarding RRM2's prognostic and predictive value in cancer research.