ARPC2 Human

What is ARPC2 and what is its basic function in human cells?

ARPC2 encodes one of seven subunits (the p34 subunit) of the human Arp2/3 protein complex. This evolutionarily conserved complex plays a critical role in regulating actin polymerization in cells, which is fundamental to cellular processes involving cytoskeletal remodeling . The Arp2/3 complex initiates the formation of new actin filaments from existing filaments, creating branched actin networks that are essential for cell movement, endocytosis, and intracellular trafficking.

For experimental validation of ARPC2 function:

• Use siRNA knockdown to observe effects on actin cytoskeleton organization

• Perform immunofluorescence microscopy with anti-ARPC2 antibodies to visualize localization

• Conduct in vitro actin polymerization assays with purified components

How is ARPC2 expression regulated in normal human tissues?

ARPC2 is widely expressed across different human tissues, though with varying expression levels. Data from the Human Protein Atlas (HPA) indicates relatively higher expression in bone marrow, lymph nodes, and blood/immune cells, with lower expression observed in brain tissues and neuronal cells . This pattern suggests tissue-specific regulation mechanisms.

For studying ARPC2 expression:

• RT-qPCR for quantitative mRNA expression analysis across tissues

• Western blotting for protein-level expression comparisons

• Immunohistochemistry for spatial distribution in tissue sections

• Analysis of promoter regions to identify tissue-specific regulatory elements

What splice variants of ARPC2 have been identified and characterized?

At least two alternatively spliced variants of ARPC2 have been well-characterized, with additional variants described but not fully characterized regarding their full-length nature . The functional differences between these variants remain an active area of research.

Methodological approach for splice variant analysis:

• RT-PCR with variant-specific primers

• Northern blotting to validate transcript sizes

• Cloning and sequencing of variants

• Functional studies comparing activities of different isoforms

What are the known protein interactions of ARPC2?

ARPC2 has been demonstrated to interact with Cortactin, which is an important regulator of actin assembly and cell migration . As part of the Arp2/3 complex, ARPC2 also interacts with the other six subunits of the complex and with various nucleation-promoting factors.

To study protein interactions:

• Co-immunoprecipitation followed by mass spectrometry

• Yeast two-hybrid screening

• Proximity ligation assays

• FRET-based interaction studies in living cells

How does ARPC2 expression correlate with cancer prognosis across different tumor types?

The following table summarizes key cancer types where ARPC2 overexpression correlates with poor survival outcomes:

For prognostic analysis:

• Kaplan-Meier survival analysis with optimal statistical cutoff values

• Univariate and multivariate Cox regression analyses

• Integration of multiple survival endpoints (OS, DSS, PFI)

• Correlation with clinicopathological parameters

What mechanistic roles does ARPC2 play in cancer progression and metastasis?

Experimental studies, particularly in hepatocellular carcinoma (HCC), demonstrate that ARPC2 silencing significantly inhibits cell proliferation, migration, and invasion, while ARPC2 overexpression promotes these processes . These findings suggest that ARPC2 may enhance cancer cell motility through its role in actin cytoskeleton remodeling, which is essential for invasive and metastatic behaviors.

To investigate mechanisms:

• Perform in vitro migration and invasion assays with ARPC2 knockdown/overexpression

• Use live-cell imaging to visualize cytoskeletal dynamics

• Analyze downstream signaling pathways affected by ARPC2 modulation

• Develop in vivo metastasis models to validate in vitro findings

How does ARPC2 expression relate to the tumor microenvironment and immune infiltration?

ARPC2 expression shows significant correlations with tumor microenvironment (TME) characteristics, including stromal and immune cell infiltration in various cancer types . This suggests that ARPC2 may influence or be influenced by the tumor immune microenvironment, potentially affecting immunotherapy responses.

Research approach:

• Use ESTIMATE algorithm to calculate stromal and immune scores

• Apply CIBERSORT to determine relative proportions of 22 infiltrating immune cell types

• Perform correlation analyses between ARPC2 expression and immune cell subsets

• Validate findings with multiplexed immunohistochemistry or flow cytometry of tumor samples

What is the relationship between ARPC2 genetic alterations, DNA methylation, and expression in tumors?

Evidence suggests that genetic alterations and DNA methylation patterns in tumor tissues may contribute to aberrant ARPC2 expression . Understanding these regulatory mechanisms could reveal potential therapeutic targets or biomarkers.

Methodological considerations:

• Analyze copy number variations using GISTIC algorithm

• Perform bisulfite sequencing to profile methylation patterns in promoter regions

• Integrate multi-omics data (genomic, epigenomic, transcriptomic)

• Use CRISPR-based epigenome editing to validate functional impacts of methylation sites

What are the optimal methods for measuring ARPC2 expression in clinical samples?

Researchers have successfully employed multiple complementary techniques to assess ARPC2 expression in clinical samples:

RT-qPCR protocol specifics:

• RNA extraction using TRIzol reagent or commercial kits

• cDNA synthesis with oligo(dT) primers

• Use of reference genes like GAPDH or β-actin for normalization

• Optimization of primer design to distinguish splice variants

Immunohistochemistry considerations:

• Tissue fixation in 10% formalin, paraffin embedding

• Antigen retrieval methods optimization

• Primary antibody dilution (1:500 recommended for ab133315, Abcam)

• Quantification through average optical density measurement using Image-Pro Plus 6.0

Western blotting parameters:

• Sample preparation with RIPA buffer containing protease inhibitors

• Protein quantification using BCA assay

• SDS-PAGE with 10-12% gels

• Transfer optimization and blocking conditions

What bioinformatic tools and databases are most useful for ARPC2 research?

Pan-cancer analysis has utilized several key databases and tools that are particularly valuable for ARPC2 research:

Database resources:

• The Cancer Genome Atlas (TCGA) for cancer expression data

• Genotype-Tissue Expression (GTEx) database for normal tissue expression

• Human Protein Atlas (HPA) for protein expression patterns

• UCSC Xena database for integrated multi-omics data

Analytical tools:

• R packages: "limma" for differential expression, "survival" and "survminer" for survival analysis

• ESTIMATE algorithm for tumor microenvironment analysis

• CIBERSORT for immune cell composition analysis

• TIMER2.0 for immune infiltration estimation

What experimental models are most appropriate for studying ARPC2 function?

Based on published research, the following experimental models have proven effective:

Cell lines:

• HCC cell lines (HCC-LM3, MHCC97-H, HepG2, huh-7) for cancer studies

• Normal liver cell line (L-02) as control

• Choose cell lines based on endogenous ARPC2 expression levels

Genetic manipulation approaches:

• siRNA transfection using TransIntroTM EL Transfection Reagent

• Plasmid-based overexpression using pcDNA 3.1(+) vector and Lipofectamine 3000

• Consider stable knockdown using shRNA for long-term experiments

• CRISPR-Cas9 for complete knockout studies

Functional assays:

• Proliferation assays (CCK-8, EdU incorporation)

• Migration assays (wound healing, transwell)

• Invasion assays (Matrigel-coated transwell)

• 3D spheroid formation for more physiologically relevant models

How should researchers analyze correlations between ARPC2 expression and clinical outcomes?

For robust statistical analysis of ARPC2 as a prognostic biomarker:

Statistical methods:

• Determine optimal expression cutoffs using statistical methods rather than median splits

• Perform both Kaplan-Meier analysis with log-rank tests and Cox regression

• Analyze multiple survival endpoints (OS, DSS, PFI) for comprehensive assessment

• Include multivariate analysis adjusting for known prognostic factors

Visualization approaches:

• Kaplan-Meier curves with hazard ratios and confidence intervals

• Forest plots for displaying results across multiple cancer types

• Box plots for expression differences between clinical subgroups

• Correlation heatmaps for relationships with clinical parameters

What are the common pitfalls in interpreting ARPC2 expression data across different cancer types?

Researchers should be aware of several potential pitfalls:

Tissue-specific considerations:

• ARPC2 has opposite prognostic implications in different cancers (e.g., negative in most cancers but positive in SKCM and THYM)

• Expression patterns may reflect tissue of origin rather than cancer-specific changes

• Consider the baseline expression in corresponding normal tissues

Technical considerations:

• Account for batch effects when combining data from different sources

• Be aware of platform-specific biases in expression quantification

• Consider sample purity and heterogeneity in tissue samples

• Validate findings using multiple technical approaches

How can researchers integrate multi-omics data to understand ARPC2 regulation and function?

Integrative approaches provide the most comprehensive understanding:

Data integration strategies:

• Correlate expression with copy number and methylation data

• Perform pathway enrichment analysis using associated genes

• Use protein-protein interaction networks to identify functional modules

• Apply machine learning for pattern recognition across data types

Validation approaches:

• Confirm key findings with independent datasets

• Use experimental models to validate computational predictions

• Apply single-cell approaches to resolve cellular heterogeneity

• Consider longitudinal samples to capture dynamic changes

What are the unexplored aspects of ARPC2 function in normal physiology?

While ARPC2's role in cancer has been increasingly studied, several aspects of its normal function remain to be fully elucidated:

Research opportunities:

• Detailed characterization of tissue-specific splice variants

• Role in immune cell function given high expression in bone marrow and lymphoid tissues

• Developmental regulation during embryogenesis and tissue differentiation

• Potential non-canonical functions beyond actin cytoskeleton regulation

What therapeutic strategies targeting ARPC2 show promise for cancer treatment?

Given ARPC2's role in cancer progression, several therapeutic approaches warrant investigation:

Potential strategies:

• Direct inhibition of ARPC2 or the Arp2/3 complex

• Targeting upstream regulators of ARPC2 expression

• Exploiting synthetic lethality with other cytoskeletal regulators

• Combination approaches with immunotherapy based on TME correlations

For preclinical evaluation:

• Test cytoskeletal inhibitors in ARPC2-high vs. ARPC2-low cancer models

• Evaluate effects on tumor growth and metastasis in vivo

• Assess potential toxicities in normal tissues

• Identify predictive biomarkers for response