CPNE1 Antibody

What is CPNE1 and why is it a significant research target?

CPNE1 (Copine-1) is a calcium-dependent phospholipid-binding protein located on human chromosome 20q11.21, encoding 537 amino acids with multiple splice forms. It represents the first discovered member of the copine family, with significant involvement in crucial cellular processes including apoptosis, growth control, autophagy, mitosis, inflammation, exocytosis, cytoskeletal organization, and gene transcription. CPNE1 has emerged as an important research target because it demonstrates significant upregulation in multiple tumor types compared to normal tissues, suggesting its potential role in carcinogenesis and tumor progression .

Research has established CPNE1's involvement in the development of various cancers, including breast cancer, non-small cell lung cancer, prostate cancer, liver cancer, thyroid cancer, and osteosarcoma. Its expression levels correlate with TNM staging, lymph node metastasis, and distant metastasis in several cancer types, making it a valuable biomarker for prognosis and potential therapeutic target .

What are the standard methods for detecting CPNE1 expression in tissue samples?

The detection of CPNE1 expression in tissue samples typically employs complementary techniques to assess both protein and mRNA expression levels. For protein detection, immunohistochemistry (IHC) represents the gold standard method. This involves preparing paraffin-embedded histological specimens cut into 4-μm thick sections, followed by dewaxing and rehydration procedures. After blocking endogenous peroxidase activity and non-specific binding, slides are incubated with a primary antibody against human CPNE1 (typically at 1:100 dilution) for 18 hours at 4°C, followed by incubation with horseradish peroxidase-conjugated secondary antibody. The immune reaction is developed with 3,3′-diaminobenzidine-tetrahydrochloride-dihydrate (DAB), and slides are counterstained with hematoxylin for microscopic analysis .

For mRNA expression analysis, quantitative real-time PCR (qPCR) using SYBR-Green is commonly employed. This requires RNA extraction, cDNA synthesis, and amplification using specific primers for CPNE1, with GAPDH frequently used as an internal control. The relative gene expression levels are typically calculated using the 2^-ΔΔCt method . Western blotting provides another reliable protein detection method, using anti-CPNE1 antibodies typically at 1:1000 dilution, with visualization through enhanced chemiluminescence (ECL) detection systems .

How can researchers validate CPNE1 antibody specificity for experimental applications?

Validating CPNE1 antibody specificity is crucial for ensuring reliable experimental results. A comprehensive validation approach should incorporate multiple complementary methods. First, researchers should perform Western blot analysis using both positive control samples (tissues or cell lines known to express CPNE1, such as HCC cell lines) and negative control samples (tissues with minimal CPNE1 expression). A specific antibody will demonstrate a single band at the expected molecular weight of CPNE1 (approximately 59 kDa) .

Researchers should also conduct peptide competition assays, where the antibody is pre-incubated with the immunizing peptide before application to the sample. This should eliminate specific staining if the antibody is truly specific. Additionally, siRNA knockdown experiments provide valuable validation data - cells transfected with CPNE1-specific siRNA should show reduced staining compared to control siRNA-transfected cells when probed with the CPNE1 antibody. This approach was effectively demonstrated in studies with osteosarcoma cell lines .

Cross-reactivity testing with other copine family members (CPNE2-9) should be performed to ensure the antibody does not recognize related proteins. Finally, comparing results from multiple antibodies that recognize different epitopes of CPNE1 can provide additional confirmation of specificity and reliability for experimental applications.

What cellular localization pattern should researchers expect when using CPNE1 antibody for immunostaining?

When using CPNE1 antibody for immunostaining, researchers should expect to observe both nuclear and cytoplasmic localization patterns, as demonstrated in multiple tissue types. In osteosarcoma tissues specifically, CPNE1 protein has been observed to localize in both the nuclei and cytoplasm of tumor cells . This dual localization pattern reflects CPNE1's diverse functional roles in different cellular compartments.

The staining intensity and distribution pattern will vary depending on the tissue type and pathological conditions. In hepatocellular carcinoma (HCC) tissues, for example, strong positive CPNE1 staining has been observed compared to normal liver tissue . In contrast, cartilage tumor tissues demonstrate minimal CPNE1 detection . Researchers should anticipate that malignant tissues generally exhibit stronger CPNE1 immunostaining compared to adjacent normal tissues across multiple cancer types, including gastric cancer and clear cell renal cell carcinoma .

When conducting subcellular localization studies, preparing appropriate positive and negative controls is essential for accurate interpretation of results. The specific fixation method and antigen retrieval protocol may influence the observed localization pattern, so optimization for each tissue type and antibody is recommended for reliable results.

How can researchers effectively design CPNE1 knockdown experiments to study its functional roles?

Designing effective CPNE1 knockdown experiments requires careful consideration of several key factors. Based on published successful approaches, researchers should first select appropriate cell lines that express detectable levels of CPNE1, such as Saos-2 and HOS cells for osteosarcoma studies, or HepG2 and MHCC-97H cells for hepatocellular carcinoma research . RNA interference (RNAi) technology has proven effective for CPNE1 silencing, with documented siRNA sequences such as 5′-CACACAACTGGTCTCATACTT-3′ demonstrating successful knockdown .

For transient knockdown, researchers should transfect cells with CPNE1-specific siRNA using standard transfection reagents like Lipofectamine. For stable knockdown, lentiviral vector systems expressing CPNE1-targeted shRNA sequences have shown reliable results. These vectors should include appropriate selection markers (e.g., GFP) to verify infection efficiency, which should exceed 90% for robust experimental outcomes .

Verification of knockdown efficiency is crucial and should be performed at both mRNA level (via qPCR) and protein level (via Western blot) 48-72 hours post-transfection. A proper experimental design should include appropriate controls: a non-silencing (scrambled) siRNA sequence (e.g., 5′-TTCTCCGAACGTGTCACGT-3′) as a negative control, and untreated cells as baseline controls . Functional assays following knockdown should be selected based on the specific research question, with proliferation, colony formation, invasion, and migration assays being particularly informative for cancer-related studies.

What signaling pathways should researchers investigate when studying CPNE1's molecular mechanisms?

When investigating CPNE1's molecular mechanisms, researchers should focus on several key signaling pathways that have been implicated in CPNE1's functional effects. The AKT/P53 pathway has been directly linked to CPNE1 activity in hepatocellular carcinoma, where CPNE1 knockdown inhibited this pathway, resulting in decreased proliferation, migration, and invasion of HCC cells . Conversely, CPNE1 overexpression demonstrated opposite effects, confirming this regulatory relationship.

The EGFR/STAT3 signaling pathway is another critical target for investigation, particularly in clear cell renal cell carcinoma, where CPNE1 has been shown to promote proliferation and migration through activation of this pathway . Researchers should examine key components of this pathway through Western blot analysis following CPNE1 manipulation.

Additionally, CPNE1 has been demonstrated to regulate several other important cancer-related proteins, including Ras, MEK-1/2, WNT1, β-catenin, cyclin A1, IRAK2, and cIAP2 . The WNT/β-catenin pathway deserves particular attention as it plays crucial roles in tumor development and progression. Western blot analysis following CPNE1 silencing has shown downregulation of these proteins, suggesting direct or indirect regulatory relationships .

For myogenesis studies, the PERK-eIF2α pathway should be investigated, as CPNE1 has been implicated in regulating myogenesis through this mechanism . In designing pathway studies, researchers should employ both gain-of-function (overexpression) and loss-of-function (knockdown) approaches to comprehensively characterize CPNE1's regulatory effects.

How should researchers interpret conflicting CPNE1 expression data between mRNA and protein levels?

When researchers encounter discrepancies between CPNE1 mRNA and protein expression levels, several methodological and biological factors should be considered for proper interpretation. First, post-transcriptional regulation mechanisms may account for these differences. CPNE1 may be subject to microRNA-mediated regulation, as suggested by miRNA-target enrichment analyses conducted through the LinkedOmics database . These regulatory mechanisms could result in reduced protein levels despite high mRNA expression.

Post-translational modifications and protein stability factors may also contribute to discrepancies. Researchers should investigate potential protein degradation pathways affecting CPNE1, such as the ubiquitin-proteasome system or autophagy-lysosomal degradation. Examining CPNE1 protein half-life through cycloheximide chase assays could provide valuable insights.

From a methodological perspective, researchers should critically evaluate the specificity of antibodies used for protein detection. Different antibodies may recognize distinct CPNE1 isoforms or epitopes affected by post-translational modifications. Similarly, primers used for mRNA quantification should be evaluated for their ability to detect all relevant CPNE1 splice variants.

When reporting conflicting data, researchers should incorporate multiple detection methods and time points in their experimental design. Temporal dynamics may explain apparent discrepancies, as protein expression often lags behind mRNA changes. When available, single-cell analysis approaches can provide clarification by revealing heterogeneity within bulk samples that might contribute to observed discrepancies.

What are the key considerations for investigating CPNE1's role in immune response within the tumor microenvironment?

Investigating CPNE1's role in immune response within the tumor microenvironment requires careful consideration of several key factors. CPNE1 expression has been significantly correlated with immune infiltration and immune response regulation in multiple cancer types. Specifically, the level of CPNE1 expression has shown significant positive correlation with infiltration of B cells, CD8+ T cells, CD4+ T cells, macrophages, neutrophils, and dendritic cells in hepatocellular carcinoma .

Researchers should employ comprehensive immune profiling approaches when studying CPNE1 in this context. The ESTIMATE algorithm can be applied to expression data to calculate stromal, immune, and estimate scores, providing insights into the tumor microenvironment composition . The CIBERSORT deconvolution algorithm is valuable for calculating fractions of 22 types of tumor-infiltrating immune cells (TIICs) and comparing these between high and low CPNE1 expression groups .

Special attention should be given to immune checkpoint molecules, as high CPNE1 expression has been associated with elevated expression of CD8+ T cell exhaustion markers, including CTLA4, PDCD1, and LAG3 . This suggests CPNE1 may play a role in T cell exhaustion mechanisms that inhibit cell proliferation and cytotoxic potential of CD8+ T cells, potentially contributing to immune evasion in cancer.

When designing experiments, researchers should consider both in vitro co-culture systems with immune cells and in vivo models that preserve the complex tumor microenvironment. Single-cell RNA sequencing approaches can provide valuable insights into cellular heterogeneity and specific immune cell subpopulations affected by CPNE1 expression. Flow cytometry analysis of tumor-infiltrating lymphocytes following CPNE1 manipulation can further validate findings regarding immune cell composition and activation states.

What are the optimal conditions for CPNE1 overexpression in experimental models?

The optimal conditions for CPNE1 overexpression in experimental models depend on several key factors that researchers should carefully consider. For transient overexpression in cell culture systems, the mouse Cpne1 sequence can be effectively amplified using Prime STAR Max DNA polymerase with specific primers (forward: CGCAAATGGGCGGTAGGCGTG, reverse: TTGGCTGCCCTTTCACTTCC). Successful transfection has been achieved using Lipofectamine 3000 following the manufacturer's protocol .

For stable overexpression, lentiviral vector systems have demonstrated high efficiency. Researchers studying hepatocellular carcinoma have successfully used Hep3B cells for CPNE1 overexpression through plasmid transfection . When implementing lentiviral transduction approaches in primary cells like satellite cells, a protocol using 4 μl of lentivirus solution with polybrene (5 μg/ml) has proven effective. The medium containing lentivirus should be carefully removed after 24 hours and replaced with fresh medium, with transduced cells being cultured for an additional 3 days to achieve optimal expression .

Verification of overexpression efficiency is critical and should be performed at both mRNA level (via RT-PCR) and protein level (via Western blot) . Researchers should also consider the impact of overexpression on cell viability and proliferation, as CPNE1 has been shown to affect these parameters in various cell types. When designing functional studies following overexpression, appropriate controls (empty vector transduction) must be included, and researchers should be aware that excessive overexpression might trigger cellular stress responses that could confound experimental results.

How can researchers effectively correlate CPNE1 expression with clinical outcomes in patient samples?

Effectively correlating CPNE1 expression with clinical outcomes in patient samples requires a multifaceted approach combining robust molecular analysis with comprehensive clinical data. Researchers should first establish reliable CPNE1 detection methods in their specific tissue of interest. Immunohistochemistry on tissue microarrays allows for high-throughput analysis of CPNE1 protein expression across large patient cohorts, with precise scoring systems to quantify expression levels .

Researchers should also investigate correlations between CPNE1 expression and specific clinicopathological features. Studies have shown significant associations between CPNE1 expression and sex, age, cancer stage, tumor grade, invasion range, and distant metastasis in various cancer types . For example, in clear cell renal cell carcinoma, CPNE1 expression correlates significantly with grade, invasion range, stage, and distant metastasis .

To enhance the credibility of findings, researchers should validate their results in independent patient cohorts or through publicly available databases such as TCGA, HCCDB, and the Kaplan-Meier Plotter database .

What are the most reliable positive and negative control tissues for CPNE1 antibody validation?

When validating CPNE1 antibodies, selecting appropriate positive and negative control tissues is crucial for ensuring reliable results. Based on the literature, several tissues and cell lines have demonstrated consistent CPNE1 expression patterns that make them suitable controls for validation studies.

For positive control tissues, hepatocellular carcinoma (HCC) samples represent reliable options, as CPNE1 has been consistently reported to show strong positive staining in HCC tissues compared to normal liver tissue . This differential expression has been validated across multiple HCC cohorts in the HCCDB database and confirmed through immunohistochemistry in the Human Protein Atlas (HPA) database . Similarly, osteosarcoma tissues have demonstrated high CPNE1 expression, with 84% of patient samples (21/25) showing positive staining for CPNE1, making them valuable positive controls .

For negative or low-expression control tissues, cartilage tumor tissues have been reported to exhibit minimal CPNE1 detection and can serve as appropriate negative controls . Normal liver tissue adjacent to HCC also demonstrates significantly lower CPNE1 expression compared to tumor tissue and can function as a comparative control .

In terms of cell lines, HCC cell lines show overexpression of CPNE1 compared to most tumor types, as demonstrated by the Cancer Cell Line Encyclopedia (CCLE) . Specifically, HepG2 and MHCC-97H for hepatocellular carcinoma, and Saos-2 and HOS cells for osteosarcoma have been used successfully in CPNE1 studies and can serve as positive control cell lines . Researchers should verify CPNE1 expression in their specific lots of cell lines before using them as controls.

How should researchers approach CPNE1 co-immunoprecipitation experiments to identify interaction partners?

When designing co-immunoprecipitation (Co-IP) experiments to identify CPNE1 interaction partners, researchers should implement a comprehensive strategy that maximizes specificity while minimizing false positives. The approach should begin with careful cell lysis using buffers that preserve protein-protein interactions, typically containing 1% NP-40 or Triton X-100, 150 mM NaCl, 50 mM Tris-HCl (pH 7.5), supplemented with protease and phosphatase inhibitors.

For the immunoprecipitation step, researchers can employ either antibody-based or tag-based approaches. When using CPNE1 antibodies directly, they should be pre-validated for IP applications to ensure they can effectively capture the native protein. Protein A/G magnetic beads offer advantages over traditional agarose beads due to lower non-specific binding. Alternatively, expressing tagged versions of CPNE1 (such as FLAG, HA, or Myc) allows for highly specific capture using well-characterized anti-tag antibodies, though researchers must verify that the tag does not interfere with CPNE1's interactions.

Control immunoprecipitations are crucial and should include isotype-matched irrelevant antibodies for antibody-based Co-IP or empty vector-transfected cells for tag-based approaches. Crosslinking approaches using formaldehyde or DSP (dithiobis[succinimidylpropionate]) can stabilize transient interactions but may introduce artifacts if not carefully controlled.

For identifying novel interaction partners, mass spectrometry analysis of co-immunoprecipitated proteins represents the gold standard approach. This should be complemented with reverse Co-IP experiments, where antibodies against suspected interaction partners are used to immunoprecipitate complexes, followed by Western blotting for CPNE1.

Based on current knowledge of CPNE1's functions, researchers should focus on potential interactions with components of the AKT/P53 pathway, EGFR/STAT3 signaling, WNT/β-catenin pathway, and proteins involved in immune response regulation . Proximity ligation assays can provide additional evidence for direct physical interactions in situ, complementing Co-IP findings.

What is the significance of CPNE1 expression in different cancer types, and how should researchers interpret comparative data?

The significance of CPNE1 expression varies across cancer types, but a consistent pattern of upregulation in malignant tissues compared to normal counterparts has been observed. In hepatocellular carcinoma (HCC), CPNE1 is significantly overexpressed at both mRNA and protein levels compared to adjacent normal tissues, as validated across 10 HCC cohorts in the HCCDB database . Similarly, in osteosarcoma, 84% of patient samples (21/25) demonstrated positive CPNE1 expression, significantly higher than in cartilage tumor tissues . Gastric cancer tissues also show elevated CPNE1 protein expression compared to adjacent normal tissue .

The functional significance varies by cancer type but demonstrates common themes. In osteosarcoma, CPNE1 silencing inhibits proliferation, colony formation, invasion, and metastasis, while enhancing chemosensitivity to cisplatin and adriamycin . In HCC, CPNE1 knockdown inhibits the AKT/P53 pathway, reducing proliferation, migration, and invasion capabilities .

When comparing CPNE1 expression across cancer types, researchers should standardize their analysis methods and use appropriate statistical approaches to account for inter-study variations. Meta-analysis techniques can help integrate findings across multiple studies and cancer types. Researchers should also consider tissue-specific functions of CPNE1 when interpreting results, as its role may be context-dependent despite the consistent pattern of upregulation in malignancy.

How can researchers effectively study the relationship between CPNE1 and immune infiltration in tumor tissues?

Studying the relationship between CPNE1 and immune infiltration in tumor tissues requires a systematic approach combining computational analysis with experimental validation. Researchers should first utilize computational tools to establish correlations between CPNE1 expression and immune parameters. The ESTIMATE algorithm can be applied to expression data from cohorts like TCGA to calculate stromal, immune, and estimate scores, providing initial insights into the relationship between CPNE1 expression and immune cell content in the tumor microenvironment .

The CIBERSORT deconvolution algorithm represents a powerful tool for calculating fractions of 22 types of tumor-infiltrating immune cells (TIICs) based on gene expression data . This approach has revealed that high CPNE1 expression is associated with elevated infiltrations of CD8+ T cells, plasma cells, regulatory T cells, follicular helper T cells, and CD4 memory activated T cells, while correlating with lower infiltrations of neutrophils, CD4 memory resting T cells, and M2 macrophages in clear cell renal cell carcinoma .

For experimental validation, multiplex immunohistochemistry or immunofluorescence allows simultaneous detection of CPNE1 and immune cell markers in tissue sections. Flow cytometry analysis of dissociated tumor samples can provide quantitative assessment of immune cell populations in relation to CPNE1 expression levels. Single-cell RNA sequencing offers the highest resolution for characterizing immune cell subpopulations and their relationship with CPNE1 expression.

Researchers should pay particular attention to immune checkpoint molecules, as high CPNE1 expression has been associated with elevated expression of T cell exhaustion markers including CTLA4, PDCD1, and LAG3 . Functional studies using co-culture systems of tumor cells with immune cells following CPNE1 manipulation can provide mechanistic insights into how CPNE1 influences immune cell function and recruitment.

What experimental approaches should researchers use to investigate CPNE1's potential as a therapeutic target?

Investigating CPNE1's potential as a therapeutic target requires a comprehensive experimental strategy spanning from in vitro mechanistic studies to in vivo efficacy and safety assessments. Researchers should begin with target validation experiments that definitively establish CPNE1's role in disease pathogenesis. RNA interference approaches have demonstrated that CPNE1 silencing significantly inhibits cancer cell proliferation, colony formation, invasion, and migration in multiple cancer types, providing strong rationale for therapeutic targeting .

For therapeutic development, several complementary approaches should be considered. Small molecule inhibitors targeting CPNE1's calcium-binding domains or protein-protein interaction surfaces represent one strategy. Researchers can employ high-throughput screening assays using recombinant CPNE1 protein to identify lead compounds, followed by medicinal chemistry optimization. Structure-based drug design approaches may be feasible as information about CPNE1's structure becomes available.

Another promising approach involves developing antibody-based therapeutics targeting CPNE1. This could include conventional antibodies for cells expressing CPNE1 on their surface, or antibody-drug conjugates that deliver cytotoxic payloads specifically to CPNE1-expressing cells. Functional screening assays measuring cell proliferation, migration, and invasion can identify antibodies with therapeutic potential.

Gene therapy approaches using siRNA, shRNA, or CRISPR-Cas9 technology represent alternatives for CPNE1 targeting. These approaches have shown promise in preclinical models, as demonstrated by successful CPNE1 knockdown using lentiviral vectors in osteosarcoma models .

Combination therapy studies are essential, as CPNE1 inhibition has shown enhanced sensitization to chemotherapeutic agents like cisplatin and adriamycin in osteosarcoma cells . Researchers should investigate synergistic effects with standard-of-care treatments across different cancer types. Additionally, exploring combinations with immune checkpoint inhibitors is warranted, given CPNE1's correlation with immune infiltration and T cell exhaustion markers .

How should researchers design experiments to resolve contradictory findings about CPNE1 function?

Resolving contradictory findings about CPNE1 function requires a systematic experimental approach that accounts for contextual factors and methodological variations. Researchers should first perform a comprehensive meta-analysis of existing literature to identify specific contradictions and their potential sources. These may include differences in cell types, experimental conditions, detection methods, or data interpretation approaches.

To address tissue-specific or context-dependent effects, researchers should conduct parallel experiments using multiple cell lines derived from different tissues or disease states. This approach has revealed that while CPNE1 is consistently upregulated in various cancer types, its downstream effects may involve distinct signaling pathways in different contexts. For example, CPNE1 activates the AKT/P53 pathway in hepatocellular carcinoma , while promoting EGFR/STAT3 signaling in clear cell renal cell carcinoma .

Methodological standardization is crucial for resolving contradictions. Researchers should employ multiple complementary techniques to measure the same parameter—for instance, assessing CPNE1 expression by both qPCR and Western blot, or evaluating cell proliferation through multiple assays such as CCK8, colony formation, and EdU incorporation. This multi-method verification can identify technique-specific artifacts that might contribute to contradictory findings.

For genetic manipulation studies, researchers should implement both gain-of-function (overexpression) and loss-of-function (knockdown/knockout) approaches in the same experimental system. This bidirectional manipulation provides stronger evidence for CPNE1's specific role and can resolve contradictions that might arise from examining only one direction of change.

Time-course experiments are essential when contradictory findings might result from temporal dynamics. CPNE1's effects may vary at different time points following expression changes, particularly regarding complex phenotypes like immune infiltration or metastasis. Similarly, dose-response relationships should be carefully examined, as different levels of CPNE1 expression might elicit qualitatively different cellular responses.

What is the current consensus on CPNE1's prognostic value across different cancer types?

In gastric cancer, elevated CPNE1 protein expression has been significantly correlated with advanced tumor-node-metastasis (TNM) stage, indicating its association with more aggressive disease . Similarly, in clear cell renal cell carcinoma, CPNE1 expression significantly correlates with adverse clinicopathological features, including higher grade, greater invasion range, advanced stage, and distant metastasis .

Multivariate analyses have established CPNE1 as an independent prognostic indicator in HCC, suggesting its value extends beyond association with established prognostic factors . The underlying mechanisms appear consistent across cancer types, with CPNE1 promoting cellular proliferation, migration, and invasion while inhibiting apoptosis, though the specific signaling pathways may vary by tissue context.

The strength of evidence varies by cancer type, with the most robust data available for HCC, osteosarcoma, and renal cell carcinoma. As research expands to additional cancer types, a more comprehensive understanding of tissue-specific variations in CPNE1's prognostic significance may emerge. The current consensus strongly supports CPNE1's value as a prognostic biomarker that could inform clinical decision-making and treatment stratification across multiple cancer types.

What biological mechanisms explain CPNE1's effects on tumor migration and invasion?

CPNE1's effects on tumor migration and invasion are mediated through multiple interconnected biological mechanisms, supported by evidence across several cancer types. At the molecular level, CPNE1 regulates critical signaling pathways that drive metastatic behavior. In hepatocellular carcinoma, CPNE1 activates the AKT/P53 pathway, with knockdown studies demonstrating that CPNE1 silencing inhibits this pathway, resulting in decreased migration and invasion capabilities . Conversely, CPNE1 overexpression enhances AKT activation, promoting metastatic potential.

In clear cell renal cell carcinoma, CPNE1 promotes migration through activation of the EGFR/STAT3 signaling pathway . This activation leads to increased expression of genes associated with epithelial-to-mesenchymal transition (EMT), a process fundamental to cancer cell invasion and metastasis. The regulation of cytoskeletal organization represents another key mechanism, as CPNE1 has been implicated in modulating cellular morphology and motility through its interactions with cytoskeletal components.

CPNE1 also influences the expression of matrix metalloproteinases (MMPs) and other extracellular matrix (ECM) remodeling enzymes. In osteosarcoma, CPNE1 silencing studies have shown downregulation of proteins associated with invasion and metastasis . Specifically, CPNE1 knockdown in osteosarcoma cells downregulates the expression of key signaling molecules including Ras and MEK-1/2, which are critical regulators of cell migration pathways .

The WNT/β-catenin pathway represents another mechanism through which CPNE1 promotes invasion. CPNE1 silencing in osteosarcoma cells downregulates WNT1 and β-catenin expression . This pathway is well-established in promoting cancer cell invasion through regulation of target genes involved in cell adhesion, migration, and ECM degradation.