CCDC115 Antibody

What is CCDC115 and why is it important in cancer research?

CCDC115 (Coiled-coil domain containing 115) is an accessory component of vacuolar-ATPase that plays crucial roles in cellular processes including cell division and growth . Recent studies have identified CCDC115 as significantly upregulated in tumor tissues of liver hepatocellular carcinoma (LIHC) patients compared to normal tissues . The importance of CCDC115 in cancer research stems from its correlation with poor prognosis in LIHC patients and its involvement in cancer-related pathways, specifically the PI3K-Akt pathway . In vitro experiments have confirmed that CCDC115 expression significantly affects proliferation potential, metastasis, and sorafenib resistance of liver cancer cells . These findings suggest that CCDC115 could serve as both a diagnostic and prognostic biomarker for LIHC, making it a valuable target for cancer research.

What applications are CCDC115 antibodies commonly used for in research?

CCDC115 antibodies are utilized in multiple research applications to detect and study this protein. Based on available commercial antibodies, the most common applications include:

• Western Blot (WB): For detecting CCDC115 protein expression levels in cell or tissue lysates, allowing quantitative comparison between different samples .

• Enzyme-Linked Immunosorbent Assay (ELISA): For quantitative measurement of CCDC115 protein in solution .

• Immunohistochemistry (IHC): For visualizing CCDC115 expression patterns in tissue sections, helping researchers understand its localization in tumors versus normal tissues .

• Immunocytochemistry (ICC): For examining CCDC115 localization within cultured cells .

• Flow Cytometry (FC): For analyzing CCDC115 expression in individual cells within a heterogeneous population .

When selecting a CCDC115 antibody, researchers should consider which applications they need and choose antibodies specifically validated for those applications, as performance can vary significantly between different experimental contexts.

How do I choose the appropriate CCDC115 antibody for my research?

Selecting the appropriate CCDC115 antibody requires consideration of several key factors:

• Host Species and Clonality: Commercial CCDC115 antibodies are available as both monoclonal (from providers like LSBio, Invitrogen, and Novus Biologicals) and polyclonal (from providers like NSJ Bioreagents, Biorbyt, and Assay Genie) . Monoclonal antibodies offer higher specificity but recognize a single epitope, while polyclonal antibodies provide stronger signals by recognizing multiple epitopes but may have higher background.

• Validated Applications: Ensure the antibody has been validated for your specific application. For example, the CCDC115 Polyclonal Antibody (PACO08221) has been validated for Western blot and ELISA applications , while others may be validated for IHC, ICC, or FC .

• Species Reactivity: Confirm the antibody reacts with your species of interest. The PACO08221 antibody, for instance, demonstrates reactivity with human, mouse, and rat samples .

• Target Region: Consider whether the antibody targets a region of interest in the CCDC115 protein, especially if studying specific isoforms, as two isoforms of the human protein are produced by alternative splicing .

• Validation Data: Review the validation data provided by manufacturers, including Western blot images or IHC staining patterns, to ensure the antibody performs as expected .

A comprehensive comparison of available antibodies, such as the one provided by Antibodypedia listing 73 antibodies from 21 providers , can help researchers make informed decisions based on these criteria.

What controls should I include when using CCDC115 antibodies?

When using CCDC115 antibodies, including appropriate controls is essential for ensuring experimental validity and accurate interpretation of results:

• Positive Control: Include samples known to express CCDC115. Based on expression data, certain cancer tissues, particularly liver hepatocellular carcinoma, show elevated CCDC115 expression . Cell lines derived from these cancers would serve as appropriate positive controls.

• Negative Control: Include samples with minimal or no CCDC115 expression. While the search results don't specifically mention tissues with low CCDC115 expression, normal adjacent tissues from cancer patients could serve as relative negative controls, as CCDC115 was significantly elevated in LIHC tumor tissues compared to normal tissues .

• Loading Control: For Western blot applications, include a loading control such as GAPDH, which was used in the qRT-PCR experiments described in the research .

• Isotype Control: For immunohistochemistry or flow cytometry, include an isotype control (an antibody of the same isotype but not specific to your target) to assess non-specific binding. The isotype for many CCDC115 antibodies is IgG .

• Knockdown/Knockout Control: If possible, include samples where CCDC115 has been knocked down or knocked out to confirm antibody specificity.

• Technical Controls: Include a no-primary-antibody control to assess secondary antibody specificity and background signal.

Including these controls helps validate antibody specificity and ensures that the observed signals are truly representative of CCDC115 expression.

How can I validate the specificity of a CCDC115 antibody for my research?

Validating the specificity of a CCDC115 antibody requires a multi-faceted approach to ensure reliable research outcomes:

• Gene Silencing Experiments: Implement CCDC115 knockdown using siRNA or shRNA techniques followed by Western blot analysis. A specific antibody should show reduced or absent signal in knockdown samples. Researchers studying CCDC115 in liver cancer have used such approaches to confirm antibody specificity and to study the effects of CCDC115 on cancer cell behavior .

• Overexpression Systems: Conversely, overexpress CCDC115 in cells with low endogenous expression and verify increased signal detection. This reciprocal validation strengthens confidence in antibody specificity.

• Peptide Competition Assay: Pre-incubate the antibody with the immunizing peptide before application in your experiment. A specific antibody will show significantly reduced signal when its binding sites are blocked by the competing peptide.

• Cross-validation with Multiple Antibodies: Utilize different CCDC115 antibodies recognizing distinct epitopes. Concordant results across different antibodies strongly indicate specific detection. The availability of 73 antibodies from 21 providers offers ample opportunity for cross-validation .

• Mass Spectrometry Confirmation: For ultimate validation, immunoprecipitate CCDC115 using your antibody and confirm the identity of the precipitated protein using mass spectrometry.

• Multiple Sample Types Analysis: Test the antibody across different sample types (cell lines, tissue samples) with known CCDC115 expression patterns. For instance, compare LIHC tumor tissues with normal adjacent tissues, as CCDC115 is significantly upregulated in LIHC .

• Isotype Controls: Include appropriate isotype controls matching the CCDC115 antibody's host species and isotype (IgG for many commercial antibodies) to distinguish specific binding from non-specific interactions.

This comprehensive validation strategy ensures that experimental findings can be confidently attributed to CCDC115 rather than non-specific interactions.

What is the relationship between CCDC115 expression and cancer prognosis?

Research has revealed significant correlations between CCDC115 expression and cancer prognosis, particularly in liver hepatocellular carcinoma (LIHC):

• Adverse Prognostic Indicator: Higher CCDC115 expression has been identified as an adverse prognostic biomarker in LIHC, with elevated expression correlating with poorer prognosis for patients . This was demonstrated through extensive statistical analyses using LIHC patient databases.

• Prognostic Risk Model: A risk model based on CCDC115 expression has shown high accuracy in predicting the prognosis of LIHC patients . This model was established using the least absolute shrinkage and selection operator (LASSO) Cox regression model and validated across multiple datasets (TCGA-LIHC, GSE14520, and ICGC-LIHC-JP).

• Tumor Stage Independence: While CCDC115 expression was significantly elevated in LIHC tissues compared to normal controls, interestingly, there was no significant difference in expression levels between different cancer stages and tumor grades . This suggests that CCDC115 may be more involved in the initiation rather than progression of liver cancer.

• Association with TP53 Mutations: LIHC patients with TP53 mutations showed increased CCDC115 expression compared to those with TP53 non-mutations , indicating that higher CCDC115 expression may be associated with genetic instability in cancer.

• Survival Outcomes: CCDC115 expression correlates with progression-free survival (PFS) and disease-specific survival (DSS) in LIHC patients, based on data from the UCSC Xena database .

• Potential Therapeutic Target: The correlation between CCDC115 expression and poorer prognosis suggests that targeting CCDC115 could potentially enhance the efficacy of liver cancer therapy .

Researchers investigating cancer prognosis should consider including CCDC115 antibody-based analyses in their studies, especially when focusing on liver cancer and potentially other cancer types where CCDC115 expression is altered.

How does CCDC115 interact with cancer-related pathways and immune checkpoint molecules?

CCDC115 has been found to interact with several cancer-related pathways and immune factors, making it a potentially important player in cancer biology:

• PI3K-Akt Pathway Involvement: Research has demonstrated that CCDC115 is significantly involved in the PI3K-Akt pathway , a central signaling network that regulates cell cycle progression, metabolism, and survival. In vitro experiments confirmed that CCDC115 expression affects key protein expression in this pathway.

• Effect on Malignant Cell Behavior: Experimental manipulation of CCDC115 expression (both downregulation and upregulation) has been shown to significantly impact the proliferation potential and metastatic capacity of liver cancer cells . This indicates that CCDC115 directly influences fundamental cancer cell behaviors.

• Drug Resistance Modulation: CCDC115 expression has been linked to sorafenib resistance in liver cancer cells . Sorafenib is a standard treatment for advanced hepatocellular carcinoma, suggesting that CCDC115 may influence treatment outcomes in liver cancer patients.

• Correlation with Immune Markers: CCDC115 expression has been positively correlated with human leukocyte antigen (HLA) molecules and immune checkpoint molecules (ICMs) in LIHC patients . This suggests a potential role for CCDC115 in the tumor immune microenvironment.

• Immune Cell Infiltration: Analysis using TIMER2.0 revealed correlations between CCDC115 expression and infiltration levels of various immune cells, including T cells (CD8+, CD4+, Th1, Th2, Tregs), B cells, monocytes, macrophages (including M2 phenotype), neutrophils, dendritic cells, cancer-associated fibroblasts, myeloid-derived suppressor cells, and natural killer cells .

• Co-expression Network: Weighted gene co-expression network analysis (WGCNA) and protein interaction network construction identified genes and proteins that interact with CCDC115 , providing insights into its functional role in cancer cellular pathways.

Researchers studying cancer pathways and tumor immunology should consider investigating CCDC115's role using specific antibodies to further elucidate these interactions and their implications for cancer progression and treatment.

What are the optimal protocols for Western blotting with CCDC115 antibodies?

Optimizing Western blot protocols for CCDC115 detection requires attention to several key parameters:

• Sample Preparation: Extract total protein from cells or tissues using TRIZOL reagent or other suitable lysis buffers, as described in previous CCDC115 research . For liver cancer studies, cell lines like HepG2, Hep3B, or patient-derived samples would be appropriate.

• Protein Quantification: Standardize protein loading using BCA or Bradford assays to ensure equal amounts (typically 20-40 μg) across all lanes.

• Gel Selection: Use 10-12% SDS-PAGE gels for optimal resolution of CCDC115, which has a molecular weight that requires appropriate separation.

• Transfer Conditions: Transfer to PVDF or nitrocellulose membranes using standard transfer buffers. Semi-dry transfer systems at 15-20V for 30-45 minutes typically work well for proteins in CCDC115's molecular weight range.

• Blocking Conditions: Block membranes with 5% non-fat dry milk or BSA in TBST for 1 hour at room temperature to minimize non-specific binding.

• Primary Antibody Dilution: For CCDC115 antibodies, optimal dilutions vary by manufacturer but typically range from 1:500 to 1:2000. The CCDC115 Polyclonal Antibody (PACO08221), for example, has been validated for Western blot applications , though specific dilution recommendations should be followed from the manufacturer's guidelines.

• Incubation Conditions: Incubate with primary antibody overnight at 4°C with gentle rocking for optimal antigen-antibody binding.

• Washing Steps: Wash membranes thoroughly (4-5 times, 5 minutes each) with TBST to remove unbound antibody and reduce background.

• Secondary Antibody Selection: Use HRP-conjugated secondary antibodies matching the host species of your primary antibody (often rabbit for polyclonal CCDC115 antibodies) .

• Detection Method: Develop using enhanced chemiluminescence (ECL) reagents and image using a digital imaging system. For weaker signals, consider using more sensitive detection reagents or longer exposure times.

• Loading Control: Include GAPDH as a loading control as used in previous CCDC115 research , with primers forward: AGAAGGCTGGGGCTCATTTG, reverse: GAGGGGCCATCCACAGTCTTC.

• Quantification: Analyze band intensities using software like ImageJ, normalizing CCDC115 expression to loading controls for accurate comparison between samples.

Following these optimized protocols will help ensure specific and sensitive detection of CCDC115 protein in Western blot applications.

How can I optimize immunohistochemistry protocols for CCDC115 detection in cancer tissues?

Optimizing immunohistochemistry (IHC) protocols for CCDC115 detection in cancer tissues requires careful attention to several critical parameters:

• Tissue Processing and Fixation: Fix tissues in 10% neutral buffered formalin for 24-48 hours, followed by paraffin embedding. Over-fixation can mask epitopes, while under-fixation may compromise tissue morphology.

• Section Thickness: Cut tissue sections at 4-5 μm thickness for optimal antibody penetration and signal detection.

• Antigen Retrieval: This step is crucial for CCDC115 detection. Use heat-induced epitope retrieval (HIER) with citrate buffer (pH 6.0) or EDTA buffer (pH 9.0). Test both to determine which provides optimal staining, as CCDC115 epitope accessibility may vary depending on the antibody used.

• Endogenous Peroxidase Blocking: Incubate sections with 3% hydrogen peroxide for 10-15 minutes to block endogenous peroxidase activity, which can cause background staining.

• Antibody Selection: Choose CCDC115 antibodies specifically validated for IHC. Several commercial antibodies have been validated for this application, including those from LSBio (LS-C134110), Invitrogen (MA5-24495), and Novus Biologicals (H00084317-M05) .

• Antibody Dilution and Incubation: Start with the manufacturer's recommended dilution (typically 1:100 to 1:500 for IHC). Optimize by testing a range of dilutions. Incubate overnight at 4°C in a humidified chamber for maximum sensitivity.

• Detection System: Use a polymer-based detection system for enhanced sensitivity without increased background. For CCDC115, which may show variable expression levels across different cancer tissues , high-sensitivity detection systems are recommended.

• Counterstaining: Use hematoxylin for nuclear counterstaining, but adjust the intensity to ensure it doesn't mask positive CCDC115 staining.

• Controls:

  • Positive Control: Include liver hepatocellular carcinoma tissue sections known to express CCDC115 .

  • Negative Control: Include normal liver tissue adjacent to tumors, which shows lower CCDC115 expression compared to tumor tissue .

  • Technical Control: Include sections processed without primary antibody to assess non-specific binding of the detection system.

• Scoring System: Implement a standardized scoring system for CCDC115 expression, considering both staining intensity (0-3+) and percentage of positive cells. This allows for quantitative comparison across different samples and patient groups, as was done in studies examining CCDC115 expression across different cancer stages and grades .

• Digital Imaging Analysis: Consider using digital image analysis software for more objective quantification of CCDC115 expression, particularly when correlating expression levels with clinical outcomes as in prognostic studies .

Following these optimized protocols will enable reliable detection and assessment of CCDC115 expression in cancer tissues, facilitating research into its role as a prognostic biomarker.

What are the best approaches for quantifying CCDC115 expression in patient samples?

Accurate quantification of CCDC115 expression in patient samples is essential for clinical and translational research. Several complementary approaches can be employed:

• Quantitative Real-Time PCR (qRT-PCR):

  • Extract total RNA using TRIZOL reagent as described in previous CCDC115 studies .

  • Synthesize cDNA using a reliable first-strand synthesis kit (such as Transcriptor First Strand cDNA Synthesis Kit).

  • Perform qRT-PCR using SYBR Green-based detection with validated CCDC115 primers:
    Forward: TACGTCAGGCTCAAGCAAGC
    Reverse: CCAGTCAATGCGGTTCTGGA

  • Use GAPDH as a reference gene for normalization.

  • Calculate relative expression using the 2^-ΔΔCT method.

  • Run parameters: 95°C for 10 min; followed by 45 cycles of (95°C for 10s, 58°C for 14s, 72°C for 20s) .

• Western Blot Quantification:

  • Perform Western blot as described earlier using validated CCDC115 antibodies .

  • Capture digital images of blots within the linear range of detection.

  • Use densitometry software (ImageJ, Bio-Rad Image Lab) to quantify band intensities.

  • Normalize CCDC115 signal to loading controls (GAPDH, β-actin).

  • Compare expression across patient groups using appropriate statistical tests (t-test, ANOVA, or non-parametric equivalents based on data distribution) .

• Immunohistochemistry Scoring:

  • Stain patient tissue sections using optimized IHC protocols with validated CCDC115 antibodies .

  • Implement a semi-quantitative scoring system combining:

    • Staining intensity (0: negative, 1: weak, 2: moderate, 3: strong)

    • Percentage of positive cells (0-100%)

    • Calculate H-score (intensity × percentage) for more nuanced quantification.

  • Use digital pathology platforms for automated scoring to reduce observer bias.

  • Compare scores across different patient groups (e.g., different cancer stages, grades) .

• ELISA-Based Quantification:

  • For blood or other fluid samples, use ELISA with validated CCDC115 antibodies .

  • Generate standard curves using recombinant CCDC115 protein.

  • Calculate absolute protein concentrations in patient samples.

  • Compare levels between patient groups using appropriate statistical methods.

• Flow Cytometry:

  • For cellular samples (blood, dissociated tissues), use flow cytometry with validated CCDC115 antibodies .

  • Quantify both percentage of CCDC115-positive cells and mean fluorescence intensity.

  • Correlate with clinical parameters and outcomes.

• Multi-parametric Analysis:

  • Combine multiple quantification methods for robust analysis.

  • Correlate CCDC115 expression with clinicopathological parameters and survival data.

  • Use statistical methods like Kaplan-Meier analysis and log-rank tests to associate expression levels with patient outcomes, as demonstrated in previous CCDC115 research .

These approaches provide complementary data on CCDC115 expression at mRNA and protein levels, enabling comprehensive assessment of its role as a biomarker in patient samples.

How do I interpret conflicting results regarding CCDC115 expression across different cancer types?

Interpreting conflicting CCDC115 expression data across cancer types requires a systematic analytical approach:

• Context-Dependent Expression Patterns: Research has shown that CCDC115 expression varies significantly across cancer types. It is upregulated in some cancers (CHOL, ESCA, HNSC, LIHC, PCPG, DLBC, PAAD) but downregulated in others (COAD, GBM, KICH, KIRC, KIRP, LUAD, PRAD, THCA, UCEC) . These differences likely reflect tissue-specific functions of CCDC115 and different pathogenic mechanisms across cancer types.

• Methodological Considerations:

  • Platform Differences: Expression data may vary depending on whether it was generated using microarray, RNA-seq, or protein-based methods. For CCDC115, studies have utilized multiple databases including TCGA, GTEx, and GEO datasets .

  • Antibody Specificity: Different antibodies may recognize different epitopes or isoforms of CCDC115, as two isoforms of the human protein are produced by alternative splicing .

  • Sample Processing: Variations in tissue preservation, RNA/protein extraction methods, or IHC protocols can influence detection sensitivity.

• Analytical Framework:

  • Metadata Analysis: Examine patient demographics, disease stage, treatment history, and tissue sampling methods across studies.

  • Statistical Rigor: Assess statistical methods used, sample sizes, and whether appropriate controls were included.

  • Multi-cohort Validation: Prioritize findings validated across multiple independent cohorts, such as the CCDC115 expression data that was validated across TCGA-LIHC, GSE14520, and ICGC-LIHC-JP datasets .

• Biological Interpretation Strategies:

  • Pathway Context: Analyze CCDC115 in the context of its associated pathways (e.g., PI3K-Akt) across different cancer types .

  • Mutation Landscape: Consider the genetic background of different cancers. For example, CCDC115 expression was found to be higher in LIHC patients with TP53 mutations compared to those without .

  • Cellular Function: Consider tissue-specific roles of CCDC115 and how these might contribute to different expression patterns.

• Integration Approaches:

  • Multi-omics Integration: Combine transcriptomic, proteomic, and functional data to build a comprehensive understanding.

  • Systems Biology Perspective: Use network analysis tools like WGCNA (as used in CCDC115 research) to understand gene co-expression patterns across cancer types .

  • Meta-analysis: Perform formal meta-analysis when sufficient comparable datasets are available.

By systematically addressing these aspects, researchers can better interpret apparently conflicting CCDC115 expression data, recognizing that such variations may reflect genuine biological differences rather than methodological inconsistencies, and potentially reveal cancer type-specific roles of CCDC115.

What are common challenges when working with CCDC115 antibodies and how can they be addressed?

Researchers working with CCDC115 antibodies may encounter several challenges that can affect experimental outcomes. Here are common issues and their solutions:

• Low Signal Intensity:

  • Challenge: Weak or undetectable CCDC115 signal despite proper technique.

  • Solutions:

    • Optimize antibody concentration through titration experiments.

    • Extend primary antibody incubation time (overnight at 4°C).

    • Use more sensitive detection systems (enhanced chemiluminescence for WB; polymer-based detection for IHC).

    • Ensure effective antigen retrieval for IHC applications.

    • Consider signal amplification methods like TSA (Tyramide Signal Amplification).

• High Background/Non-specific Binding:

  • Challenge: High background obscuring specific CCDC115 signal.

  • Solutions:

    • Increase blocking time and concentration (5% BSA or milk).

    • Optimize antibody dilution (more dilute can reduce non-specific binding).

    • Include 0.1-0.3% Triton X-100 in washing buffers for more thorough washing.

    • Perform additional washing steps.

    • Consider using monoclonal antibodies for higher specificity .

    • For polyclonal antibodies, affinity purification against the target antigen can improve specificity .

• Inconsistent Results Across Experiments:

  • Challenge: Variable CCDC115 detection between replicate experiments.

  • Solutions:

    • Standardize all protocol parameters (sample preparation, incubation times, temperatures).

    • Prepare larger batches of working solutions to use across experiments.

    • Include consistent positive controls in each experiment.

    • Consider lot-to-lot variation in antibodies; purchase larger lots when possible.

    • Document detailed protocols and any modifications.

• Cross-reactivity Issues:

  • Challenge: Antibody detecting proteins other than CCDC115.

  • Solutions:

    • Validate antibody specificity using CCDC115 knockdown/knockout controls.

    • Perform peptide competition assays to confirm specific binding.

    • Consider using multiple antibodies targeting different CCDC115 epitopes .

    • Check for potential cross-reactivity with proteins of similar structure or sequence.

• Isoform-Specific Detection Problems:

  • Challenge: Selective detection of specific CCDC115 isoforms, as two isoforms of the human protein are produced by alternative splicing .

  • Solutions:

    • Verify which isoform(s) your antibody detects by consulting manufacturer data.

    • Select antibodies based on epitope location relative to known isoform differences.

    • Use multiple antibodies targeting different regions to detect all relevant isoforms.

    • Correlate protein detection with mRNA isoform expression data.

• Antigen Masking in Fixed Tissues:

  • Challenge: Poor detection in formalin-fixed, paraffin-embedded tissues.

  • Solutions:

    • Optimize antigen retrieval conditions (buffer pH, heating time, temperature).

    • Test both heat-induced and enzymatic antigen retrieval methods.

    • Consider using frozen sections when possible.

    • Minimize fixation time during sample processing.

• Quantification Challenges:

  • Challenge: Difficulty in reliably quantifying CCDC115 expression.

  • Solutions:

    • Use digital image analysis software for consistent quantification.

    • Include appropriate housekeeping controls for normalization.

    • Develop standard curves using recombinant CCDC115 protein.

    • Use multiple quantification methods (protein and mRNA level) for validation.

Addressing these challenges through systematic optimization and validation will improve the reliability and reproducibility of CCDC115 antibody-based experiments.

How can I correlate CCDC115 expression with patient outcomes in my research?

Correlating CCDC115 expression with patient outcomes requires rigorous methodological approaches and statistical analyses. Based on successful CCDC115 prognostic research , here is a comprehensive framework:

Following this comprehensive framework will enable robust correlation of CCDC115 expression with patient outcomes in clinical research.

What emerging techniques could enhance CCDC115 detection and functional analysis?

Several cutting-edge techniques are poised to revolutionize CCDC115 detection and functional analysis in cancer research:

• Spatial Transcriptomics and Proteomics:

  • Single-cell Spatial Transcriptomics: Technologies like Visium (10x Genomics) or GeoMx DSP (NanoString) could map CCDC115 mRNA expression within the tumor microenvironment with spatial resolution, revealing heterogeneity not detectable in bulk analyses.

  • Multiplexed Ion Beam Imaging (MIBI): Allows simultaneous detection of 40+ proteins, including CCDC115 and its interaction partners, while preserving spatial information in tissue sections.

  • Imaging Mass Cytometry: Combines immunohistochemistry with mass spectrometry for high-dimensional protein profiling at subcellular resolution, enabling detailed mapping of CCDC115 in relation to multiple markers.

• Advanced Antibody-Based Technologies:

  • Proximity Ligation Assay (PLA): Detects protein-protein interactions involving CCDC115 in situ, potentially revealing associations with PI3K-Akt pathway components identified in previous research .

  • Highly Multiplexed Immunofluorescence: Techniques like CODEX or Opal can detect CCDC115 alongside numerous other markers in a single tissue section, enabling comprehensive phenotyping.

  • Nanobodies and Aptamers: Smaller binding molecules offering improved tissue penetration and epitope access for CCDC115 detection.

• Genome and Proteome Editing Approaches:

  • CRISPR-Cas9 Knock-in: Endogenous tagging of CCDC115 with fluorescent proteins or epitope tags for live-cell imaging and pull-down experiments without antibody dependencies.

  • Base Editing and Prime Editing: Precise modification of CCDC115 to study structure-function relationships with minimal off-target effects.

  • Degron Systems: Targeted protein degradation approaches allowing temporal control of CCDC115 levels to study dynamic processes.

• Single-Cell and Subcellular Analysis:

  • Single-Cell Proteomics: Mass spectrometry-based approaches to quantify CCDC115 protein levels in individual cells within heterogeneous populations.

  • Live-Cell Super-Resolution Microscopy: Nanoscale visualization of CCDC115 localization and dynamics in living cancer cells.

  • Spatial Proteomics: Techniques like hyperplexed immunofluorescence or CODEX to map CCDC115 distribution across subcellular compartments and within the tumor microenvironment.

• Systems Biology Approaches:

  • Multi-omics Integration: Combining transcriptomics, proteomics, and metabolomics data to place CCDC115 in a comprehensive cellular network context.

  • Advanced Pathway Analysis: Network-based approaches beyond traditional enrichment analysis to better understand CCDC115's role in complex cellular processes.

  • Machine Learning for Pattern Recognition: Identifying subtle patterns of CCDC115 expression and its association with clinical outcomes across large datasets.

• Liquid Biopsy Applications:

  • Circulating Tumor DNA (ctDNA) Analysis: Detecting CCDC115 mutations or copy number alterations in blood samples.

  • Exosomal Protein Analysis: Measuring CCDC115 in exosomes as a potential non-invasive biomarker.

  • Circulating Tumor Cell (CTC) Profiling: Analyzing CCDC115 expression in CTCs to monitor disease progression and treatment response.

• Translational Models:

  • Patient-Derived Organoids: Testing CCDC115 targeting in 3D cultures preserving patient tumor architecture and heterogeneity.

  • Humanized Mouse Models: Evaluating CCDC115's role in cancer progression and immune response in models with human immune system components.

  • In Silico Modeling: Using structural biology and computational approaches to predict CCDC115 interactions and druggable sites.

These emerging techniques promise to provide deeper insights into CCDC115's biology, potentially accelerating its development as a diagnostic, prognostic, and therapeutic target in cancer.

How might CCDC115 antibodies be used in developing targeted cancer therapies?

CCDC115 antibodies hold significant potential for advancing targeted cancer therapies, particularly in liver hepatocellular carcinoma (LIHC) where CCDC115 has been identified as an adverse prognostic biomarker . Here are comprehensive approaches for their therapeutic application:

• Antibody-Drug Conjugates (ADCs):

  • Mechanism: CCDC115-targeting antibodies conjugated to cytotoxic payloads could deliver potent anti-cancer agents specifically to cancer cells overexpressing CCDC115.

  • Payload Selection: Based on CCDC115's role in sorafenib resistance , ADCs incorporating payloads with different mechanisms of action could overcome treatment resistance.

  • Development Strategy: Screen validated CCDC115 antibodies for internalization efficiency, a critical property for effective ADCs.

• Bispecific Antibodies:

  • Immune Engagement: Bispecific antibodies linking CCDC115 on tumor cells to CD3 on T cells could redirect T cell cytotoxicity to CCDC115-expressing tumors.

  • Pathway Inhibition: Design bispecifics targeting both CCDC115 and key PI3K-Akt pathway components identified as correlated with CCDC115 for simultaneous blockade of multiple cancer-driving mechanisms.

  • Enhanced Therapeutic Index: The strong differential expression of CCDC115 between tumor and normal tissues in LIHC provides a favorable therapeutic window for targeted approaches.

• Antibody-Based Imaging and Theranostics:

  • Imaging Applications: Radiolabeled CCDC115 antibodies could enable PET/SPECT imaging for tumor detection, staging, and treatment response monitoring.

  • Theranostic Approach: The same antibody platform could be used for both imaging (diagnostic radioisotopes) and therapy (therapeutic radioisotopes), enabling personalized treatment.

  • Patient Selection: Imaging with CCDC115 antibodies could identify patients likely to benefit from CCDC115-targeted therapies.

• Antibody-Directed Enzyme Prodrug Therapy (ADEPT):

  • Two-Step Strategy: CCDC115 antibody-enzyme conjugates localize to tumors, followed by administration of a non-toxic prodrug that the enzyme converts to an active cytotoxin specifically at the tumor site.

  • Bystander Effect: This approach could address tumor heterogeneity by affecting both CCDC115-expressing and adjacent cells.

• CAR-T and CAR-NK Cell Therapy:

  • Target Validation: Given CCDC115's elevated expression in LIHC and correlation with poor prognosis , it represents a potential CAR-T/NK target.

  • Antibody Fragment Utilization: Single-chain variable fragments (scFvs) derived from validated CCDC115 antibodies could be incorporated into CAR constructs.

  • Safety Engineering: Include safety switches or tunable designs to mitigate potential toxicity, especially important given CCDC115's expression in normal tissues albeit at lower levels .

• Therapeutic Modulation of CCDC115-Related Pathways:

  • Combination Therapy: Use CCDC115 antibody-based targeting in combination with PI3K-Akt pathway inhibitors, based on the established correlation between CCDC115 and this pathway .

  • Resistance Reversal: As CCDC115 affects sorafenib resistance , antibody-based CCDC115 inhibition could potentially resensitize resistant tumors to standard therapies.

• Antibody-Enabled Target Validation and Drug Discovery:

  • Functional Screening: Use CCDC115 antibodies in high-throughput screens to identify compounds that modulate CCDC115 expression or function.

  • Structural Studies: Employ antibodies in co-crystallization studies to reveal druggable binding sites on CCDC115.

  • Mechanism Elucidation: Apply antibodies in mechanistic studies to better understand how CCDC115 contributes to cancer progression and drug resistance .

• Antibody-Guided Drug Delivery Systems:

  • Nanoparticle Targeting: Functionalize drug-loaded nanoparticles with CCDC115 antibodies for targeted delivery to tumor cells.

  • Enhanced Permeability and Retention: Combine passive targeting (EPR effect) with active targeting via CCDC115 antibodies for improved tumor accumulation.

The clinical development of these approaches should be informed by the established role of CCDC115 in cancer biology, particularly its association with the PI3K-Akt pathway and impact on sorafenib resistance in liver cancer .