CDCA8 Antibody

What is CDCA8 and what cellular functions does it regulate?

CDCA8 (Cell Division Cycle Associated 8) is a critical regulator of mitosis that plays essential roles in cell division processes. It functions as a component of the chromosomal passenger complex (CPC), which is crucial for proper chromosome segregation and cytokinesis during cell division. Bioinformatic analyses have demonstrated that CDCA8 is involved in multiple crucial cellular processes and pathways related to cell cycle regulation . Dysregulation of CDCA8 expression has been linked to aberrant cell proliferation and cancer progression, making it a molecule of significant interest in oncology research .

Which cancer types show significant CDCA8 dysregulation?

CDCA8 dysregulation has been documented across numerous cancer types:

What are the standard methods for detecting CDCA8 expression in experimental settings?

Multiple complementary techniques are utilized to detect and quantify CDCA8 expression:

• Immunohistochemistry (IHC): Used to detect CDCA8 protein in tissue sections of HCC, prostate cancer, and other malignancies . The Human Protein Atlas (HPA) database contains reference immunohistochemical images for CDCA8 in various normal and cancerous tissues.

• Western Blotting: Employed to detect CDCA8 protein expression in cell lines, particularly for validating knockdown experiments as demonstrated in LNCaP and DU-145 prostate cancer cells .

• Reverse transcription-quantitative PCR (RT-qPCR): Measures CDCA8 mRNA expression in cell lines, as validated in lung adenocarcinoma cell lines (A549 and H1299) compared to control cells (Beas-2B and NL-20) .

• Bioinformatic Analysis: Multiple studies utilize databases including TCGA, GEO, Oncomine, GEPIA, and HPA to analyze CDCA8 expression patterns across cancer types and normal tissues .

How should IHC protocols be optimized for CDCA8 detection in tissue samples?

For optimal CDCA8 detection via immunohistochemistry, researchers should follow these methodological considerations:

• Tissue Preparation: Use sections of approximately 4 μm thickness from formalin-fixed paraffin-embedded tissues .

• Deparaffinization and Antigen Retrieval: Thoroughly deparaffinize tissue sections in xylene followed by a graded ethanol series. Antigen retrieval is critical for optimal antibody binding and signal detection .

• Primary Antibody Incubation: Incubate slides with appropriately diluted anti-CDCA8 primary antibody at 4°C overnight to ensure complete and specific binding .

• Detection System: A detection system such as I-View 3,3'-diaminobenzidine (DAB) provides reliable visualization of CDCA8 staining .

• Controls: Include positive controls (tissues known to express CDCA8, such as cancer tissues) and negative controls (normal tissues with lower CDCA8 expression and antibody exclusion controls) to validate staining specificity .

• Analysis: Quantify staining intensity and percentage of positive cells using standardized scoring systems to enable comparison across samples.

How can CDCA8 expression be reliably compared across different cell lines?

To ensure reliable comparison of CDCA8 expression across different cell lines, implement these complementary approaches:

• RT-qPCR Analysis: Quantify CDCA8 mRNA levels using validated primers and reference genes for normalization. This approach has been successfully employed to compare expression between normal lung epithelial cells and lung cancer cell lines , as well as between normal prostate epithelial cells (RWPE-1) and prostate cancer cell lines (LNCaP, DU-145, and PC-3) .

• Western Blot Analysis: Detect and quantify CDCA8 protein levels, normalizing to established loading controls. Include both normal and cancer cell lines as demonstrated in the prostate cancer study, which showed higher CDCA8 expression in LNCaP, DU-145, and PC-3 compared to RWPE-1 .

• Bioinformatic Validation: Supplement wet-lab findings with database analyses using resources like the Cancer Cell Line Encyclopedia (CCLE) to examine CDCA8 expression across multiple cell lines .

• Standardized Conditions: Maintain consistent cell culture conditions, harvest cells at similar confluency, and process all samples simultaneously to minimize technical variability.

• Statistical Analysis: Apply appropriate statistical tests (t-tests, ANOVA) to determine significance of observed differences in expression levels .

What knockdown strategies are most effective for studying CDCA8 function?

Based on published research, several knockdown strategies have proven effective for studying CDCA8 function:

• shRNA-Mediated Knockdown: The prostate cancer study successfully used CDCA8 shRNA primers synthesized by GenePharma (Shanghai, China) cloned into a PLKO vector . This approach produced stable knockdown cell lines after lentiviral transduction and puromycin selection.

• siRNA Transfection: Specific siRNA targeting CDCA8 can be transfected into cells using Lipofectamine 2000 reagent following manufacturer's protocols, as described in the ovarian cancer study .

• Validation Methods:

  • Knockdown efficiency should be verified at both mRNA level (RT-qPCR) and protein level (Western blotting)

  • Visual confirmation via fluorescence microscopy when using fluorescently tagged constructs

  • Functional assays to assess the impact of CDCA8 knockdown on cell proliferation, migration, and invasion

• Controls: Include appropriate negative controls (non-targeting shRNA/siRNA) to distinguish between specific effects of CDCA8 depletion and non-specific effects of the knockdown procedure .

How can CDCA8 expression be linked to cell cycle regulation and cancer progression?

CDCA8's role in cell cycle regulation and cancer progression can be investigated through several complementary approaches:

• Cell Cycle Analysis: As CDCA8 is a mitosis-related gene , analyze its expression across different cell cycle phases using synchronized cell populations and correlate with established cell cycle markers.

• Functional Studies: The impact of CDCA8 manipulation on cancer hallmarks can be assessed through:

  • Knockdown or overexpression experiments followed by cell proliferation assays

  • Cell migration and invasion assays

  • Apoptosis assessment

  • Colony formation assays

• Pathway Analysis: GSEA (Gene Set Enrichment Analysis) can identify pathways associated with CDCA8 expression . Research has shown that CDCA8 regulates the expression of tumor-related proteins such as P53, PPAR, and MYC, and activates the Notch pathway in hepatocellular carcinoma .

• Protein-Protein Interaction Networks: PPI network analysis can reveal key proteins interacting with CDCA8 , providing insights into its molecular mechanisms.

• In vivo Models: While not explicitly detailed in the search results, xenograft models using CDCA8-manipulated cells would provide important validation of in vitro findings.

The comprehensive approach utilized in the prostate cancer study demonstrated that CDCA8 knockdown significantly impacted cancer cell behavior, providing a model for investigating CDCA8's role in other cancer types .

What is the relationship between CDCA8 expression and immune cell infiltration?

Analysis of the relationship between CDCA8 expression and immune cell infiltration reveals complex interactions within the tumor microenvironment:

• Correlation with Immune Cell Types:

  • CDCA8 expression is significantly positively correlated with infiltration levels of multiple immune cells, including B cells, CD8+ T cells, CD4+ T cells, and macrophages in prostate cancer

  • Particularly strong correlation with activated CD4+ T cells (rho = 0.465, p < 0.01), Th2 cells, and memory B cells in prostate cancer

  • The pan-cancer study also found positive associations between CDCA8 expression and CD8+ and CD4+ T cells across multiple cancer types

• Relationship with Chemokines: CDCA8 expression positively correlates with chemokines of the CCL family in prostate cancer, suggesting a role in immune cell recruitment .

• Analysis Tools:

  • The TIMER algorithm effectively determines relationships between CDCA8 expression and immune cell infiltration

  • The TISIDB online database provides comprehensive analysis of CDCA8's connections with the tumor immune microenvironment

• Epigenetic Connections: Reduced CDCA8 promoter methylation levels observed in KIRC, LUAD, and SKCM tissues compared to normal controls may connect to immune response modulation.

These findings suggest that CDCA8 potentially influences tumor immune surveillance mechanisms, which has implications for understanding immunotherapy response in patients with altered CDCA8 expression.

How do genomic and epigenetic factors influence CDCA8 expression in cancer?

Multiple genomic and epigenetic mechanisms regulate CDCA8 expression in cancer:

• Promoter Methylation:

  • Hypomethylation of the CDCA8 promoter is observed in multiple cancer types including KIRC, LUAD, and SKCM

  • Reduced methylation correlates with increased CDCA8 expression, suggesting epigenetic control of its transcription

• Mutation Analysis:

  • CDCA8 demonstrates a low mutation rate in most cancer types studied

  • The UCSC Xena database has been used to analyze gene copy number, methylation, and somatic mutations of CDCA8 in urinary tumors (PCa, ACC, KIRP, and KIRC)

• Transcription Factor Regulation:

  • Cistrome and JASPAR websites have been employed to identify potential transcription factors controlling CDCA8 expression

  • MYBL2 has been identified as a transcription factor that targets CDCA8 in ovarian cancer

• Copy Number Variation:

  • CNV analysis of CDCA8 in prostate cancer, adrenocortical carcinoma, and kidney cancers has been performed

  • These alterations may contribute to aberrant CDCA8 expression in cancer tissues

Understanding these regulatory mechanisms provides insight into potential therapeutic approaches targeting CDCA8 expression in various cancer types.

How should CDCA8 expression data be normalized and statistically analyzed?

For robust analysis of CDCA8 expression data, researchers should implement these normalization and statistical strategies:

• Normalization Approaches:

  • For RT-qPCR: Normalize to validated reference genes that show stable expression across the samples being compared

  • For RNA-Seq data: Apply appropriate transformations such as log2(TPM+1) as utilized by GEPIA

  • For protein quantification: Normalize Western blot data to established loading controls

  • For IHC analysis: Implement standardized scoring systems considering both staining intensity and percentage of positive cells

• Statistical Methods for Group Comparisons:

  • For two-group comparisons: Paired or unpaired Student's t-test, Mann-Whitney U test based on data distribution

  • For multiple group comparisons: Two-way ANOVA with Sidak's multiple comparisons test

  • Significance criteria: p < 0.05 is typically considered statistically significant, with multiple levels often indicated (*p < 0.05, **p < 0.01, ***p < 0.001)

• Survival Analysis:

  • Kaplan-Meier plots with log-rank tests to calculate hazard ratios (HR) and p-values

  • Univariate and multivariate Cox regression models to identify independent prognostic factors

• Diagnostic Performance Assessment:

  • ROC curve analysis to evaluate sensitivity and specificity, as demonstrated in the prostate cancer study where CDCA8 showed high diagnostic utility (AUC = 0.843)

• Quality Control:

  • Visualize data distribution using box plots to ensure homogeneity and proper normalization

  • Apply appropriate transformations to achieve normal distribution when necessary

How can conflicting CDCA8 expression data across studies be reconciled?

When confronted with discrepant CDCA8 findings across studies, consider these reconciliation strategies:

• Cancer Type-Specific Effects:

  • CDCA8's significance varies across cancer types. For example, it showed statistical significance in predicting outcomes in the GEO database but not in Oncolnc for bladder cancer

  • Analyze cancer subtypes separately, as molecular heterogeneity within a cancer type may result in varying CDCA8 impacts

• Methodological Differences:

  • Compare technical approaches (RT-qPCR, IHC, bioinformatics) used across studies

  • Assess antibody clones and detection methods, which may yield different results

  • Consider paired versus unpaired analyses; the prostate cancer study demonstrated that paired analysis removing heterogeneity strengthened findings

• Sample Characteristics:

  • UALCAN analysis demonstrated CDCA8 upregulation patterns vary based on clinical variables such as cancer stage, race, and gender

  • Stratify analyses by these variables to identify subgroup-specific patterns

• Multi-database Validation:

  • Cross-validate findings using multiple databases (TCGA, GEO, Oncomine, GEPIA)

  • Prioritize results replicated across independent cohorts

• Functional Validation:

  • In vitro and in vivo studies provide critical biological context for interpreting conflicting expression data

  • The prostate cancer study validated bioinformatic findings using cell line experiments , demonstrating the importance of experimental verification

How might CDCA8 serve as a therapeutic target in cancer treatment?

CDCA8's potential as a therapeutic target stems from several key characteristics:

What are the most promising research directions for understanding CDCA8's molecular mechanisms?

Future research on CDCA8's molecular mechanisms should focus on these promising directions:

• Structural Studies:

  • Detailed structural analysis of CDCA8 protein and its interactions could inform targeted drug development

  • Identification of critical binding domains and post-translational modifications affecting function

• Comprehensive Interactome Mapping:

  • Expand on the protein-protein interaction (PPI) network analyses to identify all critical CDCA8 interactions

  • Apply techniques such as BioID, proximity ligation assays, or mass spectrometry of immunoprecipitated complexes

• Pathway Integration:

  • Further explore CDCA8's role in the Notch pathway activation in hepatocellular carcinoma

  • Investigate connections between CDCA8 and tumor-related proteins such as P53, PPAR, and MYC

• Single-cell Analysis:

  • Apply single-cell technologies to understand heterogeneity in CDCA8 expression within tumors

  • Correlate with cell states and differentiation trajectories

• Immune Regulation Mechanisms:

  • Deeper investigation of how CDCA8 influences the tumor immune microenvironment

  • Studies of CDCA8's impact on specific immune cell function rather than just correlation with infiltration levels

• Animal Models:

  • Develop genetically engineered mouse models with CDCA8 alterations to study its role in cancer initiation and progression

  • Humanized mouse models could enable study of CDCA8's effects on human immune responses to cancer

• Epigenetic Regulation:

  • Further exploration of how promoter methylation and other epigenetic mechanisms regulate CDCA8 expression

  • Investigation of potential epigenetic therapies to modulate CDCA8 levels