CCDC43 Human

What is CCDC43 and what is its basic characterization?

CCDC43 (coiled-coil domain containing 43) is a human gene located on chromosome 17q23.3 with NCBI Gene ID 124808. It encodes a protein containing coiled-coil domains, which are structural motifs characterized by repeated heptad patterns that form alpha-helical coiled structures. The human CCDC43 protein is identified as CCD43_HUMAN in protein databases, with molecular weight approximately 25 kDa .

To characterize CCDC43, researchers typically employ a combination of genomic, transcriptomic, and proteomic approaches. The Harmonizome database indicates that CCDC43 has 3,470 functional associations with biological entities spanning 7 categories extracted from 61 datasets, suggesting extensive involvement in cellular processes . For basic characterization studies, quantitative real-time PCR (qPCR) is commonly employed to measure mRNA expression levels across tissues, while western blotting using specific antibodies enables protein expression analysis.

What is the expression pattern of CCDC43 in normal human tissues?

CCDC43 shows differential expression across various human tissues and cell types. According to the Allen Brain Atlas and BioGPS datasets, CCDC43 expression profiles have been documented in various brain regions and developmental stages . For researchers investigating tissue-specific expression patterns, the following methodological approaches are recommended:

• RNA-sequencing analysis of tissue panels to quantify transcript levels

• Immunohistochemistry (IHC) using validated anti-CCDC43 antibodies (typically at 1:100-1:500 dilution) for spatial protein localization

• Single-cell RNA sequencing to identify cell type-specific expression patterns

• Tissue microarray analysis for high-throughput screening across multiple tissues

When conducting expression studies, researchers should use appropriate housekeeping genes as controls (GAPDH, β-actin) and include positive and negative tissue controls to validate antibody specificity.

How can researchers reliably detect CCDC43 at protein and transcript levels?

For robust detection of CCDC43, researchers should implement multiple complementary approaches:

Transcript Level Detection:

• Quantitative real-time PCR (qPCR) using validated primers specific to CCDC43 mRNA

• Primer design should span exon-exon junctions to avoid genomic DNA amplification

• Northern blot analysis for transcript size confirmation

• RNA-seq for genome-wide expression profiling

Protein Level Detection:

• Western blot analysis using validated antibodies against CCDC43

• Immunoprecipitation followed by mass spectrometry for interaction studies

• Immunohistochemistry or immunofluorescence for localization studies

For western blot analysis, cells should be lysed in RIPA buffer with protease inhibitors, separated on 10-12% SDS-PAGE gels, transferred to PVDF membranes, and probed with primary antibodies against CCDC43 (typically at 1:1000 dilution). Signal detection should use enhanced chemiluminescence (ECL) systems with appropriate secondary antibodies .

What is the association between CCDC43 expression and cancer progression?

CCDC43 has been identified as significantly overexpressed in multiple cancer types, including colorectal cancer (CRC) and gastric cancer (GC). High expression of CCDC43 protein correlates with adverse clinicopathological features and poor prognosis in these malignancies .

In colorectal cancer, CCDC43 overexpression is associated with:

For gastric cancer, CCDC43 expression was found to be closely related to:

• Tumor differentiation status

• Lymph node metastasis

• Poor prognosis

To study these associations, researchers typically employ tissue microarrays with paired tumor and adjacent normal tissues, scoring CCDC43 expression using standardized criteria. For example, an intensity score ≥2 with at least 50% CCDC43-positive cells is considered high expression, while <50% CCDC43-positive cells or an intensity score <2 is regarded as low expression . Kaplan-Meier survival analysis with log-rank tests should be used to correlate expression levels with patient outcomes.

How does CCDC43 contribute to cell proliferation and cell cycle regulation?

CCDC43 plays a significant role in cell proliferation and cell cycle regulation in cancer cells. Experimental evidence demonstrates that:

• CCDC43 overexpression significantly promotes cell proliferation compared to control cells, as measured by EdU incorporation assays .

• CCDC43 knockdown increases the proportion of cells in G0/G1 phase while decreasing the proportion in S phase, indicating cell cycle arrest .

• Mechanistically, CCDC43 silencing decreases expression of cell cycle-related proteins including Cyclin D1, CDK4, and CDK6, but does not affect Cyclin B1 levels .

To investigate CCDC43's role in proliferation, researchers should:

• Use stable CCDC43 overexpression and knockdown cell models

• Employ EdU incorporation assays for S-phase analysis

• Conduct flow cytometry with propidium iodide staining for cell cycle distribution

• Analyze expression of cell cycle regulators by western blot

• Perform real-time cell proliferation assays (e.g., xCELLigence or IncuCyte systems)

What is the role of CCDC43 in cancer cell invasion and metastasis?

CCDC43 significantly enhances cancer cell invasion and metastasis capabilities through several mechanisms:

• Wound healing assays demonstrate that CCDC43 overexpression promotes cell migration .

• Transwell invasion assays show increased invasive ability in CCDC43-overexpressing cells compared to controls .

• CCDC43 expression is particularly elevated in lymph node metastatic cancer tissues .

• Mechanistically, CCDC43 promotes epithelial-mesenchymal transition (EMT) through TGF-β signaling pathway activation .

For researchers investigating CCDC43's role in invasion and metastasis, the following methodological approaches are recommended:

• Transwell migration and invasion assays using Matrigel-coated chambers

• 3D spheroid invasion assays in collagen matrices

• Live-cell imaging of collective cell migration

• In vivo metastasis models using tail vein injection or orthotopic implantation

• Analysis of EMT markers (E-cadherin, vimentin, Snail, Slug) by western blot and immunofluorescence

What transcription factors regulate CCDC43 expression?

Two key transcription factors have been identified as direct regulators of CCDC43 expression:

• FOXK1 (Forkhead Box K1): In colorectal cancer, FOXK1 directly binds and activates the human CCDC43 gene promoter. Promoter assays demonstrated that FOXK1 is a direct transcriptional activator of CCDC43, and a positive correlation between FOXK1 and CCDC43 expression was observed in CRC cells .

• YY1 (Yin Yang 1): In gastric cancer, transcription factor YY1 directly binds to the CCDC43 gene promoter, leading to overexpression of CCDC43. Chromatin immunoprecipitation (ChIP) assay and luciferase reporter assay confirmed YY1's role in promoting CCDC43 expression .

For researchers investigating transcriptional regulation of CCDC43, the following methods are recommended:

• Chromatin Immunoprecipitation (ChIP) assays to confirm direct binding of transcription factors to the CCDC43 promoter

• Luciferase reporter assays with wild-type and mutant CCDC43 promoter constructs

• Electrophoretic mobility shift assays (EMSA) to confirm DNA-protein interactions

• CRISPR/Cas9-mediated deletion of putative binding sites to validate functional importance

What downstream signaling pathways are affected by CCDC43?

CCDC43 influences several key signaling pathways that contribute to cancer progression:

• TGF-β Signaling: CCDC43 induces epithelial-mesenchymal transition (EMT) through the TGF-β signaling pathway in colorectal cancer cells .

• ADRM1-Ubiquitin-Proteasome Pathway: In gastric cancer, CCDC43 upregulates and stabilizes ADRM1, resulting in altered ubiquitin-mediated proteasomal degradation. This CCDC43-ADRM1 axis promotes proliferation, invasion, and metastasis of gastric cancer cells .

To investigate these pathways, researchers should:

• Analyze phosphorylation status of pathway components (e.g., Smad2/3 for TGF-β signaling)

• Employ ubiquitination assays to assess protein stability

• Use specific pathway inhibitors to validate dependency

• Perform rescue experiments by simultaneously manipulating CCDC43 and pathway components

• Utilize proteomics approaches to identify novel interaction partners

How does the CCDC43-mediated epithelial-mesenchymal transition (EMT) proceed in cancer cells?

CCDC43 promotes EMT in cancer cells, which is critical for increased invasion and metastasis. The process involves the following mechanisms:

• Activation of TGF-β signaling pathway components

• Downregulation of epithelial markers (E-cadherin)

• Upregulation of mesenchymal markers (N-cadherin, vimentin)

• Increased expression of EMT-related transcription factors (Snail, Slug, Twist)

For researchers studying CCDC43-mediated EMT, recommended methodological approaches include:

• Immunoblotting and immunofluorescence analysis of EMT markers

• Morphological assessment of cell phenotype transitions

• RT-qPCR analysis of EMT-related gene expression

• TGF-β pathway inhibition studies to determine dependency

• Cell adhesion and extracellular matrix interaction assays

• Analysis of cell cytoskeleton rearrangements using phalloidin staining

What are the optimal methods for modulating CCDC43 expression in experimental models?

Researchers can modulate CCDC43 expression through several approaches, each with specific advantages for different experimental questions:

Overexpression Systems:

• Plasmid-based transient transfection using vectors like pENTER-FLAG-CCDC43

• Lentiviral or retroviral transduction for stable overexpression

• Inducible expression systems (e.g., Tet-On/Off) for temporal control

• CRISPR activation (CRISPRa) for endogenous gene upregulation

Knockdown/Knockout Systems:

• siRNA transfection for transient knockdown

• shRNA for stable knockdown

• CRISPR/Cas9-mediated knockout for complete gene deletion

• CRISPR interference (CRISPRi) for transcriptional repression

For establishing stable cell lines, cells transfected with expression vectors should be selected with appropriate antibiotics (e.g., puromycin at 2 μg/ml for 4 weeks) . Expression changes should be validated at both mRNA level (RT-qPCR) and protein level (western blot) before proceeding with functional assays.

What are the most informative in vitro functional assays for studying CCDC43?

To comprehensively characterize CCDC43 function, researchers should employ multiple complementary assays:

Proliferation Assays:

• EdU incorporation assay to measure DNA synthesis

• CCK-8 or MTT assays for cell viability

• Colony formation assay for long-term proliferative capacity

• Real-time cell analysis for proliferation kinetics

Cell Cycle Analysis:

• Flow cytometry with propidium iodide staining

• Immunoblotting for cell cycle regulators (Cyclins, CDKs)

• EdU pulse-chase experiments for S-phase analysis

Migration and Invasion Assays:

• Wound healing/scratch assay for collective migration

• Transwell migration assay for individual cell migration

• Matrigel invasion assay for invasive capacity

• 3D spheroid invasion assays for more physiologically relevant models

EMT and Signaling Pathway Analysis:

• Immunoblotting for EMT markers and signaling proteins

• Immunofluorescence for morphological and protein localization changes

• RT-qPCR for gene expression changes

• Luciferase reporter assays for pathway activation

What in vivo models are appropriate for studying CCDC43 function?

In vivo models provide crucial insights into CCDC43's role in tumor growth and metastasis in a physiologically relevant context:

Xenograft Models:

• Subcutaneous injection of CCDC43-modulated cancer cells into immunodeficient mice

• Orthotopic implantation for tissue-specific microenvironment effects

• Patient-derived xenografts for clinical relevance

Metastasis Models:

• Tail vein injection for experimental metastasis

• Splenic injection for liver metastasis (particularly relevant for GI cancers)

• Orthotopic implantation with spontaneous metastasis monitoring

Genetic Mouse Models:

• Conditional knockout or overexpression models using tissue-specific Cre-loxP systems

• CRISPR/Cas9-mediated genome editing in mice

When designing in vivo experiments, researchers should:

• Include appropriate sample size calculations based on expected effect sizes

• Use both gain- and loss-of-function approaches

• Employ multiple tumor models to establish generalizability

• Analyze both primary tumor growth and metastatic spread

• Validate in vivo findings with matched tumor tissue analysis (IHC, RNA-seq)

How can CCDC43 expression be evaluated in clinical samples?

For clinical research applications, CCDC43 expression can be evaluated using several methods, each with specific advantages:

Immunohistochemistry (IHC):

• Most commonly used for clinical specimens

• Allows assessment of protein expression and localization

• Semi-quantitative scoring systems can be employed (e.g., intensity score ≥2 with at least 50% CCDC43-positive cells considered high expression)

• Enables correlation with clinicopathological features

RT-qPCR:

• Quantitative assessment of mRNA levels

• Requires careful sample collection to preserve RNA integrity

• Needs appropriate reference genes for normalization

RNA In Situ Hybridization:

• Allows visualization of mRNA in tissue context

• Useful when antibodies are not specific or available

Tissue Microarrays:

• Enables high-throughput analysis across multiple samples

• Useful for large cohort studies

• Can be combined with digital pathology for quantitative analysis

For robust clinical studies, researchers should:

• Include matched tumor and adjacent normal tissues

• Ensure appropriate sample size with power calculations

• Use validated antibodies with positive and negative controls

• Employ blinded assessment by multiple pathologists

• Correlate expression with comprehensive clinicopathological data

What is the potential of CCDC43 as a therapeutic target in cancer?

Based on its role in cancer progression, CCDC43 represents a promising therapeutic target:

• The FOXK1-CCDC43 axis in colorectal cancer and the YY1-CCDC43-ADRM1 axis in gastric cancer provide multiple intervention points .

• Inhibition of ADRM1 has been shown to reverse the function of CCDC43 in gastric cancer both in vitro and in vivo, suggesting a potential therapeutic strategy .

• As a promoter of EMT and metastasis, targeting CCDC43 could reduce cancer spread.

Potential therapeutic approaches include:

• Small molecule inhibitors targeting CCDC43-protein interactions

• Antisense oligonucleotides or siRNA for CCDC43 knockdown

• Disruption of the FOXK1-CCDC43 or YY1-CCDC43 transcriptional regulation

• Targeting downstream effectors like ADRM1

• Combination approaches targeting both CCDC43 and its regulatory pathways

Researchers developing CCDC43-targeted therapies should:

• Perform high-throughput screens to identify inhibitors

• Develop target engagement assays

• Validate specificity using knockout/knockdown models

• Assess effects on normal cells to predict toxicity

• Evaluate combination potential with standard therapies

How does CCDC43 expression correlate with treatment response and patient outcomes?

Understanding the relationship between CCDC43 expression and clinical outcomes is crucial for its development as a biomarker:

• High expression of CCDC43 protein is associated with tumor progression and poor prognosis in patients with colorectal cancer .

• In gastric cancer, CCDC43 expression is closely related to tumor differentiation, lymph-node-metastasis, and prognosis .

• The mechanistic involvement of CCDC43 in cell proliferation, invasion, and EMT suggests it may influence response to therapies targeting these processes.

To investigate correlations with treatment response, researchers should:

• Conduct retrospective analyses of CCDC43 expression in pre-treatment biopsies

• Perform stratified analyses based on treatment modalities

• Use multivariate analyses to control for confounding variables

• Develop predictive models incorporating CCDC43 with other biomarkers

• Validate findings in independent patient cohorts

What are the current knowledge gaps in CCDC43 research?

Despite progress in understanding CCDC43's role in certain cancers, several knowledge gaps remain:

• The normal physiological function of CCDC43 in healthy tissues remains poorly characterized

• The complete three-dimensional structure of CCDC43 protein has not been determined

• Comprehensive protein interaction networks of CCDC43 are not fully elucidated

• The role of CCDC43 in cancer types beyond colorectal and gastric cancer requires investigation

• The potential for post-translational modifications of CCDC43 affecting its function is unexplored

• The role of CCDC43 in cancer stem cell maintenance or drug resistance has not been studied

To address these gaps, researchers should consider:

• Knockout mouse models to understand physiological function

• Structural biology approaches (X-ray crystallography, cryo-EM)

• Unbiased interactome studies using BioID or proximity labeling

• Pan-cancer analyses of CCDC43 expression and function

• Mass spectrometry to identify post-translational modifications

What emerging technologies can advance CCDC43 research?

Several cutting-edge technologies hold promise for advancing CCDC43 research:

Single-Cell Technologies:

• Single-cell RNA-seq to identify cell populations expressing CCDC43

• Single-cell proteomics for protein-level analysis

• Spatial transcriptomics to understand CCDC43 expression in tissue context

CRISPR-Based Technologies:

• CRISPR screens to identify synthetic lethal interactions with CCDC43

• Base editing for precise modification of CCDC43 regulatory elements

• CRISPRi/a for temporal control of expression

Advanced Imaging:

• Super-resolution microscopy for subcellular localization

• Live-cell imaging with fluorescent tags to track dynamics

• Intravital imaging for in vivo visualization

Computational Approaches:

• AI-based prediction of protein structure and interactions

• Network biology to map CCDC43's position in cellular pathways

• Machine learning for biomarker development

Organoid Models:

• Patient-derived organoids to study CCDC43 in more physiologically relevant systems

• CRISPR-engineered organoids with CCDC43 modifications

• Co-culture systems to examine microenvironmental influences