COLEC10 Antibody

Where is COLEC10 protein typically expressed in cells and tissues?

COLEC10 protein (CL-L1) demonstrates a specific cellular localization pattern that reflects its function as a secreted protein. Immunofluorescence studies in ATDC5 cells (a murine chondrocyte cell line) have shown that CL-L1 is predominantly expressed in the Golgi apparatus, colocalizing with the trans-Golgi network marker 58K, consistent with its role as a secreted peptide . Additionally, CL-L1 colocalizes with laminin, a major component of the basal lamina . In mouse embryos, CL-L1 shows strong expression in the liver and submucosal palatal region, with particular localization in the basal membrane of the epithelium in the palate shelf of the maxilla . This expression pattern is crucial for researchers to consider when designing immunohistochemistry or immunofluorescence experiments targeting COLEC10.

What are the recommended fixation methods for COLEC10 antibody immunohistochemistry?

For optimal COLEC10 antibody staining in immunohistochemistry, researchers should consider the protein's cellular localization and structural characteristics. Based on experimental protocols used in published research, 4% paraformaldehyde fixation for 10-15 minutes at room temperature is recommended for cultured cells, while 24-hour fixation is suitable for tissue samples. Since COLEC10 protein localizes to the Golgi apparatus and associates with the extracellular matrix component laminin, antigen retrieval using citrate buffer (pH 6.0) may be necessary to unmask epitopes after formalin fixation. When designing immunohistochemistry experiments, it's important to note that COLEC10 expression varies by tissue, with particularly strong expression in liver tissue and epithelial structures during development . Permeabilization with 0.1-0.5% Triton X-100 may be required to access intracellular pools of the protein while maintaining extracellular matrix associations.

How can researchers validate COLEC10 antibody specificity?

Validating COLEC10 antibody specificity requires a multi-pronged approach to ensure reliable experimental results. Researchers should:

• Perform western blot analysis using positive control samples (such as liver tissue extracts) alongside negative controls. COLEC10 protein appears at approximately 34 kDa under reducing conditions.

• Include genetic controls by using COLEC10 knockout/knockdown cells or tissues as negative controls. Research has utilized various COLEC10 mutant constructs including ArgXTer, Gly77Glufs*66, and Cys176Trp mutations to validate antibody specificity .

• Conduct immunoprecipitation followed by mass spectrometry to confirm that the antibody is pulling down COLEC10 rather than cross-reactive proteins.

• Compare results from multiple antibodies targeting different epitopes of COLEC10 to confirm consistent staining patterns.

• Use recombinant COLEC10 protein for antibody pre-absorption tests to demonstrate binding specificity.

Experimental data has shown that wild-type COLEC10 expression can be detected in both cell lysates and supernatants from transfected HEK293 and HeLa cells, while certain mutations (such as ArgXTer and Gly77Glufs*66) result in no detectable protein, providing useful controls for antibody validation .

What are the optimal conditions for western blot analysis using COLEC10 antibodies?

For optimal western blot detection of COLEC10 protein, researchers should follow these methodological guidelines:

• Sample preparation: Extract proteins using RIPA buffer supplemented with protease inhibitors. For secreted COLEC10, concentrate cell culture supernatants using TCA precipitation or centrifugal filters.

• Gel electrophoresis: Use 10-12% SDS-PAGE gels for optimal resolution of COLEC10 (approximately 34 kDa).

• Transfer conditions: Transfer proteins to PVDF membranes at 100V for 60-90 minutes in Tris-glycine buffer with 20% methanol.

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

• Primary antibody incubation: Dilute COLEC10 antibody (typically 1:500-1:2000, depending on the specific antibody) in blocking buffer and incubate overnight at 4°C.

• Detection system: Use an HRP-conjugated secondary antibody followed by ECL detection.

• Controls: Include positive controls (liver tissue extracts or COLEC10-transfected cells) and negative controls (COLEC10 knockout cells or tissues).

Research has shown that wild-type COLEC10 produces detectable bands in both cell lysates and culture supernatants, while certain mutations (Cys176Trp) result in detectable protein in cell lysates but reduced secretion, which should be considered when interpreting western blot results .

What are the key considerations for immunofluorescence experiments with COLEC10 antibodies?

When conducting immunofluorescence experiments with COLEC10 antibodies, researchers should consider the following methodological aspects:

• Fixation: Use 4% paraformaldehyde for 10-15 minutes at room temperature to preserve protein localization while maintaining antigenicity.

• Permeabilization: Apply 0.1-0.2% Triton X-100 for 5-10 minutes to access intracellular COLEC10 while preserving Golgi structure.

• Blocking: Block with 5-10% normal serum (from the species of the secondary antibody) with 1% BSA to reduce background.

• Antibody dilution: Typically use COLEC10 antibodies at 1:100-1:500 dilution, optimized through titration experiments.

• Co-staining markers: Include organelle markers such as 58K for Golgi apparatus and extracellular matrix proteins like laminin for colocalization studies .

• Controls: Use COLEC10 knockdown cells and secondary-only controls to validate specificity.

• Imaging parameters: Capture images using confocal microscopy to accurately assess subcellular localization, particularly the Golgi apparatus localization pattern characteristic of COLEC10 .

• Analysis: Quantify colocalization with appropriate markers using software like ImageJ with the JACoP plugin for Pearson's correlation coefficients.

Research has demonstrated that COLEC10 shows distinct localization patterns, particularly in the Golgi apparatus and in association with laminin in the extracellular matrix, which should be evident in properly executed immunofluorescence experiments .

How does COLEC10 expression impact hepatocellular carcinoma (HCC) prognosis?

COLEC10 expression has emerged as a significant prognostic indicator in hepatocellular carcinoma based on multiple patient cohorts. Comprehensive analysis of COLEC10 expression in HCC reveals:

These findings collectively establish COLEC10 as a valuable prognostic biomarker in HCC, with potential applications in risk stratification and treatment planning for HCC patients.

What molecular mechanisms underlie COLEC10's tumor suppressor function in HCC?

COLEC10 exhibits tumor suppressor functionality in HCC through multiple molecular mechanisms that regulate key cancer-related pathways:

• Cell cycle regulation: Overexpression of COLEC10 induces G0/G1 cell cycle arrest in HCC cells, thereby inhibiting cell cycle progression and restraining cancer cell proliferation .

• Epithelial-mesenchymal transition (EMT) inhibition: COLEC10 suppresses EMT by increasing expression of the epithelial marker E-cadherin while decreasing expression of mesenchymal markers N-cadherin and Vimentin .

• Hedgehog signaling pathway modulation: COLEC10 overexpression leads to significant downregulation of key proteins within the Hedgehog signaling pathway, which has been implicated in HCC progression and metastasis .

• PI3K-AKT pathway inhibition: COLEC10 reduces phosphorylation levels of both PI3K and AKT, thereby attenuating this signaling cascade that regulates cell cycle, proliferation, apoptosis, and metabolism in HCC .

• Protein-protein interactions: Bioinformatic analyses suggest potential interactions between COLEC10 and proteins CCBE1 and FCN3, both of which are downregulated in tumor tissues and correlate with patient survival .

These mechanistic insights provide a foundation for understanding COLEC10's role in HCC pathogenesis and suggest potential avenues for targeted therapeutic development in HCC patients with low COLEC10 expression.

How can COLEC10 be targeted in functional studies of hepatocellular carcinoma?

To effectively investigate COLEC10's function in hepatocellular carcinoma, researchers can employ several methodological approaches:

• Gene overexpression studies: Transfect HCC cell lines (such as Hep3B and SMMC7721) with COLEC10 expression vectors to assess the impact on cell proliferation, migration, and invasion. Cell proliferation can be evaluated using CCK-8 assays, while migration and invasion can be assessed through Transwell assays .

• RNA interference approaches: Use siRNA or shRNA targeting COLEC10 in HCC cell lines with relatively high endogenous COLEC10 expression to evaluate the effects of COLEC10 knockdown.

• CRISPR/Cas9 gene editing: Generate COLEC10 knockout HCC cell lines to thoroughly investigate loss-of-function effects on cancer-related phenotypes.

• Mutation studies: Introduce specific COLEC10 mutations (such as Cys176Trp) that affect protein secretion to investigate the importance of secreted versus intracellular COLEC10 in tumor suppression .

• Pathway analysis: Assess the impact of COLEC10 modulation on key signaling pathways (Hedgehog, PI3K-AKT, EMT) using western blot analysis of pathway components and phosphorylation status .

• Cell cycle analysis: Use flow cytometry to evaluate cell cycle distribution following COLEC10 overexpression or knockdown .

• In vivo models: Establish xenograft models using COLEC10-overexpressing or COLEC10-knockout HCC cells in nude mice to evaluate effects on tumor growth, which has been highlighted as an important future direction in COLEC10 research .

These approaches provide a comprehensive toolkit for investigating COLEC10's tumor suppressor function in HCC and may lead to novel therapeutic strategies targeting this gene or its downstream pathways.

What are common challenges in detecting COLEC10 protein and how can they be addressed?

Researchers frequently encounter several challenges when detecting COLEC10 protein, each requiring specific methodological solutions:

• Low endogenous expression levels: COLEC10 may be expressed at low levels in many cell types, making detection difficult.

  • Solution: Use sensitive detection methods such as enhanced chemiluminescence for western blots or amplification steps in immunohistochemistry (e.g., tyramine signal amplification). Consider concentrating samples through immunoprecipitation before western blotting.

• Antibody cross-reactivity: Some COLEC10 antibodies may cross-react with other collectin family members.

  • Solution: Validate antibody specificity using COLEC10 knockout/knockdown controls and perform pre-absorption tests with recombinant proteins. Use multiple antibodies targeting different epitopes to confirm results.

• Secretion-dependent detection: Since COLEC10 protein is secreted, significant amounts may be lost in standard cellular preparations.

  • Solution: Analyze both cell lysates and concentrated culture supernatants when performing western blots. Research has demonstrated that wild-type COLEC10 produces detectable protein in both fractions, while certain mutations (such as Cys176Trp) result in reduced secretion .

• Fixation-sensitive epitopes: Some epitopes may be altered during standard fixation procedures.

  • Solution: Compare multiple fixation methods (paraformaldehyde, methanol, acetone) to determine optimal conditions for specific antibodies.

• Tissue-specific expression patterns: COLEC10 expression varies significantly across tissues.

  • Solution: Include appropriate positive control tissues (such as liver) when optimizing detection protocols .

By implementing these methodological approaches, researchers can overcome common detection challenges and achieve more reliable COLEC10 protein analysis.

How can researchers design experiments to investigate COLEC10's interaction with other proteins?

To effectively study COLEC10's protein-protein interactions, researchers should employ a multi-faceted experimental approach:

• Co-immunoprecipitation (Co-IP):

  • Perform immunoprecipitation with COLEC10 antibodies followed by western blotting for suspected interacting partners (e.g., CCBE1 and FCN3) .

  • Use epitope-tagged COLEC10 (FLAG, HA, or Myc) to facilitate pull-down experiments with high-quality commercial antibodies.

  • Include appropriate controls: IgG control immunoprecipitations and COLEC10 knockout/knockdown samples.

• Proximity ligation assay (PLA):

  • Use this technique to visualize protein interactions in situ, providing spatial information about where COLEC10 interactions occur within cells.

  • This is particularly useful for confirming interactions in the Golgi apparatus or extracellular matrix where COLEC10 localizes .

• Bimolecular fluorescence complementation (BiFC):

  • Express COLEC10 and potential interacting proteins as fusion constructs with complementary fragments of a fluorescent protein.

  • Interaction brings the fragments together, reconstituting fluorescence that can be visualized microscopically.

• GST pull-down assays:

  • Use recombinant GST-tagged COLEC10 domains to identify direct binding partners from cell lysates.

  • This approach can help map specific interaction domains within COLEC10.

• Mass spectrometry-based interactomics:

  • Perform immunoprecipitation of COLEC10 followed by mass spectrometry to identify novel interaction partners.

  • Compare results from different cell types relevant to COLEC10 function (liver cells, embryonic tissues).

• Mammalian two-hybrid system:

  • Use this system to confirm direct interactions and map interaction domains in a cellular context.

• Co-localization studies:

  • Perform immunofluorescence microscopy to assess spatial overlap between COLEC10 and potential interacting proteins, particularly focusing on Golgi apparatus and extracellular matrix locations .

Bioinformatic analyses have identified potential interactions between COLEC10 and proteins CCBE1 and FCN3, making these high-priority candidates for experimental validation using the methods described above .

What controls should be included when studying the effects of COLEC10 mutations?

When investigating COLEC10 mutations, researchers should implement a comprehensive set of experimental controls to ensure valid and interpretable results:

• Expression controls:

  • Wild-type COLEC10: Always include wild-type COLEC10 expression as a positive control to establish baseline expression, localization, and function .

  • Empty vector: Include cells transfected with the same vector lacking the COLEC10 insert to control for effects of transfection and vector expression.

  • Untransfected cells: Include completely untransfected cells to establish baseline cellular phenotypes.

• Mutation-specific controls:

  • Known loss-of-function mutations: Include previously characterized mutations like ArgXTer or Gly77Glufs*66, which research has shown produce no detectable protein .

  • Secretion-defective mutations: Include mutations like Cys176Trp, which produces detectable intracellular protein but exhibits defective secretion .

  • Site-directed mutagenesis controls: When introducing point mutations, include control mutations at non-critical residues to confirm specificity of the observed effects.

• Cellular compartment controls:

  • Analyze both cell lysates and culture supernatants when studying secreted proteins like COLEC10 .

  • Include markers for different cellular compartments (e.g., Golgi apparatus, endoplasmic reticulum) in localization studies.

• Functional controls:

  • Rescue experiments: Attempt to rescue phenotypes of COLEC10 knockout/knockdown cells by reintroducing wild-type or mutant COLEC10 to demonstrate specificity.

  • Dose-response relationships: Test multiple expression levels to establish relationships between COLEC10 levels and observed phenotypes.

• Experimental validation methods:

  • Use multiple detection methods (western blot, ELISA, immunofluorescence) to confirm results across different experimental platforms .

  • Perform timecourse experiments to distinguish between primary and secondary effects of COLEC10 manipulation.

Research has demonstrated the utility of these controls, showing that wild-type COLEC10 is detectable in both cell extracts and supernatants, while certain mutations affect protein production (ArgXTer, Gly77Glufs*66) or secretion (Cys176Trp) .

How can COLEC10 be studied in the context of 3MC syndrome?

Studying COLEC10 in the context of 3MC syndrome requires specialized methodological approaches that address both developmental aspects and molecular mechanisms:

• Patient-derived samples:

  • Analyze COLEC10 mutations in patient samples using targeted sequencing or whole-exome sequencing.

  • Establish fibroblast or lymphoblastoid cell lines from affected individuals to study cellular phenotypes.

• Functional characterization of mutations:

  • Express wild-type and mutant COLEC10 constructs (such as ArgXTer, Gly77Glufs*66, and Cys176Trp) in cell culture systems to assess protein expression, secretion, and function .

  • Use ELISA and western blotting to quantitatively assess differences in protein expression and secretion between wild-type and mutant COLEC10 .

• Developmental models:

  • Study COLEC10 expression in mouse embryo models, focusing on tissues relevant to 3MC syndrome phenotypes, such as the palatal shelf of the maxilla where CL-L1 is expressed in the basal membrane of the epithelium .

  • Generate COLEC10 knockout or knock-in mouse models mimicking human mutations to recapitulate developmental abnormalities.

• Cell migration assays:

  • Use agarose spot assays to assess the impact of COLEC10 mutations on cell migration, which is critical in developmental processes .

  • Compare migration patterns between cells expressing wild-type and mutant COLEC10 constructs.

• Protein interaction studies:

  • Investigate interactions between COLEC10 and other proteins implicated in 3MC syndrome, such as COLEC11 (which encodes CL-K1).

  • Assess how 3MC-associated mutations affect these protein interactions.

• Pathway analysis:

  • Examine the impact of COLEC10 mutations on developmental signaling pathways relevant to craniofacial development.

  • Study the relationship between COLEC10 and extracellular matrix components like laminin, with which it colocalizes .

These methodological approaches provide a comprehensive framework for understanding COLEC10's role in 3MC syndrome and the molecular mechanisms underlying the developmental abnormalities associated with COLEC10 mutations.

What are the most effective animal models for studying COLEC10 function?

Several animal models offer distinct advantages for investigating COLEC10 function, each with specific methodological considerations:

• Mouse models:

  • Conventional knockout models: Complete deletion of COLEC10 allows assessment of its role in development and physiological processes. These models are particularly valuable for studying developmental aspects relevant to 3MC syndrome .

  • Conditional knockout models: Tissue-specific or inducible deletion of COLEC10 helps distinguish between developmental and adult functions while avoiding embryonic lethality if present.

  • Knock-in models: Introduction of specific mutations identified in human patients (such as Cys176Trp) enables direct study of pathogenic mechanisms .

  • Reporter models: Fusion of reporter genes (GFP, LacZ) to COLEC10 facilitates visualization of expression patterns during development and in adult tissues.

• Xenograft models:

  • Implantation of human HCC cells with manipulated COLEC10 expression into immunodeficient mice allows evaluation of COLEC10's tumor suppressor role in vivo .

  • These models are particularly useful for testing the effects of COLEC10 overexpression or knockdown on tumor growth, metastasis, and response to therapies.

• Zebrafish models:

  • Rapid development and optical transparency make zebrafish valuable for studying COLEC10's role in embryogenesis.

  • CRISPR/Cas9-mediated knockout or knockdown using morpholinos can reveal developmental functions.

  • Time-lapse imaging allows real-time visualization of developmental processes influenced by COLEC10.

• Cell-based models:

  • While not animal models per se, organoids derived from mouse or human tissues can recapitulate 3D tissue architecture for studying COLEC10 function in a physiologically relevant context.

  • Hepatic organoids are particularly relevant for studying COLEC10's role in liver biology and HCC .

When selecting an animal model, researchers should consider the specific aspects of COLEC10 function they aim to study, whether developmental roles relevant to 3MC syndrome or tumor suppressor functions relevant to HCC .

How can researchers integrate COLEC10 findings into multi-omics analyses of cancer?

Integrating COLEC10 findings into multi-omics cancer analyses requires sophisticated methodological approaches to connect gene expression with broader molecular contexts:

• Transcriptomic integration:

  • Analyze COLEC10 co-expression networks across multiple cancer types using RNA-seq data from resources like TCGA and GEO.

  • Apply weighted gene co-expression network analysis (WGCNA) to identify modules of genes that correlate with COLEC10 expression.

  • Research has identified 97 genes with Spearman's correlation greater than 0.55 as co-expressed with COLEC10 across both cBioPortal and LinkedOmics databases .

• Proteomic correlation:

  • Correlate COLEC10 expression with protein expression patterns to identify post-transcriptional regulatory mechanisms.

  • Focus on proteomics of the Hedgehog and PI3K-AKT signaling pathways, which have been linked to COLEC10 function in HCC .

• Epigenomic analysis:

  • Examine DNA methylation patterns at the COLEC10 promoter across cancer types to identify epigenetic regulatory mechanisms.

  • Correlate chromatin accessibility data with COLEC10 expression to identify regulatory elements.

• Mutation context:

  • Analyze the mutational landscape of tumors in relation to COLEC10 expression.

  • Identify mutations in genes that correlate with altered COLEC10 expression or function.

• Pathway enrichment analysis:

  • Conduct Gene Set Enrichment Analysis (GSEA) to identify biological pathways and processes associated with COLEC10 expression.

  • Focus on pathways implicated in COLEC10 function, such as EMT, Hedgehog signaling, and PI3K-AKT pathways .

• Clinical correlation:

  • Integrate COLEC10 expression data with clinical parameters (tumor stage, survival outcomes) to develop prognostic models.

  • Research has demonstrated that COLEC10, along with CCBE1 and FCN3, can be incorporated into a risk score formula that effectively stratifies HCC patients into high-risk and low-risk groups with significantly different survival outcomes .

• Single-cell analysis:

  • Analyze COLEC10 expression at the single-cell level to identify cell populations where it may play particularly important roles.

  • Examine relationships between COLEC10 expression and cell states/phenotypes in the tumor microenvironment.

These methodological approaches provide a framework for contextualizing COLEC10 within the broader molecular landscape of cancer, potentially leading to improved prognostic models and therapeutic strategies.

What statistical approaches are most appropriate for analyzing COLEC10 expression data in cancer studies?

When analyzing COLEC10 expression data in cancer studies, researchers should consider these statistical methodologies to ensure robust and clinically relevant results:

How should researchers interpret contradictory findings regarding COLEC10 function?

When confronted with contradictory findings regarding COLEC10 function, researchers should employ these methodological approaches to resolve discrepancies:

• Context-dependent analysis:

  • Systematically evaluate differences in experimental contexts (cell types, disease models, experimental conditions) that might explain contradictory results.

  • COLEC10 may function differently in different tissues or disease states, as evidenced by its dual roles in developmental disorders (3MC syndrome) and cancer (HCC) .

• Tissue-specific expression patterns:

  • Analyze tissue-specific expression and function of COLEC10, recognizing that its role may vary across tissues.

  • Research has shown distinct expression patterns in the liver and palatal regions during development, suggesting tissue-specific functions .

• Isoform-specific effects:

  • Investigate whether different COLEC10 isoforms might have distinct or even opposing functions.

  • Ensure experimental approaches distinguish between isoforms and their potential differential effects.

• Temporal considerations:

  • Assess whether COLEC10's function changes during development, disease progression, or cellular differentiation.

  • Developmental roles may differ substantially from functions in adult tissues or disease states.

• Dose-dependent effects:

  • Evaluate whether COLEC10 exhibits dose-dependent effects, with different outcomes at low versus high expression levels.

  • Design experiments with gradient expression levels rather than simple overexpression/knockdown approaches.

• Methodology assessment:

  • Critically evaluate methodological differences between studies (antibody specificity, knockout strategies, overexpression systems).

  • Consider whether differences in COLEC10 detection methods might contribute to contradictory findings .

• Integrative analysis:

  • Perform meta-analyses or systematic reviews of COLEC10 literature to identify patterns across studies.

  • Use computational approaches to integrate findings across multiple datasets and experimental systems.

• Replication studies:

  • Design experiments that directly attempt to replicate contradictory findings under identical conditions.

  • Include positive and negative controls that align with previous studies to ensure comparability.

By systematically applying these approaches, researchers can resolve apparent contradictions and develop a more nuanced understanding of COLEC10's context-dependent functions across different tissues and disease states.

What bioinformatic tools are most useful for analyzing COLEC10 in the context of protein interaction networks?

To effectively analyze COLEC10 within protein interaction networks, researchers should utilize these specialized bioinformatic tools and methodological approaches:

• Protein-Protein Interaction Databases:

  • STRING (Search Tool for the Retrieval of Interacting Genes/Proteins): Provides comprehensive PPI networks with confidence scores. Research has used STRING to construct PPI networks for COLEC10, revealing significant enrichment (PPI enrichment P-value <1.0 × 10−16) .

  • BioGRID: Offers curated interaction data from high-throughput and low-throughput experiments.

  • IntAct: Provides molecular interaction data with detailed experimental evidence.

• Network Analysis Platforms:

  • Cytoscape: Enables visualization and analysis of complex networks. Studies have used Cytoscape to visualize PPI networks involving COLEC10, CCBE1, and FCN3 .

  • NetworkAnalyst: Facilitates gene expression profiling and network-based meta-analysis.

  • GeneMANIA: Predicts gene function based on a large set of functional association data.

• Co-expression Analysis Tools:

  • LinkedOmics: Enables multi-omics data analysis in cancer. Research has used LinkedOmics to identify 9,220 genes associated with COLEC10 expression through co-expression analysis .

  • cBioPortal: Provides visualization and analysis of cancer genomics data. Studies have identified genes with Spearman's correlation greater than 0.55 as co-expressed with COLEC10 across both cBioPortal and LinkedOmics databases .

  • WGCNA (Weighted Gene Co-expression Network Analysis): Identifies modules of highly correlated genes.

• Pathway Enrichment Analysis:

  • DAVID (Database for Annotation, Visualization and Integrated Discovery): Performs functional annotation and pathway enrichment.

  • GSEA (Gene Set Enrichment Analysis): Determines whether predefined gene sets show statistically significant differences between conditions.

  • Enrichr: Provides comprehensive gene set enrichment analysis.

• Structural Prediction Tools:

  • AlphaFold: Predicts protein structure, which can inform potential interaction interfaces.

  • PRISM: Predicts protein-protein interactions based on structural information.

  • HADDOCK: Models protein-protein complexes through docking.

• Database Integration Platforms:

  • HCCDB: Specialized for hepatocellular carcinoma database integration. Research has used HCCDB to demonstrate alterations in the PPI network of COLEC10 between HCC tissues and adjacent non-tumor tissues .

  • DisGeNET: Integrates human gene-disease associations from various expert curated databases.

By leveraging these bioinformatic tools, researchers can comprehensively analyze COLEC10's position within protein interaction networks, identify key interaction partners, and elucidate its functional role in both physiological and pathological contexts.