ANXA10 Human

What is ANXA10 and what are its primary functions in human cells?

ANXA10 belongs to the annexin protein superfamily, which typically bind negatively charged phospholipids in a calcium-dependent manner. Unlike most annexins, ANXA10 possesses only one calcium-binding site and exhibits unique nuclear localization . ANXA10 functions primarily as a regulator of cell proliferation and differentiation in human cells . It is predominantly expressed in normal epithelial tissues, with high expression in the gastrointestinal tract and liver .

The protein plays important roles in maintaining cellular homeostasis and has been identified as a potential tumor suppressor in multiple cancer types. For experimental investigation of ANXA10 function, researchers typically use immunoblotting techniques with specific antibodies at concentrations of 1/1000 for Western blot and 1/500 for immunohistochemistry applications .

How is ANXA10 expression distributed across normal human tissues?

ANXA10 shows tissue-specific expression patterns in humans, with prominent expression in the gastrointestinal tract and liver . In normal pancreatic tissue, ANXA10 expression is typically negative or very low, as demonstrated by immunofluorescence studies using anti-ANXA10 antibodies . This baseline distribution pattern is important for researchers to establish when investigating altered ANXA10 expression in disease states.

When studying tissue distribution, researchers should consider using immunohistochemistry with appropriate controls, using antibodies such as recombinant anti-Annexin A10 (Abcam, ab213656, 1:1000) . For dual staining experiments, researchers have successfully employed rabbit anti-human ANXA10 antibody (1:250, Abcam) alongside other markers to visualize co-expression patterns .

What techniques are most effective for detecting ANXA10 expression in human samples?

Multiple complementary techniques can be employed to effectively detect ANXA10:

• Western Blot: Using anti-ANXA10 antibodies at 1/1000 dilution with appropriate protein loading (30 μg of whole cell lysate is typically sufficient). The predicted band size for ANXA10 is 37 kDa .

• Immunohistochemistry (IHC): Effective using antibodies at 1/500 dilution on paraffin-embedded tissue sections. Antigen retrieval with sodium citrate solution is recommended, followed by detection using polymer horseradish peroxidase systems .

• Immunofluorescence (IF): Particularly useful for co-localization studies. Double IF staining can be achieved using rabbit anti-human ANXA10 antibody (1:250) with DyLight 488 anti-rabbit IgG (green) for visualization, combined with DAPI counterstaining for nuclei .

• Transcriptomic analysis: RNA sequencing or microarray data can quantify ANXA10 mRNA expression, with data normalization to transcripts per million (TPM) and log2 transformation (log2(TPM + 1)) .

The selection of technique should be guided by your specific research question, available samples, and desired quantitative or qualitative outcomes.

What is the role of ANXA10 in cancer progression and how does its expression change across different cancer types?

ANXA10 demonstrates significant tumor type-specific expression patterns and functions in cancer:

Liver Hepatocellular Carcinoma (LIHC):

• ANXA10 is consistently downregulated in LIHC compared to normal tissue

• Low expression correlates with vascular invasion and early recurrence

• In vitro experiments demonstrate that ANXA10 upregulation inhibits LIHC cell proliferation and migration

Pancreatic Ductal Adenocarcinoma (PDAC):

• ANXA10 expression is significantly associated with PDAC and its precursor lesions (p<0.0001)

• Increased expression in high-grade pancreatic intraepithelial neoplasias (PanINs) and intraductal papillary mucinous neoplasms (IPMNs)

• Co-expression with CD24 is highly correlated with PDAC and high-grade neoplastic lesions

Other Cancers:

• Homozygous somatic deletion of ANXA10 or downregulation of its mRNA is associated with poor survival in gastric and bladder cancers

For researchers investigating cancer progression, it's important to note that ANXA10 may function as either a tumor suppressor or oncogenic factor depending on the cancer type and context. Methodologically, combined analysis of ANXA10 with other markers (such as CD24 in pancreatic cancer) can improve diagnostic accuracy, with receiver operating characteristic (ROC) analysis showing AUC of 0.911 when distinguishing early-stage PDAC from low-grade PanINs .

What mechanisms regulate ANXA10 expression in hepatocellular carcinoma?

Several regulatory mechanisms have been identified that control ANXA10 expression in HCC:

• Genetic alterations: High frequency loss of chromosome 4q, where ANXA10 is located, is observed in HCC .

• Transcriptional repression: ZNF281 (Zinc Finger Protein 281) has been identified as a transcriptional repressor of ANXA10. ZNF281 drives invasion and metastasis of HCC partially through transcriptional repression of ANXA10 by recruiting the NuRD complex .

• Epigenetic regulation: While not explicitly detailed in the search results, epigenetic mechanisms likely play a role in ANXA10 downregulation in HCC.

Research methodologies to investigate these mechanisms include:

• Copy number variation analysis to detect chromosomal deletions

• Chromatin immunoprecipitation (ChIP) to identify transcription factor binding

• Expression correlation studies between ZNF281 and ANXA10

• Functional studies using overexpression or knockdown of regulatory factors

Understanding these regulatory mechanisms provides potential therapeutic targets for restoring ANXA10 expression in HCC.

How can ANXA10 be utilized as a prognostic biomarker in human cancers?

ANXA10 shows significant potential as a prognostic biomarker in multiple cancer types:

In Liver Hepatocellular Carcinoma (LIHC):

• Downregulation of ANXA10 correlates with worse clinical outcomes

• ANXA10 expression is significantly linked with clinicopathological features

• A three-gene prognostic signature rooted in ANXA10-related immunomodulators has been determined to be an independent prognostic predictor

In Pancreatic Cancer:

• ANXA10 expression patterns distinguish between low-grade and high-grade pancreatic lesions

• Combined with CD24, ANXA10 shows improved performance in differentiating PanIN-3 from PanIN-1/2 lesions (AUC = 0.841)

• The combination of ANXA10 and CD24 scores has an AUC of 0.911 to distinguish early-stage PDAC from low-grade PanINs

Methodologically, researchers can implement ANXA10 as a prognostic biomarker through:

• Immunohistochemical scoring systems based on both staining intensity and proportion of positive cells

• Statistical models incorporating ANXA10 expression with other clinical parameters

• Development of nomograms to predict survival with good accuracy

• Time-dependent receiver operating characteristic (ROC) curve analysis to assess predictive accuracy

When evaluating ANXA10 as a biomarker, researchers should consider standardized scoring methods such as multiplication of proportion score (0-4) by intensity score (0-3), with a multiplication product ≥2 considered positive .

What is the relationship between ANXA10 expression and immune cell infiltration in cancer?

ANXA10 expression has been linked to tumor immune microenvironment characteristics:

• Bioinformatic analyses using tools such as TIMER, CIBERSORT, and ssGSEA algorithms have revealed associations between ANXA10 expression and immune cell infiltration in hepatocellular carcinoma .

• The "SCNA" module in TIMER can be used to evaluate the relation between immune cells and somatic copy number alterations of ANXA10 .

• The "Lymphocyte" module in TISIDB has been applied to determine the effect of ANXA10 expression and copy number alterations on the content of tumor-infiltrating lymphocytes (TILs) .

• ANXA10 expression is significantly linked with immunocytes and multiple cancer-related pathways .

Methodologically, researchers investigating this relationship should consider:

• Using computational approaches like single-sample Gene Set Enrichment Analysis (ssGSEA) with markers specific to immune cell types

• Applying the CIBERSORT methodology to deconvolute immune cell populations

• Correlating ANXA10 expression with immunomodulators using Spearman correlation tests

• Conducting functional enrichment analysis of relevant immunomodulators through platforms like WebGestalt

These approaches provide valuable insights into how ANXA10 may influence the tumor immune microenvironment, potentially informing immunotherapy strategies.

What experimental approaches are recommended for studying ANXA10 function in human cancer cells?

Several experimental approaches have proven effective for investigating ANXA10 function:

• Expression modulation:

  • Overexpression using vectors containing the ANXA10 gene to study tumor-suppressive effects

  • RNAi-mediated knockdown to investigate consequences of ANXA10 loss

  • CRISPR/Cas9 gene editing for complete knockout studies

• Functional assays:

  • Proliferation assays to assess growth-regulatory functions

  • Migration and invasion assays to evaluate effects on cell motility

  • Colony formation assays to determine effects on clonogenicity

  • Apoptosis assays to investigate cell death regulation

• Molecular interaction studies:

  • Co-immunoprecipitation to identify protein binding partners

  • Chromatin immunoprecipitation to study DNA interactions

  • Calcium-binding assays to evaluate calcium-dependent functions

• In vivo models:

  • Xenograft models using cell lines with modulated ANXA10 expression

  • Patient-derived xenografts to maintain tumor heterogeneity

  • Animal experiments following ARRIVE guidelines

When designing experiments, researchers should include appropriate controls and consider the cellular context, as ANXA10 functions may be tissue-specific. For protein detection, researchers have successfully used antibodies from commercial sources such as Abcam (ab227556 at 1/1000 dilution for Western blot and 1/500 dilution for IHC; ab213656 at 1:1000 for IHC) .

How can ANXA10 expression analysis be integrated into clinical research protocols?

Integration of ANXA10 expression analysis into clinical research protocols requires standardized approaches:

• Tissue sample collection and processing:

  • Fresh tissue should be fixed in formalin and embedded in paraffin

  • Slicing into 3 μm thick sections is recommended for optimal staining

  • Antigen retrieval using sodium citrate solution is crucial for antibody binding

• Standardized immunohistochemistry protocols:

  • Use of polymer horseradish peroxidase detection systems

  • Consistent antibody dilutions (e.g., 1:1000 for Abcam ab213656)

  • Implementation of scoring systems that account for both staining intensity and proportion of positive cells

• Data integration with clinical parameters:

  • Correlation with clinicopathological features

  • Development of prognostic models incorporating ANXA10 expression

  • Construction of nomograms for survival prediction

• Ethical considerations:

  • Approval from ethical committees (as seen in studies conducted at institutions like the Second Affiliated Hospital of Chongqing Medical University)

  • Informed consent from all participating patients

  • Adherence to guidelines such as the Helsinki declaration

Researchers should ensure proper documentation of all methodological details to facilitate reproducibility and comparison across studies.

What is known about ANXA10 as a therapeutic target in cancer treatment?

While research is still developing, several aspects of ANXA10 as a therapeutic target have emerged:

• Tumor suppressor restoration:

  • In liver hepatocellular carcinoma, where ANXA10 is downregulated, strategies to restore expression may have therapeutic potential

  • In vitro trials have shown that ANXA10 upregulation inhibits LIHC cell proliferation and migration

• Targeting regulatory mechanisms:

  • Inhibition of transcriptional repressors like ZNF281 could potentially restore ANXA10 expression in HCC

  • Understanding the recruitment of the NuRD complex by ZNF281 to repress ANXA10 provides potential intervention points

• Biomarker-guided treatment:

  • ANXA10 expression patterns could guide treatment decisions, particularly in combination with other markers

  • The three-gene prognostic signature based on ANXA10-related immunomodulators might help stratify patients for different treatment approaches

What are the optimal antibody selection criteria for ANXA10 detection in different applications?

Selecting appropriate antibodies for ANXA10 detection requires consideration of several key factors:

• Application specificity:

  • For Western blot: Rabbit Polyclonal Annexin A10/ANXA10 antibody (e.g., ab227556) at 1/1000 dilution has been validated

  • For IHC-P: The same antibody has been used successfully at 1/500 dilution

  • For double immunofluorescence: Rabbit anti-human ANXA10 antibody at 1:250 dilution combined with species-appropriate secondary antibodies (e.g., DyLight 488 anti-rabbit IgG)

• Validation status:

  • Preference for antibodies cited in publications

  • Confirmation of reactivity with human samples

  • Verification of specificity through positive and negative controls

• Epitope recognition:

  • Antibodies raised against recombinant fragment proteins within human ANXA10 provide good specificity

  • Consider the isoform recognition profile when multiple ANXA10 variants exist

• Detection system compatibility:

  • For IHC, polymer horseradish peroxidase detection systems have shown good results

  • For IF, fluorophore-conjugated secondary antibodies with appropriate spectral separation for multi-labeling experiments

When reporting antibody usage in research, detailed documentation of catalog numbers, dilutions, incubation conditions, and detection systems should be provided to ensure reproducibility.

What bioinformatic approaches are most effective for analyzing ANXA10 in multi-omics datasets?

Several bioinformatic approaches have proven effective for analyzing ANXA10 in multi-omics contexts:

• Transcriptomic analysis:

  • Differential expression analysis using TCGA data (374 tumor tissues and 50 normal tissues)

  • Normalization to transcripts per million (TPM) with log2(TPM + 1) transformation

  • GEO dataset analysis (e.g., GSE54236, GSE14520) for validation

• Immune infiltration analysis:

  • TIMER (Tumor IMmune Estimation Resource) for evaluating immune cell infiltration

  • Single-sample Gene Set Enrichment Analysis (ssGSEA) using the R package GSVA

  • CIBERSORT methodology for immune cell population deconvolution

• Clinical correlation tools:

  • TISIDB for exploring relationships with clinical parameters and tumor-infiltrating lymphocytes

  • Integration of copy number alterations with expression data

• Pathway and functional analysis:

  • WebGestalt for functional enrichment analysis

  • Analysis of m6A modification pathways

  • Competing endogenous RNA (ceRNA) network analysis

• Prognostic model development:

  • Stepwise variable selection using Akaike information criterion in Cox models

  • Time-dependent receiver operating characteristic (ROC) curve analysis

  • Kaplan-Meier survival curve analysis and log-rank tests

These approaches can be integrated to provide comprehensive insights into ANXA10 biology and clinical significance. For reproducibility, researchers should document software versions, parameters, and statistical thresholds used in their analyses.

What are the most promising areas for future investigation of ANXA10 in human disease?

Several promising research directions for ANXA10 investigation emerge from current findings:

• Mechanistic studies of tumor suppression:

  • Elucidation of the molecular pathways through which ANXA10 inhibits cancer cell proliferation and migration

  • Investigation of context-dependent functions explaining differential roles in different cancer types

• Immune regulation:

  • Further characterization of the relationship between ANXA10 and immune cell infiltration

  • Exploration of ANXA10's potential role in immunotherapy response prediction

• Translational applications:

  • Development and validation of ANXA10-based biomarker panels for early cancer detection

  • Evaluation of therapeutic strategies targeting ANXA10 regulatory mechanisms

• Multi-omics integration:

  • Comprehensive integration of genomic, transcriptomic, and proteomic data to understand ANXA10 regulation

  • Investigation of epigenetic mechanisms controlling ANXA10 expression

• Structure-function relationships:

  • Detailed characterization of ANXA10's unique calcium-binding properties

  • Investigation of nuclear localization mechanisms and nuclear functions

These research directions have potential to advance our understanding of ANXA10 biology and its applications in cancer diagnosis, prognosis, and treatment.

How can contradictory findings about ANXA10 function in different cancer types be reconciled?

The apparent contradictory roles of ANXA10 across different cancer types can be addressed through several methodological approaches:

• Tissue context consideration:

  • Systematic comparison of ANXA10 function in different tissue microenvironments

  • Investigation of tissue-specific binding partners and signaling pathways

• Isoform-specific analysis:

  • Determination whether different ANXA10 isoforms predominate in different tissues

  • Investigation of isoform-specific functions through targeted experimental designs

• Temporal dynamics assessment:

  • Evaluation of ANXA10's role at different stages of cancer progression

  • Investigation of whether initial tumor suppressive functions can switch to oncogenic roles during disease evolution

• Molecular interaction mapping:

  • Comprehensive interactome analysis across different cell types

  • Identification of context-specific protein-protein interactions

• Integrated pathway analysis:

  • Systematic comparison of ANXA10-associated pathways across cancer types

  • Network analysis to identify common and divergent signaling nodes

These approaches require rigorous experimental design with appropriate controls, multiple complementary techniques, and validation across independent cohorts to reconcile seemingly contradictory findings.