REG1A Human

What is REG1A and what is its basic function in human physiology?

REG1A (Regenerating islet-derived protein 1-alpha) belongs to the REG family of proteins, which are calcium-dependent lectins structurally similar to each other . The human REG family consists of multiple members including REG1α, REG1β, REG III, REG IV, and HIP/PAP . These proteins were initially identified for their function in pancreatic islet regeneration, with REG1A specifically named for its role in β-cell regeneration in rat pancreatic islets .

How is REG1A expression typically measured in clinical samples?

REG1A expression can be measured at both the transcriptional (mRNA) and protein levels through several validated methodologies:

For transcriptional analysis:

• Quantitative real-time PCR (qRT-PCR) is commonly employed to assess REG1A expression in both blood and tissue samples, as demonstrated in studies examining DKD cohorts .

• RNA sequencing and microarray analysis have been utilized to identify differential expression of REG1A between diseased and healthy tissues, as seen in colorectal cancer studies analyzing GEO databases (GSE18105, GSE20916, GSE28000, and GSE44076) .

For protein detection:

• Immunohistochemistry (IHC) staining is effective for visualizing REG1A protein expression in tissue samples, allowing researchers to assess both expression levels and localization patterns .

• Serum REG1A levels can be measured using enzyme-linked immunosorbent assays (ELISA), which enables clinical correlation studies .

The choice of methodology should be determined by the specific research question, sample availability, and required sensitivity/specificity parameters.

What are the normal expression patterns of REG1A in different human tissues?

REG1A shows tissue-specific expression patterns in healthy individuals, with its expression predominantly observed in:

• Pancreatic islet cells, where it plays a role in β-cell function and regeneration

• Gastrointestinal epithelial cells, particularly in the stomach and colon, suggesting roles in gastrointestinal homeostasis

• Renal tissues, where baseline expression is typically low in healthy individuals

Expression levels of REG1A remain relatively low in most normal tissues but become significantly upregulated in pathological conditions such as diabetes, diabetic kidney disease, and colorectal cancer . This characteristic differential expression between healthy and pathological states contributes to REG1A's potential utility as a biomarker.

How does REG1A function as a biomarker for Diabetic Kidney Disease (DKD)?

REG1A demonstrates considerable promise as a diagnostic biomarker for DKD through multiple lines of evidence:

• Expression analysis: REG1A is significantly upregulated in both blood and kidney samples of DKD patients compared to healthy controls (HC), with clear statistical significance (p<0.001) .

• Diagnostic efficacy: As an individual marker, REG1A demonstrates high diagnostic value with an Area Under the Curve (AUC) of 0.912 in development cohorts . When combined with RUNX3, the diagnostic efficiency increases further to 0.917 in development sets and 0.948 (95% CI: 0.898-0.998) in validation sets .

• Clinical correlation: REG1A expression positively correlates with multiple clinical indicators of kidney dysfunction, including:

  • Serum creatinine (SCr)

  • Urinary albumin creatinine ratio (UACR)

  • Fasting blood glucose (FBG)

  • HbA1c levels

  • C-peptide (C-P)

• Prognostic value: Kaplan-Meier analysis reveals that patients with high REG1A expression have an increased risk of developing DKD approximately 12 years after diabetes onset . The risk is particularly high when both REG1A and RUNX3 are elevated (HR = 6.459), with rapid increase in DKD risk after 7-8 years of diabetes .

These findings suggest that REG1A's elevation in DKD relates to both islet dysfunction and renal injury mechanisms, making it a valuable marker for monitoring disease progression and risk stratification in diabetic patients.

What molecular mechanisms connect REG1A to colorectal cancer progression?

REG1A contributes to colorectal cancer (CRC) progression through several interconnected molecular pathways:

• β-catenin/MYC axis activation: REG1A promotes CRC proliferation and metastasis by activating the β-catenin/MYC signaling pathway . This activation leads to downstream effects that enhance tumorigenic properties.

• Glycolytic metabolism regulation: REG1A upregulation drives aerobic glycolysis, a hallmark of cancer metabolism . The mechanism involves a β-catenin/MYC axis-mediated glycolysis upregulation, enhancing energy production required for rapid tumor growth .

• Epigenetic regulation: REG1A expression itself is modulated by METTL3 through m6A methylation, indicating that epitranscriptomic modifications play a role in REG1A-mediated oncogenesis .

• Cell cycle and apoptotic resistance: Experimental evidence shows that REG1A overexpression accelerates cell cycle progression and prevents apoptosis in CRC cells, while its knockdown reverses these effects .

• Migration and invasion promotion: REG1A enhances the migratory and invasive capabilities of CRC cells, contributing to metastatic potential .

Notably, inhibition of the Wnt/β-catenin/MYC axis or glycolysis process using specific inhibitors effectively abolishes the malignant behaviors driven by REG1A , confirming the mechanistic relationship and suggesting potential therapeutic approaches.

How does REG1A expression correlate with clinical outcomes in different diseases?

REG1A expression demonstrates significant correlations with clinical parameters and outcomes across multiple pathologies:

In Diabetic Kidney Disease:

• REG1A levels correlate positively with indicators of renal dysfunction, including elevated serum creatinine and UACR

• REG1A expression correlates negatively with estimated glomerular filtration rate (eGFR), indicating more severe kidney damage with higher REG1A levels

• High REG1A expression is associated with faster progression to DKD, particularly when combined with elevated RUNX3 expression (HR = 6.459)

• REG1A correlates with metabolic parameters including C-peptide, HbA1c, and fasting blood glucose, linking it to both kidney function and diabetes severity

In Colorectal Cancer:

These correlations highlight REG1A's potential value as both a diagnostic and prognostic marker across different disease contexts, with particular significance in predicting disease progression and patient outcomes.

What is the relationship between REG1A and RUNX3 in disease pathogenesis?

The relationship between REG1A and RUNX3 reveals a complex interplay in disease development, particularly in diabetic kidney disease:

• Co-expression pattern: REG1A and RUNX3 expression levels demonstrate a positive correlation (r=0.3) in patient samples, suggesting potentially coordinated regulatory mechanisms or shared pathways .

• Synergistic diagnostic value: When combined, REG1A and RUNX3 provide superior diagnostic efficacy for DKD compared to either marker alone, with validation cohorts showing an impressive AUC of 0.948 .

• Complementary pathogenic roles:

  • REG1A is associated with both islet dysfunction and renal impairment, showing significant elevation in diabetes mellitus patients even without kidney disease

  • RUNX3 appears more specific to renal impairment, without significant elevation in diabetes without kidney complications

  • RUNX3 functions downstream in the TGF-β signaling pathway, a critical mediator of diabetic kidney injury

• Risk stratification: When both markers are elevated, patients show dramatically increased DKD risk (HR = 6.459) compared to patients with elevation of only one marker or neither marker . The Kaplan-Meier curves demonstrate that patients with high expression of both REG1A and RUNX3 had the worst prognosis among all four possible expression pattern groups .

• Mechanistic differences: While both contribute to DKD, their mechanisms differ:

  • RUNX3 regulates the TGF-β1/Smad signaling pathway in high glucose-treated renal tubular epithelial cells

  • RUNX3 is implicated in diabetic vascular endothelial dysfunction

  • REG1A appears to function primarily through effects on islet cells and subsequent metabolic dysregulation

This relationship suggests that combined assessment of both markers provides more comprehensive disease monitoring than either alone, capturing multiple aspects of diabetic kidney disease pathophysiology.

What experimental designs are most effective for studying REG1A function?

When investigating REG1A function, researchers should consider multiple experimental approaches to comprehensively characterize its roles:

In vitro cellular studies:

• Gain and loss of function experiments: Overexpression and knockdown/knockout studies, as demonstrated in colorectal cancer cell lines (SW620 and HCT116), provide direct evidence of REG1A's effects on cellular functions .

• Functional assays to assess:

  • Proliferation (EdU incorporation, CCK-8 assays, colony formation)

  • Cell cycle progression (flow cytometry)

  • Apoptosis (Annexin V/PI staining)

  • Migration and invasion (Transwell assays with/without Matrigel)

  • Metabolic activity (glycolysis measurement, lactate production)

Clinical validation studies:

• Multi-cohort design: The implementation of both development and validation cohorts strengthens biomarker research, as seen in the DKD studies that validated findings across independent patient sets .

• Cross-sectional and longitudinal approaches: Combining cross-sectional analysis (DKD vs. healthy controls) with longitudinal follow-up (diabetes patients with/without DKD development) provides insights into both diagnostic and prognostic value .

• Multi-specimen analysis: Comparing REG1A expression in different sample types (blood, kidney tissue) confirms the biomarker's utility across specimen types .

Mechanistic investigations:

• Pathway analysis: Gene Set Enrichment Analysis (GSEA) using public datasets (TCGA) to identify associated pathways, as performed in colorectal cancer research identifying glycolysis as a key REG1A-associated process .

• Protein-protein interaction studies: To elucidate how REG1A interacts with signaling components like the β-catenin/MYC axis .

• Use of specific inhibitors: Pharmacological inhibition of suspected downstream pathways can confirm mechanistic relationships, as demonstrated by the reversal of REG1A effects using Wnt/β-catenin/MYC axis inhibitors .

These complementary approaches allow researchers to build a comprehensive understanding of REG1A from molecular mechanisms to clinical applications.

What are the optimal methods for detecting and quantifying REG1A in different sample types?

The detection and quantification of REG1A require careful consideration of sample type, research question, and available resources:

For blood/serum samples:

• Quantitative PCR (qPCR): Provides sensitive measurement of REG1A transcripts, as demonstrated in validation studies with 141 blood samples from biospecimen banks .

• ELISA assays: Enable protein-level quantification in serum, allowing for correlation with clinical parameters .

• Normalization considerations: For transcriptional analysis, appropriate reference genes must be selected; for protein quantification, total protein concentration or other stable serum proteins can serve as normalizers.

For tissue samples:

• Immunohistochemistry (IHC): Allows visualization of REG1A protein expression and localization within tissue architecture, with semi-quantitative scoring systems to assess expression levels .

• RNA extraction and qPCR: Provides quantitative measurement of REG1A transcripts in tissue homogenates .

• Laser capture microdissection: May be employed for more precise isolation of specific cell populations expressing REG1A.

• Western blotting: Offers protein-level confirmation of REG1A expression and opportunity to examine post-translational modifications.

For analysis of large datasets:

• Bioinformatic approaches: Analysis of public repositories (GEO databases, TCGA) provides valuable insights into REG1A expression across large patient cohorts .

• ROC curve analysis: Establishes diagnostic efficacy through Area Under the Curve (AUC) determination .

• Correlation analysis: Spearman or Pearson correlation coefficients help establish relationships between REG1A and clinical parameters .

Quality control considerations:

• Sample handling: Standardized collection, processing, and storage protocols are critical for reliable results.

• Technical replicates: Multiple measurements reduce technical variability.

• Positive and negative controls: Include samples with known REG1A status to validate assay performance.

• Calibration curves: For absolute quantification, particularly in protein assays.

The choice of method should be guided by the specific research question, with consideration of sensitivity, specificity, and technical feasibility requirements.

How can researchers address data interpretation challenges when studying REG1A as a biomarker?

Researchers face several challenges when interpreting REG1A biomarker data, requiring methodological solutions:

Establishing appropriate cutoff values:

• ROC curve analysis with Youden index calculation helps determine optimal cutoff values for categorizing high versus low REG1A expression .

• In DKD studies, researchers established specific cutoffs (0.84 for REG1A and 1.45 for RUNX3) based on statistical optimization .

• Validation across independent cohorts strengthens the reliability of these cutoff values.

Addressing confounding factors:

• Multivariate analysis: Statistical adjustment for potential confounders (age, gender, comorbidities) ensures REG1A's independent predictive value.

• Stratified analysis: Examining REG1A performance within subgroups can identify population-specific considerations.

• Careful cohort selection: The DKD study separated diabetic patients with and without kidney disease to isolate kidney-specific effects .

Distinguishing correlation from causation:

• Complementary experimental validation: In vitro studies demonstrating functional consequences of REG1A manipulation support causal relationships suggested by correlative clinical data .

• Temporal relationships: Longitudinal studies showing REG1A elevation preceding clinical outcomes strengthen causality arguments.

• Dose-response relationships: Demonstrating that higher REG1A levels correlate with more severe outcomes supports biological plausibility .

Integrating with other biomarkers:

• Combinatorial approaches: Analyzing REG1A alongside complementary markers (like RUNX3) improves predictive performance .

• Calibration curves: These assess the agreement between predicted and actual outcomes, as demonstrated in both development and validation cohorts for DKD prediction .

• Hazard ratio calculations: Quantifying risk associated with different marker combinations (as in the four-group analysis of REG1A and RUNX3) .

Addressing disease specificity:

• Comparative analysis across conditions: Studies comparing REG1A in DKD versus other chronic kidney diseases help establish specificity .

• Sample size considerations: Researchers acknowledged limitations of insufficient sample sizes when comparing diagnostic efficacy in different kidney diseases .

Through these methodological approaches, researchers can maximize the validity and utility of REG1A as a biomarker while acknowledging and addressing potential limitations.

How should researchers design clinical studies to validate REG1A as a biomarker?

Effective clinical validation of REG1A as a biomarker requires rigorous study design, incorporating several critical elements:

Cohort selection and characterization:

• Multi-stage approach: Implement separate development and validation cohorts to establish and then confirm findings .

• Comprehensive clinical characterization: The DKD study included detailed baseline information (age, gender, duration of diabetes, BMI, blood pressure) and laboratory parameters (HbA1c, FBG, lipid profiles, renal function indicators) .

• Appropriate control groups: Include both healthy controls and disease-specific controls (e.g., diabetes without kidney disease) to assess biomarker specificity .

Sample size considerations:

• Power calculations: Determine required sample sizes based on expected effect sizes and desired statistical power.

• The DKD validation study utilized 50 DKD patients, 50 diabetes patients without DKD, and 41 healthy controls, providing adequate power for their analyses .

Longitudinal follow-up:

• Time-to-event analysis: Kaplan-Meier survival analysis with DKD development as the endpoint event allows assessment of REG1A's prognostic value .

• Appropriate follow-up duration: Studies should account for the natural history of the disease (e.g., 7-12 years for diabetes complications) .

Analytical validation:

• Calibration curves: Assess agreement between predicted and observed outcomes to validate predictive models .

• Sensitivity and specificity determination: ROC curve analysis with AUC calculation quantifies diagnostic performance .

• Combination marker strategies: Evaluating REG1A alongside complementary markers (RUNX3) strengthens clinical utility .

Standardization and quality control:

• Sample collection and processing protocols: Standardize procedures to minimize pre-analytical variability.

• Technical validation: Include replicate measurements and quality control samples.

• Method consistency: Maintain consistent analytical approaches between development and validation cohorts.

Ethical considerations:

• Institutional review board approval: Studies involving human participants require proper ethical review, as noted in the DKD research approved by Shenzhen People's Hospital .

• Informed consent procedures: Though written consent requirements may vary based on study design and institutional requirements .

By incorporating these design elements, researchers can produce robust evidence supporting REG1A's utility as a clinically valuable biomarker.

What contradictory findings exist in REG1A research and how can they be reconciled?

Several apparent contradictions exist in the REG1A literature, requiring careful interpretation and reconciliation:

Contradictory roles in islet function:

• Contradiction: Some studies suggest REG1A indicates islet β-cell apoptosis and declining function, while others propose it stimulates islet regeneration and cell proliferation .

• Reconciliation approach: These seemingly opposing functions may represent context-dependent effects or temporal dynamics in REG1A activity. Researchers should design time-course experiments examining REG1A expression and function at different stages of islet injury and recovery.

Tissue-specific versus systemic effects:

• Contradiction: REG1A shows significant elevation in both local tissues (kidney, colorectal tumors) and circulation (serum), raising questions about its primary site of action .

• Reconciliation approach: Consider REG1A as both a paracrine and endocrine signaling molecule. Experimental designs should incorporate tissue-specific knockdown/overexpression alongside systemic manipulation to distinguish local from distant effects.

Diagnostic specificity challenges:

• Contradiction: REG1A elevation occurs in multiple pathologies (DKD, CRC, possibly other conditions), potentially limiting its disease-specific diagnostic value .

• Reconciliation approach:

  • Develop disease-specific cutoff values through ROC analysis in different pathologies

  • Implement combinatorial biomarker panels (e.g., REG1A + RUNX3 for DKD)

  • Consider REG1A as a "risk factor" rather than a specific diagnostic marker

  • Design studies comparing REG1A expression across multiple disease states

Age-related findings:

• Contradiction: While most studies show age as a risk factor for diabetic complications, some findings indicated aging as potentially protective against DKD .

• Reconciliation approach: Stratified analysis examining REG1A expression and function across different age groups with consideration of diabetes onset age (youth-onset versus adult-onset) might resolve this discrepancy.

Causality versus consequence:

• Contradiction: It remains unclear whether REG1A elevation is a cause or consequence of pathological processes .

• Reconciliation approach: Longitudinal studies examining REG1A dynamics before clinical disease manifestation, combined with mechanistic studies using inducible expression systems, can help establish temporal relationships and causality.

Researchers should acknowledge these contradictions explicitly in study design and interpretation, employing complementary methodologies and integrating findings across research groups to develop a more complete understanding of REG1A biology.

What are the most promising therapeutic applications targeting REG1A?

Based on current research findings, several therapeutic strategies targeting REG1A show potential for clinical development:

In Diabetic Kidney Disease:

• Early identification and intervention: Using REG1A (with RUNX3) as a predictive biomarker could enable preventive strategies for high-risk diabetic patients approximately 7-8 years before DKD development .

• REG1A antagonism: Development of antibodies or small molecules inhibiting REG1A function might mitigate renal injury mechanisms in diabetes.

• Pathway-specific interventions: Given REG1A's association with renal impairment markers, targeting downstream pathways might preserve kidney function in diabetes.

In Colorectal Cancer:

• REG1A inhibition: The study demonstrating that REG1A knockdown attenuated malignant properties suggests direct targeting could have therapeutic value .

• Metabolic intervention: Targeting the REG1A-mediated glycolysis pathway provides a metabolic vulnerability for therapeutic exploitation .

• Wnt/β-catenin/MYC axis inhibition: Since REG1A acts through this pathway, existing inhibitors of this signaling cascade might counteract REG1A-driven oncogenesis .

• Combination strategies: Pairing REG1A-targeting approaches with standard chemotherapeutics might enhance treatment efficacy, particularly since previous research showed REG1A inhibition enhances sensitivity to 5-Fluorouracil in CRC cells .

• m6A modification targeting: Given that REG1A is regulated by METTL3-mediated m6A modification, epitranscriptomic modulators might provide an upstream intervention point .

Technological approaches:

• RNA interference: siRNA or antisense oligonucleotides targeting REG1A mRNA could reduce expression in disease settings.

• CRISPR-based approaches: For localized applications, gene editing to modify REG1A expression might prove valuable.

• Neutralizing antibodies: Development of antibodies targeting secreted REG1A protein could prevent receptor activation.

Implementation considerations:

• Tissue-specific delivery systems to target REG1A inhibition to affected tissues while minimizing systemic effects

• Biomarker-guided treatment selection to identify patients most likely to benefit from REG1A-targeted therapies

• Combination strategies addressing multiple aspects of disease pathophysiology

These approaches represent promising avenues for translating REG1A research into clinical applications, though further preclinical validation and safety assessments are required before human trials.

What unresolved questions remain in REG1A research?

Despite significant advances, several critical questions about REG1A remain unresolved and warrant further investigation:

Molecular mechanism questions:

• What are the precise receptor(s) for REG1A in different cell types, particularly in renal and colorectal tissues?

• How does REG1A interact with the Wnt/β-catenin pathway at the molecular level in colorectal cancer?

• What is the exact relationship between m6A modification by METTL3 and REG1A expression/function?

• Are there post-translational modifications of REG1A that affect its function in different disease contexts?

Diagnostic application questions:

• What is the optimal timing for REG1A measurement to predict DKD development in diabetic patients?

• How does REG1A perform as a biomarker in ethnically diverse populations?

• What is the specificity of REG1A for DKD versus other forms of chronic kidney disease? (Noted as a limitation requiring further study)

• Can REG1A be effectively measured in urine samples, potentially offering a less invasive biomarker approach?

Functional biology questions:

• What explains the dual roles of REG1A in both indicating β-cell apoptosis and potentially stimulating islet regeneration?

• How does REG1A contribute to the microenvironment changes in both kidney disease and cancer?

• Are there REG1A polymorphisms that affect disease susceptibility or progression?

• What is the relationship between REG1A and the immune system in different disease contexts?

Therapeutic development questions:

• Is REG1A inhibition likely to have significant side effects given its roles in multiple tissues?

• What are the optimal strategies to deliver REG1A-targeting therapeutics to specific tissues?

• Could REG1A serve as a companion diagnostic for patient selection in clinical trials?

• What combination strategies might enhance the efficacy of REG1A-targeted approaches?

Technical research needs:

• Validation of REG1A at the protein level in kidney tissues, as noted as a limitation in the DKD research

• Development of standardized assays for REG1A detection in clinical laboratories

• Creation of improved animal models that better recapitulate REG1A biology in human disease

Addressing these unresolved questions will require multidisciplinary approaches and collaboration between basic scientists, clinical researchers, and technology developers.