ADPRH Human

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

ADPRH (ADP-ribosylarginine hydrolase) functions as a critical modulator of CD8+ T cell functions in humans, playing an essential role in immune regulation. The protein catalyzes the removal of ADP-ribose from arginine residues in proteins, effectively reversing mono-ADP-ribosylation—a post-translational modification that affects protein function and cellular signaling. In normal physiological conditions, ADPRH helps maintain cellular homeostasis by regulating the ADP-ribosylation status of various target proteins involved in immune function, metabolism, and cell proliferation pathways .

Research has demonstrated that ADPRH expression varies significantly between normal and pathological tissues, with dysregulation of this enzyme being implicated in several disease processes, particularly cancer development. Its role extends beyond basic cellular metabolism to influence immune surveillance mechanisms, which explains its emerging significance in tumor immunity research .

How is ADPRH expression typically measured in human tissue samples?

ADPRH expression in human tissue samples can be measured through multiple complementary techniques, depending on whether researchers are targeting transcript or protein levels:

Transcriptomic analysis methods:

• Real-time PCR (rt-PCR): Used for quantitative measurement of ADPRH mRNA expression, as validated in studies comparing LGG and normal brain tissues

• RNA sequencing: Provides comprehensive transcriptomic profiling, allowing for comparative analysis across different databases such as GEPIA and GLIOVIS

Proteomic analysis methods:

• Immunohistochemistry (IHC): Enables visualization and semi-quantitative assessment of ADPRH protein expression in tissue sections, as demonstrated in studies using the Human Protein Atlas (HPA)

• ELISA: While not specifically mentioned for ADPRH in the search results, similar protein quantification techniques could be applied following the principles outlined for other proteins

What are the normal expression patterns of ADPRH across different human tissues?

ADPRH exhibits tissue-specific expression patterns in normal human tissues, with research data from the Human Protein Atlas (HPA) providing valuable baseline information for comparative studies. In normal brain tissue, ADPRH shows relatively low expression levels compared to certain pathological states, particularly low-grade gliomas .

The baseline expression of ADPRH in normal tissues serves as a critical reference point for researchers investigating its dysregulation in disease states. According to immunohistochemistry data from HPA, ADPRH protein expression in normal brain tissue is significantly lower than in LGG samples, making it a potentially useful biomarker for distinguishing normal from neoplastic neural tissue .

When designing studies to investigate ADPRH expression, researchers should include appropriate normal tissue controls matched for anatomical location, age, and other relevant demographic factors to establish accurate baseline comparisons. This practice helps ensure that observed alterations in expression are genuinely associated with the pathological condition under investigation rather than normal tissue heterogeneity .

How does ADPRH expression differ between low-grade gliomas and normal brain tissue?

ADPRH expression shows consistent and significant upregulation in low-grade glioma compared to normal brain tissue, as demonstrated through multiple independent analytical approaches. This differential expression has been validated at both the transcriptomic and proteomic levels:

Transcriptomic evidence:

• GEPIA database analysis demonstrated overexpression of ADPRH in LGG samples compared to normal brain tissues

• GLIOVIS database analysis confirmed these findings across multiple datasets

• Validation cohort containing 18 LGG samples and 10 non-tumor brain samples further corroborated ADPRH upregulation in LGG tissues

Proteomic evidence:

• Immunohistochemistry (IHC) analysis revealed remarkably higher ADPRH protein expression in LGG samples compared to normal brain tissue

The expression level of ADPRH also shows positive correlation with tumor grade, with higher expression observed in WHO grade III compared to grade II gliomas based on data from both TCGA and CGGA cohorts . This gradient of expression suggests that ADPRH upregulation may be associated with glioma progression and potentially more aggressive disease phenotypes.

What is the prognostic value of ADPRH expression in low-grade glioma patients?

ADPRH expression has demonstrated significant prognostic value in low-grade glioma patients, with high expression levels consistently associated with poorer clinical outcomes. Comprehensive multivariate analyses have established ADPRH as an independent prognostic biomarker:

Survival outcomes associated with high ADPRH expression:

Multivariate Cox analysis findings:
The expression level of ADPRH remains an independent prognosis-predicting index even after adjusting for established clinical variables including age, gender, and IDH mutation status, as shown in the table below:

The remarkably high hazard ratio (HR) for ADPRH expression (2.837 in TCGA cohort and 1.633 in CGGA cohort) with highly significant p-values (<0.001 in both cohorts) underscores its powerful prognostic potential . These findings suggest that ADPRH expression analysis could significantly enhance current prognostic models for LGG patients and potentially inform clinical decision-making regarding treatment strategies and surveillance protocols.

How do researchers analyze the relationship between ADPRH expression and tumor immune infiltration?

Researchers employ several specialized databases and analytical approaches to investigate the relationship between ADPRH expression and tumor immune infiltration:

Primary analytical platforms:

• TIMER database: Used to assess correlations between ADPRH expression and various tumor immune infiltrating cells (TIICs)

• ESTIMATE database: Provides algorithmic scores that predict the presence of infiltrating stromal and immune cells in tumor samples based on gene expression data

Methodological approach:

• Gene expression data for ADPRH and immune cell markers are extracted from cancer genomics databases (TCGA, CGGA)

• Correlation analyses are performed between ADPRH expression levels and immune cell signature genes

• Statistical methods, including Pearson or Spearman correlation coefficients, are calculated to quantify relationships

• Visualization techniques such as scatterplots and heatmaps are employed to represent correlation patterns

Research has demonstrated that ADPRH expression has strong correlations with tumor immune infiltrating cells in LGG, suggesting its potential involvement in shaping the tumor immune microenvironment . This relationship may partially explain the mechanism by which ADPRH impacts patient prognosis, as tumor immune infiltration patterns are known to significantly influence cancer progression and treatment response.

When designing studies to investigate these relationships, researchers should consider the heterogeneity of immune cell populations within tumors and employ multiple analytical approaches to validate findings, including both computational and experimental validation methods.

What are the recommended experimental controls when studying ADPRH expression in human tissues?

When designing experiments to study ADPRH expression in human tissues, researchers should implement a comprehensive set of controls to ensure reliable and reproducible results:

Essential experimental controls:

• Tissue-matched normal controls:

  • Non-tumor brain samples from the same anatomical region as the tumor samples being studied

  • Age-matched control tissues to account for age-related changes in ADPRH expression

• Technical controls for expression analysis:

  • Positive and negative controls for immunohistochemistry (IHC) staining

  • Reference genes/housekeeping controls for rt-PCR (such as GAPDH, β-actin, or 18S rRNA)

  • Calibration standards for quantitative protein assays

  • Isotype controls for antibody-based detection methods

• Biological reference controls:

  • Gradient of expression samples (when available) showing different levels of ADPRH expression

  • WHO grade II and grade III glioma samples to account for grade-dependent expression variations

  • IDH-mutant and IDH-wildtype samples to control for molecular subtype variations

• Validation cohort:

  • Independent sample set for validation of initial findings, as demonstrated in studies using multiple cohorts (TCGA, CGGA, and independent validation cohorts)

Implementing these controls helps researchers distinguish true biological signals from technical artifacts and allows for more robust interpretation of ADPRH expression data in the context of human pathologies, particularly in cancer research.

How should researchers design experiments to study the functional role of ADPRH in immune regulation?

Designing experiments to elucidate the functional role of ADPRH in immune regulation requires a multi-faceted approach that combines molecular, cellular, and systems biology techniques:

In vitro experimental approaches:

• Gene modulation studies:

  • CRISPR-Cas9 mediated knockout of ADPRH in relevant cell lines (glioma cells, immune cells)

  • shRNA or siRNA-mediated knockdown for transient suppression of ADPRH expression

  • Overexpression systems using viral vectors to assess gain-of-function effects

  • Inducible expression systems to study temporal aspects of ADPRH function

• Co-culture systems:

  • Co-cultures of glioma cells with immune cells (particularly CD8+ T cells) with modulated ADPRH expression

  • Transwell systems to distinguish between contact-dependent and soluble factor-mediated effects

  • Measurement of immune cell activation markers, cytokine production, and cytotoxic activity

• Functional assays:

  • ADP-ribosylation assays to measure enzymatic activity of ADPRH

  • T cell proliferation and activation assays in the presence or absence of ADPRH

  • Cytokine production profiling using multiplex assays or ELISA

  • Cytotoxicity assays to assess the impact on immune-mediated tumor cell killing

In vivo approaches:

• Orthotopic glioma models with ADPRH-modified tumor cells

• Humanized mouse models to study human immune cell interactions with ADPRH-expressing tumors

• Analysis of tumor growth, immune infiltration, and survival outcomes

Systems biology approaches:

• Correlation of ADPRH expression with immune cell signatures using TIMER and ESTIMATE databases

• Gene Set Enrichment Analysis (GSEA) to identify biological pathways impacted by ADPRH expression

• Integration of multi-omics data (transcriptomics, proteomics, epigenomics) to build comprehensive network models

These experimental approaches should be designed with appropriate controls and replicated across multiple experimental systems to establish robust evidence for the functional role of ADPRH in immune regulation, particularly in the context of cancer immunology.

What methodology should be followed for assessing ADPRH as a prognostic biomarker in clinical samples?

Assessing ADPRH as a prognostic biomarker in clinical samples requires a systematic methodology that encompasses sample collection, processing, analysis, and statistical validation:

Sample collection and processing protocol:

• Cohort selection criteria:

  • Clear inclusion/exclusion criteria (e.g., specific cancer type, complete follow-up information, no comorbidity, defined tumor grade according to WHO classification)

  • Stratification by relevant clinical variables (age, gender, tumor grade, molecular subtypes)

  • Balanced representation of outcome groups to ensure statistical power

• Sample preparation:

  • Standardized tissue collection and preservation methods (fresh frozen or FFPE)

  • Consistent RNA/protein extraction protocols to minimize technical variability

  • Quality control measures for nucleic acid and protein integrity

Analytical methodology:

• Expression analysis:

  • Quantitative measurement of ADPRH using rt-PCR for mRNA expression

  • Immunohistochemistry (IHC) for protein expression evaluation with standardized scoring systems

  • Consider multiplex approaches to simultaneously assess ADPRH and related markers

• Statistical analysis protocol:

  • Kaplan-Meier survival analysis with log-rank test to assess the association between ADPRH expression and survival outcomes (OS, PFS)

  • Determination of optimal cut-off values for high versus low expression groups

  • Univariate and multivariate Cox regression analysis to evaluate ADPRH as an independent prognostic factor

  • Adjustment for established prognostic variables (age, gender, tumor grade, IDH status)

  • Calculation of hazard ratios (HR) and 95% confidence intervals (CI)

• Validation strategies:

  • Internal validation using bootstrapping or cross-validation

  • External validation in independent cohorts (as demonstrated with TCGA and CGGA datasets)

  • Comparison with established prognostic markers to determine added value

• Reporting standards:

  • Adherence to REMARK (REporting recommendations for tumor MARKer prognostic studies) guidelines

  • Clear presentation of statistical methods, cut-off determination, and adjusted variables

  • Transparent reporting of limitations and potential confounding factors

By following this methodological framework, researchers can rigorously assess the prognostic value of ADPRH expression and its potential clinical utility as a biomarker for patient stratification and treatment planning.

How can Gene Set Enrichment Analysis be applied to understand ADPRH's role in signaling pathways?

Gene Set Enrichment Analysis (GSEA) represents a powerful computational method for interpreting gene expression data in the context of biologically relevant gene sets, providing critical insights into ADPRH's role in cellular signaling pathways:

GSEA implementation for ADPRH studies:

• Methodological approach:

  • Samples are divided into groups based on ADPRH expression levels (typically using median expression as a threshold)

  • Gene expression profiles from each group are analyzed against reference gene sets (e.g., h.all.v7.1.symbols.gmt)

  • Enrichment scores are calculated to determine whether members of a gene set tend to occur toward the top or bottom of the ranked list

  • Statistical significance is assessed with p-values and false discovery rate (FDR) metrics (pathways with p < 0.05 and FDR < 0.05 are considered significantly enriched)

• Key analytical parameters:

  • Reference database selection (e.g., MSigDB, KEGG, GO)

  • Appropriate normalization of gene expression data

  • Statistical thresholds for significance determination

  • Visualization methods for pathway enrichment results

GSEA findings related to ADPRH in LGG:

GSEA has revealed that high ADPRH expression in LGG is associated with upregulation of several critical signaling pathways:

• P53 signaling pathway: Involved in tumor suppression and cell cycle regulation

• KRAS signaling pathway: Connected to cell proliferation and oncogenesis

• IL6/JAK-STAT3 signaling: Critical for immune regulation and inflammatory responses

• TNF-beta signaling pathways: Important for immune function and cell death regulation

These pathway associations provide mechanistic insights into how ADPRH might influence tumor progression and immune responses in LGG. For example, the connection to IL6/JAK-STAT3 signaling suggests that ADPRH may modulate inflammatory processes within the tumor microenvironment, potentially affecting immune cell recruitment and function.

When applying GSEA to ADPRH research, investigators should consider integrating results with experimental validation to confirm the functional relevance of identified pathways in specific biological contexts.

What statistical approaches are most appropriate for analyzing the correlation between ADPRH expression and clinical outcomes?

Analyzing the correlation between ADPRH expression and clinical outcomes requires robust statistical methodologies that can account for the complexity of clinical data and potential confounding variables:

Recommended statistical approaches:

The statistical significance of results should be rigorously assessed, with p-values < 0.05 generally considered statistically significant, though stricter thresholds may be appropriate for multiple comparisons. As demonstrated in published research, ADPRH expression maintained statistical significance (p < 0.001) as an independent prognostic factor in multivariate Cox regression models across different cohorts .

How can researchers integrate multi-omics data to better understand ADPRH function in human diseases?

Integrating multi-omics data provides a comprehensive approach to understanding ADPRH function in human diseases, allowing researchers to explore complex biological systems at multiple molecular levels:

Multi-omics integration strategies for ADPRH research:

• Data collection across omics platforms:

  • Genomics: Identification of genetic variants, copy number alterations, and structural variations affecting ADPRH

  • Transcriptomics: Gene expression analysis of ADPRH and related pathway genes using RNA-seq or microarrays

  • Proteomics: Protein expression and post-translational modifications of ADPRH using mass spectrometry or antibody-based methods

  • Epigenomics: DNA methylation patterns, histone modifications, and chromatin accessibility around the ADPRH locus

  • Metabolomics: Metabolite profiles associated with altered ADPRH function

  • Immunomics: Immune cell profiling and cytokine patterns in relation to ADPRH expression

• Computational integration frameworks:

  • Network-based approaches: Construction of gene-protein interaction networks with ADPRH as a focal point

  • Pathway enrichment methods: Multi-omics GSEA to identify consistently enriched pathways across data types

  • Bayesian integration: Probabilistic models combining evidence from multiple data sources

  • Machine learning approaches: Supervised and unsupervised learning algorithms to identify patterns across omics layers

  • Similarity network fusion: Integration of networks from individual omics data into a comprehensive similarity network

• Specific analytical approaches:

  • eQTL analysis: Identifying genetic variants that influence ADPRH expression

  • Protein-protein interaction mapping: Identifying ADPRH interaction partners across different cellular contexts

  • Correlation analysis: Examining relationships between ADPRH expression and immune infiltration scores from TIMER and ESTIMATE databases

  • Multi-omics clustering: Identifying disease subtypes based on integrated molecular profiles

  • Systems biology modeling: Developing mathematical models of cellular processes involving ADPRH

• Visualization and interpretation tools:

  • Circos plots: Visualizing multi-omics data in a circular layout

  • Heatmaps and clustering diagrams: Representing patterns across multiple datasets

  • Network visualization tools: Displaying interaction networks involving ADPRH

  • Pathway visualization: Mapping multi-omics data onto biological pathway diagrams

By integrating data across multiple molecular levels, researchers can generate more robust hypotheses about ADPRH function in disease contexts, identify novel biomarker combinations with improved predictive power, and uncover potential therapeutic targets within ADPRH-associated pathways.

How might ADPRH be targeted for therapeutic intervention in gliomas?

Based on its role in glioma progression and immune modulation, ADPRH represents a promising target for therapeutic intervention through several potential approaches:

Potential therapeutic strategies targeting ADPRH:

• Direct enzymatic inhibition:

  • Development of small molecule inhibitors targeting ADPRH's catalytic domain

  • Structure-based drug design utilizing crystallographic data of ADPRH protein

  • High-throughput screening of compound libraries to identify lead molecules

  • Rational modification of known ADP-ribosylation pathway inhibitors

• Gene expression modulation:

  • Antisense oligonucleotides (ASOs) designed to reduce ADPRH mRNA expression

  • RNA interference approaches using siRNA or shRNA delivery systems

  • CRISPR-Cas9 gene editing for permanent modification of ADPRH expression

  • Epigenetic modulators targeting ADPRH gene promoter regions

• Immunotherapeutic approaches:

  • Exploiting the relationship between ADPRH and tumor immune infiltrating cells (TIICs)

  • Combination therapies targeting ADPRH alongside immune checkpoint inhibitors

  • Development of vaccines targeting cells with high ADPRH expression

  • Adoptive cell therapies using T cells engineered to target ADPRH-expressing cells

• Pathway-based interventions:

  • Targeting downstream effectors in ADPRH-associated signaling pathways:

    • P53 signaling pathway modulators

    • KRAS pathway inhibitors

    • IL6/JAK-STAT3 signaling antagonists

    • TNF-beta signaling modifiers

• Personalized therapeutic approaches:

  • Stratification of patients based on ADPRH expression levels for treatment selection

  • Development of companion diagnostics to identify patients likely to respond to ADPRH-targeted therapies

  • Monitoring of ADPRH expression as a biomarker for treatment response

Given the established correlation between high ADPRH expression and poor clinical outcomes in LGG patients, therapeutic strategies aimed at reducing ADPRH activity or expression could potentially improve survival outcomes. Additionally, the connection between ADPRH and immune infiltration suggests that combining ADPRH-targeted therapies with immunotherapeutic approaches might yield synergistic effects.

What challenges exist in translating ADPRH research findings into clinical applications?

Translating ADPRH research findings into clinical applications faces several significant challenges that must be addressed through methodical research and development approaches:

Technical and biological challenges:

• Target specificity concerns:

  • Ensuring therapeutic interventions specifically target ADPRH without affecting related enzymes

  • Addressing potential compensatory mechanisms that might emerge following ADPRH inhibition

  • Developing delivery systems that can effectively cross the blood-brain barrier for glioma applications

• Biomarker standardization issues:

  • Establishing standardized assays for ADPRH detection across different clinical laboratories

  • Determining clinically relevant cut-off values for "high" versus "low" ADPRH expression

  • Addressing potential heterogeneity in ADPRH expression within tumors

• Model system limitations:

  • Current preclinical models may not fully recapitulate the complex interactions between ADPRH, tumor cells, and the immune microenvironment

  • Patient-derived xenografts may not preserve the immune context relevant to ADPRH function

  • In vitro systems might not reflect the three-dimensional architecture of tumors

Clinical translation challenges:

• Study design considerations:

  • Need for prospective validation of ADPRH as a prognostic biomarker beyond retrospective analyses

  • Requirement for multi-center studies with diverse patient populations

  • Integration of ADPRH assessment into existing clinical workflows

• Regulatory pathways:

  • Demonstrating analytical validity, clinical validity, and clinical utility of ADPRH as a biomarker

  • Navigating regulatory approval processes for ADPRH-targeted therapies

  • Addressing potential off-target effects and safety concerns

• Implementation barriers:

  • Cost-effectiveness considerations for ADPRH testing in clinical settings

  • Training requirements for pathologists and laboratory personnel

  • Integration with existing molecular testing panels for gliomas

Strategic approaches to overcome challenges:

• Collaborative research networks:

  • Multi-institutional collaborations to pool resources and patient samples

  • Public-private partnerships to accelerate therapeutic development

  • Data sharing initiatives to enhance statistical power and reproducibility

• Technological innovations:

  • Development of simplified, cost-effective ADPRH detection methods

  • Integration of artificial intelligence for image analysis of ADPRH immunohistochemistry

  • Novel drug delivery systems specifically designed for CNS applications

• Clinical trial designs:

  • Basket trials grouping patients based on ADPRH expression across different cancer types

  • Adaptive trial designs allowing for modification based on interim results

  • Incorporation of ADPRH as a stratification factor in conventional clinical trials

Addressing these challenges systematically will be crucial for successfully translating the promising research findings on ADPRH into clinically meaningful applications that benefit patients.

How can researchers design clinical trials to evaluate ADPRH-based prognostic models or therapeutic interventions?

Designing clinical trials to evaluate ADPRH-based prognostic models or therapeutic interventions requires careful consideration of trial objectives, patient selection, endpoints, and analytical approaches:

Prognostic model validation trial design:

Therapeutic intervention trial design:

By implementing these clinical trial design elements, researchers can systematically evaluate the clinical utility of ADPRH as both a prognostic biomarker and therapeutic target, potentially leading to improved patient stratification and novel treatment approaches for glioma patients.

What are the most promising future directions for ADPRH research in human diseases?

The emerging understanding of ADPRH's role in human diseases, particularly in low-grade gliomas, points to several promising research directions that could significantly advance both basic science knowledge and clinical applications:

Mechanistic investigations:

• Detailed characterization of ADPRH's role in modulating CD8+ T cell functions and its impact on tumor immune surveillance

• Exploration of the molecular mechanisms connecting ADPRH to the P53, KRAS, IL6/JAK-STAT3, and TNF-beta signaling pathways identified through GSEA

• Investigation of potential ADP-ribosylation targets in the context of glioma progression and immune regulation

• Elucidation of how ADPRH affects the composition and function of tumor immune infiltrating cells

Expanded biomarker applications:

• Evaluation of ADPRH expression across a broader range of cancer types beyond LGG

• Integration of ADPRH with other molecular markers to develop composite prognostic signatures

• Assessment of ADPRH as a potential predictive biomarker for response to specific therapies, particularly immunotherapies

• Development of liquid biopsy approaches for non-invasive monitoring of ADPRH expression

Therapeutic development:

• Design and screening of small molecule inhibitors specifically targeting ADPRH

• Exploration of RNA-based therapeutics to modulate ADPRH expression

• Investigation of combination approaches targeting ADPRH alongside immune checkpoint inhibitors

• Development of strategies to normalize the tumor immune microenvironment by modulating ADPRH activity

Clinical translation initiatives:

• Prospective clinical validation of ADPRH as a prognostic biomarker in multi-institutional studies

• Incorporation of ADPRH assessment into clinical trial designs as a stratification factor

• Development of standardized assays for ADPRH detection suitable for clinical implementation

• Investigation of ADPRH in the context of treatment resistance and disease recurrence

These research directions hold significant promise for advancing our understanding of ADPRH biology and leveraging this knowledge to improve patient outcomes. The strong prognostic value of ADPRH in LGG (HR 2.837 in TCGA cohort, p<0.001) provides compelling rationale for continued investigation of this biomarker in both research and clinical settings.

How can researchers address current data gaps and contradictions in ADPRH research?

Addressing data gaps and resolving contradictions in ADPRH research requires systematic approaches to enhance data quality, improve methodological consistency, and promote collaborative research efforts:

Strategies to address methodological inconsistencies:

Addressing specific data gaps:

• Biological mechanisms:

  • Further investigation of the molecular mechanisms by which ADPRH influences immune cell function

  • Detailed characterization of the ADP-ribosylation targets regulated by ADPRH in different cell types

  • Studies on the interplay between ADPRH and other post-translational modification systems

• Clinical contexts:

  • Expansion of research beyond LGG to other cancer types and non-oncological conditions

  • Investigation of ADPRH in treatment-resistant populations

  • Studies on the impact of current therapeutic modalities on ADPRH expression and function

  • Assessment of ADPRH in minimal residual disease and recurrence settings

• Technological developments:

  • Implementation of single-cell analysis techniques to address tumor heterogeneity

  • Application of spatial transcriptomics to understand ADPRH expression in the context of tissue architecture

  • Development of in vivo imaging approaches to monitor ADPRH activity non-invasively

Collaborative research initiatives:

• Data sharing frameworks:

  • Establishment of centralized repositories for ADPRH-related data

  • Implementation of standardized data formats and reporting guidelines

  • Development of data integration tools for cross-study comparisons

• Multi-institutional collaboration:

  • Formation of research consortia focused on ADPRH biology

  • Coordinated biospecimen collection with standardized processing

  • Multi-center validation studies with harmonized protocols