ALDOB Human

What is ALDOB and what is its primary function in human metabolism?

ALDOB (Aldolase B) is an enzyme encoded by the ALDOB gene that plays a critical role in carbohydrate metabolism. It catalyzes the cleavage of fructose-1,6-bisphosphate and fructose-1-phosphate in the glycolytic pathway (GO: 0006096). In February 2018, ALDOB was identified as the most significantly up-regulated transcript among those related to glycolysis in rectal cancer transcriptome analysis. The enzyme is primarily expressed in the liver, kidney proximal tubule cells, and small intestine, where it serves as a key metabolic regulator for fructose processing. Unlike other aldolase isoforms, ALDOB has a distinctly higher catalytic efficiency for fructose-1-phosphate, making it crucial for dietary fructose metabolism in these tissues .

How is genetic variation in ALDOB related to hereditary fructose intolerance?

Hereditary fructose intolerance (HFI) is an autosomal recessive disorder caused by mutations in the gene coding for aldolase B. Individuals with HFI develop severe, potentially life-threatening manifestations when exposed to even minute amounts of fructose. The mutations reduce or eliminate ALDOB enzymatic activity, resulting in accumulation of fructose-1-phosphate in hepatocytes and proximal kidney tubule cells following fructose consumption. This accumulation causes rapid ATP depletion, leading to acute hypoglycemia, renal tubular acidosis, and hyperuricemia .

The most common pathogenic variants include c.448G>C (p.Ala150Pro) with an ExAC frequency of 0.2% and c.548T>C (p.Leu183Pro). HFI prevalence in central Europe is estimated at 1:26,100, making heterozygous carriers relatively common. No clear genotype-phenotype correlations have been identified; clinical severity appears dependent on individual nutritional habits .

What techniques are most effective for studying ALDOB expression in tissue samples?

For comprehensive analysis of ALDOB expression, researchers should implement multiple complementary techniques:

For mRNA quantification:

• RNA sequencing (RNA-seq) through databases like TCGA, GEO, and ArrayExpress

• Quantitative real-time PCR (qRT-PCR) for validation studies

For protein detection:

• Immunohistochemistry (IHC) - Particularly useful for visualizing spatial distribution in tissues

• Western blotting for semi-quantitative protein analysis

• Proteomic analysis via the Clinical Proteomic Tumor Analysis Consortium (CPTAC)

For structural analysis:

• The AlphaFold Protein Structure Database to predict 3D protein structure of ALDOB

The Human Protein Atlas (HPA) database provides valuable immunohistochemistry images that visualize ALDOB protein expression patterns in normal and pathological tissues. For maximum reliability, researchers should cross-validate findings using both mRNA and protein detection methods to account for potential post-transcriptional regulation .

How does ALDOB expression correlate with clinical parameters in cancer patients?

ALDOB expression shows significant associations with multiple clinical parameters across cancer types:

In rectal cancer patients:

• High ALDOB expression correlates with:

  • More advanced post-CCRT pT status (P = .002)

  • Positive pre- and post-CCRT nodal metastasis (P ≤ .001)

  • Lymphovascular invasion (P = .015)

  • Perineural invasion (P = .023)

  • Poor response to neoadjuvant chemoradiotherapy (P < .001)

In kidney clear cell renal cell carcinoma (KIRC):

• ALDOB expression shows significant relationships with:

  • T-stage progression

  • N-stage status

  • M-stage status

  • Advanced pathological grade

These correlations, validated through logistic regression analyses, suggest ALDOB may be involved in mechanisms driving disease progression and treatment resistance .

What evidence supports ALDOB as a prognostic biomarker in cancer?

Multiple lines of evidence establish ALDOB as a valuable prognostic biomarker:

In kidney clear cell renal cell carcinoma (KIRC):

• ROC analysis demonstrates ALDOB's superior prognostic performance:

  • 1-year survival AUC = 0.74

  • 3-year survival AUC = 0.63

  • 5-year survival AUC = 0.66

Validation methodology:

• Multiple independent cohorts (1,070 KIRC tissues and 409 normal tissues) from GEO, TCGA, and ArrayExpress databases

• Kaplan-Meier survival analysis with Log-Rank testing

• Univariate and multivariate Cox regression analysis identifying ALDOB as an independent prognostic predictor

• Construction of nomograms for 1-year, 3-year, and 5-year OS, DSS, and PFS prediction

This extensive validation across multiple datasets consistently identifies ALDOB as a robust prognostic marker, particularly in KIRC, suggesting its potential clinical utility in patient risk stratification .

What methodologies can researchers use to integrate ALDOB into multivariate prognostic models?

Researchers can follow this methodological framework to develop ALDOB-integrated prognostic models:

• Data collection and preprocessing:

  • Extract ALDOB expression data and clinicopathological variables from appropriate databases

  • Apply normalization techniques to ensure comparability across samples

  • Handle missing data through appropriate imputation methods

• Prognostic model development:

  • Perform univariate Cox regression to identify significant prognostic variables

  • Use multivariate Cox regression to determine independent predictors

  • Apply LASSO regression for feature selection among ALDOB and related genes

• Nomogram construction:

  • Integrate T stage, N stage, M stage, AJCC stage, pathological grade, and ALDOB expression

  • Develop separate nomograms for different survival endpoints (OS, DSS, PFS)

• Model validation:

  • Calculate concordance index (C-index) using 1,000 bootstrap resamples

  • Generate calibration curves to assess predictive accuracy

  • Perform internal and external validation using independent cohorts

• Statistical analysis tools:

  • Employ R software (version 4.2.0) with specialized packages

  • Use "glmnet" and "survival" packages for LASSO regression

  • Apply "ConsensusClusterPlus" for patient subtyping based on ALDOB expression

This systematic approach ensures rigorous development and validation of clinically applicable prognostic models incorporating ALDOB expression.

How does ALDOB interact with the tumor microenvironment and immune infiltration?

Recent research has uncovered significant relationships between ALDOB expression and the tumor immune microenvironment:

Experimental findings:

• In vivo and in vitro experiments demonstrate that ALDOB may regulate tumor growth by modulating CD8+ T cell infiltration

• These findings are consistent with computational immune infiltration analyses showing correlations between ALDOB expression and immune cell populations

Analytical approaches for researchers:

Research implications:

• ALDOB may have potential reference value for immunotherapy response prediction

• Understanding ALDOB's influence on the tumor immune microenvironment could inform therapeutic strategies targeting metabolic pathways in cancer

This emerging area represents a frontier in ALDOB research, linking metabolic functions with immunological processes in the cancer microenvironment.

What is the relationship between ALDOB and epigenetic modifications in cancer?

Researchers investigating epigenetic regulation of ALDOB should focus on these methodological approaches:

m6A methylation analysis:

• Examine correlations between ALDOB and 23 types of m6A regulators, including:

  • Eight writers (METTL3, METTL14, RBM15B, RBM15, VIRMA, WTAP, CBLL1, ZC3H13)

  • Two erasers (FTO, ALKBH5)

  • Thirteen readers (YTHDF1, YTHDF2, YTHDF3, YTHDC1, YTHDC2, HNRNPC, HNRNPA2B1, IGF2BP1, IGF2BP2, IGF2BP3, LRPPRC, FMR1, ELAVL1)

Analytical methodologies:

• Extract RNA methylation data from appropriate databases

• Calculate Pearson correlation coefficients between ALDOB and m6A regulator expression

• Apply LASSO regression to identify significant associations

• Validate findings using experimental approaches such as:

  • Methylated RNA immunoprecipitation sequencing (MeRIP-seq)

  • RNA methylation assays

  • Gene knockdown/overexpression studies

How can researchers address contradictory findings regarding ALDOB's role across different cancer types?

The contradictory findings regarding ALDOB's roles in different cancers present both challenges and opportunities for researchers. A methodological framework to address these contradictions includes:

Systematic approach for reconciling contradictory findings:

• Conduct meta-analysis across cancer types:

  • Pool data from multiple studies using random-effects models

  • Perform subgroup analyses by cancer type, stage, and molecular subtype

  • Identify factors that modify ALDOB's prognostic significance

• Investigate tissue-specific contexts:

  • Compare ALDOB's molecular interactions across tissue types

  • Analyze protein-protein interaction networks using STRING database

  • Perform differential pathway enrichment analysis between cancer types

• Examine functional mechanisms:

  • Design experiments to test ALDOB's function in multiple cell line models

  • Use CRISPR-Cas9 to modulate ALDOB expression in different cancer contexts

  • Measure metabolic parameters and oncogenic properties

• Consider genetic and epigenetic modifiers:

  • Analyze how genomic alterations affect ALDOB function

  • Investigate epigenetic regulation patterns across cancer types

  • Examine interactions with tissue-specific transcription factors

This methodological framework can help researchers systematically investigate and explain why ALDOB might act as a tumor suppressor in some contexts while promoting progression in others .

How should experimental studies be designed to investigate ALDOB's functional impact on cancer progression?

Researchers should consider this methodological framework when designing experiments to investigate ALDOB's functional impact:

In vitro experimental design:

• Cell line selection:

  • Use multiple cell lines representing the cancer type of interest

  • Include cell lines with varying baseline ALDOB expression

  • Consider primary patient-derived cells when available

• ALDOB modulation approaches:

  • Generate stable ALDOB knockdown using shRNA or CRISPR-Cas9

  • Create ALDOB overexpression models using lentiviral vectors

  • Consider inducible expression systems for temporal control

• Functional assays:

  • Proliferation: MTT/XTT assays, colony formation, cell counting

  • Migration/invasion: Transwell assays, wound healing

  • Metabolism: Seahorse analysis, lactate production, glucose consumption

  • Apoptosis: Annexin V/PI staining, caspase activity

In vivo experimental design:

• Animal models:

  • Xenograft models using ALDOB-modulated cell lines

  • Orthotopic implantation for tissue-specific effects

  • Consider genetically engineered mouse models when appropriate

• Analytical endpoints:

  • Tumor growth kinetics and final tumor volume/weight

  • Metastatic burden assessment

  • Survival analysis

  • Immunohistochemical analysis of harvested tumors

• Mechanistic investigations:

  • Analyze tumor microenvironment changes (CD8+ T cell infiltration)

  • Examine signaling pathway alterations

  • Perform metabolomic profiling of tumors

These systematic approaches enable rigorous assessment of ALDOB's functional impact on cancer progression, providing mechanistic insights beyond correlative observations .

What statistical approaches are most appropriate for analyzing ALDOB expression data in relationship to clinical outcomes?

Researchers should employ these statistical methodologies when analyzing ALDOB expression in relation to clinical outcomes:

Basic statistical approaches:

• Mann-Whitney U test or Student's t-test for comparing expression between two groups

• Kruskal-Wallis H test with Dunn's test for multiple group comparisons

• Pearson or Spearman correlation for continuous variable associations

Survival analysis methods:

• Kaplan-Meier curves with Log-Rank test for survival differences

• Cox proportional hazards model for univariate and multivariate analysis

• Harrell's concordance index (C-index) for predictive accuracy assessment

Advanced statistical considerations:

• Optimal cutpoint determination:

  • X-tile software or maxstat R package to identify optimal expression thresholds

  • Validation of cutpoints in independent cohorts

• Model development and validation:

  • Training/validation set approach (typical split: 70%/30%)

  • Cross-validation techniques (e.g., 10-fold cross-validation)

  • Bootstrap resampling (1,000 iterations recommended)

• Handling confounding factors:

  • Propensity score matching for balanced comparisons

  • Stratified analysis by relevant clinical variables

  • Interaction term analysis in regression models

Statistical software recommendations:

• R software (version 4.2.0) with specialized packages:

  • "survival" for survival analysis

  • "glmnet" for LASSO regression

  • "survminer" for visualization

  • "timeROC" for time-dependent ROC analysis

Statistical significance should typically be set at P < 0.05, with appropriate multiple testing corrections when necessary.

How can mass spectrometry be optimized for detecting and quantifying ALDOB protein in clinical samples?

Researchers can optimize mass spectrometry protocols for ALDOB analysis following these methodological guidelines:

Sample preparation optimization:

• Tissue processing:

  • Flash-freeze samples immediately after collection

  • Consider laser capture microdissection for cellular specificity

  • Optimize protein extraction buffers (RIPA vs. urea-based) for ALDOB recovery

• Protein digestion protocols:

  • Compare trypsin, Lys-C, and combination digestion approaches

  • Optimize enzyme-to-protein ratios (typically 1:20 to 1:100)

  • Consider extended digestion times (12-16 hours) at controlled temperatures

• Enrichment strategies:

  • Evaluate immunoprecipitation with ALDOB-specific antibodies

  • Test fractionation methods (SCX, high-pH RP) to reduce sample complexity

  • Consider phosphopeptide enrichment to study ALDOB post-translational modifications

MS acquisition parameters:

• MS instrument selection:

  • High-resolution instruments (Orbitrap, Q-TOF) for discovery phase

  • Triple quadrupole instruments for targeted quantification

• Acquisition methods:

  • Data-dependent acquisition (DDA) for discovery

  • Parallel reaction monitoring (PRM) or multiple reaction monitoring (MRM) for targeted quantification

  • Data-independent acquisition (DIA) for comprehensive peptide coverage

• ALDOB-specific considerations:

  • Target multiple unique peptides across different regions of ALDOB

  • Include both common and variant-specific peptides when analyzing genetic variants

  • Monitor post-translational modifications (phosphorylation, acetylation)

Quantification strategies:

• Relative quantification:

  • Label-free quantification using MS1 intensity or spectral counting

  • Isobaric labeling (TMT, iTRAQ) for multiplexed comparison

• Absolute quantification:

  • Stable isotope-labeled standard (SIS) peptides for ALDOB

  • AQUA peptide approach for precise quantification

  • QconCAT strategy for multi-peptide quantification

These optimized mass spectrometry approaches enable reliable detection and quantification of ALDOB protein in clinical samples, facilitating translational research applications .

How should researchers address contradictory data between ALDOB mRNA and protein expression in cancer samples?

When facing discrepancies between ALDOB mRNA and protein expression, researchers should implement these methodological approaches:

Analytical framework for resolving mRNA-protein discrepancies:

• Validation of measurement techniques:

  • Confirm antibody specificity through multiple approaches:

    • Western blot with recombinant protein controls

    • Knockdown/overexpression validation

    • Multiple antibodies targeting different epitopes

  • Validate RNA measurement methods:

    • qRT-PCR with multiple primer sets

    • RNA-seq with appropriate quality controls

    • In situ hybridization for spatial validation

• Investigation of post-transcriptional mechanisms:

  • microRNA regulation:

    • Identify miRNAs targeting ALDOB through bioinformatic prediction

    • Perform correlation analysis between miRNA and ALDOB expression

    • Conduct luciferase reporter assays to validate direct targeting

  • RNA stability analysis:

    • Actinomycin D chase experiments to measure ALDOB mRNA half-life

    • RNA-binding protein immunoprecipitation to identify regulators

    • Analysis of alternative splicing patterns

• Protein-level regulation assessment:

  • Proteasomal degradation:

    • Proteasome inhibitor studies (MG132, bortezomib)

    • Ubiquitination analysis via immunoprecipitation

    • Half-life determination via cycloheximide chase

  • Post-translational modifications:

    • Phosphorylation, acetylation, or glycosylation analysis

    • Mass spectrometry to identify modification sites

    • Functional impact assessment of modifications

• Integrative data analysis:

  • Stratify samples by mRNA-protein correlation status

  • Identify clinical or molecular features associated with discrepancies

  • Develop multivariate models incorporating both mRNA and protein data

This systematic approach helps researchers uncover biological mechanisms underlying ALDOB regulation and ensures accurate interpretation of seemingly contradictory expression data .

How might ALDOB testing be implemented in clinical pathology workflows for cancer patients?

Implementation of ALDOB testing in clinical pathology requires standardized protocols and careful workflow integration:

Clinical implementation framework:

• Specimen requirements and processing:

  • Tissue: FFPE or fresh frozen tumor sections (recommended minimum: 1 cm²)

  • Blood: Plasma or serum (potential circulating tumor DNA analysis)

  • Processing timeline: Within 30 minutes of collection for fresh samples

  • Fixation protocol: 10% neutral buffered formalin, 6-24 hours for optimal IHC results

• Testing methodologies with clinical validity:

  • Immunohistochemistry:

    • Antibody selection: Validated antibodies with demonstrated specificity

    • Scoring system: H-score (0-300) with median cutoff (e.g., 195) for high vs. low expression

    • Quality controls: Positive (liver tissue) and negative controls on each slide

  • RT-qPCR:

    • Validated primer sets targeting conserved ALDOB regions

    • Reference gene normalization (GAPDH, ACTB, B2M)

    • Established Ct thresholds for expression categorization

• Reporting standards:

  • Standardized terminology for expression levels

  • Inclusion of relevant prognostic and predictive information

  • Integration with existing molecular pathology reporting

• Quality assurance measures:

  • Inter-laboratory proficiency testing

  • Regular concordance testing between methods

  • Slide exchange programs for IHC standardization

• Implementation timeline:

  • Validation phase: 3-6 months

  • Limited clinical implementation: 6-12 months

  • Full integration into routine practice: 12-18 months

This structured approach ensures reliable, clinically meaningful ALDOB testing that can inform treatment decisions and prognostication .

What is the most effective study design for evaluating ALDOB as a predictive biomarker for treatment response?

To rigorously evaluate ALDOB as a predictive biomarker, researchers should implement this methodological framework:

Optimal study design elements:

• Prospective-retrospective design (initial validation):

  • Utilize archived specimens from completed randomized trials

  • Ensure treatment arms with and without the therapy of interest

  • Implement blinded ALDOB assessment

  • Predefine statistical analysis plan and cutpoints

• Prospective observational study (clinical validation):

  • Multi-institutional cohort with standardized treatment protocols

  • Baseline ALDOB assessment before treatment initiation

  • Predefined response criteria (RECIST 1.1, pathological response)

  • Minimum follow-up period appropriate for the cancer type

• Prospective interventional trial (clinical utility):

  • Biomarker-stratified design or biomarker-strategy design

  • Power calculations based on preliminary data

  • Incorporation of ALDOB status in treatment decision algorithm

  • Collection of quality of life and economic outcomes

Statistical considerations:

• Sample size calculation accounting for expected biomarker prevalence

• Interaction testing between ALDOB status and treatment effect

• Multivariable analysis adjusting for known prognostic factors

• Predefined subgroup analyses based on molecular or clinical features

Biospecimen considerations:

• Collection at multiple timepoints (baseline, during treatment, progression)

• Multiple testing methodologies (IHC, RNA-seq) for cross-validation

• Central laboratory assessment with rigorous quality control

• Exploration of dynamic changes in ALDOB expression during treatment

This comprehensive approach provides the methodological rigor needed to establish ALDOB as a clinically useful predictive biomarker .

What are the most promising therapeutic approaches targeting ALDOB in cancer?

Researchers exploring ALDOB-targeted therapeutics should consider these methodological approaches:

Therapeutic targeting strategies:

• Direct enzymatic inhibition:

  • Structure-based drug design utilizing AlphaFold-predicted ALDOB structure

  • High-throughput screening of small molecule libraries

  • Allosteric modulators targeting regulatory sites

  • Validation in enzymatic assays with purified ALDOB protein

• Gene expression modulation:

  • RNA interference approaches (siRNA, shRNA)

  • CRISPR-Cas9 gene editing (knockout, knockdown)

  • Antisense oligonucleotides targeting ALDOB mRNA

  • Epigenetic modulators affecting ALDOB promoter methylation

• Protein-protein interaction disruption:

  • Identification of critical ALDOB interaction partners via proteomic approaches

  • Peptide mimetics targeting interaction interfaces

  • Small molecule disruptors of protein complexes

  • Validation using co-immunoprecipitation and FRET assays

• Metabolic pathway intervention:

  • Targeting upstream or downstream metabolites in ALDOB pathway

  • Synthetic lethality approaches in ALDOB-high tumors

  • Combination with other metabolic inhibitors

  • Metabolic flux analysis to confirm mechanism of action

Experimental validation hierarchy:

• In vitro screening in multiple cell line models

• Ex vivo testing in patient-derived organoids

• In vivo efficacy in xenograft and syngeneic models

• Toxicity assessment in appropriate animal models

• Combination studies with standard-of-care treatments

These approaches provide a structured framework for developing ALDOB-targeted therapeutic strategies with potential for clinical translation .

How does ALDOB interact with cellular metabolism reprogramming in the context of cancer progression?

To investigate ALDOB's role in metabolic reprogramming, researchers should implement these methodological approaches:

Experimental framework for metabolic studies:

• Metabolic profiling techniques:

  • Targeted LC-MS/MS for glycolytic intermediates and related metabolites

  • 13C-glucose and 13C-fructose tracing to track carbon flux

  • Seahorse analysis for glycolytic rate and mitochondrial respiration

  • NMR-based metabolomics for comprehensive metabolite detection

• Enzyme activity assessments:

  • Spectrophotometric assays for ALDOB enzymatic activity

  • Metabolic flux analysis using isotope-labeled substrates

  • In situ enzyme activity measurements in tissue sections

  • Correlation of activity with protein/mRNA expression levels

• Metabolic pathway analysis:

  • Gene set enrichment analysis focusing on metabolic pathways

  • Protein-protein interaction mapping with other metabolic enzymes

  • Regulatory network analysis of metabolism-related transcription factors

  • Integration with publicly available metabolic datasets

• Functional metabolic studies:

  • Glucose/fructose dependency assays under ALDOB modulation

  • Mitochondrial function assessment after ALDOB knockdown/overexpression

  • Lipid metabolism analysis in ALDOB-altered cells

  • Nutrient deprivation studies to assess metabolic flexibility

Analytical considerations:

• Temporal dynamics of metabolic changes after ALDOB modulation

• Microenvironmental factors (oxygen, pH, nutrient availability)

• Comparison between 2D cultures and 3D models

• Integration of metabolomic, transcriptomic, and proteomic data

This comprehensive approach enables detailed characterization of ALDOB's role in cancer metabolic reprogramming, potentially revealing vulnerabilities that could be therapeutically exploited .

What techniques should be used to study ALDOB's interactome in cancer cells?

Researchers investigating ALDOB's protein interactions should employ this comprehensive methodological approach:

Interactome analysis workflow:

• Affinity purification-mass spectrometry (AP-MS):

  • Endogenous ALDOB immunoprecipitation with validated antibodies

  • Tagged-ALDOB (FLAG, HA, GFP) expression and pull-down

  • Quantitative comparison between cancer and normal cells

  • SILAC or TMT labeling for differential interaction analysis

• Proximity-based labeling methods:

  • BioID: ALDOB-BirA* fusion for biotinylation of proximal proteins

  • APEX2-ALDOB fusion for peroxidase-mediated labeling

  • Split-BioID for detecting conditional interactions

  • Controlled expression using inducible systems

• Protein-fragment complementation assays:

  • Mammalian two-hybrid screening

  • Bimolecular fluorescence complementation (BiFC)

  • NanoBiT complementation for dynamic interaction monitoring

  • FRET/BRET approaches for real-time interaction studies

• Crosslinking mass spectrometry (XL-MS):

  • Chemical crosslinking of intact cells or purified complexes

  • MS/MS analysis to identify crosslinked peptides

  • Structural modeling of interaction interfaces

  • Comparison between different cellular contexts

Computational analysis pipeline:

• Filtering against CRAPome database to remove common contaminants

• Network visualization using Cytoscape or STRING

• Gene Ontology enrichment analysis of interaction partners

• Structural modeling of protein complexes using AlphaFold-Multimer

Validation strategies:

• Co-immunoprecipitation of key interactions

• Proximity ligation assay in tissue sections

• Mutagenesis of putative interaction domains

• Functional studies of disrupted interactions

This systematic approach provides a comprehensive view of ALDOB's protein interaction network in cancer cells, potentially revealing novel regulatory mechanisms and therapeutic targets .

How should researchers design experiments to resolve contradictory findings about ALDOB's role in different cancer contexts?

To systematically address contradictory findings about ALDOB's role across cancer types, researchers should implement this experimental framework:

Comprehensive experimental design strategy:

• Standardized multi-cancer cell line panel assessment:

  • Select representative cell lines from ≥5 cancer types with contradictory ALDOB findings

  • Implement identical ALDOB modulation approaches across all lines

  • Measure consistent endpoints using standardized protocols

  • Create an "ALDOB effect score" normalizing impact across cancer types

• Context-dependent mechanism investigation:

  • Perform comparative phosphoproteomic analysis across cancer types

  • Analyze differential gene expression after ALDOB modulation

  • Map metabolic dependencies using CRISPR screening

  • Identify cancer-specific interaction partners

• Genetic background analysis:

  • Sequence cancer cells for mutations in ALDOB-related pathways

  • Engineer isogenic cell lines differing only in key genetic alterations

  • Assess how genetic context modifies ALDOB function

  • Perform synthetic lethality screens in different genetic backgrounds

• Microenvironmental influence evaluation:

  • Test ALDOB function under varying oxygen tensions

  • Assess impact of pH, nutrient availability, and ECM components

  • Co-culture with cancer-specific stromal and immune cells

  • Develop appropriate 3D models mimicking tissue-specific TME

Analytical integration framework:

• Develop computational models to predict ALDOB behavior based on cellular context

• Perform meta-analysis across all experimental conditions

• Identify decision trees for ALDOB function based on context

• Generate testable hypotheses explaining contradictory findings

This systematic approach allows researchers to methodically dissect the factors determining ALDOB's differential roles across cancer contexts, providing a cohesive understanding of its complex biology .