ADHFE1 Antibody

What is ADHFE1 and why is it important in molecular research?

ADHFE1 (Alcohol Dehydrogenase Iron Containing 1) encodes hydroxyacid-oxoacid transhydrogenase, which is responsible for the oxidation of 4-hydroxybutyrate in mammalian tissues. It belongs to the iron-containing alcohol dehydrogenase family . The protein is significant in research because:

• It catalyzes the cofactor-independent reversible oxidation of gamma-hydroxybutyrate (GHB) to succinic semialdehyde (SSA) coupled with the reduction of 2-ketoglutarate (2-KG) to D-2-hydroxyglutarate (D-2-HG)

• It has been implicated in several cancer types including breast, gastric, and colorectal cancer

• It plays a role in adipocyte metabolism and differentiation

ADHFE1 has four isoforms produced by alternative splicing with molecular weights of 50, 45, 32, and 27 kDa . Understanding its function and expression patterns is critical for research in oncology, metabolism, and cellular differentiation.

What are the key characteristics of commercially available ADHFE1 antibodies?

Commercial ADHFE1 antibodies have specific characteristics researchers should consider:

When selecting an ADHFE1 antibody, researchers should consider validation data for their specific application and tissue/cell type of interest, as performance can vary significantly between manufacturers .

How are ADHFE1 antibodies validated for research applications?

ADHFE1 antibodies undergo multiple validation steps to ensure specificity and reliability:

• Western Blot Validation: Confirms correct molecular weight bands (primarily 50 kDa, with secondary bands at 40-45 kDa) in relevant tissue/cell lysates including HT-29 cells, mouse/rat kidney and liver tissues

• Immunohistochemistry Validation: Demonstrated in human colon tissue, colon cancer tissue, and liver tissue with specific staining patterns

• Orthogonal Validation: Some antibodies undergo RNAseq validation to confirm specificity by comparing antibody signal with mRNA expression

• Cross-reactivity Testing: Evaluated against protein arrays containing hundreds of human recombinant protein fragments to ensure specificity

• Tissue Arrays: High-quality antibodies are tested on tissue arrays containing dozens of normal human tissues and common cancer types

Researchers should review validation data specific to their intended application before selecting an ADHFE1 antibody for their experiments .

What are the optimized protocols for ADHFE1 detection by Western blot?

For optimal Western blot detection of ADHFE1, follow this methodological approach:

Sample Preparation:

• Prepare lysates from tissues (kidney, liver) or cell lines (HT-29, U-87 MG) known to express ADHFE1

• Use a lysis buffer containing protease inhibitors to prevent degradation

• Load 20-30 μg of total protein per lane on a 12% SDS-PAGE gel

Protocol:

• Transfer proteins to PVDF membrane overnight at 4°C

• Block with 5% nonfat milk in PBS with 0.5% Tween 20 for 1 hour at room temperature

• Incubate with primary ADHFE1 antibody at dilutions of 1:500-1:2000 or 0.04-0.4 μg/mL overnight at 4°C

• Wash 3 times (10 minutes each) with PBS-T

• Incubate with secondary antibody (anti-rabbit) at 1:4000 dilution for 1 hour at room temperature

• Wash 3 times (10 minutes each) with PBS-T

• Visualize using chemiluminescent detection system

Expected Results:

• Primary band at 50 kDa

• Possible secondary bands at 40-45 kDa representing isoforms

• Signal strength varies by tissue type, with highest expression in adipose tissue, liver, and kidney

For validation, use positive controls from kidney or liver tissue, and consider knockdown/knockout samples as negative controls to confirm specificity .

How should immunohistochemistry protocols be optimized for ADHFE1 detection in different tissue types?

Optimizing IHC protocols for ADHFE1 detection requires tissue-specific adjustments:

General Protocol:

• Deparaffinize and rehydrate 3-5 μm tissue sections

• Perform antigen retrieval using TE buffer pH 9.0 (preferred) or citrate buffer pH 6.0 (alternative)

• Block endogenous peroxidase with 3% H₂O₂ for 10 minutes

• Apply primary ADHFE1 antibody at dilutions of 1:100-1:400 or 1:2500-1:5000 (tissue-dependent)

• Incubate overnight at 4°C

• Apply secondary antibody for 30 minutes at room temperature

• Develop with DAB chromogen kit

• Counterstain with Mayer's hematoxylin

Tissue-Specific Considerations:

For optimal results, include positive control tissues (liver or kidney) and negative controls (antibody diluent only) with each staining run. Scoring should consider both staining intensity and percentage of positive cells .

What are the most effective strategies for monitoring ADHFE1 expression changes in cell-based experiments?

To effectively monitor ADHFE1 expression changes in cellular experiments, multiple complementary approaches should be employed:

1. Quantitative RT-PCR:

• Design primers spanning exon-exon junctions to avoid genomic DNA amplification

• Use reference genes stable under your experimental conditions (GAPDH, β-actin)

• Calculate relative expression using the 2^(-ΔΔCT) method

• This approach successfully detected ADHFE1 expression changes during adipocyte differentiation and in cancer studies

2. Western Blot Analysis:

• Quantify ADHFE1 protein levels relative to loading controls (β-actin, GAPDH)

• Use densitometry software for accurate quantification

• Monitor both the 50 kDa primary band and any secondary bands at 40-45 kDa

• This method effectively detected ADHFE1 changes in vector-transfected cell lines

3. Immunofluorescence:

• Use 1:100-1:250 dilution of ADHFE1 primary antibody

• Co-stain with mitochondrial markers to confirm subcellular localization

• Include DAPI nuclear counterstain

• This approach confirmed mitochondrial localization of ADHFE1

Experimental Design Considerations:

• Include time-course analyses for dynamic processes (e.g., differentiation, drug treatments)

• Use positive controls such as cells with known ADHFE1 expression (HT-29, AGS cells)

• Consider genetic manipulation approaches (shRNA knockdown, overexpression) to validate findings

• For drug treatments affecting ADHFE1, monitor at multiple timepoints and concentrations

This multi-modal approach provides comprehensive data on ADHFE1 expression changes while confirming specificity through complementary techniques.

How can ADHFE1 antibodies be used to investigate cancer metabolism and metabolic reprogramming?

ADHFE1 antibodies are powerful tools for investigating cancer metabolism due to ADHFE1's role in producing D-2-hydroxyglutarate (D-2HG), a cancer-associated oncometabolite. Here's a methodological approach:

Metabolic Flux Analysis with ADHFE1 Detection:

• Manipulate ADHFE1 expression through overexpression or knockdown in cancer cell lines

• Perform stable isotope tracing using ¹³C-labeled glutamine or glucose

• Quantify metabolite changes using mass spectrometry

• Correlate metabolic changes with ADHFE1 protein levels via Western blot

• Assess D-2HG production in relation to ADHFE1 expression

Research has shown that ADHFE1 overexpression leads to:

• Increased D-2HG production (14.3-fold increase in acetyl-CoA levels)

• Elevated NADPH/NADP ratio

• Accumulation of TCA metabolites generated by reductive glutamine metabolism

Redox Status Assessment:

• Measure mitochondrial ROS production using fluorescent probes

• Correlate ROS levels with ADHFE1 expression detected by immunoblotting

• Assess the relationship between hypoxia, ADHFE1 levels, and metabolic adaptation

Studies show ADHFE1 induces moderate but significant increases in mitochondrial ROS, contributing to EMT in breast cancer cells .

Therapeutic Response Monitoring:

• Treat cancer cells with chemotherapeutic agents (e.g., cisplatin)

• Measure ADHFE1 expression changes via Western blot

• Correlate with drug sensitivity (IC₅₀ values)

Research indicates that ADHFE1 knockdown reduces cisplatin resistance in gastric cancer cells (IC₅₀ decreased from 7.62 to 5.05 μg/mL), while overexpression increases resistance (IC₅₀ increased to 11.58 μg/mL) .

This approach provides insights into how ADHFE1 contributes to metabolic reprogramming in cancer cells and potential therapeutic vulnerabilities.

What methodologies are effective for investigating the relationship between ADHFE1 and MYC signaling in cancer?

The relationship between ADHFE1 and MYC signaling in cancer can be investigated using these methodological approaches:

Co-expression Analysis:

• Perform dual immunostaining for ADHFE1 and MYC in tissue sections

• Quantify correlation coefficient between staining intensities

• Compare expression patterns across multiple tumor samples

Research has shown that ADHFE1 and MYC co-amplification occurs in breast tumors, with MYC induction increasing ADHFE1 expression .

Genetic Manipulation Experiments:

• Use inducible MYC expression systems:

  • Induce MYC in human mammary epithelial cells (HMEC-MYC) using 4-hydroxytamoxifen

  • Measure changes in ADHFE1 expression by Western blot and qPCR

  • Studies show MYC induction increases ADHFE1 expression

• Use MYC knockdown systems:

  • Downregulate endogenous MYC in cancer cells (e.g., SUM159T) using inducible shRNA

  • Monitor ADHFE1 expression changes

  • Research shows MYC knockdown reduces ADHFE1 expression

Promoter Analysis:

• Perform chromatin immunoprecipitation (ChIP) to assess MYC binding to the ADHFE1 promoter

• Use reporter assays to evaluate ADHFE1 promoter activity with MYC manipulation

• Interestingly, research suggests MYC may not directly activate the ADHFE1 promoter but may regulate ADHFE1 through its effects on iron metabolism

Functional Synergy Assessment:

• Perform tumor xenograft studies with cells expressing:

  • Control vector

  • ADHFE1 overexpression

  • MYC overexpression

  • Both ADHFE1 and MYC

• Measure tumor growth rates and metabolite production (particularly 4HB and 2-hydroxyglutarate)

Studies have shown ADHFE1 and MYC enhance tumor growth synergistically while increasing intratumor levels of 4HB and 2-hydroxyglutarate .

These approaches provide comprehensive insights into the mechanistic relationship between ADHFE1 and MYC signaling in cancer progression.

How can researchers investigate ADHFE1's role in tumor metastasis, particularly liver metastasis?

To investigate ADHFE1's role in tumor metastasis, particularly liver metastasis, researchers can employ the following methodological approaches:

Clinical Sample Analysis:

• Measure serum ADHFE1 levels in patients with and without metastasis using ELISA

• Correlate with established metastasis markers (CEA, CA199, AFP)

• Calculate diagnostic sensitivity and specificity

Research has shown serum ADHFE1 can discriminate gastric cancer patients with liver metastasis with 75.00% sensitivity and 86.92% specificity (AUC = 0.863) . ADHFE1 levels correlate positively with established markers (rCEA = 0.810, rCA199 = 0.788, rAFP = 0.765) .

In Vitro Migration and Invasion Assays:

• Manipulate ADHFE1 expression in cancer cell lines using overexpression vectors or shRNA

• Perform Transwell migration and invasion assays

• Quantify cell numbers and compare between experimental groups

Studies show silencing ADHFE1 significantly suppresses migration and invasion of cancer cells, while overexpression enhances these processes .

Angiogenesis Assessment:

• Collect conditioned media from cells with manipulated ADHFE1 expression

• Perform tube formation assays using endothelial cells

• Quantify angiogenic potential through tube length, branch points, and loop formation

Research demonstrates that ADHFE1 overexpression significantly enhances angiogenesis while silencing inhibits it . Since the liver is supplied by a dual blood system of arteries and portal veins, angiogenesis is critical for liver metastasis .

In Vivo Metastasis Models:

• Establish orthotopic tumor models with ADHFE1-manipulated cell lines

• Monitor metastatic spread using bioluminescence imaging

• Quantify metastatic burden through histological analysis of the liver

• Correlate metastatic potential with ADHFE1 expression

Xenograft studies have shown that cells with ADHFE1 knockdown develop smaller tumors compared to controls, indicating ADHFE1's role in tumor progression .

Therapeutic Response Monitoring:

• Treat metastatic cancer patients with chemotherapy

• Monitor serum ADHFE1 levels before and after treatment

• Correlate changes with treatment response

Studies show serum ADHFE1 levels decrease after chemotherapy in patients with liver metastasis, indicating its potential as a response biomarker .

These approaches provide comprehensive insights into ADHFE1's role in metastasis and could help develop targeted interventions.

What are common challenges in detecting ADHFE1 and how can they be overcome?

Researchers may encounter several challenges when detecting ADHFE1. Here are common issues and methodological solutions:

Challenge 1: Multiple Isoform Detection

• Issue: ADHFE1 has four isoforms (50, 45, 32, 27 kDa) produced by alternative splicing

• Solution:

  • Use antibodies targeting shared epitopes across isoforms

  • Run Western blots on 10-12% gels with extended run times to separate isoforms

  • Verify primary band at 50 kDa and secondary bands at 40-45 kDa

  • Consider using isoform-specific PCR primers to correlate protein bands with transcript variants

Studies show that in vitro translation of different ADHFE1 constructs (using M1 or M2 as start sites) produces different protein species, confirming the presence of multiple isoforms .

Challenge 2: Low Expression Levels

• Issue: ADHFE1 expression can be low in certain tissues/cell types

• Solution:

  • Increase protein loading to 40-50 μg per lane

  • Extend primary antibody incubation to overnight at 4°C

  • Use high-sensitivity chemiluminescent substrates

  • Consider enriching mitochondrial fractions as ADHFE1 is mitochondrially localized

  • For IHC, extend DAB development time and use amplification systems

Challenge 3: Cross-Reactivity Issues

• Issue: Some antibodies may cross-react with other iron-containing proteins

• Solution:

  • Use antibodies validated against protein arrays

  • Include appropriate negative controls (ADHFE1 knockdown cells)

  • Confirm specificity through peptide competition assays

  • Validate findings with a second antibody targeting a different epitope

Challenge 4: Non-specific Background in IHC

• Issue: High background can obscure specific ADHFE1 signals

• Solution:

  • Optimize antigen retrieval (TE buffer pH 9.0 preferred over citrate buffer)

  • Increase blocking time (5% BSA or 10% normal serum)

  • Add 0.1-0.3% Triton X-100 to reduce non-specific binding

  • Include avidin/biotin blocking for biotin-based detection systems

  • Use polymer-based detection systems instead of avidin-biotin methods

Challenge 5: Inconsistent Results Across Tissues

• Issue: ADHFE1 detection varies significantly between tissue types

• Solution:

  • Adjust antibody concentration based on tissue type (lower for high-expressing tissues)

  • Optimize fixation time for each tissue type

  • Use tissue-specific positive controls (kidney/liver for high expression)

  • Consider tissue-specific antigen retrieval protocols

  • Validate with orthogonal methods (e.g., RT-PCR) to confirm expression patterns

Implementing these methodological solutions will significantly improve ADHFE1 detection reliability across different experimental systems.

How can researchers ensure specificity when detecting ADHFE1 in different cellular contexts?

Ensuring ADHFE1 antibody specificity across diverse cellular contexts requires systematic validation approaches:

Genetic Manipulation Controls:

• Generate ADHFE1 knockdown/knockout cells using:

  • siRNA treatment (validated sequences: HSS134689, HSS13469, HSS175147)

  • shRNA stable cell lines

  • CRISPR-Cas9 knockout

• Use these as negative controls alongside wild-type cells

• Confirm knockdown efficiency by qRT-PCR before antibody validation

• Verify disappearance or significant reduction of the target band/signal

Studies have successfully used ADHFE1 knockdown cells to validate antibody specificity in colorectal and breast cancer research .

Overexpression Controls:

• Generate cells overexpressing tagged ADHFE1 (HA-tag, FLAG-tag)

• Use dual detection with tag-specific and ADHFE1-specific antibodies

• Confirm co-localization of signals

• Compare band patterns with endogenous ADHFE1

Research has shown that HA-tagged ADHFE1 expression in COS cells produced protein products of the same mass found upon in vitro transcription and translation, confirming antibody specificity .

Cross-Validation with Multiple Antibodies:

• Use antibodies from different vendors targeting distinct epitopes

• Compare staining patterns and band profiles

• Confirm consistent results across antibodies

• If discrepancies exist, investigate using additional validation methods

Cell Type-Specific Considerations:

Orthogonal Method Validation:

• Compare protein detection with mRNA expression (RNA-seq, qRT-PCR)

• Validate subcellular localization with fractionation studies

• Confirm function through enzymatic activity assays

• Use mass spectrometry to verify protein identity in immunoprecipitated samples

Implementing these comprehensive validation approaches ensures reliable ADHFE1 detection across experimental systems and prevents misinterpretation of results due to antibody non-specificity.

What pitfalls should researchers avoid when interpreting ADHFE1 antibody data in cancer studies?

When interpreting ADHFE1 antibody data in cancer research, several methodological pitfalls require careful consideration:

Pitfall 1: Failing to Account for Context-Dependent Expression

• Issue: ADHFE1 has contradictory roles in different cancer types

• Methodological Solution:

  • Always include appropriate tissue-matched controls

  • Compare ADHFE1 expression within the same cancer type

  • Acknowledge contradictory findings - ADHFE1 is upregulated in breast and gastric cancers but downregulated/hypermethylated in colorectal cancer

  • Avoid generalizing findings across cancer types

Pitfall 2: Overlooking Post-Translational Modifications

• Issue: ADHFE1 function may be regulated by PTMs not detected by all antibodies

• Methodological Solution:

  • Use phospho-specific antibodies when investigating signaling changes

  • Consider protein activity assays alongside expression studies

  • Examine multiple antibodies targeting different epitopes

  • Note that ADHFE1 is an iron-containing enzyme and its activity depends on iron availability

Pitfall 3: Misinterpreting Correlation with Tumor Stage

• Issue: ADHFE1 correlation with tumor stage varies by cancer type

• Methodological Solution:

  • Perform multivariate analyses accounting for confounding variables

  • Use sufficiently large sample sizes with adequate representation across stages

  • Calculate hazard ratios with appropriate confidence intervals

  • Note that in gastric cancer, ADHFE1 correlates with TNM stage, lymph node metastasis, and liver metastasis (HR: 2.61; 95% CI: 1.15-5.95)

Pitfall 4: Ignoring Metabolic Context

• Issue: ADHFE1 function is intimately linked to cellular metabolism

• Methodological Solution:

  • Measure D-2HG levels when studying ADHFE1

  • Assess metabolic state (normoxia vs. hypoxia)

  • Consider mitochondrial status and function

  • Acknowledge that ADHFE1 significantly upregulates D-2HG under hypoxic conditions

Pitfall 5: Neglecting Genetic/Epigenetic Regulation

• Issue: ADHFE1 expression is regulated by multiple mechanisms

• Methodological Solution:

  • Check for gene amplification, methylation status, and MYC co-expression

  • Perform integrated multi-omics analyses

  • Note that while ADHFE1 genomic amplification occurs in some tumors, it doesn't always correlate with protein expression

  • Consider that in basal-like breast tumors, ADHFE1 amplification may be more significant for protein expression

Pitfall 6: Overinterpreting Therapeutic Implications

• Issue: ADHFE1's therapeutic potential requires careful validation

• Methodological Solution:

  • Validate in multiple cell lines and patient-derived xenografts

  • Assess effects on non-malignant cells

  • Use appropriate dosing and timing for in vivo studies

  • Note that while ADHFE1 knockdown increases sensitivity to cisplatin in gastric cancer (IC₅₀ decreasing from 7.62 to 5.05 μg/mL), comprehensive studies across cancer types are needed

How can ADHFE1 antibodies be used to investigate the protein's role in metabolic diseases beyond cancer?

ADHFE1 antibodies can provide valuable insights into the protein's role in metabolic diseases through these methodological approaches:

Adipose Tissue Metabolism:

• Compare ADHFE1 expression between:

  • White and brown adipose tissues

  • Normal and obese subjects

  • Different adipose depots (subcutaneous vs. visceral)

• Correlate with metabolic parameters (insulin sensitivity, glucose tolerance)

Research has shown that ADHFE1 transcript is restricted to white and brown adipose tissues, liver, and kidney, with 40% downregulation in white adipose tissue of ob/ob obese mice compared to C57BL/6 mice .

Experimental Design for Adipocyte Studies:

• Differentiate preadipocytes using standard protocols

• Collect samples at multiple timepoints (0, 2, 4, 6, 8 days)

• Perform Western blot for ADHFE1 with parallel gene expression analysis

• Correlate ADHFE1 expression with differentiation markers

Studies demonstrate differentiation-dependent expression of ADHFE1 during in vitro brown and white adipogenesis, with PI 3-kinase-mediated signals maintaining basal ADHFE1 transcript levels in adipocytes .

Metabolic Pathway Analysis:

• Use ADHFE1 antibodies for co-immunoprecipitation to identify interaction partners

• Perform immunofluorescence to assess co-localization with metabolic enzymes

• Correlate ADHFE1 expression with metabolic intermediates

• Investigate connections to:

  • Pyruvate metabolism

  • Respiratory electron transport

  • ATP synthesis pathways

D-2-Hydroxyglutaric Aciduria Studies:

• Compare ADHFE1 expression and localization in patient vs. control samples

• Assess relationship between ADHFE1 levels and D-2HG accumulation

• Investigate potential therapeutic interventions targeting ADHFE1

ADHFE1 is associated with D-2-hydroxyglutaric aciduria and combined D-2 and L-2-hydroxyglutaric aciduria , suggesting its importance in these rare metabolic disorders.

Methodological Approach for Clinical Samples:

• Collect tissue or blood samples from patients with metabolic disorders

• Perform ADHFE1 immunohistochemistry or Western blot

• Correlate expression with metabolic parameters and disease severity

• Consider genetic variants that may affect ADHFE1 function

This systematic approach using ADHFE1 antibodies can reveal novel connections between this protein and various metabolic diseases, potentially identifying new therapeutic targets.

What are the most promising techniques for investigating ADHFE1's subcellular localization and its impact on cellular function?

Advanced techniques for investigating ADHFE1's subcellular localization and functional impact include:

High-Resolution Imaging Approaches:

• Super-Resolution Microscopy:

  • Use STORM or PALM imaging with fluorophore-conjugated ADHFE1 antibodies

  • Achieve 20-30 nm resolution to precisely map mitochondrial localization

  • Co-stain with mitochondrial markers (MitoTracker, TOM20) for confirmation

• Live-Cell Imaging with Tagged ADHFE1:

  • Generate cells expressing fluorescent protein-tagged ADHFE1 (GFP, mCherry)

  • Perform time-lapse imaging to monitor dynamic localization

  • Use FRAP (Fluorescence Recovery After Photobleaching) to assess protein mobility

Research has demonstrated that ADHFE1 localizes to mitochondria, which is critical for its metabolic functions .

Subcellular Fractionation with Immuno-detection:

• Isolate pure mitochondrial, cytosolic, and nuclear fractions

• Perform Western blot with ADHFE1 antibody

• Use fraction-specific markers (VDAC, α-tubulin, lamin) to confirm purity

• Quantify relative distribution across compartments

Protease Protection Assays:

• Isolate intact mitochondria

• Perform selective membrane permeabilization

• Subject to protease treatment

• Detect ADHFE1 by Western blot

• Determine intra-mitochondrial localization (matrix, inner membrane, intermembrane space)

Proximity Labeling with ADHFE1:

• Generate cells expressing ADHFE1 fused to BioID or APEX2

• Activate proximity labeling to biotinylate neighboring proteins

• Purify biotinylated proteins using streptavidin

• Identify interaction partners by mass spectrometry

• Confirm key interactions by co-immunoprecipitation with ADHFE1 antibodies

Functional Assessment Following Localization Disruption:

• Generate ADHFE1 mutants lacking mitochondrial targeting sequences

• Confirm mislocalization using immunofluorescence

• Assess impact on:

  • D-2HG production

  • Mitochondrial ROS generation

  • Metabolic pathway alterations

  • Cellular differentiation and EMT

These techniques would reveal how ADHFE1's mitochondrial localization affects its function in producing D-2HG and influencing cellular metabolism, which is particularly relevant given ADHFE1's role in increasing mitochondrial ROS and promoting EMT in cancer cells .

How can researchers integrate ADHFE1 antibody data with multi-omics approaches to understand its role in disease progression?

Integrating ADHFE1 antibody data with multi-omics approaches requires sophisticated methodological strategies:

Integrative Proteogenomic Analysis:

• Correlate ADHFE1 protein expression (immunohistochemistry, Western blot) with:

  • Genomic alterations (copy number, mutations)

  • Epigenetic modifications (methylation status)

  • Transcriptional profiles (RNA-seq)

• Perform pathway enrichment analysis to identify networks involving ADHFE1

Studies show that while genomic amplification of ADHFE1 occurs in some breast tumors, it doesn't always correlate with protein expression except in basal-like subtypes .

Metabolomics Integration:

• Measure cellular metabolites using mass spectrometry in samples with varying ADHFE1 expression

• Specifically quantify D-2HG levels and TCA cycle intermediates

• Correlate metabolite profiles with ADHFE1 protein levels detected by antibodies

• Map changes onto metabolic pathway maps

Research shows ADHFE1 overexpression leads to significant metabolic rewiring, including:

• 14.3-fold increase in acetyl-CoA

• Elevated NADPH/NADP ratio

• Accumulation of TCA metabolites from reductive glutamine metabolism

Experimental Design for Multi-omics:

Single-Cell Multi-omics Approach:

• Perform single-cell proteomics including ADHFE1 detection

• Correlate with scRNA-seq data

• Identify cell subpopulations with distinctive ADHFE1 expression patterns

• Map cellular heterogeneity in tumor samples

Clinical Sample Integration:

• Collect matched tissue samples for multi-omics analysis

• Perform tissue microarray analysis with ADHFE1 antibody

• Correlate with patient outcomes and treatment responses

• Develop predictive models incorporating ADHFE1 protein levels

Research shows ADHFE1 serves as an indicator for poor prognosis and liver metastasis in gastric cancer, with serum levels decreasing after chemotherapy .

Network Analysis Approach:

• Identify ADHFE1-interacting proteins through IP-MS

• Map ADHFE1 in protein-protein interaction networks

• Correlate network perturbations with disease progression

• Identify potential therapeutic targets within the network

This comprehensive integration of ADHFE1 antibody data with multi-omics approaches provides a systems-level understanding of ADHFE1's role in disease progression and identifies potential intervention points for therapeutic development.