PTGES3 Human

What is PTGES3 and what are its primary functions in human cells?

PTGES3, also known as p23, functions as a molecular chaperone of Hsp90 involved in protein folding and stabilization across a wide range of proteins. It plays a critical role in mediating the expression of prostaglandin E2 (PGE2), which affects multiple biological processes. In normal cellular physiology, PTGES3 participates in the COX/prostaglandin pathway primarily through PGE2 production, influencing various cellular mechanisms including protein stabilization, hormone signaling, and stress responses .

The protein is expressed across multiple tissue types, with differential expression patterns observed between normal and malignant tissues. Research demonstrates that PTGES3 is involved in several biological pathways, notably oocyte meiosis, progesterone-mediated oocyte maturation, and arachidonic acid metabolism pathways .

How is PTGES3 expression regulated in normal tissues?

In healthy tissues, PTGES3 shows differential expression patterns, with significantly lower expression observed in bile duct, esophagus, liver, and stomach compared to other tissues. This tissue-specific regulation suggests that PTGES3 expression is controlled by complex transcriptional mechanisms that may be altered in pathological conditions .

The protein operates within a network of co-expressed genes, including CACYBP, HNRNPC, and TCP1, which interact with PTGES3 in normal cellular function. Understanding these regulatory networks is essential for comprehending how dysregulation occurs in disease states .

What experimental models are most suitable for studying PTGES3 in basic research?

For basic PTGES3 research, several experimental models have proven valuable:

• Cell line models: Human cancer cell lines (particularly breast cancer and lung adenocarcinoma lines) with varying PTGES3 expression levels provide useful in vitro systems for mechanistic studies.

• Gene knockdown approaches: siRNA transfection targeting PTGES3, as demonstrated in breast cancer cell lines, offers insights into functional consequences of PTGES3 inhibition .

• CRISPR-Cas9 gene editing: For creating stable PTGES3 knockout or knock-in cell lines to study long-term effects.

• Patient-derived xenografts (PDX): These models preserve tumor heterogeneity and microenvironment influences on PTGES3 function.

• Bioinformatic approaches: Mining TCGA, GTEx, and CPTAC databases has proven valuable for analyzing PTGES3 expression patterns across normal and malignant tissues .

When selecting models, researchers should consider tissue context, expression levels, and relevant interacting partners to ensure physiological relevance.

In which cancer types is PTGES3 most significantly overexpressed?

Comprehensive pan-cancer analyses have identified PTGES3 overexpression in multiple cancer types compared to their normal tissue counterparts. Based on TCGA data analysis, PTGES3 shows significantly elevated expression in:

• Lung adenocarcinoma (LUAD)

• Liver hepatocellular carcinoma (LIHC)

• Lung squamous cell carcinoma (LUSC)

• Esophageal carcinoma (ESCA)

• Cholangiocarcinoma (CHOL)

• Head and neck squamous cell carcinoma (HNSC)

• Kidney renal papillary cell carcinoma (KIRP)

• Breast invasive carcinoma (BRCA)

• Glioblastoma multiforme (GBM)

• Stomach adenocarcinoma (STAD)

• Prostate adenocarcinoma (PRAD)

• Bladder urothelial carcinoma (BLCA)

• Colon adenocarcinoma (COAD)

• Rectum adenocarcinoma (READ)

Interestingly, PTGES3 is downregulated in kidney chromophobe (KICH), suggesting context-dependent expression patterns. At the protein level, CPTAC dataset analysis confirms higher PTGES3 expression in tumor tissues compared to normal tissues in multiple cancer types, including COAD, BRCA, KIRC, OV, UCEC, LUSC, LIHC, and HNSC .

What is the prognostic significance of PTGES3 expression in different cancer types?

PTGES3 expression has significant prognostic implications across multiple cancer types:

• High PTGES3 expression correlates with poor OS in BRCA, MESO, KIRP, LIHC, ESCA, and LUAD

• Interestingly, higher PTGES3 expression in COAD and OV patients correlates with better OS

For disease-specific survival (DSS):

• High PTGES3 expression associates with poor DSS in LUAD, ACC, LIHC, KIRP, MESO, and KICH

• Better DSS is observed in COAD and OV patients with high PTGES3 expression

These findings suggest that PTGES3 could serve as a valuable prognostic biomarker, particularly in LIHC, ESCA, MESO, and LUAD, though its prognostic significance appears to be cancer type-specific.

How does PTGES3 influence tumor microenvironment and immune regulation?

PTGES3 participates in complex immune regulatory networks within the tumor microenvironment. Research in lung adenocarcinoma has demonstrated that PTGES3 expression correlates with immune regulatory mechanisms, suggesting it may influence tumor immune evasion and response to immunotherapy .

The prostaglandin pathway, in which PTGES3 plays a key role, affects immune cell infiltration and function within the tumor microenvironment. PGE2, produced through the action of PTGES3, can modulate immune responses by:

• Influencing T cell activation and differentiation

• Affecting cytokine production profiles

• Potentially altering immune checkpoint expression

• Modulating immune cell trafficking within the tumor microenvironment

Further research is needed to fully elucidate the specific mechanisms by which PTGES3 regulates immune responses in different cancer contexts, particularly regarding its potential impact on immunotherapy efficacy.

What are the most effective methods to study PTGES3 protein-protein interactions?

Several methodologies have proven effective for investigating PTGES3 protein-protein interactions:

• Co-immunoprecipitation (Co-IP): Essential for identifying native protein complexes involving PTGES3, particularly its interaction with Hsp90 and client proteins.

• Proximity ligation assay (PLA): Allows visualization of protein interactions in situ, providing spatial context for PTGES3 interactions within cells.

• Yeast two-hybrid screening: Useful for identifying novel interaction partners of PTGES3.

• Protein microarrays: Enable high-throughput screening of potential PTGES3 binding partners.

• FRET/BRET assays: Provide dynamic information about PTGES3 interactions in living cells.

• Mass spectrometry-based interactomics: The String database has been used to speculate the possible interaction network of PTGES3 . This approach can identify both direct and indirect interactors within protein complexes.

• Cross-linking mass spectrometry (XL-MS): Offers insights into structural aspects of PTGES3 interactions.

When designing experiments to study PTGES3 interactions, researchers should consider physiological relevance of conditions and validation through multiple complementary techniques.

What are the recommended protocols for assessing PTGES3 knockdown effects in cancer cells?

Based on published research methodologies, the following protocols are recommended for assessing PTGES3 knockdown effects:

• siRNA Transfection Protocol:

  • Use validated siRNA sequences targeting PTGES3

  • Transfect at 60-70% cell confluence using lipofection or electroporation

  • Include scrambled siRNA controls

  • Validate knockdown efficiency via qRT-PCR and Western blot at 48-72 hours post-transfection

• Functional Assays:

  • Cell Viability: Perform CCK-8 assays at 24, 48, and 72 hours post-transfection

  • Cell Migration: Conduct wound healing assays by creating a scratch in confluent monolayers and monitoring closure over 24-48 hours

  • Cell Invasion: Use Matrigel-coated transwell assays

  • Apoptosis Assessment: Flow cytometry with Annexin V/PI staining

  • Cell Cycle Analysis: Flow cytometry with propidium iodide staining

• Molecular Pathway Analysis:

  • Assess expression changes in downstream effectors via Western blot

  • Evaluate PGE2 production using ELISA

  • Monitor changes in relevant signaling pathways (e.g., MAPK, PI3K/AKT)

• Rescue Experiments:

  • Perform PTGES3 re-expression experiments to confirm specificity of observed phenotypes

These protocols have been successfully implemented in breast cancer cell lines, demonstrating that PTGES3 knockdown significantly inhibits proliferation and migration .

How can researchers effectively analyze PTGES3 correlation with clinical variables in cancer patients?

To effectively analyze PTGES3 correlation with clinical variables in cancer patients, researchers should employ the following methodological approaches:

• Dataset Selection and Quality Control:

  • Utilize comprehensive datasets with robust clinical annotation (TCGA, CPTAC, GEO)

  • Ensure adequate sample sizes for statistical power

  • Implement proper normalization of gene expression data

  • Account for batch effects and other technical biases

• Statistical Analysis Methods:

  • For categorical variables: Use appropriate statistical tests (t-test, ANOVA, Chi-square)

  • For survival analysis: Employ Kaplan-Meier analysis with log-rank tests

  • For multivariate analysis: Implement Cox proportional hazards regression

  • Calculate hazard ratios with 95% confidence intervals

  • Use ROC curve analysis to assess predictive value (as demonstrated in LUAD with AUC of 0.705)

• Clinical Variable Correlation Analysis:

  • Systematically assess PTGES3 expression correlation with:

    • Cancer stage and tumor grade

    • Age, gender, and other demographic factors

    • Histological subtypes

    • Molecular subtypes

    • Treatment response parameters

• Validation Approaches:

  • Cross-validate findings in independent patient cohorts

  • Implement both internal and external validation

  • Consider paired analysis of tumor and adjacent normal tissues when available

In lung adenocarcinoma, this approach revealed significant associations between PTGES3 expression and cancer stage (p < 0.05, stage 1 vs. stage 3) and tumor grade (p < 0.001, grade 2 vs. grade 3) . Similar methodologies applied to breast cancer identified PTGES3 as part of a six-gene signature with independent prognostic value .

What are the current approaches for targeting PTGES3 in cancer therapy development?

Several approaches are being investigated for targeting PTGES3 in cancer therapy development:

• Direct Inhibition Strategies:

  • Small molecule inhibitors targeting PTGES3 protein-protein interactions, particularly its chaperone function with Hsp90

  • Peptide-based inhibitors designed to disrupt specific PTGES3 interactions

  • Structure-guided drug design focusing on key functional domains

• Drug Repurposing Approaches:

  • Molecular docking studies have identified existing compounds with PTGES3-binding potential

  • Three drugs (gedunin, genistein, and diethylstilbestrol) have been confirmed to target PTGES3, with genistein and diethylstilbestrol demonstrating stronger binding affinities than gedunin

  • These compounds significantly inhibit breast cancer cell proliferation and reduce PTGES3 expression at both protein and mRNA levels

• Gene Expression Modulation:

  • siRNA and antisense oligonucleotides targeting PTGES3 mRNA

  • CRISPR-Cas9 approaches for genetic knockdown

  • Promoter-targeted epigenetic modifiers

• Pathway-based Approaches:

  • Targeting the COX/PGE2 pathway upstream or downstream of PTGES3

  • Combinatorial approaches targeting multiple components of PTGES3-associated pathways

These approaches are at various stages of development, with drug repurposing showing particularly promising results in preclinical models of breast cancer .

How does PTGES3 inhibition affect cancer cell proliferation and migration?

PTGES3 inhibition has demonstrable effects on cancer cell behavior, particularly regarding proliferation and migration:

Effects on Cell Proliferation:
Research in breast cancer cell lines has shown that PTGES3 knockdown via siRNA transfection significantly inhibits cell proliferation, as measured by CCK-8 cell viability assays . This antiproliferative effect suggests that PTGES3 plays a critical role in sustaining cancer cell growth.

Effects on Cell Migration:
Wound healing assays following PTGES3 knockdown demonstrate significant inhibition of breast cancer cell migration . This finding indicates that PTGES3 contributes to the metastatic potential of cancer cells.

Molecular Mechanisms:
The antiproliferative and anti-migratory effects of PTGES3 inhibition likely stem from:

• Disruption of PGE2 production, which normally promotes tumor growth through upregulating anti-apoptotic genes

• Destabilization of client proteins dependent on PTGES3 chaperone function

• Interference with signaling pathways that drive cancer cell proliferation and migration

These effects have been validated using both genetic approaches (siRNA) and pharmacological inhibition with compounds like genistein and diethylstilbestrol, confirming PTGES3 as a promising therapeutic target .

What biomarkers can be used to predict response to PTGES3-targeted therapies?

While PTGES3-targeted therapies are still under development, several potential biomarkers may predict treatment response:

• PTGES3 Expression Levels:

  • Baseline PTGES3 mRNA and protein expression levels may indicate sensitivity to PTGES3 inhibition

  • Cancer types with higher PTGES3 expression (LUAD, LIHC, BRCA) might show greater response to targeted therapies

• Co-expression Biomarkers:

  • Expression of PTGES3-interacting genes (CACYBP, HNRNPC, TCP1) may serve as complementary biomarkers

  • These genes participate in functional networks with PTGES3 and could influence therapeutic response

• Pathway Activity Markers:

  • Markers of the COX/PGE2 pathway activity

  • Downstream effectors of PTGES3 signaling

  • PGE2 levels in tumor tissue or circulation

• Genetic and Molecular Features:

  • PTGES3 gene alterations and mutations, which have been observed in lung adenocarcinoma

  • Cancer molecular subtypes that show differential PTGES3 dependency

• Immune Markers:

  • Given PTGES3's role in immune regulation, markers of immune cell infiltration and activity may predict response, particularly in combination with immunotherapies

Future clinical trials of PTGES3-targeted therapies should incorporate biomarker analyses to refine patient selection and develop companion diagnostics for precision medicine approaches.

How does PTGES3 genetic variation impact its function in cancer progression?

Genetic variation in PTGES3 has significant implications for cancer biology:

Gene Alterations and Mutations:
Analysis of lung adenocarcinoma has revealed several types of PTGES3 gene alterations, though the specific functional consequences of these alterations remain under investigation . These genetic variations may:

• Alter protein structure and function

• Affect interaction with binding partners like Hsp90

• Influence subcellular localization

• Impact prostaglandin synthesis activity

Expression Quantitative Trait Loci (eQTLs):
Genetic variants affecting PTGES3 expression levels may contribute to cancer risk and progression. Comprehensive genomic analysis could identify regulatory variants that predispose to aberrant PTGES3 expression.

Splicing Variants:
Alternative splicing of PTGES3 may generate protein isoforms with distinct functional properties, potentially contributing to cancer heterogeneity and treatment response.

Copy Number Variations:
PTGES3 gene amplification could drive overexpression in certain cancer types, representing a distinct mechanism of PTGES3 upregulation separate from transcriptional activation.

Further research characterizing the functional consequences of PTGES3 genetic variation will enhance our understanding of its role in cancer progression and may reveal novel therapeutic vulnerabilities.

What are the methodological challenges in studying PTGES3 across different cancer types?

Researchers face several methodological challenges when studying PTGES3 across cancer types:

• Tissue and Context Specificity:

  • PTGES3 exhibits different expression patterns and functions across tissue types

  • Study designs must account for tissue-specific co-factors and regulatory mechanisms

  • Results from one cancer type may not generalize to others (as evidenced by opposing prognostic associations in different cancers)

• Technical and Analytical Considerations:

  • Antibody specificity issues for protein detection

  • Standardization challenges across different experimental platforms

  • Need for appropriate statistical methods to handle high-dimensional data

  • Controlling for tumor purity in correlation analyses, as demonstrated in the TIMER2.0 analysis

• Model System Limitations:

  • Cell lines may not recapitulate the complex tumor microenvironment

  • Patient-derived models are resource-intensive but provide greater clinical relevance

  • Genetically engineered mouse models may not fully replicate human PTGES3 biology

• Integration of Multi-omics Data:

  • Combining transcriptomic, proteomic, and metabolomic data presents computational challenges

  • Requires sophisticated approaches to integrate findings across different molecular levels

• Translational Barriers:

  • Bridging preclinical findings to clinical applications

  • Developing reliable biomarkers for patient stratification

  • Designing appropriate clinical trials for PTGES3-targeted therapies

Addressing these challenges requires multidisciplinary approaches and careful experimental design, as exemplified by studies that integrate computational predictions with experimental validation .

How does PTGES3 interact with other cancer-related pathways to drive tumor progression?

PTGES3 engages in complex interactions with multiple cancer-related pathways:

• Integration with Prostaglandin Signaling:

  • PTGES3 mediates PGE2 production, which activates EP receptors

  • The COX/PG pathway affects cancer progression primarily through PGE2 production

  • This pathway influences cell proliferation, apoptosis resistance, angiogenesis, and immune evasion

• Molecular Chaperone Functions:

  • As a co-chaperone of Hsp90, PTGES3 facilitates proper folding and stabilization of client proteins involved in:

    • Signal transduction

    • Cell cycle regulation

    • Steroid hormone signaling

    • Stress response pathways

• Interaction with Specific Signaling Networks:

  • Functional analysis has revealed PTGES3 enrichment in pathways including:

    • Oocyte meiosis

    • Progesterone-mediated oocyte maturation

    • Arachidonic acid metabolism

  • Co-expression analysis identified interaction with CACYBP, HNRNPC, and TCP1, suggesting broader regulatory networks

• Influence on Tumor Microenvironment:

  • PTGES3-mediated PGE2 production affects stromal cells and immune infiltrates

  • Contributes to creating an immunosuppressive microenvironment

  • May modulate tumor-associated inflammation

• Potential Role in Therapy Resistance:

  • Stabilization of client proteins involved in drug resistance mechanisms

  • Activation of alternative survival pathways under therapeutic pressure

Understanding these complex interactions is essential for developing effective PTGES3-targeted therapies and rational combination treatment strategies that address multiple aspects of PTGES3 biology.

What novel technologies are advancing our understanding of PTGES3 function?

Several cutting-edge technologies are transforming PTGES3 research:

• Single-cell Omics Technologies:

  • Single-cell RNA sequencing reveals cell-specific PTGES3 expression patterns

  • Single-cell proteomics provides insights into PTGES3 protein interactions at cellular resolution

  • These approaches help uncover heterogeneity in PTGES3 function across different cell populations within tumors

• CRISPR-based Functional Genomics:

  • CRISPR screens identify genes that synergize with PTGES3 in cancer progression

  • CRISPR activation/interference systems enable precise modulation of PTGES3 expression

  • Base editing approaches allow study of specific PTGES3 variants

• Advanced Structural Biology Methods:

  • Cryo-electron microscopy of PTGES3 complexes

  • Hydrogen-deuterium exchange mass spectrometry for dynamic interaction analysis

  • These techniques provide atomic-level insights into PTGES3 function and drug binding

• Computational Drug Discovery:

  • Machine learning approaches for PTGES3 inhibitor prediction

  • Molecular dynamics simulations of drug-PTGES3 interactions

  • Virtual screening methodologies have already identified compounds like gedunin, genistein, and diethylstilbestrol as PTGES3 inhibitors

• Spatial Transcriptomics and Proteomics:

  • Map PTGES3 expression and function within tissue architecture

  • Reveal spatial relationships between PTGES3-expressing cells and the tumor microenvironment

Integration of these technologies provides unprecedented insights into PTGES3 biology and accelerates the development of targeted therapeutic strategies.

How might combination therapies targeting PTGES3 and other pathways enhance cancer treatment?

Strategic combination approaches involving PTGES3 inhibition show significant promise:

• PTGES3 Inhibition with Immunotherapy:

  • Given PTGES3's role in immune regulation, combining PTGES3 inhibitors with immune checkpoint inhibitors may enhance anti-tumor immune responses

  • This approach could be particularly relevant in cancers where PTGES3 contributes to immunosuppression

• Targeting Multiple Components of the PGE2 Pathway:

  • Combining PTGES3 inhibition with COX-2 inhibitors or EP receptor antagonists

  • This multi-level approach may provide more complete pathway suppression than single-agent strategies

• PTGES3 and HSP90 Dual Targeting:

  • Since PTGES3 functions as an Hsp90 co-chaperone, dual inhibition may synergistically disrupt protein stabilization in cancer cells

  • This approach could address potential resistance mechanisms to single-agent therapy

• Personalized Combination Approaches:

  • Combinations based on tumor-specific genetic alterations

  • For instance, in breast cancer, PTGES3 inhibition could be combined with therapies targeting genes in the six-gene signature (APOOL, BNIP3, F2RL2, HINT3, PTGES3, and RTN3)

• Rational Drug Combinations Based on Network Analysis:

  • Computational approaches can identify synthetic lethal interactions with PTGES3

  • Network pharmacology may reveal non-obvious drug combinations with synergistic potential

Preclinical studies with genistein and diethylstilbestrol provide proof-of-concept for PTGES3-targeting combination approaches , laying groundwork for more sophisticated combinatorial strategies.

What are the most promising future research directions for PTGES3 in precision oncology?

Several promising research directions will advance PTGES3's role in precision oncology:

• Development of Selective PTGES3 Inhibitors:

  • Structure-based design of highly specific PTGES3 inhibitors

  • Optimization of lead compounds (genistein, diethylstilbestrol) for improved pharmacokinetics and reduced off-target effects

  • Creation of PTGES3 degraders using proteolysis-targeting chimera (PROTAC) technology

• Biomarker-driven Patient Stratification:

  • Identification and validation of biomarkers predicting PTGES3 dependency

  • Development of companion diagnostics for PTGES3-targeted therapies

  • Integration of the six-gene signature (including PTGES3) into clinical decision-making for breast cancer

• Therapeutic Resistance Mechanisms:

  • Understanding adaptive responses to PTGES3 inhibition

  • Identifying bypass pathways that confer resistance

  • Developing strategies to prevent or overcome resistance

• PTGES3 in Emerging Cancer Therapies:

  • Exploring PTGES3's role in response to cellular immunotherapies (CAR-T, TILs)

  • Investigating PTGES3 inhibition in combination with targeted radiotherapy

  • Assessing PTGES3 in cancer stem cell targeting approaches

• Translational Pipeline Development:

  • Design of first-in-human clinical trials for PTGES3 inhibitors

  • Establishment of pharmacodynamic markers of target engagement

  • Creation of patient-derived preclinical models for therapy testing