Recombinant Human Protein FAM87A (FAM87A)

What is FAM87A and what is its role in normal cellular function?

FAM87A is a long non-coding RNA (lncRNA) that is part of the family with sequence similarity 87. In normal cellular contexts, FAM87A appears to function as a competing endogenous RNA (ceRNA) that regulates gene expression by interacting with microRNAs, particularly miR-424-5p. While its normal physiological role is still being fully elucidated, research suggests it plays an important role in cellular homeostasis by modulating signaling pathways involved in cell proliferation, migration, and invasion .

The protein consists of 286 amino acids and has been studied primarily in the context of its regulatory functions in gene expression networks. Current evidence suggests that FAM87A is primarily localized in the cytoplasm, as demonstrated by FISH (Fluorescence In Situ Hybridization) and subcellular isolation experiments .

What expression patterns of FAM87A are observed in different tissues and disease states?

FAM87A exhibits differential expression across various tissues, with abnormal expression patterns in several pathological conditions, particularly in glioma. Research has demonstrated that FAM87A is significantly downregulated in glioma tissues and cell lines compared to normal brain tissue . This downregulation has been correlated with several clinical parameters:

• FAM87A expression is lower in patients with metastatic tumors compared to those with non-metastatic tumors

• Expression levels decrease markedly with increasing pathological stage

• Expression is notably correlated with lymphatic metastasis and TNM staging

• FAM87A expression differs between low-grade glioma (LGG) and glioblastoma (GBM)

How can recombinant FAM87A protein be used in basic research applications?

Recombinant FAM87A protein, such as the Strep-tagged version (AA 1-286) available from commercial sources, can be utilized in multiple basic research applications:

• Protein-protein interaction studies: The purified recombinant protein can serve as bait in pull-down assays to identify novel binding partners.

• Antibody validation: As a positive control in Western blots and ELISA to validate antibody specificity against FAM87A.

• Functional assays: To study the effects of exogenous FAM87A protein on cell behavior in vitro.

• Structural studies: For investigating the secondary and tertiary structure of the protein.

• Binding affinity measurements: To quantify the strength of interactions with potential binding partners including miR-424-5p .

For optimal results, researchers should verify the activity and purity of recombinant FAM87A before use, as proteins produced through different expression systems (such as Cell-free protein synthesis as mentioned in the product description) may exhibit varying levels of functionality .

What mechanisms underlie FAM87A's tumor suppressor function in glioma?

FAM87A functions as a tumor suppressor in glioma through a complex molecular mechanism involving the miR-424-5p/PPM1H axis. This mechanism can be broken down into several key components:

• Competitive binding with miR-424-5p: FAM87A acts as a competing endogenous RNA (ceRNA) by binding to miR-424-5p, which prevents this microRNA from interacting with its target mRNAs. This competitive binding has been confirmed through bioinformatics analysis, dual luciferase assays, and RNA immunoprecipitation (RIP) experiments .

• Regulation of PPM1H expression: By sequestering miR-424-5p, FAM87A prevents the suppression of PPM1H (Protein Phosphatase, Mg2+/Mn2+ Dependent 1H), allowing for its increased expression. PPM1H is a downstream target of miR-424-5p, and its expression is positively correlated with FAM87A expression in glioma tissues .

• Modulation of PI3K/Akt signaling: The FAM87A/miR-424-5p/PPM1H axis ultimately regulates the PI3K/Akt signaling pathway, which is crucial for cell proliferation, migration, and invasion .

• Regulation of EMT-related proteins: Overexpression of FAM87A has been shown to decrease the expression of invasion- and metastasis-related proteins (fibronectin, N-cadherin, vimentin, MMP9, and MMP2) while increasing E-cadherin expression, suggesting an inhibitory effect on epithelial-mesenchymal transition (EMT) .

These molecular interactions collectively contribute to FAM87A's ability to suppress glioma cell proliferation, migration, and invasion both in vitro and in vivo.

How does FAM87A expression correlate with clinical outcomes in glioma patients?

Analysis of clinical data reveals significant correlations between FAM87A expression and patient outcomes in glioma:

These findings collectively suggest that FAM87A expression levels could serve as a useful prognostic indicator for glioma patients and may help in therapeutic decision-making.

How can targeting the FAM87A/miR-424-5p/PPM1H axis be exploited for therapeutic interventions?

The FAM87A/miR-424-5p/PPM1H axis represents a promising therapeutic target for glioma treatment, with several potential intervention strategies:

• Upregulation of FAM87A: Since FAM87A functions as a tumor suppressor in glioma, therapies aimed at increasing its expression could potentially inhibit tumor growth. This might be achieved through gene therapy approaches or small molecules that enhance FAM87A expression .

• miR-424-5p inhibition: Targeting miR-424-5p with specific inhibitors (antagomirs) could mimic the effect of increased FAM87A expression, potentially restoring PPM1H expression and inhibiting tumor progression .

• PPM1H activation: Direct targeting of PPM1H, the downstream effector in this signaling axis, could provide another therapeutic approach. Compounds that increase PPM1H activity or expression might bypass the need for manipulating upstream regulators .

• Combination with existing therapies: Research has shown that overexpression of FAM87A decreases the resistance of glioma cells to temozolomide (TMZ), a standard chemotherapeutic agent for glioma. This suggests that therapies targeting the FAM87A/miR-424-5p/PPM1H axis could potentially enhance the efficacy of conventional treatments .

• PI3K/Akt pathway modulation: Since the FAM87A/miR-424-5p/PPM1H axis ultimately modulates the PI3K/Akt signaling pathway, combining FAM87A-targeted therapies with PI3K/Akt inhibitors could potentially yield synergistic effects .

These approaches represent promising avenues for future therapeutic development, though further research is needed to translate these findings into clinically viable treatments.

What are the recommended protocols for detecting FAM87A expression in different cell types and tissues?

For comprehensive analysis of FAM87A expression, researchers should employ multiple complementary techniques:

• Quantitative Real-Time PCR (qRT-PCR):

  • Extract total RNA using TRIzol reagent or commercial kits

  • Perform reverse transcription to generate cDNA

  • Design specific primers for the FAM87A transcript

  • Normalize expression to appropriate housekeeping genes (e.g., GAPDH, β-actin)

  • Include multiple biological and technical replicates for statistical validity

• In Situ Hybridization (ISH) or Fluorescence ISH (FISH):

  • For visualization of FAM87A localization within cells and tissues

  • Use specific RNA probes complementary to FAM87A sequence

  • This method can confirm cytoplasmic localization of FAM87A, as demonstrated in previous studies

• Western Blotting:

  • For protein-level detection, though challenging for lncRNAs like FAM87A

  • Use validated antibodies specific to FAM87A protein products

  • Include appropriate positive and negative controls

• Immunohistochemistry (IHC):

  • For detection in tissue samples, particularly useful for clinical specimens

  • Requires high-specificity antibodies

  • Can be combined with other markers for correlation studies

• RNA-Sequencing:

  • For comprehensive transcriptome analysis and discovery of novel variants

  • Particularly useful for identifying correlation patterns with other genes

For all methods, include appropriate controls and validate results using multiple techniques to ensure reliability and reproducibility.

What experimental approaches are most effective for studying FAM87A's interaction with miR-424-5p?

To investigate the interaction between FAM87A and miR-424-5p, researchers can employ several complementary approaches:

• Bioinformatic Prediction:

  • Utilize databases such as miRcode, TargetScan, and miRBase to predict potential binding sites

  • This serves as the starting point for experimental validation

• Dual Luciferase Reporter Assay:

  • Clone the wild-type (WT) and mutated (MUT) binding sites of FAM87A into luciferase reporter vectors

  • Co-transfect cells with these constructs and miR-424-5p mimics or inhibitors

  • Measure luciferase activity to assess direct binding

  • A reduction in luciferase activity with WT but not MUT constructs confirms direct interaction

• RNA Immunoprecipitation (RIP) Assay:

  • Use antibodies against RNA-induced silencing complex (RISC) components (e.g., Ago2)

  • Immunoprecipitate the complex and analyze the enrichment of FAM87A and miR-424-5p by qRT-PCR

  • This confirms their co-localization in the same RNA-induced silencing complex

• RNA Pull-Down Assay:

  • Use biotinylated FAM87A or miR-424-5p as bait

  • Capture interacting partners using streptavidin beads

  • Identify bound miRNAs or lncRNAs by qRT-PCR or sequencing

• Fluorescence Colocalization Studies:

  • Label FAM87A and miR-424-5p with different fluorescent markers

  • Visualize their colocalization using confocal microscopy

• Correlation Analysis in Clinical Samples:

  • Measure expression levels of both FAM87A and miR-424-5p in the same samples

  • Calculate correlation coefficients to assess their relationship in vivo

  • Previous studies have demonstrated a negative correlation between FAM87A and miR-424-5p expression in glioma samples

These methods collectively provide strong evidence for direct interaction between FAM87A and miR-424-5p, supporting the ceRNA hypothesis.

What cell models and assays are most suitable for investigating FAM87A's functional effects on cell behavior?

To comprehensively investigate FAM87A's functional effects on cell behavior, researchers should consider the following cell models and assays:

Cell Models:

• Glioma Cell Lines:

  • T98G and A172 cell lines have been successfully used in previous studies

  • These cells exhibit relatively low endogenous FAM87A expression, making them suitable for overexpression studies

  • Other glioma cell lines (U87, U251) can also be considered for comparative analyses

• Primary Tumor Cells:

  • Patient-derived primary glioma cells provide a more clinically relevant model

  • These maintain tumor heterogeneity better than established cell lines

• Normal Glial Cells:

  • For comparative studies to understand FAM87A's normal function

  • Useful for assessing the specificity of effects observed in cancer cells

• Genetic Manipulation Models:

  • Stable overexpression: Using lentiviral vectors for long-term studies

  • Knockdown: siRNA or shRNA targeting FAM87A

  • CRISPR-Cas9: For complete knockout studies or endogenous tagging

Functional Assays:

• Cell Proliferation Assays:

  • MTT assay: For assessing cell viability and proliferation

  • EdU incorporation assay: For directly measuring DNA synthesis

  • Colony formation assay: For evaluating long-term proliferative capacity

• Migration and Invasion Assays:

  • Transwell assay: For quantifying cell migration and invasion

  • Wound healing assay: For studying directional cell migration

  • 3D invasion assays: For more physiologically relevant models

• Cell Cycle and Apoptosis Analysis:

  • Flow cytometry with propidium iodide staining: For cell cycle analysis

  • Annexin V/PI staining: For apoptosis detection

  • Western blotting for cell cycle and apoptosis markers

• Drug Resistance Assays:

  • Cell viability assays with temozolomide (TMZ) treatment

  • Combination treatment studies to assess synergistic effects

• Molecular Analyses:

  • Western blotting: For assessing expression of EMT markers (E-cadherin, N-cadherin, vimentin), MMPs, and signaling proteins (PI3K/Akt pathway)

  • qRT-PCR: For gene expression analysis

  • Immunofluorescence: For protein localization studies

These cell models and assays collectively provide a comprehensive understanding of FAM87A's functional effects on various aspects of cell behavior.

How does the structure of FAM87A contribute to its function as a competing endogenous RNA?

The structure-function relationship of FAM87A as a competing endogenous RNA (ceRNA) is a complex topic that involves several aspects:

• Primary Sequence and Binding Sites:

  • The primary sequence of FAM87A contains specific miRNA response elements (MREs) that enable it to bind to miR-424-5p

  • Bioinformatic analyses and experimental validation have confirmed these binding sites

  • The specificity of these interactions is crucial for its ceRNA function

• Secondary and Tertiary Structure:

  • The three-dimensional folding of FAM87A likely creates structural motifs that facilitate miRNA binding

  • RNA structure prediction tools can provide insights into potential stem-loop structures that may serve as miRNA binding platforms

  • Structural analyses using techniques such as SHAPE (Selective 2'-hydroxyl acylation analyzed by primer extension) or RNA crystallography would be valuable for detailed characterization

• Subcellular Localization:

  • The predominant cytoplasmic localization of FAM87A, as demonstrated by FISH and subcellular isolation experiments, is consistent with its role as a ceRNA

  • This localization places it in the same cellular compartment as the miRNA machinery, enabling competition for miRNA binding

• Abundance and Stability:

  • The effectiveness of FAM87A as a ceRNA depends on its abundance relative to target mRNAs and miRNAs

  • RNA stability factors, including potential protein interactions or modifications, could influence its half-life and thus its ceRNA function

  • Understanding these factors could provide insights into the regulation of FAM87A's function

• Protein Interactions:

  • While functioning primarily as a ceRNA, FAM87A may also interact with RNA-binding proteins that could modulate its structure and function

  • RNA immunoprecipitation followed by mass spectrometry could identify such protein partners

Advanced structural studies combined with functional validations are needed to fully elucidate how FAM87A's structure enables its ceRNA function, which could potentially inform the design of RNA-based therapeutics targeting this pathway.

What is the relationship between FAM87A and the PI3K/Akt signaling pathway in glioma progression?

The relationship between FAM87A and the PI3K/Akt signaling pathway in glioma progression involves a complex regulatory network:

• Indirect Regulation via miR-424-5p/PPM1H Axis:

  • FAM87A functions as a ceRNA by sponging miR-424-5p, which leads to upregulation of PPM1H

  • PPM1H (Protein Phosphatase, Mg2+/Mn2+ Dependent 1H) is a phosphatase that can dephosphorylate and inactivate components of the PI3K/Akt pathway

  • This indirect regulation creates a signaling cascade: FAM87A → miR-424-5p ↓ → PPM1H ↑ → PI3K/Akt pathway ↓

• Effect on Pathway Activation Status:

  • Experimental evidence suggests that overexpression of FAM87A leads to decreased phosphorylation of key components in the PI3K/Akt pathway

  • This results in reduced activation of this pro-oncogenic signaling cascade

  • Western blot analysis can demonstrate changes in phosphorylation levels of PI3K, Akt, and downstream targets like mTOR

• Functional Consequences of Pathway Modulation:

  • The PI3K/Akt pathway regulates numerous cellular processes including proliferation, survival, metabolism, and migration

  • By suppressing this pathway, FAM87A ultimately inhibits these pro-oncogenic processes in glioma cells

  • This explains the observed effects of FAM87A overexpression on reducing cell proliferation, migration, and invasion

• Potential Feedback Mechanisms:

  • The PI3K/Akt pathway might also regulate FAM87A expression, creating potential feedback loops

  • Investigation of whether PI3K/Akt inhibitors affect FAM87A expression would help elucidate such mechanisms

• Therapeutic Implications:

  • Understanding this relationship suggests that combining FAM87A-targeted therapies with PI3K/Akt inhibitors might produce synergistic anti-tumor effects

  • This could lead to more effective treatment strategies for glioma patients

Further research using pharmacological inhibitors, genetic manipulation, and phosphoproteomic analyses would help to fully characterize this relationship and its implications for glioma therapy.

What additional molecular pathways might be regulated by FAM87A beyond the miR-424-5p/PPM1H axis?

While the miR-424-5p/PPM1H axis represents the most well-characterized mechanism of FAM87A action in glioma, several additional pathways may be regulated by this lncRNA:

• Potential Regulation of Other miRNAs:

  • Like many ceRNAs, FAM87A may bind and regulate multiple miRNAs beyond miR-424-5p

  • Computational predictions and RNA pull-down experiments followed by miRNA sequencing could identify additional miRNA partners

  • Each of these interactions would potentially regulate distinct downstream pathways and target genes

• Epithelial-Mesenchymal Transition (EMT) Regulation:

  • Experimental evidence shows that FAM87A overexpression affects expression of EMT markers, decreasing fibronectin, N-cadherin, vimentin, and increasing E-cadherin

  • This suggests FAM87A may regulate EMT through mechanisms potentially independent of or complementary to the miR-424-5p/PPM1H axis

  • Transcription factors driving EMT (such as SNAIL, SLUG, ZEB1/2) might be directly or indirectly regulated by FAM87A

• Matrix Metalloproteinase (MMP) Regulation:

  • FAM87A overexpression reduces MMP2 and MMP9 expression, which are crucial for extracellular matrix degradation and tumor invasion

  • The regulatory mechanisms connecting FAM87A to MMP expression warrant further investigation and may involve transcription factors or signaling pathways beyond PI3K/Akt

• Cell Cycle Regulation:

  • The inhibitory effect of FAM87A on cell proliferation suggests potential regulation of cell cycle progression

  • Analysis of cyclins, cyclin-dependent kinases (CDKs), and cell cycle inhibitors in the context of FAM87A manipulation would provide insights into these mechanisms

• Apoptosis and Cell Survival Pathways:

  • FAM87A may influence apoptotic pathways, complementing its effects on proliferation

  • Investigation of apoptosis markers and pathways (caspases, Bcl-2 family proteins) in response to FAM87A modulation would elucidate this potential function

• Interaction with RNA-Binding Proteins:

  • Beyond functioning as a ceRNA, FAM87A might interact with RNA-binding proteins to influence post-transcriptional regulation

  • RNA immunoprecipitation followed by mass spectrometry could identify protein partners and suggest additional molecular functions

• Epigenetic Regulation:

  • Some lncRNAs can influence chromatin modification and DNA methylation

  • Investigation of whether FAM87A influences epigenetic marks would reveal potential broader regulatory roles

Comprehensive transcriptomic, proteomic, and epigenomic analyses in the context of FAM87A manipulation would help identify these additional pathways and expand our understanding of FAM87A's role in cellular biology.

How should researchers interpret discrepancies between in vitro and in vivo FAM87A functional studies?

When confronted with discrepancies between in vitro and in vivo FAM87A functional studies, researchers should consider several factors to properly interpret the results:

• Microenvironmental Factors:

  • In vivo tumor microenvironment includes stromal cells, immune cells, and extracellular matrix components absent in most in vitro models

  • These factors may modulate FAM87A's function or expression

  • Analysis: Compare FAM87A effects in co-culture systems versus monoculture to partially address this discrepancy

• Compensatory Mechanisms:

  • In vivo systems often develop compensatory pathways that may not be active in vitro

  • Long-term in vivo studies might allow for adaptation that short-term in vitro studies cannot capture

  • Analysis: Temporal analysis of FAM87A effects both in vitro and in vivo can help identify delayed compensatory responses

• Dosage and Expression Level Differences:

  • Overexpression or knockdown levels achieved in vitro versus in vivo may differ significantly

  • Analysis: Quantify actual FAM87A expression levels in both systems and correlate with observed effects

  • Consider using inducible expression systems to achieve comparable expression levels

• Model Relevance:

  • Cell lines might not fully recapitulate the heterogeneity of primary tumors

  • Xenograft models using immunocompromised mice lack normal immune interactions

  • Analysis: Compare results across multiple cell lines and consider using syngeneic or genetically engineered mouse models

• Endpoint Measurements:

  • In vitro studies often measure direct cellular effects (proliferation, migration)

  • In vivo studies measure complex outcomes (tumor volume, metastasis, survival)

  • Analysis: Develop parallel assays that measure the same parameters in both systems when possible

• Pharmacokinetic Considerations:

  • For studies involving FAM87A-targeting therapeutics, drug distribution and stability differ between in vitro and in vivo settings

  • Analysis: Measure actual drug concentrations at the target site in vivo and match in vitro concentrations accordingly

When discrepancies are observed, researchers should not immediately dismiss either in vitro or in vivo results, but rather use these differences as an opportunity to uncover additional biological mechanisms. The tumor xenotransplantation assay described in the literature demonstrates that FAM87A's tumor-suppressive effects observed in vitro were consistent with in vivo findings, providing strong support for its biological role .

What statistical approaches are most appropriate for analyzing FAM87A expression data in relation to clinical outcomes?

When analyzing FAM87A expression data in relation to clinical outcomes, researchers should employ robust statistical approaches tailored to the specific questions and data types:

• Survival Analysis:

  • Kaplan-Meier Method: For visualizing survival differences between patient groups with high versus low FAM87A expression

  • Log-rank Test: To statistically compare survival curves

  • Cox Proportional Hazards Model: For multivariate analysis to assess the independent prognostic value of FAM87A while controlling for confounding factors (age, tumor grade, treatment)

  • Previous studies have successfully employed these methods to demonstrate that FAM87A expression is negatively correlated with survival rate in glioma patients

• Correlation Analyses:

  • Pearson or Spearman Correlation: To assess relationships between FAM87A expression and continuous variables (e.g., miR-424-5p expression)

  • Point-Biserial Correlation: For relationships between FAM87A expression and binary variables

  • These methods have revealed negative correlations between FAM87A and miR-424-5p expression in glioma samples

• Comparative Analyses:

  • Student's t-test: For comparing FAM87A expression between two groups (e.g., metastatic vs. non-metastatic tumors)

  • ANOVA: For comparison across multiple groups (e.g., different pathological stages)

  • Mann-Whitney U or Kruskal-Wallis: Non-parametric alternatives when data doesn't meet normality assumptions

  • These approaches have shown that FAM87A expression decreases with increasing pathological stage in glioma

• Regression Analyses:

  • Linear Regression: For modeling relationships between FAM87A expression and continuous outcome variables

  • Logistic Regression: For modeling relationships with binary outcomes (e.g., metastasis)

  • Multinomial Logistic Regression: For categorical outcomes with multiple levels (e.g., tumor grade)

• Advanced Statistical Approaches:

  • Receiver Operating Characteristic (ROC) Curve Analysis: To assess the diagnostic or prognostic value of FAM87A expression

  • Machine Learning Algorithms: For complex pattern recognition and risk stratification based on FAM87A and other molecular markers

  • Propensity Score Matching: To reduce selection bias in observational studies

• Multiple Testing Correction:

  • Bonferroni Correction: Conservative approach for multiple hypothesis testing

  • False Discovery Rate (FDR) Control: Less stringent alternative suitable for exploratory analyses

  • These corrections are crucial when analyzing FAM87A in relation to multiple clinical parameters or gene expression patterns

• Sample Size and Power Considerations:

  • A priori power analysis to determine adequate sample sizes

  • Post hoc power analysis to interpret negative findings

  • Previous studies included 76 pairs of glioma and adjacent normal tissues, providing sufficient power for detecting expression differences

Proper statistical analysis requires transparency in reporting methodologies, appropriate handling of outliers, and validation in independent cohorts whenever possible.

How can researchers integrate FAM87A expression data with other molecular profiling data for comprehensive tumor characterization?

Integrating FAM87A expression data with other molecular profiling data enables comprehensive tumor characterization and can reveal broader biological insights. Researchers should consider the following approaches:

• Multi-omics Integration Strategies:

  • Correlation Networks: Construct networks connecting FAM87A expression with other molecular features (mRNA, miRNA, protein, methylation)

  • Factor Analysis: Identify latent factors that explain patterns across different data types

  • Canonical Correlation Analysis: Find maximally correlated linear combinations of variables across different data platforms

  • Multi-omics Clustering: Group samples based on patterns across multiple data types simultaneously

• Pathway and Gene Set Enrichment Analysis:

  • GSEA (Gene Set Enrichment Analysis): Determine whether FAM87A expression correlates with specific biological pathways or functions

  • Ingenuity Pathway Analysis (IPA): Map relationships between FAM87A and other molecules in canonical pathways

  • GO (Gene Ontology) Enrichment: Identify biological processes associated with genes correlated with FAM87A

  • These approaches can extend findings beyond the known miR-424-5p/PPM1H axis to discover additional biological roles

• Integration with Clinical Data:

  • Multi-variable Regression Models: Include FAM87A expression alongside clinical variables and other molecular markers

  • Random Forest or Other Machine Learning Approaches: Develop predictive models for patient outcomes incorporating FAM87A and other features

  • Nomograms: Create graphical computational tools for individualized prognosis prediction

• Visualization Techniques:

  • Heatmaps: Display correlations between FAM87A and other molecular features

  • Circos Plots: Visualize genome-wide relationships

  • Network Diagrams: Illustrate relationships between FAM87A and interacting partners

  • These visualizations can help identify patterns not obvious from tabular data

• Public Database Integration:

  • TCGA Data Analysis: Compare FAM87A expression patterns across different cancer types

  • GEO Dataset Meta-analysis: Integrate findings from multiple independent studies

  • cBioPortal Exploration: Examine relationships between FAM87A alterations and other genomic features

  • Previous studies have successfully utilized TCGA data to compare FAM87A expression between low grade glioma and glioblastoma

• Single-cell Analysis Integration:

  • Cell Type Deconvolution: Estimate cellular composition of bulk tumor samples and correlate with FAM87A expression

  • Single-cell RNA-seq: Examine FAM87A expression in specific cell populations within heterogeneous tumors

  • Spatial Transcriptomics: Correlate FAM87A expression with spatial location within the tumor microenvironment

• Functional Validation of Integrated Findings:

  • CRISPR Screens: Identify synthetic lethal interactions with FAM87A

  • Drug Sensitivity Correlation: Determine whether FAM87A expression predicts response to specific therapies

  • Combinatorial Perturbations: Test effects of modulating FAM87A alongside other identified molecular features

By integrating FAM87A expression data with other molecular profiling data, researchers can develop more comprehensive models of tumor biology, identify potential therapeutic targets, and better predict patient outcomes. This integrated approach may reveal previously unknown functions of FAM87A beyond its established role in the miR-424-5p/PPM1H axis .

What emerging technologies could advance our understanding of FAM87A's function?

Several cutting-edge technologies show promise for deepening our understanding of FAM87A's functions:

• CRISPR-Cas13 RNA Editing:

  • Enables precise manipulation of FAM87A without altering the genome

  • Allows for targeted disruption of specific functional domains or binding sites

  • Can be used to investigate structure-function relationships with unprecedented precision

• RNA Structure Probing Technologies:

  • SHAPE-seq (Selective 2'-hydroxyl acylation analyzed by primer extension and sequencing): Maps RNA secondary structures in vivo

  • PARIS (Psoralen Analysis of RNA Interactions and Structures): Identifies RNA-RNA interactions

  • These methods could reveal how FAM87A's structure facilitates its interaction with miR-424-5p and potentially other partners

• Spatial Transcriptomics:

  • Maps gene expression within the spatial context of tissues

  • Could reveal localized FAM87A expression patterns within heterogeneous tumor microenvironments

  • May identify specific niches where FAM87A plays particularly important roles

• Single-cell Multi-omics:

  • Simultaneously profiles transcriptome, proteome, and epigenome at single-cell resolution

  • Could identify cell subpopulations where FAM87A is differentially regulated

  • May reveal cell type-specific functions of FAM87A in complex tissues

• Advanced Live Cell Imaging:

  • MS2 or Broccoli RNA Tagging Systems: Allow visualization of FAM87A dynamics in living cells

  • FRET-based Sensors: Can detect interactions between FAM87A and binding partners in real-time

  • These approaches could reveal the temporal and spatial dynamics of FAM87A function

• Liquid-liquid Phase Separation (LLPS) Analysis:

  • Investigates whether FAM87A participates in biomolecular condensates

  • Could reveal novel mechanisms of RNA compartmentalization and function

  • May explain how FAM87A concentrates with miRNAs and other regulatory factors

• High-throughput CRISPR Screening:

  • Identifies genes that synthetically interact with FAM87A

  • Could reveal additional pathways connected to FAM87A function

  • May identify potential combination therapy targets

• Nanopore Direct RNA Sequencing:

  • Provides long-read sequencing of native RNA molecules

  • Could identify FAM87A isoforms and post-transcriptional modifications

  • May reveal previously unrecognized complexity in FAM87A regulation

• Proteogenomic Approaches:

  • Combines proteomics with genomics and transcriptomics

  • Could identify proteins that interact with FAM87A

  • May reveal unexpected non-canonical functions beyond its role as a ceRNA

These technologies, particularly when used in combination, have the potential to dramatically expand our understanding of FAM87A's biological functions and therapeutic potential.

What are the challenges and considerations for developing FAM87A-based therapeutic approaches?

Developing FAM87A-based therapeutic approaches presents several challenges and considerations that researchers must address:

• Delivery Challenges:

  • RNA Stability: FAM87A as an RNA molecule is inherently unstable in physiological conditions

  • Blood-Brain Barrier (BBB) Penetration: For glioma applications, therapeutics must cross the BBB

  • Targeted Delivery: Ensuring delivery specifically to tumor cells while sparing normal tissues

  • Potential Solutions: Nanoparticle formulations, exosome-based delivery, BBB-penetrating peptides, or intranasal delivery for brain tumors

• Expression Control Challenges:

  • Dosage Optimization: Determining the optimal therapeutic level of FAM87A

  • Temporal Regulation: Controlling when and for how long FAM87A is expressed

  • Spatial Regulation: Restricting expression to target tissues

  • Potential Solutions: Inducible expression systems, tissue-specific promoters, or mRNA modifications to control half-life

• Off-target Effects:

  • miRNA Network Perturbation: FAM87A binds miR-424-5p, which may have other important targets

  • Unexpected Interactions: FAM87A may interact with molecules beyond miR-424-5p

  • Immunogenicity: Potential immune responses to the therapeutic construct

  • Monitoring Approaches: Transcriptome-wide analyses to detect off-target effects, immunological assays

• Clinical Translation Considerations:

  • Patient Selection: Identifying patients most likely to benefit (e.g., those with low endogenous FAM87A)

  • Biomarkers: Developing companion diagnostics to monitor treatment efficacy

  • Combination Strategies: Determining optimal combinations with standard treatments like temozolomide

  • Resistance Mechanisms: Anticipating and addressing potential resistance pathways

• Regulatory and Manufacturing Challenges:

  • Regulatory Framework: Navigating the evolving regulatory landscape for RNA therapeutics

  • Manufacturing Scale-up: Ensuring consistent production of high-quality RNA therapeutics

  • Stability and Storage: Developing formulations with practical shelf-life and storage requirements

  • Quality Control: Establishing appropriate standards for purity and activity

• Alternative Therapeutic Approaches:

  • Small Molecule Modulators: Compounds that increase endogenous FAM87A expression

  • miR-424-5p Inhibitors: Alternative approach that might achieve similar effects

  • PPM1H Activators: Targeting the downstream effector directly

  • PI3K/Akt Pathway Inhibitors: Complementary approach to target the same signaling cascade

• Preclinical Validation Requirements:

  • Appropriate Animal Models: Including models that recapitulate the BBB for glioma applications

  • Pharmacokinetic/Pharmacodynamic Studies: Understanding the therapeutic window

  • Toxicology Assessment: Comprehensive evaluation of potential side effects

  • Efficacy Benchmarks: Defining clinically relevant endpoints for success

While challenging, FAM87A-based therapeutic approaches hold significant promise for glioma treatment. The demonstrated tumor-suppressive effects in both in vitro and in vivo models provide strong rationale for continued development efforts .

How might FAM87A research contribute to precision medicine approaches for glioma and other cancers?

FAM87A research has significant potential to contribute to precision medicine approaches for glioma and potentially other cancers through several avenues:

• Prognostic Stratification:

  • Expression-based Biomarker: FAM87A expression levels could serve as an independent prognostic marker

  • Molecular Subtyping: FAM87A expression patterns may help define molecular subtypes with distinct clinical behaviors

  • Risk Prediction Models: Integration of FAM87A with other molecular markers could improve risk stratification

  • Current research has already demonstrated that FAM87A expression correlates with survival outcomes in glioma patients

• Predictive Biomarkers for Treatment Response:

  • Chemotherapy Sensitivity: FAM87A has been shown to influence temozolomide resistance in glioma cells

  • Targeted Therapy Selection: FAM87A status could predict response to PI3K/Akt pathway inhibitors

  • Immunotherapy Response: Potential correlations between FAM87A expression and immune microenvironment

  • Treatment Monitoring: Changes in FAM87A expression during treatment could serve as pharmacodynamic markers

• Personalized Therapeutic Targeting:

  • FAM87A Restoration: For patients with low FAM87A expression, therapies to restore expression

  • miR-424-5p Inhibition: Alternative approach for patients with high miR-424-5p levels

  • PPM1H Activation: For patients with low PPM1H expression

  • Combinatorial Targeting: Patient-specific combinations based on multi-omics profiling

• Liquid Biopsy Applications:

  • Circulating FAM87A: Potential non-invasive biomarker for disease monitoring

  • Exosomal FAM87A: May reflect tumor state without need for tissue biopsy

  • Longitudinal Monitoring: Allow for real-time assessment of treatment response and recurrence

• Tumor Heterogeneity Assessment:

  • Spatial Heterogeneity: Mapping FAM87A expression across different regions of tumors

  • Temporal Heterogeneity: Tracking changes in FAM87A expression during disease progression

  • Cellular Heterogeneity: Identifying cell populations with differential FAM87A expression

  • These approaches could inform more precise therapeutic strategies

• Integration with Radiomics:

  • Imaging-Genomics Correlations: Identifying imaging features that correlate with FAM87A expression

  • Non-invasive Prediction: Potentially predict FAM87A status from imaging characteristics

  • Response Prediction: Combined radiomic-genomic signatures to predict treatment outcomes

• Clinical Trial Design:

  • Patient Selection: Enriching trials with patients likely to respond based on FAM87A status

  • Adaptive Designs: Adjusting treatment based on changes in FAM87A-related biomarkers

  • Basket Trials: Testing FAM87A-targeted therapies across multiple cancer types with similar molecular profiles

The FAM87A/miR-424-5p/PPM1H axis represents a promising pathway for precision medicine approaches, as it provides multiple points for intervention and stratification. By understanding the complex regulatory networks involving FAM87A, clinicians may eventually be able to tailor treatment strategies to individual patients based on their molecular profiles, improving outcomes while minimizing unnecessary toxicity .