EEF1E1 Human

What is EEF1E1 and what are its primary functions in normal human cells?

EEF1E1 is a protein-coding gene that produces a protein localized in both the cytoplasm and nucleus of cells. In the cytoplasm, EEF1E1 functions as an auxiliary component of the macromolecular tRNA synthase complex. Its nuclear function becomes particularly important during DNA damage events, where it participates in DNA damage response pathways .

The protein interacts with ATM (Ataxia Telangiectasia Mutated) and ATR (Ataxia Telangiectasia and Rad3-related) kinases, which are central to DNA damage response signaling. The interaction with ATM occurs independently of p53 and is induced during genotoxic stress or cell growth. Meanwhile, interaction with ATR is enhanced following UV irradiation, suggesting a role in response to specific DNA damage types .

How is EEF1E1 structurally organized at the genetic and protein levels?

EEF1E1 encodes a protein that functions in both nuclear and cytoplasmic compartments with distinct roles. At the protein level, structural analyses reveal domains that facilitate its interactions with ATM/ATR and components of the tRNA synthase complex. Its ability to translocate between cellular compartments is critical to its function in stress response pathways .

Structurally, the EEF1E1 protein contains regions that enable it to interface with ATM/ATR kinases during DNA damage events. This structural organization is essential for its role in mediating p53 activation following cellular stress, particularly in contexts where DNA integrity has been compromised .

What is the normal expression pattern of EEF1E1 across human tissues?

While the search results don't provide comprehensive information about EEF1E1 expression across all human tissues, they do contain comparative data between normal liver tissue and hepatocellular carcinoma. In normal liver tissue, EEF1E1 expression is detectably lower compared to HCC tissue.

Analysis of data from the UCSC XENA database comparing normal (n=160) and tumor (n=371) samples showed a median difference in expression of 0.812 (0.671-0.951), with tumor samples exhibiting significantly higher expression (p<0.001) . Similarly, paired sample analysis of 50 normal and 50 cancerous tissues from the same patients showed a difference of 1.019 (0.748-1.289), which was statistically significant (t=7.572, p<0.001) .

How does EEF1E1 interact with the ATM/ATR-p53 axis in cellular stress response?

EEF1E1 serves as a critical mediator in the ATM/ATR-p53 signaling pathway during cellular stress responses. Research indicates that EEF1E1 interacts with ATM independent of p53 status, but this interaction is significantly enhanced during DNA damage events and genotoxic stress conditions. Similarly, EEF1E1-ATR interaction increases following UV irradiation .

The mouse homolog of EEF1E1 has been demonstrated to translocate to the nucleus during DNA damage events, where it actively participates in ATM/ATR-mediated p53 activation. This suggests a conserved mechanism whereby EEF1E1 contributes to stress-responsive signaling through direct physical interactions with key kinases that regulate p53 activity .

Mechanistically, EEF1E1 appears to function as a scaffold or adaptor protein that facilitates signal transduction from damage-sensing kinases (ATM/ATR) to downstream effectors like p53. This positions EEF1E1 as a potential regulator of cellular fate decisions following genotoxic insults, with implications for both normal tissue homeostasis and cancer progression .

What is the relationship between EEF1E1 expression and tumor immune microenvironment in HCC?

Analysis of EEF1E1's relationship with the tumor immune microenvironment reveals significant correlations with various immune cell populations. Using single-sample gene set enrichment analysis (ssGSEA), researchers have identified both positive and negative correlations between EEF1E1 expression and immune cell infiltration in HCC .

EEF1E1 expression showed significant negative correlations with:

• Cytotoxic cells (r = -0.247, p < 0.001)

• Dendritic cells (DC) (r = -0.250, p < 0.001)

• Neutrophils (r = -0.199, p < 0.001)

• T gamma delta cells (Tgd) (r = -0.179, p < 0.001)

• Th17 cells (r = -0.259, p < 0.001)

While positive correlations were observed with:

• Macrophages (r = 0.165, p = 0.001)

• NK cd56bright cells (r = 0.288, p < 0.001)

• T follicular helper cells (TFH) (r = 0.225, p < 0.001)

• Th2 cells (r = 0.306, p < 0.001)

These findings suggest EEF1E1 may play a role in modulating the immunosuppressive microenvironment in HCC, potentially through altering the balance between anti-tumor and pro-tumor immune populations .

How does EEF1E1 overexpression affect cellular phenotypes in cancer models?

In hepatocellular carcinoma, EEF1E1 overexpression correlates with several aggressive phenotypes. Higher EEF1E1 expression is associated with more advanced T stage, higher histologic grade, and increased vascular invasion . This suggests that EEF1E1 may promote cellular characteristics that enhance invasion and progression in liver cancer models.

The odds ratio for EEF1E1 overexpression in advanced T stages (T2, T3, T4) compared to T1 is 2.541 (95% CI: 1.678-3.875, p<0.001), indicating that EEF1E1 expression increases significantly with tumor progression. Similarly, the odds ratio for vascular invasion is 1.974 (95% CI: 1.238-3.167, p=0.004), suggesting EEF1E1 may promote invasive capabilities .

These phenotypic alterations likely result from EEF1E1's interactions with critical signaling pathways, including potential effects on the p53 pathway, though the exact mechanisms require further investigation in controlled experimental models.

What are the optimal methods for analyzing EEF1E1 expression in tissue samples?

For comprehensive analysis of EEF1E1 expression in tissue samples, researchers should employ a multi-modal approach combining RNA and protein detection methods. Based on methodologies described in current research, the following techniques are recommended:

• RNA expression analysis:

  • RNA-seq with TPM (transcripts per million) normalization followed by log2 transformation allows accurate cross-sample comparisons .

  • FPKM-to-TPM conversion is advised when working with legacy data in FPKM format .

  • For paired sample analysis, paired t-tests should be applied to detect differences between tumor and adjacent normal tissues .

• Protein expression analysis:

  • Immunohistochemistry (IHC) for spatial localization and semi-quantitative assessment .

  • Multiplex immunohistochemistry for simultaneous detection of EEF1E1 and interacting partners (ATM, p53) .

  • Western blotting for quantitative protein expression analysis.

• Data validation approaches:

  • Cross-validation using multiple databases (TCGA, GTEX, ICGC, CPTAC) .

  • Paired sample analysis when possible to control for inter-individual variation .

When publishing EEF1E1 expression analyses, researchers should report both parametric (mean ± SD) and non-parametric (median with IQR) statistics to account for potential non-normal distribution of expression data .

What statistical approaches are most appropriate for correlating EEF1E1 expression with clinical outcomes?

Based on successful approaches in EEF1E1 research, a comprehensive statistical framework for correlating expression with clinical outcomes should include:

• Survival analysis techniques:

  • Kaplan-Meier analysis with log-rank tests to compare high vs. low expression groups .

  • Univariate Cox proportional hazards regression to calculate hazard ratios (HR) and 95% confidence intervals .

  • Multivariate Cox regression to assess independence from established prognostic factors .

• Expression cutoff determination:

  • Median-based dichotomization for initial exploratory analyses .

  • ROC curve analysis to identify optimal cutoffs for outcome prediction.

  • Sensitivity analyses using different cutoffs to ensure robustness.

• Correlation with clinicopathological variables:

  • Logistic regression to calculate odds ratios for binary outcomes (e.g., presence of vascular invasion) .

  • Mann-Whitney U tests for continuous variables when normality cannot be assumed .

  • Multiple testing correction using Benjamini-Hochberg procedure for exploratory analyses.

• Validation strategies:

  • Internal validation using bootstrap or cross-validation techniques.

  • External validation in independent cohorts from different databases (e.g., ICGC, CPTAC) .

This comprehensive statistical approach ensures robust and reproducible findings when correlating EEF1E1 expression with clinical outcomes in cancer research .

How can researchers effectively investigate the mechanisms of EEF1E1 in immune microenvironment modulation?

To comprehensively investigate EEF1E1's role in immune microenvironment modulation, researchers should implement a multi-faceted approach:

• Computational immune infiltration analysis:

  • Apply the ssGSEA algorithm with validated immune cell gene signatures to quantify immune cell populations .

  • Calculate Pearson correlation coefficients between EEF1E1 expression and immune cell infiltration scores .

  • Consider correlations significant when p < 0.05 and |r| > 0.20 .

• Experimental validation techniques:

  • Multiplex immunohistochemistry to simultaneously visualize EEF1E1 expression and immune cell markers in the same tissue section .

  • Flow cytometry of dissociated tumors to quantify immune cell populations in relation to EEF1E1 expression.

  • Co-culture experiments between EEF1E1-manipulated cancer cells and various immune cell types.

• Functional assessment:

  • CRISPR-Cas9 knockout or overexpression of EEF1E1 followed by immune infiltration analysis.

  • Conditioned media experiments to assess if EEF1E1 affects immune cell chemotaxis or function via secreted factors.

  • Cytokine profiling to identify potential mediators of EEF1E1's effects on immune cells.

• Single-cell approaches:

  • Single-cell RNA sequencing to identify cell populations affected by EEF1E1 expression.

  • Spatial transcriptomics to map the relationship between EEF1E1-expressing cells and immune infiltrates.

This integrated approach enables a comprehensive understanding of EEF1E1's mechanistic role in modulating the tumor immune microenvironment, potentially revealing novel therapeutic opportunities .

How reliable is EEF1E1 as a prognostic biomarker in hepatocellular carcinoma?

EEF1E1 has demonstrated considerable promise as a prognostic biomarker in hepatocellular carcinoma across multiple independent datasets. The reliability of EEF1E1 as a prognostic indicator is supported by the following evidence:

These consistent findings across multiple cohorts using different analytical platforms (transcriptomic and proteomic) enhance the reliability of EEF1E1 as a prognostic biomarker in HCC. The particularly strong hazard ratios in both univariate and multivariate analyses suggest EEF1E1 may offer superior prognostic value compared to some traditional clinical parameters .

What is the relationship between EEF1E1 expression and response to specific cancer therapies?

While the search results don't directly address the relationship between EEF1E1 expression and therapy response, we can draw some inferences based on its molecular interactions and immune correlations:

EEF1E1's established interaction with the ATM/ATR pathway suggests it may influence response to DNA-damaging therapies such as radiation and certain chemotherapeutics. Since ATM and ATR are central to DNA damage response and repair mechanisms, EEF1E1's role in this pathway could potentially modulate therapeutic efficacy .

The significant correlations between EEF1E1 expression and various immune cell populations suggest potential implications for immunotherapy response. Specifically:

• Negative correlations with cytotoxic cells (r = -0.247, p < 0.001) and positive correlations with Th2 cells (r = 0.306, p < 0.001) suggest EEF1E1 may be associated with an immunosuppressive microenvironment, potentially diminishing response to immune checkpoint inhibitors .

• The negative correlation with dendritic cells (r = -0.250, p < 0.001) could indicate reduced antigen presentation capability, potentially affecting cancer vaccine efficacy .

• Positive correlation with macrophages (r = 0.165, p = 0.001) might suggest a relationship with tumor-associated macrophages, which could influence response to macrophage-targeting therapies .

Further research specifically designed to analyze treatment outcomes stratified by EEF1E1 expression is needed to establish definitive relationships between this marker and therapeutic responses.

How does EEF1E1 expression compare across different cancer types and what are the clinical implications?

The search results primarily focus on EEF1E1 in hepatocellular carcinoma but do mention its involvement in other cancer types. EEF1E1 has been reported to play roles in ovarian cancer, breast cancer, and non-small cell lung cancer, suggesting its relevance extends beyond liver malignancies .

While detailed cross-cancer comparisons are not provided in the search results, the consistent finding that EEF1E1 is involved in multiple cancer types suggests it may represent a common mechanism in oncogenesis or tumor progression. This broad involvement has several clinical implications:

• Potential as a pan-cancer prognostic marker:
  Given its strong prognostic value in HCC (HR = 2.581, p < 0.001) and reported involvement in multiple cancer types, EEF1E1 may serve as a cross-cancer prognostic indicator .

• Mechanistic insights into common cancer pathways:
  EEF1E1's interaction with fundamental cellular processes like the ATM/ATR-p53 axis suggests it may influence common pathways of DNA damage response and cellular stress adaptation across different cancer types .

• Therapeutic target potential:
  The involvement of EEF1E1 across multiple cancers makes it a potentially attractive therapeutic target with broad applications, particularly if it modulates essential cancer cell survival mechanisms through its interaction with the ATM/ATR pathway .

For researchers investigating EEF1E1, systematic pan-cancer analysis would be valuable to determine whether the prognostic significance and mechanistic roles observed in HCC are conserved across other malignancies.