NDUFS4 Human

What is NDUFS4 and what is its function in human cells?

NDUFS4 is the NADH:ubiquinone oxidoreductase subunit S4, a critical component of mitochondrial complex I in the electron transport chain. It functions as an accessory subunit essential for the assembly and stability of complex I, playing a vital role in cellular energy production through oxidative phosphorylation. The protein is encoded by the NDUFS4 gene and participates in the transfer of electrons from NADH to ubiquinone, contributing to ATP synthesis. Research indicates its expression varies across tissue types, with significant implications for cellular metabolism and function in both normal and pathological conditions .

How is NDUFS4 expression typically measured in human tissue samples?

NDUFS4 expression in human tissues is commonly assessed through multiple complementary methods. In clinical research settings, immunohistochemistry (IHC) on tissue microarrays (TMA) represents a standard approach, as demonstrated in studies examining NDUFS4 expression in gastric cancer tissues and gastric mucosal tissue from surgical margins . For mRNA level analysis, RT-PCR and RNA sequencing data (often analyzed through platforms like GEPIA2) provide quantitative measurements comparing expression between cancerous and normal tissues. Western blot experiments are frequently employed to detect protein expression levels across different cell lines, as shown in the comparative analysis of NDUFS4 expression in gastric carcinoma cell lines including HGC27, AGS, MKN1, and MKN45 . For broader expression pattern analysis, bioinformatics approaches using GEO datasets provide valuable insights into differential gene expression patterns.

What common experimental models are used to study NDUFS4 function in human disease?

Researchers employ diverse experimental models to investigate NDUFS4 function in human disease. In vitro models typically utilize human cancer cell lines such as HGC27, AGS, MKN1, and MKN45 for gastric cancer studies . Gene silencing approaches using siRNA transfection represent a common methodology to assess phenotypic effects of NDUFS4 downregulation, with functional assays including CCK8 for proliferation assessment, wound scratch experiments for migration analysis, and transwell experiments for invasion studies . For in vivo validation, nude mouse xenograft models provide crucial insights into tumor growth dynamics under conditions of NDUFS4 modulation. Additionally, bioinformatics approaches leveraging databases such as GEPIA2 and GEO datasets (e.g., GSE64951) enable comparative analysis of NDUFS4 expression between normal and pathological tissues across larger sample sizes . These complementary models collectively facilitate comprehensive understanding of NDUFS4's role in disease pathogenesis.

How does NDUFS4 expression correlate with different histological classifications of gastric cancer?

NDUFS4 expression demonstrates significant correlation with histological classifications of gastric cancer according to the Laurén classification system (P=0.010). Among the three major subtypes, diffuse type gastric cancer exhibits the highest proportion of high NDUFS4 expression at 68.45% (115/168 cases), compared to mixed type at 62.75% (34/51 cases) and intestinal type at 50.45% (56/111 cases) . This distribution pattern suggests potential molecular differences in NDUFS4 regulation or function across distinct histological subtypes. When examining tumor differentiation status specifically in tubular adenocarcinoma, a strong inverse relationship emerges between differentiation grade and NDUFS4 expression (P<0.001). Poorly differentiated tumors show markedly elevated NDUFS4 expression (69.67%, 147/211 cases) compared to moderately differentiated (51.43%, 54/105 cases) and well-differentiated tumors (14.29%, 2/14 cases) . These correlation patterns suggest NDUFS4 may play different biological roles depending on the histological context and differentiation status, potentially contributing to the heterogeneity of treatment responses observed in gastric cancer.

What methodological approaches are most effective for modulating NDUFS4 expression in experimental settings?

For effective NDUFS4 modulation in experimental settings, RNA interference techniques have demonstrated considerable utility. Small interfering RNA (siRNA) transfection represents a validated approach for transient NDUFS4 knockdown, with optimization of siRNA sequences crucial for achieving maximum knockdown efficiency. In published research, NDUFS4-siRNA-1 demonstrated optimal knockdown in HGC27 cell lines . For stable long-term knockdown, shRNA delivered via lentiviral vectors offers advantages for extended studies. When selecting cellular models, western blot screening across multiple relevant cell lines helps identify those with highest baseline NDUFS4 expression (e.g., HGC27 and MKN1 showed higher expression than AGS and MKN45) . For gain-of-function experiments, transfection with expression vectors containing NDUFS4 cDNA under strong promoters can achieve overexpression. CRISPR-Cas9 gene editing presents a sophisticated alternative for complete NDUFS4 knockout or precise modifications. Regardless of approach, validation through both mRNA quantification (RT-qPCR) and protein detection (western blot) is essential to confirm successful modulation before proceeding to functional assays.

How does NDUFS4 function as an independent prognostic factor in multivariate analysis?

In rigorous multivariate analysis, NDUFS4 emerges as an independent prognostic factor for gastric cancer patient outcomes. When adjusted for potential confounding variables including gender, age, Laurén classification, differentiation status, T stage, N stage, M stage, and TNM stage, high NDUFS4 expression maintains statistical significance as an independent predictor of poor prognosis with a hazard ratio of 1.557 (P=0.027, 95% CI: 1.052-2.304) . This represents a critical finding, as it demonstrates that NDUFS4's prognostic value persists even when accounting for established clinical parameters. Within the same multivariate model, only differentiation status (HR: 1.406, P=0.031) and TNM stage (HR: 2.478, P<0.001) emerged as additional independent prognostic factors . The persistence of NDUFS4 as an independent factor suggests it likely influences tumor biology through molecular mechanisms distinct from those captured by conventional histopathological and staging parameters. This statistical independence strengthens the rationale for incorporating NDUFS4 assessment into clinical prognostic evaluation frameworks for gastric cancer patients.

What are the optimal experimental controls when investigating NDUFS4 function in cancer cell lines?

When designing experiments to investigate NDUFS4 function in cancer cell lines, implementing appropriate controls is essential for ensuring valid and interpretable results. For gene silencing experiments using siRNA transfection, negative control (NC) siRNA with a scrambled sequence that doesn't target any known gene transcript serves as the critical comparison baseline . Western blot validation should quantitatively demonstrate knockdown efficiency compared to this control. When assessing phenotypic effects through functional assays such as proliferation, migration, and invasion experiments, both untransfected cells and negative control transfected cells should be included to distinguish between specific NDUFS4 knockdown effects versus general transfection-related effects . For experiments involving multiple cell lines (such as HGC27 and MKN1), consistent transfection protocols should be maintained while acknowledging potential cell line-specific variations in knockdown efficiency . In xenograft models, proper randomization of animals to treatment and control groups with equivalent initial conditions is essential. Additionally, when examining human tissue samples, appropriate control tissues (such as adjacent non-tumorous tissues or normal gastric mucosa) must be included for comparative expression analysis .

What statistical approaches are most appropriate for analyzing NDUFS4 expression data in relation to survival outcomes?

Analyzing NDUFS4 expression in relation to survival outcomes requires rigorous statistical methodologies. For categorical analysis, patient stratification into "high" and "low" expression groups based on validated cutoff points (determined through methods such as ROC curve analysis or median expression value) enables group comparisons . Kaplan-Meier survival curves with log-rank tests represent the standard approach for visualizing and testing survival differences between expression groups . Cox proportional hazards regression models should be employed for both univariate and multivariate analyses. In univariate analysis, the hazard ratio (HR) for NDUFS4 expression (high versus low) quantifies the strength of association with survival outcomes . Multivariate Cox regression incorporating relevant clinical covariates (including age, gender, histological classification, differentiation status, and TNM parameters) is crucial for determining whether NDUFS4 functions as an independent prognostic factor . Results should report hazard ratios with 95% confidence intervals and precise P-values. For continuous expression data from platforms like RNA-seq, correlation with survival can be assessed through Cox regression with continuous variables or visualization tools such as those provided by GEPIA2. All analyses should report sample sizes, follow-up duration ranges, and numbers of events to ensure transparency and reproducibility.

What are the technical considerations for validating NDUFS4 knockdown efficiency in experimental models?

Validating NDUFS4 knockdown efficiency requires comprehensive technical considerations across multiple levels. At the mRNA level, RT-qPCR using NDUFS4-specific primers with appropriate housekeeping gene normalization (typically GAPDH or β-actin) should demonstrate significant reduction compared to control conditions. For protein-level validation, western blot analysis using specific anti-NDUFS4 antibodies with proper loading controls is essential, with quantification through densitometry analysis to determine knockdown percentage . When optimizing siRNA transfection, testing multiple siRNA sequences targeting different regions of NDUFS4 mRNA is recommended to identify the most effective option, as demonstrated in the selection of NDUFS4-siRNA-1 for optimal knockdown in published research . Time-course experiments should establish the duration of knockdown effect, particularly important for interpreting longer-term functional assays. For functional validation, mitochondrial complex I activity assays or oxygen consumption rate measurements provide direct evidence of NDUFS4 functional impairment beyond mere expression reduction. In in vivo models, tissue samples from xenografts should undergo immunohistochemical validation of maintained NDUFS4 knockdown throughout the experimental period .

How should researchers interpret conflicting data regarding NDUFS4 expression patterns across different cancer types?

When encountering conflicting data regarding NDUFS4 expression patterns across cancer types, researchers should implement a systematic interpretation approach. First, methodological differences in expression detection (immunohistochemistry, RNA-seq, microarray) should be carefully evaluated, as each technique measures different molecular aspects (protein vs. mRNA) with varying sensitivity and specificity . Tissue-specific biological contexts must be considered, as NDUFS4 may have divergent functions depending on the metabolic profile of the tissue of origin. Cancer subtype heterogeneity within broadly defined cancer types may explain apparently conflicting results, as demonstrated by the significant differences in NDUFS4 expression across Laurén classification subtypes (intestinal, diffuse, and mixed) in gastric cancer . Temporal dynamics in NDUFS4 expression during cancer progression might account for discrepancies, particularly if studies sampled different disease stages. Statistical considerations including sample size, patient selection criteria, and cutoff determination methods for "high" versus "low" expression significantly impact results . Finally, molecular context, including genetic and epigenetic alterations co-occurring with NDUFS4 expression changes, may explain tissue-specific consequences of altered expression. Integration of functional validation data across different models provides the most robust framework for resolving apparent conflicts in expression pattern significance.

What bioinformatics approaches can effectively analyze NDUFS4's relationship with other genes in cancer pathways?

To effectively analyze NDUFS4's relationship with other genes in cancer pathways, several sophisticated bioinformatics approaches can be employed. Co-expression analysis using tools like WGCNA (Weighted Gene Co-expression Network Analysis) can identify genes whose expression patterns correlate with NDUFS4 across samples, potentially revealing functional relationships . Pathway enrichment analysis using platforms such as GSEA (Gene Set Enrichment Analysis), DAVID, or Metascape helps identify biological processes and signaling pathways significantly associated with NDUFS4 expression patterns. For identifying potential transcriptional regulators of NDUFS4, motif analysis of its promoter region combined with ChIP-seq data integration can reveal candidate transcription factors. Protein-protein interaction network analysis using databases like STRING or BioGRID places NDUFS4 in its functional context within the interactome. Multi-omics data integration combining transcriptomics with proteomics, metabolomics, and epigenomics provides comprehensive insights into NDUFS4's regulatory mechanisms and downstream effects. Database mining approaches using resources such as GEPIA2 and GEO datasets (e.g., GSE64951) enable validation of findings across larger cohorts . Finally, machine learning algorithms can identify complex, non-linear relationships between NDUFS4 expression and other molecular features that might not be apparent through traditional statistical approaches.

How can NDUFS4 research findings be translated into potential clinical applications?

Translating NDUFS4 research findings into clinical applications requires a strategic multiphase approach. Initially, development of standardized immunohistochemical protocols for NDUFS4 detection in clinical pathology laboratories would enable consistent assessment in patient samples . The strong correlation between high NDUFS4 expression and poor prognosis supports its development as a prognostic biomarker, potentially incorporated into integrated prognostic scoring systems alongside established parameters like TNM staging . For predictive biomarker applications, retrospective analysis of NDUFS4 expression in relation to treatment response could identify patient subgroups most likely to benefit from specific therapeutic regimens. The identification of NDUFS4 as independently associated with tumor progression parameters suggests its potential as a therapeutic target, warranting drug discovery efforts focused on modulating its activity or expression . Additionally, exploring synthetic lethality approaches—identifying compounds selectively toxic to cells with high NDUFS4 expression—represents a promising strategy. For guiding clinical trials, patient stratification based on NDUFS4 expression could enhance trial design and interpretation. Development of companion diagnostics measuring NDUFS4 expression or activity would support personalized medicine approaches. Finally, liquid biopsy applications detecting circulating NDUFS4 mRNA or protein could enable minimally invasive monitoring of disease progression and treatment response.

What are the current knowledge gaps in understanding the molecular mechanisms of NDUFS4 in human disease?

Despite significant advances, several critical knowledge gaps persist in understanding NDUFS4's molecular mechanisms in human disease. First, while correlative data strongly link NDUFS4 expression to cancer progression, the precise mechanistic pathways through which NDUFS4 promotes malignant phenotypes remain incompletely characterized . The regulatory mechanisms controlling NDUFS4 expression in different tissue contexts and disease states are poorly understood, including potential epigenetic modifications, transcription factors, and post-transcriptional regulators. The specific contribution of NDUFS4 dysfunction to mitochondrial biology beyond complex I assembly requires further investigation, particularly regarding potential non-canonical functions outside the respiratory chain. The metabolic consequences of altered NDUFS4 expression or function in cancer cells have not been comprehensively mapped, including potential effects on the Warburg effect and metabolic reprogramming . The relationship between NDUFS4 and immune cell function within the tumor microenvironment represents an unexplored area with potential implications for immunotherapy response. Additionally, while NDUFS4 mutations cause Leigh syndrome, a neurological disorder, the connection between these germline mutations and somatic alterations in cancer remains unclear. Finally, the potential role of NDUFS4 in cancer stem cell maintenance, therapy resistance mechanisms, and metastatic processes requires dedicated investigation to fully understand its contribution to aggressive disease phenotypes .

What are the technical limitations of current methods for studying NDUFS4 in human samples?

Current methods for studying NDUFS4 in human samples face several technical limitations researchers should consider. Immunohistochemistry, while valuable for spatial expression analysis, suffers from antibody specificity concerns, semi-quantitative assessment limitations, and potential variability in staining protocols across laboratories . For mRNA expression analysis, RNA degradation in archived FFPE samples presents challenges, while fresh tissue samples may not represent the heterogeneity present in whole tumors. Single-cell approaches remain technically challenging and expensive for routine NDUFS4 profiling, limiting our understanding of expression heterogeneity within tumor microenvironments. Western blot analysis requires substantial protein amounts, challenging when working with limited clinical specimens . When comparing expression across studies, non-standardized cutoff values for defining "high" versus "low" expression complicate cross-study comparisons and meta-analyses . For functional studies, primary human cells derived from patient samples often grow poorly in vitro, potentially altering their NDUFS4 expression patterns and functionality compared to their in vivo state. Finally, the technical complexity of accurately measuring mitochondrial complex I activity in human tissue samples limits our ability to correlate NDUFS4 expression with functional consequences for cellular bioenergetics. Addressing these limitations requires method standardization, technology improvement, and careful experimental design considering these constraints.

What strategies can address challenges in targeting NDUFS4 for therapeutic development?

Developing therapeutic strategies targeting NDUFS4 presents unique challenges requiring innovative approaches. Given NDUFS4's essential role in mitochondrial function, direct inhibition might cause systemic toxicity, necessitating careful therapeutic window determination between cancer cells and normal tissues . Leveraging synthetic lethality approaches by identifying compounds selectively toxic to cells with elevated NDUFS4 expression offers a more selective strategy. For delivery challenges, developing cancer-selective targeting systems such as antibody-drug conjugates or nanoparticle formulations could enhance therapeutic index. RNA interference approaches using siRNA or shRNA encapsulated in tumor-targeting delivery systems might achieve tissue-specific NDUFS4 knockdown, similar to methodologies used in experimental models . Rather than direct targeting, modulating downstream effectors of NDUFS4's oncogenic functions may provide alternative intervention points with potentially reduced toxicity. Since NDUFS4 affects cellular metabolism, combination approaches with metabolism-targeting agents might produce synergistic effects in cancer cells while sparing normal tissues. For patient selection in clinical development, developing companion diagnostics measuring NDUFS4 expression would identify patients most likely to benefit from targeted therapies . Finally, repurposing approved drugs known to modulate mitochondrial function may accelerate therapeutic development by leveraging existing safety data and providing insights into the feasibility of modulating NDUFS4-related pathways in vivo.

What considerations should inform experimental design when studying NDUFS4 in different cancer subtypes?

When studying NDUFS4 across cancer subtypes, experimental design should address several critical considerations. First, histological subtype stratification is essential, as demonstrated by significant variations in NDUFS4 expression across Laurén classification subtypes (intestinal, diffuse, and mixed) in gastric cancer . Molecular subtyping based on comprehensive genomic, transcriptomic, and proteomic profiling should complement histological classification to capture heterogeneity more completely. For cell line model selection, researchers should verify that chosen models accurately represent the molecular subtype under investigation through comparative genomic and transcriptomic profiling . Experiment timing considerations should account for potential temporal changes in NDUFS4 expression during disease progression, with dedicated studies comparing early versus advanced disease stages. Control tissue selection requires careful consideration, as appropriate normal comparators may differ across cancer subtypes (e.g., different cell types of origin) . When designing functional experiments, researchers should select phenotypic assays relevant to the specific biological behaviors of each cancer subtype, rather than applying uniform assays across all models . Microenvironmental factors should be incorporated into experimental designs, potentially including co-culture systems or conditioned media approaches to capture subtype-specific tumor-stroma interactions. Finally, validation across multiple independent patient cohorts representing different populations enhances generalizability of findings across cancer subtypes .