Prognostic biomarker NEIL3 and its association with immune infiltration in renal clear cell carcinoma

BackgroundDeep learning architectures have advanced genotype‒phenotype mappings with precision but often obscure the roles of specific genes and their interactions. Our research introduces a model-agnostic computational methodology, capitalizing on the analytical strengths of deep learning models to serve as biological proxies, enabling interpretation of key gene interactions and their impact on phenotypic outcomes. The objective of this research is to refine the understanding of genetic networks in complex traits by leveraging the nuanced decision-making of advanced models.ResultsTesting was conducted across several computational models representing varying levels of complexity trained on gene expression datasets for the prediction of the Ki-67 biomarker, which is known for its prognostic value in breast cancer. The methodology is capable of using models as proxies to identify biologically significant genes and to infer relevant gene networks from an entirely data-driven analysis. Notably, the model-derived biomarkers (p-values of 0.013 and 0.003) outperformed the conventional Ki-67 biomarker (0.021) in terms of prognostic efficacy. Moreover, our analysis revealed high congruence between model precision and the biological relevance of the genes and gene relationships identified. Furthermore, we demonstrated that the complexity of the identified gene relationships was consistent with the decision-making intricacy of the model, with complex models capturing greater proportions of complex gene–gene interactions (61.2% and 31.1%) than simpler models (4.6%), reinforcing that the approach effectively captures biologically relevant in-model decision-making processes.ConclusionsThis methodology offers researchers a powerful tool to examine the decision-making processes within their genotype–phenotype mapping models. It accurately identifies critical genes and their interactions, revealing the biological rationale behind model decisions. It also enables comparisons of decision-making between different models. Furthermore, by discovering in-model critical gene networks, our approach helps bridge the gap between research and clinical applications. It facilitates the translation of complex, model-driven genetic discoveries into actionable clinical insights. This capability is pivotal for advancing personalized medicine, as it leverages the precision of deep learning models to uncover biologically relevant genes and gene networks and opens pathways for discovering new gene biomarker combinations and previously unknown gene interactions.

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Transcription Factor MAZ Potentiates the Upregulated NEIL3-mediated Aerobic Glycolysis, thereby Promoting Angiogenesis in Hepatocellular Carcinoma

Zhang,

Wang,

Zhang

et al. 2024

CCDT

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Background: Hepatocellular carcinoma (HCC) is characterized by high vascularity and notable abnormality of blood vessels, where angiogenesis is a key process in tumorigenesis and metastasis. The main functions of Nei Like DNA Glycosylase 3 (NEIL3) include DNA alcoholization repair, immune response regulation, nervous system development and function, and DNA damage signal transduction. However, the underlying mechanism of high expression NEIL3 in the development and progression of HCC and whether the absence or silencing of NEIL3 inhibits the development of cancer remain unclear. Therefore, a deeper understanding of the mechanisms by which increased NEIL3 expression promotes cancer development is needed. Methods: Expression of NEIL3 and its upstream transcription factor MAZ in HCC tumor tissues was analyzed in bioinformatics efforts, while validation was done by qRT-PCR and western blot in HCC cell lines. The migration and tube formation capacity of HUVEC cells were analyzed by Transwell and tube formation assays. Glycolytic capacity was analyzed by extracellular acidification rate, glucose uptake, and lactate production levels. Chromatin immunoprecipitation (ChIP) and dual-luciferase reporter gene assays were utilized to investigate specific interactions between MAZ and NEIL3. Results: NEIL3 and MAZ were substantially upregulated in HCC tissues and cells. NEIL3 was involved in modulating the glycolysis pathway, suppression of which reversed the stimulative impact of NEIL3 overexpression on migration and angiogenesis in HUVEC cells. MAZ bound to the promoter of NEIL3 to facilitate NEIL3 transcription. Silencing MAZ reduced NEIL3 expression and suppressed the glycolysis pathway, HUVEC cell migration, and angiogenesis. Conclusion: MAZ potentiated the upregulated NEIL3-mediated glycolysis pathway and HCC angiogenesis. This study provided a rationale for the MAZ/NEIL3/glycolysis pathway as a possible option for anti-angiogenesis therapy in HCC.

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Prognostic biomarker NEIL3 and its association with immune infiltration in renal clear cell carcinoma

Cited by 3 publications

References 55 publications

Identification of genomic regions affecting nitrogen excretion intensity in Brown Swiss dairy cows

Identification of genomic regions affecting nitrogen excretion intensity in Brown Swiss dairy cows

A Model-agnostic Computational Method for Discovering Gene–Phenotype Relationships and Inferring Gene Networks viain silicoGene Perturbation

Transcription Factor MAZ Potentiates the Upregulated NEIL3-mediated Aerobic Glycolysis, thereby Promoting Angiogenesis in Hepatocellular Carcinoma

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