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2026
Editorial
Title
Editorial: Exploiting non-oncogene addiction for overcoming drug resistance in metastatic tumors
Abstract
Editorial on the Research Topic Exploiting non-oncogene addiction for overcoming drug resistance in metastatic tumors Acquired genetic aberrations implement new information about how tissue systems should perform (Mueller et al. 2018; Hendrix et al. 2007). Emerging tumor systems integrate oncogene-driven information by coordinating vast networks of non-oncogenes within tumor and heterogeneous adjacent stroma cells, ultimately organizing non-oncogene addiction (NOA)-driven circuitries to ensure the viability and resilience of malignant tissues (Xu et al. 2024; Blazquez et al. 2020). Integrated oncogenic-triggered information, promoting NOA genome editing, becomes conceivable in novel tissue structures and functions. However, phenomenology does not reveal how oncogenic input is integrated transcriptionally, particularly with regard to the operation of novel oncogene-driven NOA circuitries that span tumor tissue systems (Yuan et al. 2024). The reproducibility of tumor phenomenology, which facilitates traditional tumor classification, hypothesizes that similar integration of oncogenic data is associated with a distinct input of oncogenic information. However, how is oncogenic information integration formally structured at the non-oncogene level? All five papers focus on the tension between oncogenic information input and comprehensive integration of non-oncogenes. Di Marco describes Coatomer subunit zeta-1 (COPZ1), a protein exhibiting characteristics of NOA in multiple tumor types. Its expression may be normal or increased compared to non-malignant cells. COPZ1 is genome-agnostically integrated across many tumor histologies (Di Marco et al.). Knockdown of COPZ1 leads to tumor cell death in various neoplasias. Silencing COPZ1 can involve Golgi damage, blocking autophagy or the unfolded protein response, silencing the type I interferon pathway, or
Author(s)
Open Access
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Rights
CC BY 4.0: Creative Commons Attribution
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Language
English