Unraveling the role of perineural invasion in cancer progression across multiple tumor types.

IF 2.8 4区 医学 Q2 ONCOLOGY
Muqtada Shaikh, Sanket Shirodkar, Gaurav Doshi
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引用次数: 0

Abstract

Perineural invasion (PNI) refers to the infiltration of tumor cells into the connective tissue of nerves and is increasingly recognized as a pathological hallmark of multiple cancers, including pancreatic, prostate, colorectal, breast, and head and neck malignancies. PNI is associated with aggressive tumor behavior, an increased risk of recurrence, and poor patient survival. This review consolidates current mechanistic insights into PNI, highlighting immune-neural crosstalk, Schwann cell activation, tumor-nerve feedback loops, and conserved neurotrophic signaling axes. In addition to these shared features, we identify tumor-specific drivers, such as neural progenitor cell involvement in breast cancer, glutamate/NMDA receptor signaling in pancreatic cancer, and epigenetic regulators in gastric and head and neck cancers. A key clinical advancement involves integrating artificial intelligence-driven imaging and machine learning tools for enhanced detection, classification, and prognostic assessment of PNI. These technologies provide scalable, reproducible alternatives to traditional histopathology and underscore the need for standardized diagnostic protocols. By synthesizing molecular, cellular, and computational findings, this review outlines a strategic framework for identifying actionable targets within the nerve-tumor interface. Ultimately, understanding the complex biology of PNI is critical for developing targeted therapies and precision oncology strategies that may reduce recurrence and improve outcomes for patients with high-risk, nerve-invasive tumors.

揭示神经周围浸润在多种肿瘤类型癌症进展中的作用。
围神经浸润(PNI)是指肿瘤细胞浸润到神经结缔组织中,越来越被认为是多种癌症的病理标志,包括胰腺癌、前列腺癌、结直肠癌、乳腺癌和头颈部恶性肿瘤。PNI与侵袭性肿瘤行为、复发风险增加和患者生存差有关。这篇综述整合了目前对PNI的机制见解,强调了免疫-神经串扰、雪旺细胞激活、肿瘤-神经反馈回路和保守的神经营养信号轴。除了这些共同特征外,我们还确定了肿瘤特异性驱动因素,例如乳腺癌中的神经祖细胞参与,胰腺癌中的谷氨酸/NMDA受体信号传导,以及胃癌和头颈癌中的表观遗传调节因子。一项关键的临床进展包括整合人工智能驱动的成像和机器学习工具,以增强PNI的检测、分类和预后评估。这些技术为传统的组织病理学提供了可扩展、可重复的替代方案,并强调了标准化诊断方案的必要性。通过综合分子、细胞和计算方面的发现,本文概述了在神经肿瘤界面内识别可操作靶点的战略框架。最终,了解PNI的复杂生物学对于开发靶向治疗和精确肿瘤策略至关重要,这些策略可能会减少复发并改善高风险神经侵袭性肿瘤患者的预后。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Medical Oncology
Medical Oncology 医学-肿瘤学
CiteScore
4.20
自引率
2.90%
发文量
259
审稿时长
1.4 months
期刊介绍: Medical Oncology (MO) communicates the results of clinical and experimental research in oncology and hematology, particularly experimental therapeutics within the fields of immunotherapy and chemotherapy. It also provides state-of-the-art reviews on clinical and experimental therapies. Topics covered include immunobiology, pathogenesis, and treatment of malignant tumors.
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