Yu Baofa, Shao Shengwen, Ma Wenxue
Taimei Baofa Cancer Hospital, Dongping, Shandong, 271500, China; Jinan Baofa Cancer Hospital, Jinan, Shandong, 250000, China; Beijing Baofa Cancer Hospital, Beijing, 100010, China; Immune Oncology Systems, Inc, San Diego, CA, 92102, USA.
Institute of Microbiology and Immunology, Huzhou University School of Medicine, Huzhou, Zhejiang, 313000, China.
Cancer Lett. 2025 Feb 1;610:217350. doi: 10.1016/j.canlet.2024.217350. Epub 2024 Nov 22.
Pancreatic cancer remains one of the most challenging malignancies to treat due to its late-stage diagnosis, aggressive progression, and high resistance to existing therapies. This review examines the latest advancements in early detection, and therapeutic strategies, with a focus on emerging biomarkers, tumor microenvironment (TME) modulation, and the integration of artificial intelligence (AI) in data analysis. We highlight promising biomarkers, including microRNAs (miRNAs) and circulating tumor DNA (ctDNA), that offer enhanced sensitivity and specificity for early-stage diagnosis when combined with multi-omics panels. A detailed analysis of the TME reveals how components such as cancer-associated fibroblasts (CAFs), immune cells, and the extracellular matrix (ECM) contribute to therapy resistance by creating immunosuppressive barriers. We also discuss therapeutic interventions that target these TME components, aiming to improve drug delivery and overcome immune evasion. Furthermore, AI-driven analyses are explored for their potential to interpret complex multi-omics data, enabling personalized treatment strategies and real-time monitoring of treatment response. We conclude by identifying key areas for future research, including the clinical validation of biomarkers, regulatory frameworks for AI applications, and equitable access to innovative therapies. This comprehensive approach underscores the need for integrated, personalized strategies to improve outcomes in pancreatic cancer.
由于胰腺癌晚期诊断、进展迅速且对现有疗法高度耐药，它仍然是最难治疗的恶性肿瘤之一。本综述探讨了早期检测和治疗策略的最新进展，重点关注新兴生物标志物、肿瘤微环境（TME）调节以及人工智能（AI）在数据分析中的整合。我们强调了有前景的生物标志物，包括微小RNA（miRNA）和循环肿瘤DNA（ctDNA），它们与多组学检测相结合时，可为早期诊断提供更高的灵敏度和特异性。对肿瘤微环境的详细分析揭示了癌症相关成纤维细胞（CAF）、免疫细胞和细胞外基质（ECM）等成分如何通过形成免疫抑制屏障导致治疗耐药。我们还讨论了针对这些肿瘤微环境成分的治疗干预措施，旨在改善药物递送并克服免疫逃逸。此外，还探讨了人工智能驱动的分析在解读复杂多组学数据方面的潜力，以实现个性化治疗策略和对治疗反应的实时监测。我们通过确定未来研究的关键领域来得出结论，包括生物标志物的临床验证、人工智能应用的监管框架以及公平获得创新疗法。这种综合方法强调了采用综合、个性化策略来改善胰腺癌治疗效果的必要性。
