Zeng Bin, Liu Xiaoqin, Liu Jun, Qiu Hongbo, Zhang Tianwei, Chen Xiaomei, Wang Lin
Department of Oncology, Zigong First People's Hospital, Zigong Medical Science Academ, Zigong, 643200, Sichuan, China.
Sci Rep. 2025 Jul 2;15(1):23559. doi: 10.1038/s41598-025-08528-8.
Esophageal cancer is a highly lethal malignancy with high incidence and mortality rates, which continue to pose a significant threat to public health worldwide. Despite the adoption of multidisciplinary treatments, improving long-term survival rates remains a major challenge. Therefore, conducting in-depth research into the molecular mechanisms of esophageal cancer, identifying predictive biomarkers, and developing personalized treatment and monitoring strategies are crucial to enhancing clinical outcomes and patient prognosis. This study utilized advanced bioinformatics techniques to construct a gene co-expression network for esophageal cancer and compared it with sequencing data from real-world esophageal cancer samples. This approach successfully identified four key genes closely associated with the progression of esophageal cancer: MARCKSL1, MCM6, RFC4, and PLAU. A risk assessment model based on these genes demonstrated high predictive accuracy, particularly in assessing the risk of early-stage esophageal cancer, highlighting its significant potential for clinical application. Functional validation experiments and clinical data analysis further revealed that these genes play critical roles in esophageal cancer cell metastasis, glycolysis, and response to radiation therapy. These findings provide a new molecular basis for radiotherapy prognosis, helping to formulate more precise radiotherapy protocols and optimize treatment outcomes. Additionally, the regulatory networks of these genes revealed a complex molecular regulatory mechanism, offering new perspectives for precision treatment in esophageal cancer. In summary, these genes not only offer new insights into the early diagnosis and personalized treatment of esophageal cancer but also improve patient prognosis by correlating with radiotherapy response, thus laying a solid scientific foundation for future advancements.
食管癌是一种具有高发病率和死亡率的高致死性恶性肿瘤，在全球范围内持续对公众健康构成重大威胁。尽管采用了多学科治疗方法，但提高长期生存率仍然是一项重大挑战。因此，深入研究食管癌的分子机制、识别预测性生物标志物以及制定个性化治疗和监测策略对于改善临床结果和患者预后至关重要。本研究利用先进的生物信息学技术构建了食管癌基因共表达网络，并将其与来自真实世界食管癌样本的测序数据进行比较。该方法成功鉴定出与食管癌进展密切相关的四个关键基因：MARCKSL1、MCM6、RFC4和PLAU。基于这些基因的风险评估模型显示出高预测准确性，特别是在评估早期食管癌风险方面，凸显了其在临床应用中的巨大潜力。功能验证实验和临床数据分析进一步表明，这些基因在食管癌细胞转移、糖酵解以及对放射治疗的反应中发挥关键作用。这些发现为放射治疗预后提供了新的分子基础，有助于制定更精确的放射治疗方案并优化治疗结果。此外，这些基因的调控网络揭示了一种复杂的分子调控机制，为食管癌的精准治疗提供了新的视角。总之，这些基因不仅为食管癌的早期诊断和个性化治疗提供了新的见解，还通过与放射治疗反应相关联改善了患者预后，从而为未来的进展奠定了坚实的科学基础。
